Thousands of volatile organic compounds are released into the atmosphere from both human activities and natural sources. These emissions fuel complex chemical reactions that influence air quality and climate. As societies transition to cleaner energy, as temperatures rise and ecosystems respond to climate stress, the composition and amount of these emissions are shifting. Understanding these changes is crucial to predict future air quality and climate, since emissions are the basic input of any atmospheric chemical transport model. However, measuring concentrations of volatile organic compounds is often not enough to understand emissions, as the rapid chemical transformations of these reactive compounds in the atmosphere make it hard to assess their source strength and source location.

Direct airborne emission observations are a powerful tool to address this. With such airborne flux observations, it is possible to map real-world emissions of volatile organic compounds at a landscape scale of few km².  This lecture will show how airborne flux observations helped us find that changes in urban emission composition were not reflected in current emission inventories and revealed links of anthropogenic emissions with temperature. It will also highlight our current research on how climate change-driven stress can change biogenic emissions and their impact on the atmosphere.

How to cite: Pfannerstill, E. Y.: Emissions in transition: Exploring air quality-climate links from cities to forests , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1680, https://doi.org/10.5194/egusphere-egu26-1680, 2026.

A multitude of Volatile Organic Compounds (VOC) are present in the air we breathe. These airborne chemicals make up the familiar scents of flowers, fuels, and firesmoke. On a global scale, however, the single largest VOC source is the Amazon rainforest.

Each day, as the giant Amazonian ecosystem takes up carbon dioxide through photosynthesis, it also releases a fascinating cocktail of reactive chemicals into the air. These trace compounds play several roles within the forest, including protecting leaves from oxidative damage and mediating chemical communication between plants and insects. Above the canopy, they shape global atmospheric chemistry by influencing the atmospheric oxidation capacity, particle formation and the radiative budget, as well as regional clouds and precipitation.

Since 2012 we have been using sensitive mass spectrometers to characterize VOC from the 325m ATTO measurement tower in the pristine Brazilian rainforest. Initial work focused on isoprene (C5H8) and monoterpenes (C10H16), whose emissions vary between wet and dry seasons in response to light and temperature. Parallel measurements of total OH reactivity showed that many additional reactive compounds must be present, and the search for these species revealed new VOC sources and sinks, from soil, mosses and lichen.

In 2022-2023, the CAFÉ-BRAZIL airborne campaign extended these VOC measurements up to 14 km altitude across the entire Amazon basin. Even at these heights, the forest imprint is clear. Nocturnal deep convection transports substantial amounts of VOC to the upper troposphere, where they can accumulate overnight and prime the atmosphere for complex organic photochemistry at dawn. These natural chemical processes will be disrupted by continued deforestation.

 Climate models predict that the Amazon rainforest will suffer more, severe drought periods in future. Our recent measurements over the Amazon and within the BIOSPHERE 2 rainforest facility show that chiral VOCs can serve as sensitive indicators of how the forest responds to drought stress, including the extreme 2023/2024 El Niño event. Measurements of VOC in air hold the key to unlocking the complex chemical processes operating within and above the Amazon rainforest ecosystem.

How to cite: Williams, J.: From Forest to Sky: Air Chemistry over the Amazon, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16771, https://doi.org/10.5194/egusphere-egu26-16771, 2026.

The Amazon Basin (AB) is experiencing an intensification of hydroclimatic extremes, with droughts becoming more frequent, widespread, longer, and severe in the 21st-century. While precipitation deficits have historically been the primary driver of these events, the role of rising air temperatures and the consequent increase of atmospheric evaporative demand (AED) remains poorly quantified. Understanding the relative contributions of these factors is crucial for assessing AB resilience and potential tipping points under ongoing global warming. Here, we analyzed drought evolution across the AB over 45 years (1980–2024) using the Standardized Precipitation-Evapotranspiration Index (SPEI) derived from ERA5-Land reanalysis data. To isolate the contribution of atmospheric evaporative demand (CAED) to drought severity, we compared the standard SPEI with a modified SPEI version based on constant climatological AED.

Furthermore, we applied a rarity index to systematically rank drought events by intensity and spatial extent, enabling a standardized comparison of the exceptional 2023/24 event with historical benchmarks. Our analysis reveals that the 2023/24 drought (AD-23/24) was the most extreme event on record, affecting over 88% of the basin’s area and having a magnitude four times that of the average of the previous top-5 droughts. Notably, the recurrence of high-ranking drought years since 2020 underscores a persistence of extreme conditions in the 2020s. Crucially, the CAED analysis uncovers a distinct temporal regime shift occurring after 2005. While earlier droughts were primarily precipitation-driven, the post-2005 era is characterized by a predominantly evapotranspiration-driven regime, in which climate change-induced warming significantly amplifies drought intensity through increased AED. This intensification is further linked to sea surface temperature anomalies in the Tropical Indian, Tropical Pacific, and North Atlantic oceans. These findings demonstrate that the AB has entered a new hydroclimatic phase in which temperature-driven AED is overtaking precipitation deficits as the primary driver of exceptional drought events. This shift suggests that warming is likely exacerbating drought severity, posing unprecedented challenges for ecosystem stability and water security in the region.

How to cite: Albuquerque, R., M. dos Santos, D., F. V. V. Miranda, V., F. Peres, L., M. Trigo, R., M. B. Nunes, A., L. R. Liberato, M., M. Gouveia, C., and Libonati, R.: Temperature-driven shift intensifies 21st-century Amazon droughts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-557, https://doi.org/10.5194/egusphere-egu26-557, 2026.

This study provides the first high-resolution polarimetric radar observations of Overshooting Convective Storms (OCS) over the Western Ghats (WG), India using the newly installed SSPA-based X-band Radar at HACPL, Mahabaleshwar. Three post-monsoon OCS events (15, 23, and 24 October 2025) were analysed using PPI, RHI, CFAD products and ERA5 Atmospheric fields. All storms exhibited strong vertical growth, with echo-top heights of 17.6–19.8 km (15 Oct), 16.5 km (23 Oct), and 17.8 km (24 Oct), and peak reflectivity values of 59.6, 63.3, and 52.1 dBZ, respectively. Notably, significant reflectivity (>40 dBZ) persisted above 16 km, confirming deep overshooting intrusions. Polarimetric signatures showed clear mixed-phase and ice-growth processes, including KDP up to 3–4° km⁻¹, enhanced ZDR in the rainy regions, and reduced ρhv (0.92–0.96) within convective cores, indicating liquid water content, riming, and graupel/hail production. ERA5 diagnostics revealed favorable conditions for deep convection, with strong mid-tropospheric ascent (–0.6 to –0.8 Pa s⁻¹), high moisture, and pronounced convergence over the WG. These results demonstrate intense post-monsoon overshooting convection in complex terrain and highlight the capability of X-band Polarimetric radar to reveal the storm microphysics and vertical structure in an orographically challenging environment.

How to cite: Devisetty, H., Uriya Veerendra, M. K., Tyagi, B., Das, S. K., Chakravarty, K., Survase, C., and Burrala, P. K.: Radar Polarimetry to Characterize Overshooting Convection in the Western Ghats of India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-676, https://doi.org/10.5194/egusphere-egu26-676, 2026.

This study is motivated by the observation of a unique intraseasonal power in the upper tropospheric equatorial meridional winds over the Western Hemisphere during boreal winter. The presence of intraseasonal power at both westward and eastward wavenumbers is unusual and intriguing, as the dominant tropical modes of intraseasonal variability typically exhibit little amplitude in equatorial meridional winds and are instead characterized by strong zonal wind perturbations. Furthermore, the intraseasonal disturbances are confined to the upper troposphere.  The spatial structure and dynamical characteristics of these disturbances are consistent with those of mixed Rossby-gravity waves (MRGWs), indicating the presence of intraseasonal MRGWs in the atmosphere. A systematic relationship between the location and amplitude of the intraseasonal MRGWs and the upper-tropospheric westerlies suggests that background circulation is fundamental to their existence. This hypothesis is investigated through a set of diagnostic analyses guided by the dispersion relation of a linear shallow water model incorporating a homogeneous background flow. The results not only explain the emergence of intraseasonal MRGWs but also the overall distribution of meridional wind power throughout the troposphere. The strength and direction of the background flow govern the observed spatial and temporal characteristics of MRGWs via Doppler shifting of intrinsic MRGWs. Consequently, the spectral power distribution of equatorial meridional wind perturbations across pressure levels represents a composite of MRGWs that have been Doppler-shifted by a range of background flow regimes.

How to cite: Mehak, M. and Ettammal, S.: Can Background Circulation Facilitate Intraseasonal Mixed Rossby-Gravity Waves over theCentral-Eastern Pacific?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-691, https://doi.org/10.5194/egusphere-egu26-691, 2026.

Western Disturbances (WD) are key atmospheric phenomena over northern India, Pakistan, and the Western Himalayas, especially during winter months (December to February). In recent years, increasing variability in these systems has been observed across all seasons, notably pre-monsoonal months (March to May), although thorough investigation remains underexplored. The study evaluates the shifting behaviour and structure in WDs across two climatologically distinct periods – 1950 to 1976 and 1977 to 2022, corresponding to the well-documented 1976-1977 climate shift. In this study, vorticity-based WD track data, coupled with the ERA5 reanalysis dataset, have been utilised to analyse the shift. Behavioural changes are quantified through frequency trends, maximum vorticity distribution and mean track, while structural evolution is examined through composite vertical profiles of key atmospheric variables.  The study unravels notable increase in WD frequency during the pre-monsoon season in recent decades, accompanied by a westward shift in WD origins and longer track durations, thereby enhancing the potential for moisture transport. Furthermore, substantial strengthening of upper-level zonal winds, intensified mid-tropospheric convection, and atmospheric moisture availability have been observed through structural analysis. Such transformations indicate a transition of WD towards hybrid systems with enhanced convective features, thereby elevating the potential for extreme precipitation events during the pre-monsoon period. This improved understanding of the evolving WD dynamics is critical for hydrological planning, climate action, strategies and disaster preparedness in the highly vulnerable Himalayan and adjoining regions.

How to cite: Mitra, S., Sardana, D., and Agarwal, A.: Evolving Characteristics in Western Disturbances over the Hindu Kush Himalayas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-731, https://doi.org/10.5194/egusphere-egu26-731, 2026.

This work investigates a prominent dust event that occurred over Nainital, Uttarakhand, during 14-18 May 2025. Continuous micro pulse lidar (MPL) observations provided evidence of significant aerosol enhancement during the event. Distinct elevated aerosol layers were observed between 1 and 2.5 km above ground level, where backscatter coefficients increased to approximately 5×10⁻³ km⁻¹ sr⁻¹ and extinction values ranged between 0.8 and 1.2 km⁻¹. The persistence of these layers indicated long-range transport rather than local sources. Satellite-based aerosol optical depth (AOD) data from MODIS confirmed these enhancements, showing values doubling from 1.1-1.5 to above 2.2 during the peak dust intrusion. Meteorological observations documented elevated daytime temperatures between 20.1 ± 1.3 and 26.8 ± 1.6 °C and a marked reduction of relative humidity to below 50%, suppressing aerosol scavenging. Wind speeds intensified, with nocturnal maxima up to 5.6 ± 1.1 m/s and predominantly westerly to northwest directions (230°- 265°), favoring dust transport from western source regions. Synoptic-scale 850 mb wind analyses further corroborated persistent strong westerlies guiding mineral aerosols from the Thar Desert and Indo-Gangetic plains into the Himalayan foothills. The results highlight the importance of integrating lidar measurements with meteorological and reanalysis datasets to capture both vertical and horizontal characteristics of dust intrusions in mountainous regions. 

How to cite: Singh, S. K., Singh, N., Rawat, V., Chauhan, M., and Debnath, S.: Variation of atmospheric properties during a dust episode over central Himalayan region using Lidar observation and auxiliary data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1290, https://doi.org/10.5194/egusphere-egu26-1290, 2026.

With the intensification of global warming, deep convective system(DCS) precipitation over the Tibetan Plateau(TP) has become increasingly frequent, which plays a vital role in regulating the regional hydrological cycle. Previous studies have focused on instantaneous convective activity, little elaborating on the evolutionary processes and rainfall of DCSs throughout their whole lifecycle. Here, based on the continuous observations from the FY-4A geostationary satellite, this study investigates the characteristics and evolution of DCSs over TP from 2022 to 2023 through our previous full-lifecycle tracking algorithm from initiation to dissipation. Furthermore, the effects of key meteorological factors on DCSs evolution are revealed.

Results indicate that DCSs are mostly short-lived (3–6 h lifecycle), and more than 85% of convective precipitation occurs during summer from June to August. DCSs concentrate in the central-eastern TP, with an occurrence probability exceeding 12% in summer. Additionally, the area and rainfall rate of DCSs typically reach their peaks at the middle stage of the lifecycle. After the dissipation of the convective core, the persistence time of cirrus can reach 5%–28% of the core’s lifecycle. Controlled variable analysis reveals that convective available potential energy (CAPE) and precipitable water (PW) synergistically regulate the development of convective systems: under conditions of high CAPE (500-10³ J kg^-1) and high PW (>50 mm), the area of cores expands to the largest extend. However, the maximum lifecycle and peak precipitation of DCSs occur under conditions of moderate wind shear (5-10 m s^-1).

This study explores the full-lifecycle evolutionary patterns of DCS over the TP and the regulatory effects of meteorological conditions over TP, laying a theoretical foundation for future research on regional precipitation and climate change in the region.

How to cite: Guo, C., Yin, J., Pan, Z., Zang, L., and Mao, F.: Observed Lifecycle of Convective Precipitation over Tibetan Plateau Based on the FY-4A Geostationary Satellite, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1839, https://doi.org/10.5194/egusphere-egu26-1839, 2026.

How to cite: Liu, Z.:  Global-scale compound Droughts and heatwaves: inland/coastal type grouping, diversity of temperature extremes, and dynamically-based Interpretable reconstruction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2053, https://doi.org/10.5194/egusphere-egu26-2053, 2026.

Carbon dioxide removal (CDR) is critical to net-zero pathways achieving the Paris Agreement 1.5°C target, yet its effectiveness in reducing humid heat stress risks remain uncertain. Here we examine the hysteresis and reversibility of humid heat stress in China under CDR scenarios. Humid heat responds asymmetrically during warming and CO₂ removal especially in eastern and southern China, producing a hysteretic and partially reversible trajectory. This results from unequal adjustments of temperature and relative humidity, which constrain heat-stress recovery even as global temperatures decline. Moist static energy analysis indicates suppressed vertical energy export over eastern China and enhanced transport of warm moisture from tropical oceans, sustaining humid heat during CO₂ removal. Consequently, severe humid heat stress persists, with over 6.4 billion people affected, more than 66% of which is due to hysteresis. These findings highlight enduring heat-related risks and the urgent need for adaptation alongside mitigation.

How to cite: Ma, Q.: Committed and Irreversible Humid Heat Stress Risk in China Despite Carbon Dioxide Removal, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2384, https://doi.org/10.5194/egusphere-egu26-2384, 2026.

African Easterly Waves (AEWs) are a dominant synoptic-scale feature of the tropical atmosphere, widely recognized for their role as precursors of tropical cyclones and for modulating summertime rainfall over the Atlantic basin and adjacent regions. However, their potential influence on the transport of Saharan dust across the Atlantic and its impacts on air quality has received comparatively less attention. In this study, we investigate the role of AEWs in modulating Saharan dust transport and its relationship with high PM2.5 concentration episodes over the Yucatán Peninsula. Using reanalysis data, we document a pronounced seasonal cycle in dust transport, with maximum concentrations during boreal summer (June–August), coinciding with the peak activity of AEWs. Spectral analysis reveals a significant contribution at periods of 4–9 days, consistent with the characteristic timescales of AEWs. To quantify their impact on air quality, intense dust events associated with AEWs were identified based on anomalies exceeding one standard deviation and compared with episodes of poor air quality driven by particulate matter. Our results indicate that AEWs account for approximately 26–31% of PM2.5 pollution episodes linked to dust over the Yucatán Peninsula, with event durations ranging from 1 to 8 days. These findings highlight the important role of AEWs in shaping the synoptic-scale variability of aerosol transport and surface air quality in the Yucatán Peninsula and southern Mexico, underscoring their relevance beyond tropical cyclogenesis and precipitation, particularly during the boreal summer.

How to cite: Jaramillo-Moreno, A. and Vázquez-Jiménez, C. S.: African Easterly Waves as Drivers of Saharan Dust Transport and PM2.5 Extremes in the Intra-Americas Region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4071, https://doi.org/10.5194/egusphere-egu26-4071, 2026.

The Madden–Julian Oscillation (MJO) is the dominant mode of intraseasonal variability (30–90 days) in the tropical atmosphere, characterized by eastward-propagating convective and circulation anomalies that strongly modulate tropical and regional climate. Through its large-scale dynamical perturbations, the MJO influences moisture transport, low-level circulation, and precipitation over regions far removed from its primary convective center. However, its role in regulating low-level moisture fluxes over northwestern South America has received comparatively limited attention. In this study, we investigate the influence of the MJO on moisture transport toward Colombia, with particular emphasis on its modulation of the Chocó Low-Level Jet. Using MERRA-2 reanalysis, we characterize intraseasonal variability in low-level moisture advection and wind fields associated with different MJO phases defined by the Real-time Multivariate MJO (RMM) index. The analysis examines changes in low-level moisture transport, wind intensity, and large-scale convergence associated with the zonal displacement of the MJO convective envelope. Results show that the strength of the Chocó Jet depends strongly on the longitudinal position of the MJO convective center. Certain MJO phases enhance moisture transport from the eastern Pacific toward Colombia, favoring orographic ascent along the Andes and organized convection over the Colombian Pacific region, while other phases are associated with weaker moisture fluxes and reduced convergence. These findings highlight the role of the MJO in regulating intraseasonal moisture transport and low-level circulation over northwestern South America.

How to cite: Toro-Arenas, J., Jaramillo-Moreno, A., Carvajal-Serna, L. F., and Mesa-Sanchez, Ó. J.: Intraseasonal Modulation of the Chocó Low-Level Jet by the Madden–Julian Oscillation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4128, https://doi.org/10.5194/egusphere-egu26-4128, 2026.

The Indian Summer Monsoon Rainfall (ISMR), occurring from the month of June to September, is characterized by intraseasonal variability in the form of active and break spells. Monsoon breaks are periods of sparse to no rainfall, marked by positive outgoing longwave radiation anomalies, above-normal pressures, and clear-sky conditions. These monsoon breaks can have socioeconomic impacts due to their effect on crop growth stages, irrigation planning, and water management. This study aims to investigate the synoptic-scale systems responsible for the onset and sustenance of monsoon breaks over the Western Ghats and other parts of India to improve future predictability of the same. Rainfall data from 1901 to 2025 is used to identify break spells and classify them into short, moderate, and long-duration events. Subsequently, decadal rainfall composites are constructed. These composites reveal patterns of rainfall suppression and enhancement over the Indian subcontinent, equatorial Indian Ocean and West Pacific. Although the spatial structure of rainfall anomalies remains consistent, slight decadal variabilities are observed. Composite analyses of outgoing longwave radiation, and upper and lower tropospheric winds are used to diagnose the synoptic features associated with monsoon breaks. Case studies of recent drought years, 2002 and 2015, highlight the role of upper tropospheric anticyclones, northward displacement of the monsoon trough, and dry air intrusion from West Asia in sustaining and prolonging the breaks, confirming previous studies. The influence of large scale climate oscillations such as ENSO, EQUINOO, and the Boreal Summer Intraseasonal Oscillation (BSISO) on monsoon break frequency and duration is investigated using statistical and machine learning tools, with the aim of informing the development of improved predictive frameworks for monsoon breaks.

