The process of new particle formation from gas-phase precursors holds significant importance in Earth's atmosphere and introduces a notable source of uncertainty in climate change predictions. Typical conditions for new particle formation are moderate temperatures, clear sky and low background aerosol contamination. This general paradigm was challenged by the puzzling observation of frequent new particle formation in megacities. The pre-existing aerosol loadings in such environments seemed to be too high to allow clusters of being formed and grow fast enough before they encounter a collision with a background particle and get lost from the number budget.

Here, we show how nanoparticle growth in urban atmospheres is facilitated enabling efficient survival of nanoclusters providing an explanation for the occurrence of NPF in heavily polluted environments. We outline the tool set, which we have developed over the recent years to address this puzzle. Significant uncertainty in the particle number size distribution measurements and growth rate estimates were addressed through new instrumentation and analysis approaches. At the same time, we refined growth models to account for the challenges of a wide variety of potentially condensable vapors and updated our understanding of particle survival in the atmosphere.

We could demonstrate that new particle formation takes a decisive role in air quality issues in megacities, especially as nanoparticles seem to grow at surprisingly constant rates even when no new particle formation is observed. The “unique atmospheric experiment” of the Covid-19 lockdowns finally provided the chance to estimate how sensitive the urban environment is to changes in the atmospheric chemistry, especially with respect to new particle formation. While we speculated that other condensable vapors than previously thought could be part of the puzzle, we can finally show that also the population dynamics are crucial for more efficient nanoparticle survival than previously thought. However, severe challenges remain, as the outlined methodological improvements also revealed that sometimes the little ones even grow slower than expected.

How to cite: Stolzenburg, D.: How do the little ones grow? Solving the puzzling occurrence of new particle formation in megacities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2853, https://doi.org/10.5194/egusphere-egu25-2853, 2025.

Clouds are fascinating objects because of their myriad shapes and the optical phenomena that they cause. They are also scientifically challenging to understand because their formation and dissipation require knowledge about both the large-scale meteorological environment as well as about the details of cloud droplet and ice crystal formation on the microscale. While we have reduced the uncertainty in the radiative forcing of aerosol-cloud interactions over the last decades, the effect of climate change on clouds, precipitation forecasts and cloud dynamics still pose lots of open questions.

With the advancement of better in-situ and remote sensing instruments, unprecedented observations of clouds are now possible. Simultaneously, the increasing amount of computing power enables us to simulate clouds at increasingly finer scales over larger domains, making convection parameterizations obsolete and allowing us to resolve larger eddies. Cloud research is also being revolutionized by machine learning. We have used machine learning in combination with satellite data to disentangle the response of stratocumulus clouds to aerosol perturbations, for understanding how cirrus clouds respond to the presence of mineral dust as well as for classifying ice crystals down to aggregated monomer scale in in-situ measurements.

We have exploited these advancements in our CLOUDLAB project, where we employed cloud seeding technology to better our understanding of mixed-phase cloud processes: by releasing silver iodide-containing particles from uncrewed aerial vehicles in supercooled low stratus clouds over the Swiss plateau, we were able to observe and measure downstream ice crystals in a controlled way. From these measurements, we quantifed their diffusional growth rates, aggregation rates and riming rates. Additional high-resolution modeling supported the experiments and provided insights for weather forecasts and climate projections. The CLOUDLAB results can also be translated to the potential climate mitigation idea of thinning mixed-phase clouds.

I have great hope that the open questions in cloud research will be tackled by a combination of advanced measurement devices, AI-driven methods, and further advances in computing power enabling high-resolution modeling.

How to cite: Lohmann, U.: From the microscale to climate: combining observations, laboratory data, and numerical simulations for aerosol-cloud interactions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5678, https://doi.org/10.5194/egusphere-egu25-5678, 2025.

Persistent Dry Spells (PDS) during winter in the eastern Mediterranean are crucial to understanding the regional challenges of water resources and mitigating agricultural and economic impacts. Winter dry spells significantly affect ecosystem stability, public health, and socioeconomic conditions in a region susceptible to climate variability. Therefore, extending the forecast horizon of these extreme weather events to subseasonal time scales is a key challenge. With this aim, we examine the covariability of the sea surface temperature of the Indian Ocean and Persistent Dry Spells during winter over the eastern Mediterranean. The positive Indian Ocean Dipole (IOD) phase alters global circulation patterns, notably increasing the geopotential height at 500 hPa and the sea-level pressure over western Russia, eastern Europe, and the eastern Mediterranean during PDS events. Concurrently, the positive IOD phase enhances moisture fluxes and decreases sea level pressure and geopotential height at 500 hPa in the Western Mediterranean, suggesting increased cyclonic activity in that region. This type of activity probably influences the formation of PDS in the eastern Mediterranean through latent heating and the formation of ridges downstream of the cyclones. The baroclinic, subtropical, and polar regimes are large-scale synoptic regimes alternately prevailing during PDS events. Changes due to the DMI phase are not identical under these regimes and sometimes have opposite trends. The baroclinic regime is the most frequent regime during PDS events. Consequently, the average changes in pressure intensity during PDS events strongly resemble those during baroclinic days. Positive DMI case studies exemplify the effect of these large-scale regimes. We provide evidence for a link between the positive phase of IOD in December and the frequency of longer (> 15 days) PDS events. The normalized frequencies of persistent 15-20-day events under the positive dipole mode index (DMI) are ~ 2% higher than the frequency of negative DMI. The frequencies of 6-7 day events are ~20% lower. Finally, we emphasize the sensitivity of persistent dry spells during winter to event definition, the chosen precipitation data source, and threshold definitions for climate indices. These considerations are essential for improving the accuracy of regional weather and climate predictions, further enhancing our understanding of the climatic impacts of IOD and other teleconnection patterns in the eastern Mediterranean and worldwide.

How to cite: Berkovic, S. and Hochman, A.: Links between the Indian Ocean Dipole and Persistent Dry Spells in the Eastern Mediterranean Winter, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-56, https://doi.org/10.5194/egusphere-egu25-56, 2025.

Quantifying uncertainties is a key aspect of data assimilation systems since it has a large impact on the quality of the forecasts and analyses. Sequential data assimilation algorithms, such as the Ensemble Kalman Filter (EnKF), describe the model and observation errors as additive Gaussian noises and use both inflation and localization to avoid filter degeneracy and compensate for misspecifications. This introduces different stochastic parameters which need to be carefully estimated in order to get a reliable estimate of the latent state of the system. A classical approach to estimate unknown parameters in data assimilation consists in using state-augmentation, where the unknown parameters are included in the latent space and are updated at each iteration of the EnKF. However, it is well-known that this approach is not efficient to estimate stochastic parameters because of the complex (non-Gaussian and non-linear) relationship between the observations and the stochastic parameters which can not be handled by the EnKF. A natural alternative for non-Gaussian and non-linear state-space models is to use a particle filter (PF), but this algorithm fails to estimate high-dimensional systems due to the curse of dimensionality. The strengths of these two methods are gathered in the proposed algorithm, where the PF first generates the particles that estimate the stochastic parameters, then using the mean particle the EnKF generates the members that estimate the geophysical variables. This generic method is first detailed for the estimation of parameters related to the model or observation error and then for the joint estimation of inflation and localization parameters. Numerical experiments are performed using the Lorenz-96 model to compare our approach with state-of-the-art methods. The results show the ability of the new method to retrieve the geophysical state and to estimate online time-dependent stochastic parameters. The algorithm can be easily built from an existing EnKF with low additional cost and without further running the dynamical model. 

How to cite: Guillot, J.: State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-125, https://doi.org/10.5194/egusphere-egu25-125, 2025.

Since the inception of physics-informed neural networks (PINNs) by Raissi et al. in 2019, it has been seen as a promising approach to outperform conventional algorithms in terms of computational efficiency, reduced costs, and improved prediction accuracy, especially in small data regimes.PINNs incorporate known physical governing equations in the form of partial differential equations (PDEs) or ordinary differential equations (ODEs) into neural networks, and occasionally the governing equations are derived from observational or simulated data, allowing PINNs to address specific atmospheric systems.Moreover, depending on the problem being solved, most work adds the physical constraints directly into the loss or cost function, while others enhance performance using modified architectures or preprocessing techniques.In the realm of atmospheric sciences, challenges remain, including a heavy reliance on simulated data and limited use of observational datasets, which does not show the real-world applicability of PINNs. A detailed review of available results shows critical gaps in scalability, hybrid data integration, and standardization in atmospheric science.We identified a hybrid methodology by combining simulated and observational data, which includes optimizing hybrid loss functions to balance physics-based and observational accuracy, applying adaptive training techniques, and standardizing preprocessing schemes to handle multi-scale atmospheric phenomena.Results demonstrate the ability of PINNs to deliver faster computation, enhanced prediction accuracy, and robustness in sparse data environments. This highlights the transformative advantages of PINNs over traditional methods and suggests future directions for leveraging their capabilities in atmospheric science applications.

How to cite: Ekue, J. A., Hammond, D., and Agyei-Yeboah, E.: Envisioning the Role of Physics-Informed Neural Networks in Atmospheric Science: Advancements, Challenges, and Future Prospects, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-243, https://doi.org/10.5194/egusphere-egu25-243, 2025.

Accurate evaluation of cloud microphysical variables is essential for improving cloud parameterization and weather forecasting. However, obtaining high-resolution, spatially and temporally extensive observation dataset remains a challenge due to the limitations of in situ measurements. Therefore, this study addresses this gap by assessing existing equations for estimating vertically integrated liquid water content (VIL, kg/m²) from liquid water content (LWC, g/m³) using C-band dual-polarised doppler weather radar (DWR) data from IMD Jaipur station over 78 deep convective summer monsoon days in the years 2020-2022. A long-term climatological study (2003-2023) of total column cloud liquid water (TCCLW, kg/m²) from ERA5, liquid water cloud water content (LWCP, kg/m²) from MODIS and rainfall data from IMD, IMERG, and GPCP datasets is also performed. VIL is computed as the vertical integral of LWC across atmospheric layers using four reflectivity-LWC (Z-LWC) relationships and one reflectivity-differential reflectivity (Z, ZDR-LWC) relationship from existing literature. The performance of each equation is evaluated by comparing radar-derived VIL with satellite-derived parameters like MODIS cloud liquid water path (LWP, kg/m²) and TCCLW. The results show that VIL values increase with rainfall intensity and cloud vertical height, leading to higher estimation errors. Among the equations tested, the hybrid ZDR-based equation consistently demonstrated superior performance, particularly during high-intensity rainfall events, with lower root mean square error (RMSE) and mean absolute error (MAE) values which also captured more detailed spatial patterns of liquid water distribution and reduced bias, making it the most reliable estimator. Despite some limitations, such as beam blockage and slight spatial shifts due to interpolation, the study highlights the advantages of incorporating polarimetric radar products for VIL estimation. These findings provide a foundation for improving real-time precipitation forecasts and understanding cloud microphysics, with future work aimed at refining the methodology by addressing data gaps and enhancing cloud-type-specific estimators.

How to cite: Sabu, A., Syed, H. A., Das, S., Panda, S. K., Sharma, D., and Pal, J.: Evaluation of Vertically Integrated Liquid Water Content in Indian Summer Monsoon Clouds Using Dual-Polarimetric Doppler Weather Radar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-888, https://doi.org/10.5194/egusphere-egu25-888, 2025.

Air quality monitoring remains a significant challenge in urban areas, particularly where high-cost infrastructure is unavailable or difficult to maintain. Traditional monitoring systems are often limited in scope due to expense and logistical constraints, leading to data gaps, especially in resource-constrained environments. Low-cost air quality sensors have the potential to transform environmental monitoring by providing accessible, affordable tools for collecting air quality data, especially in urban settings. As part of the SCORE project, a low-cost sensor system was developed to support real-time air quality monitoring across European cities. These sensors provide a more granular understanding of air pollution trends, making air quality data collection both scalable and accessible to a wider range of stakeholders, including local communities. This presentation will highlight the deployment of these sensors in Dublin, Ireland, where they have been successfully integrated into citizen science initiatives, enabling communities to actively participate in environmental data collection and contribute to air quality management.

Ensuring data accuracy and reliability is a key challenge in the use of low-cost sensors. We will examine the technical challenges of deploying low-cost sensors, such as calibration, accuracy, and long-term reliability in small-scale urban environments. The presentation will also discuss strategies for integrating sensor data into authoritative air quality monitoring networks to enhance overall data quality and spatial coverage.

In Dublin, the citizen science air quality initiative has built strong connections between local communities, researchers and policymakers. This collaboration exemplifies how co-created initiatives, backed by accessible technology, can empower citizens and bridge the gap between public engagement and formal policy processes. The outcomes of the Dublin case study suggest broader applicability for the SCORE model in other cities facing similar air quality challenges. By offering a replicable and scalable solution, low-cost sensors provide an affordable alternative to high-end monitoring stations, enabling resource-limited municipalities to expand their air quality infrastructure. The project demonstrates how engaging local communities in the data collection process can foster long-term, sustainable environmental stewardship. These insights underscore the importance of equitable partnerships between citizens, researchers, and governments in tackling air pollution, particularly in cities where financial or technical constraints have traditionally limited comprehensive air quality monitoring.

How to cite: Daruari, H., Crowley, S., Cocco, C., and Barrón, J. P. G.: Building Equitable Air Quality Networks: Low-Cost Sensors and Community-Led Monitoring in Dublin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1073, https://doi.org/10.5194/egusphere-egu25-1073, 2025.

The forecast of monthly rainfall is a significant topic for water resource management and hydrological disaster prevention. A critical need for precise hydrological forecasts in water resource management is addressed in this study by analyzing machine learning (ML) models for precipitation forecasting in the Boudh district of Odisha, India. Although machine learning (ML) models have demonstrated significant promise in rainfall forecasting due to their high performance, often surpassing that of certain physical models, the intricate physical processes involved in rainfall creation mean that a single ML model is typically insufficient to provide reliable rainfall projections. A thorough set of meteorological parameters, including precipitation wind speed, temperature, and humidity, are utilized to create four distinct models: Support Vector Regression (SVR), long and short memory neural networks (LSTM), Bi-LSTM and Convolutional neural network with LSTM (CNN-LSTM). The performance of these models is thoroughly assessed utilizing a range of evaluation metrics. In this work, the correlations between precipitation and climate factors are assessed using the cross-correlation function (XCF). With maxima consistently reported during months across all four sites, the XCF analysis shows a number of significant trends, including a strong correlation amid precipitation and maximum temperature. Moreover, precipitation is significantly correlated with wind speed and relative humidity. The results demonstrate the effectiveness of hybridized ML techniques in raising the precision of precipitation forecasts. The CNN-LSTM models, which have R² values between 0.93 and 0.97, generally perform better. Their remarkable accuracy highlights their efficacy in precipitation forecasting, outperforming rival models during both training and testing. These findings have important ramifications for hydrological processes, particularly in Odisha's Boudh region, where sustainable water resources management depends on precise precipitation forecasting.

How to cite: Samantaray, S., Sahoo, A., and Satapathy, D. P.: Rainfall Prediction using Hybrid CNN-LSTM approach: A case study in the Boudh district, Odisha, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1542, https://doi.org/10.5194/egusphere-egu25-1542, 2025.

How to cite: Jalan, S., Sukhatme, J., and Seshadri, A.: Genesis, structure and propagation of synoptic systems over the Indian Ocean during the Northeast Monsoon , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2672, https://doi.org/10.5194/egusphere-egu25-2672, 2025.

The Indo-Gangetic plains (IGP) in India are frequently affected by fog during the winter months of December, January, and February, which manifests in severe consequences for air and road traffic, thereby leading to health as well as economic losses. This region, which includes highly populated cities like the National Capital Territory of Delhi, also experiences a high concentration of aerosols during this period. While studies have indicated the importance of the role of aerosols in fog processes in the region, the role of different aspects of aerosol-radiation interaction (ARI) has not been studied in detail for the formation of fog in the region. Current numerical weather prediction models (NWP) still struggle to predict fog accurately because of the uncertainties in the representation of processes leading to fog formation, sustenance, and dissipation. The present study aims to understand the influence of aerosols and ARI on the fog over IGP with a focus on dense fog conditions using the Delhi Model with Chemistry and aerosol framework (DM-Chem1.0), which is a high-resolution (330 m) model used for operational forecasting of wintertime visibility and air quality at the National Centre for Medium-Range Weather Forecasting (NCMRWF), India. Four experiments (along with a Control experiment) were designed to analyze how both the scattering and absorbing nature of ARI influence the evolution of dense fog from temporal and spatial perspectives. Two experiments isolated the absorbing and scattering effect of aerosols, while the third excluded both these effects. The fourth experiment analyzed pristine conditions with minimal aerosol presence. The study indicated that turning off absorption had the greatest impact, significantly increasing dense fog-impacted areas and fog-associated parameters like cloud liquid water mixing ratio and cloud droplet number concentration (CDNC). Satellite data for the absorbing aerosol index also corroborated the greater contribution of absorbing aerosols in the model domain. Further, the study also indicates the importance of a realistic representation of aerosol for better model performance during daytime. The study highlights the importance of correctly representing radiative interactions in the numerical models for fog prediction. The policy measures need to focus on regulating high aerosol concentrations over IGP to mitigate the adverse effects of fog.

How to cite: Bhati, S., Anurose, T. J., Jayakumar, A., Mohandas, S., and Prasad, V. S.: Aerosol-Radiation Interaction During Dense Fog in the Indo-Gangetic Plains Region , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3008, https://doi.org/10.5194/egusphere-egu25-3008, 2025.

This study investigated convective initiations (CIs) in the western Jiangnan region of China using radar data spanning April to September from 2018 to 2021. An integrated approach combining objective identification and subjective validation was applied to identify, track and validate CIs, resulting in a more accurate CIs dataset. Based on this dataset, this study delved into the spatiotemporal variations and key environmental conditions associated with CIs. The results indicated distinct seasonal and diurnal patterns in CIs events. Seasonally, the spatial variations of CIs were demarcated by the Nanling Mountains, exhibiting higher frequency to the south and lower to the north. Generally, the seasonal distribution of CIs followed a unimodal pattern, peaking during June to August and reaching minima in April and September. Notably, CIs exhibited a pronounced convection feature in the afternoon, particularly during June to August, when the majority of CIs occurred between 11:00 and 19:00. Furthermore, the spatial variations influenced by terrain were prominent. With the Nanling Mountains as the dividing line, CIs in the northern region were located near relatively higher mountains, while in the southern region, they were concentrated in smaller mountains and coastal areas. Utilizing the K-means clustering method, CIs that could develop into Mesoscale Convective Systems are classified into four circulation types: the Western Pacific Subtropical High (WPSH) Control type (Type I), the WPSH Edge type (Type II), the Southwest Airflow type (Type III), and the Low Trough Shear type (Type IV). CIs under Type I and II were primarily attributed to afternoon thermal convection occurring under conditions of strong moisture and thermal instability. The distribution of CIs triggers for these types tended to cluster in the vicinity of high-elevation terrain. In contrast, CIs belonging to Type III and IV were primarily driven by the synergy of abundant moisture conditions and systematic dynamic factors such as low-level jets, upper-level troughs, and shear lines. These exhibited a north-low and south-high frequency distribution, with high-frequency CIs trigger zones observed particularly in regions of strong moisture flux convergence and near complex terrain.

How to cite: Wu, Z., Han, Y., Song, N., Ye, C., and Xiang, G.: Exploring the spatiotemporal variations and key environmental conditions of convective initiations in the Western Jiangnan Region of China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3431, https://doi.org/10.5194/egusphere-egu25-3431, 2025.

In this talk, I will highlight our recent advances and findings in changes in climatic extremes over east Asia monsoon region. I will focus specifically on monsoon duration, intensity, rainfall extremes changes, and mechanism, with dynamic and thermodynamic factors controlling rainfall extremes over East Asia in late summer. Moreover, I will present our latest research on climatic extremes such as heatwaves based on dry conditions and stationary waves. Despite increasing future rainfall, rainfall extremes and rainfall variability in many areas, our recent studies suggest also an increase in drought risk over eastern Asia as a result of changes in evapotranspiration. However, the underlying mechanisms of heat waves and potential atmospheric and land feedbacks are still not fully understood. Through feedback attribution analysis, we found that there are dry and hot heat waves with very different underlying physical processes and feedbacks. The increasing global warming is expected to exacerbate atmospheric water demand, worsening future conditions of extreme droughts and heatwaves. Compound drought and heatwaves (DHW) events have much attention due to their notable impacts on socio-ecological systems. However, studies on the mechanisms of DHW related to land-atmosphere interaction are not still fully understood in regional aspects. Here, we investigate drastic increases in DHW from 1980 to 2019 over northern East Asia, one of the strong land-atmosphere interaction regions. Heatwaves occurring in severely dry conditions have increased after the late 1990s, suggesting that the heatwaves in northern East Asia are highly likely to be compound heatwaves closely related to drought. Moreover, the soil moisture–temperature coupling strength increased in regions with strong increases in DHW through phase transitions of both temperature and heat anomalies that determine the coupling strength. As the soil moisture decreases, the probability density of low evapotranspiration increases through evaporative heat absorption. This leads to increase evaporative stress and eventually amplify DHW since the late 1990s. Focusing on changes in stomatal conductance due to CO2 changes, our research results reveal an increase in surface resistance with CO2 elevation. Particularly under drought conditions, potential evapotranspiration tends to overestimate drought severity in the East Asian region by approximately 17% when scenarios considering vegetation are not taken into account. Additionally, intensified land-atmosphere interactions due to soil moisture deficiency lead to more frequent and amplified occurrences of compound heatwaves and droughts over northern East Asia. Understanding the relationship between soil moisture and vegetation can contribute to comprehending future severe droughts and heatwaves under diverse surface conditions with warming and moistening.

How to cite: Ha, K.-J., Yeo, J.-H., and Seo, Y.-W.: Dynamics and Characteristics of Climatic Extremes over East Asia Monsoon region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3896, https://doi.org/10.5194/egusphere-egu25-3896, 2025.

Based on the minute by minute precipitation observation data from 46 national weather stations in the Yangtze River Delta region of China and hourly ERA5 reanalysis data from June to August 2018 to 2021, the temporal and spatial characteristics and environmental parameters of short-term heavy precipitation were analyzed. The short-term heavy rainfall was classified and compared according to the 19 environmental parameters representing water vapor, dynamic and thermal conditions. The results showed that:(1) There were more short-term heavy rainfall in the Yangtze River Delta in summer, and 58.7% of the weather stations appeared more than 5 times a year on average; most of short-term heavy rainfall appeared in August, accounting for 40.7%; From 14:00 PM to 17:00 PM was the high incidence period of short-term heavy rainfall; The duration of short-term heavy rainfall was mostly within 60 minutes, accounting for 85.9%, and the longest process lasted 282 minutes.(2) At the beginning of short-term heavy rainfall, water vapor was sufficient, PWAT generally exceeded 63mm, and the relative humidity at 850 hPa and 700 hPa exceeded 80%; The energy condition was good, and the average value of cape was 1516.9 J/kg; The vertical wind shear of 0-6 km was mainly distributed in the range of 8.1~16.7 m/s, belonging to medium weak or weak intensity; The thickness of warm clouds was large, most of which were more than 4395.2 m, which was conducive to higher precipitation efficiency.(3) The environmental parameters of the three types of short-term heavy rainfall were quite different. The water vapor of the first type was mainly concentrated in the lower layer, with high cloud base height and large Cape value, 75% of which was more than 1700 J/kg. The thermal conditions were prominent, and the dynamic effect was weak. The water vapor of the whole layer of the second type was sufficient, and the Cape value was high, with an average value of 1401.1 J/kg, the uplift condition of the middle and low layers was the best of the three types. The water vapor, thermal and dynamic effects were relatively balanced; The third type was rich in water vapor, with prominent water vapor conditions, large vertical wind shear in the lower layer and weak thermal effect. 

How to cite: Zhang, C. and Peng, L.: Characteristics of environmental parameters of short-term heavy rainfall in the Yangtze River Delta region in summer, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3935, https://doi.org/10.5194/egusphere-egu25-3935, 2025.

A novel deep learning (DL) transformer model, named the 3D-Geoformer, has been developed for ENSO-related modeling studies in the tropical Pacific. Multivariate input predictors and output predictands are selected to adequately represent ocean-atmosphere interactions; so, this purely data-driven model is configured in such a way that key fields for the coupled ocean-atmosphere system are collectively and simultaneously utilized, including three-dimensional (3D) upper-ocean temperature and surface wind stress fields, which represents the coupled ocean-atmosphere interactions known as the Bjerknes feedback in the region. The 3D-Geoformer achieves high correlation skills for ENSO prediction at lead times of up to one and a half years. The reasons for the successful prediction with interpretability are explored comprehensively by performing perturbation experiments to predictors and quantifying input‐output relationships in predictions using the 3D-Geoformer. This is achieved by investigating how the thermal precursors contribute to ENSO prediction skills, with the dependence of the precursor representations on preconditioning multi-month input predictors elucidated. Results reveal the existence of ENSO‐related upper‐ocean temperature anomaly pathways and consistent phase propagations of thermal precursors around the tropical Pacific in the DL framework. The research demonstrates that 3D thermal fields and their basinwide evolution during multi-month time intervals act to enhance long‐lead prediction skills of ENSO. It is demonstrated that the 3D-Geoformer can not only have its ability to effectively improve prediction skills of sea surface temperature variability in the eastern equatorial Pacific, but also explain how and why it is so, thus enhancing model explainability.

How to cite: Zhou, L. and Zhang, R.: Deep learning-based ENSO modeling and its prediction and predictability study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4905, https://doi.org/10.5194/egusphere-egu25-4905, 2025.

Sudden stratospheric warming (SSW), a well-known phenomenon in the polar atmosphere, changes the distribution of various atmospheric parameters due to the enhanced activity of planetary waves. These processes produce zonal asymmetry in total ozone content (TOC) with a wave-1 pattern. However, regional characteristic properties of the Antarctic TOC anomalies that occur during the SSW life cycle have not been studied in detail. We aim to analyze the connection of zonally asymmetric variations of TOC with SSW events. The analysis is based on a time series of ten research stations in the Antarctic region and gridded fields from MSR-2 TOC data. Here, we compare the evolution of TOC and wave amplitudes in three Southern Hemisphere SSW events. The TOC time series over ten stations in the Antarctic region and superposed epoch analysis for ±60-day time lags relative to the SSW central date were used. A regional division according to the geographic location of the stations and TOC climatology was introduced. According to the TOC asymmetry pattern, a division between Eastern and Western Hemisphere stations is used. We observe zonally asymmetric ozone responses in the two hemispheres during the SSW life cycle, including distinct precursor properties before the SSW onset. This research clarifies the different SSW properties in local ozone observations under the zonally asymmetric TOC field. The previously unknown regional manifestations of Antarctic TOC anomalies in the early stage of the SSW are discussed. The role of wave-1 and the zonally asymmetric Brewer-Dobson circulation in the Eastern–Western Hemisphere difference in the Antarctic TOC variability is also discussed. We also characterize total ozone levels in the years immediately preceding and following the three most significant SSW events. We examine the influence of planetary wave activity and large-scale climate modes on the level of interannual ozone variability and its regional patterns. There is evidence that Antarctic total ozone in the years adjacent to these SSW events is reduced, which may serve as a precursor signal of these events and an indicator of their longer-lasting influence. We discuss the implications and importance of these ozone perturbations for the regional Antarctic climate.

How to cite: Milinevsky, G., Yu, R., Grytsai, A., Evtushevsky, O., Klekociuk, A., and Ivaniha, O.: Ozone anomalies over Eastern and Western Hemisphere Antarctic stations during sudden stratospheric warming life cycle, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5045, https://doi.org/10.5194/egusphere-egu25-5045, 2025.

An on-going campaign monitors the greenhouse gases emissions of biogas plants in the Grand Est region, in France, using airborne in situ CO₂ and CH₄ concentrations and wind measurements from Uncrewed Aerial System, associated with a mass balance method. During 16 days in 2024, we quantified the instantaneous emissions of 19 agricultural biogas plants, with installed methane productions ranging from 128 to 312 Nm³.h^-1_,producing biogas injected into the network mainly from manure, energy crops and agricultural wastes.

Observations obtained to date were used to quantify emissions either representative of the globality of a biogas plant or of specific targeted sources inside a site (inputs, effluents, digesters or biogas purification). Global plant methane emissions among all sites range from 1.5 to 26 kg.h^-1, with average emissions of 10 kg.h^-1_. Repeated measurements of emissions on the same site at different dates depict a significant temporal variability, however overwhelmed by the variability of emissions among all sites. We estimated instantaneous methane losses ranging from 1.7 to 10 %, comparing monitored emissions with the installed productions. Emissions of targeted sources among sites suggest that inputs and effluents might be the predominant methane sources on the sites, while biogenic CO₂ emissions might be mostly attributed to the biogas purification process.

This campaign highlighted several limits intrinsically linked with the mass balance method. One of them is the sensitivity to contamination by parasite sources, which has to be anticipated during the field campaign preparation. Another difficulty is the risk of measuring truncated plumes, as the mass balance method requires the monitoring of an entire plume cross-section to provide quantifications representative of the complete source emissions. These limitations could be overturned in the future by alternative quantification methods, such as inversion methods based on Large Eddy Simulation of the atmospheric transport, considering the highly variable nature of the turbulent plume. These new developments, associated with evolutions of the monitoring protocol, may improve the reliability and precision of the results.

How to cite: Bonne, J.-L., Dumelie, N., Lauvaux, T., Abdallah, C., Burgalat, J., Albora, G., Vincent, J., Cousin, J., Parent, F., Moncourtois, V., and Joly, L.: Lessons learned from a UAS survey of methane emissions from multiple biogas plants in France, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5789, https://doi.org/10.5194/egusphere-egu25-5789, 2025.

Based on the traditional satellite-based convective initiation (CI) detection method, an improved algorithm for the identification and tracking of CIs using satellite data has been proposed. This algorithm then undergoes spatio-temporal matching with ground-based observation data such as radar and precipitation data. Incorporating experts domain knowledge, the algorithm utilizes a subjective-objective interactive approach to complete the verification and calibration of the satellite-drived CI identification results. This process results in a high-resolution annotation dataset of convective initiation that can be used for detection and forecasting of CI and artificial intelligence models.

Firstly, within a spatial-temporal window of 30 minutes before and after the satellite CIs trigger time and a radius of 20km, the satellite-derived CIs are matched with radar-identified CIs. Additionally, within a spatial-temporal window of 60 minutes after the satellite CI trigger and extending 2km outside the CI cloud clusters movement zone, the satellite-derived CIs are also matched with precipitation data. The two matching results are combined to form a comprehensive identification of CIs. Furthermore, using a calibration system and a back-to-back verification method by forecasters, the CI annotation results are revised, resulting in a high-resolution and reliable CI annotation dataset.

Using this methodology, a high spatio-temporal resolution CI dataset was established for the years 2018-2023, which allowed for the statistical analysis of CI distributions across different precipitation levels in each month. The highest proportion of CI events occurred in August, followed by July. Among these, CI events with moderate precipitation accounted for 46.2%, weak precipitation accounted for 34.4%, and strong precipitation accounted for 19.3%.

It can be seen that there is a noticeable northward shift in the occurrence of CI events, especially those associated with heavy precipitation, from April to August. In April, these events are mainly concentrated in a few provinces in the central and southern parts of the country. Subsequently, they gradually expand from south to north, covering the entire central and eastern research area by August. In September, they retreat back to the central and southern regions. This spatial evolution pattern of CI events once again verifies that the occurrence of severe convection events is closely related to the position changes of the Intertropical Convergence Zone (ITCZ) and the monsoon.The frequency of CI occurrences has also been proven to peak between 11 a.m. and 3 p.m., regardless of precipitation intensity.

How to cite: Peng, L., Ye, C., and Ou, X.: Convection Initiation Identification and The Construction of A High-value Dataset Using the Fengyun-4A Satellite, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6767, https://doi.org/10.5194/egusphere-egu25-6767, 2025.

The tropical middle atmosphere is characterized by long-period oscillations such as the Quasi Biennial Oscillation and the Semiannual Oscillation which are primarily driven by the interaction of a broad spectrum of atmospheric waves with the background flow. Using reanalysis datasets and independent rocket soundings from a low latitude location, we identified a hitherto unreported variability in the tropical middle atmosphere that appears at a variable interval of 2-5 years in the late 20^th century and 7-9 years in the early 21^st century. The newly identified variability, Quasi-Periodic Easterly Bursts (QPEBs) as we call them, manifests as enhanced easterlies during the easterly phase of the Stratopause Semiannual Oscillation around May-July. QPEBs are found to have remote influences on the Southern Hemispheric polar vortex as well as residual circulation in the lower mesosphere. A momentum budget analysis reveals that QPEBs are found to be primarily caused by enhanced cross-equatorial advection as well as gravity wave drag. Even though a close association with the Quasi Biennial Oscillation winds is observed, the cause of the observed periodicity remains elusive.

How to cite: Koushik, N. and Kumar, K. K.: An Enigmatic Variability in the Tropical Middle Atmosphere, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7147, https://doi.org/10.5194/egusphere-egu25-7147, 2025.

In past few decades there has been a noticeable increase in the frequency and intensity of extreme rainfall events (EREs) globally, including India. The Clausius-Clapeyron relationship explains how the warmer air can significantly hold more moisture. Hence, in present climate change scenario increasing temperature along with other factors can lead to further increase in EREs. Effective management strategeis in various sectors like disaster preparedness, smart-city planning, water quality, public health, agriculture planning, etc. can get improved, through proper understanding on the distribution and frequency of EREs. Keeping in mind the socio-economic impacts of EREs; this study aimed to identify the hotspot regions for EREs in India.

India is a country with vast spatio-temporal variability in rainfall pattern. Hence, this study implemented objective criteria to identify the spatio-temporal rainfall variability of EREs over four rainfall homogeneous regions for pre-monsoon, monsoon and post-monsoon seasons. Based on frequency distribution of daily accumulated rainfall, suitable rainfall threshold values for defining EREs are identified for each homogeneous region and each season. These threshold values vary region-wise as well as season-wise. Distribution of EREs show interannual as well as seasonal variability.

Clustering algorithms, popular unsupervised Machine Learning (ML) techniques, are handy tools to identify hotspots of extreme rainfall regions with similar spatial variability. To understand the ERE distribution and to identify rainfall hotspots based on long term daily gridded rainfall data, this study implemented K-means clustering and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithms. Comparative area distribution study between K-means and DBSCAN clustering help to identify the EREs hotspots in India. Overall, the K-means method shows more scattered hotspots compared to DBSCAN method, which are further validated using Davies-Boulding Index (DBI), Silhouette score, Calinski-Harabasz (CH) score and Dunn's Index. These score analysis methods serve as potential tools to support the clustering validation method. In addition to the area distribution, this study has addressed the temporal variability of the EREs hotspots. ST-OPTICS ( Spatio-Temporal Ordering Points to Identify the Clustering Structure) algorithm results clustering of hotspots based on their spatial and temporal similarity. This study shows that ML algorithms prove to be promising techniques for detecting and analyzing spatial as well as temporal variability of EREs hotspots which is effective for future management practice in various sectors.

Keywords: Extreme Rainfall Events; DBSCAN Clustering; K-Means Clustering; ST-OPTICS.

How to cite: Putatunda, I. and Vasudevan, R.: Extreme rainfall hotspots in India based on spatio-temporal variability of rainfall using unsupervised clustering techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7852, https://doi.org/10.5194/egusphere-egu25-7852, 2025.

Agricultural yield prediction plays a crucial role in food security and economic planning, yet existing models often struggle with the complexity and high dimensionality of agricultural data. This study presents a framework that combines explainable artificial intelligence (XAI) with feature reduction methodology to enhance the accuracy and efficiency of rice yield prediction. Our approach addresses the dual challenges of model interpretability and computational efficiency while maintaining high prediction accuracy.

The framework begins with a systematic development of prediction models utilizing advanced machine learning (ML) and deep learning (DL) techniques. We implemented comprehensive pre-processing steps, including data normalization, feature engineering, and missing value handling, to ensure data quality. Our evaluation encompassed various models, including Random Forest, Gradient Boosting Machines, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks with attention mechanisms. To optimize model performance, we employed hyperparameter tuning through grid search, effectively mitigating issues of overfitting and underfitting.

A notable innovation of our framework is the incorporation of SHapley Additive exPlanations (SHAP), enabling transparent insights into the model's decision-making process. Leveraging this XAI approach, we introduced a novel feature reduction methodology that systematically identifies and removes negatively contributing features while maintaining model accuracy. Our analysis of a multivariate dataset which is a public dataset from rice fields in the an Giang province of the Mekong Delta, Vietnam, required the integration of diverse satellite datasets, including optical data from Landsat and radar data from Sentinel-1. This revealed distinct patterns of feature influence on yield prediction, facilitating the optimization of the feature set for maximum effectiveness. Key radar polarization bands, VV (Vertical-Vertical) and VH (Vertical-Horizontal), provided crucial surface backscatter data, capturing information on crop structure, growth stages, and post-harvest soil conditions. Notably, the feature min_vh consistently emerged as the most significant predictor.

The implementation of our feature reduction strategy resulted in significant improvements in both model performance and computational efficiency. By removing 15-20 number of identified negatively contributing features, we achieved approximately 3-5% improvement in prediction accuracy while substantially reducing the computational overhead and model training time. This enhancement in efficiency did not compromise the model's interpretability, demonstrating the robust nature of our framework.

Our methodology represents a significant advancement in agricultural modeling by successfully addressing the challenges of high-dimensional data while maintaining model interpretability. The framework's ability to identify and eliminate non-contributing features while improving prediction accuracy demonstrates its potential for wide-scale application in agricultural yield prediction. Furthermore, the reduced computational requirements make it a practical solution for real-world applications where computational resources may be limited.

These results validate the effectiveness of our integrated approach in handling complex agricultural data while providing actionable insights for yield prediction. The framework offers a scalable, interpretable, and computationally efficient solution that can be adapted for various agricultural prediction tasks, potentially transforming how we approach agricultural yield forecasting.

How to cite: Dey, A., Saha, A., Sarkar, S., Mondal, A., and Mitra, P.: An Explainable AI-Driven Feature Reduction Framework for Enhanced Agricultural Yield Prediction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7965, https://doi.org/10.5194/egusphere-egu25-7965, 2025.

Black carbon (BC) is a short-lived atmospheric aerosol having light absorbing properties with climate-changing potential. In addition, BC aerosols are also responsible for several adverse health effects including cardiovascular and respiratory problems. Here, we examine the long-term changes in BC, using MERRA-2 (Modern-Era Retro spective analysis for Research and Applications) and Emissions Database for Global Atmospheric Research (EDGAR) data for the period 2000–2019, and the associated health burden in rural India. This study finds a decreasing trend in BC in the rural IGP (Indo-Gangetic Plain) and NWI (North West India) during 2007–2019, at about -0.004 and –0.005 μg/m³/yr, respectively. A significant reduction in BC (from 0.03 to 0.01 μg/m³/yr after 2006) is observed in the rural Peninsular India (PI), where the reduced wind speed limits the transport of BC aerosols from other regions and thus, limits the BC concentration there. Our assessment finds that government policies such as BS (Bharat Stage) emission norms, electrification of rail routes, use of electric and compressed natural gas-based vehicles, the transformation of brick kilns to zig-zag technology, mechanised farming for on- site handling of crop residues and recent changes in atmospheric drivers (e.g. winds in IGP) contributed to this reduction in BC. However, the health burden associated with BC causes the highest all-cause mortality to be around 5,17,651 and 34,082 inhabitants in winter (December-February) and post-monsoon (October-November) seasons, respectively, in the rural IGP in the latest year 2019. In brief, the reduction of BC in rural India indicates that it complements the government policies. However, an improvement in the policy implementation might prove to be conducive to reduce the BC-driven mortality and regional climate warming.

How to cite: Pathak, M., Kuttippurath, J., and Kumar, R.:  Long-term changes in black carbon aerosols and their health effects in rural India during the past two decades (2000–2019), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9946, https://doi.org/10.5194/egusphere-egu25-9946, 2025.

The rapid urbanization of modern cities presents significant challenges, with air pollution emerging as a critical concern for public health and environmental sustainability. In Greece, while the government collects extensive air quality data as mandated by the EU Directive 2881/2024 (recast of 2008/50, 2004/107), limited efforts are made to communicate this data to the public. The existing network of large monitoring stations is often inaccessible to the pubic and primarily serving scientists and policymakers.

Addressing this gap, the FAIRCITY (ATTP4-0360457) project—a collaboration between the National Observatory of Athens, the National and Kapodistrian University of Athens and the Greek Innovation Company Energy4Smart—introduces the “Smart Stations” an innovative solution incorporating public benches powered by photovoltaics, equipped with free charging sockets for people with electrical wheelchairs as well as other smart city sevices, with embedded low cost air quality sensors, designed to make air quality data accessible, timely, and engaging. This initiative not only aligns with global sustainability goals but also serves as a model for other cities seeking to improve urban liveability. The low-cost sensors embedded within the bench at a height of approximately 3 meteres above ground, were selected based on size, technology and price criteria to continuously monitor eight key pollutants: three fractions of Particulate Matters (PM₁, PM_2.5, PM₁₀), carbon monoxide (CO), carbon dioxide (CO₂), nitrogen dioxide (NO₂), ozone (O₃) and sulfur dioxide (SO₂).

The Smart Stations are deployed in open, public spaces (e.g., commercial areas, residential zones, parks), at a distance from major pollutant sources and in collaboration with interested municipalities of the Attica Region. Their aim is to record the local air quality and pollutant diurnal variations in order to highlight the sources responsible (i.e., Korydallos high NO₂, NO, PM concentrations from traffic) and estimate the population exposure. Citizens can walk up to these stations, sit down and instantly access critical information about their local air quality from digital displays that provide in near real-time the simplified Air Quality Index (AQI) along with health protection and other environmental infomation.

Preliminary results indicate that the diurnal variations of the monitored pollutants follow closely the local anthropogenic activities (traffic by passing the area, central heating, cooking). The pollutant levels are similar across the different municipalities, presenting peaks at different times depending on the type of area. The hourly AQI is mainly affected by larger scale events such as an extensive air pollution episode or dust intrusion event.  

How to cite: Assimakopoulos, V., Fameli, K.-M., Kladakis, A., Efthymiou, C., Charalampidou, C., Sotiropoulou, M., Antoniou, I. –. M., Kytrilaki, A., Massas, A., and Assimakopoulos, M.-N.: Bridging the Gap: Smart Benches for Accessible Urban Air Quality Monitoring and Public Engagement in the Region of Attica, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10268, https://doi.org/10.5194/egusphere-egu25-10268, 2025.

Air quality monitoring is crucial for assessing environmental health and supporting mitigation strategies. This research focuses on the co-location of various low-cost particulate matter (PM) sensors—uRADMonitor, AirGradient, PurpleAir, Clarity, and sensors from community initiatives—alongside a mobile laboratory equipped with a reference-grade GRIM EDM 180 analyzer. The primary goal is to identify and quantify bias among these low-cost sensors for PM2.5 and PM10 measurements at the same location.

By systematically analyzing the measurement discrepancies, a generalized correction formula is derived, enabling the harmonization of readings across different sensor types. The corrected data will form the basis of a hybrid air quality monitoring network, which standardizes PM2.5 and PM10 concentrations regardless of the sensor manufacturer. This approach leverages the affordability and scalability of low-cost sensors while ensuring data quality comparable to reference instruments.

The results aim to address limitations in the current low-cost sensor ecosystem, enhance interoperability, and provide communities and policymakers with reliable, high-resolution air quality data. Ultimately, this study supports the development of inclusive and sustainable monitoring frameworks that empower both urban and rural regions with actionable environmental insights, using all kinds of sensors.

How to cite: Luchiian, A.: Harmonizing Low-Cost Air Quality Sensors for a Hybrid Monitoring Network, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10631, https://doi.org/10.5194/egusphere-egu25-10631, 2025.

Agricultural burning in Tucumán, Argentina, has been a major contributor to air pollution, particularly during the dry season (April to September). This environmental issue is mainly due to the limited availability of modern machinery for sustainable harvesting, leading to heavy reliance on traditional biomass burning for crop residue management. The combustion process generates large amounts of fine particulate matter (PM_2.5), which severely affects air quality and public health. To address this challenge, an inter-institutional collaboration under the Networking Initiative Breathe2Change.org, supported by the Alexander von Humboldt Foundation, facilitated the creation of the first air quality monitoring network in Tucumán. This initiative aimed to raise awareness and provide actionable data to local communities and scientists.

A custom sensor module was designed, integrating an OPC Plantower PMS5003 sensor for real-time PM_2.5 detection, CO₂ sensors using NDIR technology, as well as humidity and temperature sensors. A forced ventilation system was also incorporated to ensure representative air circulation inside the module without affecting airflow into the OPC sensor. The network, consisting of 25 sensor modules deployed throughout the 22,500 square kilometers of Tucumán, provided continuous data collection for 12 months in 2023. The data were shared on a publicly accessible data platform, developed as part of the Breathe2Change Initiative, which facilitated both citizen consultation and analysis by the scientists involved in the project.

During an initial 3-week intercomparison phase, 10 sensor modules were assessed for consistency, yielding a high correlation (R² > 0.9), confirming the reliability of the modules. Afterward, 23 of the 25 sensors were deployed across urban, suburban, and rural areas, including regions directly affected by agricultural fires. High- and low-flow reference samplers were used to collect daily PM2.5 concentrations from August to December, coinciding with the peak biomass burning period. During this period, two of the sensor modules were co-located with the reference samplers to allow for direct comparison. This phase was essential for deriving a local correction factor for the sensors.

Results showed considerably high PM2.5 concentrations, with monthly averages exceeding 60 µg/m³ in fire-impacted areas, well above the daily limits set by the World Health Organization (WHO). Even urban areas recorded average levels of 30 µg/m³, surpassing WHO guidelines. The region’s mountainous terrain and climate further exacerbated the pollution, triggering thermal inversion phenomena that trapped pollutants near ground level. Using the corrected sensor network, spatial distribution maps of PM_2.5 were generated through Kriging interpolation, revealing a strong correlation between elevated pollutant levels and fire activity. Higher PM_2.5 concentrations were observed in the central-eastern part of the province, likely linked to sugarcane production areas, and possibly influenced by rural traffic and biomass burning. Kriging analysis confirmed this spatial trend, with a marked reduction in localized concentrations after September, likely due to rainfall events.

This study underscores the degradation of air quality during biomass burning events and the need for regulatory measures and sustainable agricultural practices to mitigate environmental and health impacts. It also highlights the potential of low-cost sensors as effective tools for monitoring air pollution in resource-limited regions.

How to cite: Gibilisco, R. G., Aguilera Sammaritano, M., Reynoso Posse, F., Huber, K., Elizondo, J., Torkar, S., Saez, M. M., Scaglioti, A., Tames, F., Puliafito, E., Castellano, M. J., Diaz, M., Parellada, N., Ciancaglini, G., Schillman, B., Kurtenbach, R., Wiesen, P., Caggiano, A., Ben Altabef, A., and Teruel, M.: Spatio-Temporal Distribution of PM 2.5 and its Association with Agricultural Fires in Northern Argentina., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11635, https://doi.org/10.5194/egusphere-egu25-11635, 2025.

Severe weather events often develop rapidly and cause extensive damage, resulting in billions of dollars in losses annually. This paper explores Large Language Models (LLMs) to effectively reason about the adversity of weather hazards. To tackle this issue, we gathered National Weather Service (NWS) flood reports covering the period from June 2005 to September 2024. Two pre-trained LLMs including Bidirectional and Auto-Regressive Transformer (BART) models (large) and Bidirectional Encoder Representations from Transformers (BERT) were employed to classify flood reports according to predefined labels. These models encompass a range of sizes with parameter counts of 406 million, and 110 million parameters, respectively. We employed the Low-Rank Adaptation (LoRA) fine-tuning technique to enhance performance and memory efficiency. The fine-tuning and few-shot learning capabilities of these models were evaluated to adapt pre-trained language models for specific tasks or domains. The methodology was applied in Charleston County, South Carolina, USA— a vulnerable region to compound flooding. Extreme events reported during the training periods were unevenly distributed across training period, resulting in imbalanced datasets. The “Cyclonic” category represents significantly fewer instances in the report text data, while the “Flood” and “Thunderstorm” categories appeared more frequent.  The findings revealed that while few-shot learning significantly reduced computational costs, fine-tuned models resulted in more stable and reliable performance. Among multiple LLMs applied in this research, the BART model achieved higher F1 scores in the “Flood,” “Thunderstorm,” and “Cyclonic” categories—requiring fewer training epochs to reach optimized performance levels. Furthermore, the BERT model demonstrated a shorter overall training time (12 hours 17 minutes) compared to other LLMs, demonstrating efficient cost of computing. This comprehensive evaluation of LLMs across diverse NWS flood reports enhanced our understanding of their capabilities in text classification and offered valuable insights into leveraging these advanced techniques for weather disaster assessment.

How to cite: Neupane, A., Zafarmomen, N., and Samadi, V.: Leveraging Large Language Models for Enhancing and Reasoning Adverse Weather Hazard Classification, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13882, https://doi.org/10.5194/egusphere-egu25-13882, 2025.