How to cite: Aboobaker, J. and Singh, Dr. S.: Synoptic-Scale Mechanisms And Climate Oscillation Influences On The Monsoon Breaks Of The Indian Summer Monsoon System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4971, https://doi.org/10.5194/egusphere-egu26-4971, 2026.

This study develops a novel framework within the Weather Research and Forecast Model for modeling aerosol-cloud-lightning interactions. The framework explicitly represents aerosol-cloud interactions by prescribing aerosols with two configurations: an idealized setup, where both cloud condensation nuclei (CCN) and ice nucleating particles (IN) are assumed to have a single chemical composition and spatially uniform distributions; and a quasi-realistic configuration, with multi-species aerosols assigned spatially varying distributions, where hygroscopic components act as CCN, dust particles act as IN, and all aerosol species influence radiative transfer. Cloud microphysics is coupled with detailed charge separation and discharge processes to enable the lightning simulation. The framework is evaluated using two thunderstorms in Guangdong, China. For an isolated storm, the model successfully reproduces the observed tripolar charge structure (positive–negative–positive), demonstrating its capability in simulating cloud electrification. For a frontal storm, it captures well the observed precipitation and lightning, and shows that increasing CCN suppresses the rainfall while enhancing the lightning. Higher CCN concentrations produce more numerous but smaller cloud droplets, which suppresses the coalescence into rain droplets, allows a greater number of droplets to loft into the upper troposphere, and forms more but smaller cloud ice particles. This boosts graupel–ice collisions, intensifies non-inductive charging, strengthens the upper positive charge and the vertical electric-field gradient, ultimately increasing the lightning frequency. In contrast, no significant aerosol-induced invigoration of updrafts is observed. These results highlight the dominant role of aerosol microphysical effects over dynamical invigoration in modulating thunderstorm electrification and lightning activity.

How to cite: Wang, W., Chen, G., and Zhang, Y.: A framework for modeling aerosol-cloud-lightning interactions: Validation of charge structure and aerosol effects, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6493, https://doi.org/10.5194/egusphere-egu26-6493, 2026.

Variations in land surface characteristics directly alter surface biophysical properties like albedo, roughness length, and canopy resistance, leading to changes in surface radiative and turbulent fluxes. These changes influence sensible and latent heat fluxes, which can further regulate surface temperature, evapotranspiration, and near-surface moisture transport. Thus, variations in surface fluxes associated with changes in land-surface properties can regulate convective instability, moisture convergence, and can modulate short-range rainfall characteristics and their predictability. Such land–atmosphere feedbacks are particularly important over the Indian region, where strong seasonal contrasts and heterogeneous land surfaces play a critical role in shaping rainfall variability. Hence, this study investigates the sensitivity of short-range precipitation forecasts over India to decadal changes in land use and vegetation during the pre-monsoon and monsoon periods. USGS 24-category land use data based on the 1994 landscape is used as the control run. Seven different simulation experiments are conducted using WRF model with various land use and vegetation data from MODIS and ISRO; ie: Experiment1 (MODIS 2001, USGS LAI and VF default), Experiment 2 (MODIS 2001, LAI and VF default), Experiment 3 (MODIS 2019, LAI and VF default), Experiment 4 (MODIS 2001, with Urban Class of 2019, LAI, and VF default), Experiment 5 (MODIS 2001, with water bodies of 2019, LAI, and VF default), Experiment 6 (MODIS 2019, LAI and VF of 2019), Experiment 7 (ISRO 2018-2019, VF and LAI default). A comprehensive assessment based on quantitative error metrics and categorical forecast skill scores demonstrates statistically significant improvements in rainfall forecast performance following the assimilation of updated land-use and vegetation datasets.  These statistically robust improvements indicate that realistic representation of land-surface conditions contributes meaningfully to enhanced short-range precipitation predictability. The computed Extreme Dependency Index (EDI) values indicate an enhanced ability of the model to capture rare extreme rainfall events following the incorporation of updated land-use information. The incorporation of realistic land-use classifications derived from MODIS and ISRO datasets led to improved simulations of surface meteorological variables, including temperature, wind speed, relative humidity, surface pressure, and surface fluxes. Corresponding improvements were also observed in the vertical atmospheric profiles for wind, temperature, and specific humidity profiles. These enhancements indicate a more realistic depiction of land–atmosphere interactions and boundary-layer processes in the model.

How to cite: Putatunda, I., Vasudevan, R., and Singh, R.: Effect of decadal land use change on WRF model-simulated surface meteorological parameters over the Indian region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8881, https://doi.org/10.5194/egusphere-egu26-8881, 2026.

Rising global warming is accelerating climate extremes at regional scales worldwide. Capturing these extremes at the regional level requires high resolution climate modeling capable of representing complex topography and strong land atmosphere interactions. In this study, a high-resolution Non-Hydrostatic Regional Climate Model (NHRCM) is configured at a kilometer scale resolution (5 km) through dynamic downscaling of the MRI AGCM 3.2 outputs developed by the Meteorological Research Institute of Japan (MRI). Daily precipitation and temperature (maximum and minimum) data are generated at 5 km resolution for the historical period (1980–2000) and the future period (2081–2100) under the high emission climate scenario SSP585. The performance of the downscaled climate variables is evaluated against ERA5 Land data after resampling to the same resolution as the NHRCM. Statistical metrics and extreme climate indices are used to quantify model skill and biases at regional and sub regional scales over Pakistan. The results reveal a strong correlation in high elevation regions of Pakistan compared to the plains. After evaluating model performance, precipitation and temperature extreme indices are calculated for both historical and future periods. The findings indicate an increase in precipitation in the southern regions of Pakistan, accompanied by rising temperatures. These trends are also associated with an increase in short-duration intense rainfall events during summer and prolonged dry conditions in winter. Furthermore, the frequency of heatwaves is expected to rise by the end of the century across Pakistan, along with increasing temperatures in snow fed regions. Overall, this study highlights the added value of high-resolution nonhydrostatic regional climate modeling in understanding and assessing climate extremes over Pakistan, providing a robust foundation for future climate impact assessments and adaptation planning.

How to cite: Mamoor, M., Rahim, A., Yaseen, M., Naz, R., Kosar, T., Tariq, N., and Akif, A.: Kilometer Scale Climate Modeling of Extremes: Evaluation of the NonHydrostatic Regional Climate Model (NHRCM) over Pakistan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9211, https://doi.org/10.5194/egusphere-egu26-9211, 2026.

In this study we analyze the occurrence of cold pools in the tropical Atlantic during the ORCESTRA field campaign (August to September 2024), using data collected from over 2000 soundings. We first tested whether the method to detect cold pools based on the mixed-layer height, developed for shallow convective cold pools in the winter trades during EUREC4A, is applicable to the deep convective regime of the ITCZ. The validation by a surface-based detection method and the investigated recovery behaviour of the mixed layer after a cold pool demonstrates its applicability to this environment. On this basis, we examine the distribution and properties of cold pools within the tropical Atlantic. A total of 26% of all ORCESTRA soundings detected cold pools, compared to only 7% during the EUREC4A campaign in the winter trades. The ITCZ region with the highest moisture content and presumably deepest convection features the largest number of cold pools. This presentation will further discuss how the cold pool strength and frequency correlate with wind, moisture and stability.

How to cite: Vogel, R., Mann, L., Robbins-Blanch, N., and Rochetin, N.: Characterizing cold pools in the ITCZ using soundings from the ORCESTRA campaign, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14298, https://doi.org/10.5194/egusphere-egu26-14298, 2026.

Satellite data sets are the primary source of observations of cloud characteristics, but downward-looking passive sensors cannot see lower-level clouds obscured by higher clouds nor cloud bases. Observations of low clouds with downward-looking satellite IR are hampered by the small brightness temperature differences between the low cloud top and the underlying surface. In contrast, upward-looking thermal IR can readily distinguish warm clouds against the cold sky. By sampling thermal IR cloud characteristics across the diurnal cycle, upward looking thermal IR observations have the potential to yield improved understanding of transitions in cloudiness at sunrise and sunset and differences in the relative importance of different cloud processes with and without SW fluxes.

Our thermal IR all-sky camera was assembled from commercially available, off-the-shelf parts. The key components are a FLIR Boson thermal IR camera and a FLIR PTU-5 pan-tilt mount. The IR camera has a 50° field of view and a resolution of 640x500 pixels. To obtain imagery of the entire sky, the pan-tilt mount points the camera at 14 different directions, each varying in azimuth and elevation. The volume coverage pattern is executed once per minute, and the entire sky is sampled in less than 30 seconds. The images are then stitched together in software to yield a hemispherical array of IR brightnesses from horizon to horizon.

From the imagery we can infer cloud fraction, cloud coverage characteristics relating to the size and shapes of cloud elements, and estimate the altitude of cloud bases at all times of day. Sequences of images reveal the evolution of individual cloud elements and provide information on the phase space of cloud properties across the diurnal cycle and to related to air mass changes, such as the passage of fronts. Combined with other data from lidar and visible all sky cameras, the upward-looking thermal IR data on cloud outer surface temperature details at small spatial scale (10s of meters) and few minute time scale have high potential to yield new insights on cloud initiation and dissipation.

We will detail the performance of the thermal IR all-sky camera and analyze derived cloud characteristics in the context of data from visible wavelength all-sky imagery and additional atmospheric observations.

How to cite: Miller, M. and Yuter, S.: All-Sky Camera Upward-looking Thermal Infrared Cloud Characteristics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15029, https://doi.org/10.5194/egusphere-egu26-15029, 2026.

Reanalysis products, which blend weather model output with observations are commonly used as substitutes for observations to assess numerical weather prediction model forecast skill, the accuracy of climate model historical realizations, and AI training. However, the quality of reanalysis output is not uniform across all variables, times of day, seasons, or geographic settings. This study evaluates the strengths and weaknesses of ERA5 reanalysis (0.25° grid) over a multi-year period by comparing them to worldwide hourly surface observations from over 1200 stations, buoys, and radiosonde vertical profiles.

Our analysis focuses on several metrics across the diurnal cycle (7 AM and 3 PM local time) and during temperature outlier events (< 10th percentile and > 90th percentiles for the 30-year climatology). Results indicate that ERA5 provides reliable 2-meter air temperatures in most regions, but shows a frequent dry bias in dewpoint of greater than 3 ℃ more than 5% of the time for many stations in the Dry and Mediterranean climate zones. ERA5 often underestimates warm events (> 90th percentile), with the largest cold biases, less than -3 ℃ occurring more than 11% of the time in the Mediterranean climate zone. Temperature and dewpoint biases are amplified in complex terrain, and dewpoint biases tend to be larger near coastal locations. To investigate whether higher spatial resolution mitigates these issues, we also examine the performance of the ERA5-Land product  (0.1° grid). These findings emphasize the importance of evaluating the adequacy of purpose when using reanalysis for specific applications, since performance can vary significantly by variable, time of day, season, and climate zone.

How to cite: Lewis, W., Yuter, S., and Miller, M.: Is ERA5 Fit for Purpose? A Global Multi-Variable Evaluation of Reanalysis Strengths and Weaknesses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15265, https://doi.org/10.5194/egusphere-egu26-15265, 2026.

Persistent extreme weather anomalies lasting several weeks to months can lead to drought and compound dry–hot extremes, posing serious socio-economic risks in the Indian monsoon region. Although subseasonal-to-seasonal (S2S) prediction systems have advanced, the extent to which these models represent drought-relevant hydroclimatic variability over India has not been adequately quantified. Here, we focus on evaluating the hindcast quality of weekly accumulated precipitation and temperature from multiple S2S models with lead times up to six weeks during the JJAS season over India. These model hindcast outputs and IMD observations are regridded to a common 0.5° resolution and analyzed using deterministic forecast skill metrics at various lead times, statistical bias correction is then applied to isolate systematic model errors, followed by SPI-based drought diagnostics, and compound dry–hot extreme indices are derived and computed. The analysis reveals modest forecast skill at early lead times, followed by a systematic decline as lead time increases, with precipitation predictability deteriorating more rapidly than temperature predictability. Although the models generally capture the large-scale spatial distribution of drought-prone regions, they significantly underestimate the frequency and spatial extent of compound dry–hot conditions, exhibiting pronounced regional dependence across India. These results highlight key limitations and identify opportunities to enhance subseasonal drought early-warning systems. 

Keywords: Subseasonal-to-seasonal predictability, Indian Summer Monsoon, Drought, Climate extremes, Hindcast evaluation

How to cite: Sukumaran, S. and Agarwal, A.: S2S Forecast Skill Assessment for Summer Monsoon Drought Warning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16394, https://doi.org/10.5194/egusphere-egu26-16394, 2026.

Phthalate exposure has been rampant with the growing use of these compounds in personal care  and other plastic products. Phthalates have been associated with endocrine, neurological, and reproductive disorders resulting from their continuous release from plastic surfaces throughout their lifecycle. These endocrine disruptors are used in personal care products to increase their shelf life. This paper aims to identify phthalates and their concentration in three test materials, perfume, scented candles, and incense sticks, through a chamber experiment. This experiment aids in understanding phthalate emissions into air during the use of these materials in indoor environments. Sampling was performed using Tenax TA tubes, which were placed inside the glass chambers for 30 minutes while maintaining a positive flow rate using a mass flow controller and pump. The tubes capture gas-phase phthalates efficiently. The tubes were then further analyzed using TD-30 and GC/MS. Three phthalates, which were detected in the test materials, were dimethyl phthalate (DEP), dibutyl phthalate  (DBP), and butyl benzyl phthalate (BBP). The phthalate concentration was found to be high in incense sticks, with DEP being the most prominent phthalate, followed by DBP. Scented candles had the high concentration of DBP, followed by BBP. A similar pattern was observed in perfumes. The high concentrations of these compounds detected in the test materials underscore growing concerns about the widespread use of phthalates in  manufacturing of plastic products.

How to cite: Anshika, F. and Rappenglueck, B.: Investigation of phthalate emissions from incense stick, scented candle and perfume through a chamber experiment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18616, https://doi.org/10.5194/egusphere-egu26-18616, 2026.

Delhi is one of the most polluted megacities in the world. Since the ancient era, Delhi has been known for its rich heritage sites like the Red Fort, Humayun’s Tomb, and Qutub Minar, which are UNESCO World Heritage Sites. In this study, we investigate urban pollution-linked alterations at the heritage buildings (HBs) of Delhi, India, through a comparative characterization of deposited black crust (BC) and the underlying red sandstone (RS) collected from the exposed surfaces of the HBs. The BC on HBs can act as an integrative passive sampler of urban pollution, recording particulate matter, reactive gases (SO_x/NO_x), and associated heavy metal deposition. To achieve this objective, we applied a multi-analytical workflow combining X-ray diffraction (XRD), X-ray fluorescence (XRF), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDX), Fourier transform infrared spectroscopy (FTIR), carbonaceous analysis, and inductively coupled plasma mass spectrometry (ICP-MS) resolved phase assemblages, morphology, major-elemental composition profiles, and signatures of trace elements. The outcome suggests that the urban pollution sources, including vehicular emissions, road/construction/soil dust, industrial activity, and biomass burning, were identified as fingerprints of calcium (Ca) and sulfur (S) in the formed BC at the RS substrate. In this scenario, BC was enriched with Ca and S, which may cause sulfation phenomena to occur at the RS substrate as it contains low intrinsic Ca. Therefore, gypsum was identified as a dominant deteriorating agent, along with weddellite, bassanite, while carbon and heavy metals were also embedded in BC relative to the RS substrate. Additionally, in this work, a modeling approach is also utilized to assess the pollution dispersion impacts on the built HBs and linkage with BC deposition, which was employed using coupled WRF (Weather Research and Forecasting) and AERMOD (the American Meteorological Society/Environmental Protection Agency Regulatory Model) technique. Hence, considering these analytical and modelling approaches contributes to applying the site-specific conservation and preservation interventions in similar urban polluted environments.

How to cite: Kumar, G., Gurjar, B. R., Sharma, M., and Ojha, C. S. P.: Black crust as a passive sampler of urban pollution on the heritage buildings in Delhi, India: An analytical and modelling approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18929, https://doi.org/10.5194/egusphere-egu26-18929, 2026.

Tropical deep convection evolves as a cyclic process, but most observational and modeling studies diagnose convection through regional or domain-based contrasts, obscuring how key physical processes vary across different stages of the convective life cycle. The convective life cycle in the tropics is frequently conceptualized through recharge discharge processes. While valuable, this framework can be extended with more granular, phase specific diagnostics to better understand the distinct physical processes governing each stage of convection. Here, we build on existing phase-plane approaches to represent convection as a cyclic process, using column-integrated moist static energy (MSE) and its temporal tendency as the primary state variables. The phase plane is constructed with column integrated MSE along the horizontal axis and its temporal derivative along the vertical axis. While adhering to the established recharge–discharge paradigm, we extend this terminology by defining four distinct, cycle-consistent stages on the phase plane: Build-up, Cresting, Decay, and Recovery. Applying this framework to the Indian Summer Monsoon (ISM) core region, we map quasi-geostrophic (QG) omega-scaled precipitation components onto the MSE phase plane  to investigate the relative contributions of diabatic heating  and adiabatic forcing across the convective life cycle. These stage dependent signatures demonstrate the utility of the MSE phase plane for attributing and relative importance of dynamical and diabatic processes across the convective life cycle. Final results and extended analyses will be presented and discussed at the conference.

How to cite: Chakraborty, S., Nikumbh, A., Maithel, V., and Zore, T.: A Phase-Plane Representation of the Convective Life Cycle: Characterizing Diabatic and Adiabatic Drivers during the Indian Summer Monsoon, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19311, https://doi.org/10.5194/egusphere-egu26-19311, 2026.

In Eastern Africa, subseasonal forecasts are critical for early warning systems as climate extremes severely impact food security and livelihoods. ECMWF Artificial Intelligence Forecasting System (AIFS ENS v1.0) , an ensemble-based probabilistic data-driven forecast model developed by ECMWF offers unprecedented opportunities for regional applications through AI-driven weather prediction, but GPU compute costs and data access challenges limit deployment. As participants in the ECMWF AI Weather Quest, we developed solutions enabling cost-effective, cloud-based AIFS ensemble forecasting tailored for regional climate centers.  

We implemented a workflow (https://github.com/icpac-igad/ea-aifs) leveraging Google Cloud Platform infrastructure. Initial conditions are accessed via ECMWF's IFS data stored at AWS (Amazon Web Service) open data program at S3 Cloud storage using GRIB index-kerchunk, and VirtualiZarr methods for efficient data streaming without local storage overhead. The workflow employs experimental FP16 (half-precision) inference on AIFS ensemble models along with the standard FP32, evaluating GPU memory requirements and enabling deployment on cost-effective T4/L4 GPUs rather than expensive A100 instances.  

Verification results from the SON (September-October-November) 2025 season as part of the AI Weather Quest demonstrates that Team Fahamu's submission using AIFS ensemble forecasts for temperature and mean sea-level pressure outperforms climatology benchmarks. Regional evaluation over East Africa reveals promising subseasonal skill for temperature at lead times of 2-4 weeks—critical timescales for agricultural planning and anticipatory drought/flood action—while evaluation of precipitation forecasts is ongoing. This method provides a scalable template for regional climate centers globally to operationalize state-of-the-art AI weather models cost-effectively, advancing the democratization of advanced forecasting capabilities. 

How to cite: Koech, E., Kalladath, N., Mwanthi, A., Ogelo, A., Kinyua, J., Koros, H., Lelaono, M., Misiani, H., Bekele, T., Seid, H., Gudoshava, M., and Amdihun, A.: Cost-Effective ECMWF AIFS Ensemble Inference for Subseasonal Forecasting in East Africa , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20505, https://doi.org/10.5194/egusphere-egu26-20505, 2026.