Air quality monitoring is an essential procedure to ensure that pollutant levels remain within safe limits and do not pose a threat to public health, particularly for vulnerable populations. The deployment and maintenance of stationary air quality monitoring stations can be expensive, especially when a large number is required to create a comprehensive network. As a result, there has been growing interest in utilizing small, low-cost sensors that are easier to deploy and provide a more flexible and cost-effective alternative. In addition to these sensors, satellite systems have become valuable tools for air quality monitoring, offering high temporal resolution data that facilitates the assessment of air pollution over larger areas. This study looks into data fusion techniques to combine data from both stationary and mobile low-cost sensors with satellite data to analyze the air quality at the University of the Philippines, Diliman campus. Seven small sensors were deployed across the university, a mixed-use area with both vegetation and buildings, to measure pollutant concentrations, such as particulate matter. Satellite data from MODIS, Sentinel-5P, and ERA5 reanalysis were used to monitor aerosol optical depth (AOD), sulfur dioxide (SO2), nitrogen dioxide (NO2), and meteorological conditions. The time-series analysis focused on a three-day period during which mobile air quality data from an e-trike were collected around the university. The data from these mobile sensors, along with the stationary sensor measurements, were used to estimate PM2.5 concentrations across the campus. Kriging interpolation, a geostatistical method that estimates unknown values based on the spatial correlation of known data points, was employed to generate smooth surfaces of PM2.5 concentration across the university.  Kriging interpolation was used on the stationary sensor dataset to predict the PM2.5 levels at the location of the mobile sensors at a given timeframe. Moreover, cokriging was also applied by incorporating multiple correlated variables, improving predictions by utilizing relationships between the primary variable (PM2.5) and secondary variables, such as aerosol optical depth or SO2 and NO2 concentrations. The results obtained from both Kriging and Cokriging methods were compared with data collected from mobile sensors to assess the air quality at the University of the Philippines, Diliman. The interpolated PM2.5 values were compared with the data from the mobile sensors (SEN55 and PMS7003) as ground truth, and a mean absolute percentage error (MAPE) of 43.00% to 57.23% was obtained. Initial results of cokriging with NO2 showed MAPE of 36.67% to 52.55%. Further work is expanding the dataset and refining the interpolation models to enhance the accuracy and reliability of air quality assessments across the university. By integrating more data and conducting additional tests, this approach can provide more comprehensive air quality monitoring at reduced costs and address data gaps.

How to cite: Hizon, J. R., Torres, R. A., Togonon, A. C. E., Recto, B. A., Apostol, F. A., Magpantay, P., Eslit, J. J., Ganhinhin, J., Rosales, M., Austria, I., de Guzman, J., de Leon, M. T., Cajote, R., Co, P. J., and Ramos, R.: Air Quality Assessment In The University Of The Philippines Diliman Campus Through The Integration Of Small Sensors, Satellite Data, And Kriging Interpolation Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14960, https://doi.org/10.5194/egusphere-egu25-14960, 2025.

Fog significantly reduces visibility, impacting transportation and safety, particularly in regions like the United Arab Emirates (UAE) where it is a regular
occurrence, in particular in the winter months. This study develops a machine learning-based approach for automated fog detection and masking from near real-time observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument onboard the Meteosat Second Generation spacecraft to enhance fog detection and forecast. We evaluated six basic machine learning (ML) models trained with four different methods: (1) supervised training using SEVIRI pixel data and fog observations over airport stations; (2) as approach (1) but incorporating infrared channel data; (3) training with labeled fog and no-fog regions identified in SEVIRI night microphysics Red-Green-Blue (RGB) images through k-means clustering; and, (4) a fusion approach combining station-labeled data (approach 1) and k-means clustered-labeled data (approach 3). Among the models, the eXtreme Gradient Boosting (XGBoost) demonstrated slightly higher performance. Models trained on station data (approach 1) achieved a Probability of Detection (POD) of 0.73 and a False Alarm Ratio (FAR) of 0.11. For spatial fog masking, models trained on a combination of station-labeled and k-means cluster-labeled data (approach 4) performed best. Overall, the XGBoost method and the fusion approach (4) are recommended for fog detection and masking in the hyper-arid UAE. These findings demonstrate the potential for trained ML models to deliver accurate, near real-time fog detection and masking, enhancing monitoring over broad areas.

How to cite: Nelli, N. R., Francis, D., Cherif, C., Fonseca, R., and Ghedira, H.: Automated Nighttime Fog Detection and Masking Using Machine Learning from Near Real-Time Satellite Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14973, https://doi.org/10.5194/egusphere-egu25-14973, 2025.

The Madden-Julian Oscillation (MJO) is a crucial atmospheric phenomenon characterized by large-scale, eastward-propagating disturbances in the tropical atmosphere. It profoundly influences global climate and weather patterns and serves as a key source of predictability for subseasonal forecasts. In particular, the propagation characteristics of the MJO are critical parameters that impacts the timing and intensity of its effects. Variability in these characteristics can alter the MJO’s interaction with other climate components, thereby affecting weather patterns. Therefore, it is essential to investigate the variability of MJO propagation characteristics.

In this study, we aim to examine the changes in propagation characteristics of the MJO, such as propagation speed, across three primary regions: Indian Ocean, Maritime Continent, western Pacific. These changes will be compared between two distinct period (P1: 1979-1998, P2: 2003-2022). Furthermore, we will investigate the mechanisms driving variations in MJO propagation speed within each tropical region and assess potential future changes using reanalysis data and model outputs. By addressing these questions, this study can contribute to improve the predictability and accuracy of climate models in representing the MJO.

How to cite: Kim, H.-R. and Ha, K.-J.: Changes in MJO Propagation Characteristics and Regional Variations under a Changing Climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15784, https://doi.org/10.5194/egusphere-egu25-15784, 2025.

 In order to solve the problem of quantity traceability of precipitation phenomenon instrument, a precipitation phenomenon checking device was developed. By simulating the precipitation particles of 4.3 mm and 9.5 mm, corresponding to the velocities of 2m/s, 7M/s and 12M/s respectively, the on-site verification of the precipitation phenomenometer and the test program of the upper computer software are carried out, the relevant particle channels are recorded and displayed in the map, and the performance of the precipitation phenomenometer is judged automatically. It has many advantages, such as complete function, reasonable design, easy to carry, friendly software interface, one-button detection, automatically judge whether the equipment is qualified, and according to the template to generate a verification report. The practical application proves that the device provides a strong support for the meteorological department's equipment support personnel to carry out the verification work of the precipitation phenomenometer, improves the working efficiency, and plays a role in supervising and inspecting the quality of the precipitation phenomenometer's equipment, it has a good application prospect in the field verification of precipitation phenomenometer.

How to cite: Han, Y.: Development and application of the calibration device of precipitation phenomenometer, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17395, https://doi.org/10.5194/egusphere-egu25-17395, 2025.

The Multi-sensor Advection Diffusion Weather Research and Forecast (MAD-WRF) model is a state-of-the-art addition to the WRF model that includes a fast cloud-initialization procedure, making it more suitable for hydrometeors analysis and clouds forecasts. The MAD-WRF cloud initialization combines a cloud parameterization that infers the presence of clouds based on relative humidity with observations of the cloud mask and cloud top/base height to provide a three-dimensional cloud analysis. During the forecasts, the hydrometeors can be advected and diffused with no microphysics, in what we refer to as the MAD-WRF passive mode. Alternatively, these passive hydrometeors can be integrated into the explicitly resolved hydrometeors during a nudging phase, designated the MAD-WRF active mode (Jiménez et al., 10.1016/j.solener.2022.04.055). As such, MAD-WRF has been extensively used for solar energy predictions.

Here we have investigated the feasibility of using MAD-WRF to improve the accuracy of intense precipitation forecasts. An extreme precipitation event over Israel that led to urban floods and two casualties in Tel-Aviv during January 4^th, 2020, has been chosen as a case study. The extreme accumulated precipitation responsible for noon and early afternoon floods was triggered by a persistent cloud train that developed over the area several hours before. MAD-WRF model has been configured with 3-nested domains with 9, 3 and 1 km grid-sizes. We have run MAD-WRF in active mode incorporating satellite-retrieved cloud-top heights provided by the European Space Agency EUMETSAT in all three domains. EUMETSAT data are available in near real-time making it suitable for operational forecasts.

Independent precipitation data measured by the Israel Meteorological Service radar at Bet-Dagan (about 10 km south-east of Tel-Aviv) has been used for forecasts verification. Comparison between radar data and MAD-WRF forecasts with and without incorporation of EUMETSAT cloud-tops retrievals reveal the advantage MAD-WRF cloud initialization. The significant improvement in the forecast of the location and rate of the precipitation is observed up to 12 hours ahead in time.

On-going work focuses on the evaluation of the precipitation distributions and improvement of the forecast of dry areas.

How to cite: Gelman, A., Morin, E., Jiménez, P., Sheu, R.-S., and Rostkier-Edelstein, D.: Improving forecasts of extreme precipitation with MAD-WRF mesoscale model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17568, https://doi.org/10.5194/egusphere-egu25-17568, 2025.

Dengue fever outbreaks impose a severe healthcare burden in Vietnam, therefore the development of an early Dengue warning system is key to improve public health planning and mitigate the future burden produced by this disease. This study assessed the ECMWF ensemble re-forecast skill for relative humidity, temperature and precipitation, which are key factors for vector-borne disease transmission in Vietnam between 1-4 weeks in advance. We focused the analysis on the rainy season (May-October) using ERA5 reanalysis as a reference dataset. Re-forecast data was pre-processed using a quantile mapping technique to reduce the bias between re-forecast and observations. Results showed that corrected re-forecasts of weekly mean temperature, relative humidity and accumulated precipitation are skilful up to 2-3 weeks in advance and rank histograms verified the forecast reliability. Nonetheless the model is less skillful for the region of South Vietnam and seems to struggle at predicting extremely high/low values of temperature, relative humidity and precipitation. Results from this study demonstrate that ECMWF ensemble forecasts are suitable to use as inputs for a dengue early warning system up to 14-21 days in advance

How to cite: Perez, I., Sparrow, S., Weisheimer, A., Wright, M., and Main, L.: Verification of weather variables linked to Dengue incidence inthe sub‐seasonal scale in Vietnam, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19687, https://doi.org/10.5194/egusphere-egu25-19687, 2025.

Background: The increasing availability and usage of low-cost air quality sensors (LCS) presents both opportunities and challenges in terms of data accuracy, reliability, precision and interpretation. Various low cost sensors types differ in the degree of accuracy reliability and precision They can also be influenced by environmental conditions like temperatures and humidity. This study assesses three LCS, E-Samplers, Modulair^TM and AirQO, deployed alongside a reference-grade Beta Attenuation Monitor (BAM-1022) in Nairobi, Kenya, to upraise their performance under varying conditions and explore the strategies for calibration and integration into the monitoring networks.

Methods: The study used BAM-1022 data to validate and calibrate the LCS installed at the University of Nairobi’s Parklands Campus (27 February 2024 to 26 December 2024).  We analyzed sensor accuracy, precision and response to pollution across wet and dry seasons and varying temperature and humidity levels. We aligned the LCS data with BAM-1022 measurements using tailored correction factors and multiple linear regression (MLR) models. We used the coefficient of determination, represented by R-squared (R²), a statistical measure of how close the data from the LCS are from the data from the BAM and the Pearson correlation, r to show the strength of the linear relationship between the sensor measurements and reference measurements. Additionally, we conducted paired t-tests to determine whether statistically significant differences existed between the BAM-1022 and each LCS, and one-sample t-tests to find out if there was a statistically significant difference in the values recorded by low-cost sensors themselves. The study also explored the potential of LCS to improve spatial coverage and resolution while addressing challenges like sensor drift and environmental interference.

Results: The Modulair^TM sensor showed closer measurements in reference to BAM-1022 measurements (R²= 0.82, r =0.9458) followed by AirQO (R²=0.54, r =0.8933) and E-Sampler (R²=0.36, r =0.7166). During wet season, Modulair^TM maintained the closer measurements (R²=0.73, r =0.9123) with AirQO (R²=0.36, r =0.7219) and E-Sampler (R²=0.21, r =0.7812) showing lower alignment. Similar trend was observed in dry season with Modulair^TM (R²=0.8, r=0.8124) followed by AirQO (R²=0.51, r=0.7001) and E-Sampler (R²=0.28, r=0.6996). During high PM_2.5 concentration periods (July to December), Modulair^TM reported higher values than the BAM on certain days. AirQO generally recorded lower values except during these high concentration periods while the E-Samplers fluctuated between higher or lower values across the collocation period. Consequently, correction factors of -12.5, 31.55 and 29.65 were derived for Modulair^TM,AirQO and E-Samplers respectively. Statistical analysis revealed a significant difference between the BAM measurements and LCS (p-value < 0.001). However, no significant differences were observed between the measurements of each of the low-cost sensors.

Conclusion: The LCS can enhance air quality monitoring networks when collocated appropriately and, consistently and carefully calibrated. The readings should be corrected against reference sensor for accurate and reliable data.  Collocation with reference monitors or among the LCS units for regions with limited access to high-end monitoring infrastructure such as Nairobi is key before deployment. Air quality modeling can create a comprehensive monitoring networks hence improved spatial resolution and public health insights. 

How to cite: Nyamondo, J., Oguge, N., Anyango, S., Afulloh, A., Adera, N., and Okoth, B.: Air Quality Monitoring in Nairobi City, Kenya: Role of Collocation in Low Cost Sensor Deployment , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20267, https://doi.org/10.5194/egusphere-egu25-20267, 2025.

Tropical deep convective clouds (DCCs) play a pivotal role in Earth's hydrological cycle, with their dynamics strongly influenced by aerosols. Depending on their properties, aerosols can either invigorate or suppress cloud formation and development. Previous observational studies and cloud-resolving model simulations have shown that aerosols such as black carbon (BC) and sulfates modify cloud microphysics, affecting droplet size distribution, latent heat release, and precipitation patterns. However, the use of global climate models (GCMs) to study these aerosol-cloud interactions remain limited, despite their ability to capture large-scale circulation patterns and associated non-linear feedback. This study investigates the sensitivity of aerosol-induced cloud invigoration and suppression (AIVe) to major aerosol species during the Indian summer monsoon (ISM) season using the Community Earth System Model, specifically its atmospheric component, the Community Atmosphere Model version 5 (CESM-CAM5). The analysis focuses on DCCs over central India during the monsoon months of June–September (JJAS) for the period 2005–2008. Aerosol and cloud parameters from CESM-CAM5 simulations, conducted at 0.5-degree horizontal resolution, are compared with satellite observations. Five Atmospheric Model Intercomparison Project (AMIP)-style simulations were performed: one with aerosols at pre-industrial level (PI) levels, another at present-day (PD) levels, and three additional simulations perturbing specific aerosol species (dust, BC, and sulfate) under PD conditions to isolate their individual effects on AIVe. The findings highlight that aerosol physico-chemical properties critically influence DCC behavior. Black carbon near the boundary layer increases cloud condensation nuclei (CCN) concentrations, delaying precipitation, enhancing warm-phase invigoration, and strengthening updrafts. In the upper troposphere, BC absorbs solar radiation, causing atmospheric warming that promotes cloud deepening and cold-phase processes. Additionally, BC intensifies both shortwave and longwave heating, prolonging cloud lifetimes and supporting deeper convection. Sulfate aerosols primarily enhance warm-phase invigoration through increased CCN concentrations at lower altitudes. However, their weaker vertical transport limits their impact on cold phase processes and deep convection compared to BC and dust. Dust aerosols with high concentrations in the mid-troposphere, act as efficient ice-nucleating particles (INPs), enhancing cold phase invigoration. However, suppressed updrafts in the upper troposphere reduce their overall effect on deep convective systems, emphasizing the importance of aerosol size, number concentration, and properties in shaping AIVe. This study underscores the complex interplay between aerosol characteristics and their vertical distribution in influencing cloud dynamics during the ISM. Detailed results and further implications will be presented.

How to cite: Sharma, P., Ganguly, D., and Kant, S.: Sensitivity of Cloud Invigoration and Suppression Effects to Major Aerosol species During the Indian Summer Monsoon in a Global Climate Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-886, https://doi.org/10.5194/egusphere-egu25-886, 2025.

Black carbon (BC) aerosols have been reported to influence the precipitation patterns over South-East Asia. In this study, we present surface measurements of BC carried out from a rural location in the Western Ghats and covering all the seasons. Despite being a remote location with negligible anthropogenic emissions, the total BC concentration is strongly modulated by particles originating from fossil fuel burning (~75%). Contrary to the prominent role played by boundary layer dynamics in the diurnal variations of BC in the tropics, our measurements reveal a disconnection between boundary layer dynamics and BC concentration mostly due to the advection from a distant urban location being the dominant source of BC. However, this influence is conspicuous on the concentration of particles originating from biomass burning. Seasonal variations in the wind fields, surface temperature, and rainfall are observed to influence the BC concentration, thereby leading to distinct diurnal variations seldom reported elsewhere. Reanalysis data sets fail to capture these changing patterns in BC, with daily mean concentrations exhibiting large differences with our observations (particularly in winter months) and diurnal patterns being different throughout the season. Under this backdrop, incorporation of these measurements could possibly improve the monsoon forecast in global climate models and provide deeper insights on the role of meteorology and boundary layer dynamics on aerosol fields in complex environments.

How to cite: Sunil S, D., Narayana Sarma, A., Kudilil, S., Sreedharan Krishnakumari, S., and Krishnaswamy, K.: Modulation of temporal evolution of black carbon aerosols at a rural location in the Western Ghats by meteorology and boundary layer dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1100, https://doi.org/10.5194/egusphere-egu25-1100, 2025.

During winter, dense fog occurrences in the Indo-Gangetic Plain pose severe risks to visibility, air quality, and public health, emphasizing the need for improved fog forecasting in India. This study employs a high-resolution WRF-Chem model (2 km × 2 km) to identify optimal configurations for simulating fog in the region and investigate the impact of urbanization-induced UHI/UDI (Urban Heat Island/Urban Dry Island) and elevated emissions on the fog life cycle in and around the megacity of Delhi.

A comprehensive sensitivity analysis explores model configurations across microphysics, planetary boundary layer (PBL), land surface models (LSM), radiation schemes, chemistry, and emission inputs. Simulations of surface and vertical meteorology are evaluated against data from weather stations and radiosonde profiles, while modeled chemistry is compared with ground-based measurements. Results demonstrate that specific combinations of microphysics, PBL, and LSM schemes coupled with chemistry effectively simulate Liquid Water Content (LWC), a critical fog proxy. Modeled relative humidity, particulate matter concentrations, and fog life cycles show strong agreement with observations. We then utilize this optimized model configuration to quantitatively analyze individual and combined effects of urbanization and aerosols on fog formation.

How to cite: K Lal, A., Kunchala, R. K., and Mohan, M.: Evaluating WRF-Chem for simulating fog episodes: A Case Study from The National Capital Region Delhi, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1370, https://doi.org/10.5194/egusphere-egu25-1370, 2025.

Satellite remote sensing has the advantage of wide spatial coverage and high data consistency, which is an important technology for global atmospheric environment monitoring. However, due to the influence of cloud cover, satellite remote sensing faces the problem of data missing; moreover, the direct object of hyperspectral satellite remote sensing is the total amount of pollution gases in the atmosphere, which is different from the near-ground concentration that directly affects human health. To solve these problems, this research developed a remote sensing technology combining satellite spectral analysis and artificial intelligence. We use artificial intelligence to increase the spatial coverage of satellite and ground-based remote sensing, and make future short term predictions and their applications. Preliminary results show that the reconstruction of satellite remote sensing data supported by artificial intelligence is of great significance for environmental pollution monitoring and control.

How to cite: Liu, C., Hu, Q., Li, Q., and Zhang, C.: Spatiotemporal reconstruction of gas pollutants with high resolution and coverage using hyperspectral remote sensing and artificial intelligence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1923, https://doi.org/10.5194/egusphere-egu25-1923, 2025.

In Indian metropolitan cities, two-wheelers (2W) constitute 60–70% of traffic, making their emissions a significant contributor to urban air pollution. This study measured 2W exhaust emissions and driver exposure under real-world traffic conditions in the Chennai metropolitan area. Emission factors for CO, HC, and NO were 1.1, 0.02, and 0.03 g/km, respectively. However, limited studies on 2W are available due to the complexity of real-world measurements in Indian traffic conditions. The gaseous emissions from the measured vehicles are lower than their respective Bharat Stage (BS) standards except for CO. Personal exposure levels for PM₁₀, PM_2.5, and PM₁ were 212.5, 78.1, and 58.9 µg/m³, with the highest exposures occurring during idling and driving behind heavy-duty vehicles. The Multiple Particle Path Dosimetry (MPPD) model was used to estimate the deposition fractions in the human respiratory tract (HRT). Results indicated that PM_2.5 and PM₁ deposition fractions are higher in the pulmonary region, whereas PM₁₀ deposition is higher in the head region. 2W drivers are exposed to higher concentrations than any other motor vehicle driver. Since there is no substantiation of a tolerable limit of PM₁ exposure or a threshold beyond which no detrimental health implications occur, cautious planning is needed when developing the roads.

How to cite: Ranjan, S., K. Kuppili, S., and Nagendra SM, S.: Exploring the Nexus of Two-Wheeler Gaseous Contributions and Driver Exposure in a Million-Plus Population City, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2082, https://doi.org/10.5194/egusphere-egu25-2082, 2025.

Volcano eruption is one of the most destructive natural disasters, and its direct release of toxic gases and volcanic ash can lead to atmospheric pollution, posing significant threats to human health and ecological balance. To investigate the environmental impact of volcanic emissions, we retrieved the vertical column densities (VCDs) of sulfur dioxide (SO₂) and bromine monoxide (BrO) using the Chinese highest-resolution atmospheric trace gas remote sensing satellite payloads: the Environmental Trace Gas Monitoring Instrument (EMI) series on-board the GaoFen (GF5-02) and DaQi (DQ-1) satellites.

Here, we present our study on two significant volcanic emission events. On January 15, 2022, a violent eruption occurred near the South Pacific Island nation of Tonga, which is a typical submarine volcano. During this eruption, the volcanic plume ascended directly into the stratosphere (above 20 km), releasing a substantial amount of SO₂ and spreading rapidly westward (~30 m/s). In contrast, the majority of the BrO dispersed southeastward slowly (~10 m/s) within the altitude range of 8–15 km on January 16. The differences in eruption height and timing resulted in the transport of SO₂ and BrO in distinct directions in the Southern Hemisphere.

Another case is the Sundhnukagigar volcano on Iceland's Reykjanes Peninsula, which is a typical fissure volcano. A significant eruption began at 21:00 on August 22nd, following an earthquake swarm; this was the largest eruption in the region since December 2023. Satellite data indicated that the volcanic eruption released high concentrations of SO₂, with the maximum SO₂ VCD exceeding 15 Dobson Units (DU). By the morning of the 26th, part of the air mass had been transported northward to the Arctic Svalbard region. Simultaneously, ground observations from Ny-Ålesund revealed that an unprecedented Arctic haze event occurred, with the SO₂ VCD reaching approximately 40 times the usual level. It is also important to note that, in the context of global warming, the ongoing activity of Iceland's volcanoes will further exacerbate the melting of local glaciers and permafrost. This, in turn, disrupts the gravitational balance of the overlying crust, leading to an intensification of volcanic activity. Therefore, it is essential to employ multi-instrument, multi-scale, and high-resolution observations to monitor volcanic activity and assess its impact on both regional and global climate and the environment.

How to cite: Luo, Y., Li, Q., Wu, K., Qian, Y., Zhou, H., and Si, F.: Two Typical Case Studies of Volcanic Eruption Trace Gases Based on EMI Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2443, https://doi.org/10.5194/egusphere-egu25-2443, 2025.

As a major air pollutant and precursor of ozone (O₃), anthropogenic nitrogen oxides (NO_X = NO + NO₂) have been effectively controlled in China since peaking around 2012. However, the evolving contrast of emissions across cities and its impacts on secondary pollutants such as O₃ remain poorly understood, primarily due to the limitations of existing emission inventories. Here we track the historical high-resolution (5 km) NO_X emissions based on POMINO-OMI and POMINO-TROPOMI NO₂ VCDs, adopting our previously developed inversion, PHLET. The results demonstrate significantly weaker NO_X emission declines in economically small cities where environmental pollution received much less attentions, leading to a shift of emission burdens toward western and non-capital cities. Moreover, simulations based on GEOS-Chem indicate that such disparities in NO_X emission trends have inhibited the mitigation of O₃ mainly in the western China, and even added up to the O₃ increase in some areas of the North China Plain. Our study points to the value of satellite-based inversion to access historical environmental regulations, and emphasizes the importance of collaborative pollution control across regions for comprehensive pollution control in China and other Global South countries undergoing rapid emission changes.

How to cite: Kong, H., Lin, J., Chen, L., Zhang, Y., and Wang, S.: City-level Disparities in NOX Emission Trends and Their Inhibitory Effects on O3 Mitigation in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3222, https://doi.org/10.5194/egusphere-egu25-3222, 2025.

Atmospheric ice nucleating substances (INSs) play a crucial role in ice cloud formation above -35°C, impacting cloud radiative properties, cloud lifetime, and the hydrological cycle. Characterizing inorganic (e.g., mineral dusts, volcanic ash, metals) and organic (e.g., bacterial cells, fungal spores, pollen, and various biomacromolecules) INSs has typically involved: 1) single-particle analyses, which offer high resolution but require specialized equipment, and 2) bulk sample treatment (e.g., heat, H₂O₂, (NH₄)₂SO₄) analyses, which are more accessible but may overestimate or underestimate INS concentrations due to non-target effects. There is a need for additional methods to quantify inorganic and organic INSs concentrations in the atmosphere to test and improve climate models.

Here we show a new density gradient centrifugation method to differentiate and quantify inorganic (densities ≥ 2.1 g cm^-3) and organic INSs (densities ≤ 1.6 g cm^-3). Density gradient centrifugation was used to separate the INSs suspension into their respective density isolate. This was followed by a wash procedure consisting of sequential differential centrifugation and ultrafiltration. Lastly, the INSs were quantified using a droplet freezing assay.

Our method successfully recovered organic water-soluble INSs (lignin, birch pollen washing water and filtered Fusarium acuminatum) and organic water-insoluble INSs (Snomax and Pseudomonas syringae) in the low-density isolate. We recovered inorganic water-insoluble INSs (K-feldspar) in the high-density isolate. In an INS mixed suspension, we recovered K-feldspar in the high-density isolate and lignin in the low-density isolate both at concentrations similar to the isolated K-feldspar or lignin tests. 

This work demonstrates the broad applicability of density gradient centrifugation for characterizing a wide range of inorganic and organic atmospheric INSs.

How to cite: Uppal, G. K., Worthy, S. E., Chen, L., Yeung, C., McConville, O., and Bertram, A. K.: SeParation of Ice Nuclei via Density Layers (SPINDL): A new method for characterizing ice nuclei using density gradient centrifugation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3841, https://doi.org/10.5194/egusphere-egu25-3841, 2025.

Monitoring emissions of nitrogen dioxide is crucial for understanding the atmospheric composition and its impacts on air quality and climate. This study aims to evaluate the accuracy of retrievals of nitrogen dioxide tropospheric column by the Tropospheric Emissions: Monitoring of Pollution (TEMPO) by comparing them against retrievals of the ground-based Pandora instruments.

The TEMPO is a visible and ultraviolet spectrometer flying aboard of a commercial telecommunications satellite, Intelsat 40e, in geostationary orbit over 91˚ W longitude, thus maintaining a continuous view of North America. High resolution measurements of radiance reflected by the Earth's back to the instrument's detectors enable retrievals of columns of nitrogen dioxide involved in the chemical dynamics of Earth’s atmosphere. TEMPO V03 Level 1, 2, and 3 data were recently made available from the Atmospheric Science Data Center (ASDC) via NASA EarthData Search.

Direct-Sun Pandora spectrometer is used to retrieve columnar amounts of trace gases in the atmosphere by the means of differential optical absorption spectroscopy at numerous locations around the globe.

ASDC has developed a set of Jupyter notebooks dedicated to TEMPO vs. Pandora comparisons of the columns of individual trace gases including one dealing with NO₂ tropospheric column. The notebooks allow a user to select a specific Pandora station and a timeframe of interest. The code downloads all relevant TEMPO L2 granules as well as the Pandora dataset. The latter is sub-set to the selected timeframe. Time series of the gas column retrievals along with their uncertainties are then derived with accounting for the quality flags from both datasets. Since Pandora measurements are significantly more frequent, a procedure computing weighted averages of them at the times of TEMPO retrievals was incorporated to the notebooks allowing direct comparison of gaseous columns from two sensors against each other.

The results derived by the ASDC tool show only qualitative agreement between the TEMPO and Pandora retrievals of nitrogen dioxide tropospheric column. it was also found that the discrepancies between the two are site dependent which may point to a potential problem with Pandora quality flags. Two attempts were made to improve comparison. Since TEMPO algorithm allows for negative NO₂ tropospheric columns, such retrievals were removed from consideration. There are also multiple TEMPO retrievals accompanied by uncertainty greater that the retrieved column. Removal of such retrievals constitutes another approach to improve comparison.

The findings of this study will contribute to the understanding of the reliability and applicability of space-based trace gases monitoring for air quality applications. The results will enhance our understanding of atmospheric processes related to tropospheric NO₂.

How to cite: Radkevich, A., Mahmoud, H., and Kaufman, D.: Comparison of nitrogen dioxide tropospheric columns retrieved by TEMPO and Pandora, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4493, https://doi.org/10.5194/egusphere-egu25-4493, 2025.

Wildfires and dust storms significantly contribute to air pollution, causing adverse health impacts and intensifying various aerosol-meteorology feedbacks in the atmosphere through direct and indirect aerosol effects. These effects, however, are highly variable and depend on prevailing synoptic conditions. In April 2020, Ukraine experienced one of its most severe air pollution episodes, which had a profoundly negative impact on the Kyiv metropolitan area. This event was triggered by wildfires in the abandoned exclusion zone around the Chornobyl Nuclear Power Plant (northern Ukraine) and a dust storm that swept across the entire territory of Ukraine from the west to the east. Despite similar aerosol emissions – characterized by elevated levels of dust, organic carbon (OC), and black carbon (BC) – the atmospheric effects varied significantly under different synoptic processes during April 2020. This study presents seamless modeling results that analyze the meteorological response to direct (DAE) and indirect aerosol effects (IDAE) under varying synoptic conditions during this pollution episode in Ukraine.

Using the Environment – HIgh-Resolution Limited Area Model (Enviro-HIRLAM) at a 1.5 km horizontal resolution, four simulations/runs were conducted to investigate the role of aerosols: DAE run, IDAE run, combined aerosol effects (COMB run), and a reference (REF run) representing a standard Numerical Weather Prediction configuration without aerosol effects. The uniform and continuous effects of biomass burning and dust aerosols were primarily observed in radiation parameters, leading to a reduction in downwelling global and net short-wave radiation by 25-40 W/m². A clear correspondence between aerosol distribution and changes in the spatial patterns of other meteorological parameters was evident during the atmospheric fronts and the dust storm episode. Notably, the movement of a warm front caused near-surface air temperature to decrease and specific humidity to increase ahead of the front, with the opposite effects observed behind it. Compared to the REF run, these parameters exhibited local variations ranging from -2.6°C to +1.0°C for air temperature and from -1.5 g/kg to +1.0 g/kg for specific humidity. Aerosol effects during the stationary cold front led to an increase in air temperature and cloud liquid water content. However, transported sulfur aerosols significantly influenced these effects against the background of OC and BC emissions. In contrast, the subsequent dust storm and cold front had the opposite effect on air temperature, also impacting changes in turbulent kinetic energy. Most of these effects were associated with areas in model domain affected by elevated concentrations of dust, BC, and OC in their coarse and accumulation modes.

We acknowledge support through the grant HPC-Europa3 Transnational Access Programme for projects “Integrated modelling for assessment of potential pollution regional atmospheric transport as result of accidental wildfires”; projects Horizon Europe programme under Grant Agreement No 101137680 CERTAINTY (Cloud-aERosol inTeractions & their impActs IN The earth sYstem); project No 101036245 RI-URBANS (Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban & Industrial AreaS) and No 101056783 European Union via FOCI-project (Non-CO2 Forcers And Their Climate, Weather, Air Quality And Health Impacts).

How to cite: Savenets, M., Mahura, A., Nuterman, R., and Petäjä, T.: Direct and indirect effects of biomass burning and dust aerosols under various synoptic processes during the April 2020 pollution case in Ukraine, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4697, https://doi.org/10.5194/egusphere-egu25-4697, 2025.

Black carbon (BC), the primary light-absorbing aerosol, has significant implications for atmospheric heating and climate change, with far-reaching effects on regional air quality and public health. In Iran, BC concentrations, primarily resulting from combustion processes such as industrial emissions, vehicular exhaust, and biomass burning, constitute a significant air quality challenge, particularly in urban regions with high levels of anthropogenic activity. However, there is a lack of studies on the long-term trends of BC in Iran, particularly regarding the effects of urban growth and land use changes on air quality and human health. This study systematically analyzes trends in BC concentrations from 1980 to 2023, both on a national and regional scales, using high-resolution data from the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2).  The analysis includes temporal and spatial variations to evaluate the impact of anthropogenic and natural factors on BC levels over this period. A substantial increase in BC concentrations was observed from 1980 to 2023, followed by a decline after 2010. Regional analysis revealed higher BC levels in western Iran, driven by concentrated anthropogenic and industrial activities, compared to the sparsely populated, desert-dominated eastern regions, characterized by arid landscapes. Seasonal variations in BC concentrations were observed nationwide, with peak levels occurring in Tehran and Ahvaz during the winter. Trend analysis across various land use and land cover (LULC) types indicated that urban and agricultural expansion were the primarily drivers of increasing BC concentrations. Positive correlations were observed between the aforementioned factors and aerosol emissions, while water and grassland coverage were associated with reduced emissions in most regions. These findings underscore the necessity of expanding natural land use, such as forest coverage, and promoting sustainable urbanization as strategies to mitigate BC emissions.

How to cite: Shaheen, A., Yousefi, R., Wang, F., Kaveh-Firouz, A., and Ge, Q.: Assessing black carbon dynamics in Iran: the role of urban growth and land use changes in long-term trends (1980–2023), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5323, https://doi.org/10.5194/egusphere-egu25-5323, 2025.

Cloud droplet size distribution is essential for quantifying the role of clouds in earth system, including cloud albedo, precipitation formation, and cloud lifetime. The response of cloud droplet spectral relative dispersion (ε) to aerosol number concentration (N_a) is highly uncertain, and the role of turbulence in ε–N_a relationships is yet puzzling. This study uses large eddy simulation to examine the ε–N_a relationship and derives an expression for ε from a minimal model to elucidate this relationship. Our findings indicate that as N_a increases, ε initially decreases due to the aerosol’s effect on weakening the intensity of turbulence-induced broadening greater than its effect on weakening the intensity of condensational narrowing. However, as N_a continues to increase, ε increases due to the aerosol’s effect on weakening the intensity of condensational narrowing more significant than its effect on weakening the intensity of turbulence-induced broadening. These findings improve the understanding of the aerosol effects on cloud droplet size distribution and address the challenge of quantifying aerosol indirect effects considering turbulence, potentially leading to new cloud microphysics parameterizations.

How to cite: Chen, Y., Chen, J., and Lu, C.: Turbulence-induced Non-Monotonic Influence of Aerosols on Cloud Droplet Size Distribution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5362, https://doi.org/10.5194/egusphere-egu25-5362, 2025.

Delhi is one of the most polluted cities in the world with a rapidly growing population. Huge amount of VOCs is released into the atmosphere from both anthropogenic and biogenic emissions. Various types of VOCs are released from anthropogenic sources such as disinfectants and cleansers, paints and varnishes, wood preservatives, aerosol sprays, room fresheners, dry cleaners and organic solvents. Another important anthropogenic source is burning of fossil fuels in motor vehicles, which also releases VOCs. Various plants species also release VOCs like isoprene (biogenic VOCs) which upon oxidation with atmospheric oxidants like ozone (O₃), nitrate (NO₃) and hydroxyl radicals (OH) forms less volatile products which on further reaction forms secondary organic aerosols (SOA). VOCs are also responsible for formation of tropospheric ozone which is one of the major criterion air pollutants and causes various health issues.

Around 32 samples of VOCs have been collected in the NCT of Delhi using charcoal tubes from the selected sites, VIZ., Okhla Phase 2 (OKHL, Industrial site), Sri Aurobindo Marg (SAM, traffic intervention site), Income tax office (ITO, traffic intervention site), Jawaharlal Nehru University (JNU, Institutional site). Sample preparation has been done following the protocol given by NIOSH 1501 method for xylene analysis, which is widely accepted as a “golden standard” for Industrial Hygiene sampling. Collected samples were run on GC-FID and concentration of VOCs is determined. The average concentration of Total VOCs at SAM is found to be 382.07µg/m³ while it is 200.14, 242.63 and 452.62 µg/m³ at JNU, OKHL and ITO, respectively. Out of all the VOCS, benzene and toluene represents the highest percentage with benzene representing a percentage of 17%  and 18% at SAM, JNU, Okhla and ITO, respectively and Toluene  contributing to a percentage concentration of 15% , 13%, 16% and 15% respectively at SAM , JNU, Okhla and  ITO thus owing to high vehicular emissions in Delhi. Individual average concentration at evening is higher than individual average concentration at morning at all chosen sites.  Also individual concentration of benzene and toluene is higher than other VOCs being 64.14 µg/m³ and 59.13 µg/m³ respectively at SAM, 35.64 µg/m³ and 25.83 µg/m³ at JNU, 79.6 µg/m³ and 69.9 µg/m³ at ITO and 41.92 µg/m³ and 37.80 µg/m³ at Okhla. It has planned to evaluate both the carcinogenic and non-carcinogenic risk associated with the chosen VOCs. This research will help us to get knowledge of sources of emission of VOCs. Further we will get a knowledge of the carcinogenic and non-carcinogenic impacts of VOCs and the percentage of population in Delhi which is getting directly or indirectly exposed to the carcinogenic VOCs. Hence it would help us in determining the health risk associated with VOC emission which would help in formulating effective strategies for controlling VOC emission. This would further aid us in reducing tropospheric ozone which is also a pollutant of concern. This study can also be used further in understanding atmospheric chemical reactions, photochemical smog pollution, assessment and forecast of possible change in atmospheric environment on the regional/global scale.

How to cite: Sharma, R.: Volatile organic compounds in ambient air of Delhi, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5764, https://doi.org/10.5194/egusphere-egu25-5764, 2025.

NASA’s Atmospheric Science Data Center (ASDC) at Langley Research Center will present an overview of the Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission, focusing on the cutting-edge tools and services available to users for air quality research and environmental monitoring. TEMPO is a pioneering geostationary satellite that provides day light hourly observations of pollutants over North America, including measurements of ozone, nitrogen dioxide, and other critical pollutants.This presentation will highlight the ASDC’s role in archiving, distributing, and providing user support for TEMPO data. Attendees will be introduced to data access tools, visualization platforms, and analysis services designed to facilitate the use of TEMPO observations for scientific research and decision-making. Key resources, such as NASA Earthdata Search, Earth GIS, OPeNDAP, Worldview, Github Tutorials and Harmony services on the cloud, will be showcased, demonstrating how researchers can efficiently explore and download high-resolution data products.Additionally, the presentation will cover the application of TEMPO data in studying air quality trends, emission sources, and the impacts of pollution on public health and climate. Attendees will also gain insights into ASDC's open science initiatives, which encourage collaboration and data sharing to enhance the impact of TEMPO and NASA’s broader Earth science mission.Through this presentation, the ASDC aims to empower the scientific community with the tools and knowledge needed to harness the full potential of TEMPO data in addressing pressing environmental challenges.

How to cite: Mahmoud, H. and Radkevich, A.: Leveraging TEMPO Data: Tools and Services for Air Quality Monitoring and Research from the Atmospheric Science Data Center, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7314, https://doi.org/10.5194/egusphere-egu25-7314, 2025.

Microplastics, particularly microplastic fibers, are an emerging pollutant of growing concern, frequently detected in the atmosphere. However, recent studies emphasized the predominance of artificial and natural microfibers over microplastic fibers. Despite this, research focusing on all types of microfibers, commonly grouped as anthropogenic microfibers (MFs) remains limited, especially in residential indoor environments. Therefore, this study explored the indoor atmospheric deposition of microfibers, in the residential homes of Chennai, India, marking the first such study in the country. Additionally, workplaces, including offices, laboratories, and hostel rooms, were examined. Bedrooms (16,736±7,263 MFs/m²/day) and student hostels (5,572±2,898 MFs/m²/day) recorded the highest contamination in respective categories, and this could be attributed to the abundance of textile products, such as bedsheets, carpets, quilts, towels, and curtains in the indoors of both the rooms. MFs shorter than 500 µm dominated the samples, comprising 78.8 and 65.9 % of total MFs in residential and workplace categories, respectively. The diameter of MFs ranged from 2.02–23 µm in residential spaces and 2.04–36.4 µm in workplaces, indicating their potential to penetrate human lungs. µ-FTIR analysis revealed the predominance of semi-synthetic MFs (48.2 %), followed by natural (29.3%) and synthetic (22.5 %) MFs, underscoring the need to consider all categories of MFs. Further classification revealed rayon (94.5±6.40 %), cotton (68.1±6.12 %), and polyethylene terephthalate (PET) (48.1±11.5 %) as major MFs, indicating textiles as a significant contamination source. The detection of black rubber/latex MFs indicates additional contributions from road dust. Surface morphological analysis, correlations with environmental and meteorological factors, and backward trajectory analysis further highlighted the primary role of indoor/local sources in MFs contamination. Overall, the study emphasizes the need to monitor all categories of MFs and calls for comprehensive investigations into the impact of indoor textile products and road dust on indoor atmospheric contamination in future research.

How to cite: Jessieleena, A., Kambapalli Ezhilan, I., Chandel, A. S., D'sa, S. V., Mohamed, N., and Nambi, I.: Atmospheric deposition of anthropogenic microfibers in different indoor environments of Chennai, India , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7978, https://doi.org/10.5194/egusphere-egu25-7978, 2025.

Nitrogen dioxide (NO₂), an atmospheric pollutant produced by fossil fuel combustion in vehicles and industrial processes, is harmful to human health, worsening respiratory and cardiovascular diseases. The main effects of NO₂ pollution on human health are respiratory infections, airway inflammation, asthma, and low birth weight, among others. Vehicle traffic in cities is one of the main sources of NO₂, affecting the health of the population living near highways. The highest NO₂ concentration occurs at distances between 200 and 500 meters from high-traffic highways. The study area, the metropolitan region of Campinas (MRC), Brazil, is a technological, industrial and economic hub with 3.3 million inhabitants and busy transport corridors that connect the southeast and central-west regions of the country. It is composed of 20 municipalities and is located in São Paulo state, the most developed and populated Brazilian state. The aims of this work are to map atmospheric NO₂ pollution and estimate NO₂ concentrations near the highways in the MRC using average daily vehicle flow (DVF) and NO₂ concentrations estimated from satellite images. Data on the tropospheric vertical column of nitrogen dioxide (in mol/cm²) values from 32 daily images from the Sentinel 5P satellite TROPOMI spectrometer that were collected from April 15 to May 20, 2024, were used. During that period, there was no rain, and the sky remained clear and cloudless. The images were processed to produce NO₂ median images during the study period. The NO₂ pollution map was produced by the spline interpolation algorithm method. To estimate the concentration of NO₂ near the MRC highways, a road map was used, and a 500 m buffer was drawn around the highways. The NO₂ pollution map was combined with the buffer map, and the median NO₂ concentration within the 500 m buffer around the highways was estimated. Pearson regression analysis was performed between the average DVF and the NO₂ concentration. The results revealed a positive and significant correlation (r=0.692; p= 0.004) between the DVF and NO₂ concentration near the highways estimated from satellite data. The highest NO₂ concentrations were observed near highways SP-083 (1.5591 mol/cm²; 45,000 vehicles/day), SP-330 (1.521 mol/cm²; 38,815 vehicles/day), and SP-075 (1.485 mol/cm²; 37,813 vehicles/day). The results of this study can be used in epidemiological research to identify neighborhoods and populations that live near high NO₂ concentration highways and are exposed to respiratory and cardiovascular disease risks. In the next step of this research, the NO₂ concentration values ​​estimated from Sentinel 5P images in mol/cm² units will be converted to µg/m³ units using data from ground-based measurement stations located in the MRC. In the future, this methodology can be used to produce highway NO₂ pollution maps for areas in which ground measurement station data are unavailable.

How to cite: Ferreira, M.: Using Sentinel 5P satellite and vehicle flow data to map NO2 air pollution near highways in the Metropolitan Region of Campinas, Brazil, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9328, https://doi.org/10.5194/egusphere-egu25-9328, 2025.

Extreme weather events, such as extreme temperatures, water vapor transport, and the resulting extreme precipitation, have been occurring with increasing frequency and are projected to intensify further in a warming climate. Understanding how these events respond to climate change is critically important. Numerical models serve as essential tools for uncovering the mechanisms behind these phenomena, with spatial resolution being one of the key challenges. Leveraging advanced supercomputing resources, we have recently made significant advancements in developing high-resolution Earth system models based on the Community Earth System Model (CESM), featuring a 25 km atmospheric resolution and a 10 km oceanic resolution. Compared to the commonly used CMIP5 and CMIP6 models, the high-resolution Earth system model demonstrates substantial improvements in reproducing extreme weather events, thereby greatly enhancing the confidence in future projections.

How to cite: Gao, Y.: Enhancing the Simulation and Prediction of Extreme Temperature and Water Vapor in a Warming Climate Using a High-Resolution Earth System Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11612, https://doi.org/10.5194/egusphere-egu25-11612, 2025.

The Indo-Gangetic Plain (IGP) is a globally recognized hotspot for high aerosol loading, necessitating precise modelling to understand its spatial and temporal dynamics. This study evaluates the performance of differently parameterized Seasonal Autoregressive Integrated Moving Average (SARIMA) models in forecasting the Aerosol Optical Depth (AOD) at 550 nm retrieved from the CERES (Clouds and the Earth's Radiant Energy System) satellite platform across eight  locations: Delhi, Dhaka, Jaipur, Kanpur, Karachi, Kolkata, Lahore, and Varanasi in the IGP. Using long-term AOD datasets from CERES during the period of 2005 to 2020, we tested various SARIMA configurations to capture seasonal trends and irregular variations specific to urban environments. The SARIMA configurations tested include configure_1: (1,0,1)(1,0,1)₁₂, configure_2: (1,1,1)(1,1,1)₁₂, configure_3: (2,0,1)(2,0,1)₁₂, and configure_4: (2,1,1)(2,1,1)₁₂ These configure models were compared with CERES-derived observations for AOD at the study sites for the next two years, that is, Jan, 2021 to Dec, 2022. Each configuration was assessed for data stationarity using the Augmented Dickey-Fuller (ADF) test and if not follows, then the differentiation method has been used to stationaries the series. The Model performance was evaluated using multiple statistical metrics, including normalized Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Squared Error (RMSE), Mean Bias Error (MBE), and Mean Absolute Percentage Error (MAPE) for every configuration showed the low metric values. The result indicates high correlation coefficients, ranging from 0.54 to 0.91, and R-squared values, varying between 0.31 and 0.81 for all configurations that significantly determined the best-suited models for each location. Every modelled configuration has been checked with 95% and 99% confidence interval (with alpha=0.05 and 0.01, respectively) showing the p-value <0.001. These results emphasize the models' ability to replicate observed AOD patterns effectively. It reveal that parameter sensitivity plays a critical role in predictive accuracy, with optimal configurations varying across locations due to heterogeneity in aerosol sources and meteorological conditions. The present study underlines the importance of site-specific model tuning for reliable aerosol forecasting in densely populated and pollution-prone regions. These insights provide a foundation to enhance air quality prediction studies and address health, and climate impacts associated with aerosols in the IGP.

How to cite: Mall, A. and Singh, S.: Comparison of Differently Parameterized SARIMA Models using CERES-Derived Aerosol Optical Depth over Indo-Gangetic Plain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12277, https://doi.org/10.5194/egusphere-egu25-12277, 2025.

The aim of this study is to investigate Particulate Matter (PM) sources and mechanisms of formations over the city of Naples (Italy) and their seasonal and day-to-day variations.

We have sampled fine particles with diameter < 2.5 μm (PM_2.5) and < 10 μm (PM₁₀) daily on pre-cleaned (700 °C for 2 h) quartz filters, during the months of May and November 2016-January 2017, on top of the historical building complex in Largo San Marcellino, Naples. We have measured the concentrations of total N/C together with their isotopic composition (δ¹⁵N and δ¹³C). We have also measured the concentration of major ions and interpreted the results with data of gaseous compounds, as well as consideration of the meteorology, using data and state of the art models of atmospheric circulation (Hysplit). Our point was to show that the uncertainty associated with quantification of sources contribution with an apportionment model decreases when the model is constrained with information derived from different methods.

Seasonal differences: the results show that the concentrations of total PM₁₀/PM_2.5, N/C measured in autumn are more variable than those measured in spring. This is related to a different wind regime, whereby in spring air masses mostly originated from West and South (the “clean” Mediterranean sea), whereas in autumn the wind blew air from North (over the highly urbanized and “dirty” European continent). This interpretation is supported by the concentration of major ions showing more scattered values in autumn for species typically originating from land (K⁺, NH₄⁺, NO₃^-), with high values on the 9^th and the 26-27^th of December and the 2^nd of January 2017. However, neither the monthly mean δ¹⁵N and δ¹³C, nor the daily values corresponding to the spikes show significant changes, suggesting that the isotopic composition of total N/C has limited power in identifying changes of mean monthly sources or for the spikes. 

Day-to-day variations: a significant change of the main species measured is found around the middle of May. This event is associated with a change in weather pattern going from a typical land-sea breeze wind regime (typically causing poor air circulation and stagnation of air masses) to an intense synoptic with winds originating mostly from South/South-West (the sea). Correspondingly, there is a peak in the concentration of major ions originating mostly from land (NO₃^-, SO₄^2-, Ca₂⁺, C₂O₄^2-, K⁺) towards the end of the land-sea breeze regime (9-11^th May), followed (10-15^th May) by an increase of the concentration of major ions originating mostly from the sea (Na⁺, Mg²⁺, Cl^-). The entire period (9-14^th) is characterized by a concurrent variation of total N, C, δ¹⁵N and δ¹³C. While the changes of δ¹⁵N are caused mainly by isotope fractionations, associated with the dissociation of NH₄Cl producing NH₃ and HCl, the changes of δ¹³C are caused mostly by a change of the source of total C, associated with carbonate (CO₃^2-) apportion.