Persistent heatwaves across South Asia impose severe and growing impacts, yet the atmospheric processes that sustain extreme heat over multiple days remain incompletely understood. This study aims to determine whether heatwave persistence is driven by failures of nocturnal boundary-layer ventilation, rather than by daytime temperature extremes alone. We analyze pre-monsoon (March–May) heatwaves across South Asia (65°E–98°E, 5°N–35°N) from 1981 to 2024 using ERA5 hourly reanalysis, which includes near-surface air temperature, boundary-layer height, near-surface winds, and surface sensible heat flux. Heatwaves are identified using a percentile-based definition of daily maximum temperature, and nighttime conditions are diagnosed consistently using local solar time. Nocturnal ventilation is quantified through a physically interpretable ventilation potential combining nighttime boundary-layer height and near-surface wind speed, complemented by diagnostics of turbulent mixing and nocturnal cooling. We find that heatwave nights are consistently characterized by suppressed nocturnal ventilation, including shallow boundary layers, weak winds, and reduced turbulent exchange, and that reductions in nighttime ventilation are more strongly associated with heatwave duration and nighttime heat accumulation than daytime temperature anomalies. Composite analyses further indicate that ventilation and turbulent mixing weaken before heatwave onset and remain suppressed throughout the persistence phase, with particularly pronounced effects in humid regions such as Bangladesh. Our findings demonstrate that nocturnal boundary-layer ventilation failure is a central physical mechanism controlling heatwave persistence and suggest that incorporating nighttime atmospheric processes into heatwave monitoring and early-warning frameworks is essential for anticipating prolonged and high-impact heat extremes.

How to cite: Laskor, Md. A. H., Dipu, S. U. A., Bhuiyan, F., and Islam, A. K. M. S.: Nocturnal boundary-layer ventilation failure governs heatwave persistence in South Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21148, https://doi.org/10.5194/egusphere-egu26-21148, 2026.

Phenolic compounds are key precursors and intermediates in the formation and aging of secondary organic aerosols (SOA), particularly in biomass-burning plumes and urban atmospheres. However, their detection typically requires laboratory-based chromatographic or mass-spectrometric techniques, limiting rapid or on-site characterization. The current research present a fully 3D-printed electrode (3D-PE) platform produced via hybrid material extrusion additive manufacturing, providing a compact, low-cost, and field-deployable tool for electrochemical quantification of atmospheric phenolics. The device integrates PLA-based structural components with graphene and silver conductive layers deposited in a single manufacturing step. Cyclic voltammetry measurements demonstrate clear and distinct redox signatures for representative phenolic structures, with oxidation potentials of 0.48–0.68 V and well-resolved reduction peaks. These redox behaviors correspond to functional groups commonly found in lignin-derived and anthropogenically emitted aromatic species.

The 3D-PE operates with sample volumes as low as 50 µL, suitable for extracts from aerosol filters, cloud water, or fog samples. Its electroactive surface area (5.8–6.7 mm²) and high electron-transfer efficiency from the graphene electrode enable sensitive detection of trace phenolic compounds. The platform’s portability and rapid response offer new opportunities for quantifying oxidation intermediates during field campaigns, studying heterogeneous oxidation pathways, and investigating Secondary Organic Aerosol (SOA) formation dynamics.

This work demonstrates that additive manufacturing provides a promising route for developing next-generation, customizable atmospheric chemistry sensors. The 3D-printed electrochemical platform can complement established mass-spectrometric techniques by enabling low-cost, high-frequency measurements of reactive organic compounds that play central roles in SOA formation and atmospheric oxidative chemistry.

How to cite: Raj, A., Yadav, P., Sahai, A., and Sharma, R. S.: 3D-Printed Electrochemical Sensor for Rapid Detection of Phenolic Oxidation Products Relevant to Organic Aerosol Formation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-186, https://doi.org/10.5194/egusphere-egu26-186, 2026.

Abstract

The densely populated monsoon region of Pakistan, influenced by a diverse mix of natural and anthropogenic aerosols, provides a natural laboratory to investigate aerosol impacts on cloud properties. Using a decade-long (2015–2024) dataset from MODIS, MERRA-2, and ERA5, we examine the response of non-precipitating warm clouds to fine-mode and coarse-mode aerosols. We find positive correlations between aerosol optical depth (AOD) and cloud effective radius (CER), with stronger sensitivity to fine-mode AOD. The relationships of AOD with cloud optical thickness (COT) and liquid water path (LWP) are generally negative, but more pronounced for coarse-mode AOD. These aerosol-cloud relationships are strongly modulated by meteorological conditions: low relative humidity and low lower-tropospheric stability enhance the negative AOD–COT and AOD–LWP responses. Additionally, the sensitivity of aerosol-cloud relationships to meteorology is greater for fine-mode AOD than coarse-mode. These results highlight the importance of aerosol size and ambient meteorology in determining cloud microphysical responses, providing insight into aerosol cloud interactions in a region critical for South Asian climate.

How to cite: Anwar, K., Liu, Y., Labban, A., and Zeydan, Ö.: Aerosol-cloud interactions under fine-mode and coarse-mode aerosol conditions over the monsoon region of Pakistan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-323, https://doi.org/10.5194/egusphere-egu26-323, 2026.

Accurate retrieval of the dry-air mole fraction of CO₂ (XCO₂) is essential for tracking emissions and supporting mitigation. However, aerosols significantly alter photon path lengths through scattering and absorption, making them the largest variable error source in XCO₂ retrieval. Current efforts to improve aerosol treatment in XCO₂ retrievals, such as the Atmospheric CO₂ Observations from Space (ACOS) algorithm for the Orbiting Carbon Observatory-2 (OCO-2), primarily focus on enhancing prior estimates of aerosol optical depth (AOD), vertical distribution, and optical properties. Yet, aerosol particle size distribution (PSD) parameters—a critical microphysical factor contributing to nonlinear variations in aerosol optical properties—are held fixed and excluded from the ACOS retrieval, thereby introducing additional biases into the XCO₂ results.

To address this challenge, we developed a Boosted Aerosol-Size-Integrated XCO₂ (BASIC) retrieval algorithm that concurrently retrieves XCO₂ and aerosol PSD parameters from OCO-2 observations. Validation at five Total Carbon Column Observing Network (TCCON) sites in East Asia shows that BASIC reduces the root-mean-square error (RMSE) by 30% and 13% compared to the standard and bias-corrected OCO-2 products, respectively. The improvement primarily stems from BASIC’s ability to generate forward-modeled spectra that more closely match observations than those from ACOS, particularly in the O₂ A-band, which is highly sensitive to aerosols. These results highlight the importance of incorporating variable aerosol PSD in retrievals and demonstrate that BASIC more accurately represents aerosol effects on radiative transfer. Our findings suggest that PSD-aware retrievals can significantly improve the accuracy of satellite-derived XCO₂ estimates under highly variable aerosol loading conditions, such as those in East Asia.

How to cite: Li, Z. and Li, S.: BASIC: A Boosted Aerosol-Size-Integrated XCO2 Retrieval Algorithm, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1603, https://doi.org/10.5194/egusphere-egu26-1603, 2026.

To advance urban sustainability and achieve climate neutrality, cities are proposing various decarbonization strategies through nature-based solutions, including the implementation of green infrastructures (GI). Accurately quantifying urban biogenic CO₂ exchange is essential for developing robust greenhouse gas budgets and for distinguishing biogenic from anthropogenic contributions to atmospheric CO₂ in urban environments.

This study systematically reviews current approaches for estimating urban biogenic CO₂ fluxes from both measurement- and model-based perspectives, evaluating their advantages, limitations, and applicability in urban contexts. Building on this review, we present an optimized framework to quantify urban biospheric CO₂ fluxes using the Vegetation Photosynthesis and Respiration Model (VPRM) driven by a high-resolution land cover map and sentinel-2 satellite data. The framework is applied to the Metropolitan Area of Barcelona for April and December 2023 at a 10 m spatial resolution. Results show that evergreen needleleaf forests and croplands act as significant carbon sinks in April, with biogenic CO₂ uptake offsetting approximately 10% of anthropogenic CO₂ emissions in April and 4% in December. A cross-city comparison of urban vegetation cover, climatic conditions, and the biogenic offset effects indicates that increased vegetation cover does not necessarily translate into a proportionally stronger carbon sink. Nevertheless, this study proposes that a standardized framework for accounting for biogenic CO₂ fluxes and uptake should be established to provide critical support for GI-based mitigation strategies in urban planning.

How to cite: Luo, Q., Segura-Barrero, R., Badia, A., Lauvaux, T., Li, J., Chen, J., and Villalba, G.: Quantifying Urban Biogenic CO2 Fluxes in Greenhouse Gas Budgets: A Scalable Framework and Case Study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2329, https://doi.org/10.5194/egusphere-egu26-2329, 2026.

This study presents a comprehensive climatological analysis of aerosol optical and microphysical properties over the Eastern Mediterranean, utilizing long-term AERONET measurements from Mersin/Erdemli (IMS-METU) site. The Generalized Retrieval of Aerosol and Surface Properties (GRASP) algorithm was used to retrieve key parameters, including Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA), and Volume Size Distribution, offering robust separation of surface and aerosol properties.

Results revealed a distinct seasonal cycle in aerosol loading, with AOD peaking in summer (July-August) due to fine-mode pollution and exhibiting a secondary peak in spring (April) driven by mineral dust transport from desert regions. The annual mean SSA displays a negative spectral slope, decreasing from ~0.93 at 440 nm to ~0.90 at 1020 nm, indicating a background atmosphere dominated by fine-mode urban-industrial aerosols. Although winter months exhibit the lowest total aerosol load due to wet scavenging, they display the strongest absorption characteristics. The imaginary refractive index significantly exceeds 0.015, and SSA values drop sharply during winter, attributed to Black Carbon emissions from domestic heating. The volume size distribution maintains a bimodal structure year-round; while the coarse mode dominates during spring dust events, the fine mode contribution remains substantial across all seasons. The aerosol population over the Eastern Mediterranean is characterized as a heterogeneous mixture where the anthropogenic fine-mode background is periodically modulated by natural mineral dust intrusions and local carbonaceous emissions.

How to cite: Çetiner, A. S. and Aslanoğlu, S. Y.: Long Term Analysis of Aerosol Properties Over the Eastern Mediterranean Using Grasp Retrieved AERONET Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3325, https://doi.org/10.5194/egusphere-egu26-3325, 2026.

 Meteorological normalization of surface ozone typically relies on air temperature to proxy both photochemical activity and boundary-layer dynamics. However, this approach implicitly assumes that the thermal state adequately represents radiative energy input—an assumption that remains untested in high-elevation environments where strong solar forcing and a thin atmosphere may decouple temperature from the surface energy balance. Here, we examine how surface energy forcing modulates ozone variability independently of air temperature using continuous station-level measurements in Lhasa (3650 m a.s.l.), Tibetan Plateau. By stratifying days based on net radiative input while explicitly constraining thermal conditions through a counterfactual matched-pair analysis, we isolate energy-driven processes without invoking reanalysis-based boundary-layer estimates. Results demonstrate that high-energy states consistently exhibit enhanced morning ozone growth (median +4.3 ppb h^-1) and elevated daytime concentrations relative to temperature-matched low-energy states. These enhancements are accompanied by coherent multi-tracer responses, including moisture drying and the dilution of primary pollutants, which provide observational constraints on energy-driven vertical coupling that are distinct from temperature-dependent photochemistry. Furthermore, a rate-based robustness analysis confirms that these signals persist across varying stratification thresholds. We conclude that surface energy forcing represents a previously under-constrained structural factor in conventional ozone attribution frameworks, particularly in complex terrain where thermal and radiative states frequently decouple. 

How to cite: Zhao, C., Wang, Y., Liu, Y., Li, W., Gongga, D., Quzhen, D., Ao, Y., Yue, J., Zhong, X., and Du, X.: Surface energy forcing modulates ozone variability independently of air temperature over the Tibetan Plateau, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4386, https://doi.org/10.5194/egusphere-egu26-4386, 2026.

As growing environmental pressures challenge urban resilience, sustainability, and human well-being, the lack of high-resolution geospatial information at the urban or intra-urban scale remains a critical limitation for effective and targeted decision-making. In the context of air quality and associated health impacts, this study addresses this gap by developing the Atmospheric SpatioTemporal Emissions Model (AtmoSTEM), a high-resolution spatiotemporal framework for representing atmospheric emissions, pollutant concentrations, and population exposure at 1 km² resolution. The application focuses on the Ioannina basin in Greece, where residential biomass burning (BB) constitutes a dominant emission source, especially during the cold season, frequently leading to pollution levels exceeding the World Health Organization (WHO) Air Quality Guidelines (AQG) and EU thresholds and underscoring the need for targeted interventions.

For this purpose, a high spatiotemporal emission inventory for Residential Heating is developed. The Copernicus Atmosphere Monitoring Service (CAMS) regional emission inventory, structured according to the GNFR classification and provided at a spatial resolution of 0.05° × 0.1°, serves as the baseline dataset. The downscaling process is based on publicly available, open-access, GNFR-dependent high-resolution spatial proxies, including the Coordination of Information on population density data from Global Human Settlement (GHSL), land-use classifications from the Copernicus Land Monitoring Service (CLC 2018), the OpenStreetMap (OSM) road network, and, where applicable in coastal and maritime domains, marine traffic density from the European Marine Observation and Data Network (EMODNet). Particular emphasis is placed on refining pollutant fields that are more relevant to BB activities, thereby improving the spatial representativeness of BB emissions within urban and peri-urban environments.

To capture the temporal variability of the emissions, CAMS is combined with CAMS temporal Regional Profiles (CAMS-TEMPO), enabling the generation of analytically resolved, hourly emission estimates. Pollutant concentrations are then estimated using a Random Forest machine learning model that integrates AtmoSTEM’s high-resolution emissions, with meteorological, satellite-derived, and spatial data, as well as in-situ air quality measurements provided by the University of Ioannina. The resulting high-resolution concentration fields are evaluated against independent in-situ measurements. Additionally, BB-related PM_2.5 fields are derived and analyzed, enabling improved source-specific characterization of residential heating contributions and providing a physically consistent basis for air-quality and exposure assessments.

How to cite: Kakouri, A., Filippis, G., Korras-Carassa, M.-B., Kuenen, J., Hatzianastassiou, N., Matsoukas, C., and Kontos, T.: AtmoSTEM: A high-resolution spatiotemporal emission model for urban air quality applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5251, https://doi.org/10.5194/egusphere-egu26-5251, 2026.

We present an automated procedure that combines lidar measurements, ADS-B flight tracks and ECMWF ERA5 meteorological data to detect and characterise nighttime aircraft contrails. Measurements have been carried out at the Observatory of Haute-Provence (OHP) in France. Lidar scattering-ratio profiles were processed with a sensitivity-driven spatio-temporal discrimination algorithm to identify contrail “spots” and aggregate them into contrail signatures. A parameter score identifies an optimal discrimination threshold set that balances sensitivity and false positives. In our case, these thresholds took these values: scattering ratio SR ≈ 2.1; temporal aggregation ≈ 7.2 min; vertical separation ≈ 0.3 km. Applied to five nighttime events, the method yields mean contrail altitudes of 8.7–10.3 km, geometrical thicknesses of 0.1–1.1 km, horizontal widths 2–3 km, and optical depths (COD) of ≈0.05–0.40. Persistent contrails are associated with ice-supersaturated layers and temperatures below −41 °C. Contrail optical depth resulted well correlated with both vertical thickness and horizontal extent. We have demonstrated that combining lidar with ADS-B and ERA5 substantially improves detection and discriminates contrails from natural cirrus at night, a regime where passive satellite retrievals are limited. This approach is automatic, transferable and reproducible, offering robust validation data for satellite algorithms and improved contrail parameterizations in climate models.

How to cite: Mandija, F., Keckhut, P., Alraddawi, D., Irbah, A., Sarkissian, A., Khaykin, S., Peyrin, F., and Baray, J.-L.: Automated nighttime contrail detection using spatio-temporal clustering of Raman lidar measurements , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5443, https://doi.org/10.5194/egusphere-egu26-5443, 2026.

Smoke aerosols constitute a critical component of atmospheric pollutants and radiative forcing agents. Northeast China is frequently afflicted by smoke episodes. Driven by the combined impacts of anthropogenic emissions, residential heating, agricultural biomass burning, and other factors, this region exhibits complex aerosol characteristics with pronounced seasonal variations. This study systematically evaluates the spatiotemporal evolution of smoke aerosols from 2015 to 2023 using GEOS-Chem global simulations (2°×2.5°), along with ground-based PM2.5 measurements, sun photometer AOD measurements in Harbin, MODIS, and VIIRS fire data. We classified the region into six distinct sub-regions based on smoke concentration characteristics: four urban zones (Dalian, Shenyang, Changchun, Harbin) and two rural zones (Eastern Coastal and Western Inland). Observational and simulation data demonstrates that the model captures regional seasonal variability and annual trends. Employing the HYSPLIT model and Concentration Weighted Trajectory (CWT) analysis, we identified potential external source regions and initially assessed the relative contribution of cross-regional transport (e.g., from Siberia or North China) to smoke episodes across different seasons. This comprehensive analysis provides a scientific basis for understanding the climatic effects of aerosols and formulating refined regional air quality management strategies in Northeast China.

How to cite: Gao, X. and Miatselskaya, N.: Long-term spatiotemporal evolution and source attribution of smoke aerosols in Northeast China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8607, https://doi.org/10.5194/egusphere-egu26-8607, 2026.

The Indo-Gangetic Plain (IGP) experiences strong seasonal and spatial heterogeneity in aerosol composition, driven by variations in emissions, meteorology, and regional transport. Capturing these variations requires measurement approaches that extend beyond conventional fixed-site monitoring. In this study, we deployed a mobile lab platform equipped with aerosol instrumentation, including a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS), to investigate seasonal variability in organic aerosol (OA) sources during the post-monsoon and spring seasons across distinct urban environments in Lucknow, located in the central Indo-Gangetic Plain.

Field campaigns were conducted during the post-monsoon and spring seasons at Babasaheb Bhimrao Ambedkar University (BBAU), a site influenced by traffic near major highways, and at the Council of Scientific & Industrial Research–Central Institute of Medicinal and Aromatic Plants (CSIR-CIMAP), located adjacent to a forested area. Additional measurements were conducted during the spring season at the Uttar Pradesh Pollution Control Board (UPPCB) site, which represents a residential–commercial environment.

At each location, the mobile laboratory was operated for approximately 10–15 days, enabling continuous, near real-time characterization of fine particulate matter and associated co-pollutants. Measurements of non-refractory PM_2.5 (NR-PM_2.5) chemical composition (measured using HR-ToF-AMS) were supported by simultaneous observations of trace elements, black carbon, gaseous species, total PM_2.5 mass, and meteorological parameters. This integrated, multi-instrument framework allowed for a consistent comparison of aerosol chemical signatures across sites and seasons, while capturing short-term variability linked to local emissions, atmospheric processing, and regional transport.

Organic aerosol dominated the mass of NR-PM_2.5 across all sites and seasons, contributing more than 50% of the total NR-PM_2.5. Source apportionment using Positive Matrix Factorization (PMF) with the multilinear engine (ME-2) resolved hydrocarbon-like OA (HOA), biomass-burning OA (BBOA), oxidized biomass-burning OA (O-BBOA), and secondary oxygenated OA components (SVOOA and LVOOA). During the post-monsoon period, BBOA accounted for approximately 28–40% of total OA across the sites, indicating a strong combustion influence under shallow boundary-layer conditions. Traffic-related HOA contributed about 8–13% of OA, with enhanced fractions at the highway-influenced BBAU site, reflecting local vehicular emissions. In contrast, springtime conditions showed enhanced secondary OA contributions (70-60%), with trajectory-based analyses highlighting the role of long-range transport in shaping aerosol composition.