We conclude that the concentrations and isotopic compositions of N/C in PM are useful tools only when coupled with other tools like the analysis of the meteorology and the concentration of major ions.

How to cite: Rubino, M., Sirignano, C., Chianese, E., Hernández-Ceballos, M. Á., Angyal, A., Marzaioli, F., Di Rosa, D., Caso, G., and Riccio, A.: Multiple lines of evidence help identify the sources and formation mechanisms of Nitrogen and Carbon in Particulate Matter sampled in the historical center of Naples (Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13295, https://doi.org/10.5194/egusphere-egu25-13295, 2025.

Active and passive sensors onboard satellites and suborbital measurements have shown frequent aerosol-cloud overlapping situations over several regions worldwide on a monthly to seasonal scale. However, retrieving the optical properties of aerosols lofted over clouds poses challenges. Primarily, the assumption of aerosol single-scattering albedo (SSA) in the satellite-based algorithms is known to be one of the largest sources of uncertainty in quantifying the above-cloud aerosol optical depth (ACAOD). On the radiative forcing aspect, the sign and magnitude of the aerosol radiative forcing over clouds are determined mainly by the aerosol loading, the absorption capacity of aerosols (SSA), and the brightness of the underlying cloud cover.

We contribute to addressing the uncertainties surrounding the absorbing aerosols-cloud radiative interactions by offering a novel, NASA’s A-train-centric, one-and-half decade long (2006-2022) global retrieval product of aerosols above cloud that delivers 1) spectral ACAOD, 2) spectral SSA of light-absorbing aerosols lofted over the clouds, and 3) aerosol-corrected cloud optical depth (COD). The synergy algorithm combines lidar retrievals of ACAOD derived from the ‘De-polarization Ratio’ method applied to CALIOP and the top-of-atmosphere (TOA) spectral reflectance from OMI (354-388 nm) and MODIS (470-860 nm) sensors to deduce the joint aerosol-cloud product. The availability of accurate ACAOD accompanied by a marked sensitivity of the TOA measurements to ACAOD, SSA, and COD allow retrieval of SSA for above-cloud aerosols scenes using the ‘color ratio’ algorithm applied to UV and VIS sensors.

We will present multiyear (2006-2022), regional retrievals of UV-VIS spectral aerosol SSA above clouds, and it’s a comparison against ORACLES airborne in situ and remote sensing measurements and ground-based AERONET inversions. A preliminary uncertainty analysis suggests that an uncertainty of 20% in ACAOD can result in an error of ~0.02 at 388 nm and ~0.01 at 470 nm in the retrieved SSA from OMI and MODIS, respectively. Furthermore, the presented aerosol-cloud remote sensing algorithm assumes implications for the recently launched EarthCARE and PACE missions with potential synergy of ATLID lidar and OCI imager. The availability of the global aerosol-cloud joint product will reenergize the community by offering 1) an improved representation of aerosol extinction and absorption properties over clouds and 2) much-needed observational estimates of the radiative effects of aerosols in cloudy regions for constraining the climate models.

How to cite: Jethva, H., Torres, O., Kayetha, V., and Hu, Y.: One-and-half Decade Long Global Retrieval Dataset of UV-VIS Spectral Optical Depth and Single-scattering Albedo of Absorbing Aerosols above Clouds from A-train Active-Passive Synergy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14363, https://doi.org/10.5194/egusphere-egu25-14363, 2025.

We present the status of the Level 0-1 processor for the Tropospheric Emissions: Monitoring of Pollution (TEMPO), with a primary focus on radiometric calibration. Multiple version updates have significantly improved the TEMPO Level 1 products, enhancing the quality of Level 2 products and enabling the detection of city lights, nightglow, and aurora signals during twilight hours. However, assessments of TEMPO Level 1 data (versions 1 to 3) indicated overestimations of Sun-normalized radiances when compared to radiative transfer calculations. To investigate these biases, we compared TEMPO solar irradiance measurements to those from multiple independent instruments and a high-resolution reference solar spectrum. For Earth radiance assessments, we conducted intercomparisons with spaceborne measurements from the Advanced Baseline Imager (ABI) instruments onboard the Geostationary Operational Environmental Satellite (GOES)-16 and -19. Located at the checkout position of 89.5°W for post-launch testing, GOES-19 ABI has provided comparable viewing geometries with TEMPO (at 91.0°W) over North America. On the other hand, comparisons with GOES-16 ABI (located at 75.2°W) may require corrections for viewing angles and bidirectional reflectance distribution function (BRDF) effects due to larger differences in geometries. Additionally, we compared TEMPO Sun-normalized radiances with radiative transfer simulations over Railroad Valley, which use ground-based surface reflectance measurements as input. In this work, we present the intercomparison results and propose potential approaches to mitigate the radiometric biases.

How to cite: Chong, H., Liu, X., Houck, J., Flittner, D. E., Carr, J., and Hou, W. and the TEMPO instrument calibration team: The Tropospheric Emissions: Monitoring of Pollution (TEMPO) Level 0-1 Processor – Radiometric calibration and intercomparison, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14596, https://doi.org/10.5194/egusphere-egu25-14596, 2025.

Monoterpenes, the second most abundant biogenic volatile organic compounds globally, are crucial in forming secondary organic aerosols, making their oxidation mechanisms vital for addressing climate change and air pollution. This study utilized cyclohexene as a surrogate to explore first-generation products from its ozonolysis through laboratory experiments and mechanistic modeling. We employed proton transfer reaction mass spectrometry with NH₄⁺ ion sources (NH₄⁺-CIMS) and a custom-built OH calibration source to quantify organic peroxy radicals (RO₂) and closed-shell species. Under near-real atmospheric conditions in a Potential Aerosol Mass-Oxidation Flow Reactor, we identified 30 ozonolysis products, expanding previous data sets of low-oxygen compounds. Combined with simulations based on the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere and relevant literature, our results revealed that OH dominates over ozone in cyclohexene oxidation at typical atmospheric oxidant levels with H-abstraction contributing 30% of initial RO₂ radicals. Highly oxidized molecules primarily arise from RO₂ autoxidation initiated by ozone, and at least 15% of ozone oxidation products follow the overlooked nonvinyl hydroperoxides pathway. Gaps remain especially in understanding RO₂ cross-reactions, and the structural complexity of monoterpenes further complicates research. As emissions decrease and afforestation increases, understanding these mechanisms becomes increasingly critical.

How to cite: Li, Y., Ma, X., Lu, K., and Zhang, Y.: Investigation of the Cyclohexene Oxidation Mechanism Through the Direct Measurement of Organic Peroxy Radical, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14804, https://doi.org/10.5194/egusphere-egu25-14804, 2025.

Anthropogenic aerosols significantly deteriorate the urban air quality and climate of the western Indian region, nevertheless, the contributions from different sources (power, residential, transport and industries) to ambient particulate pollution has been uncertain. In this regard, high-resolution simulations have been conducted employing the WRF-Chem (v3.9.1) model to comprehensively assess contribution from major anthropogenic sources in post-monsoon (November 2019), when air quality is typically poor in the region. Model evaluation is conducted by comparing simulated near-surface aerosol concentrations (PM_2.5 and PM₁₀) and aerosol optical depth (AOD) against ground-based measurements (CPCB), satellite data (MODIS), and the reanalysis dataset (MERRA-2). The results show that the model captures the spatial distribution of AOD satisfactorily, with WRF-Chem simulated AOD (0.38 ± 0.10) aligning well with MERRA-2 AOD (0.54 ± 0.10) and MODIS AOD (0.50 ± 0.20). Surface PM_2.5 and PM₁₀ concentrations also meet performance metrics of Fractional Bias ≤ 60% and Fractional Error ≤ 75%, with FAC2 values of 0.9 and 0.7, respectively. Sensitivity analysis reveals spatial heterogeneity in dominant sector that contributes to PM_2.5 concentration over western India. The power sector dominates in most areas with an average contribution of ~14% from regional power sources, followed by regional industries (~12%), regional residential emissions (~9%), and regional transport (~5%). In the trans-regional emissions from the Indo-Gangetic Plain (IGP) and central India also, the power sector remains the largest contributor (~15%), followed by industry (10.5%). Our findings underscore the need for targeted emission reductions in high-impact sectors to improve air quality over western India.

How to cite: Shekhar, S., Dhaka, S., Vaishya, A., Ojha, N., Pozzer, A., and Sharma, A.: Quantifying the sources of anthropogenic aerosols over western India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14890, https://doi.org/10.5194/egusphere-egu25-14890, 2025.

Air quality and climate over the western Indian region have been shown to be strongly influenced by trans-regional anthropogenic emissions originated from the Indo-Gangetic Plain (IGP) and central India, besides the local and regional processes. Nevertheless, the relative roles of local versus remote anthropogenic processes in changing precipitation over western India have remained unclear. In this regard, numerical simulations have been conducted using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to quantify regional versus trans-regional anthropogenic effects on cloud droplet number concentration (CDNC) and precipitation during monsoon (August 2019). WRF-Chem simulations show a good agreement with the ERA5 reanalysis for cloud fraction (CF) (r = 0.88, MB = 0.08 mm/day) and accumulated monthly precipitation (AMP) (r = 0.84, MB = -0.14 mm/day). Sensitivity simulations reveal that regional plus trans-regional anthropogenic emissions enhance CDNC by up to 5.1×10⁶ number/cm² (~121% of the average CDNC over WI) but significantly reduce the precipitation by up to 45 mm (~15% of the average precipitation). The findings also revealed that the impact of trans-regional emissions in perturbing CDNC and precipitation is higher than that of regional emissions. Our results suggest that anthropogenic emissions can substantially lower water resources in this already stressed arid region in India. The study also highlights that policies need to aim emission reductions ubiquitously and not only over western India for mitigating pollution impacts on regional precipitation.

How to cite: Dhaka, S., Lakshmi, S., Vaishya, A., Ojha, N., Pozzer, A., Ansari, T., and Sharma, A.: Impacts of anthropogenic emissions on monsoon precipitation over western India: Insights from high-resolution regional modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14891, https://doi.org/10.5194/egusphere-egu25-14891, 2025.

Dhaka, the capital of Bangladesh, is currently experiencing critically alarming levels of air pollution, with its Air Quality Index (AQI) exceeding 200, indicating hazardous conditions. This study investigates the factors contributing to Dhaka's deteriorating air quality over the past two decades by integrating AQI data with Land Use and Land Cover (LULC) analyses. Particular attention is given to the impacts of major development projects, including the Metro Rail, Elevated Expressway, and International Airport Terminal 3, on the city’s air quality. Comparative assessments of AQI before and after the completion of these projects reveal a significant worsening of air quality, attributed to increased construction activity and subsequent urbanization. The rapid expansion of impervious surfaces is identified as another critical factor exacerbating the AQI. The findings emphasize the urgent need for sustainable urban planning and air quality management strategies to mitigate the adverse effects of development on public health and the environment in Dhaka.

How to cite: Akhter, J. and Rayhan, M.: Assessing the Impact of Urban Development and Land Use Changes on Dhaka's Hazardous Air Quality, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14892, https://doi.org/10.5194/egusphere-egu25-14892, 2025.

Over the past 15 years, imaging spectrometers developed at the NASA Jet Propulsion Laboratory have significantly advanced the field of remote sensing of methane (CH4) and carbon dioxide (CO2) point source emissions. This began in 2008 with airborne observations from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), 2013 with the next generation AVIRIS-NG instrument, and has culminated with the launch of NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) in 2022.

These instruments have identified thousands of CH4 and CO2 point source emissions across the oil and gas, waste, and energy sectors, contributing in some cases to emission mitigation efforts. As part of an extended mission, EMIT coverage will expand beyond the arid regions of Earth to cover terrestrial surfaces between +51.6° and −51.6° latitude, enabling direct attribution of anthropogenic emissions on a global scale. EMIT's measurements and greenhouse gas data products are accessible through NASA’s Land Processing DAAC and the U.S. GHG Center, with all associated code available as open source. These data are already being utilized by public, private, and non-profit organizations, including UNEP IMEO and the Carbon Mapper Coalition. Additionally, new airborne instruments, such as AVIRIS-3 (2023) and the planned AVIRIS-5, promise enhanced sensitivity to CH4 and CO2 point sources, offering the potential for direct comparisons with satellite-based EMIT observations.

The Carbon Investigation (Carbon-I), a proposed mission for the NASA Earth System Explorer Program, reflects a dramatic advancement in greenhouse gas mapping capability. It provides a unique combination of coverage, high spatial sampling, and very high sensitivity, to permit quantification of emissions that cannot be observed with current technology. With contiguous global observations of CH4, CO2, and CO at 300 m sampling every 28 days with targeted observations at 30 m sampling, Carbon-I will permit emission quantification at the global to regional scales as well as for localized point sources. Consistent with NASA’s Open Source Science Initiative, all Carbon-I data and code will be publicly accessible, empowering Earth Action initiatives worldwide.

How to cite: Thorpe, A., Green, R., Frankenberg, C., Michalak, A., Thompson, D., Brodrick, P., Chadwick, D., Eastwood, M., Scott, V., Frazier, W., Fahlen, J., Coleman, R. W., Xiang, C., Jensen, D., Villanueva-Weeks, C., Lopez, A., Vinckier, Q., Bender, H., Chlus, A., and Chapman, J.: Advancing Greenhouse Gas Mapping with JPL Imaging Spectrometers: AVIRIS, EMIT, and Carbon-I, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14903, https://doi.org/10.5194/egusphere-egu25-14903, 2025.

The Southern Hemisphere (SH) stratosphere circulation can be organized around the development of the low-latitude jet (LLJ) in the upper stratosphere during winter months. The LLJ is associated with weak planetary wave activity, reduced residual circulation, and connections to westerly anomalies of the middle and upper stratosphere during early and mid-winter. The 2022 Hunga eruption coinciding with an anomalously strong LLJ year. Additionally, the LLJ is linked to a persistent, strong polar vortex in the lower stratosphere during October–December. This strong vortex, primarily driven by dynamical processes in winter, is further associated with enhanced ozone losses in spring, with ozone feedback reinforcing the vortex as sunlight returns in October.

How to cite: Wang, X., Yu, W., Randel, W., and Garcia, R.: Stratospheric Circulation in the Southern Hemisphere: links to tropical winds, ozone and Hunga Eruption, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15068, https://doi.org/10.5194/egusphere-egu25-15068, 2025.

Shipping is a high-energy-consuming sector and a significant source of climate-related and harmful pollutant emissions. In response to growing environmental concerns, the maritime sector has been subject to stringent regulations aimed at reducing emissions, achieved through the adoption of alternative fuels and emission control technologies. Accurate and diverse emission factors (EFs) are critical for quantifying shipping’s contribution to current emission inventories and projecting future trends under various policy scenarios. This study presents advancements in the development of emission factors for ships, incorporating alternative fuels, biofuels and emission control technologies. The methodology integrates statistical analysis of emission data from an extensive literature review with newly acquired on-board emission measurements. To ensure high resolution and applicability across diverse operational conditions, the emission factors are formulated as functions of engine load and categorized by engine type and fuel used. The results provide insights into the emission performance of ships and intend to support the development of robust, up-to-date emission models and inventories, contributing to the broader goal of sustainable maritime transport.

How to cite: Grigoriadis, A., Chountalas, T., Fragkou, E., Chountalas, D., and Ntziachristos, L.: Advancing load-dependent emission factors for ships: Integrating alternative fuels, biofuels, and control technologies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15097, https://doi.org/10.5194/egusphere-egu25-15097, 2025.

Nitrous acid (HONO) is known to be the significant source of hydroxyl radicals (OH), impacting air quality and climate as a major oxidant in the atmosphere. Many studies have highlighted that the photolysis of HONO can produce substantial amounts of OH throughout the day. Despite the crucial role of HONO in tropospheric chemistry, more research is needed to improve understanding of global HONO budgets. To address this, we developed a prototype HONO retrieval algorithm from the Geostationary Environment Monitoring Spectrometer (GEMS). The retrieval algorithm comprises two major processes, commencing with the spectral fitting of UV spectral range (343-371 nm) using the direct fitting method to obtain the slant columns. Subsequently, the conversion of slant columns into vertical columns is achieved by applying the air mass factor. The last step involves background correction, wherein the slant column amounts of HONO included in the radiance reference spectrum are added to the differential slant columns. Enhancements of HONO resulting from wildfire events in Asia were detected using GEMS. Refining the GEMS HONO retrieval algorithm is expected to enhance our understanding of the diurnal cycle of HONO, along with tropospheric chemistry in Asia.

How to cite: Cha, H., Kim, J., Chong, H., González Abad, G., Park, S. S., and Lee, W.: Nitrous Acid (HONO) Retrievals from wildfire events by using Geostationary Environment Monitoring Spectrometer (GEMS) ultraviolet spectra, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15191, https://doi.org/10.5194/egusphere-egu25-15191, 2025.

Increasing concentrations of greenhouse gases (GHGs) in the atmosphere are the primary driver of the observed rise in global surface temperatures, meanwhile exceeding 1°C above pre-industrial levels. Addressing this challenge requires linking GHG concentrations to specific anthropogenic and natural sources as part of the global carbon budget. This study investigates the relationship between GHG concentrations measured in Thessaloniki, Greece, and potential long-range transport sources using a clustering approach.

The GHG data were obtained from the EM27/SUN Fourier Transform Infrared (FTIR) spectrometer, a ground-based low-resolution infrared spectrometer operated in the framework of the Collaborative Carbon Column Observing Network (COCCON) at a mid-latitude urban site. The instrument provides column-averaged dry air molar fractions of CH₄, CO₂, CO, and H₂O. Meteorological data for trajectory simulations were derived from the Global Data Assimilation System (GDAS) with a spatial resolution of 1° × 1°.

Clustering analysis was performed using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Seven-day kinematic back trajectories were calculated for the period 2019–2024 at two arrival heights, 1500 m and 3000 m above mean sea level. The findings aim to specify the influence of long-range transport on GHG concentrations over Thessaloniki, contributing to a more complete understanding of regional GHG source-receptor relationships and transport patterns.

How to cite: Panou, T., Mermigkas, M., Topaloglou, C., Balis, D., Dubravica, D., and Hase, F.: "Investigating Regional and Long-Range Transport Contributions to GHG Concentrations of a Mid-Latitude Urban Site" , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15708, https://doi.org/10.5194/egusphere-egu25-15708, 2025.

Black carbon (BC) aerosols, strong absorbers of solar radiation, induce atmospheric heating, altering vertical profiles of temperature, water vapor, and clouds. These impacts can lead to localized precipitation changes, and may also initiate changes to atmospheric circulation, with potentially far-reaching impacts on precipitation patterns.

While prior studies suggest BC's significant influence on precipitation, its role in both local and remote precipitation change remains insufficiently quantified. To address this gap, we explore the extent to which historical BC emissions have shaped regional precipitation. Specifically, we ask: how much could future BC changes influence regional precipitation, based on insights from the historical period?

Using the Community Earth System Model version 2 (CESM2), we have generated a 20-member ensemble of simulations of 1950–2014 with anthropogenic BC emissions fixed at 1950 levels. By comparing these to standard historical simulations with evolving emissions, we isolate the impacts of BC emission trends from 1950 to 2014 on global and regional climates.

Our results reveal that BC emissions have caused localized drying in regions of high emissions, notably over Europe during the 1980s–1990s and Eastern China in the early 21st century. Furthermore, we find indications that BC exerts a dampening effect on the most extreme precipitation events, highlighting its historical role in modulating climate extremes.

How to cite: Stjern, C. W., Samset, B. H., Alterskjaer, K., and Johansen, A. N.: Tracing Black Carbon's Historical Impact on Regional Precipitation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15895, https://doi.org/10.5194/egusphere-egu25-15895, 2025.

In 2018, international shipping accounted for significant anthropogenic greenhouse gas emissions, contributing approximately 740 million tons of CO₂ according to the voyage-based method of the Fourth International Maritime Organization (IMO) Greenhouse Gases Study [1] and 880 million tons based on the CEDS and EDGAR inventories [2]. Recognizing this impact, the IMO adopted a long-term strategy in 2023 to achieve decarbonisation of global shipping by mid-century. However, concrete measures remain under development. A recent assessment of the 2018–2022 period suggests emissions are once again approaching 2008 levels, attributed to stagnation in improving energy efficiency [3]. This highlights the urgency of evaluating the potential of operational measures to mitigate emissions.

Voyage optimization, or ship weather routing, is an operational strategy leveraging meteo-oceanographic data to minimize energy consumption. This reduction can be achieved through spatial diversions, speed variations, or a combination of both. VISIR-2 [4], an open-source Python-based model, computes least-CO₂ routes by optimizing spatial diversions. Using a validated graph-search algorithm, the model integrates ocean currents and avoids adverse sea conditions [5].

In this study, we apply VISIR-2 to an ocean-going vessel operating on the Shanghai–Los Angeles/Long Beach route, identified as one of the first green corridors of shipping [6]. Simulations are conducted for both eastbound and westbound voyages over an entire calendar year, with and without the influence of ocean currents. We evaluate the resulting CO₂ savings, analysing their dependence on engine load and environmental conditions.

These results demonstrate the potential of operational measures like voyage optimization to contribute to shipping decarbonisation. The VISIR-2 model is currently employed within the EDITO-Model Lab project [7], contributing to developing a digital twin of the ocean. This work underscores the importance of open-source tools in fostering sustainable maritime practices and achieving the IMO's decarbonisation goals.

References
[1] https://www.imo.org/en/ourwork/Environment/Pages/Fourth-IMO-Greenhouse-Gas-Study-2020.aspx
[2] Deng, S., Mi, Z. A review on carbon emissions of global shipping. Mar Dev 1, 4 (2023). https://doi.org/10.1007/s44312-023-00001-2
[3] https://www.shippingandoceans.com/post/international-shipping-emissions-return-to-peak-2008-levels-due-to-insufficient-energy-efficiency-im
[4] https://doi.org/10.5281/zenodo.8305526
[5] Mannarini, G., Salinas, M. L., Carelli, L., Petacco, N., and Orović, J.: VISIR-2: ship weather routing in Python, Geosci. Model Dev., 17, 4355–4382, https://doi.org/10.5194/gmd-17-4355-2024, 2024
[6] https://www.c40.org/news/la-shanghai-implementation-plan-outline-green-shipping-corridor/
[7] https://www.edito-modellab.eu/

How to cite: Salinas, M. L. and Mannarini, G.: Voyage Optimization with the VISIR-2 Model on the Shanghai–Los Angeles Green Corridor of shipping, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16374, https://doi.org/10.5194/egusphere-egu25-16374, 2025.

The atmosphere is a critical reservoir for and transporter of microplastics (MPs) but little is known about the nature and drivers of their regional and climatic variability. In this study, dry deposition of MPs is quantified simultaneously over a seven-day period in nine Iranian cities encompassing different populations and climates and relationships with meteorological conditions and gaseous and particulate air quality parameters investigated. Overall, deposition ranged from < 5 to > 100 MP m^-2 h^-1 and was dominated by fibres of various sizes and constructed of different polymers (mainly polyethylene, polyethylene terephthalate, polypropylene, polystyrene and nylon), and there were clear and significant differences in mean values between the different cities that were not a simple function of climate or population. On a local scale, both positive and negative relationships between MP deposition and various meteorological and air quality parameters were observed among the cities. However, the pooled depositional data for MPs and various shapes and sizes thereof exhibited significant inverse relationships with wind speed and specific measures of airborne particulate matter (e.g., dust, PM-2.5, PM-10). The results suggest that there is a broadly consistent, fibre-dominated regional population of MPs whose deposition (and presumably resuspension) is influenced by variations in wind speed, but additional location-specific factors and sources contribute to temporal variations within the different cities. Despite the relationships between deposition and some gaseous and particulate air quality parameters identified at specific locations, it may be difficult to introduce a sharp parameter that can be used as a regional proxy for MP deposition.

Acknowledgements

We thank Shiraz University and Klaipeda University for technical support. This project has received funding from the Research Council of Lithuania (LMTLT), agreement No. S-PD-24-51.

How to cite: Abbasi, S., Dzingelevičienė, R., and Turner, A.: Regional and climatic variations in atmospheric microplastic deposition, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16376, https://doi.org/10.5194/egusphere-egu25-16376, 2025.

Aerosol-cloud interactions contribute to 75–80% of the total radiative effect of aerosols and remain a major source of uncertainty in predicting future climate. Aerosols significantly influence the warm cloud properties by serving as cloud condensation nuclei (CCN). An increase in CCN leads to the formation of more numerous and smaller cloud droplets, suppresses warm rain by reducing the efficiency of collision and coalescence processes, and extends the cloud lifetime, and liquid water path (LWP) and/or cloud fraction (CF). The activation of a CCN into a cloud droplet is strongly influenced by its size and chemical composition, which subsequently affects the size distribution of cloud droplets and other cloud properties. Although the physical processes of nucleation are well documented for individual particles, the impact of aerosol size on cloud properties is often underestimated because both fine and coarse aerosols co-exist together. To bridge this gap, this study aims to address the impact of size-differentiated aerosols on warm cloud properties over the Northern Indian Ocean (NIO) by utilizing ~20 years of multi-satellite observation data.

The Arabian Sea (AS) and the Bay of Bengal (BoB) in the NIO were chosen in this study as these regions experience a continuous load of aerosols from natural and anthropogenic sources with high seasonal variations. Comparative analysis of size-segregated aerosol optical depth (AOD) revealed the dominance of coarse mode particles (c-AOD) over AS, and fine mode (f-AOD) over BoB. However, a significant increasing trend in the mean f-AOD, particularly during the post-monsoon (ON) and winter (DJF) seasons, is observed over both the AS (0.05/decade) and BoB (0.045/decade) from 2000 to 2021, primarily driven by rising anthropogenic emissions. Further, a climatological analysis of warm cloud CF during these seasons reveals a corresponding increasing trend over the AS (0.07/decade) and BoB (0.05/decade). A correlation analysis of c-AOD and f-AOD with warm CF was conducted, which revealed a stronger annual positive correlation of warm CF with c-AOD (AS: r = 0.56, BOB: r = 0.41) compared to f-AOD (AS: r = 0.37, BOB: r = 0.27). To further investigate the impact of f-AOD and c-AOD on cloud effective radius (CER) for a fixed LWP, an additional correlation analysis was performed. For low LWP (up to 70 gm^-2), an increase in CER was observed with both c-AOD and f-AOD, with a more pronounced increase in CER associated with c-AOD over both the AS and BoB regions. However, as LWP increased, f-AOD exhibited a faster decrease in CER over the BoB compared to the AS. In contrast, c-AOD consistently showed an increasing CER with rising LWP, indicating a contrasting effect relative to f-AOD. These results indicate the dominant radiative effect of fine mode aerosols on cloud formation against the classical microphysical effect of coarse mode aerosols.  Further analysis, incorporating meteorological parameters such as relative humidity and atmospheric stability, is essential to better understand these relationships and enhance the robustness of this study.

How to cite: Bangar, V. and Mishra, A. K.: Satellite-Based Analysis of Size-Segregated Aerosols and Their Effects on Warm Cloud Properties over the Northern Indian Ocean, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16951, https://doi.org/10.5194/egusphere-egu25-16951, 2025.

How to cite: Vaittinen, A., Sarnela, N., Sipilä, M., Brasseur, Z., Boyer, M., Righi, C., Thakur, R., Mazzola, M., and Quéléver, L.: New Particle Formation and Condensable Vapours in an Arctic Site: Ny-Ålesund, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18095, https://doi.org/10.5194/egusphere-egu25-18095, 2025.

The EM27/SUN instrument is a FTIR spectrometer allowing to retrieve total atmospheric column abundance of CO2, CH4, CO and H2O. LSCE is currently developing a tropical network in the framework of the OBS4CLIM French project.

OBS4CLIM aims at deploying five EM27 at observatories located in tropical (Bolivia, French Guiana, Morocco, Ivory Coast) and background regions (Amsterdam Island, Indian Ocean) for long-term observations and satellite validation purposes (TROPOMI, OCO-2/3, GOSAT, MicroCarb). The chosen stations are also part of French National Observation Service and benefit from in situ greenhouse gas measurements.

The rapid growth of this EM27/SUN network requires developing tools to ensure data quality and availability. Therefore, LSCE has developed:

- An automatic data treatment chain based on PROFFAST (developed and maintained at KIT). Two models are used as a priori profiles (GGG2020, and CAMS) allowing to retrieve daily data in near real-time (NUBICOS project).

- Automatic enclosure systems to protect the instrument from a rough environment. This system allows increasing drastically the daily observations and data availability.

Four of the five stations are fully operational. We will present in details the network construction and the first measurement results.

How to cite: Lopez, M., Cassagne, M., Leuridan, H., Ticona, L., Burban, B., Mellouki, W., Hazan, L., and Ramonet, M.: A tropical EM27/SUN network for satellite validation and long term observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18600, https://doi.org/10.5194/egusphere-egu25-18600, 2025.

Hydrogen could play a crucial role in achieving climate neutrality by serving as an energy carrier for renewable sources, offering an alternative to traditional fossil fuels. However, researchers are investigating the impact of hydrogen emissions, as its leakage into the atmosphere poses a concern due to its potential to indirectly influence methane’s atmospheric lifetime and thereby extending its greenhouse effect. Therefore, minimising hydrogen emissions would reduce any potential environmental impact while enhancing safety and efficiency throughout the hydrogen value chain. Thus far, the literature lacks a verified data inventory on the amount of hydrogen emitted from the value chain. Little to no standardized data are present for many elements of the value chain. Otherwise, when present, efforts are still needed for their collection and validation in a unique inventory. The research community needs to address this by improving the capability to quantify small and large emissions and delivering validated methodologies and techniques for measuring or calculating them. An open-access and comprehensible user-friendly tool is urgently needed to better quantify the emissions from the whole hydrogen value chain. The pre-normative research on hydrogen release assessment (NHyRA) project is specifically designed to address these urgent needs. As a first step in this process, the project defined the hydrogen value chain, identifying its main components’ typical operative conditions and recognizing the potential sources of hydrogen emissions.  The next step, the project is working to update an open-access first version of the hydrogen emissions inventory to serve as a reference for the scientific and industrial community. Therefore, by welcoming and validating any contribution of new data, including from outside the NHyRA Consortium, subsequent versions of the inventory will include a more significant amount of data for some of the archetypes (i.e. processes or equipment) in the hydrogen value chain section, to ensure consistent scenario analysis and provide mitigation action recommendations. Furthermore, the NHyRA Consortium experts have identified hydrogen detection and quantification techniques and instruments, covering those which are commercially available and emerging. In this regard, partners of the Consortium have identified three monitoring categories: Detection of emissions at the component level, Detection and quantification of emissions at the component level, and detection and quantification of emissions at the area/site level. Additionally, new or adequately adapted experimental, theoretical, and simulation methodologies will be validated to perform laboratory or in-field measurements to achieve the ambitious goal. Experimental tests will also be performed on the most critical elements of the hydrogen value chains by the partners of the Consortium. A complete picture of the hydrogen emission scenarios, applied on the middle (2030) and long (2050) term European hydrogen economy, will be developed to enable decision-makers to quantify the impact of hydrogen emissions in the energy system transition, identifying  and prioritizing effective risk mitigation actions. Finally, the project will formulate recommendations for Standards and Technical Specifications.

How to cite: Connor, A., Guzzini, A., Holewa-Rataj, J., Piras, P., Claveul, J., Robino, M., and Kostereva, A.: Pre-normative research on hydrogen release assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19010, https://doi.org/10.5194/egusphere-egu25-19010, 2025.

The effects of major greenhouse gas (GHG) emissions in West Africa remain insufficiently documented. Over two consecutive years, we monitored soil GHG emissions using a chamber-based experimental setup across four contrasting land management conditions in the Sudanian savanna. The environmental drivers of the emissions were assessed through stepwise linear regression and ANOVA statistical tests. Our results show that, regardless of land management conditions, N₂O release occurs at the highest rate in rice fields (4.29±2.9 µg N m^-2 h^-1). The soil acts as a sink for CH₄ in the forest reserve (-1.09±7.67 µg C m^-2 h^-1), whereas degraded lands, such as cropland and rainfed rice farms, exhibit CH₄ release at rates of 1.03±13.1 µg C m^-2 h^-1 and 5.93±12.28 µg C m^-2 h^-1, respectively. Livestock breeding contributes significantly to CH₄ emissions in grasslands, where the annual mean CH₄ flux is the highest (16.79±6.69 µg C m^-2 h^-1). The statistical analysis indicates that 53.8% and 50.2% variability in the CH₄ flux is explained by soil moisture and soil temperature respectively in the grassland and rice field. Soil moisture is negatively correlated with N₂O release, while the relationship with CH₄ is positive in grassland and rice fields, where higher CH₄ emissions are observed. N₂O flux shows a positive correlation with soil temperature. These findings suggest that land degradation exacerbates CH₄ emissions, and the effect of fertilizer use on biomass during the growing season increases CH₄ release in rice fields by approximately threefold. At the peak of the raining season, the forest CH₄ sink reaches the highest -6.08±14.7 µg C m^-2 h^-1 while the rainfed rice field releases 9.14±29.57 µg C m^-2 h^-1. Overall, there is intra annual variability of GHG fluxes with dry and wet years showing different magnitude of N₂O and CH₄ emissions. The patterns of GHG flux dynamics in this data-scarce region is better clarified through our investigation. We conclude that GHG emissions in response to land cover degradation and agricultural practices, such as fertilizer use, are significant in the Sudanian savanna and urgent decisions are needed to mitigate these effects.

How to cite: Oussou, F. E., Sy, S., Bliefernicht, J., Spangenberg, I., Guug, S. S., Steinbrecher, R., Schäffler-Schmidt, A., Kiese, R., Ayamba, M., Yalo, N., Asiwaju-Bello, A. Y., Sawadogo, W., Olusegun, C. F., Amekudzi, L. K., and Kunstmann, H.: Evaluation of Chamber-based soil greenhouse gas emissions in contrasted land use of the Sudanian savanna, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19307, https://doi.org/10.5194/egusphere-egu25-19307, 2025.

 Providing sufficient and nutritious food while reducing climate emissions footprints from food systems is a Grand Engineering Challenge for India. The increasing dietary emissions pose a serious threat to achieving the national net-zero goal by 2070, yet such emissions are not yet accounted for in India’s Climate Action Plans. Since the 1990s, India’s dietary transitions have been largely propelled by economic development and intensive urbanization, yet such transitions have occurred unequally between urban and rural regions across India.

The regional and temporal heterogeneity in dietary consumption patterns across different populations and the corresponding GHG emissions is not well known. Here, we apply a life-cycle approach to quantify the regional, demographical, and food commodity-specific GHG emissions (CO₂, N₂O, and CH₄) based on detailed household-expenditure data across three national-scale censuses (1999, 2011, 2022). We differentiate such emissions across twelve major food groups that are typically consumed in 88 distinct NSSO regions with demographics (rural and urban) differentiated by income. Our findings suggest that between 1999-2022, the per capita consumption of animal-based products has increased by ~20% respectively, and a ~15% decrease in wholegrain intake. Emissions from dairy (34%), wholegrain (31%), and meat (18%) food groups contributed more than 80% of total dietary emissions for 2011.

The demographical analysis suggested that household expenditure directly influences GHG emissions. For example, the highest expenditure decile of the population was 2.2 kgCO₂eq cap^-1 day^-1  with 0.7 kgCO₂eq cap^-1 day^-1  for the lowest decile in 2011Both rural and urban regions have per capita GHG emissions similarly, but the total emissions and share of food groups varied extremely with the household expenditure. The disparities in total emissions remain as high as 65% among poor and rich households, with poor houses having wholegrain-dominated emissions and rich households having dairy-dominated emissions. The spatial examination further showed the high heterogeneity in emissions among and within Indian states. Our findings highlight the opportunities and challenges in using food consumption as a lever for climate change while also reducing food inequality by shifting to healthier diets. Such findings can help strengthen State Climate Action Plans to help towards green agriculture and sustainable consumption.

How to cite: Yadav, S. and Balasubramanian, S.: Quantifying regional and temporal heterogeneity in greenhouse gas emissions from Indian diets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19539, https://doi.org/10.5194/egusphere-egu25-19539, 2025.

In this study, we conducted the estimation of shortwave aerosol radiative forcing using the aerosol optical depth (AOD) and supplementary information from the Geostationary Environment Monitoring Spectrometer (GEMS) dataset. We used the libRadtran package for the radiative transfer modeling (RTM), and used the radiative forcing values provided from the Aerosol Robotic Network (AERONET) system for the input value of RTM and the validation task. Total 6 sites in the Korean peninsula are target regions, such as Seoul (Yonsei University and Seoul National university), Anmyeon, Gwangju, Gangneung, and Ulsan. In detail, we used the climatological mean of surface albedo and asymmetry parameter at 4 shortwave channels (440, 675, 870, and 1020 nm), and used daily representative single scattering albedo provided from the GEMS dataset in order to consider the different aerosol type (dust, non-absorbing, and black carbon types). These set-up conditions were finally decided after a number of sensitivity tests. As a result, our estimation of direct aerosol radiative forcing (DARF) at the surface and top of the atmosphere (TOA) shows high correlations with the DARF from the AERONET (correlation coefficient is 0.65 to 0.85 in all 6 sites). Our estimated DARF is a little underestimated compared to the DARF of AERONET, and it seems natural due to the spatial resolution difference. With this high performance, we can provide the daytime hourly variation of DARF over the whole Korean peninsula, which can be useful information to a number of application in the future.

How to cite: Koo, J.-H., Lee, J., and Yoo, J.-A.: Estimation and validation of direct aerosol radiative forcing in the Korean peninsula using the GEMS dataset, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19628, https://doi.org/10.5194/egusphere-egu25-19628, 2025.

Background aims: Life cycle assessment (LCA) is widely used to evaluate the carbon footprint (CF) of milk production. Changes in soil organic carbon (SOC) stock play a vital role in agricultural greenhouse gas emissions. However, no consensus has been reached to incorporate SOC changes into agricultural LCA. This study aims to evaluate the CF of milk production using LCA methodology with integrating  SOC balance based on data from Viikki Research Farm at Helsinki. Methods: The CF of milk production was analyzed for 2022 and 2023 using the Solagro Carbon Calculator. Furthermore, the study explored the soil carbon and nitrogen balances using the DNDC model, for a comparison with IPCC Tier 1 & Tier 2 methods and the real measurements. Results and conclusions: Real measurements demonstrated substantial SOC loss from grassland and subsequent annual cropland, which was 607 and 3939 kg C ha^-1 in 2022 and 2023, respectively. Incorporation of those results increased the CF of milk production. Estimated based on DNDC modeling, the SOC loss exceeded the measured results in 2022 and was underestimated in 2023, while the IPCC method showed SOC sequestration in 2022. The observed emissions fluctuation between the two years was related to the rotation between perennial grass and annual crop, and harsh wintertime conditions affecting crop growth. This study underscores the importance of SOC change in agricultural LCAs. While direct measurements may have limitations, a more profound understanding of SOC dynamics and better calculation is crucial to minimize bias in CF estimations.

How to cite: Gao, Y., Hu, T., Roitto, M., Jokiniemi, T., Sandell, M., Pihlatie, M., and Tuomisto, H.: Life cycle assessment of milk production: integrating changes in soil carbon stock with eddy covariance and DNDC modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20229, https://doi.org/10.5194/egusphere-egu25-20229, 2025.

The Cerrado biome, a globally significant biodiversity hotspot, is undergoing rapid degradation primarily due to anthropogenic activities. Large-scale conversion of native vegetation for agriculture, particularly soybean and cattle ranching, and strong urbanization rates are the main drivers of the biome losses. Additionally, unsustainable water use, infrastructure development, and recurrent fires exacerbate ecosystem degradation, leading to significant biodiversity decline and ecosystem service impairment. A direct consequence of this change in land use is the generation of substantial quantities of air pollutants, mainly particulate matter of 2.5 and 10 𝜇m (PM2.5 and PM10, respectively). These particles, emitted from biomass burning, soil erosion, and dust storms, can penetrate the respiratory tract, leading to various health issues, including respiratory infections, cardiovascular disease, and increased mortality rates. Using measurements of meteorological variables and air pollutants from CETESB (Environmental Company of the State of São Paulo) from 2017 to 2023 in an important urbanized region of the Brazilian Cerrado, we characterized the seasonal distribution of PM2.5 and PM10, together with other pollutants, such as nitrogen oxides (NOx), carbon monoxide (CO) and ozone (O3). In addition, using different combinations of meteorological and air pollution variables, we trained machine learning models to predict the concentration of PM2.5 and PM10. We list Random Forest, XGBoost, and Artificial Neural Networks (ANN) among these models. Our results show that a lower concentration of air pollutants (PM10, PM2.5, CO, and NOx) is observed during summer, while, in contrast, the peak occurs during winter. This is directly related to the seasons with higher and lower precipitation rates. Curiously, O3 peaks in spring and is minimal in autumn, likely related to cloud occurrence. During the whole analyzed period, NOx, PM10, and PM2.5 exceeded the daily average limits of the World Health Organization by about 15, 22 and 35%, respectively. Regarding the predictive models, the random forest better predicted PM10 and PM2.5 concentrations. For PM10, the statistical results for the train (80% of the data)/test (20% of the data) set were R² = 0.79/ 0.92 (p-value < 0.05), with RMSE of 10.7 and 6.5 𝜇g m-3. For PM2.5, the model returned R² = 0.74/0.91, with RMSE of 4.3 and 2.6 𝜇g m-3 for the train/test set, respectively. Although not the best, the ANN also worked relatively well after proper tuning. Future investigations will extend and validate the predictions obtained in this study to other stations in the Cerrado biome with multiple models to spatialize the PM prediction and obtain the regions in which the most air pollutants are emitted. 

How to cite: Franco, M. A. and Teixeira, M.: Characterization and machine learning prediction of atmospheric pollutants in an urban region of the Cerrado biome, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20571, https://doi.org/10.5194/egusphere-egu25-20571, 2025.

Oxygenated volatile organic compounds (OVOCs) significantly contribute to the radical formation in the troposphere, enhancing atmospheric oxidation capacity and driving secondary pollutant production. However, uncertainties in OVOC emissions hinder accurate assessments of their regional impacts. This study updates OVOC emission profiles for the Yangtze River Delta (YRD) region and integrates them into the Community Multiscale Air Quality (CMAQ) model to refine OVOC estimations. The updated model effectively captures the diurnal variations of most OVOCs, significantly reducing biases compared to simulations based on previous inventories. OVOCs, particularly formaldehyde (HCHO), are key precursors of hydroperoxyl radicals (HO₂), which play a dominant role in ozone production across the YRD. Anthropogenic emissions, primarily from industrial activities and vehicular sources, account for 40−60% of total OVOCs. Sensitivity simulations reveal that reducing emissions of reactive OVOCs, such as HCHO and glyoxal, effectively lowers regional ozone levels. These findings underscore the pivotal role of OVOCs in radical chemistry and ozone formation, providing insights for mitigating ozone pollution in rapidly urbanizing regions like the YRD.

How to cite: Li, J.: Photooxidation of Oxygenated Volatile Organic Compounds as a Major Source of Hydroperoxyl Radicals Driving Ozone Formation in the Yangtze River Delta Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21194, https://doi.org/10.5194/egusphere-egu25-21194, 2025.

The spatiotemporal variation of fog reflects the complex interactions among fog, boundary layer thermodynamics and synoptic systems. Previous studies revealed that fog can present fast spatial propagation feature and attribute it to boundary layer low-level jet (BLLJ), but the effect of BLLJ on fog propagation is not quantitatively understood. Here we analyze a large-scale fog event in Jiangsu, China from 20 to 21 January 2020. Satellite retrievals show that fog propagates from southeast coastal area to northwest inland with the speed of 9.6 m/s, which is three times larger than the ground wind speeds. The ground meteorologies are insufficient to explain the fog fast propagation, which is further investigated by WRF simulations. The fog fast propagation could be attributed to the BLLJ occurring between 50 and 500 m, because the wind speeds (10 m/s) and directions (southeast) of BLLJ core are consistent with fog propagation. Through sensitive experiments and process analysis, three possible mechanisms of BLLJ are revealed: 1) The abundant oceanic moisture is transported inland, increasing the humidity of boundary layer and promoting condensation; 2) The oceanic warm air is transported inland, enhancing the inversion layer and favouring moisture accumulation; 3) The moisture advection probably promotes low stratus formation, and later it subsides to be ground fog by turbulent mixing of fog droplets. The fog propagation speed would decrease notably by 6.4m/s (66%) in the model if the BLLJ-related moisture and warm advections are turned off.

How to cite: Yan, S., Wang, H., Liu, X., Zu, F., and Liu, D.: Effect of boundary layer low-level jet on fog fast spatial propagation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1679, https://doi.org/10.5194/egusphere-egu25-1679, 2025.

During 29^th July to 1^st August in 2023, a persistent heavy rainfall event (“23·7” event) hit North China causing severe floods, enormous infrastructure damage and large economy loss. Observational analysis shows that the extremely large accumulation of precipitation and long duration of this event are closely related to a slowly moving landfall typhoon “Dusuari” over North China due to the blocking effect of an anomalous high over the mid- and high-latitude Asia. The anomalous southeasterly flow induced by the typhoon “Dusuari” and another typhoon “Kanu” over the East China Sea jointly built a highly efficient channel of water vapor supplying from southern oceans towards North China. A water vapor budget analysis indicates that precipitation of this event is mainly caused by dynamic process involving strong ascending motion. Accompanying strong water vapor transportation and convergence over North China, large amount of latent heat is released in the middle and lower troposphere. The physical mechanisms of heavy rainfall-induced diabatic heating in maintaining the precipitation over North China is further investigated using statistics analysis and numerical experiments. On one hand, the latent heating released by heavy rainfall induces significant uplifting flows which causes more precipitation. On the other hand, the heavy rainfall-induced diabatic heating contributes to enhancement of the westward extension of high-pressure dam around the Mongolian Plateau through a regional meridional circulation. This strengthened high pressure dam sustained the cyclonic circulation of “Dusuari” over North China, leading to continuous heavy rainfall there.

How to cite: Zhao, W.: Mechanisms of persistent extreme rainfall event in North China, July 2023: Role of atmospheric diabatic heating, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1762, https://doi.org/10.5194/egusphere-egu25-1762, 2025.

This study examines the spatial and temporal distributions of short-term heavy rain (SHR) in the middle Yangtze River basin (MYRB) in the summers of the past decade. SHR events are most frequent during the annual Meiyu periods, significantly contributing to total precipitation. Additionally, these events generally last longer and tend to peak at night. The occurrence of SHR events decrease from southeast to northwest, influenced by the monsoonal flow and the small-scale terrain. Moisture convergence prior to Meiyu SHR events is predominantly influenced by both southerly and easterly winds below 700 hPa. Frequent low-level jets and quasi-steady cyclonic circulation lead to strong southerly winds prevailing over the eastern MYRB, while weaker easterly winds dominate in the west. Wind profiles derived from wind profile radar products illustrate the preceding changes in wind speed, wind directions, and vertical wind shear below 4 km above ground level (AGL), as well as the timing of these changes. In the plain area of southeastern MYRB, accelerated southwesterlies are observed 3 to 4 hours before SHR events, accompanied by an intensification of southerly winds near the boundary layer top 2 hours prior. Within the hour leading up to the SHR events, wind speeds sharply rise to their peak. In front of the mountains in west MYRB, southwesterlies strengthen 5 hours in advance but then weaken as they shift to northerlies. Just before the SHR events, however, reinforced northerlies occur near the surface. In the mountainous region of western MYRB, while changes in wind speed are minimal due to topographic blocking, the frequency of southeasterly components below 2 km AGL significantly increases 4 hours before SHR events. The preceding timing of significant vertical wind shear coincides with the increase in wind speed and the change in wind direction. Understanding the detailed characteristics of wind profiles preceding the SHR events during the Meiyu seasons can provide valuable insights for localized severe weather early warning systems. 

How to cite: Wang, J., Cui, C., Wang, X., and Wang, X.: Wind profile warning characteristics of short-term heavy rain during the Meiyu season, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1763, https://doi.org/10.5194/egusphere-egu25-1763, 2025.

Convective clouds during the Mei-yu season contribute significantly to the total rainfall and related disasters over the middle and lower reaches of the Yangtze River in China. Studying the effects of aerosols on convective clouds is of great importance to weather and climate research. However, there are still many open questions to address. This study investigated the effects of aerosol on convections with different cloud geometrical thickness (CGT) bins during the 2018 Mei-yu season, which lasted for 17 days from 18 June to 5 July. Contrasting aerosol effects on shallow and deep convective clouds were revealed by means of anthropogenic aerosol experiments in the Weather Research and Forecasting model with Chemistry (WRF-Chem). Specifically, increased anthropogenic aerosols lead to a 9% reduction in total rainfall and a 7.17% decrease in convection occurrences during the Mei-yu season. After adopting a methodology that stratifies the convective clouds by fixing the CGT, we found that increasing aerosols suppress shallow convections with CGT less than 4 km and invigorate deep convections with CGT greater than 4 km. Increased aerosols enhance the scattering of shortwave radiation, resulting in cooling of the surface air and increasing the stability of the regional lower atmosphere, potentially suppressing shallow convection. Meanwhile, in deep convection, with its stronger updraft and more latent heat, convective invigoration occurs under polluted conditions due to the aerosol-related microphysical and dynamical responses. Considering the high-humidity environment during the Mei-yu season, additional relative humidity tests show that the competing aerosol effects come from convective core invigoration and convective periphery processes which enhance evaporation and dissipation, demonstrating relative humidity is a critical factor in maintaining the net aerosol effects on convections. These results contribute to a better understanding of the effects of anthropogenic aerosols on convections during the Mei-yu season and the competing effects of aerosols depending on the ambient environmental conditions.