The use of a mobile laboratory enabled rapid deployment across diverse land-use environments while maintaining consistent instrumentation and methodology, allowing robust inter-site and inter-seasonal comparisons. This approach emphasises the significance of high-resolution mobile observations for elucidating the fine-scale spatial variability and seasonal evolution of organic aerosol sources in the complex urban regions of the IGP.

How to cite: Lakra, A., Tripathi, S. N., Sethi, D., Kumar, A., Bhowmik, H. S., and Shukla, A. K.: Seasonal variation of organic sources during the post-monsoon and spring seasons across multiple urban sites of the Indo-Gangetic Plain using a mobile lab platform, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8822, https://doi.org/10.5194/egusphere-egu26-8822, 2026.

The impact of increasing anthropogenic hydrogen (H₂) emissions on Earth’s radiative balance depends on the soil microbial H₂ sink—the largest and most uncertain term in the global H₂ budget. Soil moisture is a primary but poorly quantified control regulating the soil sink. Here, we assess the sensitivity of microbial H₂ oxidation to soil moisture in laboratory experiments with temperate and arid soils spanning distinct textures. H₂ oxidizer activity is observed down to –70 to –100 MPa water potentials across soils, which are among the driest conditions reported for microbial activity and are much drier than assumed in global simulations of H₂. Using genome-resolved meta-omics, we link H₂ oxidation dynamics in temperate soils to specific desiccation-adapted microbial taxa that contribute differentially to H₂ uptake along the moisture gradient. Through global simulations, we show that our observationally constrained drier moisture threshold increases the contribution of arid and semi-arid regions for soil H₂ uptake by 4-7 percentage points (pp), while decreasing the contribution of temperate and continental regions (−7 pp). Our results highlight the importance of H₂ uptake under extreme hydrological conditions, particularly the roles of desertification, dryland expansion, and H₂-oxidizer ecophysiology in modulating long-term changes in H₂ uptake.

How to cite: Reji, L., Bertagni, M., Paulot, F., Qin, Q., and Zhang, X.: Global Implications of a Low Soil Moisture Threshold for Microbial Hydrogen Uptake , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11855, https://doi.org/10.5194/egusphere-egu26-11855, 2026.

A closed to open-celled transition of mixed-phase clouds within a marine cold air outbreak (MCAO), driven by an occluded cyclone over the Nordic Seas, is interrogated with data acquired during the Cold Air Outbreak Experiment in the Sub-Arctic Region (CAESAR) field campaign on 29 February 2024. The understanding of these transitions is a challenge for numerical prediction models due to their small scale but important for weather and climate prediction, as open celled conditions have stronger updrafts supporting locally high precipitation rates along with a lower cloud fraction (lower albedo) than closed celled conditions. Measurements indicate that a stratiform (closed cell convective) cloud deck with cloud top heights of ~1230 m and liquid water paths (LWP) of ~130 gm^-2, within a boundary layer with aerosol number/CCN surpassing 600 cm^-3, deepen to heights of ~1520 m with LWPs of ~270 gm^-2 over 250 km of fetch, before transitioning into an open-celled convective structure with cloud tops reaching up to 2220 m. Cooling free tropospheric temperatures with fetch, which reduce the inversion strength thereby enhancing growth by entrainment may encourage boundary layer growth. Open-cells are  more glaciated than closed-cells with mean LWPs falling from 270 gm^-2 to 80 gm^-2 across the transition, however isolated peaks of LWP within updrafts of open cells occasionally surpass 500 gm^-2. Minimal secondary ice production (SIP) is observed in closed cells with ice nucleating particle and ice number concentrations ~2 L^-1 with cloud temperatures between -20^oC and -15^oC. In open cellular convection (cloud temperatures between -22^oC and -15^oC), ice number concentrations reach ~10 L^-1 indicating SIP. High aerosol concentrations are hypothesized to support the maintenance of closed-celled convection, with 80% of drops smaller than 10 μm reducing the riming efficiency. Small droplets also limit the production of freezing drizzle, which is hypothesized to limit the potential of SIP due to freezing/fragmentation within closed cell convection. Only after aerosol concentrations are depleted through scavenging and/or entrainment are SIP processes able to become more effective and precipitation particles able to grow large and dense enough to reach the surface and form cold pools breaking up the cloud deck. Plumes of warm moist air lifting off the ocean surface, juxtaposed with cold pools and entrainment events penetrating to the surface are documented using the Multi-function Airborne Raman Lidar (MARLi) in the first observations of its kind.

How to cite: Ephraim, S., Zuidema, P., Bansemer, A., Cai, L., Cruikshank, O., Evengeliou, N., French, J., Geerts, B., Grasmick, C., Massling, A., McFarquhar, G., Noel, G., Petters, M., Rosky, E., Skov, H., Snider, J., Tatro, T., Wang, Z., Woods, S., and Zhang, L.: Closed to Open-Celled Mixed-Phase Cloud Transition Over the Nordic Seas Under High Aerosol Loading, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12630, https://doi.org/10.5194/egusphere-egu26-12630, 2026.

Urban vegetation plays a key role in modulating atmospheric carbon dioxide (CO₂) in megacities. However, studies that explicitly quantify the effect of urban vegetation on CO₂ remain scarce. This study investigates how vegetation cover affects CO₂ mixing ratios in the Metropolitan Area of São Paulo (MASP) during 2020–2022, using four monitoring sites with contrasting vegetation fractions: Pico do Jaraguá (PJ; 59.75%), IAG (36.38%), ICESP (22.42%), and UNICID (10.42%). Vegetation cover was derived from Sentinel-2 Level-2A imagery using NDVI-based pixel classification, while CO₂ observations were obtained from the METROCLIMA network and analyzed together with concurrent meteorological variables (temperature, humidity, and wind). The analysis comprised characterizing temporal variability and quantifying vegetation effects using regression models and probability distribution functions (PDFs). Clear seasonal and diurnal patterns were observed, with lower CO₂ concentrations during summer and afternoon hours (420-414 ppm), and higher values during winter and nighttime periods (447-425 ppm). The greener and less urban site, PJ, exhibited the lowest and most stable CO₂ levels, whereas the highly urban UNICID site showed the highest average mixing ratios. Elevated CO₂ values at IAG (428.30 ppm in summer and 435.49 ppm in winter), despite substantial vegetation cover, suggest the influence of local emissions and boundary-layer dynamics, while relatively low CO₂ values at ICESP (422.10 ppm in summer and 428.03 ppm in winter) likely reflect the elevated measurement height (~100 m a.g.l.), which favors regional-scale mixing and reduces sensitivity to local emission sources. NDVI revealed a bimodal phenological cycle (April–May and October–November), which was mirrored by CO₂ variability at PJ. Among 132 fitted PDFs, the Normal Inverse Gaussian distribution best captured CO₂ variability, with greener sites showing flatter and more symmetric distributions and urban sites showing increased skewness and peakedness. Regression results indicate a significant vegetation signal: a 0.1 increase in NDVI was associated with CO₂ reductions of ~3.92 ppm (PJ), 2.81 ppm (IAG), and 7.66 ppm (ICESP; likely conditional on site features), while no significant effect was detected at UNICID. Overall, urban vegetation influences both mean CO₂ levels and their distributional characteristics, supporting the role of green infrastructure and phenology in urban carbon management.

Keywords: carbon dioxide, vegetation cover, linear regression, Metropolitan Area of São Paulo.

How to cite: Piscoya, J., Franco, M. A., and Andrade, M. D. F.: Influence of Vegetation Cover on Atmospheric CO2 Mixing Ratios in the São Paulo Metropolitan Area, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13522, https://doi.org/10.5194/egusphere-egu26-13522, 2026.

Aerosol optical depth (τ) is routinely reported at wavelengths that are not directly measured by ground-based sun photometers, in particular at 550 nm for satellite validation and at longer wavelengths for short-wave infrared applications. These values are typically obtained by spectral interpolation or extrapolation, most often using linear or quadratic regressions in logarithmic space. However, the uncertainty structure of such regressions is frequently treated incorrectly, because measurement uncertainties in τ are absolute and approximately wavelength-independent in linear space, and therefore become wavelength-dependent in logarithmic space. As a result, the measurement covariance matrix must be explicitly accounted for in log–log regression, although this is rarely done in practice. This study provides both a formal and a practical framework for estimating τ at non-measured wavelengths together with its associated uncertainty. A rigorous formulation is presented for linear and quadratic regression in logarithmic space, including the propagation of random and systematic (bias-related) errors from the original spectral measurements to the interpolated or extrapolated wavelength.

Sensitivity analyses based on synthetic aerosol optical depth spectra generated with the GRASP forward model are used to compare six different approaches for deriving τ(550) and τ(2000), including linear and quadratic regressions over different spectral ranges as well as the GRASP-AOD method. When the covariance matrix is treated correctly, quadratic log–log regression is found to be the most robust method for estimating τ(550), and its results become essentially independent of the chosen spectral range. In contrast, when the covariance matrix is neglected, the same regression becomes highly sensitive to the selected wavelengths, and artificially improved performance is obtained when restricting the fit to the central AERONET channels. These findings are confirmed using real AERONET observations. When the full covariance treatment is applied, differences between estimates obtained using different spectral ranges remain below 0.002 at all sites analysed. When it is ignored, root-mean-square differences exceeding 0.01 are observed at sites dominated by fine-mode aerosols.

Finally, the uncertainty propagation framework is applied to real data and shows that the uncertainty of interpolated τ follows the expected Beer–Lambert law governing sun-photometer measurements, scaling with optical air mass. This provides an independent validation of the formal error model. Overall, this work establishes a consistent methodology for spectral interpolation and extrapolation of τ, ensuring both accurate values and physically meaningful uncertainties for satellite validation and related applications.

How to cite: Torres, B., Dubovik, O., Toledano, C., Fuertes, D., Momoi, M., Kazadzis, S., Marbach, T., Lind, E., Roman, R., Veloso Varela, M., and Barreto, A.: Inferring aerosol optical depth at unmeasured wavelengths from ground-based spectral photometer data: uncertainty-consistent regression, sensitivity tests, and application to real data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13698, https://doi.org/10.5194/egusphere-egu26-13698, 2026.

Past aerial hyperspectral mapping campaigns and pilot studies have demonstrated that highly concentrated plumes are a significant portion of California’s total methane emissions, including many unintentional leaks that can be fixed quickly when operators are notified. Satellite plume imagers such as Planet’s Tanager offer the capacity for repeated observations of known methane infrastructure, with enough spatial resolution and sensitivity to address a significant fraction of these leaks by identifying source facilities and contacting operators. Here we present system design and first results from the California Satellite Methane Project (CalSMP), a comprehensive multi-sector effort to notify individual operators of plumes within days of detection and ensure prompt mitigation when possible through a mix of direct regulation and voluntary dialogue with operators.

In May 2025, CARB began retrieving low-latency Tanager plume detections purchased from Carbon Mapper. Using a cloud-based system developed in-house, CARB employees oversee a semi-automated process to assign plumes to a source and facilitate information exchange with operators. The system generates a notification email with instructions and response forms tailored to the specific facility type (e.g. landfill, oil and gas, dairy biogas). These responses allow us to categorize emissions across sectors by emission type (e.g. unintentional, temporary, process) as well as identify sector-specific components or infrastructure (landfill gas collection system, gas well stuffing box) and details of any repairs.

CARB plans to expand its spatiotemporal coverage through additional satellites, with increased automation as we scale up. While the project’s initial focus is direct repair of unintentional leaks, operator responses also effectively survey underlying causes of point-source emissions and can inform future efforts to improve industry operational practices. CARB has dedicated community outreach funds to ensure methane observations are accessible, understandable, and useful to communities, and is committed to sharing technical details, project design, and lessons learned with other jurisdictions to maximize global mitigation efforts.

How to cite: Phillips, D., Yang, E., Schroeder, J., Zelinka, S., Frausto-Vicencio, I., Fibiger, D., and Herner, J.: From Detection to Mitigation: The California Satellite Methane Project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14127, https://doi.org/10.5194/egusphere-egu26-14127, 2026.

We quantify climate effects of Tambora-scale eruptions under current and future warming using the SOCOL-MPIOM coupled atmosphere-chemistry-ocean model for 2020, SSP2-4.5, and SSP3-7.0 (2080) scenarios.

Global cooling amplifies counterintuitively with background warming: SSP3-7.0 shows 44% stronger cooling than present-day due to polar vortex intensification increasing from 5.5% to 44.5%. Regional responses reveal complex patterns: winter exhibits Arctic warming (+0.4-0.6°C) simultaneous with tropical cooling (up to -8°C) and continental extremes (up to +15°C). Summer brings widespread continental cooling (-2 to -4°C). Monsoon systems weaken by 20-35% while mid-latitude winter precipitation intensifies by 20-40%.

Results demonstrate that volcanic impacts under anthropogenic warming generate spatially heterogeneous extremes rather than uniform cooling, critical for agricultural and water resource risk assessment.

How to cite: Tkachenko, M. and Eugene, R.: Climate Response to Tambora-Scale Volcanic Eruptions Under Present and Future Climate Conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14645, https://doi.org/10.5194/egusphere-egu26-14645, 2026.

Cloud detection over snow- or ice-covered (S/IC) surfaces remains a critical challenge in satellite remote sensing. The cloud-like high surface albedo and ice-cloud-like brightness temperatures of these surfaces often lead to systematic misclassification in visible- and infrared-based algorithms, including the threshold-based cloud detection applied to Sentinel-5P atmospheric composition retrievals. Misclassified clear-sky scenes can introduce biases in the retrieved total columns of ozone, sulfur dioxide, and nitrogen dioxide, while misclassified cloudy scenes reduce the spatial coverage of satellite products.

To address this challenge, we develop a global cloud detection algorithm based on absorption images derived from Sentinel-5P oxygen A-band observations. The algorithm exploits the reduction of oxygen absorption in cloudy pixels, as cloud layers reflect solar radiation before it reaches the underlying surface, thereby shortening the radiative transfer path in the atmosphere and reducing absorption along the path. In addition, spatial texture information extracted from oxygen absorption images is incorporated to enhance sensitivity to optically thin and broken clouds, enabling more robust discrimination between clouds and bright underlying surfaces. This physical mechanism makes the algorithm insensitive to surface type, rendering it particularly suitable for global cloud detection, including over S/IC surfaces.

Validation against CALIPSO demonstrates a marked improvement in cloud detection performance across diverse surface and cloud conditions. The proposed algorithm achieves an overall accuracy of 91%, compared with 85% for the Suomi-NPP product and 48% for the operational Sentinel-5P product. Improvements are particularly pronounced over S/IC surfaces, where detection accuracy increases by 15% relative to Suomi-NPP. Additionally, detection accuracy for optically thin clouds improves by 20% globally, with the largest gains (up to 52%) observed over S/IC surfaces. These results demonstrate the value of oxygen absorption and spatial texture features for cloud detection, especially over S/IC surfaces, and support improved quality and consistency of satellite-based atmospheric observations over polar and other bright-surface regions.

How to cite: Liu, H. and Li, S.: Cloud Detection over Snow- or Ice-covered Surfaces Using Oxygen A-Band Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15531, https://doi.org/10.5194/egusphere-egu26-15531, 2026.

Simulations of aviation's air quality impacts near airports are critical for enhancing our understanding of how aviation impacts air quality regionally, and the potential effects of sustainable alternatives. In order to better understand such impacts, a research to investigate the effects of the resolution of the simulation grid and of the meteorology inputs on the air quality impact estimates due to aircraft emissions, specifically in the context of stretched gnomonic cubed-sphere grids for the simulation of a specific area was conducted. Such grids allow for the possibility of having a region with finer grid elements while coarsening the grid outside a specified area.

The research questions that the present research effort aims to address are: which parameter (grid resolution or meteorology resolution) impacts most the simulations, how grid and meteorology resolution impact air quality estimates, and whether stretched grids can be used for regional simulations.

To address the matter, we use the distributed-memory, high-performance version of the GEOS-Chem atmospheric chemistry-transport model to simulate the evolution of aviation attributable to Landing and Take-Off operations (LTO) emissions throughout the year of 2019. The LTO emissions were obtained from the OpenAVEM emissions inventory, whereas the remaining non-aviation emissions were taken from the default GCHP databases.

Three different grid resolutions were chosen to evaluate the impact of the horizontal grid resolution: C24, C36, and C48, with grid cell lengths ranging between 40 km, 30 km, and 20 km, respectively. All grids use the same stretch parameters, i.e., target latitude, target longitude, and stretch factor. These parameters were set so as to have a finer resolution around Europe. For the meteorology sensitivity, two resolutions were used, 2 ° ×2.25 ° and 0.5 ° ×0.625 ° from MERRA2 for the three grid resolutions aforementioned.

A comparison between the area weighted concentrations for NO₂, PM_2.5, and O₃ showed that the resolution of the meteorology plays a more important role than the horizontal grid resolutions, for the resolutions tested. For the human health impacts, the deaths attributable to each component have also been estimated and compared for each grid resolution.

How to cite: Kavabata, L., Quadros, F., Meijer, V., Snellen, M., and Dedoussi, I.: Horizontal grid and meteorology resolution impacts on aviation’s air quality impacts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15593, https://doi.org/10.5194/egusphere-egu26-15593, 2026.

Abstract. This work presents the incorporation of a tropospheric isoprene oxidation scheme into an Earth System Model to enhance the simulation of tropospheric ozone levels. Numerical experiments were performed using two distinct model setups: one accounting for isoprene oxidation and another in which this chemical pathway was not considered.

Keywords: Isoprene, tropospheric ozone, atmospheric chemistry, MIM1 mechanism

Understanding the processes of tropospheric ozone formation is of key importance both for the development of air quality control measures and for climate prediction, especially under conditions of changing anthropogenic and biogenic emissions. While on the global scale the production of tropospheric ozone is primarily governed by the oxidation of carbon monoxide and methane, in densely populated and industrial regions non-methane volatile organic compounds (NMVOCs) become the dominant contributors. Among these NMVOCs, isoprene plays a particularly important role, with the majority of its atmospheric emissions originating from vegetation.

The aim of this study is to further develop the INM RAS–RSHU chemical–climate model [1], which is a component of the Earth System Model (ESM), with an emphasis on a more accurate representation of tropospheric chemical processes. The primary focus is on the implementation of an improved chemical mechanism designed to enhance the accuracy of simulated concentrations of key atmospheric gaseous components. One of the main criteria in selecting the mechanism is achieving an optimal balance between the level of chemical detail and the computational efficiency of the model.

As part of the model development, a comparative analysis of several widely used chemical mechanisms was performed, including the Mainz Isoprene Mechanism (MIM1) [2], comprising 16 species and 44 reactions; MIM2, with 69 species and 178 reactions [3]; the Model for Ozone and Related Chemical Tracers (MOZART), including 151 species and 287 reactions [4]; and the Regional Atmospheric Chemistry Mechanism (RACM), which includes more than 100 species and 363 reactions [5]. Based on the results of this analysis, the MIM1 mechanism was considered the most appropriate for initial implementation in the ESM, as it was decided to begin with the most compact option while still providing sufficient accuracy in representing key tropospheric chemical processes.

To assess the impact of the MIM1 mechanism, two numerical experiments were conducted using identical model settings and boundary conditions. In the control simulation, a basic tropospheric chemistry scheme without isoprene was applied, whereas the MIM1 experiment implemented the full isoprene oxidation mechanism, including 44 chemical reactions.

 The study and the set of numerical experiments are aimed at optimizing the chemical component of the INM RAS–RSHU chemical–climate model in order to improve the accuracy of representing tropospheric processes while maintaining high computational efficiency. The obtained results provide a solid basis for further investigation of the interactions between chemical and dynamical processes in the atmosphere and will contribute to the development of approaches for forecasting atmospheric composition and its impact on regional and global climate change.

How to cite: Okulicheva, A., Tkachenko, M., and Smyshlyaev, S.: Numerical modeling of tropospheric chemistry in an Earth System Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17672, https://doi.org/10.5194/egusphere-egu26-17672, 2026.

The influx of anthropogenic metals into the atmosphere is expected to increase substantially due to the rapid growth of the space industry. More than 20 elements from re-entering spacecraft have been identified in sulphuric acid droplets in the Junge layer, with several estimated to surpass the background level from cosmic dust. While the atmospheric impact of these particles is uncertain, they have been widely hypothesised, including ozone destruction, increased polar stratospheric cloud formation, harmful surface deposition, a perturbed radiative balance and in turn, changes to global circulation.

The spacecraft ablation process and subsequent formation of space debris particles (SDPs) are not well defined. The dominant constituent of spacecraft is aluminium. If vaporised, aluminium is expected to undergo a series of reactions to form aluminium hydroxide (Al(OH)3). The initial form and size of the particles will strongly influence the coagulation, global transport, and atmospheric lifetime of the particles. Constraining these factors is vital to accurately assessing the impact SDPs have on the atmosphere.

This work provides an update on the work presented at EGU2025 (Egan et al., Modelling impacts of ablated space debris on atmospheric aerosols, EGU25-4460), using the Whole Atmosphere Community Climate Model with the Community Aerosol and Radiation Model for Atmospheres (WACCM-CARMA) to simulate the production and transport of SDPs. This work investigates the sensitivity of the initial particle radius to the transport, lifetime and surface deposition of particles.

How to cite: Hawkins, S., Egan, J., Plane, J., Marsh, D., and Feng, W.: Modelling the transport of ablated space debris particles in the atmosphere, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19003, https://doi.org/10.5194/egusphere-egu26-19003, 2026.

Solid fuels such as fuelwood (FW), coal balls (CB), dung cake (DC), and agricultural residue (AR) are widely used for domestic heating and cooking in developing countries, particularly in rural and peri-urban regions. Combustion of these fuels is a major source of carbon-based gaseous emissions, notably carbon monoxide (CO) and carbon dioxide (CO₂), contributing to indoor air pollution, adverse health effects, and climate change. The emission characteristics of these gases are strongly influenced by fuel moisture content, elemental composition, and inorganic constituents. This study presents the development of emission factors (EFs) for CO and CO₂ from commonly used solid fuels and evaluates materials-based mitigation strategies using a laboratory-designed fixed-bed reactor system with a combustion chamber simulating real-world burning conditions.

Fuel samples were collected from representative domestic sources, air-dried, pulverized, and homogenized prior to analysis. Moisture content was determined gravimetrically by oven drying at 105 °C. Ultimate analysis of carbon (C), hydrogen (H), nitrogen (N), sulfur (S), and oxygen (O) was performed using a CHNS/O elemental analyzer, while anionic and cationic species were quantified using ion chromatography. Combustion experiments for emission factor development were conducted in a custom-designed fixed-bed reactor equipped with a controlled burning chamber to simulate domestic heating conditions. The reactor enabled stable combustion, controlled airflow, and downstream integration of mitigation materials for post-combustion treatment of exhaust gases. It also includes real-gas cylinders to generate the gas mixture representing smoke.

The results revealed notable variability in fuel composition and emission behavior. FW exhibited relatively efficient combustion with lower CO emissions, while DC, with higher moisture and lower carbon content, produced higher CO levels. CB showed high CO emissions despite its carbon content, whereas AR displayed intermediate emission characteristics. Elevated levels of alkali metals and anions, particularly in DC, were associated with reduced combustion efficiency and increased CO formation.

The hazards of these gases demands for removal. In this study, removal experiments were carried out by integrating advanced functional materials into the exhaust section of the fixed-bed reactor. Porous and nanostructured materials such as graphene-based materials, biochar, graphitic carbon nitride (g-C₃N₄), zeolites, metal–organic frameworks (MOFs), covalent organic frameworks (COFs), and silica-based materials were evaluated for CO oxidation and CO₂ capture. Material characterization using Brunauer–Emmett–Teller (BET) surface area analysis, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) confirmed high specific surface area, well-developed porous structures, and the presence of reactive surface functional groups, which directly enhance adsorption and catalytic conversion of carbonaceous gases.

Overall, the study demonstrates that fuel chemical composition and combustion conditions strongly influence CO and CO₂ emission factors from domestic heating activities. The integration of a designed fixed-bed reactor with a burning chamber and advanced materials-based mitigation strategies provides a robust experimental framework for reducing carbon-based emissions and improving air quality in regions dependent on traditional solid fuels.

How to cite: Sahu, D. and Pervez, S.: Emission of Carbon Monoxide (CO) and Carbon Dioxide (CO2) from Household Solid Fuels Burning Practices: Development of Real World Emission Factor and Removal Methods, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20738, https://doi.org/10.5194/egusphere-egu26-20738, 2026.

Field-based observations of Carbon dioxide (CO₂) exchange between soils and atmosphere are critical to accurately account for terrestrial carbon cycling in data-scarce West African savanna ecosystems. This study quantified soil CO₂ fluxes over two consecutive years (2023–2024) using a static chamber approach across four contrasting land-use systems namely forest, grassland, cropland, and rice fields. Measurements were conducted on weekly basis using replicated chambers to assess both spatial heterogeneity and interannual variability. Soil CO₂ fluxes were analysed in relation to key environmental drivers, including water-filled pore space (WFPS) and soil temperature, using mixed-effects statistical models to account for repeated chamber measurements. Across all land uses, CO₂ emissions increased markedly in 2024 compared to 2023. Median seasonal CO₂ fluxes ranged from 0.59 to 1.46 t C ha⁻¹ season⁻¹ in forest systems, 1.91 to 5.07 t C ha⁻¹ season⁻¹ in grasslands, 1.75 to 5.09 t C ha⁻¹ season⁻¹ in croplands, and 1.84 to 2.61 t C ha⁻¹ season⁻¹ in rice fields. Grasslands and croplands consistently exhibited the highest CO₂ emissions, with maximum values reaching 7.18 and 5.38 t C ha⁻¹ season⁻¹, respectively, highlighting the strong influence of land management and disturbance intensity. Forest soils showed comparatively lower CO₂ fluxes, reflecting reduced soil disturbance and more stable microclimatic conditions. Statistical analyses revealed that soil temperature was a dominant driver of CO₂ emissions across all ecosystems, while soil moisture exerted a secondary but significant control, particularly in managed systems. Higher WFPS and elevated soil temperatures during the wet season were associated with enhanced CO₂ release, indicating intensified microbial respiration and root activity. Interannual contrasts suggest that wetter and warmer conditions in 2024 amplified soil respiration across all land uses. Overall, our results demonstrate pronounced spatial and temporal variability in soil CO₂ fluxes in the Sudanian savanna and underscore the sensitivity of carbon emissions to land-use change and hydro-climatic variability. These findings provide critical baseline data for improving regional carbon budgets and for informing mitigation strategies in data-scarce tropical savanna regions.

How to cite: Oussou, F. E. and the WASCAL CONCERT Team: Assessing Spatial and temporal heterogeneity of Soil Carbon emissions across anthropized land use Gradient in the Sudanian savanna, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21511, https://doi.org/10.5194/egusphere-egu26-21511, 2026.

Anthropogenic fires linked to the burning of agricultural biomass residues represent a recurring environmental disturbance in the province of Tucumán, northwest Argentina. These events are predominantly associated with land clearing and post-harvest burning in sugarcane fields concentrated in the region’s lowland plains, where agro-industrial activity is most intense. Such practices contribute significantly to greenhouse gas (GHG) emissions, vegetation degradation, and a range of socio-economic impacts. This study integrates satellite-derived fire indices with environmental economic tools to quantify the spatial and temporal effects of fire over the past five years (2021–2025) and assess their implications for climate change mitigation and policy.

Multispectral data from Sentinel‑2 and atmospheric composition products from Sentinel‑5P were processed via Google Earth Engine to calculate vegetation and fire severity indices including NDVI, dNBR, and BAI. Additionally, tropospheric CO and CO₂ concentrations were used to evaluate atmospheric impacts. The spatial distribution of fire activity—primarily in the eastern and southern lowlands—was cross-referenced with the Global Fire Emissions Database (GFED) and IPCC Tier 1 emission factors to estimate fire-related GHG emissions. Preliminary analyses indicate an average of 45,000 ha affected annually, mainly in sugarcane-dominated landscapes, resulting in estimated emissions of approximately 382,500 t CO₂-equivalent per year.

To evaluate broader socio-environmental impacts, economic losses were estimated across multiple dimensions: reduced land productivity, costs of ecosystem restoration, loss of ecosystem services (e.g. carbon sequestration, water retention), and public health expenses related to degraded air quality. Additional indirect impacts include traffic accidents due to smoke-induced low visibility and recurring property damage reported in local media. These preliminary estimates suggest combined annual damages of approximately USD 46.5 million, underscoring the considerable burden imposed by current fire management practices.

It is important to note that this work presents ongoing research, and all results are preliminary. The estimates provided will be further refined through continued integration of field data, emission modeling, and economic valuation methods.

This integrative approach demonstrates the value of combining Earth observation technologies with environmental economics to support climate-oriented decision-making. By quantifying the environmental and economic impacts of anthropogenic fires, this study provides critical evidence for the development of cross-sectoral policies aimed at regulating biomass burning, improving land management practices, and strengthening resilience to climate risks. The case of Tucumán underscores the urgent need for sustainable alternatives to current residue management practices and for aligning agricultural production with mitigation goals.

How to cite: Reynoso Posse, F., Zbrun Luoni, J. P., and Aguilera Sammaritano, M.: Greenhouse gas emissions and socio-environmental costs of anthropogenic fires in Tucumán (Argentina): A remote sensing and environmental economics approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21709, https://doi.org/10.5194/egusphere-egu26-21709, 2026.

The new satellite missions including active sensors (e.g. EarthCare), passive multi-angular polarimeters (e.g. PACE/SPEXone, PACE/HARP-2) and single-viewing instruments (e.g. OLCI), together with synergies among existing sensors, are foreseen to characterize aerosols and clouds with high accuracy. However, robust validation activities are essential to ensure the quality of the new satellite products.

In this study, we focus on the evaluation of the aerosol optical properties synergistically retrieved from three sensors, i.e., TROPOMI, OLCI-A, and OLCI-B, within the framework of the AIRSENSE ESA project (https://www.grasp-earth.com/portfolio/airsense/). The derived optical properties include the aerosol optical depth (AOD), Ångström exponent (AE), coarse- and fine-mode AOD, and single-scattering albedo (SSA). Validation is performed against ground-based sun-photometer observations from five ACTRIS/AERONET stations across Europe (https://aeronet.gsfc.nasa.gov/). The results show good agreement for AOD with root-mean-square errors (RMSE) ranging from 0.006 to 0.09. In contrast, AE and SSA show lower agreement, with RMSE values of 0.27 and 0.02, respectively, at the Limassol station, even when quality flags are applied.

Moreover, we evaluate the aerosol properties retrieved using PACE/SPEXone observations. PACE (Plankton, Aerosol, Cloud, and ocean Ecosystem) mission was launched in February 2024 and employs advanced passive polarimetric observations to enhance the aerosol characterization. In addition to aerosol optical properties (e.g., AOD, AE) the PACE/SPEXone products generated within the framework of AIRSENSE, include the aerosol layer height (ALH), a parameter that is critical for quantifying aerosol-cloud interactions. Since EarthCARE/ATLID provides vertically resolved aerosol profiles, it offers an independent reference for the assessment of ALH. Here, we present first comparison results of the PACE/SPEXone ALH product over the ocean, produced with two algorithms, RemoTAP and FastMAPOL, compared to EarthCARE/ATLID weighted backscatter heights. Overall, RemoTAP ALH products are systematically lower than those derived from EarthCARE/ATLID, whereas FastMAPOL retrieves a larger number of ALH estimates but exhibits lower overall agreement with the EarthCARE/ATLID reference. As a next step, we intend to expand the area of interest and increase the number of collocations.

Acknowledgements:

This research is financially supported by the PANGEA4CalVal project (Grant Agreement 101079201) funded by the European Union  and the AIRSENSE (Aerosol and aerosol cloud Interaction from Remote SENSing Enhancement) project, funded by the European Space Agency under Contract No. 4000142902/23/I-NS.

How to cite: Voudouri, K. A., Tsekeri, A., Karipis, A., Litvinov, P., Lopatin, A., Dubovik, O., Hasekamp, O., and Amiridis, V.: Evaluation of new polarimetric products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21713, https://doi.org/10.5194/egusphere-egu26-21713, 2026.

This work evaluates EarthCARE cloud products against ground-based Cloudnet retrievals at multiple sites in Europe. We focus on the comparison between the EarthCARE synergetic target classification product (AC-TC), with the Cloudnet target classification product, both derived from the synergy of lidar/radar measurements. As the two classifications have different aerosol/cloud types, a new common classification with the following classes is defined and used for direct comparison: Unknown, Clear, Liquid (Droplets T>0°C), Supercooled Liquid (Droplets T<0°C), Drizzle or rain, Drizzle & droplets, Ice, Ice & droplets, Melting ice possibly coexisting with droplets, Insects, Aerosol. Each AC-TC or Cloudnet target is assigned with a new class. Spatiotemporal collocation criteria are considered, along with visual inspection of the collocated scenes, to limit the dataset in homogenous scenes where the satellite and suborbital platform has detected similar clouds. Additionally, retrieved ice and liquid water cloud contents from Cloudnet and EarthCARE are compared to evaluate cloud microphysical properties. Τhe geographical diversity of the Cloudnet network, provides the advantage of investigating different atmospheric conditions in terms of clouds and aerosols, with abundant ice cloud occurrences in the northern sites and frequent liquid water clouds at the southern sites. This analysis aims to assess the consistency of cloud categorization and microphysical retrievals between the satellite and suborbital measurements, and to investigate the strengths and limitations of both approaches.

How to cite: Tsikoudi, I., Marinou, E., Pfeitzenmaier, L., Mashon, S., O'Connor, E., Karkani, D., Karipis, A., Voudouri, K. A., Kollias, P., Treserras, B. P., and Battaglia, A.: Cloud typing and microphysics: An EarthCARE-Cloudnet Comparison, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21851, https://doi.org/10.5194/egusphere-egu26-21851, 2026.

Remote sensing of aerosol variability over the Central Himalayas remains challenging because of complex terrain, strong elevation gradients in surface reflectance, and frequent cloud and snow contamination, which can bias passive aerosol optical depth (AOD) retrievals. In this study, we examine an apparent MODIS-derived AOD enhancement in 2011 over the Central Himalayas that deviates from the expected seasonal pattern of reduced aerosol loading during the monsoon and post-monsoon periods. The central objective is to determine whether this apparent anomaly represents a physically meaningful aerosol enhancement or is influenced by retrieval limitations in high-relief environments. We evaluate the MODIS anomaly using collocated CALIPSO observations, including vertically resolved aerosol extinction profiles and aerosol-layer optical depths. CALIPSO measurements show no evidence of persistently elevated aerosol layers corresponding to the MODIS enhancement, and aerosol extinction remains vertically shallow, indicating that the observed AOD anomaly is not associated with strong free-tropospheric aerosol intrusion. These results suggest that the apparent MODIS “spike” likely reflects a column-integrated enhancement dominated by near-surface aerosol and/or terrain–cloud–snow-related retrieval effects rather than a sustained elevated aerosol event. This study highlights the importance of integrating active lidar profiling with passive satellite retrievals to improve the interpretation of aerosol anomalies over mountainous regions and strengthens the basis for aerosol–cloud interaction assessments in the Himalayas.

How to cite: Bello, A., Bhardwaj, A., and Sam, L.: CALIPSO validation of an apparent MODIS AOD spike over the Central Himalayas (2011), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23052, https://doi.org/10.5194/egusphere-egu26-23052, 2026.

Tropospheric Emissions: Monitoring of Pollution (TEMPO) is observing air quality and
atmospheric composition over North America from a geostationary orbit since its operations
started in August 2023. TEMPO observes the continent every 40 to 60 minutes at a spatial
resolution on the order of ~ 2 x 4.5 km 2 . Together with the Geostationary Environment
Monitoring Spectrometer (GEMS, launch 2020) monitoring Asia and the Sentinel-4/UVN
(launch 2025) monitoring Europe, TEMPO is part of the current global constellation of
geostationary sensors devoted to the observation of air quality. Like GEMS and Sentinel-4/UVN,
TEMPO uses backscattered ultraviolet and visible solar radiation to retrieve atmospheric
amounts of key trace gases and aerosols associated with air quality and atmospheric chemistry.
Among the species retrieved from TEMPO observations of nitrogen dioxide and formaldehyde
are important to understand emissions and atmospheric chemistry, including the formation and
destruction of tropospheric ozone.

After multiple version updates over the first two years of the mission, the TEMPO Level 2 NO 2
and HCHO products have undergone significant enhancements to improve the performance and
accuracy of the slant column retrievals, air mass factor calculations and post-processing
corrections including destriping for NO 2 and background for HCHO. We illustrate the
performance of both retrievals (version 3 & 4), evaluating their fitting uncertainty and showing

comparisons with independent correlative measurements and other satellite products showcasing
small noise levels and remarkable accuracy with well quantified biases. We continue by
illustrating the capacity of TEMPO products focusing on different case studies showing
TEMPO’s high temporal and spatial resolution. We finalize discussing aspects of the retrieval
subject to improvement and our plans to address them.

How to cite: Gonzalez Abad, G., Nowlan, C. R., Chance, K., Liu, X., Chong, H., Fasnacht, Z., Flittner, D. E., Ghahremanloo, M., Henderson, B., Hou, W., Houck, J., Judd, L., Knowland, K. E., Shah, V., Wales, P., Qin, W., Valin, L., and Wang, H.: First two years of TEMPO nitrogen dioxide and formaldehyde observations:algorithm status and highlights, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23274, https://doi.org/10.5194/egusphere-egu26-23274, 2026.

The Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission is part of a global constellation of geostationary satellites, along with GEMS and Sentinel-4, dedicated to monitoring air quality across the Northern Hemisphere. TEMPO is the first geostationary satellite instrument to monitor air pollutants over North America on an hourly basis at nearly neighborhood-scale resolution, covering an area from Mexico City to the Canadian oil sands and from the Atlantic to the Pacific Ocean.TEMPO measures backscattered ultraviolet and visible radiation to observe several trace gases important to air quality, including ozone, nitrogen dioxide, and formaldehyde, with observations every 40–60 minutes and at a high spatial resolution of approximately 2 × 4.75 km². TEMPO was successfully launched in April 2023 and began nominal operations in October 2023. Since then, it has been continuously monitoring atmospheric pollutants across its observation domain.
This presentation summarizes the Version 4 (V04) updates and improvements to the TEMPO total-ozone (O3TOT) and ozone-profile (O3PROF) retrieval algorithms. This presentation also presents the evaluation of the upcoming V04 TEMPO O3TOT product through comparisons of total ozone columns (TOCs) with measurements from other satellite instruments (e.g., OMPS and TROPOMI) and ground-based instruments, including Pandora, Brewer, and Dobson spectrometers. The V04 TEMPO O3PROF algorithm, which is UV-only, is validated through comparisons of ozone profiles, tropospheric ozone, and 0–2 km ozone columns with those from the Tropospheric Ozone Lidar Network (TOLNet) and aircraft observations, as well as through validation with MLS, EPIC, TROPOMI, and OMI observations.