How to cite: liu, L.: Contrasting aerosol effects on shallow and deep convections during the Mei-yu season in China , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1775, https://doi.org/10.5194/egusphere-egu25-1775, 2025.

The present study assesses the simulated precipitation and cloud properties using three microphysics schemes (Morrison, Thompson, and MY) implemented in the Weather Research and Forecasting model. The precipitation, differential reflectivity (Z_DR), specific differential phase (K_DP) and mass-weighted mean diameter of raindrops (Dm) are compared with measurements from a heavy rainfall event that occurred on 27 June 2020 during the Integrative Monsoon Frontal Rainfall Experiment (IMFRE). The results indicate that all three microphysics schemes generally capture the characteristics of rainfall, Z_DR, K_DP, and Dm, but tend to overestimate their intensity. To enhance the model performance, adjustments are made based on the MY scheme, which exhibited the best performance. Specifically, the overall coalescence and collision parameter (Ec) are reduced, which effectively decreases Dm and makes it more consistent with observations. Generally, reducing Ec leads to an increase in the simulated content (Qr) and number concentration (Nr) of raindrops across most time steps and altitudes. With a smaller Ec, the impact of microphysical processes on Nr and Qr varies with time and altitude. Generally, the autoconversion of droplets to raindrops primarily contributes to Nr, while the accretion of cloud droplets by raindrops plays a more significant role in increasing Qr. In this study, it is emphasized that even the precipitation characteristics could be adequately reproduced, accurately simulating microphysical characteristics remains challenging and it still needs adjustments in the most physically based parameterizations to achieve more accurate simulation.

How to cite: Zhou, Z.: An evaluation and improvement of microphysical parameterization for a heavy rainfall process in Meiyu season, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1816, https://doi.org/10.5194/egusphere-egu25-1816, 2025.

In this talk, we consider a mathematical model of cloud physics that consists of the Navier-Stokes equations coupled with the cloud evolution equations for water vapor, cloud water, and rain. In this model, the Navier-Stokes equations describe weakly compressible flows with viscous and heat conductivity effects, while microscale cloud physics is modeled by the system of advection-diffusion-reaction equations. We aim to explicitly describe the evolution of uncertainties arising from unknown input data, such as model parameters and initial or boundary conditions. The developed stochastic Galerkin method combines the space-time approximation obtained by a suitable finite volume method with a spectral-type approximation based on the generalized polynomial chaos expansion in the stochastic space. The resulting numerical scheme yields a second-order accurate approximation in both space and time and exponential convergence in the stochastic space. Our numerical results demonstrate the reliability and robustness of the stochastic Galerkin method. We also use the proposed method to study the behavior of clouds in certain perturbed scenarios, for example, the ones leading to changes in macroscopic cloud patterns as a shift from hexagonal to rectangular structures.

How to cite: Chertock, A.: Stochastic Galerkin method for cloud simulation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1939, https://doi.org/10.5194/egusphere-egu25-1939, 2025.

From 08:00 on July 29 to 08:00 on August 2, 2023, under the influence of typhoon "Dussuri", an extremely heavy rainstorm process occurs in Hebei and Beijing. The precipitation in some areas of the windward slope of Taihang Mountains exceeds 250mm, and in some areas it exceeds 500mm. The distribution of heavy precipitation is basically consistent with the terrain of the windward slope. Using the 6-minute radar retrieved wind field network data developed by the CMA (China meteorological administration) Meteorological Observation Center for analysis, it is found that from 13:00 on July 29th to 20:00 on August 1st, a southeast-oriented ultra-low-level jet greater than 12 m/s was maintained in the 925-hPa field over Hebei and Beijing. The angle between the jet and the Taihang Mountains is almost 90°, and at the same time, a 850-hPa typhoon trough stays on the windward slope for a long time, resulting in stable and less movement of heavy precipitation echoes. This series of factors together led to the occurrence of the extremely heavy rainstorm process. Using the ERA5 hourly reanalysis data as the initial field and based on the WRF4.5 model, a sensitivity test is conducted on this process using three-layer bidirectional nesting (grid spacing of 9km, 3km, and 1km, respectively). The experiment reduces the Yanshan and Taihang Mountains to half of their original heights and 50 meters, respectively (equivalent to the altitude of Beijing). The experimental results indicate that: (1) Precipitation impact: Due to the easterly winds brought by typhoons, the eastern side of Taihang Mountains is on the windward slope, which has a significant impact on precipitation. When the height of Taihang Mountains decreases, the precipitation intensity significantly weakens; When the terrain height drops to 50m, the precipitation location is biased to the west compared to the actual situation. (2) The experiment showed that the blocking effect of Taihang Mountains formed mesoscale low vortex and convergence line on the windward slope. When the height of Taihang Mountains drops to half of its original height or only 50 meters, the mesoscale low vortex and convergence line move westward to Shaanxi Province. (3) The vertical profile analysis along the east-west direction of Taihang Mountains shows strong upward movement in the windward slope area, with positive vorticity in the lower level and negative vorticity in the upper level. When the height of Taihang Mountains decreases, the upward movement significantly weakens, and the positive and negative vorticity weakens until it disappears, indicating that the dynamic effect of terrain has a significant impact on precipitation processes. (4) The Yanshan Mountains are oriented east-west, and parallel to the environmental winds. Therefore, when its height decreases, its impact on physical quantities such as precipitation, wind field, vertical velocity, and vorticity is relatively small.

Key words: terrain, "23.7" extremely heavy rainstorm, analysis

How to cite: huang, X., wu, Z., xue, F., and fu, C.: Analysis and research on the impact of terrain on the "23.7" extremely heavy rainstorm, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2024, https://doi.org/10.5194/egusphere-egu25-2024, 2025.

 Fog, rain, snow, and icing are the high-impact weather events often lead to the highway blockings, which in turn causes serious economic and human losses. At present, there is no clear calculation method for the severity of highway blocking which is related to highway load degree and economic losses. Therefore, there is an urgent need to propose a method for assessing the economic losses caused by high-impact weather events that lead to highway blockages, in order to facilitate the management and control of highways and the evaluation of economic losses. The goal of this work is to develop a method to be used to assess the high impact weather (HIW) effects on the highway blocking. Based on the K-means cluster analysis and the CRITIC (Criteria Importance through Intercriteria Correlation) weight assignment method, we analysed the highway blocking events occurred in Chinese provinces in 2020. Through cluster analysis, a new method of severity levels of highway blocking is developed to distinguish the severity into five levels. The severity levels of highway blocking due to high-impact weather are evaluated for all weather types. As a part of calculating the degree of highway blocking, the highway load in each province is evaluated. The economic losses caused by dense fog are specifically assessed for the entire country.

How to cite: Liu, D., Jing, T., Yan, M., and Gultepe, I.: A New Method for Calculating Highway Blocking due to High Impact Weather Conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2044, https://doi.org/10.5194/egusphere-egu25-2044, 2025.

I will introduce a flux globalization-based well-balanced path-conservative central-upwind scheme on Cartesian meshes for the two-dimensional (2-D) two-layer thermal rotating shallow water equations. The scheme is well-balanced in the sense that it can exactly preserve a variety of physically relevant steady states. In the 2-D case, preserving general "moving-water" steady states is difficult, and to the best of our knowledge, none of existing schemes can achieve this ultimate goal. The proposed scheme can exactly preserve the 𝑥- and 𝑦-directional jets in the rotational frame as well as certain genuinely 2-D equilibria. Numerical experiments demonstrate the performance of the proposed scheme in computationally non-trivial situations: in the presence of shocks, dry areas, non-trivial topographies, including discontinuous ones, and in the case of hyperbolicity loss. The scheme works equally well in both the 𝑓-plane and beta-plane frameworks.

How to cite: Kurganov, A., Cao, Y., Liu, Y., and Zeitlin, V.: Flux Globalization-Based Well-Balanced Path-Conservative Central-Upwind Scheme for Two-Dimensional Two-Layer Thermal Rotating Shallow Water Equations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2053, https://doi.org/10.5194/egusphere-egu25-2053, 2025.

Proper scoring rules are an essential tool to assess the predictive performance of probabilistic forecasts. However, propriety alone does not ensure an informative characterization of predictive performance and it is recommended to compare forecasts using multiple scoring rules. With that in mind, interpretable scoring rules providing complementary information are necessary. We formalize a framework based on aggregation and transformation to build interpretable multivariate proper scoring rules. Aggregation-and-transformation-based scoring rules can target application-specific features of probabilistic forecasts, which improves the characterization of the predictive performance. This framework is illustrated through examples taken from the weather forecasting literature and numerical experiments are used to showcase its benefits in a controlled setting. Additionally, the framework is tested on real-world data of postprocessed wind speed forecasts over central Europe. In particular, we show that it can help bridge the gap between proper scoring rules and spatial verification tools.

How to cite: Pic, R., Dombry, C., Naveau, P., and Taillardat, M.: Interpretable ultivariate scoring rules based on aggregation and transformation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2224, https://doi.org/10.5194/egusphere-egu25-2224, 2025.

The middle and lower reaches of the Yangtze River (MLYR) suffers from extreme precipitation (EP) during summer, which has a huge impact on human society and ecosystem. However, the large spreads among climate models hinder their application in future risk assessment. In this work, four typical synoptic patterns (SPs) triggering EP over MLYR are identified based on the clustering algorithm. And we found a significant linear correlation between the CMIP6 (sixth phase of Coupled Model Intercomparison Project) models’ ability to reproduce the observed typical SPs in present-day climate and the projected future changes of EP over MLYR. Then we proposed an emergent constraint method for EP projections based on this linear correlation and the observed SPs. Using this method, the model spread is evidently narrowed, which increases the credibility of projected future EP changes.

How to cite: Hu, Y., Lin, Y., Bao, J., and Deng, Y.: Constraining Future Changes in Extreme Precipitation Using Typical Synoptic Patterns, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2650, https://doi.org/10.5194/egusphere-egu25-2650, 2025.

From July 29 to August 1, 2023, extreme heavy rainfall occurred in the Chinese HUABEI region. Heavy rainstorm occurred in the most areas of Beijing, Tianjin and Hebei province. The daily precipitation of 14 national meteorological observatories  exceeded the historical extreme value. The process intensity exceeded the three extreme rainstorm processes in the history of HUABEI region. Studying the causes of extreme heavy precipitation in HUABEI and evaluating the predictive performance of the model for extreme heavy precipitation is beneficial for improving the application and forecasting ability of the model. This article analyzes the weather scale characteristics and anomalies of this precipitation process from factors such as height field, wind field, divergence field, vorticity field, and water vapor. The dynamic and thermal structure of the vortex and the cause of the upper level continental high  are analyzed using the method of cyclone phase space map and full type vorticity equation. Finally, the predictive ability of the model for extreme precipitation is tested. The following main conclusions have been drawn:(1) The precipitation process is divided into two stages. Before the 31st, it was caused by the residual vortex circulation of the "Dussuri", with strong precipitation intensity and range. After the 31st, it was formed by the convergence of the easterly jet on the west side of the subtropical high pressure and its interaction with the terrain. Precipitation was mainly concentrated in the northern part of China, with weaker rainfall intensity compared to the previous period.(2) The key impact systems of this process are the 200hPa high trough and continental high pressure, the 500hPa blocking high pressure, and the residual circulation of the low-level "Dussuri". The divergence in front of the 200hPa high altitude trough is beneficial for maintaining upward movement in the North China region; At 500hPa, there is a blocking high pressure in the northern and eastern parts of North China, which is conducive to the maintenance of low-level vortex systems. The "Dussuri" convergence circulation is the triggering system of the process.(3) The water vapor conditions during this process were exceptionally good, mainly consisting of three water vapor transport paths: the southerly water vapor transport of the South China Sea monsoon, the eastward water vapor transport of the residual circulation of "Dussuri", and the southeast water vapor transport path of typhoon "Kanu".(4) During the northward movement, the residual vortex of the Dussuri maintains a quasi symmetric and warm center structure, with weak cold advection in the upper level of the vortex on the 30th.(5) The uneven vertical distribution of condensation latent heat heating generates negative vorticity in the upper troposphere, ensuring the stable maintenance of continental high pressure.(6) In global model forecasting, the CMA model cannot report a blocking high pressure above 96 hours of time. The EC deterministic model can predict heavy precipitation processes within a 120 hour time frame, and the ensemble forecast can have a predictable time frame of up to 7 days.

How to cite: guan, Y.: Analysis and Model Verification of Extreme rainfall Processes in Huabei of China in 2023, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2697, https://doi.org/10.5194/egusphere-egu25-2697, 2025.

This article introduces the five-year research plan of the project and the preliminary progress made over the past two years: 1. Implemented tracking observation experiments on the Mei-Yu frontal extreme precipitation associated in the middle and lower reaches of the Yangtze River for the years 2023 and 2024; 2. Investigated the triggering and maintenance mechanisms of extreme precipitation related to multi-scale interactions and associated thermodynamic conditions; 3. Conducted studies on the microphysical structure and evolution simulation of extreme precipitation. To be specific, the mechanism of low-level jet formation is analyzed during the rainy season in the Yangtze River Basin in 2024.

How to cite: Cui, C. and Wang, B.: Preliminary results on the Mei-Yu Frontal Heavy Rainfall Tracking Observation Experiment and Related Studies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3219, https://doi.org/10.5194/egusphere-egu25-3219, 2025.

In this study, the microphysical characteristics of summer and winter liquid rainfall are analyzed by 4 Parsivel sites in Hubei Province in the middle reaches of the Yangtze River during 2015-2018. The possible reasons for summer and winter DSD differences are also discussed. The main conclusions are summarized as follows:

(1) Hubei Province is dominated by stratified rainfall in winter, while summer includes convective, stratified, and mixed rainfall. Compared with winter, the average rain rate and D_m in summer are larger, the number concentration N_w is relatively smaller, while difference between δ_M is very small. The PDF distribution of D_m peak value are about 1.0 mm both in summer and winter, and the D_m data is skewed to the right while the N_w show the opposite.

(2) With increasing rain rate, the D_m increases in both summer and winter. For rain rate R < 2 mm h^-1, there are larger D_m and smaller N_w in summer than that in winter, while for the rain rete R > 2 mm h^-1 shows the opposite.

(3) There are differences in the μ-λ and Z-R relationships between summer and winter in the middle reaches of the Yangtze River. The relationships also different from those in the lower reaches of the Yangtze River.

(4) The middle reaches of the Yangtze River are mainly influenced by the warm and humid air transport originates in the subtropical South Indian Ocean. In summer, the convective rainfall raindrops grow by collision–coalescence mechanism, and the break-up mechanism also plays an important role which makes smaller diameter. The ice particles could grow sufficiently and fall to the ground with enough time by the accretion mechanism in winter.

In summary, this study gives an insight into the seasonal characteristics of rainfall microphysics in summer and winter, which are very useful for radar QPE and numerical forecasting models modify in the middle reaches of the Yangtze River. However, due to the limitation of observation data, more types of observation data and numerical models simulation should be included to understand the mechanism of the microphysical processes for future reach.

How to cite: Wang, B. and Fu, Z.: The seasonal characteristics of summer and winter raindrops size distribution in Central China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3232, https://doi.org/10.5194/egusphere-egu25-3232, 2025.

Climate model output at 30 European airports (including 25 of the busiest) is used to investigate summer take-off distance required – TODR – and maximum take-off mass – MTOM – and how they may change in the future. We compare data from 2035–2064 to a historical baseline of 1985–2014 using three future forcing scenarios which represent low (SSP1-2.7), medium (SSP3-7.0), and high (SSP5-8.5) future emissions trajectories defined by the widely used Shared Socioeconomic Pathways, SSPs.

This work presents data for the A320 aircraft manufactured by Airbus but the calculation framework is widely applicable to any similar fixed-wing aircraft and uses entirely open-access input data.

We use 10 models from the 6th Coupled Model Intercomparison Project (CMIP6) which have a range of equilibrium climate sensitivity values; a measure of the amount of global warming they give for a doubling of carbon dioxide concentrations.

We use a numerical scheme which considers the resultant forces on an aircraft in the runway acceleration phase of its take-off and show that 30-year average values of TODR could increase by up to 100 m by mid-century. There is, however, significant variability since daily data is used throughout.

We quantify the changing probability distribution of TODR using kernel density estimation and illustrate this using an example showing how increases in extreme daily maximum temperature could alter distributions of TODR.

Additionally, we project that the 99th percentile (a one in a hundred day event) of the TODR from 1985-2014 may by exceeded on as many as half the summer days for some sites in the future.

Four of the airports studied (Chios, Pantelleria, San Sebastian and Rome Ciampino) have runway lengths which are shorter than the TODR when the aircraft is carrying its maximum payload. This means that the weight they carry must be reduced to fulfil safety constraints, which will only become more stringent as temperatures increase further. Relative to the mean weight-restriction amount for the historical period, we find that the number of passengers may have to be reduced by up to 10-12 passengers per flight, again accompanied by a significantly increased chance of exceeding extreme historical values.

How to cite: Williams, J., Williams, P., Guerrini, F., and Venturini, M.: Climate change will increase aircraft take-off distances and reduce payloads, but by how much?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3272, https://doi.org/10.5194/egusphere-egu25-3272, 2025.

Based on the observational and forecast datasets from precipitation merging product, radiosonde, Doppler radar, wind profiler radar and ECMWF product, the evolution and causes of the heavy precipitation process of Meiyu in the middle and lower reaches of the Yangtze River in China from June 21 to 22, 2024 were analyzed. The results show as the followings. (1) The heavy precipitation was mainly distributed in the northern part of Hunan Province, the southeastern part of Hubei Province and the western part of Anhui Province, with the main period from 15:00 on June 21 to 15:00 on June 22, especially in the early morning of June 22. The rain belt was located to the north of the subtropical high, in the north of the low-level jet, and at the front side of the moving trough line. (2) The K index exceeded 38℃ in all areas, and the CAPE before and after this heavy precipitation process was over 800 J/kg and less than 100 J/kg, respectively, indicating the evolution characteristics of unstable atmospheric stratification as well as the energy accumulation and release. (3) In the early stage of this process, the surface high temperature was distributed to the south of Wuhan, and the near-surface convergence line extended from the eastern part of Henan Province to the central part of Hubei Province. In the middle stage of this process, the convergence line moved eastward. In the later stage of this process, there was a significant cold pool over the land surface along the Yangtze River. The near-surface high temperature and convergence line were the triggering mechanisms of the heavy precipitation, while the cold pool led to the gradual weakening of the precipitation. (4) The water vapor flux was mainly located in the northern part of Hunan Province, the eastern part of Hubei Province as well as the southern part of Anhui Province, and gradually moved eastward. The flux values in the middle and lower layers were relatively high in the early morning of June 22. There were two water vapor transport belts in the lower layer, corresponding to different heavy precipitation centers. (5) The approximately east-west oriented echo band moved from west to east through the forms of merging, strengthening and dissipating. The south side of the echo band was the mesoscale linear or hook-shaped strong echo accompanied by high echo top and strong VIL. The meso-β scale convective system was composed of several meso-γ scale convective cells, and the meso-γ scale convective cells caused strong cumulative precipitation through the ‘train effect’.

How to cite: Zhou, H., Ge, N., and Qi, W.: Evolution and Cause Analysis of a Heavy Precipitation Process of Meiyu Along Yangtze River, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3417, https://doi.org/10.5194/egusphere-egu25-3417, 2025.

IAGOS, or the In-service Aircraft for a Global Observing System project, is a European Infrastructure project consisting of scientific measurement packages attached to commercial aircraft. Operating since 1994, this programme provides a unique long-timeseries dataset of flight data across the globe, with thousands of flights per year providing a strong base for statistical studies.

Here, we use flight times derived from IAGOS metadata to quantify the role of the El Nino - Southern Oscillation (ENSO), the Quasi-Biennial Oscillation, the solar cycle and the North Atlantic Oscillation (NAO) on trans-Atlantic flight times. We do this both by subsetting the data in various ways and via regression methods. This allows us to statistically assess the effects of these large-scale atmospheric-dynamical processes on trans-Atlantic flight times. We also calculate the additional costs associated with these effects in terms of both carbon dioxide emissions and fuel costs, allowing us to understand how climate processes drive them.

Depending on season and direction of flights, we show that these four climate indices can explain as much as 1/3 of the total variance in trans-Atlantic flight times. At a flight-time level and particularly in winter, the NAO dominates flight times and is the most important factor in one-way fuel costs: flights at peak NAO+ can be as much as 83 minutes longer than the equivalent flight at peak NAO- when crossing the Atlantic. However, at a whole-dataset level, ENSO is shown to be much more important in driving net round-trip costs. We further estimate that the monthly cost of these four climate indices can be as high as 100 kT of additional CO2 or USD 20 million at 2023 flight volumes and fuel prices.

How to cite: Wright, C.:  IAGOS estimates of climate-process costs for trans-Atlantic flights, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3842, https://doi.org/10.5194/egusphere-egu25-3842, 2025.

The MicroWave Humidity Sounder II (MWHS II) is a cross-track microwave sounder flying on FengYun (FY)-3C satellite. It has 15 channels ranging from 89.0 to 191.0 GHz, eight (channels 2-9) of which are located near 118.75 GHz along an oxygen absorption line, five (channels 11-15) of which are located near 183.31 GHz water vapor absorption line and the remaining two channels 1 and 10 are two window channels centered at 89.0 and 150.0 GHz. A new precipitation detection algorithm for 118GHz channels was developed based on the radiation characters of the double O₂ absorption bands (118 and 50-60 GHz). Since both of the 118 GHz and 50-60 GHz oxygen absorption bands are sensitive to atmospheric temperature, the radiation observed in the two bands has a specific inherent constraint relationship under the clear-sky conditions. However, the frequencies of 118 GHz channels are approximately twice that of the 50-60 GHz channels, and the two bands have different absorption and scattering characteristics for atmospheric hydrometeors. The radiance transfer mode VDISORT was used to simulate the sensitivity of the 118 GHz and 50-60 GHz channels to five kinds of hydrometeors (cloud water, rainwater, ice, snow, and graupel) in the cloud atmosphere. The results show that the 50-60 GHz channels are more sensitive to rainwater, and the 118 GHz channels are more sensitive to the other four types of hydrometeors. Therefore, the inherent constraint of the observational radiance between 118 GHz and 50-60 GHz channels under clear-sky condition is no longer valid for a cloudy scenario. In this paper, the machine learning system TensorFlow was used to construct a model for predicting the brightness of 118 GHz channels using 50-60 GHz observations under clear-sky conditions, and the accuracy of the prediction model was validated using independent samples. Then this neural network-based predictive model was used for 118 GHz channel precipitation detection. When the difference between actual observed and predicted bright temperature for 118 GHz channel is more massive than three times of the standard deviation of the prediction model, it is thought that the MWHS II observation is contaminated by precipitation or cloud. At last, this new precipitation detection algorithm for 118 GHz was validated by simulated measurements. The results show that both the precipitation detection POD (test probability) and PC (correct rate) for 118 GHz channels are above 90%.

How to cite: Guo, Y.: A precipitation detection algorithm for 118GHz channels based on FY-3C MWHS II and FY-3C MWTS II, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3880, https://doi.org/10.5194/egusphere-egu25-3880, 2025.

Precipitation nowcasting plays an essential role in operational weather forecasting services. Sudden precipitation events have significant socio-economic impacts, including natural disasters like flash floods. This challenge is becoming increasingly critical as climate change alters weather patterns and the frequency of extreme weather events continues to increase.

Over the last decade, radar observations, offering high temporal and spatial resolution, have facilitated the development of machine learning methods for precipitation nowcasting. Once trained, these methods are well suited to processing large datasets with low latency, especially in a real-time context. Recent advances in the field of nowcasting have focused on optimizing model architectures, improving loss functions for imbalanced data, and integrating multivariate inputs, including radar and satellite observations.

This study explores some critical hyperparameters, such as temporal context length, edge effect during training, influence of the output horizons prediction, and convolution kernel size. To do this, we investigate the performance of several models, including both machine learning approaches from different families, in particular SmaAt-Unet, ConvLSTM , and DGMR (trained on UK rains) , as well as non-machine learning methods such as  STEPS. An eleven years consistent radar precipitation dataset covering the Paris region was set up from Météo-France mosaic. Nine years were used for training machine learning models, and two years were reserved to evaluate the models’ performances. To assess the model in different weather conditions, the data set is divided into four groups with distinct characteristics corresponding to various meteorological phenomena. To ensure consistent evaluation, we evaluated the models on the same two-year test dataset, focusing on three criteria, namely: spatial consistency (Pearson correlation coefficient), location accuracy (CSI), and precipitation intensity (MSE).

Our analysis reveals that machine learning models consistently outperform traditional optical flow methods, with notable variations in performance across timescales and rainfall intensities. We also highlight that performance is nearly identical for all models in the presence of stratiform rain, while there are substantial differences in the convective rain group. Additionally, we show that for deep learning models, considering edge effects during training prevents the propagation of inevitable errors and helps avoid the appearance of ghost rain cells at the edges of the map. Furthermore, we show that the size of the kernels of the first layers plays an important role and must be large enough to allow correlation between distant pixels.

Finally, our study provides guidelines for the development of precipitation nowcasting models.

How to cite: Guigal, B., Chazottes, A., Barthès, L., Viltard, N., Le Bouar, E., Moreau, E., and Mallet, C.: Methodological Focus on Hyperparameters for Different Rain Nowcasting Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4138, https://doi.org/10.5194/egusphere-egu25-4138, 2025.

This study introduces Aeolus 2.0^{[1, 2]}, a novel multilayer moist-convective Thermal Rotating Shallow Water (mcTRSW) model designed to simulate atmospheric dynamics under various forcings, such as increased radiative or thermal forcing, as well as the effects of latent heat release and radiative transfer on meso- and large-scale dynamics. The model incorporates a novel moist-convective scheme that respects conservation laws, a new bulk aerodynamic scheme for sea surface evaporation and sensible heat flux, and provides a computationally efficient yet physically robust framework, bridging the gap between idealized models and complex general circulation models. Aeolus 2.0 integrates barotropic and baroclinic processes, enabling detailed investigations of phenomena such as zonal wind variability, heatwaves, and seasonal energy fluxes.

The model has already been applied to various atmospheric phenomena, such as simulating the Madden-Julian Oscillation (MJO)^[3], large-scale localized extreme heatwaves^[4], and atmospheric responses to increased radiative forcing during solstices and equinoxes^[1]. In this presentation, we showcase the results of the latter. The findings highlight significant changes in zonal wind velocity and meridional temperature gradients, with notable hemispheric asymmetry. Specifically, increased radiative forcing enhances subtropical westerly jet velocities and mid-latitude temperatures during the solstices, while reducing polar cyclone zonal wind velocities in the affected hemisphere. Poleward eddy heat fluxes were consistently observed across hemispheres, and heatwave intensity and duration were amplified over both land and ocean regions.

References:

[1] Rostami, M., Petri, S., Fallah, B., Fazel-Rastgar, F. (2025). Aeolus 2.0's thermal rotating shallow water model: A new paradigm for simulating extreme heatwaves, westerly jet intensification, and more. Physics of Fluids, 37 (1), 016604. https://doi.org/10.1063/5.0244908.

[2] Rostami, M., Petri, S., Guimaräes, S.O., Fallah, B. (2024). Open-source stand-alone version of atmosphere model Aeolus 2.0 Software. Geoscience Data Journal, 11, 1086–1093. https://doi.org/10.1002/gdj3.249. (Link to Zenodo: https://doi.org/10.5281/zenodo.10054154)

[3] Rostami, M., Zhao, B. & Petri, S. (2022). On the genesis and dynamics of madden–Julian oscillation-like structure formed by equatorial adjustment of localized heating. Quarterly Journal of the Royal Meteorological Society, 148, 3788–3813.  https://doi.org/10.1002/qj.4388.

[4] Rostami, M., Severino, L., Petri, S., & Hariri, S. (2023). Dynamics of localized extreme heatwaves in the mid-latitude atmosphere: A conceptual examination. Atmospheric Science Letters, e1188. https://doi.org/10.1002/asl.1188 .

How to cite: Rostami, M., Petri, S., Fallah, B., and Fazel-Rastgar, F.: On the Dynamical Core of Aeolus 2.0: An Atmospheric Model Using a Moist-Convective Thermal Rotating Shallow Water Framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5038, https://doi.org/10.5194/egusphere-egu25-5038, 2025.

Based on the brightness temperature observed by the Fengyun-4A satellite, eight hundred mesoscale convective systems (MCSs) are identified in the middle reaches of the Yangtze River Basin during the warm seasons of 2018–2021, and these MCSs are categorized into the quasistationary (QS) type and the outward-moving (OM) type. Afterward, the initiations of the MCSs are backward tracked using a hybrid method of areal overlapping and optical flow. Then, the intensity, evolution and distribution of cloud-to-ground (CG) lightning and radar composite reflectivity (CR) associated with MCSs are explored.

The QS-MCSs primarily occur in July and August and are mainly initiated in the afternoon. The OM-MCSs mostly occur in June and July with two initiation peaks at noon and late night, respectively. The QS-MCSs are mainly initiated in mountainous areas. In contrast, the OM-MCSs are mainly initiated in plain areas. Compared to the OM-MCSs, the QS-MCSs show notable diurnal variation in intensity and develop more rapidly. The geographical distribution of CG lightning associated with MCSs shows that the highest occurrence tends to appear over the transition zone of the Poyang Lake Plain and the surrounding mountains. The CG lightning associated with MCSs features a relative lower proportion of negative CG lightning occurrences. An overall negative correlation between brightness temperature and the peak current of CG lightning is documented with seasonal variations. The advection of ice particles associated from convective cores into nearby stratiform regions caused by relatively stronger mid-to-upper-level winds, may explain the positive correlations in May and September. A time lag of 0–2 h between the CG lightning occurrence peak and the MCS extent maximum is found. As the MCS develops, the proportion of convective clouds decreases, the proportion of nonprecipitating anvil increases, and the proportion of stratiform consistently maintains 50%–60% of the MCS extent, dominating throughout its life span. The main region for stratiform is primarily in the southern part of the MCS, while convective clouds are mainly in the northern part, possibly due to the influence of the Meiyu front.

How to cite: Sun, J. and Fu, Y.: The Intensity, Evolution, and Distribution of Cloud-to-Ground Lightning and Radar Reflectivity throughout the Life Cycle of Mesoscale Convective Systems over Southern China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5335, https://doi.org/10.5194/egusphere-egu25-5335, 2025.

How to cite: Cottrell, F., Barrett, P., Abel, S., Whitall, M., Williams, K., and Field, P.: Informing the Unification of a Single Cloud Fraction Scheme in the Met Office’s Unified Model  , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5755, https://doi.org/10.5194/egusphere-egu25-5755, 2025.

Cloudburst is a new post-processing system at the Met Office, leveraging Amazon Web Services (AWS) to provide a route for easy deployment of post processing pipelines allowing for the generation of replacement data as legacy sources are retired. The focus is primarily on generating diagnostics where consistency across multiple variables is required to provide a coherent weather narrative. Thus far all provided parameters have utilised the Met Office’s global and UK deterministic models but the system is made to be versatile so ensemble forecasts could be used in future.

The diagnostics generated in Cloudburst use code from the open-source IMPROVER (Integrated Model post-PROcessing and VERification) repository, which offers a versatile toolbox of post-processing plugins. By enhancing this toolbox with new plugins and functionalities, we promote the reusability of post-processing components, fostering collaboration between the Blended Probabilistic Forecast team and the Cloudburst team. Any code added to the IMPROVER repository by Cloudburst is made as adaptable as possible so that it could be applied to deterministic forecasts or ensemble members.

In this presentation we will describe the first diagnostic generated within Cloudburst: precipitation type. This diagnostic was required to be consistent with the rain and snow rate so these were also rederived from the precipitation rate. Precipitation type, along with rain and snow rates, have now been operationalised and the data sent downstream for customers.

How to cite: Spelman, M.: Cloudburst: A Platform for Running Post-Processing Workflows at the Met Office, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5870, https://doi.org/10.5194/egusphere-egu25-5870, 2025.

Rainfall nowcasting algorithms rely primarily on extrapolation, where recent radar rainfall observations are projected forward in time based on a motion field that is determined with past data. While additional (stochastic) processes may be incorporated, as is for example done in the pySTEPS models, extrapolation remains the fundamental mechanism. Although the motion field estimates are robust, they assume a steady state in the motion field for the future. This assumption can face significant challenges in maintaining accuracy over time, especially during convective weather events characterized by rapid changes in precipitation patterns and their movement.

In this study, we focus on three objectives: 1) identifying the current errors and uncertainties in the steady-state motion field derivation using pySTEPS, 2) the construction of a dynamic motion field derivation approach using a new deep-learning model, MotioNNet, and 3) the development of ensemble motion fields for MotioNNet. MotioNNet is a U-Net based deep-learning architecture, which uses the past radar images (five in this study) in combination with the estimated static motion field from pySTEPS to estimate the deviation from the provided static motion field per grid cell with increasing lead time. For the ensemble generation in MotioNNet, we tested probabilistic techniques such as SpatialDropout and Monte Carlo dropout.

We trained and tested our model on C-band weather radar data from the Royal Netherlands Meteorological Institute (KNMI), using 10,000 rainfall events. These events were selected to include cases with both intense precipitation and significant motion errors. Our results show that the static motion field approach results in average motion field errors of 1 – 3 km h^-1 at the start of the forecast and increases to 4 – 8 km h^-1 (on average, and locally sometimes much higher) at a lead time of 90 minutes. The dynamic motion field estimates of MotioNNet improve the motion prediction accuracy by approximately 13%. The improvement is much higher for structured and stable events (up to 45%), but almost negligible for localized thunderstorm events. The results of the ensemble construction in MotioNNet indicate that MotioNNet is capable of adding perturbations in space where most uncertainty takes place, especially for structured and stable events. This is an advantage compared to the spatially uniform approach of pySTEPS. However, the spread of the ensembles is still underestimated, even more so than with pySTEPS, indicating that the uncertainty in the forecast is not yet well represented.

We conclude that the hybrid MotioNNet approach can substitute and enhance parts of the motion field module in pySTEPS. MotioNNet refines initial motion field estimates, rather than replacing them, which leads to a modular approach that fits well in the overall pySTEPS framework. We expect that the dynamic motion field approach from MotioNNet will aid in further enhancing the predictability of (high-intensity) rainfall events for short lead times, especially for structured events where motion errors currently play a role in the forecast error.

How to cite: Imhoff, R., Blázquez Martín, D. A., Taormina, R., and Schleiss, M.: Data-driven dynamic motion field generation for rainfall nowcasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6014, https://doi.org/10.5194/egusphere-egu25-6014, 2025.

The spatiotemporal dependence structure in postprocessed weather forecast variables is essential for reliable hydrological and socio-economic applications. However, in univariate postprocessing, where statistical or advanced machine learning techniques are applied independently in each margin, the multivariate dependence structure present in the raw ensemble forecasts is lost. To restore the disrupted spatial or temporal dependence structure of univariately postprocessed forecasts, copula-based methods are traditionally applied as an additional step that utilizes dependency information from raw ensemble forecasts or historical observations. However, such a two-step framework faces difficulty incorporating exogenous variables to model the dependence structure. To overcome these limitations, a multivariate non-parametric data-driven distributional regression postprocessing technique based on a generative neural network is employed to draw samples directly from multivariate predictive distribution as output [1]. This study focuses on preserving temporal dependency and investigates the performance of a multivariate generative model against two-step approaches to postprocess a 2-meter temperature forecast with a one-month lead time over the Trentino-South Tyrol region in the northeastern Italian Alps. The forecast dataset is a fifth-generation seasonal weather forecast system (SEAS5) generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), which has a 0.125° x 0.125° horizontal grid resolution with 25 ensemble members over a reforecast period from 1981 to 2016. The reference dataset is the high-resolution (250 m x 250 m) gridded observational data over the region. The results are presented using multivariate proper scoring rules (i.e., energy and variogram scores) to measure the overall discrepancy and dependence structure in the postprocessed forecast. The performance analysis reveals that the multivariate generative postprocessing model outperforms the two-step approach over the entire region.

References:

[1] Chen, J., Janke, T., Steinke, F. & Lerch, S. Generative Machine Learning Methods for Multivariate Ensemble Postprocessing. Ann. Appl. Stat. 18, 159–183 (2024).

How to cite: Uttarwar, S. B., Chen, J., Lerch, S., and Majone, B.: Lead time-dependent postprocessing of 2-meter temperature forecast using a multivariate generative machine learning model , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6937, https://doi.org/10.5194/egusphere-egu25-6937, 2025.

This paper proposes a novel method for predicting icing on overhead contact lines by integrating physical modeling with Transformer-based deep learning, addressing the limitations of traditional meteorological models in complex weather conditions and terrains. The method combines physical factors such as meteorological data (e.g., temperature, humidity, wind speed) and topographic features to construct a physical model for initial predictions, while leveraging the Transformer model's robust capability in processing time-series data to capture the nonlinear dynamics of the icing process. Experimental results demonstrate that the proposed method significantly outperforms traditional single meteorological models in prediction accuracy across various weather conditions, particularly excelling in extreme weather and complex terrain scenarios. This approach provides reliable technical support for disaster prevention, mitigation, and early warning systems in the transportation sector, offering substantial practical value for engineering applications.

How to cite: Huai, X., Kang, W., Li, B., Luo, J., Dai, W., and Liu, R.: A Study on Catenary Icing Prediction Method Integrating Physical Modeling and Transformer-Based Deep Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7540, https://doi.org/10.5194/egusphere-egu25-7540, 2025.

Based on the minute-resolution meteorological elements data observed at 70 automatic weather stations in Jiangsu, the second-resolution sounding data of 3 sounding stations and the fog droplet spectrum data of 21 dense fog events, from January 1, 2013 to December 31, 2023, the spatial and temporal distribution, boundary layer structure and microphysical structure characteristics of the fog at different grades in Jiangsu were analyzed. The results show that in recent years, the number of fog hours in Jiangsu are distributed along the Yangtze River and to the north along the Huaihe River. The average annual fogging time at each station is 318.5h, the strong dense fog and extremely dense fog were mainly concentrated along the Huaihe River and its north, accounting for 16.4% of the total fog hours. The probability of occurrence of fog in Jiangsu is the highest at 05:50, and the probability of occurrence of fog in winter, spring, summer and autumn is the highest at 07:10, 05:50, 05:20 and 05:50, respectively. The temperature structure of fog at different grades between 0 and 1500 m has inversion layer, and with the increase of fog intensity, the inversion intensity increases. And the relative humidity is saturated in the lower layer, but with the increase of fog intensity, the relative humidity of upper layer decreases. With the increase of fog intensity, the number of fog drops of different sizes all increase, and the spectrum of fog drops expands obviously when strong dense fog or extremely dense fog occurs.

How to cite: Wang, H., Zhang, Z., and Liu, D.: Characteristics of the Macro- and Micro-Structures of Different Grades of Fog in Jiangsu, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7697, https://doi.org/10.5194/egusphere-egu25-7697, 2025.

Atmospheric convection plays a fundamental role in the vertical redistribution of atmospheric constituents, in driving atmospheric circulation, and in creating severe weather conditions that put life and property at risk. Cloud and precipitation processes in convection and their related release of latent heat are coupled to the rate of vertical air motion in convective updrafts and downdrafts. Observations of vertical air motion in convection have generally been confined to suborbital observations of limited areas and periods of time, but understanding the global distribution of convection is very much needed.

The NASA Atmosphere Observing System (AOS) was formulated based on the NASA 2017 Earth Science Decadal Survey to address key objectives tied to aerosols, clouds, convection, and precipitation. As of March 2024, the AOS constellation consists of four individual projects: 1) AOS-Storm, in partnership with JAXA and CNES, flying in a 55° inclined orbit and focusing on convective precipitation, vertical air motions, and convective ice cloud properties; 2) AOS-Sky, a satellite carrying a suite of passive sensors including a multi-angle polarimeter, passive microwave radiometer, and thin ice cloud far infrared imaging radiometer flying in tandem with a CSA-provided spacecraft (called HAWCsat) carrying aerosol and moisture limb imagers; 3) an Italian Space Agency led mission, in partnership with NASA, carrying a multi-frequency elastic backscatter lidar with Raman channels for measurement of aerosol, cloud, ocean, and land properties; and 4) an expected cloud profiling radar to be competed as part of an announcement of opportunity.

This talk will focus on the AOS-Storm project consisting of the JAXA Precipitation Measuring Mission (PMM) and the CNES Convective Core Observations through MicrOwave Derivatives in the trOpics (C²OMODO) mission, with NASA providing a spacecraft bus for one of the CNES radiometers and launch of both satellites.  The PMM mission includes a JAXA-provided spacecraft and Ku-band Doppler radar that will provide radar reflectivity across a 255-km swath (similar to TRMM and GPM) and Doppler velocity measurements at nadir in moderate to strong convective systems. The CNES C²OMODO mission consists of two identical passive microwave radiometers (channels near 89, 183, and 325 GHz) flying in tandem with a temporal spacing expected to be in the 30-120 second range. The time-differenced passive microwave brightness temperatures will characterize the rate of change of ice water path and anvil size as well as the vertical flux of ice mass. We will highlight recent simulations of expected performance for measurements of vertical air motions and ice water path in convective clouds.

How to cite: Braun, S., Kollias, P., Gong, J., Liu, Y., Takahashi, N., Kubota, T., Brogniez, H., Amiot, T., Yorks, J., and Cecil, D.: Future Satellite Observations of the Dynamics and Microphysics of Convection from the NASA Atmosphere Observing System (AOS), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7761, https://doi.org/10.5194/egusphere-egu25-7761, 2025.

EGU abstract 2025

NP5.2 EDI: Advances in statistical post-processing, blending, and verification of deterministic and probabilistic forecasts

The challenge of uncertain observations: Probabilistic verification of decadal predictions with high temporal resolution

Verification plays an important role in the evaluation and the development of climate predictions. With new developments in the field and ever larger availability of computational resources, temporal high resolutions become an option. But we often do not make use of the full temporal distribution and much too often we still rely on temporal averages to reduce the dimensionality of the data to make a verification with common metrics manageable. One of the reasons is the challenge how to verify in an understandable manner probabilistic model predictions with probabilistic, uncertain observations.

Tools for probabilistic verification are available, like the Continuous Rank Probability Score (CRPS), but are often defined for perfect observations. Furthermore, many tools are for the wider community hard to comprehend and are as such often not applied. This poses the question on how to verify predictions on the basis of current imperfect usage of metrics within the field and how to communicate prediction skill in general. 

This contribution will address two main approaches and apply it to the comparison between a decadal prediction and the associated projection (historical simulation), with an assimilation simulation as an observational reference. In the first we will ask how to communicate verification results for a wider community. For this we will look at framing the skill as yearly matchups between the two model results. Basing on the Integrated Quadratic Distance each year determines which model result is closer to the observations and the years how often one result was better than the other leads to our verification result. In a second approach it will be discussed to find modifications of some of the most applied metrics in our field, Anomaly Correlation (ACC) and Root-Mean Square (RMS), towards uncertain observations. While these metrics are imperfect, they allow an easy communication for people already applying them. Differences in their interpretation will be discussed, giving us insights about how uncertain observations change our understanding of a good prediction. We address also significance estimation and it will be highlighted why we need to find easy comprehendible approaches to handle uncertain observations in the future.

How to cite: Düsterhus, A.: The challenge of uncertain observations: Probabilistic verification of decadal predictions with high temporal resolution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8034, https://doi.org/10.5194/egusphere-egu25-8034, 2025.

Bias correction techniques are often used as effective and reliable approaches to improve the representation of current and past conditions in climate models. This study aims to evaluate the performance of Quantile Delta Mapping (QDM) as a bias correction method for daily precipitation simulations from climate models: the Icosahedral Nonhydrostatic Model (ICON), the Regional Climate Model COSMO-CLM (CCLM), and the Regional Climate Model (REMO) at a spatial resolution of 3 km over Germany. The dataset consists of historical observations from HYRAS and climate model simulations between 1961 and 1990, split into a calibration period (1961–1980) and an independent validation period (1981–1990). To assess performance, we considered four aspects: 1) sequence of events, 2) distribution of values, 3) spatial structure, and 4) visual inspection of distance metrics, ultimately providing an integrative qualitative ranking across these aspects. Performance metrics included correlation, Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), and error metrics such as BIAS, mean square error (MSE), and root mean squared error (RMSE). Additional metrics considered were the Kolmogorov-Smirnov (KS) statistic, Perkins Skill Score (S_score), probability density function (PDF), 80th, 90th, and 95th percentiles, and spatial autocorrelation. As a preliminary assessment of the simulated precipitation from ICON, results show only slight improvements in the time and spatial distribution of precipitation metrics. For example, the KS statistic improved from 0.0314 to 0.0190, while the S_score improved from 0.0314 to 0.0195 when comparing HYRAS vs. ICON raw and HYRAS vs. ICON bias-corrected using QDM, respectively. Therefore, limited improvement is expected from bias correction when the climate model already performs well, whereas significant improvements can be achieved when the climate models perform only acceptably.

How to cite: Espitia, E., Díaz Esteban, Y., Haupt, M., Adakudlu, M., Vlachopoulos, O., and Xoplaki, E.: A multi-criteria evaluation of the performance of bias correction using Delta Quantile Mapping for simulated precipitation over Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8427, https://doi.org/10.5194/egusphere-egu25-8427, 2025.

How to cite: Ben Bouallegue, Z. and Taillardat, M.: The crossing-point quantile: an optimal point-forecast in terms of ROC areas. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8449, https://doi.org/10.5194/egusphere-egu25-8449, 2025.

How to cite: Hurst, K. and Evans, G.: Improvements to NWP visibility forecasts using statistical post-processing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9468, https://doi.org/10.5194/egusphere-egu25-9468, 2025.

Lightning, hail, severe turbulence and severe icing associated with cumulonimbus clouds (Cb) present a significant safety hazard to air traffic and can impact the comfort and timeliness of a flight. The World Area Forecast System (WAFS) facilitates safe and efficient flight planning by providing global forecasts of key meteorological hazards. The next generation of WAFS will provide probabilistic forecasts of these hazards, including cumulonimbus clouds.

At the Met Office, these forecasts are currently made using three simple threshold tests applied to parameters from MOGREPS-G, a global NWP ensemble. These thresholds are used as a proxy for the occurrence of cumulonimbus clouds in the NWP data.

In this work, a series of deep learning models have been trained to predict the occurrence of cumulonimbus in global satellite observations using a wider set of parameters from the control member of MOGREPS-G. The purpose of the training is for the deep learning model to learn the representation of a cumulonimbus in the NWP data in a supervised manner. The model predictions are then applied to the whole ensemble to produce a probability forecast of cumulonimbus occurrence.

A range of loss functions were used during model training and verification to account for spatial information at a range of scales. Different loss functions were also used to enhance the reward for correct forecasts of the relatively rare cumulonimbus clouds.

Some of the trained models are shown to have greater skill than a baseline using the threshold test method. The model characteristics change depending on the choice of loss function used during training.

Further work is needed to explore how to make predictions at a range of lead times and how to use inputs from the whole ensemble.

How to cite: Creswick, A.: A deep learning approach for probabilistic forecasts of cumulonimbus clouds from NWP data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9783, https://doi.org/10.5194/egusphere-egu25-9783, 2025.

Title: Probabilistic Postprocessing of Hourly Precipitation Ensemble Forecasts Using UNet 

Authors: Marcos Esquivel-González, Albano González, Juan Carlos Pérez, Juan Pedro Díaz, Pierre Simon Tondreau

Affiliation of authors: Grupo de Observación de la Tierra y la Atmósfera (GOTA), Avenida Astrofísico Francisco Sánchez, s/n, La Laguna, 38200, Canary Islands, Spain

Abstract: Reliable precipitation forecasting is crucial in sectors like public safety, agriculture and water management. Numerical Weather Prediction (NWP) models, which form the backbone of modern forecasting, are prone to errors due to their limitations and the chaotic behavior of equations, requiring postprocessing to improve accuracy and quantify uncertainties. Thus, this study evaluates probabilistic postprocessing models tailored for the Canary Islands, with the aim of enhancing Weather Research and Forecasting (WRF) ensemble forecasting accuracy in hourly precipitation forecast. UNet-based models were explored using two approaches,  one incorporating  the full set of km-scale convection-permitting ensemble forecast simulations (25) and another applying dimensionality reduction via Principal Component Analysis (PCA) and feature selection methods. These models were compared to traditional benchmarks like the Censored Shifted Gamma Distribution (CSGD) with Ensemble Model Output Statistics (EMOS) and the Analog Ensemble method. In the analysis of the results, not only the reliability of the predictions for the set of available meteorological stations was considered, but also the generalization capacity of the UNet models to obtain precipitation predictions for the whole region.

In general, UNet models outperformed traditional approaches. The UNet with PCA excelled in probabilistic and deterministic metrics but struggled in regions without weather station data. Conversely, the UNet with feature selection, while slightly less accurate overall station locations, showed better generalization to unseen locations, maintaining consistent performance across the region and reducing computational demand. Additionally, the Integrated Gradients technique, an interpretability method that quantifies the contribution of each input feature to a model’s predictions by analyzing gradients, was employed to evaluate the impact of input variables on model performance. This analysis revealed that the integration of digital terrain elevation data significantly contributed to the UNet's outputs, underscoring the importance of topographic data in rainfall prediction.

How to cite: Esquivel González, M., González, A., Pérez, J. C., Díaz, J. P., and Tondreau, P. S.: Probabilistic Postprocessing of Hourly Precipitation Ensemble Forecasts Using UNet, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9837, https://doi.org/10.5194/egusphere-egu25-9837, 2025.

High-resolution climate projections are crucial for assessing the future impacts of climate change. Statistical, dynamic, or hybrid climate data downscaling is often employed to create the datasets required for impact modelling. In this study, we utilize the COSMO-CLM (CCLM) version 6.0, a regional climate model, to investigate the advantages of dynamically downscaling a general circulation model (GCM) from CMIP6, with a focus on Central Asia (CA). The CCLM, running at a 0.22° horizontal resolution, is driven by the MPI-ESM1-2-HR GCM (at 1° spatial resolution) for the historical period 1985–2014 and projections for 2019–2100 under three shared socioeconomic pathways (SSPs): SSP1-2.6, SSP3-7.0, and SSP5-8.5 (Fallah et al., 2025). Using the CHIRPS gridded observation dataset for evaluation, we assess the performance of the CCLM driven by ERA-Interim reanalysis over the historical period.

The added value of CCLM, particularly over mountainous areas in CA, is evident, with a reduction in mean absolute error and bias of climatological precipitation by 5 mm/day for summer and 3 mm/day for annual values (Fallah et al., 2024). While no error reduction is achieved for winter, the frequency of extreme precipitation events improves in the CCLM simulations. Future projections indicate an increase in the intensity and frequency of extreme precipitation events in CA by the century’s end, particularly under the SSP3-7.0 and SSP5-8.5 scenarios. The number of days with more than 20 mm of precipitation increases by more than 90, and the annual 99th percentile of total precipitation increases by over 9 mm/day in mountainous areas.

A convolutional neural network (CNN) is also trained to map GCM simulations to their dynamically downscaled CCLM counterparts. The CNN successfully emulates the GCM-CCLM chain across large areas of CA but demonstrates reduced skill when applied to other GCM-CCLM chains. This downscaling approach and CNN architecture provide an alternative to traditional methods and could be a valuable tool for the scientific community involved in downscaling CMIP6 models (Harder et al., 2023).

In future work, we aim to extend this approach by training a neural network model to map the available GCM-RCM model chains for CORDEX-EU and applying the trained model to decadal prediction ICON simulations. This will enable the production of CORDEX-EU-like regional ICON simulations, bridging the gap between global and regional climate information on decadal timescales. By integrating decadal predictions into the framework, we aim to enhance the usability of regionalized climate data for short-term climate planning and decision-making.

References:

• Fallah, B., Russo, E., Menz, C., Hoffmann, P., Didovets, I., and Hattermann, F. F.: Anthropogenic influence on extreme temperature and precipitation in Central Asia, Sci. Rep., 13, 6854, https://doi.org/10.1038/s41598-023-33921-6, 2023.
• Fallah, B., Menz, C., Russo, E., Harder, P., Hoffmann, P., Didovets, I., and Hattermann, F. F.: Climate Model Downscaling in Central Asia: A Dynamical and a Neural Network Approach, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2023-227, accepted, 2025.
• Harder, P., Hernandez-Garcia, A., Ramesh, V., Yang, Q., Sattegeri, P., Szwarcman, D., Watson, C., and Rolnick, D.: Hard-Constrained Deep Learning for Climate Downscaling, J. Mach. Learn. Res., 24, 1–40, 2023.

How to cite: Fallah, B. and Rostami, M.: Precipitation Downscaling Using Dynamical and Neural Network Approaches., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10090, https://doi.org/10.5194/egusphere-egu25-10090, 2025.

Forecasts on transportation meteorology, such as pavement temperature, are becoming increasingly important in the face of global warming and frequent disruptions from extreme weather and climate events. In this study, we propose a pavement temperature forecast model based on stepwise regression—model output statistics (SRMOS) at the short-term timescale, using highways in Jiangsu, China, as examples. Experiments demonstrate that the SRMOS model effectively calibrates against the benchmark of the linear regression model based on surface air temperature (LRT). The SRMOS model shows a reduction in mean absolute errors by 0.7–1.6 °C, with larger magnitudes observed for larger biases in the LRT forecasts. Both forecasts exhibit higher accuracy in predicting minimum nighttime temperatures compared to maximum daytime temperatures. Additionally, it overall shows increasing biases from the north to the south, and the SRMOS superiority is greater over the south with larger initial LRT biases. Predictor importance analysis indicates that temperature, moisture, and larger-scale background are basically the key predictors in the SRMOS model for pavement temperature forecasts, of which the air temperature is the most crucial factor in the model’s construction. Although larger-scale circulation backgrounds are generally characterized by relatively low importance, their significance increases with longer lead times. The presented results demonstrate the considerable skill of the SRMOS model in predicting pavement temperatures, highlighting its potential in disaster prevention for extreme transportation meteorology events.

How to cite: Zhu, S., Lyu, Y., Wang, H., Zhou, L., and Zhu, C.: Pavement Temperature Forecasts Based on Model Output Statistics: Experiments for Highways in Jiangsu, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10119, https://doi.org/10.5194/egusphere-egu25-10119, 2025.

Raw forecasts, be they weather or hydrological, suffer from the inevitable errors stemming from either model structures or initial conditions estimation. With forecasting being a critical component in addressing challenges in flood control, reservoir and hydropower operation, and other fields related to the environment, energy and public safety, improving forecasting skill is increasingly necessary. Post-processing methods can help in this regard and can help improve forecast accuracy and reliability. Non-Homogeneous Gaussian Regression (NGR) and Bayesian Model Averaging (BMA) are the two most commonly used methods when it comes to post-processing probabilistic forecasts, and they have shown to be similarly efficient in many studies. For case studies where there are several distinct forecasts for one single observation, NGR risks losing information on uncertainty by aggregating the forecasts even though it accounts for heteroscedasticity. BMA, on the other hand, evaluates distinct model components and utilizes them accordingly, while assuming all the forecasts are alike in their under/overdispersion. This work introduces a mixed NGR-BMA approach for calibrating air temperature forecasts with lead-times of 1-10 days where the forecasts are first processed with NGR and then corrected once more by BMA according to a priori information on the skill of model components. This way, the upsides of each method is maintained through post-processing. The results generally show that the higher the lead-time, the more the proposed method outperforms either BMA or NGR taken individually. 

How to cite: Oghbaei, B. and Arsenault, R.: Using Non-Homogeneous Gaussian Regression to incorporate heteroscedasticity when post-processing air temperature forecasts by Bayesian Model Averaging, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10245, https://doi.org/10.5194/egusphere-egu25-10245, 2025.

Operational weather forecasting has traditionally relied on physics-based numerical weather prediction (NWP) models, but the rise of AI-based weather emulators is reshaping this paradigm. However, most data-driven models for medium-range forecasting still face limitations, such as a narrow range of predicted variables and low effective spatiotemporal resolution. This presentation will compare the strengths and weaknesses of these two approaches, using Environment and Climate Change Canada’s Global Environmental Multiscale (GEM) model and Google DeepMind’s GraphCast model. It will demonstrate that GraphCast outperforms GEM in predicting large-scale features, particularly for longer lead times.

Building on these findings, we propose a new hybrid NWP-AI system, in which GEM’s large-scale state variables are spectrally nudged towards GraphCast’s inferences, while GEM continues to generate fine-scale details critical for weather extremes. Results show that this hybrid system improves GEM’s forecast accuracy, reducing RMSE for the 500-hPa geopotential height by 5-10% and extending predictability by 6-12 hours in the extratropics, peaking at day 7 of the forecast. It also yields significant improvements in tropical cyclone trajectory prediction without degrading intensity forecasts. Unlike state-of-the-art AI-based models, the hybrid system ensures meteorologists retain access to all forecast variables, including those critical for high-impact weather. Preparations are currently well underway for the operationalization of this hybrid system at the Canadian Meteorological Centre. 

How to cite: Separovic, L., Husain, S., Caron, J.-F., Aider, R., Buehner, M., Chamberland, S., Creese, C., Lapalme, E., McTaggart-Cowan, R., Subich, C., Vaillancourt, P., Yang, J., and Zadra, A.: Leveraging Data-Driven Weather Forecasting for Improving Numerical Weather Prediction Skill Through Large-Scale Spectral Nudging, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10701, https://doi.org/10.5194/egusphere-egu25-10701, 2025.

Water vapour fluxes, originating mainly from the Atlantic, North Africa, and the Mediterranean region, play a critical role in shaping the climate dynamics of the Mediterranean Basin, especially during the summer months. These fluxes significantly influence relative humidity levels in the troposphere, affecting both local and regional weather patterns, such as intense rainfall events and prolonged droughts, while also contributing to the amplification of heatwaves through enhanced surface radiation trapping. This study uses observational data collected during the Mediterranean Experiment for Sea Salt and Dust Ice Nuclei (MESSA-DIN) from July to September 2021 in Soverato, southern Italy, to characterise the synoptic conditions of the severe summer of 2021.

A combination of ground-based remote sensing instruments revealed intense and persistent water vapour transport in the mid-troposphere. ERA5 data were used to identify the moisture dynamics over the Mediterranean Basin. The comparison between ERA5 reanalysis data and ground-based measurements further highlighted discrepancies in the representation of water vapour, particularly a dry bias in relative humidity in the range between 500 hPa and 300 hPa. While ERA5 provided a coherent and detailed representation of synoptic patterns and showed general agreement in the time evolution of the atmospheric vertical structure with observations, it exhibited a dry bias in relative humidity (RH) values compared to a ground-based microwave profiler (MWP). However, the magnitude of the bias also depends on the bias affecting the MWP retrieval, typically within 10-15% RH in the mid-troposphere. ERA5 also overestimates the presence of both cold and warm clouds, while ground instruments detected much less frequent cloud cover. This emphasizes the need for improving reanalysis performance in complex coastal and orographic settings. The bias in ERA5 was further assessed using GRUAN data from the Potenza station and regular upper-air data from Mediterranean stations.

The study underscores the importance of ground-based measurements, such as those from microwave radiometers, in improving weather forecasts for extreme events. Despite their lower vertical resolution, these instruments—both on their own and when combined with higher-resolution measurement techniques such as Raman lidars and upper-air soundings—provide continuous, real-time measurements of atmospheric water vapour. These measurements are essential for enhancing our understanding of water vapour fluxes and their impact on cloud formation, as well as for improving the accuracy of high-resolution forecasting models, especially in the representation of extreme weather events in the Mediterranean and Central Europe.

How to cite: Madonna, F., Gandolfi, I., Rosoldi, M., Karimian Saracks, F., Hesham Essa, Y., and Salicone, G.: The Role of Water Vapour in Shaping Mediterranean Summer Climate: Findings from MESSA-DIN 2021 measurement campaing in southern Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11378, https://doi.org/10.5194/egusphere-egu25-11378, 2025.

Machine learning-based models are rapidly transforming medium-range weather forecasting. The European Centre for Medium-Range Weather Forecasts (ECMWF) has developed the Artificial Intelligence Forecasting System (AIFS), a state-of-the-art data-driven model combining a graph neural network encoder-decoder with a sliding window transformer processor. Trained on ECMWF's ERA5 re-analysis and operational numerical weather prediction analyses, AIFS demonstrates exceptional deterministic forecast skill across upper-air variables, surface weather parameters, and tropical cyclone tracks.

Building on this foundation, ECMWF has introduced AIFS-CRPS, a probabilistic extension of AIFS designed for ensemble forecasting. AIFS-CRPS is obtained by training a stochastic model with the Continuous Ranked Probability Score (CRPS) as its loss function. It addresses uncertainties and generates highly skilful probabilistic forecasts. For medium-range timescales, AIFS-CRPS matches or outperforms ECMWF’s physics-based Integrated Forecasting System ensemble across key variables and lead times.

This presentation will highlight recent advancements in deterministic and probabilistic forecasting with AIFS, showcasing its operational readiness and its potential to redefine medium-range forecasting at ECMWF.

How to cite: Hahner, S. and the AIFS-Team: The AIFS: ECMWF’s data-driven weather forecasting system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12077, https://doi.org/10.5194/egusphere-egu25-12077, 2025.

Urban resilience to extreme weather events is increasingly threatened by the intensification of short-duration rainfall, often leading to urban flooding. This study focuses on improving the prediction of heavy rainfall in the Metropolitan Area of Barcelona, located on the Catalan Mediterranean coast in the northeast of the Iberian Peninsula, using high-resolution radar products and rain gauge data. Despite the decrease in average of annual rainfall in the AMB over recent decades, the intensity rates of some storm events are among the highest of the existing series, with occasional convective events causing urban flooding and severe disruptions for the urban region. The latest climate change reports (IPCC 2022) point towards an increase in frequency and intensity of heavy rainfall events in the region.

An extensive dataset of rainfall days spanning from 2014 to 2022 is analysed, including volumetric radar products (VIL, Echo Top), surface rainfall measurements, and incident reports. A bottom-up approach is used to identify 45 intense convective days with significant impacts in the study region. A radar-based nowcasting approach is introduced, utilizing a two-dimensional radar product with three-dimensional atmospheric information to enhance early warnings in the urban region, with high spatial resolution. This approach focuses on the convective parts of storms through Vertical Integrated Liquid (VIL) density-based tracking and nowcasting with six-minute temporal updates to characterize storm centroids and their evolution. The density of VIL (DVIL), derived from radar composites, provides vertical storm structure information in a two-dimensional format, enabling faster data processing without losing volumetric capabilities.

The findings reveal spatial coherence between maximum DVIL intensities and maximum rainfall locations, with all events exceeding the 2.5 g/m³ DVIL threshold coinciding with high-intensity rainfall. Centroid trajectories show seasonal patterns, with some summer events originating from scattered sources and moving more slowly, while some autumn ones align along the coast and propagating inland. The time lag between initial DVIL detection and peak precipitation for the analysed days ranges from 30 minutes to over two hours, offering critical lead times for early warnings.

This study demonstrates the strengths and limitations of DVIL as a predictor of heavy rainfall in urban areas. The RaNDeVIL module shows promise for operational nowcasting, with necessary improvements to address complex interactions of the storm dynamics and more complex modelling to nowcast longer timescales. These advancements aim to enhance resilience to intense precipitation in the Metropolitan Area of Barcelona under changing climatic conditions.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 101037193.

How to cite: Esbri, L., Rigo, T., Llasat-Botija, M., and Llasat, M. C.: Using VIL density for identification of storm nuclei, tracking and nowcasting in the Barcelona Metropolitan Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13191, https://doi.org/10.5194/egusphere-egu25-13191, 2025.

In discussions about climate change, the focus is usually on rising temperatures. However, it is important to understand the significant impact of climate change on the entire weather system. The cloud feedback mechanism is one of the most complex factors in the climate system. This is because clouds can have a heating and cooling effect at the same time, and this balance has a significant influence on the global radiation balance. To understand how all the different factors work together to create a complex system, we need to look closely at how these factors have changed over time.

The aim of this research is to examine changes in cloud cover and convective parameters, as well as the background, causes and effects of these changes in Central Europe between 1983 and 2022. The research uses data from the ERA5 reanalysis database. Aside from the analysis of environmental conditions, an objective cyclone identifying method is used to determine regions under low- or high-pressure weather system influence.  

The statistical analysis shows that in general, the decrease in ERA5 low-level cloud cover is associated with an increase in cloud base. Medium- and high-level cloud cover, however, is influenced by changes in large-scale circulation systems.

Low-level cloud cover decrease in the northern regions of the study area is likely due to increasing temperatures and decreasing boundary layer humidity. Though temperatures in the Mediterranean region also have risen, the increase in the frequency of negative NAO situations, and an increase in Mediterranean cyclone and low-pressure system activity - the latter of which is likely induced by the higher evaporation of the Mediterranean Sea - resulted in the increase in cloud cover over the central Mediterranean region. We have also observed an increase in the CAPE (convective area pressure energy) in the Mediterranean during the summer months, which leads to an increase in the frequency of heavy thunderstorms and extreme precipitation events in this area, contributing to the intensification of weather extremes in the region. Changes over the study area are not linear but show a region dependent 10-20 years periodical pattern which is also investigated.

How to cite: Soós, V. and Hajnalka, B.: Impact of climate change on ERA5 cloud cover and convective parameters in Central Europe (1983-2022), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13477, https://doi.org/10.5194/egusphere-egu25-13477, 2025.

Based on hourly precipitation data, the warm-sector rainfall events in Beijing-Tianjin-Hebei region are selected and classified using objective methods. There are 33 warm-sector rainfall events in this region from 2010 to 2023. They mainly occur during June and August with the most in July. The average lifetime of these warm-sector rainfall events is 5.44 h. The warm-sector rainfall events are mainly concentrated in the center of the Beijing-Tianjin-Hebei region, and the frequency of occurrence in the east is higher than that in the west. The frequency of occurrence in Beijing is much higher than that in other regions, and it is mainly concentrated in the terrain bell mouth of northeast Beijing. According to the circulation situation that generates warm-sector rainfall, three types of precipitation are obtained: low-vortex type, shear-line type and southerly-wind type. The occurrence months, starting times and locations of warm-sector rainfall events in different types are slightly different. Based on the analysis of the synthetic circulation situation, the dynamic, water vapor and low-level vertical motion conditions of the low-vortex type is most favorable for warm-sector rainfall. The vertical upward movement of shear line warm-sector rainfall events is strong in Beijing; The dynamic condition of southerly-wind type is the weakest, but the water vapor condition is more favorable and the occurrence is related to the topographic distribution of  Beijing-Tianjin-Hebei.

How to cite: Liu, R.: Selection and Classification of Warm-Sector Rainfall Events in Beijing-Tianjin-Hebei, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14147, https://doi.org/10.5194/egusphere-egu25-14147, 2025.

Precipitation nowcasting, which entails high-resolution forecasting of precipitation events within 1–2 hours, is significant to daily life and professional activities. Nevertheless, accurate short-term precipitation forecasting remains a considerable challenge at present. Traditional numerical weather prediction, which relies on intricate physical equations to simulate the Earth's atmospheric state, necessitates substantial computational resources and frequently yields lower accuracy for small-scale forecasts, thereby failing to meet the demands of precipitation prediction in complex regions. Most deep learning methodologies concentrate exclusively on the spatiotemporal prediction of a singular precipitation variable, thereby neglecting the dynamic spatiotemporal relationships between precipitation and other meteorological data within the meteorological system. Moreover, due to the rapid pace of climate change, long-term time series data is often inadequate for accurately addressing precipitation forecasting for extreme weather events, since past meteorological time series data may not accurately reflect the current atmospheric conditions. There is an urgent need to rely on short-term time series for prediction tasks. However, most current methods that rely on short-term time series for prediction perform poorly in forecasting moderate to heavy precipitation events. Inspired by spatiotemporal information transformation schemes, we introduce a spatiotemporal information(STI) transformation equation from chaotic dynamics into the field of computer vision and develop a neural network model framework based on spatiotemporal information transformation. This framework maps high-dimensional spatial information to the temporal information of future precipitation information, thereby facilitating the integration of dynamic spatiotemporal relationships between various meteorological data and precipitation, and enabling the mutual transformation of spatiotemporal information for enhanced forecasting accuracy. Furthermore, we propose an adaptive gradient loss function designed to improve the model's sensitivity to learning moderate-intensity precipitation. This research utilizes the US SEVIR dataset for training and testing, which encompasses data such as satellite visible light, infrared temperature, humidity, and cloud precipitation while employing multiple meteorological data for precipitation forecasting over the subsequent hour. We selected the Structural Similarity Index, Peak Signal-to-Noise Ratio, False Alarm Rate, Critical Success Index, and Heidke Skill Score as both quantitative and qualitative evaluation metrics. Experimental results demonstrate that the STI framework reduces the model's error in moderate to heavy precipitation events, making the model more sensitive to severe rainfall events. Furthermore, when the STI framework is integrated into other deep learning models and retrained, it further enhances their precipitation prediction accuracy. This finding indicates that the STI framework effectively captures the dynamic spatiotemporal relationships between various meteorological and precipitation data.

How to cite: Hu, J., Liu, D., Huang, X., and Wu, X.:  Spatiotemporal Information Transformation for Precipitation Nowcasting Using Multi-Meteorological Factors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14517, https://doi.org/10.5194/egusphere-egu25-14517, 2025.

Moist convection in the Maritime Continent (MC) is typically driven by synoptic disturbances: Northerly Cold Surge (NCS), Borneo Vortex, and Madden-Julian Oscillation (MJO). One or more of these tropical disturbances can control the convective behaviour in the MC, resulting in changes in the diurnally forced convection, cloud populations and diurnal precipitation. This investigation analyses a record extreme rainfall event on Java Island around New Year's Eve 2020, the highest amount of rainfall recorded in the capital city of Indonesia, Jakarta. We use reanalysis data from ECMWF Reanalysis v5 (ERA5) to identify and analyse the southward propagation of the NCS. Satellite measurements from the Himawari-8 Advanced Himawari Imager and satellite-derived cloud physical properties reveal the cloud signatures of the NCS. High-resolution Weather Research & Forecasting Model (WRF) simulations were performed to understand the mesoscale dynamic process of the NCS's interaction with the enhanced precipitation at the diurnal scale.

Our results suggest that this extreme event resulted from the interaction of an NCS event and the diurnally forced convection. A persistent northwesterly wind near the surface over the Java Sea induced an intense low-level wind convergence from the meridional moisture transport associated with the NCS and the equatorial trough over Java. This promoted the necessary unstable conditions for organised convection during the afternoon-evening. The cloud populations and diurnal cycle of heavy rainfall in western Java were affected by the frontal region of the NCS with the offshore propagating land breeze from Java and Sumatra, as well as the intense convergence of moisture air in the internal seas of the MC. Our analysis also suggests that the presence of this strong cross-equatorial flow in the MC induced moisture transport from the southern part of Sumatra to the western region of Java. The findings outlined here could be utilised to enhance our understanding of severe weather in the MC.

How to cite: Lopez-Bravo, C.: A high-resolution modelling and observational analysis of an extreme rainfall event driven by the Northerly Cold Surge and intraseasonal tropical variability in Jakarta: January 2020, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14567, https://doi.org/10.5194/egusphere-egu25-14567, 2025.

In evaluating multivariate probabilistic forecasts predicting vector quantities such as a weather variable at multiple locations or a wind vector, an important step is the assessment of their calibration and reliability. Here, we focus on the Gaussian Box ordinate transform (BOT), which is appropriate if the forecasts and observations are multivariate normal. The BOT is based on the Mahalanobis distance of the observation vector and the estimated Gaussian mean and asymptotically standard uniform if the forecasts and the observation are drawn from the same multivariate Gaussian law. However, for small ensemble sizes combined with high dimensionality, deviation from uniformity is substantial even for reliable forecasts, resulting in hump-shaped or triangular BOT histograms. To circumvent this problem, we derive an ensemble size and dimension-dependent fair version of the Gaussian BOT, where the uniformity holds for any combination of these parameters. With the help of a simulation study, first, we assess the behaviour of the fair BOT for various dimensions, ensemble sizes, and types of calibration misspecification. Then, using ensemble forecasts of vectors consisting of multiple combinations of upper-air weather variables, we demonstrate the usefulness of the fair BOT when multivariate normality is only an approximation.

*Research was supported by the Hungarian National Research, Development and Innovation Office under Grant No. K142849.

How to cite: Baran, S. and Leutbecher, M.: Fair Box ordinate transform for multivariate Gaussian forecasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15060, https://doi.org/10.5194/egusphere-egu25-15060, 2025.

Convective events pose a significant threat to society due to the associated heavy rainfall, large hail, strong winds, and lightning. Location and timing determination of convective precipitation is still a challenge for modern meteorology. Despite the good skills of current weather forecasting tools in the prediction of the large-scale environment facilitating the onset of convective phenomena, the multitude of spatial scales involved in such events makes their characterization, observation, and forecast a difficult task. The problem is further complicated by their rapid temporal development, which lasts from minutes to a few hours depending on the specific case.

Recent research indicates that the predictability of these events can be strongly improved accounting for local meteorological observations. 

The goal of the ICREN (Intense Convective Rainfall Events Nowcasting) project is to enhance the nowcasting of convective events by:

• exploiting the information made available by local standard and non-conventional observations of meteorological variables

• integrating physically based Numerical Weather Prediction (NWP) models with data-driven black box Neural Networks (NNs). 

The NWP model is used to support the NN by means of pseudo-observations (forecasted variables); while the fast computational speed of the NN enables advancing predictions in time and generating ensemble forecasts of convective phenomena.

The project is carried out in the Seveso River basin (almost 300 km²) in Northern Italy. In this region, convective events trigger floods and flash floods heavily impacting the large urban area of Milan.

Within the project, the Weather Research and Forecasting (WRF) NWP model is employed. By using three nested grids, the model achieves a 2 kkm x 2 km spatial resolution over the test area. To optimize the prediction of meteorological variables required by the NN, the model assimilates lightning observations and GNSS-derived Zenith Tropospheric Delays (ZTDs), both of which enhance the representation of local atmospheric humidity.

Several NN models have been trained on standard meteorological data, GNSS ZTDs, and radar-derived parameters—including the position, velocity, and attenuation of convective cells—to identify the architecture best suited for predicting 10-minute accumulated rainfall from 10 minutes up to 1 hour following the detection of a convective event in the test area.

The best-performing models are used to generate ensemble predictions of rainfall events by suitably perturbing the input variables.

Results from the WRF model, the NN predictions and the ensemble forecasts will be presented along with initial integration outcomes for selected convective events occurring in the test area in 2019.

This work is supported by the ICREN-PRIN project (MUR- CUP: D53D23004770006). 

How to cite: Venuti, G., Song, X., Federico, S., Guariso, G., Sangiorgio, M., Pasquero, C., Hassantabar Bozroudi, S. H., Mohamed, A. B. E. A., Zaf, R. D., Luini, L., Nebuloni, R., and Realini, E.: Ensemble Convective Rainfall Nowcasting by integrating Numerical Weather Prediction models and Neural Networks: the ICREN project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15639, https://doi.org/10.5194/egusphere-egu25-15639, 2025.

Mesoscale vortices in the boundary layer are characterized by short lifespans, small spatial scales, and difficulty in prediction, leading to their frequent oversight in operational forecasting. This oversight often results in lower accuracy for precipitation forecasting associated with these vortices. From April 2 to April 3 2023, a squall line event triggered by vortices extending from the lower troposphere to the boundary layer occurred across eastern Hubei to western Anhui. This event developed ahead of a shallow mid-tropospheric trough, while the lower levels were influenced by southwest flow. High-resolution numerical simulations successfully reproduced the evolution of the vortex and the organizational development of the squall line. Dynamic diagnosis revealed that the nocturnal boundary layer vortex (925 hPa) was initiated by the intensification of the nocturnal jet and the blocking effect of terrain. Subsequently, through vertical advection of horizontal vorticity from boundary layer to lower level, the vortex at the lower troposphere (850 hPa) developed and intensified. Later, under the combined influence of horizontal divergence and horizontal advection, the vortex rapidly strengthened, creating favorable convergence conditions for the squall line's development due to the northerly flow west of the vortex and the southwest flow south of it.

How to cite: Zhang, Y., Xi, X., and Sun, J.: The formation and evolution mechanism of the boundary layer vortex east of thesecond-step terrain along the middle reaches of the Yangtze River, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15786, https://doi.org/10.5194/egusphere-egu25-15786, 2025.

Climate forecasts at seasonal timescales are critical for various sectors, and play a key role in decision-making processes, helping to mitigate risks associated with climate variability and extreme events. However, model outputs are typically insufficient for many practical applications due to coarse resolution and systematic biases, requiring the employment of post-processing techniques to enhance their usability and target stakeholders’ interest such as early warning systems. Post-processing techniques such as downscaling and bias correction can translate model outputs into higher-resolution, bias-corrected forecasts that are more relevant and best appropriate for local applications. We present a physics-informed CNN-based framework for downscaling and bias correction of ECMWF SEAS5.1 seasonal temperature and precipitation forecasts over Europe from 1° to ~1.2km, which represents a downscaling factor of ~60. The approach considers several climate drivers of atmospheric surface variables from SEAS5.1 as input and takes European Meteorological Observations at 1.2 km as ground truth data. We use an analog-based approach to account for the mismatch between long-range model outputs and observations due to model drifting, which is a problem for supervised neural networks algorithms running on climate datasets. Finally, we present a detailed evaluation of the performance for the period 2017-2022, by comparing our results to the raw output. In most cases, the post-processed forecasts outperform the raw predictions in terms of bias reduction, spatial representation and capturing the extremes. This work has potential implications for reducing uncertainties, improving spatial representation, and addressing systematic biases present in raw ECMWF seasonal products.

How to cite: Díaz Esteban, Y., Lin, Q., Heidari, F., Espitia Sarmiento, E. F., and Xoplaki, E.: Improving seasonal forecasts for early warning systems in Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15861, https://doi.org/10.5194/egusphere-egu25-15861, 2025.

The importance of precipitation nowcasting is gradually expanding due to the increasing frequency and intensity of localized rainfall caused by climate change. The growth and decay processes of precipitation are critical factors influencing the accuracy of precipitation nowcasting, necessitating advanced modeling approaches. This study proposes a novel methodology that integrates artificial intelligence (AI) with high-resolution radar data to predict the growth and decay processes of precipitation, incorporating these predictions into a radar-based nowcasting model. In this study, AI was applied to predict radar-based precipitation intensity change rates up to two hours ahead, and these predictions were integrated into a precipitation nowcasting model. The AI effectively learned the spatiotemporal patterns of nonlinear precipitation evolution using the RainNet architecture. The AI was trained on three years (2021 – 2023) of radar-derived precipitation intensity change rates, with one year (2020) used for validation to evaluate its performance. The nowcasting model was developed using cross-correlation techniques to calculate motion vectors of the precipitation system at different spatial scales, and a semi-Lagrangian backward extrapolation method was employed for precipitation prediction. Integrating AI-predicted precipitation intensity change rates into the nowcasting model resulted in significant improvements in prediction performance. The results showed a 10% improvement in precipitation prediction accuracy compared to the baseline nowcasting model that did not incorporate AI-based precipitation intensity change rate predictions. The model effectively captured rapid changes in precipitation intensity, demonstrating the utility of AI-based predictions for short-term nowcasting. This study highlights the potential of combining traditional nowcasting models with AI techniques, presenting a promising approach for enhancing precipitation prediction accuracy.

This research was supported by the "Development of radar based severe weather nowcasting technology (KMA2021-03122)" of "Development of integrated application technology for Korea weather radar" project funded by the Weather Radar Center, Korea Meteorological Administration.

How to cite: Kim, K.-H., Ko, K., and Nam, K.-Y.: Enhancing Radar-Based Precipitation Nowcasting Model with AI-Predicted Precipitation Intensity Change Rates, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16130, https://doi.org/10.5194/egusphere-egu25-16130, 2025.

How to cite: Franch, G., Tomasi, E., de Kock, S., Angelinelli, M., and Cristoforetti, M.: RUSH: A Novel Fully AI-driven Framework for Seamless Integration of Observations and Global AI Forecasts in Short-term Weather Prediction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16651, https://doi.org/10.5194/egusphere-egu25-16651, 2025.

Parametric approaches to post-processing methods are widely used today, as they provide full predictive distributions for the weather variable of interest. These methods rely on training data consisting of historical forecast-observation pairs to estimate their parameters. Consequently, post- processed forecasts are generally restricted to locations with accessible training data. To overcome this limitation, we introduce a general clustering-based interpolation technique that extends calibrated predictive distributions from observation stations to any location within the ensemble domain where ensemble forecasts are available. Using the ensemble model output statistics (EMOS) post-processing technique, we conduct a case study based on 10-m wind speed ensemble forecasts from the European Centre for Medium-Range Weather Forecasts.  The results illustrate the effectiveness of the proposed method, demonstrating its advantages over both regionally estimated and interpolated EMOS models as well as raw ensemble forecasts.

Reference:  Baran, S. and Lakatos, M. (2024) Clustering-based spatial interpolation of parametric post-processing models. Wea. Forecasting  9, 1591-1604.

Research was supported by the Hungarian National Research, Development and Innovation Office under Grant No. K142849.

How to cite: Nagy-Lakatos, M. and Baran, S.: Clustering-based spatial interpolation of parametric post-processing models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16934, https://doi.org/10.5194/egusphere-egu25-16934, 2025.

Radar nowcasting methodologies have evolved from traditional optical flow and extrapolation techniques to advanced deep learning algorithms. However, accurately modeling growth and decay processes remains a significant challenge. This study explores spatio-temporal statistical models inspired by physics-based stochastic partial differential equations (SPDEs). Specifically, the solution to the advection-diffusion PDE is framed as a vector autoregressive process with coloured noise, characterized by non-uniform spectral properties.

We investigate the stochastic component using Gaussian Processes (GPs) and Gauss Markov Random Fields (GMRFs), evaluating covariance structures such as exponential, squared exponential, and dynamically weighted covariance and precision matrices. Nowcasts employing state-dependent GPs and GMRFs are assessed over lead times ranging from 15 minutes to 2 hours. The approach is tested on simulated data and UK precipitation events from the Met Office Nimrod system, focusing on a 200 km × 200 km region. Training data spans January 2014 to December 2020, with observational dimensions on the order of 10^4. To enable computationally efficient Bayesian inference, we utilize sparse matrix methods and Laplace approximations.

How to cite: Atureta, V., Siegert, S., and Challenor, P.: Exploring spatiotemporal vector autoregressive models for radar nowcasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17194, https://doi.org/10.5194/egusphere-egu25-17194, 2025.

Senegal, located in the West Sahel region, frequently experiences flooding driven by mesoscale convective systems (MCSs), which contribute 90% of the region’s rainfall. Current early warning systems for hydrological extremes struggle with timely and accurate predictions, necessitating advancements in precipitation nowcasting. Nowcasting describes short-term weather forecasts with a lead time of typically less than two hours. In this region traditional numerical weather models have limited accuracy in predicting short-term events, and nowcasting models therefore outperform numerical weather prediction in this time frame. Precipitation nowcasts can be helpful in supporting and informing decision makers on time to adapt to the risk and protect society from hydrological extremes.

A major challenge in developing warning systems for this region is the lack of radar data coverage, which is typically used in nowcasting models, compounded by a sparse ground-based observational network. Increasing the data availability and understanding the properties of MCSs could enhance the predictability of regional weather conditions, which is a primary objective of the High-resolution weather observations East of Dakar (DakE)-project. During the project, 14 automated weather stations have already been installed east of Dakar.

The objective of this study, which is part of the DakE-project, is to integrate the in-situ station data with satellite data to develop a precipitation nowcasting model that is optimally adapted to local conditions considering different spatial and temporal scales. An optical flow routine, based on statistical extrapolation of the current state of the atmosphere, is used for this purpose. To incorporate a stochastic term, which represents the unpredictable component, the STEPS (short-term ensemble prediction system) approach is applied. The skill of the forecast depends, among other things, on the geographical location, the spatial and temporal scales and the meteorological conditions, since developments that do not fulfil the steady-state assumption, such as the initiation, growth and termination of convective systems, are not resolved. The next step is to investigate whether these shortcomings can be compensated by implementing machine learning approaches.

References:

Anderson, Seonaid R., et al. "Nowcasting convective activity for the Sahel: A simple probabilistic approach using real‐time and historical satellite data on cloud‐top temperature." Quarterly Journal of the Royal Meteorological Society150.759 (2024): 597-617.

Mathon, V., Laurent, H., & Lebel, T. (2002). Mesoscale convective system rainfall in the Sahel. Journal of Applied Meteorology and Climatology, 41(11), 1081-1092.

Pulkkinen, S., Nerini, D., Pérez Hortal, A. A., Velasco-Forero, C., Seed, A., Germann, U., & Foresti, L. (2019). Pysteps: An open-source Python library for probabilistic precipitation nowcasting (v1. 0). Geoscientific Model Development, 12(10), 4185-4219.

Taylor, Christopher M., et al. "Nowcasting tracks of severe convective storms in West Africa from observations of land surface state." Environmental Research Letters 17.3 (2022): 034016.

Keywords: Nowcasting, Senegal, Mesoscale Convective System, Precipitation

How to cite: Berghöfer, M.-B., Monroy, D. L., and Härter, J. O.: Nowcasting precipitation events from mesoscale convective systems for Dakar, Senegal , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17279, https://doi.org/10.5194/egusphere-egu25-17279, 2025.

Accurate forecasting of solar power generation is crucial for grid operators, as location-dependent photovoltaic (PV) installations exhibit diverse production patterns. The need for high temporal and spatial resolution, combined with the inherent variability of PV outputs, presents significant challenges for forecasting and post-processing across different time horizons. This study addresses these challenges in post-processing optimal point forecasts for PV sites across multiple forecasting ranges, with the aim of providing seamless output for end-users in the energy sector. Specifically, we focus on two-day-ahead PV site forecasts, with an emphasis on a highly resolved nowcasting range (from minutes to hours ahead) and a smooth transition to short-range forecasts. Advanced machine learning techniques, gridded meteorological models, and a variety of location-specific data sources are employed to enhance our post-processing approach for optimal site forecasts.

Focusing on an Austrian case study, we develop a post-processing framework based on machine learning approaches for time-series forecasting, with particular emphasis on Long Short-Term Memory (LSTM) models compared to more classical methods such as Random Forest (RF) and Multiple Linear Regression (MLR). Our primary objective is to smoothly post-process and identify transitions among a set of range-specific, mostly gridded background models spanning various spatial and temporal resolutions. The post-processed models used as input primarily represent irradiance and related parameters. Our work integrates IrradPhyD-Net, a high-resolution AI-based nowcasting model, with AROME, a limited-area Numerical Weather Prediction (NWP) model for the alpine region, providing valuable physical information extending into the short- and medium-range. To exploit the location-specific characteristics of the site, we incorporate additional time-series models that capture the climatology and trends of PV, irradiance, and strongly correlated parameters identified during pre-processing. Given the substantial and growing input data needs of AI and machine learning, we build on our previous contributions by integrating semi-synthetic data to address challenges posed by limited or inconsistent historical PV data, thereby improving model stability. In this context, additional data sources, such as satellite-based CAMS radiation time-series and ERA-5 reanalysis, are essential.

By leveraging skillful input models, supported by synthetic data, our post-processing framework demonstrates strong forecast skill across the studied ranges. Thus, sourcing and transforming data from multiple inputs proves to be an effective way to achieve seamless, high-skill forecasts while maintaining high temporal resolution for nowcasting.

How to cite: Papazek, P., Gfäller, P., and Schicker, I.: Hybrid Post-Processing for Solar Power: Bridging Nowcasting to Short-Range  , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17282, https://doi.org/10.5194/egusphere-egu25-17282, 2025.

Gridscale forecasts of surface weather delivered by operational global NWP suffer from biases which depend strongly on the weather situation and on geographical factors. Such biases also plague re-analyses, such as ECMWF’s ERA5, as operational models are the engines of those re-analyses. This presentation will itemise a number of different gridscale biases identified through a conditional verification exercise in which millions of station measurements were compared with short range Control run forecasts of the ECMWF operational ensemble. We will postulate what physical reasons might underpin these biases. There is for example a strong dependence of rainfall forecast bias on model near surface relative humidity, which seems to relate to the handling of droplet evaporation and other cloud physics processes. All such errors can in principle be addressed via ECMWF’s “ecPoint” post-processing approach; indeed the conditional verification activity here was managed via ecPoint calibration software. The resulting corrections will be illustrated.

Whilst data-driven AI models are currently delivering better predictions of the synoptic pattern than classical physics-based global NWP, the fact remains that those AI models are generally using unadjusted re-analyses for training, and so the situation-dependant biases will clearly put a cap on skill attainable by them for surface weather parameters, even when the forecast synoptic pattern is ‘perfect’. Some ECMWF views on how to overcome this barrier, to deliver even better predictions, will be very briefly presented.

How to cite: Hewson, T.: Using Conditional Verification to describe Situation-dependant Model Biases for Surface Weather – Applications and Implications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18177, https://doi.org/10.5194/egusphere-egu25-18177, 2025.

This research aims to examine the evolution of the large-scale localized buoyancy anomalies in mid-latitude regions, investigating the adjustments in the atmosphere for moist-convective environments. For the global dynamical simulation, the two-layer moist-convective thermal rotating shallow water (mcTRSW) model Aeolus2.0 with intermediate complexity was employed. The concept of two interacting layers enabled the study of the dynamics of localized extreme heatwaves in baroclinic and barotropic situations. The model initialization comprises daily averaged velocity and potential temperature variables from ERA5 data. The results reveal the presence of a circular positive buoyancy anomaly in the lower layer, while the upper layer shows opposite circular rotation wind movement for some of the cases analyzed. The condensed liquid water content anomaly evolution shows that baroclinic localized buoyancy perturbation should play an important role for increased cloud formation and condensation, as a result of the heatwave propagation in the atmosphere for those extreme forcings. Comparing the strong and weak buoyancy anomalies results, we can notice the prolonged effects of baroclinic initial condition over the barotropic case.

How to cite: Oliveira Guimarães, S., Rostami, M., and Petri, S.: An Intermediate Complexity Approach to the Dynamics of Localized Extreme Heatwaves in the Mid-Latitude Atmosphere for moist-convective environments using Aeolus2.0, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18630, https://doi.org/10.5194/egusphere-egu25-18630, 2025.

Extreme rainfall events represent a substantial risk to regions across the globe, including the Central Europe. The 2002 Central European flood was a devastating natural disaster affecting countries like Germany, Austria, the Czech Republic, and Hungary. Intense rainfall, saturated soils, and overflowing rivers caused severe flooding, displacing many and leading to significant loss of life. With damages exceeding €20 billion, it remains one of Europe’s most costly flood events, heavily impacting historic cities such as Prague and Dresden (Chorynski et al., 2012).

The spatial and temporal resolution of climate models can present challenges when simulating extreme rainfall events at regional or local scales in term of both the intensity and spatial distribution of precipitation. Therefore, In this study the implementation of high-resolution RCMs with "explicit" convection has been applied which directly resolves deep convection on the model grid without relying on parameterization schemes, known as convection-permitting (CP) models (Prein et al., 2013a,b). This study evaluates the performance of RegCM5 in simulating two consecutive extreme rainfall events (6–7 and 11–13 August 2002) over Central Europe and the Czech Republic, comparing 12 km and 3 km i.e. CP-RCM simulations along with sensitivity of planetary boundary layer (PBL) scheme Holtslag and UW. The results reveal significant discrepancies in the 12km RCM simulations, particularly in Czech Republic, where they struggle to capture the rainfall patterns of both events. The model configurations with UW PBL closely follow the observed extreme rainfall patterns, demonstrating improved alignment with the events. While CP simulations improve the representation of small-scale processes, accurately capturing localized extreme events, particularly the first spell, remains challenging. These findings highlight the potential of CP-RCM simulations for extreme precipitation in terms of climate adaptation, infrastructure development, and policy planning to mitigate the potential risks

How to cite: Verma, S., Crespo, N. M., Belda, M., Halenka, T., Huszar, P., and Holtanova, E.: Assessing the Performance of Convection-Permitting Regional Climate Models in Simulating the 2002 Extreme Rainfall Event Over Central Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19296, https://doi.org/10.5194/egusphere-egu25-19296, 2025.

Anemoi is an open-source framework co-developed by ECMWF and several European national meteorological services to build, train, and run data-driven weather forecasts. Its primary goal is to empower meteorological organisations to train machine learning (ML) models using their data, simplifying the process with shared tools and workflows.
Designed for modularity and flexibility, Anemoi offers key components for efficient data-driven forecasting. The framework is organised into distinct Python packages covering the entire machine learning lifecycle—from the creation of customised datasets from diverse meteorological sources to the development and training of advanced deep learning graph models. Once a model is trained, Anemoi enables users to run it for inference, using the outputs of physics-based NWP analyses or ensembles as initial conditions, while maintaining comprehensive lineage tracking.
Anemoi has already been applied in experimental operational forecasting, including ECMWF’s Artificial Intelligence Forecasting System (AIFS). It has supported models utilising stretched grid and limited-area configurations. These applications demonstrate Anemoi’s potential to enhance forecasting accuracy by integrating ML techniques into existing systems.
More than just a technical framework, Anemoi represents a collaborative effort among meteorological services, researchers, and technologists, fostering knowledge exchange and innovation.

How to cite: Prieto Nemesio, A., Nerini, D., Wijnands, J., Nipen, T., and Chantry, M.: Anemoi: A New Collaborative Framework for Data-driven Weather Forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19431, https://doi.org/10.5194/egusphere-egu25-19431, 2025.

Seamless prediction systems provide frequently updated forecasts across different timescales by combining observations, such as weather radar data, with numerical weather prediction (NWP) models. These systems are increasingly needed by users like hydrological services, local authorities, renewable energy operators, and smartphone apps to make better and earlier decisions. This is especially true for precipitation, which is highly variable in space and time and strongly influences downstream models like (urban) hydrology. To achieve this, forecasts must not only be fast and accurate but also come with calibrated ensembles to estimate uncertainty and propagate errors properly.
In Belgium, Project IMA (inspired by the Japanese word for "now" or "soon") is the seamless prediction system developed by the Royal Meteorological Institute (RMI). It uses RMI’s observation network, including RADQPE for gauge-corrected precipitation estimates, the pysteps-be probabilistic rainfall nowcasting system, the INCA-BE nowcasting system, and the ACCORD NWP models ALARO and AROME. Unlike many other systems, Project IMA offers seamless ensemble precipitation nowcasts for lead times up to 6 hours, updated every 5 minutes, designed to improve flash flood predictions and quantify their uncertainty.
This presentation will showcase recent developments in Project IMA, including updates to the open-source pysteps framework, such as an improved runtime efficiency, code structure and better representation of extremes. We will discuss new deep learning-based methods for blending forecasts to extend their lead time and improve accuracy, calibration, and usefulness for end users such as hydrologists, crisis managers and water authorities.
Project IMA aims to ensure a rapid transfer from research to operations and encourages open-source contributions to ensure transparency and reproducibility. It supports the United Nations’ “Early Warnings for All” initiative, which strives to make forecasts more accessible and actionable by 2027.

How to cite: De Cruz, L., De Kock, S., Van Ginderachter, M., Reyniers, M., Deckmyn, A., Dehmous, I., Dewettinck, W., Erdmann, F., Imhoff, R., Moraux, A., Reinoso-Rondinel, R., Veldhuizen, M., Casey, J. J., Faleu Kemajou, L., Kumar, A., and Van Nieuwenhuize, V.: Advances in Project IMA, the Seamless Prediction Programme of the Royal Meteorological Institute of Belgium, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19706, https://doi.org/10.5194/egusphere-egu25-19706, 2025.

How to cite: Lakatos-Szabó, M.: A comparative study of imputation methods for improving statistical post-processing of weather forecasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19873, https://doi.org/10.5194/egusphere-egu25-19873, 2025.

The Asian Summer Monsoon Anticyclone (ASMA) plays a critical role in trapping, transporting, and redistributing water vapour in the upper troposphere and lower stratosphere, particularly into the extratropical lower stratosphere. Comparison of ERA5 reanalysis data with remote sensing data and simulations with the model ICON-CLM in convection-parameterized (12 km grid spacing) and convection-permitting (3.3 km) setups indicate that the transport into the ASMA is overestimated in ERA5 over the Tibetan plateau (Singh & Ahrens 2023). This presentation critically discusses the water vapour transport into the upper-troposphere/lower-stratosphere by deep convective events over the Tibetan plateau and the Himalayas – an area identified as hotspot for troposphere-stratosphere exchange (Škerlak et al. 2014) using convection-parameterized reanalysis data. Our investigations use a decade-long ICON-CLM climate-like simulation (Collier et al. 2024) performed as a contribution to the CORDEX flagship pilot study Convection-Permitting Third Pole (CPTP).

References

Collier, E., N. Ban, N. Richter, B. Ahrens, D. Chen, X. Chen, H-W. Lai, R. Leung, L. Li, T. Ou, P.K. Pothapakula, E. Potter, A. F. Prein, K. Sakaguchi, M. Schroeder, P. Singh, S. Sobolowski, S. Sugimoto, J. Tang, H. Yu, C. Ziska: The First Ensemble of Kilometre-Scale Simulations of a Hydrological Year over the Third Pole. Clim Dyn. https://doi.org/10.1007/s00382-024-07291-2, 2024

Singh, P., B. Ahrens: Modeling Lightning Activity in the Third Pole Region: Performance of a km-Scale ICON-CLM Simulation. Atmosphere, 14(11), 1655, DOI: 10.3390/atmos14111655, 2023

Škerlak, B., M. Sprenger, and H. Wernli: A global climatology of stratosphere–troposphere exchange using the ERA-Interim data set from 1979 to 2011. English. Atmospheric Chemistry and Physics 14 (2), 913–937. doi: 10.5194/acp-14-913-2014, 2014

How to cite: Ahrens, B. and Singh, P.: Moist convection and tracer transport in and out of the Asian Summer Monsoon Anticyclone, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20360, https://doi.org/10.5194/egusphere-egu25-20360, 2025.

Reliable climate forecasts are crucial for adapting to future challenges, particularly in urban flood management, where pluvial flooding poses a significant threat. This study focuses on the verification and enhancement of rainfall data for urban flood modelling by analysing critical aspects such as total depth, intensities, seasonality, dry weather periods, and spatial distribution during extreme storm events.

In Graz, Austria, a network of 23 high-resolution precipitation measurement stations covering 120 km², including 13 stations with over a decade of data, was utilized to calibrate a regional climate model through a downscaling approach. This provided minute-level rainfall data for each station, enabling a detailed comparison of historical measurements from the past 10 years with climate model outputs for the current state of the climate. Subsequently, changes in key rainfall characteristics were assessed for the near future (2040–2050) and far future (2090–2100).

Our analysis evaluated yearly precipitation totals, spatial rainfall distribution, intensity-duration-frequency (IDF) functions, and the seasonality of extreme rainfall events. The results revealed promising alignment with historical data, though discrepancies were noted for shorter durations and seasonal shifts. Specifically, heavy rainfall events were projected to occur more frequently in autumn in the future, a trend absent in historical observations.

This study underscores the importance of statistically robust downscaling and verification techniques in blending observational and model-based forecasts to enhance the reliability of climate predictions. These advancements provide critical insights for urban flood resilience planning and illustrate the evolving nature of extreme rainfall under changing climatic conditions.