How to cite: Park, J., Liu, X., Bak, J., Chong, H., Chance, K., Hou, W., Houck, J., Abad, G. G., Nowlan, C. R., Wang, H., Yang, K., Flynn, L. E., Haffner, D. P., Flittner, D. E., Knowland, K. E., Johnson, M., Demetillo, M. A. G., Spurr, R., Li, C., and Zhao, X. and the TEMPO Validation Team and NASA's GMAO Team: The Status of the TEMPO Total-Ozone and Ozone-Profile Algorithm: V04 Updates and Comprehensive Evaluations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23276, https://doi.org/10.5194/egusphere-egu26-23276, 2026.

Low-cost air quality sensors (LCS) offer opportunities for expanding air monitoring networks in regions where reference-grade instrumentation is limited. Within the framework of the Improving Air Quality in West Africa (IAQWA) project, we deployed Real-time Affordable Multi-Pollutant sensors (RAMPs) in Abidjan (Côte d'Ivoire) and Accra (Ghana), to characterize fine particulate matter (PM_2.5) in contrasting urban environments. Prior to field deployment, each RAMP underwent a co-location period with reference monitors, and city-specific multilinear calibration models were developed incorporating both RAMP-reported PM_2.5 and relative humidity (RH). These calibration models were applied to correct the sensor data and improve measurement reliability under varying atmospheric conditions.

From February 2020 to June 2021, five (5) measurement sites in Abidjan and four (4) sites in Accra were monitored using a 15-second temporal resolution. These sites were selected to represent the dominant pollution sources in West Africa, particularly domestic fires and road traffic. The calibrated dataset enabled comparative analysis of diurnal, daily, and seasonal PM_2.5 variability. Both cities exhibited pronounced morning PM_2.5 peaks associated with traffic, while evening increases were more visible in residential areas, indicating contributions from domestic combustion. Seasonal contrasts were marked, with highest concentrations occurring during the long dry season (Harmattan), when long-range Saharan dust transport significantly enhanced particulate loading. During an intense dust episode in January 2021, calibrated RAMP data underestimated PM_2.5 relative to reference measurements, highlighting a known limitation of optical LCS under high mineral dust conditions.

Annual mean PM₂.₅ concentrations ranged from 17 to 26 µg m^-3 across sites, exceeding both the 2005 and 2021 WHO air quality guidelines. Variability within each city, especially between traffic-influenced and urban background locations, was greater than variability between the two cities. These findings demonstrate both the value of rigorously calibrated low-cost sensors for improving air quality knowledge in data-scarce urban regions, and the need for sensor performance considerations in environments influenced by episodic dust intrusions.

How to cite: Bahino, J., Giordano, M., Beekmann, M., Ramanchandran, S., and Yoboué, V.: Field-Calibrated Low-Cost Sensor Networks for PM2.5 Monitoring in West African Urban Environments: Insights from Abidjan and Accra, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-130, https://doi.org/10.5194/egusphere-egu26-130, 2026.

Harmful Algal Blooms (HABs) represent a growing threat to marine ecosystems, aquaculture, and public health in the Central Eastern Arabian Sea (CEAS). This study utilized 18S rRNA metabarcoding to characterize the absolute abundance and community composition of potentially toxigenic diatoms and dinoflagellates in the coastal waters of Goa. The analysis reveals a distinct and alarming prevalence of multiple genera associated with diverse toxin syndromes.

The dataset was dominated by a massive proliferation of the dinoflagellate Karenia (linked to Neurotoxic Shellfish Poisoning and ichthyotoxicity), which reached extreme abundances exceeding 51,000 reads per sample at the most impacted sites. Co-occurring with this bloom, spatially distinct hotspots of Paralytic Shellfish Toxin (PST) producers were identified, specifically Alexandrium and Gymnodinium spp., with Alexandrium counts peaking at over 5,200 reads. Notably, the potent PST producer Alexandrium tamiyavanichii was positively identified, alongside detections of Gymnodinium catenatum.

The diatom community also exhibited significant toxicity potential; the Amnesic Shellfish Poisoning (ASP) genus Pseudo-nitzschia displayed high relative abundance (up to ~3,700 reads), including the presence of P. pungens. Furthermore, vectors for Diarrhetic Shellfish Poisoning (DSP), including Dinophysis spp. and Phalacroma rotundatum, and Yessotoxin producers (Lingulodinium polyedra, Gonyaulax spinifera) were ubiquitously present at lower background levels.

These findings highlight a complex, multi-risk scenario where ASP, PSP, NSP, and DSP vectors coexist within the same coastal system. The distinct spatial separation observed between peak Karenia, Alexandrium, and Pseudo-nitzschia events suggests that heterogeneous environmental drivers are influencing specific HAB assemblages. This data underscores the critical need for broad-spectrum toxin monitoring beyond single-species surveillance in the region.

How to cite: Zedi, S. and Khandeparker, R.: Hidden Hazards in the Central Eastern Arabian Sea: Metabarcoding Reveals Co-occurrence of ASP, PSP, and NSP Vectors in the Coastal Waters of Goa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-414, https://doi.org/10.5194/egusphere-egu26-414, 2026.

Secondary organic aerosol (SOA) has been shown to significantly impact climate, air quality, and human health. Hydroxyl dicarboxylic acids (OHDCA) are generally of secondary origin and ubiquitous in the atmosphere, with high concentrations in South China. This study explored the formation of representative OHDCA species based on time-resolved measurements and explainable machine learning. Malic acid, the most commonly studied OHDCA, had higher concentrations in the noncontinental air (63.7 ± 33.3 ng m^–3) than in the continental air (7.5 ± 1.4 ng m^–3). Machine learning quantitatively revealed the high relative importance of aromatics and monoterpenes SOA, as well as aqueous processes, in the noncontinental air, due to either shared precursors or similar formation pathways. Isoprene SOA, particle surface area, and ozone corrected for titration loss (O_x) also elevated the concentrations of malic acid in the continental air. Aqueous photochemical formation of malic acid was confirmed given the synergy between LWC, temperature, and O_x. Moreover, the OHDCA-like SOA might have facilitated a relatively rare particle growth from early afternoon to midnight in the case with the highest malic acid concentrations. This study enhances our understanding of the formation of OHDCA and its climate impacts.

How to cite: Li, H. and Lyu, X.: Investigating the formation mechanisms of hydroxyl dicarboxylic acids based on machine learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2796, https://doi.org/10.5194/egusphere-egu26-2796, 2026.

Air quality over western India is impacted by both regional emissions and transport from the Indo-Gangetic Plain (IGP). Effective mitigation requires identifying the dominant emission sectors that govern regional air quality during different seasons. In this regard, the present study examines the impact of emissions from major anthropogenic sectors (power, residential, transport and industries) on ambient fine particulate matter (PM_2.5)concentrations over the western Indian region. For this, high-resolution (12km x 12km) regional model (WRF-Chem v3.9.1) simulations have been conducted using the EDGAR v5.0 inventory for anthropogenic emissions. Simulations are conducted for the year 2019, for winter and post-monsoon seasons when PM_2.5 concentrations are typically elevated in this region, and anthropogenic emissions effects are the highest. During winter, the residential sector is seen to dominate, contributing 20% to PM_2.5 concentration over western India, followed by power (9%) and industry (∼8%). The trans-regional pollution from the IGP and central India is also dominated by residential emissions (25%), followed by the power sector (∼8%). In contrast to winter, the dominant source during post-monsoon is the power sector (∼14%), followed by the industry (∼12%) and the residential (∼9%) sectors. Trans-regional impact also shows a similar pattern and dominance of power (∼15%) and industrial (∼10%) sectors. This seasonal shift in the dominant sector is driven by the seasonal variation in emissions. The results also reveal large spatial heterogeneity in sectoral influence, highlighting that dominant emission sources vary at the local scale within western India in both seasons. The study offers model-based insights for more effective planning of regional air pollution mitigation in western India.

How to cite: Shekhar, S., Dhaka, S., Vaishya, A., Ojha, N., Pozzer, A., and Sharma, A.: Sector-segregated anthropogenic impacts on PM2.5 over western India based on regional air quality modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5792, https://doi.org/10.5194/egusphere-egu26-5792, 2026.

 Background and Objective

Exposures to fine and ultrafine particles (i.e., PM_2.5 and PM₁) are widely accepted as a major environmental risk factor and is known to cause adverse human health outcomes. Most epidemiological research as well as regulatory frameworks rely on using PM_2.5 as the ‘reference’ exposure metric to assess health risks. However, this approach does not adequately quantify size-specific PM effects that are critical for dose-based health assessments. Size-segregated particulate matter assessment of exposures and effects are limited in resource-limited settings. The objective of this study is to estimate lung deposition doses for size-fractionated PM measured using low-cost sensors.

Methods

We utilized the data obtained from an ongoing study conducted in South Indian villages. Here, the household energy use is dominated by biomass combustion and the adoption of cleaner cooking fuels like liquefied petroleum gas (LPG) is relatively low. Ambient PM measurements were carried out continuously over a period of 1 year in 80 rural households in southern India, using real-time, optical, low-cost PM sensors. In order to capture the household-level exposure characteristics, indoor PM measurements were also carried out in a subset of households. Minute averaged, PM mass concentrations in three discrete size fractions: PM₁, PM₂.₅ and PM₁₀ were provided by the low-cost sensors.  The temporal variability in PM concentrations was derived using the time-series data obtained from the sensors. Daily and monthly mean concentrations captured the short-term exposure peaks as well as day to day variability.

Results  

A mathematical model using a non-linear least squares method was developed to transform the measured PM concentrations into a continuous size distribution. Respiratory deposition doses were estimated by feeding the size distribution to a computational model of the lung designed to simulate the spatial and temporal distribution of particles within the human respiratory system, incorporating various deposition models. The estimates of deposition doses ranged from ~0.2µg/min to ~1µg/min in the total lung. The coarse particles contributed to about 20% of the total lung dose, whereas the remaining 80% of the respiratory dose was predominantly of fine and ultrafine particles.

Conclusions

This study demonstrates that physiologically relevant, size-fractioned lung deposition doses can be estimated using limited size-bin data obtained from low-cost sensors. Since low-cost air quality monitoring networks are critical in regions that lack regulatory-grade instrumentation, the proposed analytical framework provides a benchmark for translating low-cost sensor-based air pollution measurements into relevant health-based dose metrics. The proposed analytical framework can be readily modified to incorporate satellite-derived PM inputs alongside low-cost sensor data, enabling improved spatial scaling of size-resolved, dose-relevant exposure estimates.

How to cite: Sai Ganesan, K. D., Puttaswamy, N., Sendhil, S., Natesan, D., Ramasami, R., Desai, M., Pillarisetti, A., Vakacherla, S., Krishnan, R., Sambandam, S., Ramaswamy, P., and Balakrishnan, K.: Estimating Size-Resolved Lung Deposition Doses of Particulate Matter (PM) Using Low-Cost Sensor Data in Rural India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6264, https://doi.org/10.5194/egusphere-egu26-6264, 2026.

The growing urgency of climate action at the city level has led to an exponential rise in documents that describe a city’s policy, action plan, or progress towards climate action. The increased number of documents has made it increasingly difficult for governments to track commitments and compare approaches across jurisdictions. These documents are essential for informed decision-making, but extracting useful information from unstructured PDF reports remains a largely manual, resource-intensive, and inconsistent process. Recent advances in AI and large language model (LLM) based document understanding offer strong potential, but their application in urban climate governance workflows is still limited. Integrating AI-driven document analysis into this workflow offers opportunity for building scalable, standardized, and transparent climate policy assessment.

This study presents an AI-assisted natural language processing (NLP) pipeline that automatically extracts, segments, and classifies climate actions from diverse policy documents. The workflow integrates layout-aware text extraction with an action-segmentation mechanism to identify action statements across heterogeneous formats. A fine-tuned, two-stage ClimateBERT classifier then categorizes actions: Stage 1 differentiates mitigation and adaptation measures (F1 = 93%), while Stage 2 assigns domain-specific sub-categories, achieving 92% F1 for mitigation and 91% for adaptation. An equity-detection module further identifies references to vulnerable groups, inclusivity, and justice-oriented themes.

The pipeline significantly reduces manual review effort and enhances consistency in understanding climate action. By enabling standardized comparisons, the approach directly supports mayors, policymakers, and urban practitioners in evaluating progress and designing more effective and equitable interventions.

As AI capabilities advance, such automated tools will strengthen climate governance by improving the accessibility, reliability, and strategic value of climate policy data.

How to cite: Sahu, S. and Shanker, S. and the MU NLP team: Automated Analysis of City Level Climate Action Plans using Natural Language Processing Technique, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6386, https://doi.org/10.5194/egusphere-egu26-6386, 2026.

Nitrogen oxides (N₂O, NO, and NO₂) serve as critical linkages connecting climate systems, ecosystems, and atmospheric chemistry, with soils acting as a primary natural source. Adopting a multi-scale framework spanning global, regional, and field scales, we systematically examine the spatiotemporal heterogeneity of nitrogen oxide emissions from cropland soils. Spatially, emissions exhibit a latitudinal gradient, decreasing from low to high latitudes, with hotspots concentrated in agriculturally intensive regions. Temporally, emissions display multi-scale rhythmic patterns aligned with crop growth stages, seasonal cycles, and diurnal variations, tightly coupled to soil carbon-nitrogen transformation processes. From the perspective of carbon-nitrogen coupling mechanisms, we reveal how land management practices—including nitrogen fertilization, conservation tillage, and precision irrigation—regulate emissions by modulating soil organic carbon content, carbon-nitrogen ratios, and pore structure. Concurrently, climate change drivers such as rising temperatures, elevated CO₂ concentrations, and extreme precipitation alter microbial-mediated carbon-nitrogen transformation efficiency, collectively shaping the core mechanisms governing nitrogen oxide emissions. A meta-analysis further investigates light effects on soil nitrogen oxide emissions, demonstrating significant impacts: light exposure increased N₂O and NO fluxes by 57.28% and 116.19%, respectively. Notably, heightened UV-B radiation reduced N₂O emissions by 6.85%, whereas shading increased them by 77.23%, with crop-specific responses observed. Mechanistically, light regulates emissions by modifying soil physicochemical properties and restructuring nitrogen-cycling microbial communities. Current emission mitigation faces challenges, including underdeveloped monitoring systems, limited prediction accuracy due to multifactor coupling complexities, and poor regional adaptability of existing technologies. Integrating multi-source data (field observations, remote sensing inversion, laboratory experiments) with advanced modeling approaches—such as climate-soil-crop coupling models and machine learning algorithms—offers viable pathways to enhance emission prediction precision and optimize mitigation strategies. Looking ahead, priorities include establishing multi-scale automated monitoring networks, developing carbon-nitrogen coupling-driven predictive models, promoting regionally tailored carbon sequestration and nitrogen emission reduction technologies, and combining policy incentives with public engagement to reduce uncertainties in global carbon-nitrogen cycle projections. These efforts aim to strengthen scientific support for sustainable agricultural development.

How to cite: Li, D. and Shen, W.: Nitrogen oxide emissions from cropland soil: spatiotemporal heterogeneity, carbon-nitrogen coupling mechanisms, and mitigation strategies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9022, https://doi.org/10.5194/egusphere-egu26-9022, 2026.

ABSTRACT

The Taihu Lake region has experienced rapid land use intensification, characterized by conversions from natural wetlands (NW) to conventional rice-wheat rotation fields (RW) and further to greenhouse vegetable fields (GH), driven by economic interests. While such transformations are widespread, their combined effects on greenhouse gas (GHG) emissions and underlying soil microbial mechanisms remain poorly understood. This integrated study addresses these gaps through multi-faceted analyses of GHG fluxes, soil microbial communities, and nitrogen (N)-cycling functional genes across NW, RW, and GH sites. Two-year in-situ field experiments revealed significant GHG emission shifts: land use intensification reduced methane (CH₄) emissions (NW: 970.66 ± 100.09 kg C ha⁻¹; RW: 896.71 ± 300.44 kg C ha⁻¹; GH: 71.23 ± 63.62 kg C ha⁻¹) but markedly increased nitrous oxide (N₂O) emissions (NW: 3.35 ± 0.44 kg N ha⁻¹; RW: 14.38 ± 4.09 kg N ha⁻¹; GH: 81.62 ± 4.89 kg N ha⁻¹). Global warming potential followed the order RW > NW > GH, indicating intensified comprehensive greenhouse effects during NW→RW conversion and mitigation during RW→GH conversion. Microbial community analyses showed land use intensification directly altered bacterial and fungal compositions, with stronger impacts on bacteria. Bacterial communities correlated closely with soil NO₃⁻-N, pH, and electrical conductivity, exhibiting decreased deterministic processes (opposite to fungi). Arable lands (RW/GH) displayed more complex microbial networks, and seasonal variations (notably summer) influenced microbial diversity and function, though less strongly than land use effects. Integrating quantitative PCR and metagenomics uncovered microbial mechanisms driving N₂O emissions: intensification reshaped N-cycling microbial communities, depleting nitrogen fixation, dissimilatory nitrate reduction to ammonium, and anammox marker genes in GH soils. Denitrifying communities segregated similarly to total N-cycling assemblages, with increased network complexity but divergent stability. Critically, intensification amplified N₂O emission potential by elevating Pseudomonadota harboring nirK/norB genes (and associated communities) while reducing nosZ (encoding N₂O reductase) abundance—directly linking microbial functional imbalance to emission increases. Collectively, this study demonstrates that land use intensification in the Taihu Lake region drives GHG emission trade-offs (reduced CH₄ but amplified N₂O) and restructures soil microbial communities and N-cycling functions. These findings highlight the need to prioritize microbial functional balance (e.g., restoring nosZ-carrying taxa) in mitigation strategies, providing critical insights for sustainable land management in wetland-agricultural transition zones.

Acknowledgment

This study was funded by the National Key Research and Development Program of China (2023YFF0805403, 2025YFD1700403) and National Natural Science Foundation of China (42377311).

How to cite: Shen, W., Xiong, R., Qin, D., and Gao, N.: Integrated impacts of land use intensification on greenhouse gas emissions and soil microbial communities in the Taihu Lake Region: patterns, mechanisms, and implications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9373, https://doi.org/10.5194/egusphere-egu26-9373, 2026.

Dense fog events across India severely disrupt aviation, surface transportation, and daily activities during winter months, with northern districts experiencing extended periods of visibility issues. Building upon the WRF-based ensemble fog forecasting over the Indo-Gangetic Plain and BOFFIN-Melbourne's Bayesian Decision Network framework, this study proposes a continuous risk monitoring and decision support system at district-level (admin-2).

The operational system will conduct daily continuous risk assessment, leveraging satellite observations from MODIS/VIIRS/INSAT-3D and the ECMWF IFS ensemble forecasts (51 members, 0.25° resolution) including probabilistic meteorological predictions of temperature, dewpoint, wind speed, boundary layer height, relative humidity profiles, and cloud cover, to characterize antecedent fog conditions and to establish baseline occurrence patterns.

 A Bayesian Network will integrate these layers to provide real-time short-term forecasts using pre-defined conditional probability tables which encode relationships between stable boundary layer conditions, radiative cooling, and regional fog formation mechanisms. The operational output of the algorithms will be in the form of traffic light decision matrix for each district: Green (Minimal/Low risk - Monitor), Yellow (Moderate risk - Be Aware), Orange (High risk - Be Prepared), Red (Extreme risk - Take Action).

This paper will present the validation results from pilot districts and the development framework for scaling to nationwide continuous risk assessment, demonstrating the system's potential for proactive decision-making in transportation management, aviation operations, and public safety advisories.

References

• Parde, Avinash N., et al. "Operational probabilistic fog prediction based on ensemble forecast system: A decision support system for fog." Atmosphere 13.10 (2022): 1608.
• Boneh, Tal, et al. "Fog forecasting for Melbourne Airport using a Bayesian decision network." Weather and Forecasting 30.5 (2015): 1218-1233.

How to cite: Guttikunda, S. K., Kalladath, N., Tucci, R. R., Ouma, J., Amdihun, A., and Dammalapati, S. K.: Fog Risk Monitoring and Assessment for India Using Bayesian Networks and ECMWF IFS Ensemble Prediction System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9696, https://doi.org/10.5194/egusphere-egu26-9696, 2026.

Understanding vegetation responses to climate variability is essential for assessing long-term ecosystem dynamics. Leaf Area Index (LAI) is a widely used variable to characterise vegetation state and productivity. However, attributing observed global LAI trends to specific climatic drivers remains challenging due to non-linear interactions, strong spatial heterogeneity, and scale-dependent processes.

This study is conducted within the framework of the PROFECIA project, which aims to improve the monitoring and interpretation of vegetation responses to climate change by combining remote sensing observations and artificial intelligence techniques. We analyse global LAI trends over the period 1982–2022 using the GEOV2-AVHRR long-term satellite record and examine their relationship with trends in key climatic variables obtained from the ERA5 reanalysis, including temperature, precipitation, radiation, and several indicators of water availability and drought conditions. All trends are computed consistently over the 1982-2022 temporal record to ensure a homogeneous assessment of long-term vegetation–climate relationships at the global scale.

The vegetation–climate relationships are modelled using a suite of machine learning approaches, including tree-based methods and neural networks, designed to capture non-linear responses across diverse climatic and ecological conditions. Particular emphasis is placed on the role of the training strategy: different spatio-temporal sampling schemes are evaluated to assess their impact on model performance, robustness, and generalisation capability when analysing long-term trends at the global scale.

To move beyond purely predictive modelling, the study systematically applies explainable artificial intelligence (XAI) techniques to interpret the trained models. Methods such as SHAP-based attribution and partial dependence analyses are used to quantify the relative contribution of individual climatic drivers to observed LAI trends and to examine how these contributions vary across regions and time periods.

Overall, this work highlights the importance of combining robust machine learning training strategies with interpretability tools to improve the attribution of long-term vegetation trends to climatic drivers, providing new insights into global vegetation–climate interactions over the last four decades.

How to cite: García-Diaz, D., Aguilar, F., Schauman, S., and Verger, A.: Machine learning analysis of global LAI trends and their relationship with climate variability (1982–2022), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10657, https://doi.org/10.5194/egusphere-egu26-10657, 2026.

Particulate matter (PM) pollution represents a significant public health concern, in Lima, Peru. This issue is further compound by the lack of accurate forecasting tools due to limited monitoring networks. This study addresses this gap by developing and validating a hybrid model combining a Multilayer Perceptron (MLP) neural network and a type of rule-based Cellular Automata (CA) simulation. This model simulates and forecasts the spatiotemporal dispersion of PM10 and PM2.5. Using a decade of historical PM data (2015-2024) from seven monitoring stations and NASA's meteorological data, an optimized MLP was trained to learn the complex, non-linear transition rules from 47 engineered features. The model demonstrated remarkable performance in historical validation (R² > 0.90), outperforming standard baseline models. When fed with weather forecast data, the model can operate as an Early Warning System (EWS), providing a reliable prediction horizon to anticipate the exceedance of Air Quality Standards. The resulting hotspot maps accurately identify high-risk areas, confirming the potential of this hybrid model as a robust, proactive, and quantitative tool for air quality management and public health protection in complex urban environments.

How to cite: Maita, B., Condezo, P., Llanto, J., Huaman, S., and Sanabria, J.: A Hybrid Neural Network and Cellular Automata Model for spatiotemporal Forecasting of PM10 and PM2.5 in Lima, Peru, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10823, https://doi.org/10.5194/egusphere-egu26-10823, 2026.

In India, ensuring clean air for all is vital and should not be limited to urbanites. However, the current air quality monitoring networks and clean air strategies are limited to cities. Notably, the air quality status across regions is yet to be measured, and comprehensive regional management plans are non-existent. To address these significant research gaps, the Ambient air quality Monitoring over Rural areas using Indigenous Technology (AMRIT) project was envisioned and implemented by the Centre of Excellence Advanced Technologies for Monitoring Air-quality iNdicators (CoE – ATMAN), Indian Institute of Technology Kanpur. For the first time, over 1,400 low-cost PM_2.5 sensors were installed across the states of Uttar Pradesh and Bihar at the block level. The sensor locations encompass a diverse range of land-use and land-cover categories, demographics, and communities. By leveraging a dense network of low-cost sensors, we developed a data-driven machine learning framework to delineate airsheds for regional air quality management for the first time. We utilized a recurrent neural network–based long short-term memory (LSTM) and hierarchical clustering algorithms with PM_2.5 and meteorological data to delineate airsheds. The LSTM embeddings learn latent representations from PM_2.5–ventilation coefficient (VC) time series, capturing spatiotemporal patterns and inter-variable relationships. These embeddings were then hierarchically clustered to delineate airsheds. Applying this framework, our results for Bihar show five distinct airsheds; three prevail in the north of the Ganges River, and two prevail in the south of the Ganges. Notably, Airshed 1, located in the northwest region, is highly polluted. However, during the post-monsoon and winter across the airsheds, PM_2.5 levels were two to three times higher than the national standard (60 µg/m³), and on ~90% of days, people breathe unhealthy air. Similarly, we identified multiple distinct airsheds in Uttar Pradesh, as well as common airsheds that prevail across Bihar and Uttar Pradesh states. This emphasizes not only shared regional influences but also the need for an integrated approach to reduce PM_2.5 pollution. Therefore, the identified airsheds would be instrumental in targeting the reduction of fine particulate pollution across the Bihar and Uttar Pradesh states. Furthermore, the scalable, data-driven airshed delineation framework using low cost sensors could be implemented across India and potentially globally. Thus, this will facilitate airshed-based air quality management plans and integrated policy interventions to ensure “clean air for all.”

Keuwords: Air Quality; Low-Cost Sensors; Airshed;  Indo-Gangetic Plain; Machine Learning

How to cite: P Chandrasekaran, A., Tripathi, S. N., Godhani, N., Pandey, M., Rai, P., Agrawal, N., Kumar, A., and Ballav, S.: Regional Air Quality Management: A Scalable, Data-Driven Airshed Framework using Low-Cost Sensors across the Indo-Gangetic Plain, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11502, https://doi.org/10.5194/egusphere-egu26-11502, 2026.

Indoor environments account for most human exposure to air pollution, yet indoor air quality (IAQ) monitoring and control remain fragmented across devices, platforms, and proprietary building automation systems. Commercial IAQ monitors and smart thermostats are widely available, but they typically operate in closed ecosystems with limited interoperability. In parallel, open-source communities have demonstrated the potential of low-cost sensing networks, yet these solutions rarely connect to building-level control systems capable of simultaneously reducing pollutant exposure and energy use. To address this gap, we present an open, interoperable framework that integrates open-source and commercial technologies for IAQ monitoring, data management, and automated building control[1]. The framework, developed within the EU-funded healthRiskADAPT project, is built on an open, production-ready IoT infrastructure for indoor environments. At the edge, low-cost sensor nodes collect and transmit environmental data. A web-based interface allows users to register locations, nodes, and sensors, and provides near-real-time visualization, historical analytics, and an interactive map of the sensor network. Beyond monitoring, the framework enables direct integration with commercial control devices such as smart thermostats, smart plugs, and filtration systems. This interoperability supports data-driven control strategies, including increasing ventilation during indoor pollution events, activating filtration during periods of poor outdoor air quality, and dynamically adjusting HVAC operation to balance comfort, energy use, and exposure reduction. By combining continuous mass-balance modeling[2] with real-time sensor data, the system will deliver actionable indoor-outdoor (I/O) ratios and exposure indicators. These outputs could drive automated responses but also support informed user behavior, such as choosing higher-efficiency filters during high-pollution episodes, using kitchen exhaust during cooking, or understanding the trade-offs between energy costs and health risks. In this way, the platform functions not only as a control system but also as an educational and decision-support tool for occupants and building managers. This presentation demonstrates how open-source hardware, open APIs, and modular integration pathways can create a flexible, transparent, and scalable ecosystem for IAQ management. The framework supports diverse use cases, homes, schools, workplaces, and research settings, while offering a roadmap toward energy-efficient, healthier indoor environments driven by interoperable technologies rather than isolated products.

[1] https://particularmatter.org

[2] Dallo, Federico, Thomas Parkinson, Carlos Duarte, Stefano Schiavon, Chai Yoon Um, Mark P. Modera, Paul Raftery, Carlo Barbante, and Brett C. Singer. "Using smart thermostats to reduce indoor exposure to wildfire fine particulate matter (PM2. 5)." Indoor Environments 2, no. 2 (2025): 100088.

How to cite: Dallo, F., Tenti, L., Palo, A., Parkinson, T., and Duarte, C.: Open-Tool Frameworks for Cross-Platform Indoor Monitoring and Optimized Air Cleaning Strategie, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14158, https://doi.org/10.5194/egusphere-egu26-14158, 2026.

Fine particulate matter (PM_2.5) is among the foremost environmental determinants of human health, contributing to cardiovascular disease, respiratory illness, and premature mortality. In rapidly urbanizing regions of the Global South, accurate spatial characterization of PM_2.5 exposure requires spatially continuous concentration surfaces that also provide reliable uncertainty estimates, yet ground-based monitoring networks remain severely sparse. Kolkata, India’s third-largest metropolitan area (population 14.9 million), exemplifies this challenge: only seven regulatory monitoring stations cover the entire city, leaving large areas unobserved.

This study evaluates how different PM_2.5 surface generation strategies—satellite-based machine learning (ML) and spatial interpolation—differ not only in predictive accuracy but also in their ability to provide decision-relevant uncertainty under sparse monitoring conditions. Using six years of daily observations (2019–2024), we compare two complementary approaches. The first employs satellite-based ML, integrating Sentinel-5P trace gases, MODIS aerosol optical depth, ERA5 meteorological reanalysis, and static urban features (VIIRS nightlights, population density) to predict PM_2.5. The second evaluates spatial interpolation methods—ordinary kriging, inverse distance weighting (IDW), and simple averaging—using station observations alone.

For satellite-based ML (Random Forest), the station-level model achieved R² = 0.79 under leave-one-station-out (LOSO) validation, while grid-based model trained on kriging-interpolated targets reached R² = 0.70 under temporal out-of-sample validation (train: 2019–2022, test: 2023–2024). Feature importance analysis consistently identified dewpoint temperature, air temperature, and surface albedo as dominant predictors, indicating that atmospheric conditions exert stronger control on PM_2.5 variability than emission proxies or land-use variables.

For spatial interpolation evaluated under daily LOSO, all methods achieved comparable point prediction accuracy (R² ≈ 0.85). However, uncertainty calibration diverged sharply. Ordinary kriging achieved 88% empirical coverage for nominal 95% prediction intervals (90% when including observation noise)—approaching theoretical calibration—whereas IDW and simple averaging exhibited severe under-coverage (45–52%), substantially underestimating true prediction error.

These findings yield three key insights: (1) satellite-derived predictors enable spatially complete PM_2.5 estimation beyond monitoring locations, though with moderate accuracy; (2) when temporally aligned station data are available, interpolation achieves higher point accuracy than satellite-based ML; and (3) regardless of estimation strategy, only geostatistical approaches provide uncertainty estimates suitable for health-protective decision-making. We conclude that hybrid frameworks combining satellite-based spatial prediction with kriging-derived uncertainty characterization offer a principled pathway for generating spatially complete and risk-aware PM_2.5 maps in data-sparse urban environments.

How to cite: Raj, A., Dasgupta, T., Sinha, M., and Mitra, A.: Satellite-Based PM2.5 Estimation in Data-Sparse Urban Environments: Comparing Machine Learning and Geostatistical Approaches in Kolkata, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18107, https://doi.org/10.5194/egusphere-egu26-18107, 2026.

Machine Learning models are rapidly becoming popular for complementing, enhancing, and in some cases, replacing traditional numerical models. This study presents a data-driven framework for predicting 24-hour tropical cyclone intensification over the North Indian Ocean using supervised machine learning and ERA5 reanalysis data. Cyclones that formed over Bay of Bengal and the Arabian Sea during the period 1990–2024 are considered here.  We have integrated environmental parameters from ERA5 with intensity records from the IBTrACS archive, excluding early developmental stages and retaining only dynamically mature systems. Intensification is formulated as a binary classification problem based on the sign of the 24-hour change in maximum sustained wind speed. While this captures general strengthening behaviour, it does not distinguish between moderate and rapid intensification, nor does it estimate the magnitude of intensity change. Five machine learning models—Logistic Regression, Random Forest, Extra Trees, Support Vector Machine, and Multilayer Perceptron—are trained and evaluated. Results indicate that the Random Forest classifier has achieved the highest accuracy. Feature-importance analysis reveals strong physical consistency, highlighting the dominant roles of upper-level circulation, sea surface temperature, vertical wind shear, and atmospheric moisture in regulating short-term intensification. Cyclone Montha (2025) is used as a test case to illustrate the model's real-world applicability and is validated outside of historical data. The model-predicted intensification probability is estimated as 0.943, which indicates good performance. Although a single case study does not constitute statistical validation, this illustrates the applicability of data-driven models in tropical cyclone intensity estimation. The results encourage further investigations into the use of such data-driven models in tropical cyclone intensity prediction, which aids disaster management efforts.

How to cite: Madhu, D., Binu, N. M., and Ramesh, M. V.: Machine Learning-Based Prediction of Tropical Cyclone Intensification Over the North Indian Ocean Using ERA5 Reanalysis , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20844, https://doi.org/10.5194/egusphere-egu26-20844, 2026.

Traditional solid fuels are extensively used for domestic heating and cooking in developing countries, especially in rural and semi-urban regions. Combustion of these fuels is a major source of nitrogen oxides (NO_x), sulfur dioxide (SO₂), and volatile organic compounds (TVOCs), which significantly contribute to air pollution, respiratory disorders, secondary aerosol formation, and atmospheric photochemical reactions. The generation and release of these pollutants are strongly influenced by fuel moisture content, elemental composition, and inorganic constituents. This study presents a comprehensive investigation of the chemical characteristics of commonly used solid fuels and evaluates the potential of advanced functional materials for mitigating NO_x, SO₂, and TVOC emissions from domestic combustion sources.

Representative fuel samples, including fuel wood (FW), coal balls (CB), dung cake (DC), and crop residues (CR), were obtained from the Raipur–Durg–Bhilai region of Chhattisgarh, India, selected based on their prevalence and area-specific usage patterns. The samples were air-dried, pulverized, and homogenized prior to analysis. Moisture content was determined gravimetrically by oven drying at 105 °C. Ultimate analysis of carbon (C), hydrogen (H), nitrogen (N), sulfur (S), and oxygen (O) was performed using a CHNS/O elemental analyzer. Ionic species, including nitrate, sulfate, chloride, and major alkali and alkaline earth metals, were quantified using ion chromatography to assess their role in pollutant formation and combustion behavior. These chemical parameters were used to infer emission potential for NO_x, SO₂, and TVOCs.

NO emissions were generally higher for AR and DC, while FW showed the lowest NO EF. SO₂ emissions followed a similar trend, with DC producing the highest levels and FW the lowest. TVOC emissions were elevated for fuels with higher moisture and inorganic content, such as AR and DC, whereas FW exhibited the lowest TVOC emission potential. CB displayed intermediate to high emissions, with particularly high TVOC formation due to its variable composition. Emission factors developed in simulated experimental chambers were validated against real-world measurements, indicating that domestic household emissions closely correspond to chamber-based estimates.

To address post-combustion emission control, advanced materials including graphene-based materials, biochar, graphitic carbon nitride (g-C₃N₄), metal oxides (MnO₂/TiO₂), zeolites, metal–organic frameworks (MOFs), covalent organic frameworks (COFs), and silica-based adsorbents were considered for NO_x, SO₂, and TVOC mitigation. Materials were characterized using BET surface area analysis, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM), confirming high surface activity and strong gas affinity. Their physicochemical properties, high specific surface area, tunable pore size distribution, and surface functional groups, enable efficient adsorption and catalytic transformation of pollutants. Graphene-based materials and biochar adsorb acidic gases through π–π interactions and surface oxygen functional groups, while g-C₃N₄ facilitates photocatalytic oxidation of NO_x under visible light. Metal oxides such as MnO₂/TiO₂ catalyze the oxidation of SO₂ to sulfate and TVOCs to less harmful products via surface redox cycles. Zeolites and MOFs provide selective adsorption of NO_x and TVOCs through microporous confinement and acid–base interactions.

How to cite: Pervez, S., Sahu, D., Pervez, Y. F., Karbhal, I., and Deb, M. K.: Hazardous gaseous pollutants (NOx, SO2, TVOCs) emission from solid fuels combustion and their mitigation using novel adsorbent materials, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21338, https://doi.org/10.5194/egusphere-egu26-21338, 2026.

Correction of high-frequency spectral losses is a major technical challenge of the eddy covariance (EC) technique. If not properly accounted for during post-processing, these losses can result in a systematic underestimation of the measured gas fluxes exchanged between the ecosystem and the atmosphere. To address this issue, several methods have been developed, with experimental approaches relying on the definition of a transfer function and its associated cut-off frequency to describe the EC system as a first order low-pass filter.

One still debated yet fundamental choice is whether to use power spectra or co-spectra to derive the system cut-off frequency. In this study, we present a systematic, multi-site, data-driven comparison of the these two methods. To do so, we used one year of CO₂ and H₂O data from all of 38 ICOS Class 1 and Class 2 stations (Integrated Carbon Observation System, www.icos-cp.eu), all equipped with a standard setup comprising the LI-7200 enclosed path analyser and the HS-50 sonic anemometer.

We showed that the corrections were limited for both approaches, especially for CO₂, ranging from 1 to 1.2, generally higher for H₂O, ranging from 1 to 2, and overall consistent across sites. This highlighted the good spectral performance of the enclosed path analyser as well as the effectiveness of the setup standardisation. Nonetheless, the results showed that differences in correction factors between the methods existed. They were analysed for all sites, separately for stable and unstable conditions. They increased with atmospheric stability and attenuation level, and decreased with measurement height above the canopy. In particularly, they were systematically the highest in stable conditions. However, when assessing the impact of the two corrections on cumulative u*-filtered fluxes, we found that rejections of most stable conditions through this standard post-processing filtering led to differences under 3% for CO₂ in 89% of sites and under 6% for H₂O in 79% of sites.

With this specific experimental setup, we suggest prioritising the co-spectral for two main reasons. First, sensor separation is a dominant part of the high-frequency attenuation and is treated experimentally in the co-spectral method, whereas the spectral approach relies on a fully theoretical formulation. Second, the spectral method requires a robust denoising procedure, which is not needed in the co-spectral approach. Finally, while recognising its crucial importance at a network level, we highlight the complexity of having a fully automatic pipeline for spectral corrections.

How to cite: Faurès, A., Papale, D., Nicolini, G., Sabbatini, S., and Heinesch, B.: A multi-site comparison of spectral and co-spectral approaches for correction of turbulent gas fluxes with ICOS set-up, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21667, https://doi.org/10.5194/egusphere-egu26-21667, 2026.

How to cite: Mazur, A., Tabalchuk, T., and Wyszogrodzki, A.: Impact of different surface temperature perturbation schemes on the simulation of nocturnal temperature inversions in ensemble modeling , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-530, https://doi.org/10.5194/egusphere-egu26-530, 2026.

ACCORD is a consortium made up of 26 Meteorological Services (https://www.accord-nwp.org/ ). The primary objective is to provide the consortium's member services with state-of-the-art numerical weather prediction (NWP) limited-area model codes. A substantial part of the ACCORD codes are shared with IFS-ARPEGE.