How to cite: Pichler, M. and Muschalla, D.: Spatial and Temporal Verification of High-Resolution Modelled Rainfall Data for Urban Flood Risk Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21081, https://doi.org/10.5194/egusphere-egu25-21081, 2025.

A 20-member WRF-based regional Ensemble Prediction System (WEPS) is operationally run at the Central Weather Administration (CWA) to provide up to 5-day ensemble forecasts over the East Asian region with a 15-km grid spacing and a 3-km nest centered over Taiwan. Since becoming operational in 2011, WEPS has been under continuous development that aims to improve the construction of its perturbations in initial conditions (IC), boundary conditions (BC), as well as model uncertainties due to numerical approximations and physical parameterizations. Among these uncertainties, construction of IC perturbations for WEPS is the focus of this study.

An initialization method named ensemble partial cycling (EnPC) is proposed for the WEPS. The EnPC method combines partial cycling data assimilation (DA) and the ensemble of DA approach with an additional blending procedure that merges large-scale global features with small-scale regional information, leveraging the DA efforts from the deterministic system of CWA. EnPC is compared with three other initialization methods that are popularly used for regional ensemble forecasting, including dynamic downscaling from a global EPS, Ensemble Adjustment Kalman Filter (EAKF) based regional ensemble DA, and a blended version of the two, the last of which is equivalent to the current operational configuration of WEPS. Among all 4 methods, EnPC is the only method that allows separate initializations for the parent and the nested domains while the initialization for the nested domain in the other three methods is simply a downscale-interpolation from the corresponding parent grid.

Several sets of WEPS experiments are conducted over a 5-week period, including five typhoons. EnPC-initialized WEPS forecasts are found to be comparable to the dynamically downscaled forecasts in many evaluation metrics and have more accurate near-surface forecasts over the first 12 h and better precipitation forecast discrimination ability for typhoon events. Compared to the EAKF and the blended methods, forecasts initialized from EnPC have overall smaller errors in most of the evaluation metrics by both deterministic and probabilistic measures and better spread-to-error ratios. As an alternative initialization method, EnPC not only adds some regional benefits on top of downscaling, but also shows some advantages over the operational method. With the planned retirement of EAKF and the anticipation of a more unified production suite at CWA, EnPC will replace the current operational method.

How to cite: Su, Y.-J., Wu, T.-C., Li, C.-H., Lien, G.-Y., and Hsieh, C.-H.: Initializing the Taiwan WRF-based Regional Ensemble Prediction System with an Ensemble Partial Cycling Strategy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2643, https://doi.org/10.5194/egusphere-egu25-2643, 2025.

Numerical Weather Prediction (NWP) models with a horizontal resolution of 2 km or finer need detailed information for estimating the initial state of the atmosphere. Ground-based remote-sensing instruments like Sodars, Doppler lidars and Profilers provide already meteorological information of the Atmospheric Boundary Layer (ABL). Although observational networks have been extended over the years, there are still gaps in data gathering particular on the finer scales. Therefore we have commenced research to investigate data from third parties. Here we focus on wind-information in the ABL from recreational Hot-air Balloon (HaB) flights. In the basic equipment of a HaB pilot there is a professional navigation device, which is compulsory for safety reasons. Similarly to routinely launched weather balloons, the Global Navigation Satellite System (GNSS)-data from consecutive positions and the elapsed time are the basis of the calculation of the horizontal wind vector. Yearly about 6000 flights take place in the Netherlands, mainly during the morning- and evening transition. As soon as the surface is covered with snow and when convection is strongly reduced, flights may also occur during the day. The HaB data are validated with observations from the meteorological site of Cabauw and we compare the HaB winds with mast data and other available observations like a RASS wind profiler. To explore the possibilities of this new type of wind observations in more complex terrain, we will present the results of an intriguing HaB flight in  Austria, revealing a striking mountain-valley circulation. We also compare the HaB data with the results of an NWP model and we will report about a first attempt to assimilate the HaB data in a NWP model. 

How to cite: de Bruijn, E. I. F. (.: Sensing the Wind with Hot-air Balloons and their Application in an NWP model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3280, https://doi.org/10.5194/egusphere-egu25-3280, 2025.

NASA is developing an Earth Science Modeling Strategy. This is motivated by recommendation 4.2 from the United States National Academies of Sciences, Engineering, and Medicine, Thriving on Our Changing Planet: A Midterm Assessment of Progress Toward Implementation of the Decadal Survey (2024, https://doi.org/10.17226/27743): “To ensure continued advances in modeling in conjunction with Earth observation: NASA should develop a long-term strategic plan for a strong sustained commitment to Earth system modeling in concert with observations. Success in observation-driven modeling holds the key for maintaining the end-to-end capability that has served NASA well in its effectiveness and service to society.” Moreover, one of the main objectives of NASA’s Earth Science to Action Strategy (https://science.nasa.gov/earth-science/earth-science-to-action/) is to Deliver Trusted Information to Drive Earth Resilience Activities. This Objective will rely on comprehensive Earth system modeling as a key result that will enable NASA to advance and integrate Earth science knowledge to empower humanity to create a more resilient world.

In this presentation we will overview the approach being taken to develop the Earth Science Modeling Strategy, within NASA and with the broader community.  Some of the key aspects being considered include comprehensive state-of-science modeling representation of the coupled Earth system, from global to local scales, analyses and predictions at different lead times, from short term predictions to climate projections, and using ensembles. Another key facet is data assimilation into Earth system models and improved utilization and demonstration of the value of Earth observations.  It is envisioned that bold innovation, new disruptive technologies, including artificial intelligence and machine learning, and utilization of large Earth science datasets will be key enablers for cutting edge research, increased understanding of the Earth system, and improved ability to provide actionable Earth science information for societal applications to meet broad user needs. Modeling underpins NASA’s Earth Science to Action strategy as a key capability for advancing foundational knowledge, Earth system science and applied research, as well as increasing societal value of NASA’s data and information leading to improved public understanding. 

How to cite: Stajner, I.: Development of NASA’s Earth Science Modeling Strategy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3327, https://doi.org/10.5194/egusphere-egu25-3327, 2025.

Super typhoons can pose severe threats to coastal cities. For instance, Typhoon Yagi caused hundreds of fatalities and extensive property damages while sweeping across southern China and Southeast Asia in 2024. Accurately predicting the dynamic motion and intensity of super typhoons in high resolution is critical for effective disaster prevention and mitigation. Over the past few decades, the accuracy of typhoon track predictions has notably improved due to the advancements in global numerical weather prediction models. However, their capability to assess typhoon intensity and dynamic structure is still limited by coarse spatiotemporal resolution. The application of physics-based regional models, such as the Weather Research and Forecasting (WRF) model, presents a promising solution to this challenge.

To simulate high-resolution wind fields during super typhoons through WRF, it is essential to determine optimal and robust physical parameterization schemes. In previous studies, sensitivity analysis is often carried out solely based on the error criteria related to typhoon track and intensity, which are inadequate for the performance evaluation of local wind simulation. Additionally, there is a lack of consistent physical parameterization settings for different super typhoons. Furthermore, due to the inherent biases and model errors, a dynamic bias correction strategy is required for local wind forecasting. To this end, we aim to develop an integrated framework in this study that combines typhoon simulation, multi-metric evaluation, and dynamic bias correction.

The super typhoons that have significantly impacted Hong Kong over the past two decades have been chosen as study cases, i.e. Hato, Mangkhut, and Saola. A series of numerical experiments were designed to assess the impact of various physical models. By comparing simulation results with best track data and field observations from the Hong Kong Observatory and the Shenzhen Meteorological Gradient Mast, the multi-metric evaluation method provides a comprehensive understanding of both global and local wind field simulation performance. The best-performing physical models were thereby identified, achieving consistent typhoon tracks (MAE < 30 km), relatively accurate typhoon intensity predictions (RMSE < 5 m/s), and highly correlated wind fields (r > 0.9) between simulation and observation results. To further reduce the effects of systematic biases, a dynamic linear bias correction strategy was introduced to adjust local wind predictions dynamically based on real-time observations. Given the time-evolving local wind data, the linear bias correction factor can reach convergence and provide reliable forecast corrections with a lead time of approximately 15 hours. The proposed framework shows great potential to enhance disaster warning systems and improve local wind prediction accuracy in typhoon-prone regions.

How to cite: Wang, F. and Wang, L.: A multi-metric evaluation and dynamic correction framework for local wind field prediction during super typhoons, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3638, https://doi.org/10.5194/egusphere-egu25-3638, 2025.

How to cite: Corbella, C.: Reconstructing Daily Weather in the Early 19th Century: New Insights from Data Assimilation , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3762, https://doi.org/10.5194/egusphere-egu25-3762, 2025.

Ocean surface albedo (OSA) plays a important role in the energy balance of the climate systems. In climate models, it is typically treated as a constant or represented by a simplistic function of Solar Zenith Angle (SZA). However, research by Jin (2011) indicates that OSA can be significantly influenced by whitecaps under moderate to high surface wind conditions (denoted as Jin11 scheme). Whitecap coverage is a key factor in this parameterization, often expressed as a power function of surface wind speed. Given that water depth and wave height are associated with wave breaking—of which whitecaps are the primary manifestation—the ratio of theoretical wave height to water depth has been incorporated into the Jin11 scheme for adjustment. This modification reflects the characteristic that certain areas are more prone to whitecaps under identical wind conditions.

In this study, we incorporated this improved OSA parameterization scheme into the Community Earth System Model Version 2 (CESM2) and conducted coupled simulation experiments. The numerical results show an alleviation of the excessive reduction in sea surface temperature in the equatorial ocean, the North Pacific subtropical gyre circulation, and the southern westerly wind belt as simulated by the Jin11 scheme. Additionally, longwave radiative heating in the tropical regions is significantly altered after accounting for the wave breaking factor. Precipitation simulations over the northwest Pacific, the tropical Indian Ocean, and the Indo-Pacific Convergence Zone show improvements, while the induced substantial changes in latent heating have further affected vertical motion and convective activity.

How to cite: Jing, X. and Wang, L.: Development of a Ocean Surface Albedo Scheme with Wave Breaking Factor, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3809, https://doi.org/10.5194/egusphere-egu25-3809, 2025.

In this study, a latent heat nudging lightning data assimilation (LDA) method independent of the flash rate was developed and tested with data from the Lightning Mapping Imager (LMI) onboard the Feng-Yun-4A (FY-4A) satellite based on the Weather Research and Forecasting (WRF) model. In this LDA method, the positive temperature perturbations at the lightning location are first calculated by the difference between the moist adiabatic temperature of a lifted air parcel and the model temperature. The positive temperature perturbations in the mixed-phase region are then assimilated by a nudging method to adjust the latent heat within the convective system. Meanwhile, the water vapor mixing ratio is adapted to the temperature perturbations accordingly to constrain the relative humidity to remain unchanged. This method considers the physical nature of the convective system, in contrast with other LDA methods that establish an empirical or statistical relationship between the lightning flash rates and model variables.

The impact of this LDA method on short-term (≤6 h) forecasts was evaluated using two severe convective events in eastern China: a multi-region heavy rainfall event and a thunderstorm high-wind event. The results showed that LDA could add thermodynamic information associated with the convective system to the WRF model during the nudging period, leading to a more reasonable storm environment. In the forecast fields, the simulations with LDA produced more realistic convective structures, resulting in an improvement in forecasts of precipitation and high winds.

How to cite: Gao, Y. and Wang, X.: Impact of Assimilating FY-4A Lightning Data with a Latent Heat Nudging Method on Short-Term Forecasts of Severe Convective Events in Eastern China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3879, https://doi.org/10.5194/egusphere-egu25-3879, 2025.

    The level of uncertainty of reanalysis datasets varies greatly based on the quality and amount of available observations and the uncertainty of physical parameterizations used in the background forecast model. Ensemble data assimilation (EDA) schemes are used to quantifiy this combined uncertainty. However, isolating the effects of observational and model uncertainties based on a given ensemble reanalysis is not straightforward. Here, we use the 9 member EDA ensemble produced for the ECMWF 5th Generation Reanalysis product (ERA5) to investigate synoptic scale model uncertainty and its connection to the occurrence of specific weather regimes.

    To control for ensemble spread caused by observation uncertainty - especially on long time scales - we devise grid-point-wise statistical models for the logarithmic ensemble variance with temporal predictors. We use a binary segmentation algorithm to objectively identify change points in ensemble spread time-series caused by abrupt changes in the observation system.

    The set of statistical models allows for statements about the relative impact of changes in the observation system on the total background forecast uncertainty between different grid-points. After filtering out the impact of changes in the observation uncertainty, we obtain a long time series of model uncertainty estimates, which we analyze climatologically with respect to flow characteristics, regime structure and impact of physical parameterizations. This provides regions of high model uncertainty for the respective regimes as well as differences in the role of model uncertainty among the regimes.

How to cite: Schoeller, H. and Pfahl, S.: Isolating the effects of model uncertainty on ensemble reanalysis data and their relation to North Atlantic flow regimes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4243, https://doi.org/10.5194/egusphere-egu25-4243, 2025.

The Numerical Weather Prediction (NWP) gray zone (GZ) represents a critical challenge in modeling, occurring at spatial resolutions typically ranging from approximately 500 m to 5 km, depending on factors such as the modeling framework, the prevailing atmospheric conditions, and the geographical context where neither full parameterization nor explicit simulation of physical processes is feasible. Within this range, convection parameterizations often become unreliable, particularly for cumulus clouds and turbulence, leading to uncertainties in weather forecasts. High-resolution models (below 4 km) assume explicitly resolved convection, yet this approach does not consistently improve prediction accuracy. Recent advancements in scale-aware parameterizations offer a promising solution, enabling a gradual transition from parameterized to resolved convection, enhancing model performance and reducing biases within the GZ. To explore these challenges, the Weather Research and Forecasting (WRF) model was employed to simulate eight precipitation events across Schleswig-Holstein and Baden-Württemberg in Germany, all exceeding the severe weather threshold of 40 mm/h (warning level 3) set by the German Weather Service. A comprehensive suite of 1,440 simulations was conducted, combining 10 microphysics schemes, 6 cumulus schemes, 8 event cases, and 3 spatial setups. The model setups included a single domain with a 9 km grid size and two two-way nesting configurations with spatial resolutions of 9 km and 3 km. To investigate the role of convection schemes in the convective GZ and the benefits of higher spatial resolution, simulations at 3 km resolution were run both with and without active convection schemes. Initial and boundary conditions were provided by the ERA5 dataset at a spatial resolution of 0.25°. A detailed performance analysis was carried out using pairwise comparisons and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which ranked the parameterization combinations based on multiple criteria. Results revealed that non-convection-permitting setups performed better during summer precipitation events, where convection is more localized and intense. On the other hand, winter events, influenced by larger-scale processes, showed similar accuracy between convection-permitting and non-convection-permitting configurations. Interestingly, increasing resolution from 9 km to 3 km did not consistently improve model performance. Furthermore, the best-performing parameterizations at 9 km resolution outperformed those at 3 km across all configurations, challenging the common assumption that higher resolution inherently improves model accuracy. These findings emphasize the need to carefully balance resolution and parameterization choices in severe weather forecasting, particularly for convective systems. The study underscores the critical influence of model physics and nesting configurations on simulation outcomes, offering valuable insights for future research and operational modeling efforts.

How to cite: Sotiropoulou, R.-E. P., Stergiou, I., Traka, N., Kaskaoutis, D. G., and Tagaris, E.: Enhancing Precipitation Predictions in the WRF Model: The Role of Convection Schemes and Increased Spatial Resolution in the Convective Gray Zone, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5157, https://doi.org/10.5194/egusphere-egu25-5157, 2025.

The Geostationary Interferometric Infrared Sounder (GIIRS) on board the Fengyun 4B (FY-4B) satellite is the first hyperspectral interferometer flying in geostationary orbit. It can provide atmospheric information with high spatial and temporal resolution, which has significant potential for application in regional numerical weather prediction (NWP) models. Due to the high correlation between the infrared hyperspectral channels, it is critical to accurately characterize the inter-channel observational error correlation (IOEC) for assimilating the GIIRS radiance data effectively. This study firstly constructed an observation error covariance matrix for considering the inter-channel correlation of FY-4B GIIRS radiance data based on the NWP system developed by the China Meteorological Administration Beijing Urban Meteorological Institute (CMA-BJ). There was a strong error correlation between adjacent channels and channels with similar detection for the GIIRS radiances. Single-point observation assimilation experiments indicated that the IOEC had a significant impact on the magnitude and structure of temperature and humidity analysis increments. Two groups of assimilation experiments over a 10-day period were carried out and compared. The results showed that an average improvement of 1.5% could be obtained in the RMSE of the temperature and humidity forecasts within the first 12 hours incorporated the IOEC. With the IOEC, a positive impact was also achieved on the precipitation forecast skill, although it was not significant.

How to cite: Xie, Y.: Assimilation and Impact of FY-4B GIIRS Radiances in CMA-BJ Numerical Weather Prediction System, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5543, https://doi.org/10.5194/egusphere-egu25-5543, 2025.

        An ensemble down-selection method is proposed to improve the analysis and forecast with a small ensemble,  reducing computational needs. A usual problem with ensemble down-selection is that, despite of the reduction of forecast error, ensemble spread sharply decrease. To limit ensemble spread collapse, this study introduces two variations of a novel down-selection method seeking to minimize the sub-ensemble’s Continuous Ranked Probability Score (CRPS), thereby preserving ensemble spread while minimizing forecast error. The approaches are then tested with a regional-scale model whose precipitation forecast we seek to improve. The precipitation forecast performance of sub-ensembles obtained by these CRPS-based methods is evaluated against the full ensemble, and 100 randomly down-selected sets using various verification metrics measuring precipitation forecast skill. Results demonstrate that the CRPS-based sub-ensembles improve probabilistic forecast accuracy by achieving lower CRPS with the lowest Root Mean Square Error (RMSE) value, especially for short forecasts, without increasing false alarms. Additionally, the Brier Score shows improved forecasts, while Fraction Skill Score (FSS) confirms the improved spatial accuracy in light precipitation. These findings suggest that CRPS-based methods are viable sub-ensembling approaches for balancing accuracy, reliability, and computational efficiency in operational forecasting. By preserving ensemble spread, they improve the sub-ensemble's capacity to represent uncertainty, offering a practical and robust solution for ensemble down-selection.

How to cite: Lee, M.-T., Yau, M.-K., Jacques, D., and Fabry, F.: Ensemble precipitation down-selection methods using Continuous Ranked Probability Score (CRPS): Balancing accuracy and spread under computational constraints, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6903, https://doi.org/10.5194/egusphere-egu25-6903, 2025.

A parameterisation suite is the combination of all parameterisation schemes that is used by a numerical model of the atmosphere. These parameterisation (or “physics”) suites are widely seen as the most uncertain components of atmospheric models.  

In MUMIP we compare deterministic parameterisation suites from across different modelling centres under common prescribed large-scale dynamics. In the first MUMIP experiment, these dynamical tendencies have been derived by coarse-graining the convection-permitting ICON DYAMOND simulation to 0.2 degree resolution. We use these realistic spatiotemporal dynamical patterns to drive millions  of single column model simulations over the tropical Indian Ocean with prescribed SSTs. We use this data to estimate the uncertainty from their physics across four models, each using their default convection-parametrised physics suites. The models are: IFS, GFS, RAP and ARPEGE.

The distributions of precipitation rate, convective available potential energy (CAPE), convective inhibition (CIN) and level of neutral buoyancy are analysed, as well as individual model tendencies and rate of change of CAPE and CIN as a function of lead time and, for instance, the diurnal cycle . We find notable differences across the physics suites and even more strongly between convection-parameterised physics suites and the convection-permitting ICON DYAMOND benchmark. Furthermore, we relate these diagnostics to biases in temperature and specific humidity. We also develop a framework for the detection of statistical relations among diagnostics and/or their change. The framework may for instance be used to quantify the impact of spin-up compared to persistence ("memory") and randomness within a dataset and to identify similarity in the physics across modelling centres.

In this contribution some of the early results of the international MUMIP project will be presented and we hope to encourage other researchers to use and/or complement the data of MUMIP. Please refer to https://mumip.web.ox.ac.uk for details of how to get involved.   

How to cite: Groot, E., Christensen, H., Sun, X., Newman, K., Lfarh, W., Roehrig, R., Singh, K., Lambert, H., Beck, J., Williams, K., Bernadet, L., and Berner, J.: Precipitation rate, convective diagnostics and spin-up compared across physics suites in the model uncertainty model intercomparison project (MUMIP) , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7206, https://doi.org/10.5194/egusphere-egu25-7206, 2025.

FengYun-3E (FY-3E), the fifth satellite in China's second-generation polar-orbiting satellite FY-3 series, was launched on 5 July 2021. FY-3E carries a third-generation microwave temperature sounder (MWTS-3) and a second-generation microwave humidity Sounder (MWHS-2). In this study, the influence of assimilating FY-3E MWTS-3 and MWHS-2 clear-sky radiance data on tropical cyclone forecasts in a regional model is investigated through a series of data assimilation experiments. More than five typhoons from the northwest Pacific Ocean during 2024 typhoon season are selected for the numerical experiments of assimilation and forecasts, and the assimilation effects of FY-3E MWTS-3 and MWTS-2 are carefully evaluated. The results show that assimilation of MWTS-3 and MWTS-2 has positive impact on typhoon track forecasts, especially for forecasts beyond 12 hours, in terms of intensity forecasts, the impact of the data is neutral or slightly positive.

How to cite: Zhang, L., Niu, Z., Weng, F., and Huang, W.: Direct Assimilation of FY-3E Microwave Sounding Channel Data in a Regional Model to Improve Typhoon Forecasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7592, https://doi.org/10.5194/egusphere-egu25-7592, 2025.

In general, data assimilation systems analyze values on regularly distributed grids according to the irregularly distributed observations. As long as a data assimilation system was established on a certain grid, it cannot adapt to another grid. In this study, the gridless method was introduced into the three-dimensional variation (3DVar) system. Compared with grid-based method, the gridless method uses discrete points for calculation and does not require grid division, thus being immune to grid distribution. Therefore, the data assimilation system based on gridless method can adapt to most model grid structures without the need to write new code. In the data assimilation system based on gridless method, the Cressman analysis technique is adopted as observation operator and the physical transformation matrix is handled using the Taylor expansion method. The idealized experiments based on the Rankine vortex demonstrate that the 3DVar system based on gridless method can handle structured grid, unstructured grid, and mixed (structured and unstructured) grid. Furthermore, the study showed that data assimilation can be performed simultaneously for different grid resolutions, resulting in higher consistency between the grids than when data assimilation is performed separately. 

How to cite: Yuan, X.: Application of Gridless Method in Data Assimilation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7684, https://doi.org/10.5194/egusphere-egu25-7684, 2025.

This study has been inspired by the activities developed within the IRIDE project in the service chain on “Hydro-meteorological mapping and monitoring atmospheric structure”. The work presents a preliminary evaluation of three numerical weather prediction models, WRF (Weather Research and Forecasting), ICON (ICOsahedral Non-hydrostatic), and COSMO (COnsortium for Small-scale MOdelling), by comparing synthetic and observed brightness temperatures (BTs) from the Meteosat Second Generation geostationary satellite. Synthetic satellite images were generated using the Radiative Transfer for TOVS (RTTOV) model, version 13.2. The analysis spans a verification period of over one month, with all models operating at a horizontal resolution of approximately 2 km and a temporal resolution of 1 hour.

A special focus of the study is the evaluation of the models' ability to reconstruct the intense weather events that struck some Italian regions during the recent years. This severe event caused widespread damage and highlighted the critical need for accurate and timely forecasting capabilities. By analyzing the models' performance during this extreme weather event, we aim to identify strengths and limitations in their ability to simulate localized and high-impact phenomena.

To assess the performance of the models, key verification metrics were calculated to provide a quantitative basis for understanding the accuracy and reliability of the models in predicting atmospheric conditions as represented by BTs.

The results of the verification are thoroughly discussed, with particular emphasis placed on their broader implications for both the development and refinement of numerical weather prediction models. This discussion delves into how these findings can inform improvements in various aspects of model design, from enhancing their ability to simulate complex physical processes to addressing persistent biases and inaccuracies. Differences in model performance are meticulously analyzed to identify potential sources of error, which may arise from a range of factors such as deficiencies in physical parameterizations, limitations in boundary condition specifications, or inaccuracies stemming from radiative transfer assumptions. 

These analyses aim to provide a deeper understanding of the underlying causes of discrepancies, paving the way for more targeted adjustments. This work represents a significant contribution to the ongoing evolution of high-resolution numerical weather prediction models, offering a wealth of valuable insights for both researchers striving to push the boundaries of modeling capabilities and operational forecasters seeking to improve real-time prediction accuracy. By shedding light on the intricate interplay between model dynamics and observational data, it underscores the importance of continuous innovation and refinement in the pursuit of more reliable and precise forecasting tools.

How to cite: Giugliano, G., Fedele, G., Bonfiglio, A., Campanale, A., Raffa, M., Antonelli, P., and Mercogliano, P.: Performance Evaluation of High-Resolution Numerical Weather Prediction Models Using MSG Brightness Temperatures, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9393, https://doi.org/10.5194/egusphere-egu25-9393, 2025.

2023 was record-breaking for wildfires in Canada with unprecedented impacts on local ecosystems as well as large scale smoke hazards. These exceptional fire impacts rose the public demand for accurate forecasts of smoke plumes as well as analysis of air quality impacts. However, fire smoke plumes are extreme air quality events with exceptionally high concentrations and related uncertainties fall outside statistical ranges. These particular conditions induce specific challenges for data assimilation algorithms, because error estimates need to capture the high uncertainties and spatial gradients. At the same time, operational forecast systems require high computational efficiency to deliver fast, yet accurate forecasts to the public.

This study explores the potential of a novel assimilation approach, called parametric Kalman filter (PKF), for operational air quality forecasting during extreme air quality events. By explicitly evolving the main error parameters, the PKF has been proven to provide accurate uncertainty estimates at very low computational costs. In this work, a dynamical propagation of error standard deviations is implemented in the Canadian atmospheric-chemical forecast model GEM-MACH. This extended forecast model is applied to a case study of Quebec wildfires in early July 2023. First results indicate that the forecast error distributions during this events can be sufficiently approximated by a passive error-tracer. It is demonstrated that vertical diffusion is a critical component for dynamical error forecasting of extreme air quality events. The error standard-deviation forecasts are used in the current objective analysis (OA) for surface air quality at ECCC (Environment and Climate Change Canada) and compared to operational OA results.

How to cite: Vogel, A., Ménard, R., Abu, J., and Chen, J.: Potential of error-evolving tracer forecasts for operational assimilation of PM2.5 during wildfire smoke episodes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9428, https://doi.org/10.5194/egusphere-egu25-9428, 2025.

Precipitation datasets derived from reanalysis products play a critical role in weather forecasting and hydrological applications. This study aims to assess the performance of two distinct reanalysis precipitation products, i.e., the first-generation Chinese global land-surface reanalysis precipitation product (CRA40) and the fifth-generation European reanalysis precipitation product (ERA5), over mainland China. The assessment is conducted with daily-scale gridded-point rain gauge data obtained from Chinese surface meteorological stations as reference, and the continuous and categorical statistical indicators as assessment metrics. The findings of this study are as follows: 1) CRA40 outperforms ERA5 in terms of the 13-year daily mean precipitation and seasonal daily precipitation. CRA40 shows better correlation coefficients (0.97), relative biases (5.25%), root mean square errors (0.34 mm), and fractional standard errors (0.05). 2) Both CRA40 and ERA5 generally exhibit an overestimation of precipitation over mainland China. The degree of overestimation is particularly pronounced in dry climatic regions (e.g., Xizang-Qinghai plateau, Xinjiang province), while wet regions (e.g., the middle and lower reaches of Changjiang River, and South China) demonstrate relatively less overestimation. 3) ERA5 shows better performance in the detection of daily precipitation than CRA40. Neither CRA40 nor ERA5 can well capture heavy precipitation events. These findings are expected to advance our understanding of the strengths and limitations of the reanalysis precipitation products, CRA40 and ERA5, over China.

How to cite: Zhou, Z., Chen, S., Li, Z., Li, Y., and Wei, C.: Performance Assessment of CRA40 and ERA5 Precipitation Products over China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9643, https://doi.org/10.5194/egusphere-egu25-9643, 2025.

The KIM(Korean Integrated Model)-based local ensemble model, which has a 3km horizontal resolution and 13 members, was developed to improve the prediction of heavy rainfall. The members of the local ensemble model were generated by downloading the KIM global ensemble model. The initial and boundary fields for the members were provided by the KIM global ensemble model. The local ensemble model covers the Korean Peninsula and surrounding areas and produces a 5-day forecast twice a day.
With the introduction of the KIM local ensemble model, the CSI score for precipitation were improved alleviating the underestimation of precipitation in the KIM global model.  In summer, the 75% and 90% percentiles of the local ensemble model show the best performance in heavy rainfall forecasting, while in winter, the median provides the best results.
The analysis verification(RMSE) results also showed that the KIM local ensemble model generally provided improved outcomes compared to the KIM regional model and exhibited similar performance to the UM (Met Office Unified Model)-based local ensemble.

The summer season on the Korean Peninsula is characterized by frequent extreme rainfall events, and this extreme rainfall presents a major challenge for forecasters in producing accurate forecasts. Therefore, various strategies using local ensembles have been developed to predict these extreme rainfall events. Probability matching and percentiles are representative methods, and by employing these techniques, many of the issues associated with the underestimation of extreme rainfall in numerical weather prediction have been largely addressed.

Keywords: local ensemble model, regional model, member, RMSE, CSI, underestimation, extreme rainfall, probability matching, percentiles

How to cite: Shin, H., Kim, E. J., Yun, S., Park, J.-I., Ha, J.-C., and Kim, D.-J.: Development and Application of KIM-based Local Ensemble Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10029, https://doi.org/10.5194/egusphere-egu25-10029, 2025.

State augmentation in a data assimilation cycle can be used as an objective method to estimate uncertain empirical constants in parameterization schemes. In this approach, empirical parameters are appended to the model state vector. They cannot be observed, but, like any other unobserved state variable, they can be updated based on their correlations with the model equivalents of observable quantities. State-parameter correlations are likely flow-dependent, therefore they are best estimated with an ensemble of simulations.

Despite its potential usefulness in parameterization design, ensemble-based parameter estimation has been used so far as a way of accounting for model errors in the assimilation process, and as a method to increase ensemble spread. In this study, we discuss if and how it can aid parameterization optimization. As a case study, we consider a simple first-order parameterization of turbulence in the atmospheric boundary layer. We run several idealized assimilation experiments, partly in a perfect-model scenario (the forecast ensemble and the nature run providing the assimilated observations are instances of the same model), partly in a more realistic imperfect-model scenario (the models providing the forecast ensemble and the nature run the have different formulations).

We demonstrate that, in our case, sensible parameter estimation results are obtained only under restrictive conditions. First, initial conditions must be very accurate, so that the spread of the forecast ensemble is determined primarily by the uncertain parameter. Second, the error variance of the assimilated observations must be low enough for the state perturbations induced by the estimated parameter to be accurately sampled. We show that, when these conditions are met, optimized parameters can compensate for sources of model error, and argue that this property can be used to extend the flexibility of parameterization schemes. For instance, this could be achieved by using parameter estimation experiments to populate lookup tables for adaptive parameters.

How to cite: Serafin, S. and Weissmann, M.: Optimizing parameterization schemes with ensemble-based parameter estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10218, https://doi.org/10.5194/egusphere-egu25-10218, 2025.

On 16 April 2024, the United Arab Emirates experienced torrential rain, with values exceeding 200 mm in less than a day.  In this study the WRF-based forecasting system Weather On Demand (WOD), developed by the Belgingur consortium in Iceland, is employed to explore the medium-range forecasts of the event, by simulations with initial values at different times.  A simulation of the event with 120h lead time was very bad, while a 72h simulation was quite accurate in terms of reproducing an extreme precipitation event.  A comparison of the simulations corresponding to the two forecasts reveals that the error in the 120h simulation is related to incorrect advection of dry air from the desert into the path of the convective storm across the Persian Gulf. The incorrect advection is associated with a wrong perturbation in the atmospheric flow, extending throughout the troposphere.  This perturbation error is associated with an erroneous simulation of a mesoscale convective complex occurring in the vicinity of Bahrain a day before the 16 April event. 

How to cite: Ólafsson, H. and Rögnvaldsson, Ó.: Analysis of an incorrect forecast of a torrential rain event in the UAE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10827, https://doi.org/10.5194/egusphere-egu25-10827, 2025.

This presentation will provide an overview of recent scientific and technological developments at ECMWF, including the 2024 upgrade of the Integrated Forecasting System (IFS).

IFS Cycle 49r1 was implemented operationally on 12 November 2024 and substantially improved near surface wind and temperature predictions, especially in the Northern Hemisphere winter season. Key changes to the forecast model in Cycle 49r1 included the introduction of the Stochastically Perturbed Parametrisations (SPP) scheme for model uncertainty, improvements in wave and convective physics, and updates to land surface and atmospheric composition models.

Updates to data assimilation and observation usage in Cycle 49r1 included the assimilation of 2m temperature observations, assimilation of additional satellite data over sea ice, improved modelling of ocean emission and reflection and all-sky assimilation of AMSU-A, several changes to the land data assimilation system, and the introduction of version 13.2 of the radiative transfer model, RTTOV. The grid spacing of the Ensemble of Data Assimilations (EDA) was also reduced from 18 km to 9 km, with the inner loop minimisation grid reduced from 100 km to 40 km.

The impacts of Cycle 49r1 include substantial improvements to 2m temperature and 10m wind speed forecasts, increased spread for tropical cyclones intensity forecasts, a slight reduction of extreme wind forecast errors, and changes to representation of snow cover and snow density. The land data assimilation and model changes lead to a systematic reduction of soil moisture and higher spatial variability in soil moisture levels. At sub-seasonal lead times, Cycle 49r1 has small but statistically robust impacts on ensemble spread, which are driven by the switch to SPP scheme for model uncertainty. These changes are most evident in the tropics, where ensemble spread in the free atmosphere is reduced by several per cent, which represents a slight improvement in ensemble reliability relative to Cycle 48r1. In addition, Cycle 49r1 slightly improves the skill of Madden–Julian Oscillation (MJO) forecasts during weeks 3-4.

Cycle 49r1 is also the foundation for Cycle 49r2, a non-operational IFS cycle that will introduce new versions of the NEMO4/SI3 ocean and sea-ice model and underpin the 6^th generation atmosphere and ocean reanalyses (ERA6/OCEAN6), the new version of the atmospheric composition reanalysis (EAC5), and the next seasonal prediction system (SEAS6).  

In parallel to ongoing development of the IFS, ECMWF has developed the Anemoi machine learning toolbox to facilitate the development of data-driven weather prediction models, including deterministic and ensemble variants of the ECMWF AIFS. Real-time evaluation of pre-operational AIFS configurations has demonstrated that they are capable of very skilful medium-range forecasts for a range of upper-atmosphere variables, surface weather variables, and tropical cyclone tracks. The first operational version of the AIFS will be implemented later this year.

Finally, higher-resolution modelling capabilities are being accelerated by Digital Twin developments under the European Commission Destination Earth programme, which is building km-scale capability for a range of potential future HPC architectures. Major efforts are being invested in the code scalability of the Integrated Forecasting System to be able to run on GPUs and investigate alternative dynamical core options.

How to cite: Roberts, C., Rabier, F., Flemming, J., and English, S.: Recent progress and outlook for the ECMWF Integrated Forecasting System , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10861, https://doi.org/10.5194/egusphere-egu25-10861, 2025.

Improving the representation of the initial state of the atmosphere in the Numerical Weather Prediction (NWP) model is critical for advancing the quality of weather forecasts which are vital for our daily life. Wind, cloud and precipitation are driving factors for Earth’s water and energy cycles and sometimes they can represent weather-related threats. Uncertain measurements of these variables present challenges for NWP models.

The WIVERN (Wind Velocity Radar Nephoscope) mission (Illingworth et al., 2018) is one of two candidate missions in Phase A studies for potential selection as the Earth Explorer 11 mission under the European Space Agency’s FutureEO programme. WIVERN would be the first-ever satellite to measure global in-cloud winds. The data from WIVERN is expected to provide significant benefits across multiple sectors, including advancing our understanding of weather phenomena, validating climate statistics, and improving the NWP models performance.

We focus on the NWP performance after assimilating WIVERN Doppler data, specifically Line of Sight (LoS) winds, for the high-impact case study of the Medicane Ianos, occurred in mid-September 2020 in the central Mediterranean. The experimental results of WIVERN Doppler assimilation are compared with those obtained from the output of similar experiments assimilating other data types: the Advanced SCATterometer (ASCAT) radar data, radiosoundings, and Atmospheric Motion Vectors (AMV).

WIVERN pseudo-observations were generated by running an ensemble of WRF at a 4 km horizontal resolution, using the European Centre for Medium range Weather Forecast – Ensemble Prediction System (ECMWF-EPS) analysis/forecast cycle issued at 12 UTC on 16 September 2020 as initial and boundary conditions. The approach consisted of the following steps:

• The WRF model was run using the initial and boundary conditions from all 51 ECMWF-EPS members.
• The forecast trajectories of Medicane Ianos from the 51 WRF ensemble members were compared to the observed trajectory.
• The best WRF member, i.e., the one with the closest agreement between the simulated and observed trajectories, was selected.
• Pseudo-observations were generated from the output of the selected WRF best member.
• These pseudo-observations were assimilated into all other members of the WRF ensemble.

For consistency, all observations in this study were pseudo-observations. Assimilation and forecast were performed at 12 UTC on 17 September, followed by a 24-hour forecast.

The trajectories followed by the Medicane are evaluated considering the assimilation of different data sources. Results show marginal improvement of the Ianos’ trajectory when radio-soundings or Atmospheric Motion Vector (AMV) are assimilated, while the trajectory forecast is substantially improved by ASCAT data assimilation (20% improvement). The assimilation of WIVERN data is very important, as the trajectory forecast was improved by over 40%.

A similar positive impact is shown when WIVERN data are assimilated together with other data sources. Specifically, two additional experiments were conducted: in the first, all data sources except WIVERN were assimilated, while in the second, WIVERN data were included. The results show an important improvement of over 10% in the trajectory forecast of Medicane Ianos when WIVERN data are used in combination with ASCAT, AMV and radio-soundings observations.

References

Battaglia, A., et al., 2022, https://doi.org/10.5194/amt-15-3011-2022.

Illingworth, A. J., et al., 2018, DOI: 10.1175/BAMS-D-16-0047.1, 1669-1687.

How to cite: Federico, S., Torcasio, R. C., Transerici, C., Montopoli, M., Pourshamsi, M., and Battaglia, A.: Assimilation of WIVERN Doppler Data in Weather Research and Forecasting (WRF) Model for the Medicane Ianos: A Comparison with Alternative Data Sources, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11144, https://doi.org/10.5194/egusphere-egu25-11144, 2025.

National Oceanic and Atmospheric Administration’s (NOAA’s) Environmental Modeling Center (EMC) is a lead developer of operational Numerical Weather Prediction (NWP) systems at the National Weather Service (NWS), which are used for the protection of life and property and the enhancement of the economy. EMC transitions to operations and maintains more than 20 numerical prediction systems that are used by NWS, NOAA, other United States (U.S.) federal agencies, and various other stakeholders. These systems are developed through a close collaboration with academic, federal and commercial sector partners. EMC maintains, enhances and transitions-to-operations numerical forecast systems for weather, ocean, climate, land surface and hydrology, hurricanes, and air quality for the U.S. and global domains.

NOAA’s operational predictions are transitioning to the Unified Forecast System (UFS) framework in order to simplify the operational prediction suite of modeling systems. The UFS is being designed as a community-based, comprehensive atmosphere-ocean-sea-ice-wave- aerosol-land coupled Earth modeling system with coupled data assimilation and ensemble capabilities, organized around applications spanning from local to global domains and predictive time scales ranging from sub-hourly analyses to seasonal predictions.  Disparate legacy operational applications that have been developed and maintained by EMC in support of various stakeholder requirements are being transitioned to the UFS framework. The transition started several years ago and is planned to continue over the next few years. Fewer resulting applications will consolidate NCEP’s Production Suite that shares a set of common scientific components and technical infrastructure.  This streamlined suite is expected to accelerate the transition of research into operations and simplify maintenance of operational systems.

There is also a major development effort in the area of AI/ML for NWP, and EMC has stepped up its efforts in adopting and testing the new technologies that show significant promise in revolutionizing operational NWP for NOAA.  

This talk describes major development and operational implementation projects at EMC over the last couple of years, and progress in advancing UFS applications for operations. We will also present EMC plans for AI/ML for NWP, within the overall NOAA strategy, and how planned efforts link with other modeling efforts within NOAA, in the broader U.S. and international community.

How to cite: Tallapragada, V., Kleist, D., Yang, F., Barton, N., Carley, J., and Levit, J.: NOAA’s Environmental Modeling Center Update: Transitioning to Unified Forecast System Applications for Operations: Accomplishments till date and Future Plans, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11527, https://doi.org/10.5194/egusphere-egu25-11527, 2025.

The GNSS Radio Occultation (RO) Modeling Experiment (ROMEX) seeks to quantify the benefit of the increasing quantity of RO observations available for use in operational numerical weather prediction (NWP) systems and products.  ROMEX includes participation from multiple operational NWP centers and NWP models, among them are NASA’s Global Earth Observing System (GEOS) model produced and run at the Global Modeling and Assimilation Office (GMAO).  The design of the numerical experiments core to ROMEX include:  1) a control experiment that includes all the RO observations currently used operationally with the sole exception of those from commercial sources and 2) a ROMEX experiment that adds to the control over 25 thousand additional RO profiles per day from commercial RO providers, with both experiments run over the three-month period of September through November 2022.  The ROMEX experiment greatly augments the relatively small subset of the currently available commercial RO profiles which have been purchased for routine use in operational NWP by the various NWP centers.  While this smaller subset of commercial RO profiles currently used in operations has been shown to have a positive impact on NWP forecasts, the additional impact from the ROMEX RO dataset has yet to be determined and is the focus of ROMEX.  Results from GEOS are presented, including the impact on both analyses and forecasts over the study period and statistics using the forecast sensitivity-based observation impact (FSOI) method.

How to cite: Murphy, Jr, M. J., Chattopadhyay, M., El Akkraoui, A., Gelaro, R., Anthes, R. A., and Jin, J.: Assimilation of High-volume commercial GNSS Radio Occultation (RO) Observations during ROMEX in NASA’s Global Earth Observing System, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12320, https://doi.org/10.5194/egusphere-egu25-12320, 2025.

Hailstorms are highly localized severe weather events that can cause extensive damage to agriculture, infrastructure, and property, necessitating accurate forecasting for effective risk mitigation. The Weather Research and Forecasting (WRF) model, a numerical model which is able to simulate features from a wide range of scales, offers a range of physics parameterizations to simulate sub-grid scale processes which are essential for hail storm forecast. However, the vast number of possible configurations complicates the identification of an optimal setup for hail simulation. This study leverages a genetic algorithm (GA) to systematically optimize WRF physics parameterizations for hail prediction over Central Europe, focusing on the severe hail events of June 2022.

The GA framework encodes WRF physical parameterizations configurationsas individuals within a population, evolving through selection, crossover, and mutation across multiple generations. Fitness is evaluated using the F2 score, prioritizing recall to address the imbalance between observed hail and non-hail events. By exploring over 2.4 million potential configurations, the GA provides the best combinations of physical parametrizations to capture the spatial and temporal characteristics of hailstorms. The results show that this methodology enables the exploration of a wide range of possible configurations, demonstrating its potential to optimize parameterizations for high-impact weather events effectively. This novel methodology represents a substantial step toward advancing hail forecasting capabilities using high-resolution NWP models.

How to cite: Guerrero-Calzas, I., Rossetto, L., Cortés Fité, A., Hanzich, M., and Miró, J. R.: Applying a Genetic Algorithm to Optimize Hail Prediction Using the Weather Research and Forecasting Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12631, https://doi.org/10.5194/egusphere-egu25-12631, 2025.

Climate change is already here, but our understanding of its local impacts in Israel is still lacking. Although large networks of in-situ observations cover Israel, and there is an increasing amount of information coming from satellites, there are still spatial and temporal gaps that are not expected to be solved in the coming decades. The problem is more pronounced in Israel than in other locations due to its complex terrain and high climate variability. These characteristics necessitate more observations (relative to other regions) to reliably sample the regional variability and allow for regular temporal and spatial data interpolation. Reanalysis datasets have become more popular in the last few decades due to their regularity in space and time, which is achieved by combining observations with model outputs using a predefined data assimilation method. However, current reanalysis products are still too coarse to represent the high climate variability in Israel, and therefore, their use is limited. In this presentation, we will describe our effort to generate a prototype high-resolution convection-permitting ensemble-based data-assimilation system and a reanalysis product for Israel.

How to cite: Strobach, E., Rozenstein, O., and Rostkier-Edelstein, D.: A Prototype High-Resolution Data-Assimilation System for Israel, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12777, https://doi.org/10.5194/egusphere-egu25-12777, 2025.

 The Korea Meteorological Administration(KMA) is producing an impact-based forecast data based on Multi-Model Ensemble(MME) system which integrates Unified Model(global, global ensemble, local, and local ensemble models), ECMWF(global and global ensemble models) and KIM(Korean Integrated Model) global model for heat waves (HW) and cold waves (CW). MME-based impact forecast(MEPS) determines the impact(safe, concern, caution, warning, alarm) by using the probability of occurrence of maximum feels-like temperature for HW and lowest temperature for CW in Korea. 
   The distribution from 93 MME members was converted to a GEV(Generalized Extreme Value) distribution until 2023, but there is a problem that only the daily temperature can be considered. Definition of HW and CW should take into account the 2-day duration/falling temperature compared to the previous day. Therefore, the probability calculation method was modified with the ratio of the number of members satisfying the HW and CW condition among all members and its performance was compared with the previous method. 
  Verification was conducted by evaluating how well impact-based forecast was matched to the observed impact in 177 regions about 1~9 forecast day. HW was verified for August and September 2023, and CW was verified for December 2023 and January 2024.
  As a result, in the case of HW forecasting, impact-based forecast with new method showed a little better performance than previous method with GEV. New method has better Bias at concern(4-9day), warning(3-9day), alarm, and Equitable Thread Score at safe(2-9day), concern(2-9day), alarm. In addition, there are cases in which the definition of guidance is more accurately satisfied compared to the previous MEPS guidance, which was overestimated. Also, new method required much less calculation time than previous method. On the other hand, new method are not applied to CW MEPS due to its overall low performance. 
  It is presumed that the reason for the degration of performance of new method for CW is that the probability table for determining the impact in the probability distribution has not been tuned. If this table is optimized, it is expected that the performance can be improved in CW case, too.

How to cite: Yun, S., Shin, H.-C., Ha, J.-C., and Kim, D.: Improvement of Impact-based Forecast Using Multi Model Ensemble in 2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13971, https://doi.org/10.5194/egusphere-egu25-13971, 2025.

Aircraft data are considered one of the best platforms for obtaining atmospheric spatial information in the observation gap over the ocean. The National Institute of Meteorological Sciences (NIMS) has operated an atmospheric research aircraft to mitigate this observation gap. In particular, the dropsonde and AIMMS-20 systems installed on the aircraft generate vertical distributions of meteorological variables over the ocean, and these specialized observation data enhance the accuracy of the initial model fields. These aircraft observation data provide continuous distributions of meteorological variables and significantly contribute to improving the performance of numerical predictions. In this study, we evaluated the effectiveness of data assimilation (DA) on the prediction of severe meteorological phenomena affecting the Korean Peninsula using high-resolution numerical modeling using atmospheric research aircraft observation data. To analyze the sensitivity of the difference in the background error covariance in the data assimilation method, three sets of simulation experiments were performed. First, an experiment was conducted using the background error covariance option CV3 based on the NMC method, which is suitable for simple settings or when the computational resources are limited. Second, an experiment using option CV5 is suitable for studying more complex situations or high-accuracy forecasts. This option generates a covariance structure that adapts to atmospheric conditions by using an ensemble-based method. The last is an experiment using the CV7 option, which is a hybrid background error covariance option that combines static methods (such as CV3) and ensemble-based methods (such as CV5), and has the advantage of combining climate statistics and flow-dependent features to improve model prediction performance.

How to cite: Han, S.-B., Goo, T.-Y., Jung, S.-P., Kim, M.-S., Kang, D.-D., and Lee, C.: Sensitivity analysis of severe weather events to different background error covariances in meteorological aircraft data assimilation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14710, https://doi.org/10.5194/egusphere-egu25-14710, 2025.

Extreme rainfall events often lead to significantly heavier rainfall over urban areas compared to their surrounding regions. Predicting these positive urban precipitation anomalies during heavy rainfall remains a critical challenge in numerical weather modeling. This study explores the sensitivity of the Weather Research and Forecasting (WRF) model to approximately 70 combinations of parameterization schemes, including microphysics, cumulus convection, planetary boundary layer options, and urban canopy model schemes, focusing on urban precipitation anomalies.

The analysis is based on two significant heavy rainfall events over Chennai, India: October 22, 2006, and November 8, 2015. Model simulations are validated against Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation data to evaluate their ability to capture urban anomalies. High-resolution simulations demonstrate that specific combinations of parameterization schemes, particularly those incorporating multi-level urban canopy models, enhance the model’s capacity to predict significant positive anomalies during intense rainfall events.

The findings underscore the critical role of urban canopy models in shaping precipitation intensity and spatial distribution and the interplay between cumulus convection and boundary layer processes in driving urban precipitation dynamics. These insights provide practical guidance for optimizing WRF parameterization settings, advancing the accuracy of urban-scale weather prediction, and deepening the understanding of urban hydrometeorological processes.

How to cite: Kalamalla, L. and Satyanarayana, A.: Sensitivity Analysis of Parameterization Schemes in the WRF Model for Predicting Urban Precipitation Anomalies Over Chennai, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15854, https://doi.org/10.5194/egusphere-egu25-15854, 2025.

Land surface temperature (LST) on Iceland has been retrieved by means of remote sensing for the period 2001-2023.  The trend in the data shows substantial geographical variability on different scales and is partly very different from the general upward trend in the 2 m temperatures.  There are areas with strong negative trend and other areas with strong positive trend.  The variability may be attributed to changes in snow cover and vegetation.  Impact of volcanic eruptions and retreat of glaciers are also detected.  The results suggest that using data decades back in time to train forecasting models may lead to systematic errors in surface temperature forecasts.        

How to cite: Rousta, I. and Ólafsson, H.: Land surface temperature trends in Iceland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17036, https://doi.org/10.5194/egusphere-egu25-17036, 2025.

Persistence is the first approximation to seasonal and sub-seasonal temperature forecasting.  In the present study, summer and autumn temperature persistence in mean monthly temperatures in the circumpolar Arctic is explored in time-series of monthly mean data.The temperature correlations extend from being negative to very high. 

The spatial variability of temperature persistence may be linked to the Bowen ratio, static atmospheric stability, snow cover and sea-ice extent.  The variability in these factors may contribute directly to seasonal variability in the radiation budget as well as in surface fluxes.  In some regions there are also detectable impacts that can be associated with regional circulation patterns.

How to cite: ekrami, N. and Olafsson, H.: Arctic Temperature Persistence in Summer and Autumn and Seasonal Forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17466, https://doi.org/10.5194/egusphere-egu25-17466, 2025.

Atmospheric profiles are indispensable for operational weather forecasting across a wide range of scales and latitudes. Despite their importance, the assimilation of tropospheric wind and temperature profiles remains a complex task with considerable potential to markedly improve weather predictions. This research investigates the impact of Ground-based Microwave Radiometer profile measurements on Numerical Weather Prediction (NWP) using a real rainfall case study. Employing the Local Error-Subspace Transform Kalman filter (LESTKF), we assimilate temperature and wind profiles derived from the Ground-based Microwave Radiometer observation network. The coupled WRF-PDAF (Parallel Data Assimilation Framework) system is utilized to conduct twin experiments. These experiments, which vary observation variables and localization distances, offer valuable insights into the assimilation process. The study evaluates potential configurations for future profile measurements and discusses recommended localization distances. The results demonstrate that incorporating multiple observation variables leads to substantial forecast improvements compared to using individual variables alone. The research culminates in a recommendation for an optimal localization distance, which has the potential to enhance the accuracy and reliability of weather forecasting.

How to cite: Shao, C., Guo, Y., and Dong, Y.: The Impact of Ground-based Microwave Radiometer Data Assimilation: A Case Study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18433, https://doi.org/10.5194/egusphere-egu25-18433, 2025.

Numerical Weather Prediction (NWP) has recently lost its hegemony in weather forecasting, as more machine-learning-based models achieve results comparable to NWP. It turns out that data-driven models are capable of identifying patterns and distilling physical laws that, until now, have only been formulated by atmospheric physics specialists. Although ML-based models are already being used by meteorological institutes alongside NWP-based models, this does not mean that NWP will fade into irrelevance. In this talk, we will show how NWP and ML can interoperate to achieve the shared goal of providing more accurate weather forecasts. From NWP providing high-quality training data for ML models to ML models replacing specific parameterizations, the spectrum of collaboration is vast. ML models cannot succeed without high-quality training data provided by NWP, and NWP can benefit from this new technology by incorporating ML models in places where conventional physics parameterizations are found lacking. None of the currently successful ML-based models would exist without the high-quality reanalysis data generated through numerical models. Decades of expertise and extensive research in numerical modelling now serve as a solid foundation for the remarkable achievements of data-driven applications. The future of weather forecasting is built on this synergy — numerical modelling and machine learning working together to achieve what neither could accomplish on its own.

How to cite: Rognvaldsson, O. and Stanislawska, K.: Numerical Weather Prediction meets Machine Learning - a synergy for better forecasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18692, https://doi.org/10.5194/egusphere-egu25-18692, 2025.

Medicanes are very dangerous meteorological phenomena with large uncertainty on genesis and intensification usually case dependent. The peculiarity of medicane Daniel analyzed in this study is the long life and strong tropical-like characteristics even on land with baroclinic atmosphere. It is essential to deepen the knowledge of these events to improve operational forecasts and waring systems. In this perspective, the models used in this analysis have reported results sufficiently close to observations. In particular, the WRF model performed better in terms of temporal synchronization of the phenomenon, internal structure of the cyclone and spatial distribution of precipitations; while ICON better modeled lower layers and highlighted different feature on track and tropical transition.
For both the models the tracks obtained from the simple algorithm used are discrete, with major errors in the initial phase. The landfall was simulated with acceptable errors. Minimum Mean Sea Level Pressure values are modeled as lower than the observed one, with WRF simulating a most intense cyclone. Wind speed data correctly passed the threshold for classification as a Category 1 hurricane, although WRF overestimated the mid-tropospheric wind. The Hart's Cyclone Phase Space diagram consistently highlighted the tropical phase of the medicane with a symmetric deep warm core at its most intense period, but the values of the parameters differ from simulation to simulation. Finally, the point values of precipitation are not satisfactory in any model even if the field of cumulated precipitation is consistent with the observations.

How to cite: Serafini, P., Ricchi, A., Marsigli, C., and Ferretti, R.: Multi-model high resolution analysis of Mediterranean Hurricane Daniel with WRF and ICON, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19128, https://doi.org/10.5194/egusphere-egu25-19128, 2025.

The evaluation of two reanalysis precipitation datasets, CRA40 and ERA5, was conducted over the Ganjiang River Basin, utilizing precipitation records from 37 ground rainfall gauges and surface-observed stream flow data spanning from January 1998 to December 2008. Both CRA40 and ERA5 were found to effectively capture the spatial and temporal precipitation characteristics at the basin scale. However, significant differences in precipitation quality were observed between the two. ERA5 demonstrated superior accuracy in depicting short-term precipitation changes, particularly on a daily basis. In contrast, CRA40 exhibited better performance on a monthly scale, offering more stable and long-term precipitation trends. Simulations of stream flow using the VIC hydrological model driven by these two precipitation products revealed that (1) CRA40 outperformed ERA5 in both daily and monthly stream flow simulations, with a higher Nash-Sutcliffe Efficiency (NSE, 0.65 for CRA40 vs. 0.6 for ERA5) and a greater correlation coefficient (CC, 0.96 for CRA40 vs. 0.91 for ERA5). Although ERA5 had a relatively good CC (0.86 and 0.93 respectively), its NSE was notably poor (0.29 and 0.30 respectively); (2) both CRA40 and ERA5 tended to overestimate stream flows in the basin, especially during the flood season (April-September), with ERA5's overestimation being more evident. This study is anticipated to offer a foundation for selecting reliable reanalysis products for precipitation and hydrological simulations in the Ganjiang River Basin.

How to cite: li, Z., Zhou, Z., and Chen, S.: Hydrological Evaluation of CRA40 and ERA5 Reanalysis Precipitation Products over Ganjiang River Basin in Humid Southeastern China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20414, https://doi.org/10.5194/egusphere-egu25-20414, 2025.

We introduce a paradigm shift for developing parameterizations of subgrid motion in numerical weather prediction (NWP) models, which is based on recent developments in the theory of computational fluid dynamics.  The governing equations of an NWP model are based on the same Navier-Stokes (N-S) equations used in computational fluid dynamics.  They must be averaged over a grid-cell volume before transforming into discrete forms in order to be solved numerically.  Consequently, extra terms of turbulent subgrid motion appear in these governing equations that must be approximated or parameterized.  The recent development of the formal theory on the N-S equations filtered via numerical discretization concludes that such parametrizations cannot be exact for the equations to be solvable numerically.  Approximation in these parameterizations is necessary for the N-S equations to be solvable as a well-posed problem.  Practically, this has two implications for parameterizing subgrid motion in NWP models.  First, the development of the parameterizations of subgrid motion is required to be driven by improving the accuracy of the parameterizations to address specific prominent performance issues of an NWP model, and the improvement should be based on observations of forecast variables and subgrid processes for it to be physically relevant.  Second, the parameterizations should be as simple as possible for feasible performance tuning based on observations and for computational efficiency.  In this presentation, we will use two examples to discuss what the problem-driven and observation-based approach means in developing subgrid convection and turbulent mixing parameterizations in NWP models.  We will use the two examples to advocate that the problem-driven and observation-based approach should be used to develop all subgrid physics parameterizations in weather and climate models for developing parameterizations of subgrid motion.

How to cite: Bao, J.-W. and Michelson, S.: A paradigm shift for developing parameterizations of subgrid motion in NWP models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20640, https://doi.org/10.5194/egusphere-egu25-20640, 2025.

We present an investigation in which two planetary boundary layer (PBL) schemes are compared at the parameterized physical process level in a fog simulation case study.  The two PBL schemes in question are two options in the Unified Forecast System (UFS) for global and regional applications.  We investigated the difference between the two schemes using both 3-D regional and single-column configurations of the UFS.  We found that there are no significant differences in terms of parameterized physical processes.  The two schemes differ mainly in the closure assumptions and the magnitudes of parameters used in the parameterization formulations, resulting in more or less success in simulating the development of the observed fog layer.  Both schemes have their own error characteristics in representing essential processes for fog formation and dissipation, pointing to the uncertainty in PBL process parameterizations when observations and realistic large-eddy simulations are insufficient for process evaluation.

How to cite: Grell, E., Michelson, S., and Bao, J.-W.: A process-level comparison of two PBL schemes in the Unified Forecast System in a fog case study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20650, https://doi.org/10.5194/egusphere-egu25-20650, 2025.

Recent advances in artificial intelligence (AI), specifically with applications of deep learning, have brought paradigm-shifting changes to Numerical Weather Prediction.  Recent AI-based NWP systems have rivaled traditional physics-based global NWP systems according to some verification metrics.  However, the performance of these systems for extreme events, and their implications for atmospheric predictability, has not yet been fully explored.    In this study, the practical predictability for winter storms in eastern North America will be compared using forecasts generated by several traditional NWP and AI-NWP systems.   In addition to domain-wide verification statistics, the realism of cyclone structure and evolution will be evaluated at different forecast lead times.  We plan to discuss the ensemble predictability of events, evaluating the sensitivity of the AI-NWP systems to initial condition perturbations, with implications for data assimilation.  Finally, at the mesoscale, we will demonstrate a convection initiation nowcasting system that utilizes deep learning to generate probabilities of new convection forming at lead times under one hour, which we interpret using explainable AI and uncertainty quantification.

How to cite: Greybush, S. and Spallone, C.: Implications of AI for Atmospheric Predictability of Convection and Winter Storms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20960, https://doi.org/10.5194/egusphere-egu25-20960, 2025.

Traditional ensemble forecasting based on numerical weather prediction (NWP) models, is constrained by the need for massive computational resources, resulting in limited ensemble sizes. Although emerging artificial intelligence (AI)-based weather models offer high forecast accuracy and improved computational efficiency, they still face considerable challenges in ensemble forecasting applications.

In this study, we propose a fast, physics-constrained perturbation scheme through self-evolution dynamics of AI-based weather model for ensemble forecasting of tropical cyclones (TCs). These initial perturbations are conditioned on specific amplitude and spatial characteristics, exhibiting physically reasonable dynamical growth and spatial covariance. Based on this perturbation scheme, the TC track ensemble forecasts within the AI-based model significantly outperform those from the European Centre for Medium-Range Weather Forecasts (ECMWF) in both deterministic and probability metrics. Notably, we conduct TC track forecasts with 2000 members for the first time, achieving further enhanced forecast skill in probability distribution and extreme scenario of TC movement.

How to cite: Pu, J., Mu, M., Feng, J., Zhong, X., and Li, H.: A Fast Physics-based Perturbation Generator of Machine Learning Weather Model for Efficient Ensemble Forecasts of Tropical Cyclone Track, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2160, https://doi.org/10.5194/egusphere-egu25-2160, 2025.

Incorporating appropriate physical constraints to data assimilation is of great significance for the assimilation of disastrous weather data assimilation and numerical forecasting. Generally, model constraints are often difficult to describe complex sub-grid physical processes with strong nonlinearity and discontinuity, due to difficulties in developing the tangent linear and adjoint. In 4DVar, simplified physical process schemes are often used instead. With the development of artificial intelligence (AI) technology, complex sub-grid physical processes can be probably considered in variational constraints. On the basis of momentum equation constraints, this study introduces sub-grid boundary layer turbulent friction effects through machine learning (ML) and adds them into momentum equation constraints. Firstly, a deep neural network model is used to train the horizontal momentum tendency simulated by the YSU boundary layer parameterization scheme of WRF model. Secondly, under the Ensemble-Var framework of WRFDA, the momentum tendency of the boundary layer is introduced into the weak constraint of the horizontal momentum equation of variational method. The boundary layer turbulent friction term is implemented by embedding a deep neural network model, and its tangent and adjoint operators are developed to construct a ML-DA scheme. Finally, a physical constraint scheme considering the turbulent friction effect of boundary layer is established for data assimilation. The new assimilation scheme is applied to the radial wind assimilation of coastal radar. Numerical simulation experiments on different typical landing typhoons show that the new scheme better described the boundary layer four-force balance during the data assimilation process. Assimilating the direct-observed wind field, the thermal fields such as pressure and temperature are also improved. The new scheme plays a positive role in typhoon intensity and structure forecasting.

How to cite: Li, X.: Implementing the sub-grid boundary layer turbulent effects into variational constraints trough machine learning and its impact on typhoon assimilation , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2217, https://doi.org/10.5194/egusphere-egu25-2217, 2025.

This study investigates the uncertainties of two AI-driven meteorological models, Pangu-Weather and Fuxi, in the forecasts of tropical cyclones (TCs) from perspective of target observations. The conditional nonlinear optimal perturbation (CNOP) method is used to identify the sensitive areas for target observations, and the TCs in the Northwest Pacific and Bay of Bengal (BoB), with the latter being often referred as “BoB storms”, are investigated. The results suggest that the predictability of the “Pangu-Weather” model with respect to the BoB storm tracks is limited within 24 hours, and model error effects dominate the uncertainty of the forecasts after 24 hours; while for the TCs in the Northwest Pacific, the Fuxi model is shown to be strongly sensitive to initial perturbations and provide much accurate sensitive areas for target observations associated with TC track forecasts. These results illustrate the uncertainties of the two AI models and provide a theoretical basis for implementing field campaigns for target observations using AI models.

How to cite: Duan, W.: Uncertainty of AI models in tropical cyclone forecasts: target observation perspective, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2261, https://doi.org/10.5194/egusphere-egu25-2261, 2025.

The operational Tropical Regional Atmospheric Model System (TRAMS) model often underestimates the initial typhoon intensity when using the global analysis field as the initial condition. The tropical cyclone (TC) initialization scheme developed based on incremental analysis updates (IAU) technique can help reduce the initial bias. However, the IAU-based TC initialization scheme only adjusted the wind field at the analysis moment, with other variables adjusted implicitly under the constraints of the model according to the gradually inserted wind increment (named “univariate adjustment scheme” hereafter). The univariate adjustment scheme required approximately 3  to reach a dynamic equilibrium state, limiting the use of hourly TC observation information and dissipating too much meaningful short-wave information of the adjustment increment. To reduce the equilibrium adjustment time, this study constructed a multivariate adjustment IAU-based TC initialization scheme by introducing the gradient wind balance and hydrostatic balance as large-scale constraints. The case sensitivity tests of TC Hato (1713) demonstrated that the multivariate adjustment scheme can reduce the IAU relaxation time to 1  and slightly improve TC forecasts. By incorporating the equilibrium assumption as a strong constraint for the TC axisymmetric component, the multivariate adjustment scheme achieved a faster convergence of the model to its equilibrium state, reducing the loss of useful observed information. This conclusion was further confirmed with 12 other TCs. The IAU-based multivariate adjustment initialization scheme developed in this study provides a foundation for 4-D initialization with hourly TC observations.

How to cite: Zhang, S., Xu, D., Tian, W., and Zhang, B.: Multivariate adjustment in the IAU-based tropical cyclone initialization scheme  in TRAMS model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3579, https://doi.org/10.5194/egusphere-egu25-3579, 2025.

At RIKEN, the Japan’s national flagship research institute for all sciences, we have been exploring several attempts to integrate data assimilation (DA) and AI/ML. DA integrates the (usually process-driven) model and data, while AI/ML is purely data driven and is proven to be very powerful in many applications. An example is to integrate data-driven AI/ML-based precipitation nowcasting with process-driven numerical weather prediction (NWP). We developed a nowcasting system based on a convolutional long short term memory (LSTM) which takes several time steps of 2-D precipitation image data to predict future images. NWP with radar DA produces future precipitation images, which can be input to the data-driven LSTM to further improve the predicted images. Another example is to develop ML’ed observation operators for satellite radiances. We obtained an improvement by purely ML’ed observation operators without any information from a physically based radiative transfer model. The third example is to use DA with an ML’ed surrogate model for producing more accurate analyses for further training the ML’ed surrogate model. We found that DA with flow-independent background error covariance could produce more accurate ML’ed surrogate model, but ensemble-based DA resulted in a mixed situation probably because the ensemble forecasts by the ML’ed surrogate model may not produce proper error covariance. We also explored developing a limited-area ML’ed surrogate NWP model in collaboration with IMT-Atlantique. In this presentation, we will share the most recent activities of integrating DA and AI/ML at RIKEN.

How to cite: Miyoshi, T., Otsuka, S., Liang, J., Goodliff, M., Saliou, G., Ouala, S., and Tandeo, P.: RIKEN’s activities to integrate data assimilation and AI/ML, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4099, https://doi.org/10.5194/egusphere-egu25-4099, 2025.

Recently, artificial intelligence (AI) models have upended numerical weather prediction (NWP) by achieving performance comparable to or even surpassing that of physics-based NWP models while also significantly reducing computational costs. However, these AI solutions generally operate with initial conditions produced by NWP data assimilation, which remains costly and can suffer from approximations. We introduce OMG-HD, an end-to-end AI weather forecasting model designed to make predictions directly from observational data, including surface observations, radar, and satellite, thus bypassing the data assimilation step. OMG-HD provides kilometer-scale, 12-hour forecasts across the contiguous United States (CONUS) that exhibit greater skill than the leading operational NWP models. Compared to the High-Resolution Rapid Refresh (HRRR), we achieve a 13-48% improvement in RMSE for 2-meter temperature, 10-meter wind speed, 2-meter specific humidity, and surface pressure. These results demonstrate the feasibility of AI-driven end-to-end approaches for operational weather forecasting free of NWP data, offering a promising step towards faster and more accurate weather forecasts to support weather-dependent decision-making.

How to cite: Dong, H.: OMG-HD: A High-Resolution AI Weather Model for End-to-End Forecasts from Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4604, https://doi.org/10.5194/egusphere-egu25-4604, 2025.

The Met Office in the UK is exploring the use of ensemble sensitivity analysis (ESA) as an operational tool to support its upcoming focus on its global ensemble system. Ensemble sensitivity analysis is a technique that identifies atmospheric flow features throughout a forecast period that are relevant to high-impact forecast aspects such as high winds, heavy precipitation, and extreme temperatures (known as response functions). ESA typically highlights the importance of the position or magnitude of features like upper-level troughs, ridges, and wind maxima/minima in the jet stream, as well as structure in low-level pressure and moisture fields, to the response function. Since ESA also identifies specifically how features are associated with differences in high-impact response functions (e.g., an eastward shift of a 300hPa geopotential height trough off the U.S. east coast might be associated with heavier precipitation two days later in the UK), it can add substantial value to the forecasting process through forecaster awareness. This value can be realized through both improved dynamical understanding of high-impact flows and ensemble subsetting, a method that weights ensemble members more if they are more skillful in sensitive areas.

The Met Office in the UK has created a real-time ESA tool for initial evaluation to understand its value in the forecasting process. Wind, precipitation, temperature, and visibility response functions to seven-day forecast time over the UK, both coverage and maximum values, serve as the response functions. Sensitivities to geopotential height and wind speed aloft, surface pressure, and simulated water vapor imagery are produced every six hours from the response function backward to initial forecast time. This presentation involves what operational forecasters and research personnel have learned from day-to-day ensemble sensitivity fields, the use of ESA in the forecasting process, and the climatological nature of sensitivity. Future plans for the Met Office in the UK ESA tool will also be discussed.

How to cite: Ancell, B., Willington, S., Titley, H., Jones, C., Walker, B., Semple, A., Hicks, R., Relton, P., Barciela, R., Etheridge, D., and Roberts, N.: Ensemble Sensitivity Analysis in the Operational Met Office in the UK Ensemble System, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5156, https://doi.org/10.5194/egusphere-egu25-5156, 2025.

The occurrence of severe weather events has shown increasing frequency and intensity due to global climate change. The Korean Peninsula, characterized by complex inland orography and surrounded by seas on three sides, exhibits diverse meteorological phenomena significantly influenced by seasonal wind regimes. While forecasters traditionally analyze synoptic conditions using locally available observations, the irregular spatial and temporal distribution of these observations limits their ability to conduct comprehensive three-dimensional analyses. Furthermore, although global model analysis fields have been widely used for operational forecasting, their coarse spatial and temporal resolutions constrain the real-time analysis of localized severe weather events. To address these limitations and enhance nowcasting capabilities, the Korea Meteorological Administration (KMA) has implemented the Korea Analysis System (KAS) since May 2024, a real-time analysis system that utilizes a high-resolution regional model to provide rapid updates of current atmospheric conditions essential for monitoring and predicting mesoscale weather phenomena.

This study evaluates KAS's effectiveness in reproducing real-time atmospheric phenomena and its practical utility for severe weather analysis through extensive synoptic case studies. KAS generates hourly three-dimensional nowcast analysis fields at 3 km resolution by integrating 15 categories of synoptic and non-synoptic observational data with the operational Korean Integrated Model-regional (KIM-regional) forecast fields, assimilating observations up to 15 minutes past each hour to provide near real-time atmospheric conditions. The system demonstrated remarkable capability in capturing critical meteorological features across various weather regimes. During summer, KAS effectively identified precursors of convective precipitation by analyzing real-time low-level convergence zones, dewpoint depression fields, high equivalent potential temperature areas, and vertical p-velocity distributions. The system's skew T-log P diagrams revealed significant Convective Available Potential Energy (CAPE) values, providing quantitative measures of atmospheric instability and potential for convective cloud development and subsequent precipitation in specific regions. In winter scenarios, KAS accurately depicted strong wind variations, including northwesterly cold air flows and easterly winds associated with orographic precipitation. Notably, the system's thermal advection analysis fields effectively identified regions of warm air advection and their interaction with cold air masses, providing crucial indicators for potential snowfall accumulation zones, particularly in areas where warm maritime air masses encounter pre-existing cold air.

The results validate KAS's capability to provide forecasters with coherent three-dimensional nowcast analyses, overcoming the limitations of traditional forecasting methods based on irregularly distributed observations and coarse-resolution global model analyses. This advancement establishes a foundation for improved real-time severe weather detection and forecast accuracy across the Korean Peninsula and East Asia region.

Acknowledgement: This work was supported by Development of Numerical Weather Prediction and Data Application Techniques (KMA2018-00721).

How to cite: Kim, M., Lee, E., Kang, Y., and Lee, Y.: A case study on synoptic analysis using the Korea Analysis System (KAS) to enhance severe weather monitoring over the Korean Peninsula, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5510, https://doi.org/10.5194/egusphere-egu25-5510, 2025.

Cold waves are one of the most frequent extreme weather events in the winter mid- and high-latitude regions of the Northern Hemisphere. Due to their sudden onset, persistence, and wide-ranging effects, they often cause significant economic and social losses. Although progress has been made in short-term cold wave forecasting, sub-seasonal (3-4 weeks) forecasting remains a major challenge due to the loss of initial condition information, and the complex and nonlinear external forcings. Current numerical models, which dominate operational cold wave forecasting, are computationally expensive and difficult to run large-scale simulations. In contrast, machine learning models, particularly FuXi-S2S developed by Fudan University, offer significant potential due to their efficiency and accuracy in sub-seasonal forecasting.

In the preliminary work, the researcher identified the spatiotemporal characteristics and circulation evolution of cold events in Eurasia, proposing the "Cold Arctic-Warm Continent" mode and its interaction with tropical Pacific signals. Despite improvements in understanding the mechanisms of cold waves, predicting their occurrence at the sub-seasonal scale remains difficult due to uncertainties and complex nonlinear processes. Therefore, exploring new machine learning-based forecasting methods is essential to improve prediction accuracy.

The goal of this study is to: 1) identify key pre-cold wave factors at the sub-seasonal scale in China; 2) develop a probabilistic forecasting scheme based on FuXi-S2S with physically constrained perturbations. The research methodology includes composite analysis and the design of initial perturbations for the FuXi-S2S model with physical constraints, aimed at improving forecast accuracy. By comparing ensemble and deterministic forecasts, this study will evaluate the effectiveness of the proposed scheme and contribute to early warning strategies for cold wave events.

How to cite: Liu, Q.: Research on Subseasonal Ensemble Forecasting of Cold Surges over China Based on Physically-Constrained Perturbations in AI-based Weather Prediction Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7063, https://doi.org/10.5194/egusphere-egu25-7063, 2025.

In recent years, AI-based weather forecasting models have matched or even outperformed numerical weather prediction systems. However, most of these models have been trained and evaluated on reanalysis datasets like ERA5. These datasets, being products of numerical models, often diverge substantially from actual observations in some crucial variables like near-surface temperature, wind, precipitation and clouds - parameters that hold significant public interest. To address this divergence, we introduce WeatherReal, a novel benchmark dataset for weather forecasting, derived from global near-surface in-situ observations. WeatherReal also features a publicly accessible quality control and evaluation framework. This paper details the sources and processing methodologies underlying the dataset, and further illustrates the advantage of in-situ observations in capturing hyper-local and extreme weather through comparative analyses and case studies. Using WeatherReal, we evaluated several data-driven models and compared them with leading numerical models. Our work aims to advance the AI-based weather forecasting research towards a more application-focused and operationready approach.

How to cite: Jin, W., Weyn, J., and Dong, H.: WeatherReal: A Benchmark Based on In-Situ Observations for Evaluating Weather Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7596, https://doi.org/10.5194/egusphere-egu25-7596, 2025.

Forecast errors of numerical weather prediction consist of model errors and the growth of initial condition errors, while the initial condition is often optimized based on short-term forecasts. Thus it is difficult to untangle the initial condition error and model error, but it is essential to infer model errors not just for prediction but also for data assimilation (DA). A hybrid deep learning (DL) and DA method is proposed here, aiming to correct model errors. It uses a convolutional neural network (CNN) to extract characteristics of initial conditions and forecast errors, and then provides estimations for model errors. The CNN-based model error estimation method can consider the model error resulted from inaccurate model parameters, or simultaneously consider the model error and initial condition error. Based on the Lorenz05 model, offline and online experiments demonstrate that the CNN-based model error estimation method can effectively correct model errors resulted from inaccurate model parameters, including the forcing F, coupling coefficient c, and relative scale b. For both online and offline model error estimations, simultaneously considering model errors and initial condition errors are beneficial to infer the model errors, compared to considering model errors only. Moreover, using the observations to verify the forecasts has advantages over using the analyses, to estimate the model errors. Using observations can also achieve a faster convergence of model error estimation with online DA than using analyses.

How to cite: Peng, Z., Lei, L., and Tan, Z.-M.: A hybrid deep learning and data assimilation method for model error estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7652, https://doi.org/10.5194/egusphere-egu25-7652, 2025.

Operational numerical weather prediction (NWP) systems consist of three fundamental components: the global observing system for data collection, data assimilation (DA) for generating initial conditions (referred to as analysis), and the forecasting model to predict future weather conditions. While NWP have undergone a quiet revolution, with forecast skills progressively improving over the past few decades, their advancement has slowed due to challenges such as high computational costs and the complexities associated with assimilating an increasing volume of observational data and managing finer spatial grids. Advances in machine learning offer an alternative path towards more efficient and accurate weather forecasts. The rise of machine learning based weather forecasting models has also spurred the development of machine learning based DA models or even purely machine learning based weather forecasting systems. This paper introduces FuXi Weather, an end-to-end machine learning based weather forecasting system. FuXi Weather employs specialized data preprocessing and multi-modal data fusion techniques to integrate information from diverse sources under all-sky conditions, including microwave sounders from 3 polar-orbiting satellites and radio occultation data from Global Navigation Satellite System. Operating on a 6-hourly DA and forecasting cycle, FuXi Weather independently generates robust and accurate 10-day global weather forecasts at a spatial resolution of 0.25°. It surpasses the European Centre for Mediumrange Weather Forecasts (ECMWF) high-resolution forecasts (HRES) in terms of predictability, extending the skillful forecast lead times for several key weather variables such as the geopotential height at 500 hPa from 9.25 days to 9.5 days. The system’s high computational efficiency and robust performance, even with limited observations, demonstrates its potential as a promising alternative to traditional NWP systems.

How to cite: Sun, X.: A data-to-forecast machine learning system for global weather, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7911, https://doi.org/10.5194/egusphere-egu25-7911, 2025.

Southern France is often affected by heavy-precipitation events (HPEs) leading to severe flooding in the regions of Occitanie, Provence-Alpes-Côte d'Azur, and Corsica. In many cases, these HPEs are triggered by a strong south-easterly moisture transport from the Mediterranean lower atmosphere (below 850 hPa). To date forecasting the magnitude and location of HPEs is challenging for numerical weather prediction models (NWP): forecasting false positive alarms as well as false negative HPEs need to be minimized. We hypothesize that an accurate distribution of water vapor is hypothesized to be a key for improvements.

During the WaLiNeAs experiment (Flamant et al., 2021), which took place in fall-winter of 2022, water-vapor measurements of eight Raman lidar systems were performed over Southern France, Spain and Corsica. From this unique data set, we use the data of the two lidar systems operated by the Universita della Basilicata (Toulon and Camargue), the lidar data from the Institut Pierre-Simon Laplace (IPSL) at Montpellier and the ARTHUS lidar system (Lange et al., 2019) operated by the University of Hohenheim on the island of Corsica (Ajaccio). The first three systems were aligned along the Coast with an average distance of 100 km between each other.

The water vapor lidar data applied in our simulation experiment were collected during a strong precipitation event between 14-16 November 2022. Observed rainfall near the coast was between 100 and 150 mm/24h, however over the sea extreme precipitation with up to 300 mm/24h was observed.

To investigate the impact of additionally assimilating water vapor data of the four lidar systems on the prediction of this challenging to forecast precipitation event, the Weather Research and Forecasting (WRF)-NOAHMP 3DEnVar system is used on a convection permitting resolution with 10 ensemble members. The assimilation follows the setup of Thundathil et al. (2021) with a rapid update cycle length of 42 h with hourly update intervals. The results show an analysis increment of up to 2 g/kg in the lowest 2000 m. We present further results of the spatial-temporal impact of the assimilation of the water-vapor lidar data on the analysis and the forecasts up to 24 h.

References

Flamant, C., et al. (2021): A network of water vapor Raman lidars for improving heavy precipitation forecasting in southern France: introducing the WaLiNeAs initiative. Bulletin of Atmospheric Science and Technology (2021) 2: 10. https://doi.org/10.1007/s42865-021-00037-6

Lange, D., Behrendt, A., & Wulfmeyer, V. (2019). Compact operational tropospheric water vapor and temperature Raman lidar with turbulence resolution. Geophysical Research Letters, 46, 14844–14853. https://doi.org/10.1029/2019GL085774

Thundathil, R., Schwitalla, T., Behrendt, A. & Wulfmeyer, V. (2021) Impact of assimilating lidar water vapour and temperature profiles with a hybrid ensemble transform Kalman filter: Three-dimensional variational analysis on the convection-permitting scale. Q J R Meteorol Soc, 147(741, 4163–4185. Available from: https://doi.org/10.1002/qj.4173

How to cite: Schwitalla, T., Lange, D., Behrendt, A., Wulfmeyer, V., Chazette, P., Di Girolamo, P., Lagarrigue, J., Laly, F., Di Paolantonio, M., Summa, D., and Totems, J.: Assimilation of water vapor lidar data from the WaLiNeAs experiment to decrease false positives and negatives in heavy precipitation forecasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11811, https://doi.org/10.5194/egusphere-egu25-11811, 2025.

One of the most critical tools to mitigate the impact of urban flash floods is having an effective and timely early-warning system. The Argentine National Meteorological Service (SMN) is actively working in this direction through the PREVENIR Argentina-Japan cooperation project, which aims to develop an impact-based early-warning and emergency management system for urban flash floods in two Argentine target basins by 2027. As the current SMN operational system consists of 4-km resolution deterministic and warm-start probabilistic forecasts, to provide a more accurate and timely precipitation forecast, under PREVENIR, we are developing a higher-resolution (2-km), rapid-update data assimilation and numerical weather forecasting system coupling the Local Ensemble Transform Kalman Filter (LETKF) with the Weather Research and Forecasting (WRF) model. The system ingests local data from automated surface weather stations and C-band Doppler weather radars to obtain a 40-member analysis ensemble every 5 minutes, and 10-h 20-member extended forecasts every 30 minutes. This work aims to evaluate the performance of the WRF-LETKF prototype system based on a 4-day case study of almost continuous precipitation over the Buenos Aires region in March 2024, which led to urban floods in one of the pilot basins. A preliminary comparison with Radar Quantitative Precipitation Estimation (RQPE) indicates a good performance of the precipitation forecasts and added value for early warning and decision-making.

How to cite: Maldonado, P., Amemiya, A., Dillon, M. E., Gacitua Gutierrez, J., Cutraro, F., Casaretto, G., Ruiz, J., Pulido, M., Garcia Skabar, Y., and Miyoshi, T.: The PREVENIR rapid-update data assimilation and short-range numerical weather prediction system prototype: an urban flood case study over Buenos Aires. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12662, https://doi.org/10.5194/egusphere-egu25-12662, 2025.

Data assimilation (DA) is a statistical approach used to estimate the states of physical systems by integrating prior model predictions (background states x_b​) with observational data (y). This integration produces an accurate estimate, called analysis states (​x_a), by sampling or maximizing the posterior likelihood p(xx_b, y). In weather forecasting, background states are generated by imperfect models, and the likelihood p(xx_b) is often unknown. Observations, sourced from diverse instruments, are mapped to model space using observation operators (H). Effective DA algorithms must accurately estimate p(xx_b) while accommodating various observation operators, including those involving sparse, noisy or irregular data.

Traditional DA methods, such as variational assimilation, assume that the background error (​x - x_b) follows a Gaussian distribution independent of x_b​. This allows explicit computation of p(xx_b, y) and optimization via techniques like gradient descent. While robust to various observation operators, these methods depend heavily on expert knowledge to construct error correlations and are limited by their Gaussian assumptions.

Generative neural networks, particularly diffusion models, have emerged as alternatives for modeling p(xx_b). Notable examples include SDA and DiffDA, which use diffusion models to learn background distributions. SDA incorporates observations via diffusion posterior sampling, while DiffDA employs the repaint technique. These approaches improve on traditional methods by capturing more complex distributions but often struggle with sparse, irregular observations. For instance, DiffDA assumes grid-aligned data, while SDA relies on assumptions that can reduce accuracy in real-world scenarios.

In this research, we aim to develop a neural network-based data assimilation algorithm that not only captures the non-Gaussian characteristics of the conditional background distribution for enhanced accuracy but also effectively assimilates data under real-world observations (sparse, noisy and outside of the grid). We introduce VAE-Var, a novel data assimilation algorithm in which a variational autoencoder is first employed to learn the conditional background distribution and then the decoder component is utilized to construct a variational cost function, which, when optimized, yields the analysis states.

Key advantages of VAE-Var include:

• This algorithm inherits the framework of traditional variational assimilation by explicitly modeling the posterior probability function p(xx_b, y) and maximizing it to derive the analysis states. As a result, compared to other neural network data assimilation methods such as SDA and DiffDA, VAE-Var can better handle different types of observation operators, particularly irregular observations that do not fall on the grid points of the physical field.
• Unlike traditional variational assimilation algorithms, VAE-Var alleviates the dependence on expert knowledge for constructing the conditional background distribution, enabling the model to effectively capture non-Gaussian structures. This makes VAE-Var perform better in sparse observational settings.

Experiments with the FengWu weather forecasting system at 0.25° resolution show that VAE-Var achieves higher accuracy than DiffDA and traditional algorithms (interpolation and 3DVar) in sparse observational settings. When integrated with FengWu, VAE-Var reliably assimilates real-world GDAS prepbufr observations over a one-year period.

How to cite: Xiao, Y., Jia, Q., Chen, K., Xue, W., and Bai, L.: Variational Autoencoder-Enhanced Variational Methods for Data Assimilation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14520, https://doi.org/10.5194/egusphere-egu25-14520, 2025.

Accurate and reliable precipitation nowcasting plays a critical role in disaster prevention and mitigation. The heavy precipitation forecast is a challenging task for most deep learning (DL)-based models. To address this challenge, we develop a novel DL architecture called “multi-scale feature fusion” (MFF) that can give skillful precipitation forecast with a lead time of up to 3 h. The MFF model uses convolution kernels with varying sizes to create multi-scale receptive fields. This helps to capture the movement features of precipitation systems, such as their shape, movement direction, and speed. Additionally, the architecture makes use of the mechanism of discrete probability to reduce uncertainties and forecast errors, enabling it to predict heavy precipitation even at longer lead times. Four-year radar observation data from 2018 to 2021 are used for model training, and the data of 2022 for model testing. The MFF model is compared with three existing extrapolative models: time series residual convolution (TSRC), optical flow (OF), and UNet. The results show that MFF achieves superior forecast skills with high probability of detection (POD), low false alarm rate (FAR), small mean absolute error (MAE), and high structural similarity index (SSIM). Particularly, MFF can predict high-intensity precipitation fields at 3 h lead time, while the other three models cannot. Furthermore, MFF shows improvement in the smoothing effect of the forecast field, as observed from the results of radially averaged power spectral (RAPS).   

How to cite: Chen, S., Huang, Q., and Tan, J.: Skillful Precipitation Nowcasting Based on Multi-scale fusion and Radar Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14682, https://doi.org/10.5194/egusphere-egu25-14682, 2025.

 Dual-polarimetric (dual-pol) radar variables, such as differential reflectivity (Z_DR) and specific differential phase (K_DP), provide valuable information about hydrometeor types, sizes, and water content. A dual-pol radar operator that applies scattering calculations using the T-matrix method for rain and the Rayleigh scattering approximation for snow and graupel can more accurately translate model variables into observed variables. Assimilating dual-pol radar variables in numerical weather prediction models enhances the forecast accuracy for evolving mesoscale precipitation events. Therefore, developing advanced radar observation operators capable of calculating dual-pol radar variables using microphysical variables is crucial.

In this study, an improved observation operator (K-DROP; KNU dual-pol radar observation operator) is developed. The K-DROP restricts the distribution of mixed-phase hydrometeors in regions with strong vertical motions, thereby reducing overestimation of radar variables near the melting layer. Additionally, by incorporating the observed snow axis ratios for cold rain process, the calculation of  as a constant value in subfreezing regions is corrected. Observed maximum hydrometeor radius data are also applied, reducing overestimations of  and in warm regions. Experiments using LETKF are conducted for both convective and stratiform precipitation cases and compared with the previous observation operator without modifications. While the previous operator improved forecast accuracy compared to control experiments without DA, it showed limited improvements near the melting layer due to reduced hydrometeor mixing ratios and increased downdrafts. In contrast, K-DROP produced more realistic radar variables, stronger updrafts, and higher correlations with observations. These improvements are particularly effective for convective precipitation with localized heavy rainfall, demonstrating the importance of assimilating dual-pol radar variables containing water content information.

Key words: Dual-polarization radar operator, Radar data assimilation, Observation operator, Precipitation forecasting.

Acknowledgments: This work was supported by the National Research Foundation (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A2C1012361), the Korea Meteorological Administration Research and Development Program under Grant RS-2023-00237740 and the Brain Korea 21 program.

How to cite: Lee, J.-W., Min, K.-H., and Lee, G.: Improved Model Prediction with Dual-polarimetric Radar Operator in Ensemble Data Assimilation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14792, https://doi.org/10.5194/egusphere-egu25-14792, 2025.

El Niño-Southern Oscillation (ENSO) is the dominant atmosphere-ocean coupled mode of year-to-year variations in the tropical Pacific. It shows diverse spatiotemporal characteristics and casts major influences on seasonal predictions of global weather-climate extrema. Despite numerous dynamical and statistical models for ENSO prediction and predictability studies, they are commonly subjected to one-to-three issues among less skillful simulation of El Niño diversity, huge requirements of computational resources and a low robustness in statistics. Here, an efficient deep-learning model involving nonlinear coupling of multiple variables is independently developed to study the predictability of two types of El Niño events related to initial uncertainty, which is the first kind of predictability problem. The model can skillfully simulate statistically robust features of observed El Niño diversity in terms of periodicity, amplitude, and seasonal phase-locking. Using this model, we have revealed mathematically several new types of fastest-growing initial errors in two types of El Niño predictions based on a novel concept of conditional nonlinear optimal perturbation (CNOP), especially including one that can strengthen central Pacific types of events, which is rarely investigated before. Moreover, CNOPs are superimposed into a numerical model, GFDL CM2p1, for comprehensive validation and growth mechanism mining, which demonstrates the consistent dynamical evolutions of initial errors in both numerical and AI models. Our study represents the first attempt to explore the first kind of ENSO predictability problem from perspectives of nonlinear error evolving dynamics using a data-driven model. This is of great importance as it offers us sufficient confidence to perform ENSO-related (such as the Madden-Julian Oscillation, etc.) mechanisms and predictability studies for future data assimilations and observation programs without strongly relying on dynamical numerical models.

How to cite: Qin, B. and Mu, M.: The First Kind of Predictability Problem of El Niño Predictions in a Multivariate Coupled Data-driven Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14917, https://doi.org/10.5194/egusphere-egu25-14917, 2025.

In this paper, we explore the prevalent issue of underestimation of extreme precipitation values in deep learning models utilized for precipitation forecasting. We emphasize that this challenge arises from the double penalty phenomenon, which is exacerbated by the joint effect of the commonly adopted mean squared error (MSE) loss function and the intrinsic uncertainty of forecasting tasks. Drawing inspiration from probability-matching ensemble forecasting, we introduce Sort Loss, a straightforward yet highly effective deep learning loss function. By leveraging the ordinal relationships within meteorological data, Sort Loss circumvents the positional information-related double penalty problem. Experimental results from precipitation nowcasting and short-term forecasting tasks demonstrate that Sort Loss effectively diminishes the distributional discrepancies between model forecasts and actual observations. Consequently, it significantly enhances forecasting performance in heavy rainfall scenarios, while simultaneously maintaining stability across other weather conditions. This study offers a novel perspective on optimizing deep learning models for weather forecasting and showcases the potential of applying Sort Loss to improve the accuracy of extreme weather predictions.

How to cite: Cao, Y., Chen, L., and Feng, J.: Imroving Extreme Precipitation Prediction Accuracy: A Novel Probability-Matching-Based Loss Function for Deep Learning Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15517, https://doi.org/10.5194/egusphere-egu25-15517, 2025.

Data assimilation (DA) is the best tool for assessing the state of time-evolving chaotic systems. An estimate (analysis) is derived by combining Information from the most recent and previous batches of observations, the latter of which are carried forward by first guess forecasts started from previous analyses. Error variance in successful DA cycles fluctuate around an expected value. What factors determine this value?

Three parameters are found to describe the level of analysis error variance ( ), or the amount of Information in state estimates: the level of Information extracted from a recent set of observations by a DA system at analysis time ( ), the growth rate of error in the first guess ( ), and the relative weight used for combining Information from the latest set of observations and the first guess ( ). A key recognition of this study is that in DA systems with stationary performance, the gain of Information from the most recent observations, and the loss of Information due to chaotic error growth, in an expected sense, must be equal.

Exploiting this equilibrium relationship, error variance or Information in a state estimate can be expressed as a function of the three driving parameters. Analysis Information linearly and exponentially depend on  and alpha, respectively, while the optimal weight  is found to be a simple function of the rate of error growth. An evaluation of four operational DA systems reveals that their quality is driven by the amount of observational Information they each extract from the a virtually common set of globally available observations. The ECMWF analysis performs best, with two and a half times lower error variance than in any other system. A simple global adjustment of the relative weight between observations and the first guess may yield an 11-43% reduction in error variance.

How to cite: Feng, J.: An Equilibrium Between the Observational Gain and Chaotic Loss of Information, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20424, https://doi.org/10.5194/egusphere-egu25-20424, 2025.

Accurate characterization of meteorological conditions across urban regions is crucial for developing equitable energy and climate solutions. However, this task faces significant challenges: observational data often lacks spatial and temporal continuity, while numerical weather prediction models can struggle to localize severe weather events due to substantial latency between observation collection and forecast production. To address these challenges we introduce a multi-modal foundation model that rapidly integrates heterogeneous in situ and satellite data to produce a gap-filled global state. Our results show that the atmospheric structures predicted by our model are consistent with observed phenomena such as liquid and frozen precipitation and convection. Further, we apply our model to produce reanalysis and forecast datasets of solar irradiance for renewable energy applications. We also discuss ongoing work to connect global and local systems through a regional high-resolution foundation model, which is driven by multi-modal observations and the dynamics captured by the global model. This research aims to build predictive understanding of environmental systems and their interactions with built environments by improving our ability to forecast phenomena such as cold and warm fronts, convective weather, and their impacts on health, safety, and energy supply and demand.

How to cite: Vandal, T., Duffy, K., and Nachmany, Y.: A Multi-Modal Observation-Driven Foundation Model for Global Data Assimilation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21364, https://doi.org/10.5194/egusphere-egu25-21364, 2025.

Forecasting monsoon precipitation over Arizona is challenging due to its complex terrain since the model grid structure may misrepresent topographic details and the sparse observation network is insufficient for initialization of the model at the scale of the topography, particularly the spatial distribution of moisture. Our study aims to evaluate the monsoon precipitation forecast skill over Arizona by conducting an Observing System Experiment (OSE), or “data denial study” using the Data Assimilation Research Testbed (DART) to assimilate Global Positioning System precipitable water vapor (GPS-PWV) into the advanced version of the Weather Research and Forecast (WRF) model. The High-Resolution Rapid Refresh (HRRR) model is used as the initial and boundary conditions. The hourly GPS-PWV data were collected from 30 sites across Arizona characterized by a very nonuniform distribution with clusters of observations separated by large spatial gaps.

The precipitation event of interest occurred on 16 August 2021 with convective initiation developing over the Mogollon Rim in the afternoon and precipitation occurring over Flagstaff, Sedona, and Prescott as the increasingly well-organized mesoscale convective system propagated southwestward to the Arizona-California border. The amount of total precipitation recorded by NOAA’s MRMS (Multi Radar – Multi Sensor) system was 25 - 60 mm within the 12- hour period of 00 UTC 16 August to 12 UTC 16 August. In this study we initiated the forecast at 06 UTC 15 August with 40 ensemble members and assimilated the hourly GPS-PWV data over the 6h period from 1200-1800 UTC, after which we ran a deterministic forecast using the mean ensemble data assimilation analysis at 18 UTC as the initial condition for this “free forecast”.

We discovered that this forecast and assimilation system was sensitive to the specification of the initial state of the atmosphere, the radius of influence in the Ensemble Kalman Filter data assimilation system, and the model physics. Therefore, we tested the simulation using a variety of horizontal and vertical localizations and microphysics schemes to find a configuration resulting in the least-bias PWV. We used this optimal configuration to forecast 24 other precipitation events occurring in the 2021 monsoon season.

Our results show: 1) GPS-PWV data assimilation reduced forecast PWV errors across the model domain. 2) GPS-PWV data assimilation increased instability (due to moistening) of the pre-convective atmosphere over the Mogollon Rim and southeastern Arizona by as much as 1000 J/kg. 3) GPS-PWV data assimilation maintained these more favorable atmospheric conditions for convection and improved precipitation forecasts for at least 6 hours into the free forecast period but became too moist afterward. 4) The results revealed a surprising dry bias of 3-4 mm PWV in the HRRR model (used for initial conditions) compared to the actual GPS-PWV values, and this bias was maintained in the WRF model control run without GPS-PWV data assimilation for at least 18h. 

How to cite: Risanto, C. B., Arellano, Jr., A. F., Koch, S., Castro, C. L., Shohan, S., and Adams, D. K.: Impacts of Assimilating GPS-PWV in Convective-permitting Model on Forecasting Monsoon Precipitation over Arizona Complex Terrain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-100, https://doi.org/10.5194/egusphere-egu25-100, 2025.

Forecasting convective storms is one of the most challenging tasks in Numerical Weather Prediction (NWP). Data Assimilation (DA) methods improve the initial condition and subsequent forecasts by combining observations and previous model forecasts (background). Weather radar provides a dense source of observations in storm monitoring. Therefore, assimilating radar data should significantly improve storm forecasting skills. However, extrapolating rainfall patterns (nowcasting) from radar data is often better than numerical model-based forecasting with DA in the first 2 or 3 hours (Fabry and Meunier, 2020). This is related to the fact that the radar data only provides information on the precipitation pattern and intensity in the area affected by the storm. Furthermore, it does not directly provide information on other variables that are strongly linked with the storm, such as temperature, wind, and humidity, either within the precipitation region or in the areas far from the storm. One potential solution to this problem is to use machine learning (ML) techniques to construct the DA observations operator to generate a model-equivalent of the radar data. In this approach, NWP model fields (temperature, wind components, relative humidity, precipitation) would serve as input, and radar observations would be the output of an encoder-decoder neural network. The constructed observation operator describes a non-linear relationship between the NWP model storm-related variables and radar observations, spreading radar information to other variables and potentially enhancing storm forecasting skills.

How to cite: Stefanelli, M., Zaplotnik, Z., and Skok, G.: A neural network-based observation operator for weather radar data assimilation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-420, https://doi.org/10.5194/egusphere-egu25-420, 2025.

Abstract

To enhance the accuracy of rainfall estimation using remote sensing data, such as radar and satellite, it is crucial to improve the accuracy of the estimation relationships. Rainfall estimation is influenced by various factors, including rainfall type, geographical characteristics (e.g., inland and oceanic rainfall), and orographic rainfall features. Developing estimation formulas that account for variations in rainfall characteristics based on topography (elevation) and seasonal temperature changes is essential. Ensuring the reliability of observation data used in deriving these formulas is a top priority for achieving accurate rainfall estimation. This study evaluates the effectiveness of utilizing rain gauge data under varying wet-bulb temperature conditions to improve the reliability of rainfall analysis. The analysis employed disdrometer data collected over five years (2020–2024), applying channel-based particle diameter information and number concentration-based variable calculation methods to enhance the generalizability of the findings. Quantitative comparisons of rain gauge observation accuracy under different wet-bulb temperature conditions were conducted, alongside an analysis of the temperature ranges in which two types of rain gauges (tipping-bucket and weighing gauges) could be effectively utilized. Furthermore, we assessed the quality management of rain gauge data preprocessing for raindrops across various temperature conditions. The results indicate that when the wet-bulb temperature exceeded 2°C, the difference (RMSE) in rainfall between disdrometer and rain gauge observation data was less than 0.2 mm. However, this difference increased significantly to over 0.4 mm when the wet-bulb temperature was below 2°C, with particularly large differences exceeding 1.0 mm when disdrometer data were not preprocessed. These discrepancies reflect variations in hydrometeor characteristics and particle fall velocities due to temperature changes. This study underscores the necessity of establishing meteorological conditions for rainfall analysis.

Keywords: Disdrometer, Wet-bulb temperature, Long-term observation, Hydrometeor

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (RS-2022-NR071182).

How to cite: Kim, H.-J., Suh, S.-H., Byun, J., and Jun, C.: Validation of Rainfall Data Analysis Using Disdrometer Data Under Wet-Bulb Temperature Conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2686, https://doi.org/10.5194/egusphere-egu25-2686, 2025.

In the wake of the continuous expansion and refinement of the ground automatic meteorological observation network in China, the development of an effective quality control system for ground automatic station observation data has become an urgent task of great significance in the field of meteorology. Although extensive research has been conducted on quality control techniques for traditional ground observation meteorological variables such as precipitation, temperature, and pressure both domestically and abroad, the exploration of quality control strategies for precipitation phase observation data remains relatively limited.This research endeavor undertakes the utilization of upper-air and manual ground observation datasets covering the period from 2000 to 2014. Through a comprehensive analysis and selection process, meteorological factors that exert a pronounced influence on precipitation phase are identified and optimized. Subsequently, the random forest algorithm is applied to establish a quality control model for the automatic observation data of three primary precipitation phases: rain, snow, and sleet. Employing this meticulously constructed quality control model, an in-depth quality assessment is carried out on the ground automatic precipitation phase observation data collected during the period from 2015 to 2023, after the discontinuation of manual observations. A total of 15,806 station-records are flagged as suspicious or incorrect. It is observed that the stations with such data anomalies are preponderantly located in regions with sparse human habitation and challenging maintenance conditions, such as the Qinghai-Tibet Plateau, the Tianshan Mountains, and the mountainous areas in northern Heilongjiang. In contrast, regions like Guangdong, Guangxi, Yunnan, Fujian, and Hainan exhibit relatively high data quality, with the eastern regions generally outperforming the western ones (Figure 1).For the identified suspicious data, a rigorous manual verification procedure is implemented. For example, at 14:00 on January 31, 2019, the quality control results for Wuqia, Akto, and Kashgar stations in Xinjiang indicated snowfall, yet the automatic observations registered precipitation. With the ground temperatures of these stations being -10°C, -6°C, and -6°C respectively, it is meteorologically implausible for rain to occur in Xinjiang during winter. Hence, the automatic precipitation observations at these stations are deemed incorrect. After conducting a substantial amount of manual verification on other suspicious and incorrect data, it is determined that the identification accuracy rate of this quality control method surpasses 98.5%. Presently, this research outcome has been successfully incorporated into the operational quality control framework for ground automatic precipitation phase observation.

Figure 1 Frequency Diagram of Stations with Suspected or Incorrect Precipitation Phase Quality Control from 2015 to 2023

How to cite: Ou, X., Lin, H., and Huang, X.: Research on Quality Control Methodology of Automatic Precipitation Phase Observation Data Based on Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3384, https://doi.org/10.5194/egusphere-egu25-3384, 2025.

Variational data assimilation theoretically assumes Gaussian-distributed observational errors, yet actual data often deviates from this assumption. Traditional quality control methods have limitations when dealing with nonlinear and non-Gaussian-distributed data. To address this issue, our study innovatively applies two advanced machine learning (ML) based quality control (QC) methods, Minimum Covariance Determinant (MCD), and Isolation Forest to process precipitable water (PW) data derived from satellite FengYun-2E (FY2E). We assimilated the ML QC-processed TPW data using the Gridpoint Statistical Interpolation (GSI) system and evaluated its impact on heavy precipitation forecasts with the Weather Research and Forecasting (WRF) v4.2 model. Both methods notably enhanced data quality, leading to more Gaussian-like distributions and marked improvements in the model’s simulation of precipitation intensity, spatial distribution, and large-scale circulation structures. During key precipitation phases, the Fraction Skill Score (FSS) for moderate to heavy rainfall generally increased to above 0.4. Quantitative analysis showed that both methods substantially reduced Root Mean Square Error (RMSE) and bias in precipitation forecasting, with the MCD method achieving RMSE reductions of up to 58% in early forecast hours. Notably, the MCD method improved forecasts of heavy and extremely heavy rainfall, whereas the Isolation Forest method demonstrated superior performance in predicting moderate to heavy rainfall intensities. This research not only provides a basis for method selection in forecasting various precipitation intensities, but also offers an innovative solution for enhancing the accuracy of extreme weather event predictions.

How to cite: Shen, W., Chen, S., Xu, J., Zhang, Y., Liang, X., and Zhang, Y.: Enhancing Extreme Precipitation Forecasts through Machine Learning Quality Control of Precipitable Water Data from Satellite FengYun-2E: A Comparative Study of Minimum Covariance Determinant and Isolation Forest Methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3931, https://doi.org/10.5194/egusphere-egu25-3931, 2025.

The accuracy of numerical weather prediction models is highly dependent on the precision of the initial conditions, especially for forecasting storms and convective-scale weather events. Radars, with their ability to capture the internal structure and important microphysical and dynamical processes within convective systems, play a crucial role in improving weather forecasts at convective scales. Unlike conventional single-polarization radar, dual-polarization radar additionally provides information on the types and sizes of hydrometeor particles. As a result, polarimetric radar data (PRD) is a valuable data source for data assimilation (DA). Despite its potential, PRD is not yet directly assimilated into operational convection-permitting numerical models. This limitation arises from several challenges, including the highly non-linear nature of observation operators for polarimetric variables and the difficulty of estimating model error at convective scales, which require further research.

Our study primarily aims to directly assimilate PRD within an idealized setup. To accomplish this, Observation System Simulation Experiments (OSSEs) were conducted to simulate the evolution of a long-lived supercell using the ICOsahedral Nonhydrostatic (ICON) model with a two-moment microphysics scheme. For the assimilation of PRD data, the Kilometer-scale Ensemble Data Assimilation (KENDA) system was utilized, which incorporates the Local Ensemble Transform Kalman Filter (LETKF), along with the polarimetric radar forward operator EMVORADO-POL developed at the Deutscher Wetterdienst (DWD). In the current idealized setup, two types of DA experiments were conducted: a reference experiment that assimilated only non-polarimetric variables, such as reflectivity and radial velocity, and an experiment that assimilated differential reflectivity (ZDR) in addition to the non-polarimetric variables. The results from both experiments were compared, and appropriate thresholds and equivalents of noreflectivity data for polarimetric data were examined. Additionally, the sensitivity to DA settings, such as localization radius and the number of ensemble members, was also tested.

How to cite: Bardachova, T., Ramezani Ziarani, M., and Janjic, T.: Direct assimilation of  dual-polarization radar data in the idealized setup, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4163, https://doi.org/10.5194/egusphere-egu25-4163, 2025.

Forecasting precipitation in tropical regions is challenging because of substantial errors in both the models and the initial conditions. The interaction between tropical waves and convection suggests possible predictability. Therefore, an accurate representation of these waves in the models and initial conditions is important for increasing the accuracy of precipitation forecasts. This study intends to improve the predictive reliability of the ICON (Icosahedral Nonhydrostatic) global model for tropical weather events, such as tropical waves and the Madden-Julian Oscillation (MJO). We initially analyze the ability of data assimilation (DA) to conserve total energy, enstrophy, moist static energy, and other physical properties. Then, we implement an advanced DA technique, the Quadratic Programming Ensemble (QPEns), with a moist static energy constraint. Preliminary findings show that the moist static energy constraint, together with accurate wind and humidity data, decreases forecast errors and improves tropical wave representation. This induces more reliable long-term precipitation forecasts.

How to cite: Ramezani Ziarani, M., Ruckstuhl, Y., and Janjic, T.: Enhancing Tropical Weather Forecasts with Constrained Data Assimilation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4313, https://doi.org/10.5194/egusphere-egu25-4313, 2025.

This research investigates the dynamics of the Sea Breeze Front (SBF) in the southwestern Caspian Sea, specifically focusing on Bandar Anzali, Iran. Utilizing two years of observational data alongside Weather Research and Forecasting (WRF) model simulations, the study examines the meteorological characteristics and behaviors associated with SBF events. SBF days were identified by analyzing land-sea temperature contrasts, supported by wind shifts, temperature decreases, increases in humidity, and cloud formation.

In-depth analysis reveals consistent atmospheric patterns during SBF events, such as temperature variations and notable wind shifts. The intensity of the land-sea thermal contrast is influenced by both local topography and atmospheric stability. A detailed case study of March 4, 2022, highlighted key meteorological changes, including temperature drops and wind direction shifts. While the WRF model accurately captured temperature and pressure variations, it slightly underestimated humidity and dew point.

Machine learning techniques, particularly K-means clustering, were employed to classify distinct atmospheric regimes linked to SBF occurrences. The clustering analysis identified two primary atmospheric patterns: cold, humid air masses favorable to SBF development, emphasizing the significant role of land-sea temperature gradients and local wind dynamics.

This study highlights the value of combining observational data, numerical simulations, and machine learning techniques to better understand coastal mesoscale processes. The findings provide fresh insights into SBF behavior in the Caspian region, with implications for enhancing coastal weather forecasting and management. Future work should focus on improving the accuracy of WRF model simulations and further examining the impact of regional topography on SBF dynamics.

Keywords: Sea Breeze Front, Machine Learning, WRF, K-means Clustering, Temperature Gradient, Caspian Sea,.

How to cite: Sepehri, J.: Exploring the Dynamics of Sea Breeze Fronts in the Southwestern Caspian Sea: Analysis Using Observational Data, WRF Simulations, and Machine Learning Approaches, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7413, https://doi.org/10.5194/egusphere-egu25-7413, 2025.

New constellations to provide high-resolution atmospheric observations from microwave sounders operating in low-earth orbit are now coming online and are demonstrating the potential to provide operationally useful data. The first of these missions, the NASA TROPICS (Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats) Earth Venture (EVI-3) mission, was successfully launched into orbit on May 8 and May 25, 2023 (two CubeSats in each of the two launches).  TROPICS is now providing nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. TROPICS is providing rapid-refresh microwave measurements (median refresh rate of better than 60 minutes early in the mission with four functional CubeSats, and now approximately 70-90 minutes with three functional CubeSats) over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm lifecycle. Hundreds of high-resolution images of tropical cyclones have been captured thus far by the TROPICS mission, revealing detailed structure of the eyewall and surrounding rain bands.  The new 205-GHz channel in particular (together with a traditional channel near 92 GHz) is providing new information on the inner storm structure, and, coupled with the relatively frequent revisit and low downlink latency, is already informing tropical cyclone analysis at operational centers.  A neural network algorithm to retrieve the atmospheric temperature and moisture vertical profiles has recently been developed and validated, with retrieval uncertainties approaching those of state-of-the-art microwave sounders, but with much better revisit rate. In this presentation, we highlight the use of these high-revisit thermodynamic data from TROPICS to better characterize storm structure and environmental conditions over a variety of cases over the nearly two-year mission lifetime to date.

How to cite: Blackwell, W. and the TROPICS Science Team: Investigations of Tropical Cyclone Thermodynamic Structure using NASA TROPICS Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7546, https://doi.org/10.5194/egusphere-egu25-7546, 2025.

 The current study utilizes the operational data assimilation system of Deutscher Wetterdienst (DWD) to investigate the impacts of improved radar forward operator. First of all, it is shown that for experiments in which both conventional and radar data are assimilated and the latent heat nudging (LHN) is applied, the one with improved operator (i.e., with improved Mie-scattering scheme and accounting for beam broadening effect and etc.) exhibits neutral impacts on short-term forecasts. However, in subsequent experiments, in which conventional data are not assimilated and the LHN is switched off, the one with improved operator not just reduces representation error during data assimilation cycles but also enhances the short-term forecast skills. In addition, it is found that the subsequent experiments result in much shorter observation error correlation length scales for radar reflectivity data, indicating that the application of the LHN or assimilation of conventional data may increase the length scales. 

How to cite: Zeng, Y., de Lozar, A., Feng, Y., Blahak, U., Khosravianghadikolaei, K., and Janjic, T.: Study on representation error of radar data in convective scale data assimilation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8227, https://doi.org/10.5194/egusphere-egu25-8227, 2025.

Hydrometeor formation and cloud processes occur at very small spatial scales and cannot be explicitly resolved on the numerical grids of weather prediction models. Parameterizations of these processes are a necessary component of forecast models and are known to be a major source of forecast error. Two main challenges arise. First, the mismatch between the effective resolution of prediction models and the resolution of satellite observations leads to representativeness errors. Second, sub-optimal parameterizations induce systematic errors in hydrometeor fields and cloud properties. In the presence of such model biases, the assimilation of cloud-scale observations can be detrimental to the analysis. It remains an open question how to properly account for the scale mismatch and for systematic errors when assimilating small-scale observations into a larger-scale numerical model.

In this work, we compare different approaches for assimilating radiance observations that contain unresolved scales, such as data thinning, use of superobservations, and a multi-scale decomposition of the two-dimensional cloud field. We study the problem using observing system simulation experiments (OSSEs) performed with the WRF model and the DART EAKF assimilation system. The nature run is a high-resolution large eddy simulation (dx=250 m) of deep moist convection developing in moderate wind shear, which supports organization into multicell storms. Synthetic satellite imagery is computed from the nature run using operators available through RTTOV. Several 40-member km-scale ensemble experiments (dx=2 km) evaluate the impact of assimilating thinned, averaged, or multi-scale radiance observations.

How to cite: Kugler, L., Serafin, S., and Weissmann, M.: Dealing with unresolved scales of motion and systematic errors in the assimilation of cloud-affected satellite radiances, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10275, https://doi.org/10.5194/egusphere-egu25-10275, 2025.

Accurate representation of atmospheric dynamics at convection scale still represents a major challenge for numerical models and a critical aspect in operational weather predictions. Reliable forecast initial conditions, generated by the data assimilation cycle using new observations coming from different platforms, are crucial to improve the forecast accuracy in deep convection environments. In this work, the ICOsahedral Non-hydrostatic (ICON) model is run at convection-permitting scale over the Italian domain, in combination with the Kilometre-scale Ensemble Data Assimilation (KENDA) system, to test the model performance on a poorly-predicted extreme convective storm in the Marche region, Italy, on 15 September 2022. We show here the positive impact of data assimilation at convection scale on the forecast of this event, which allows to improve the localization and the intensity of the storm although substantial underestimation of precipitation still persists. The relative impact of different observations datasets is evaluated, starting from conventional and radar data operationally assimilated for numerical weather predictions over Italy. After pointing out the importance of low-level moisture convergence in the process of convection initiation and the significant undersampling of humidity field in conventional data, the added value of humidity-sensitive microwave radiances from polar satellites is analyzed. Observations sensitive to mid-lower tropospheric humidity in clear-sky conditions are employed, taken from the Microwave Humidity Sounder instrument, still little investigated in limited-area models at many numerical weather prediction centers. In order to better exploit the information content of microwave satellite observations, the preliminary development towards all-sky assimilation is presented.

How to cite: Grenzi, M., Gastaldo, T., Poli, V., Marsigli, C., Janjic, T., Cacciamani, C., and Carrassi, A.: Assimilating multi-platform observations to improve severe convection forecasting in the ICON model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10874, https://doi.org/10.5194/egusphere-egu25-10874, 2025.

Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation have been proposed which allow for using new input data without retraining the model. They could also dramatically accelerate the costly data assimilation process used in operational regional weather models. Here, in a central US testbed, we demonstrate the viability of score-based data assimilation in the context of realistically complex km-scale weather. We train an unconditional diffusion model to generate snapshots of a state-of-the-art km-scale analysis product, the High Resolution Rapid Refresh. Then, using score-based data assimilation to incorporate sparse weather station data, the model produces maps of precipitation and surface winds. The generated fields display physically plausible structures, such as gust fronts, and sensitivity tests confirm learnt physics through multivariate relationships. Preliminary skill analysis shows the approach already outperforms a naive baseline of the High-Resolution Rapid Refresh system itself. By incorporating observations from 40 weather stations, 10% lower RMSEs on left-out stations are attained. Despite some lingering imperfections such as insufficiently disperse ensemble DA estimates, we find the results overall an encouraging proof of concept, and the first at km-scale. It is a ripe time to explore extensions that combine increasingly ambitious regional state generators with an increasing set of in situ, ground-based, and satellite remote sensing data streams.

How to cite: Manshausen, P., Cohen, Y., Pathak, J., Pritchard, M., Garg, P., Mardani, M., Kashinath, K., Byrne, S., and Brenowitz, N.: Diffusion Model Data Assimilation of Sparse Weather Station Observations at Kilometer Scales, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10945, https://doi.org/10.5194/egusphere-egu25-10945, 2025.

Surface observations can provide crucial information for NWP models. If not assimilated carefully, however, they can degrade forecast accuracy, especially in complex terrains like the Alps. The horizontal and vertical covariances of climatological background error covariances used in the three-dimensional variational (3DVar) data assimilation (DA) method can produce unrealistic increments over sloped terrain. For instance, an observation from a valley station can still generate increments at the mountaintop, even though the valley observation may not accurately represent the mountaintop's weather conditions. 
We used a hybrid three-dimensional ensemble variational (Hybrid-3DEnVar) DA method to address this issue, incorporating a 50-member convection-permitting ensemble. This method was recently tested in Geosphere Austria's convective scale limited-area NWP model AROME at a 2.5 km horizontal resolution. We assimilated 2-meter temperature, 2-meter relative humidity, geopotential, and 10-meter wind components from 680 surface stations, including the Austrian TAWES network and SYNOP observations from neighbouring countries. 400 stations were actively assimilated from the observation dataset, and the rest were used to verify the analysis.  
Our results present the effectiveness of this newly tested Hybrid-3DEnVar against GeoSphere Austria's operational 3DVar in assimilating surface observations over complex Alpine terrain.

How to cite: Jyoti, K., Griewank, P., Meier, F., and Weissmann, M.: Comparison of Hybrid-3DEnVar against 3DVar for the assimilation of surface observations over the Alpine terrain in AROME-Austria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11180, https://doi.org/10.5194/egusphere-egu25-11180, 2025.

In an effort to enhance storm-scale data assimilation and prediction, we have recently updated the atmospheric Model for Prediction Across Scales (MPAS-A; Skamarock et al. 2012), coupled to the Ensemble Kalman Filter (EnKF) Data Assimilation Research Testbed (DART) system (Ha et al., 2017), for regional analysis using variable-resolution capabilities. In this talk, we will introduce unique features of the interface, leveraging the model's native coordinate both horizontally (e.g., unstructured meshes) and vertically (e.g., terrain-following height), and demonstrate its suitability for storm-scale DA. As its robustness was demonstrated in the U. S. National Severe Storms Laboratory (NSSL)'s Warn-On-Forecast framework during tornado watches in 2024, the performance of regional ensemble analysis incorporating storm-scale data assimilation using radars and cloud water path from the GOES-R satellite will be presented.

How to cite: Ha, S. and Park, J.: Convective-scale ensemble data assimilation using unstructured meshes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12280, https://doi.org/10.5194/egusphere-egu25-12280, 2025.

Assimilating visible satellite observations has become an increasingly active research topic and has been shown to provide valuable information for improving weather forecasts. The assimilation of these observations, however, is challenging due to operator deficiencies and model deficiencies in the representation of clouds. Our work focuses on evaluating the potential of the RTTOV observation operator to simulate visible satellite images in the convection-permitting AROME-Austria model, which is operational at Geosphere Austria. Specifically, we examine the systematic deviations caused by operator and model errors in all-sky conditions. In cloudy conditions, we build on findings from preceding studies and conduct sensitivity tests to evaluate model equivalents with modified operator settings. In clear-sky conditions, we aim to evaluate and mitigate the systematic deviations caused by orographic shadowing and high-albedo surfaces with the help of an advanced visible operator developed by DWD. A summer month of 3-hourly forecasts from 6 UTC to 18 UTC provides the basis for this analysis. Our findings aim to address operator deficiencies and model inconsistencies, laying the groundwork for integrating visible observations as a new observation type into the AROME-Austria model.

How to cite: Chkeir, S., Griewank, P., Scheck, L., Meier, F., Wittmann, C., and Weissmann, M.: Evaluation of visible satellite images from AROME-Austria as preparation for assimilating visible observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12711, https://doi.org/10.5194/egusphere-egu25-12711, 2025.

Extreme weather events associated with deep moist convection pose significant social risks, requiring advanced technologies for anticipatory measures. Numerical forecasting, particularly at convection-resolving scales, relies heavily on high-quality initial conditions obtained through the assimilation of complex remote-sensing-based observations. Integrating these observations into assimilation systems presents challenges due to the nonlinear relationships between observed quantities and model variables. This research explores an iterative implementation of the Local Ensemble Transform Kalman Filter based on the tempering of the observation likelihood (tempered LETKF), which can partially handle these non-linearities.

In this work, we use an N-variable Lorenz model for its simplicity and low computational cost to evaluate the performance of the method against the traditional implementation of the LETKF. We conducted comparisons under various levels of uncertainty concerning both the model and the observations. Additionally, we tested the behavior of the system for different ensemble sizes and for varying degrees of tempering. The initial findings show notable enhancements in the estimation of initial conditions and the stability of the data assimilation cycle, indicating potential benefits in more realistic model applications. The encouraging results motivate further research on tempering methods in mesoscale modeling systems, especially for predicting severe weather events linked to deep moist convection.

How to cite: Gacitua Gutierrez, J., Ruiz, J. J., Pulido, M., Dillon, M. E., García Skabar, Y., Maldonado, P., Otsuka, S., Amemiya, A., Miyoshi, T., and Pajarola, R.: Tempered local ensemble transform kalmann filter: simple model experiments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12910, https://doi.org/10.5194/egusphere-egu25-12910, 2025.

This study investigates sensitivity of convection to coincident atmospheric environment at meso-gamma spatial scales. The data used for the study comprise a large ensemble of three-dimensional high-resolution local domains that were extracted from cloud-resolving model simulations of different cases of subtropical and tropical convection over land and ocean.  The simulations were produces as part of the NASA (National Aeronautics and Space Administration) INCUS (Investigation of Convective Updrafts) mission (van den Heever, 2021). The simulations include a diverse set of convective morphologies associated with different synoptic environments (Marinesku et al., 2024 ).  Each neighborhood domain of dimensions 25.6 x 25.6 x 18 km, respectively in latitudinal, longitudinal and vertical direction, is centered on a deep convection core vertical profile that was selected for tracking convective cloud evolutions for the purpose of investigations within the INCUS mission  (Sokolowsky et al. 2024 ). The ensemble of neighborhoods used in this study therefore represent a distribution of local convective states and the associated environments embedded in a wide range of larger scales environments. 

To capture relationships between convection and environment states over a range of spatial scales contained within the neighborhoods in a concise manner, the analysis was performed in a phase space of two-dimensional horizontal scales spectral powers that are associated with leading vertical principal components of the physical variables representing the convection and environment. The convection state  was represented by vertical velocity and total condensate, and the environment by temperature, humidity, divergence and vorticity. 

The main finding is that the variability of vertical velocity and total condensate states at the convective scales (< 10 km)  exhibits high insensitivity to the variability of the neighborhood domain average environment states.   In contrast, notable co-variance was found between the vertical velocity and environment states at the convective scales, especially with the temperature mid-to-upper tropospheric warming variability and the variability of divergence in mid-troposphere and above 10 km.  For the total condensate, significant co-variance was exhibited also between its neighborhood domain average and the the convection scale environment.  This relationship reflects impact of the convective processed on the environment states including coupling between the convection dynamics and microphysics.

In the context of convective scale data assimilation the findings suggest that for representation of the convection state variability at the convective scales would be weekly constrained  in the absence of convective scale observations of the environment states. 

References

Marinescu, P.J., van den Heever, S.C., Grant, L.D., Bukowski, J. and Singh, I., 2024. How Much Convective Environment Subgrid Spatial Variability Is Missing Within Atmospheric Reanalysis Data Sets?. Geophysical Research Letters, 51(24), p.e2024GL111856.

Sokolowsky, G.A., Freeman, S.W., Jones, W.K., Kukulies, J., Senf, F., Marinescu, P.J., Heikenfeld, M., Brunner, K.N., Bruning, E.C., Collis, S.M. and Jackson, R.C., 2024. tobac v1. 5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena. Geoscientific Model Development, 17(13), pp.5309-5330.

van den Heever, S. C. (2021). NASA selects new mission to study storms, impacts on climate models. NASA Earth (https://www.nasa.gov/press‐release/nasa‐selects‐new‐mission‐to‐study‐storms‐impacts‐on‐climate‐models)

How to cite: Vukicevic, T., Prasanth, S., Haddad, Z., Posselt, D., and Hristova-Veleva, S.: Sensitivity of convection to environment within local neighborhoods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16093, https://doi.org/10.5194/egusphere-egu25-16093, 2025.

Localization is essential for any ensemble-based data-assimilation system for numerical weather prediction, and most localization approaches are distance-based. For example, in the observation-space localization used by the Deutscher Wetterdienst (DWD), the localization is a function of the distance between a model grid point and an observation location. Observation-space localization for satellite observations is especially challenging because they do not have a constant or well-defined observation location. Instead, the observed signal may originate from various vertical levels and is affected by the presence of clouds. We derive an optimal localization for all-sky visible and infrared satellite observations over Germany by minimizing the difference between the DWD operational analysis and radiosonde profiles in a 1-month cycled assimilation experiment that excluded radiosondes. We use reconstructed partial analysis increments (PAI) to approximate a wide range of localization settings without needing to rerun the costly month-long experiment. We find that visible satellite observations require no localization, but that infrared observations deteriorate the analysis if they are not localized carefully.

How to cite: Griewank, P., Parker, M., Necker, T., Miyoshi, T., Schomburg, A., Diefenbach, T., and Weissmann, M.: Optimal vertical localization for the assimilation of cloud-affected satellite observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16990, https://doi.org/10.5194/egusphere-egu25-16990, 2025.

The Global-to-Regional ICON (GLORI) Digital Twin is a configurable on-demand global-to-regional short-range high-resolution digital twin based on ICON. It is developed in a tri-lateral cooperation between Germany, Italy and Switzerland.

GLORI provides short-range global predictions down to the storm-scale (~3 km horizontal) and on-demand high resolution (~ 500 m) predictions for selected regions, like the Alpine domain or the Italian peninsula. It includes an uncertainty estimation through ensemble forecasts for global and regional scales. The data assimilation system is ensemble-based, both for the global and the regional components. The GLORI Digital Twin aims at providing forecasts down to the application level, for a range of use cases including flood forecasting, urban heat island and urban flooding events, mineral dust predictions for energy applications and pollen predictions.

Moving to higher resolution requires improvements both in the model and in the data assimilation system. We test the ICON model in complex topography and highlight its behaviour in dependence of conditions like stable boundary layer, flow interacting with the orography, convection development, different soil textures and urban areas. In that, GLORI can also be seen as testing environment for the development of hectometric scale modeling. The research focuses also on data assimilation at higher resolution, both for the global and for the regional runs, and on the usage of high-resolution observations. The assimilation of the radar data of the three partner countries is tested over the Alpine domain, aiming at the improvement of the prediction of convective events. Dedicated studies focus on direct assimilation at 1 km resolution. This goal demands a rigorous evaluation of the entire assimilation workflow, including observation thinning, averaging strategies, localization, and observation error quantification. The impact of performing data assimilation at 2 km resolution with nesting at 1 km is then compared with direct assimilation in the 1km domain. The performance of the Digital Twin is assessed on high-impact weather events, considering in particular convective development leading to severe weather and the recent flood events.

How to cite: Marsigli, C. and the GLORI Team: The GLObal-to-Regional ICON (GLORI) Digital Twin: towards hectometric scale predictions for high-impact weather, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17981, https://doi.org/10.5194/egusphere-egu25-17981, 2025.

Satellite data assimilation is progressing beyond the conventional “clear-sky” approach towards the “all-sky” approach. While the former eliminates observations affected by clouds, the latter assimilates all observations including clear-sky, cloudy and precipitation conditions. The exploitation of cloud affected radiances is a promising endeavour as these observations are directly related to particularly challenging weather phenomena (e. g. convection, frontal systems, low stratus, and fog). This study focuses on the assimilation of clear and cloud affected (all-sky) radiances, from the 6.2 μm and 7.3 μm channel sensitive to water vapour in the upper troposphere using satellite data from SEVIRI, an instrument onboard Meteosat-10. The goal is to describe the improvements in short range forecasts in the high-resolution limited area Numerical Weather Prediction Model (NWP), AROME (Application of Research to Operations at MEsoscale) used at GeoSphere Austria. 3D-Var data assimilation experiments were performed to study the impact of all-sky vs clear-sky. A significant challenge is accurately representing observation errors, which are influenced by the complex and variable nature of clouds. This work implements an observation error model that dynamically adjusts error values based on cloud amount. The model addresses the increased uncertainties in cloud-dense regions by assigning higher observation errors, while clearer areas receive lower error values, in alignment with the need for spatially adaptive error characterization in all-sky conditions. Results demonstrate that the cloud-dependent error model leads to more Gaussian departures which can be expected to improve the assimilation of cloud-affected radiances, leading to better initial conditions and refined representations of atmospheric states and consequently the forecast.   

How to cite: Neduncheran, A., Meier, F., Wittmann, C., Weissmann, M., and Griewank, P.: Optimizing all-sky infrared radiance assimilation with dynamic cloud-dependent error modeling , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18449, https://doi.org/10.5194/egusphere-egu25-18449, 2025.

Flash droughts are a unique natural hazard characterized by their sudden onset and rapid intensification. Accurate and reliable forecasts on subseasonal-to-seasonal (S2S) timescales are crucial for effective preparation and mitigation of the impacts of these events. To enhance the accuracy of soil moisture predictions—a key factor in identifying flash droughts—we propose a hybrid modeling approach that integrates state-of-the-art dynamical forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) with deep learning techniques (DL).

We use a set of DL models of different complexity for post-processing soil moisture forecasts to not only improve S2S forecasts by correcting systematic errors inherent in numerical weather prediction models, but also to enhance the spatial resolution of the forecasts.  This downscaling process is crucial as it addresses a common limitation in S2S forecasts, the coarse spatial resolution that can overlook some variations in soil moisture at a higher spatial scale. By using deterministic inputs, such as the mean and spread from the ensemble forecasting system, we further assess forecast uncertainty through dropout neural networks via Monte Carlo (MC) sampling. This technique allows us to generate probabilistic forecasts by applying MC dropout during the testing phase, thereby generating probabilistic forecasts. Our results show that the DL models outperform the S2S forecasts and lead to skillful S2S forecasts. This advanced modeling framework aims to deliver skillful soil moisture S2S forecasts, ultimately contributing to more effective strategies for managing and mitigating the effects of flash drought events.

How to cite: Otero Felipe, N., Özer, A., and Ma, J.: Deep Learning Postprocessing to Enhance Subseasonal Soil Moisture Forecasts Across Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-168, https://doi.org/10.5194/egusphere-egu25-168, 2025.

An accurate knowledge of precipitation data at high spatio-temporal resolution is crucial for hydrological forecasting, meteorological analysis, and climate studies. This is especially true in  mountainous areas, where traditional climate models struggle to accurately predict precipitation due to factors such as low spatial resolution and where rain gauges are sparse. High-elevation areas are particularly relevant as they act as reservoirs of water resources and are characterized by elevation-dependent climate change signals (Pepin et al., 2022). By leveraging the good performances of the satellite-based IMERG (Integrated Multi-satellitE Retrievals for GPM) rainfall product and the realism of the ERA5 atmospheric reanalysis, we aim to produce a multi-decadal daily rainfall product at the IMERG spatial resolution (roughly 8 km) over the Greater Alpine Region (GAR). To achieve this, we employ advanced machine learning techniques designed to capture the complex, non-linear relationships inherent in atmospheric processes.  

Twenty years of IMERG data (from 2001 to 2020) are used to train and test various types of machine learning algorithms to estimate daily precipitation maps starting from some ERA5 atmospheric fields including mid-tropospheric temperature and winds; vertically integrated ice, liquid water and water vapour contents; total precipitation, and other relevant variables. In addition to these atmospheric fields, a high-resolution elevation dataset (ETOPO) is used to represent the intricate terrain of the Alps. The Recursive Feature Elimination (RFE) technique is employed to select key input variables, introducing effective predictors and enhancing the understanding of the influence of physical atmospheric variables and their inter-relationships in mountainous regions. ERA5 total precipitation, vertically integrated ice and water vapour content appear to be the three most relevant input fields for an optimal estimate of IMERG precipitation. Among the algorithms tested (XGBoost, Random Forest, Convolutional Neural Networks, Deep Neural Networks), XGBoost (XGB) is found to be the most reliable and computationally efficient.

The results show a spatiotemporal RMSE improvement of approximately 15 percent, decreasing from 5.18 mm/day (between ERA5 and IMERG) to 4.37 mm/day (between XGB and IMERG). On a seasonal basis, the RMSE is higher in summer and fall, where higher mean precipitation intensities are observed. Also, in terms of changes with the terrain height, the RMSE follows quite tightly the mean precipitation elevation dependence. The XGB model is used to backward extend the IMERG dataset so that precipitation biases and trends can be computed over a multi-decadal time range. These findings demonstrate the potential of machine learning to improve the accuracy of ERA5 rainfall data, which can be exploited to advance our understanding of the emerging elevation-dependent climate change signal. 

How to cite: Goudarzi, I., Fazzini, D., Pasquero, C., Meroni, A. N., and Borgnino, M.: A machine learning-based backward extension of IMERG daily precipitation over the Greater Alpine Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-369, https://doi.org/10.5194/egusphere-egu25-369, 2025.

The increasing frequency and severity of extreme weather events, such as heavy rainfall and flooding, emphasize the urgent need for advanced early warning systems. Short-duration rainfall extremes, exacerbated by climate change, significantly increase flood risks, particularly in urban coastal cities like Mumbai. Mumbai's vulnerability arises from rapid urbanization, its coastal location, and variable topography, which contribute to significant spatial variability in rainfall. We have used Global Forecast System (GFS) data to identify key predictors for high-resolution, 3-hour rainfall forecasts for Mumbai. The GFS variables were selected using a correlation matrix. We have used past 3-hour observed rainfall data from Automatic Weather Stations (AWS) across 15 locations in Mumbai (2015–2023) along with selected GFS variables, which include Precipitable Water, Precipitation Rate, Relative Humidity, and Total Cloud Cover, to forecast rainfall for one timestep ahead. The dataset was divided into 80% for training and 20% for testing. We employed a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model to enhance forecast accuracy. The CNN captures spatial features, while the LSTM models temporal dependencies, effectively addressing the challenges of hyperlocal rainfall forecasting. Further, we incorporated a weighted Mean Squared Error (MSE) loss function to prioritize extreme rainfall events (≥95^th percentile). The results indicate that using CNN-LSTM models reduced the Root Mean Square Error (RMSE) by 9.41% -12.38% and increased the Correlation Coefficient (CC) by 70.4%-113% compared to GFS models. At the 95^th percentile, the Hit Rate (HR) improved by 233% -483.3%, while the False Alarm Rate (FAR) decreased by 7%-16.2%. Using weighted MSE also enhanced performance, increasing the HR by 255.5%-583.3% at the 95^th percentile and reducing the FAR by 7% -13.2%. Implementing weighted MSE as a loss function resulted in a reduction in RMSE by 9.94% -12.86% and an increase in CC by 85.2%-126%. This study highlights that the hybrid CNN-LSTM model, combined with a weighted MSE loss function, demonstrates superior capability in accurately forecasting 3-hourly extreme rainfall events in Mumbai, providing critical advancements for early warning systems and flood risk mitigation.

How to cite: Tripathy, P., Murtugudde, R., Karmakar, S., and Ghosh, S.: Enhancing Hyperlocal 3-Hourly Rainfall Forecasting for Mumbai Using a Hybrid CNN-LSTM Model., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1037, https://doi.org/10.5194/egusphere-egu25-1037, 2025.

We present Prithvi-Typhoon, an innovative adaptation of the Prithvi WxC weather foundation model for tropical cyclone intensity prediction. Through a novel three-stage progressive fine-tuning framework, we bridge the gap between general weather forecasting and specialized tropical cyclone prediction. The model integrates multi-source data from tropical cyclones (1987-2023), incorporating satellite observations, reanalysis products, and historical records. Our architecture features domain-specific feature extraction and multi-scale integration, enabling adaptive balance between local storm features and global atmospheric patterns.

Evaluation results demonstrate substantial improvements over existing methods. Notably, Prithvi-Typhoon shows enhanced skill in predicting rapid intensification events, outperforming both traditional numerical models and existing deep learning approaches. This work represents a advancement in applying foundation models to extreme weather prediction, offering a computationally efficient solution while maintaining physical consistency.

How to cite: Meng, F.: Prithvi-Typhoon: A Foundation Model Approach for Enhanced Tropical Cyclone Intensity Prediction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1738, https://doi.org/10.5194/egusphere-egu25-1738, 2025.

Accurate high-resolution spatio-temporal weather forecasting is vital for advancing our understanding of regional weather dynamics and improving meteorological applications. Traditional forecasting relies on numerical weather prediction (NWP) models, which are computationally demanding, particularly when implemented for large domains and high-resolution grids. Recently, Deep Learning (DL) has emerged as a powerful alternative, leveraging historical data to identify patterns and predict future atmospheric conditions. In this work, we develop a regional DL-based forecasting system tailored for the Arabian Peninsula (AP), a region with unique climatic conditions characterized by extreme temperatures and high wind energy potential. Therefore, it serves as an ideal case study for regional weather forecasting. The developed system forecasts hourly meteorological variables such as wind speed, wind direction, and temperature at a 5 km spatial resolution up to 48 hours ahead, with a focus on key vertical levels relevant to wind energy applications. Two forecasting approaches are explored: recursive forecasting, which iteratively advances fine-scale spatio-temporal states over time, and downscaling, which refines coarse-resolution forecasts of the meteorological variables into their high-resolution counterparts.  Additionally, we propose a combined approach that integrates these methods by combining fine-scale dynamics propagation with coarse-scale to fine-scale refinement. The frameworks were evaluated both qualitatively and quantitatively, demonstrating that while recursive forecasting accumulates errors over time, the downscaling approach effectively produces high-resolution forecasts. The combined approach significantly improves the forecasting precision, offering robust performance at early time steps and reduced error accumulation over extended forecasting horizons.

How to cite: Resifi, S., Al Aawar, E., Dasari, H., Jebari, H., and Hoteit, I.: Regional High-Resolution Weather Forecasting over the Arabian Peninsula: A Data-Driven Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1879, https://doi.org/10.5194/egusphere-egu25-1879, 2025.

Extreme climate events or tails of natural hazard distributions tend to be the most damaging in terms of societal impacts. While traditional physics-based approaches are suitable for gaining mechanistic understanding and for process-based studies, they may not be adequate for characterizing probabilistic risk from extreme events. Here, we demonstrate a ML/AI-based approach for estimating probabilistic risk from tropical cyclones (TCs) and the associated coastal flooding. First, we simulate nearly one million TCs for the current and future climates using the Risk Analysis Framework for Tropical Cyclones (RAFT), a hybrid model that combines physics with deep neural networks. Subsequently, we apply RAFT-simulated TC tracks to DeepSurge, an AI-based storm surge model that is trained on a large number of ADCIRC simulations in the North Atlantic. Our results suggest a significant increase in storm surge risk for the northern Gulf Coast, Florida and some areas near the mid-Atlantic. Also importantly, we show that ML/AI can be leveraged effectively to address the potential issue of ‘Grey Swan’ TCs and their impacts.

How to cite: Balaguru, K., Rice, J., Judi, D., Sun, N., and Daniel, B.: Addressing the US Tropical Cyclone-Storm Surge risk using RAFT-DeepSurge, an advanced AI-based approach , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2013, https://doi.org/10.5194/egusphere-egu25-2013, 2025.

We developed a data-driven system for joint prediction of daily precipitation (Pr) and near-surface temperature (T2m) over the global domain by utilizing NASA’s satellite observations and the associated reanalysis products, with the focus on S2S hydrologic forecasting. Our approach is based on a well-established methodology of linear inverse modeling modified and adapted by our science team for high-resolution modeling of precipitation. The key element of this new methodology is the usage of a so-called pseudo-precipitation (PP) variable, equal to the actual Pr where precipitation is occurring and, otherwise, equal to the (negative) air-column integrated water-vapor saturation deficit — the amount of water vapor to be added to the air column to achieve saturation at each vertical level. The model’s jointly obtained Pr and T2m forecasts are then validated against the observed fields as usual.

The above model is shown to be an efficient tool for emulating daily sequences of global coupled T2m and Pr fields with spatiotemporal characteristics strikingly similar to the observed characteristics. We used a large (100-member) ensemble of our statistical model’s hindcasts of precipitation over global domain to predict probabilities of weekly and biweekly precipitation amounts in one of the three categories (below normal, normal, and above normal) and compared these hindcasts with those based on the NASA GEOSS2S v2p1 model (4-member ensemble), calibrated using extended logistic regression. While the statistical model’s S2S precipitation forecast skill is somewhat lower than that of the reference NASA state-of-the-art system, it exhibits similar geographical and seasonal distributions, which warrants further research. We are currently looking into incorporating automated ML/AI feature identification techniques into our existing set up (with a linear activation function), to fine-tune the model learning and improve its predictive potential.

How to cite: Kravtsov, S., Robertson, A., Yuan, J., and Ghadamidehno, M.: Emulation and S2S probabilistic prediction of 2-m temperature and precipitation over the global domain using linear inverse modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3286, https://doi.org/10.5194/egusphere-egu25-3286, 2025.

AI weather models are becoming valuable tools for predicting the weather. While AI models’ general forecasts are known to be skillful, their forecast skill of extreme events is not fully understood. The 2021 Pacific Northwest (PNW) heatwave is a good case study for AI models because it falls outside of the distribution of heat waves in AI model training datasets.

Here, we investigate the forecast performance of 8 AI models (AIFS, Gencast, NeuralGCM, Graphcast, Fuxi, Pangu, Fourcastnet, FourcastnetV2) of the 2021 PNW heatwave. Despite the event being out of the training dataset distribution, their forecast performance is comparable to that of a state-of-the-art numerical weather prediction model (IFS). Specifically, AI models and IFS can accurately forecast the heatwave for lead times less than 7 days.

Two recent studies suggest the predictability barrier of the PNW heatwave may be due to an initial condition observation error. Leach et al. (2024) found that the 26th ensemble member of a 250 member IFS forecast accurately forecasts the heatwave 12 days in advance. Vonich and Hakim (2024) used backpropagation in Graphcast to find an optimal initial condition that leads to an accurate forecast 10 days in advance. Are these initial conditions robust across an ensemble of AI models? And do these initial conditions point to a unique solution?

We find a large spread in forecast accuracy when running the 8 AI models with the Leach et al. (2024) and Vonich & Hakim (2024) initial conditions. Furthermore we ran 1000 member ensembles in NeuralGCM and find initial conditions that lead to an accurate long-term forecast are not unique. These results suggest that the improvement in forecast accuracty to certain initial conditions may not necessarily be due to the initial conditions being closer to ground truth but rather they are due to cancelation of model error.

How to cite: Miyawaki, O., Fei, C., Li, S., Patel, D., Sarro, G., Zhang, H., Marchakitus, A., Hassanzadeh, P., Abbot, D., Weare, J., Nakamura, N., and Shaw, T.: On the robustness of AI model forecast skill and initial condition uncertainty of the 2021 Pacific Northwest Heatwave, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4576, https://doi.org/10.5194/egusphere-egu25-4576, 2025.

In recent decades, the prediction of precipitation has become a central focus for atmospheric scientists and weather forecasters. In particular, improving the predictability of rapidly forming rainfall events is critical for protecting lives and property. The island of Ischia, located in the Campania region of Italy, has experienced several landslides and flash floods in recent years with catastrophic effects. To mitigate these geohydrological hazards on this island, we propose a method for short-term rainfall forecasting, with "short-term" defined as a time frame up to six hours. Accurate predictions are essential, as they enable timely implementation of protective measures to safeguard the population.

Accurately predicting rainfall is a complex task influenced by numerous factors, including humidity, temperature, pressure, and wind speed. Historically, rainfall nowcasting has primarily relied on numerical weather prediction (NWP) models. However, this approach has notable limitations, such as high computational requirements and significant processing time, which make NWP models less practical for short-term forecasts.

In the past decade, machine learning (ML) models have revolutionized the way complex problems are addressed and solved, offering solutions that are both fast and highly efficient. Within this domain, deep neural networks (DNNs) a subset of ML have become increasingly prevalent for tackling complex problems using large datasets. Among these, U-Net, a specific DNNs architecture, has proven to be one of the most effective and accurate models for prediction tasks when the input data is image-based. However, achieving high accuracy with such models requires careful preprocessing of the dataset to enhance the model’s ability to effectively learn from the data. Additionally, properly tuning the model's hyperparameters is crucial for optimizing its performance.

In this study, we propose an enhanced U-Net model for nowcasting rainfall with a 120-minute lead time. The input data consists of rainfall radar data and rain gauge measurements. Furthermore, the study evaluates the model's performance under different training scenarios, comparing its efficacy when using only rainfall radar data versus an integrated dataset combining radar and rain gauge data. It is worth noting that the model operates in a regression framework, where the labels or outputs are the rain gauge readings with a 120-minute lead time.

How to cite: Taromideh, F., Santonastaso, G. F., and Greco, R.: Application of a novel deep learning model for precipitation nowcasting , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4735, https://doi.org/10.5194/egusphere-egu25-4735, 2025.

Accurate air temperature (T_a)  forecasting in urban areas is crucial for various socio-economic aspects, including risk warning and optimization of electricity systems. However, forecasting within urban environments faces substantial challenges due to the coarse spatial resolution and inadequate urban representation in numerical weather prediction (NWP) models. In this study, we present a novel multimodal deep learning framework that learns local dynamics from ground-level weather stations while effectively informing large-scale weather patterns for short-range (1- 24  hour lead time) T_a forecasting. The framework first employs graph neural networks (GNNs) to model intra-city spatiotemporal dynamics across 35 weather stations, achieving over 12% forecast improvement compared to modeling individual time series, primarily through mean state regularization. We further develop an end-to-end multimodal framework by integrating the GNN with synoptic weather patterns, achieving an additional 23% improvement, with particular expertise in winter and capturing cold spell events. Our study demonstrates the effectiveness of incorporating multi-scale information from diverse data sources and reveals that weather patterns within approximately 2000 km are critical for local city-scale forecasting. This framework can be readily adapted to other urban areas and will benefit significantly from the increasing deployment of smart IoT sensors to effectively address intra-city temperature heterogeneity.

How to cite: Wang, H. and Yang, J.: Multimodal Deep Learning Framework for Urban Air Temperature Forecasting: Bridging Local and Synoptic Scales, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4940, https://doi.org/10.5194/egusphere-egu25-4940, 2025.

Precipitation nowcasting is a long-standing challenge due to the inherent unpredictability, which often lead to significant risks and damage. While traditional approaches focus on modeling the nonlinear relationship between initial precipitation states and future states, these methods often fail to capture accurate precipitation dynamics, such as its distribution and intensity. The absence of guidance from physical theory limits data-driven methods in disclosing the chaotic nature of precipitation. To address this, we integrate Prandtl’s mixing length theory from fluid dynamics with diffusion models commonly used in computer vision to enhance the prediction of precipitation distributions and details over the next 200 minutes. This integration accounts for the turbulent properties of precipitation, improving both accuracy and granularity in forecasts. Additionally, we leverage multi-source data, particularly lightning observations, to train a control network for our diffusion model. This enhancement allows for more accurate and controllable predictions of precipitation initiation, decay, and overall spatial-temporal patterns. Our approach advances the state of the art in precipitation nowcasting, offering a robust framework that bridges physical theory with modern deep learning techniques.

How to cite: Li, D., Deng, K., Zhang, D., Leng, H., Ren, K., and Song, J.: Precipitation nowcasting diffusion model based on turbulence theory and multi-source data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5319, https://doi.org/10.5194/egusphere-egu25-5319, 2025.