During phase 1 of ACCORD (2021-2025), collaborative working methods have been drastically modernized. Code management has greatly benefited from the implementation of the ACCORD software forge (Github) as well as from the expanded use of a testing tool which enables component-wise testing of new code versions. The adaptation of the codes to new HPC architectures (CPU-GPU accelerators) has largely progressed in close collaboration with MF (ARPEGE) and ECMWF (IFS).

Research on the current (semi-implicit semi-Lagrangian spectral) dynamical kernel of ACCORD models continues, notably through an extensive reformulation of the semi-implicit operator. In addition, the alternative FVM (Finite Volume Model) code, initially developed at ECMWF, is being studied. SURFEX (https://www.umr-cnrm.fr/surfex/ ) has become the main code infrastructure for modeling surface processes and the surface-atmosphere interface. Efforts devoted to developing new options for very high-resolution modeling are increasing: dynamical kernel, 3D aspects of turbulence and radiation, refined surface characteristics, all for models at the hectometer scale. This trend is largely driven by user needs.

In data assimilation, a major advance is the near-operational status of flow-dependent algorithms (EnVar-type algorithms coded in the OOPS software framework). ACCORD has maintained first-rate expertise in preprocessing observations for assimilation. This applies both to satellite data (infrared or microwave, in polar orbit or geostationary orbit) and to ground-based networks of various types (radar, surface networks, citizen observations) or aircraft (Mode-S). In probabilistic forecasting and ensemble methods (EPS), scientific collaboration focuses (among other aspects) on ensemble perturbation methods. Several approaches for model perturbations have been studied, such as tendency perturbations (SPPT), model parameter perturbations (SPP or RP), and surface field perturbations.

A new scientific strategy was approved by the Assembly of Directors in December 2024 for the next Programme phase (2026-2030). The main objectives include:

• Continue modernizing the collaborative working methods.

• Continue adapting the codes for CPU-GPU architectures.

• Continue research on the various model components with an increased focus on very high resolution and high-impact weather forecasting.

• Develop a research infrastructure enabling process-oriented meteorological evaluation of models using specialized observations.

• Continue to develop DA algorithms with flow dependence, leveraging observations from the coming years (MTG etc.).

• Pursue and strengthen scientific collaboration on ensemble forecasting systems.

In connection with the rapid evolution of data driven forecast tools, ACCORD members want to be proactive in AI initiatives in Europe (within EUMETNET and with ECMWF). The scientific strategy foresees exploring hybrid "AI-physical NWP" solutions.

In the presentation, a few keynote features of the ACCORD scientific strategy will be further addressed.

How to cite: Fischer, C.: The ACCORD consortium and its scientific strategy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1684, https://doi.org/10.5194/egusphere-egu26-1684, 2026.

Accurate prediction of tropical cyclones remains a major challenge for numerical weather prediction, particularly for storm intensity, structure, and track. Progress depends on effectively assimilating both passive and active microwave observations, which together provide complementary insights into atmospheric temperature and moisture, cloud microphysics, and precipitation. Passive microwave measurements from low-Earth orbiting satellites, including those in low-inclination orbits, provide frequent sampling that is particularly valuable for tuning temperature and water vapor initial conditions. Observing system experiments with NOAA’s Hurricane Analysis and Forecast System (HAFS) and NASA’s Global Earth Observing System (GEOS) show that assimilating these data leads to more realistic storm structures and measurable improvements in forecasts of intensity and track.

Spaceborne radar observations provide vertical detail on clouds and precipitation. Instruments such as the GPM Dual-frequency Precipitation Radar and EarthCare’s Cloud Profiling Radar are now supported in the Community Radiative Transfer Model (CRTM) through a new spaceborne radar forward model and a Discrete Dipole Approximation–based scattering database. These capabilities are being implemented and tested within the Joint Effort for Data assimilation Integration (JEDI) framework, developed collaboratively by JCSDA, NASA, NOAA, and international partners. Early results from experiments assimilating GPM-DPR observations within NOAA’s GEOS system and CloudSat CPR data within NOAA’s HAFS demonstrate improvements in the analysis of cloud and precipitation structures.

How to cite: Moradi, I., Zhu, Y., Kalluri, S., and Todling, R.: Advancing Assimilation of Microwave and Radar Observations in the NWP Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1749, https://doi.org/10.5194/egusphere-egu26-1749, 2026.

A new assimilation module for AGRI (Advanced Geostationary Radiation Imager) carried on Fengyun-4B (FY-4B) is developed within the WRFDA (Weather Research and Forecasting model’s Data Assimilation) system. The impacts of assimilating FY-4B AGRI clear-sky and all-sky data are evaluated with cycling assimilation experiments for the typhoon Doksuri (2023) and Talim (2023) forecast. For typhoon Doksuri, compared with the benchmark experiment, AGRI assimilation brings a better analysis and forecast for atmospheric variables (wind, temperature and humidity). Meanwhile, clear error reductions for typhoon track and intensity forecasts are achieved with clear-sky and all-sky AGRI assimilation. The positive impact on landing precipitation prediction is also obtained by verifying with GPM (Global Precipitation Measurement) precipitation data. Moreover, AGRI all-sky assimilation yields better typhoon forecasts than clear-sky assimilation. In addition, the channel selection sensitivity for AGRI assimilation is also assessed with group experiments. It is suggested that with the assimilation of a new water vapor channel at 7.42 μm, which is newly added in FY-4B, multiple-channel assimilation shows greater benefit for forecast than single-channel assimilation. The same positive impact of AGRI assimilation is also present in typhoon Talim (2023) forecast. Overall, the all-sky assimilation of FY-4B AGRI water vapor channels data is beneficial for the typhoon forecast. 

How to cite: Yang, C., Zhong, T., and Min, J.: A preliminary study of FY-4B AGRI all-sky assimilation by WRFDA for tropical cyclones , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1848, https://doi.org/10.5194/egusphere-egu26-1848, 2026.

The impact of radiance data assimilation from Advanced Technology Microwave Sounder (ATMS) into the Center for Weather Forecasting and Climate Studies of the National Institute for Space Research (CPTEC/INPE), Brazil, is investigated and discussed in this work. The recent discontinuation of the Advanced Microwave Sounding Unit (AMSU-A) in the NOAA series satellites (NOAA-15, 18, and 19) gives the ATMS sensor greater relevance in the process of assimilating microwave radiance data, and this fact is the motivation of this study. The ATMS data used in this study are from the Joint Polar Satellite System (JPSS) series and the National Polar-orbiting Partnership (NPP) satellites. Therefore, understanding and studying the impact of assimilating ATMS sensor radiance channels becomes essential for developing a more realistic numerical weather forecast, especially in extreme atmospheric conditions such as storms, cyclones, and hurricanes. Thus, this work presents results on the impact of ATMS sensor data assimilation on the numerical forecast of Melissa hurricane, which reached category five on October 27, 2025, in the Caribbean Sea, Central America. The Numerical Modeling and Data Assimilation System (SMNA) is used in this study, which is composed of a Gridpoint Statistical Interpolation (GSI) System coupled to the Brazilian Global Atmospheric Model (BAM). Other types of data, such as radiance from AMSU-A data from MetOp (Meteorological Operational satellite programme) satellites, Atmospheric Motion Vectors (AMV) data from geostationary and polar orbit satellites, radio occultation GNSS (Global navigation Satellite System) data, dropsondes, radiosondes, and pilot balloons, were also used in the assimilation process. Different experiments were conducted to explore the radiance channels available in the ATMS sensor and assess their contribution to the improvement of the predictability of the Melissa hurricane. The results reported here are the first using the ATMS data in this center, and they are essential for establishing a consistent radiance database for the assimilation process in the new Brazilian climate prediction model being developed by CPTEC/INPE and partners, the Model for Ocean-laNd-Atmosphere PredictioN (MONAN), in phase of imprementation and test.

Keywords: Radiance, ATMS, Data assimilation, Numerical weather prediction, Melissa hurricane.

Acknowledgment: This work was supported by the National Council for Scientific and Technological Development - CNPq (Process No. 304388/2022-0).

How to cite: Viezel, C., Sapucci, L., and Ranieri, V.: Accessing the contribution of Advanced Technology Microwave Sounder (ATMS) in the Brazilian Numerical Modeling and Data Assimilation System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2151, https://doi.org/10.5194/egusphere-egu26-2151, 2026.

Can we control the butterfly effect? This study addresses the fundamental question of whether we can control chaotic weather systems by taking advantage of their sensitivity to initial conditions. Specifically, we explore a theoretical framework to control the system beyond the predictability limit, where infinitesimal perturbations grow to alter the macroscopic trajectory. Based on the Control Simulation Experiment (CSE) framework, we focus on the Duality Principle, which posits that the control problem is mathematically dual to data assimilation (DA). In this view, adding interventions to nature for control is equivalent to adding analysis increments to correct the model forecast for DA. Therefore, controllability can be understood as the synchronization of the nature trajectory with a target model trajectory, analogous to filter convergence in DA. Using the Lorenz 63 model, we present a compelling case study that highlights an apparent paradox within this duality. Our previous paper showed that intervening only in the z-variable was effective for controlling the full system ("z-only intervention"). However, in the dual problem of DA, observing only the z-variable leads to filter divergence ("z-only observation"). Why does intervention succeed where observation fails, despite their theoretical duality? In this presentation, we address this asymmetry and discuss the underlying dynamics of the target trajectory. Based on the Duality Principle, we establish a theory for controlling chaotic systems beyond the predictability limit, opening new pathways for mitigating extreme weather events.

How to cite: Miyoshi, T.: Harnessing the Butterfly Effect: A Duality-Based Framework for the Efficient Control of Extreme Weather, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2914, https://doi.org/10.5194/egusphere-egu26-2914, 2026.

The rapid development of AI weather foundation models, such as ECMWF’s AIFS-ENS, promises to revolutionize operational forecasting by delivering competitive skill at a fraction of the computational cost of traditional numerical weather prediction (NWP). However, a critical gap remains: these models currently lack native, robust mechanisms for assimilating real-time, novel observation types, particularly in data-sparse regions. We present a preliminary framework for the first integration of ensemble data assimilation with AIFS-ENS.

This study uses a unique observational capability from Sorcerer long-duration stratospheric balloons, the only platform currently capable of providing simultaneous, high-frequency vertical soundings and multi-day Lagrangian float trajectories from the upper troposphere (12–14 km). To quantify the unique value of this multi-modal data, we conduct a series of Observing System Experiments (OSEs) assimilating: (1) vertical "yo-yo" profiles only, (2) Lagrangian drift velocities only, and (3) a combined hybrid dataset.

We investigate the hypothesis that while vertical soundings constrain thermodynamic profiles, the assimilation of continuous Lagrangian drift data provides a superior constraint on the upper-level wind field and jet stream positioning. We present an assessment of the technical feasibility of assimilating these diverse geometries into an AI-based background and offer a preliminary evaluation of their relative impact on forecast spread and error reduction. This work represents a novel step toward "observation-adaptive" AI prediction, exploring how next-generation hardware and machine learning models can be coupled to close global observing gaps.

How to cite: Tian, X. and Tindle, A.: Assimilation of Long Duration Stratospheric Balloon Drift and Soundings in AI Weather Foundation Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3090, https://doi.org/10.5194/egusphere-egu26-3090, 2026.

Evaluation of short-range precipitation forecasts is sensitive to the spatial non-uniformity of surface observing networks. Gridded NWP precipitation forecasts are commonly verified against AWS/ASOS rain-gauge observations using point-based binary verification. However, this method is affected by a grid-to-point representativeness mismatch and an uneven station distribution, which can bias domain-aggregated verification scores. Consequently, domain-aggregated verification scores can be disproportionately influenced by observations from densely monitored areas.
Beyond sampling biases arising from network non-uniformity, point-based binary verification is also prone to the double-penalty effect. Displacement of precipitation features can be counted simultaneously as a miss at observation sites and a false alarm nearby. Collectively, these limitations motivate verification frameworks that better reflect the spatial nature of precipitation.
Here we assess how observation-network non-uniformity and verification configuration influence reported precipitation forecast skill over the Korean Peninsula. As a practical step to reduce network-density effects, we interpolated point-based precipitation into a gridded observation field and performed area-based verification in parallel with conventional point-based verification for an annual set of short-range forecasts. The results show that reported skill is highly sensitive to the verification framework; for some precipitation thresholds, the area-based approach yields Critical Success Index (CSI) values that are approximately 50% higher than those from point-based verification. The findings highlight that verification design can materially affect the interpretation of precipitation forecast performance in non-uniform observing networks and underscore the need for spatially representative verification frameworks.

How to cite: Oh, S. M., Kim, J. E., and Kang, H.-S.: Impact of observation network non-uniformity on precipitation forecast verification and skill scores, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3622, https://doi.org/10.5194/egusphere-egu26-3622, 2026.

Tropical cyclone (TC) ensemble forecasting faces a fundamental dilemma. Parameter-based approaches provide accurate track and intensity estimates but lack the continuous spatial fields needed for impact assessment, whereas ensemble-mean approaches offer complete meteorological patterns yet generate unphysical artifacts such as multiple eyes and weakened intensity due to spatial misalignment. Here we present TC-SuperEns, a two-stage framework that resolves this issue through machine learning optimization and physics-based reconstruction. The first stage learns adaptive weights for key TC parameters from historical forecast errors across seven models, while the second stage uses these parameters as dynamical constraints to align ensemble members and reconstruct physically consistent fields. Validation for 2023-2024 Northwest Pacific TCs shows 15-25% improvement in 72-hour track accuracy compared with operational models, along with notable gains in intensity relative to ECMWF's IFS. By unifying discrete parameters and continuous fields into one coherent product, the framework enhances forecast realism and interpretability for effective warning and response.

How to cite: Zhang, X., Chen, X., Liang, Y., Lin, S.-J., Liang, Z., and Song, Q.: High-fidelity tropical cyclone prediction improves public risk communication and disaster mitigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4651, https://doi.org/10.5194/egusphere-egu26-4651, 2026.

Low Earth Orbit (LEO) observations are fundamental to global Numerical Weather Prediction (NWP), with the Joint Polar Satellite System (JPSS) serving as a critical pillar for monitoring extreme weather events such as wildfires, hurricanes, and floods. To ensure data continuity into the 2030s, NOAA is transitioning from the current JPSS era—supported by Suomi-NPP, NOAA-20, and NOAA-21—toward future missions, including JPSS-3, JPSS-4, and the innovative Near Earth Orbit Network (NEON) program.

Scientific experiments and formulation studies are critical to developing requirements for future sensor capabilities within the NEON architecture. The primary objective is to demonstrate how next-generation initiatives—such as the QuickSounder and the Series-1 microwave sounder missions—will mitigate data gaps and reduce systemic risks to NWP while enhancing Earth system prediction through technical innovation and commercial data integration. Series-1 will host the Sounder for Microwave-Based Applications (SMBA), a successor to the current Advanced Technology Microwave Sounder sensor on JPSS and QuickSounder missions. It features enhanced capabilities, such as hyperspectral measurements, to mitigate the potential influence of Radio Frequency Interference (RFI) while improving vertical resolution.

To develop a credible mission architecture responsive to program constraints, the LEO program sponsored several Observing System Experiments (OSEs) and Observing System Simulation Experiments (OSSEs). Data from various spaceborne platforms were assimilated alongside conventional observations to evaluate performance across diverse metrics, including root mean square errors and ensemble spread differences in atmospheric profiles for key forecast variables—such as temperature, water vapor, geopotential height, and wind fields—and Forecast Sensitivity-based Observation Impacts. Results from the OSSEs provide quantitative evidence of how various observations influence the accuracy of atmospheric profiling and NWP. Experiments also assessed the impact of microwave and infrared observations on tropical cyclone track and intensity prediction. Furthermore, OSSEs explored the complementarity of current satellite assets with a proposed "geostationary ring" of infrared sounders.

This presentation outlines NOAA’s strategic roadmap for evolving LEO capabilities. This roadmap emphasizes international collaboration, commercial satellite data, and the strategic deployment of advanced sensors to ensure a robust, high-fidelity Earth observation network for the coming decades. Finally, the presentation highlights the high impact of microwave and infrared soundings on NWP models.

How to cite: Kalluri, S.:  Advancing Global Numerical Weather Prediction: Strategic Roadmaps and Experimental Evaluations of NOAA’s Next-Generation LEO Constellations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5876, https://doi.org/10.5194/egusphere-egu26-5876, 2026.

Background and Objectives

As typhoons intensify due to climate change, typhoon modification technology has gained renewed attention. Japan's Moonshot Goal 8 program, launched in 2022, has accelerated technological development in this area. However, social implementation requires addressing not only technical feasibility but also ethical, legal, and social implications (ELSI). While previous studies have qualitatively organized the expectations and concerns held by people regarding typhoons and typhoon modification technologies through dialogue sessions, large-scale investigations of public cognitive structures remain limited. This study aimed to elucidate people’s perceptions regarding expected benefits, ELSI concerns, and relevant stakeholders through a nationwide questionnaire survey.

Methods

1,000 participants comprised across Japan, recruited through a web-based survey with quota sampling by gender, age, and residential region. The questionnaire assessed expected benefits (9 items), ELSI concerns (15 items), stakeholders (14 items), all rated on five-point scales. Cluster analysis was conducted for each domain, followed by two-way ANOVA with residential region (four prefectures most affected by typhoons vs. others) and cluster.

Results

Cluster analysis of expected benefits revealed three clusters: disaster risk reduction, social and broader benefits, and economic applications of technological development. Two-way ANOVA revealed a significant main effect of cluster (F = 270.87, p < .01, η² = .21), with disaster risk reduction rated highest. Neither regional main effect nor interaction was significant.

Cluster analysis of ELSI revealed six clusters: environmental and ecological risks, technological uncertainty and governance, economic costs, decreased disaster preparedness awareness, transformation of social structures and views of nature, and risks of misuse and international conflict. Both cluster (F = 120.13, p < .01, η² = .11) and regional main effects (F = 5.52, p < .05, η² = .01) were significant, with four prefectures showing heightened ELSI concerns. In addition, concerns regarding the cluster of economic costs were the highest.

Cluster analysis of stakeholders revealed five clusters: policy and technical experts, local practitioners and economic actors, general citizens and disaster victims, education and ethics professionals and foreign governments. The cluster main effect was significant (F = 363.12, p < .01, η² = .27), with policy and technical experts deemed most essential. A significant interaction (F = 2.97, p < .05) indicated that four prefectures prioritized consultation with foreign governments over input from education and ethics professionals.

Conclusions

The results of this study indicate that public recognizes both multifaceted expected benefits and ELSI regarding typhoon modification technology. Prefectures of typhoon-affected regions exhibit higher concern of ELSI. In addition, public emphasizes the involvement of stakeholders and recognizes the need for inclusive consensus-building that includes citizens and disaster victims, rather than leaving technological decision-making solely to experts. These findings highlight the importance of communication and consensus-building frameworks that take public awareness structures into account in societal decision-making related to typhoon modification technologies.

How to cite: Su, Y. and Matsuyama, M.: Public Perception of Typhoon Modification Technology: Examining Expected Benefits, ELSI, and Stakeholders, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6219, https://doi.org/10.5194/egusphere-egu26-6219, 2026.

Accurate medium-range weather guidance is essential, yet end-users lack clear, location-specific evidence on where forecasts are predictable and which freely available global systems perform best. We present a global evaluation of three conventional numerical weather prediction (NWP) models (ICON, IFS, GEFS) and an AI forecast model (AIFS) for 1-10-day lead times, focusing on four near-surface variables (2-m air temperature, precipitation, 10-m wind speed, and 2-m relative humidity). Using 00 UTC cycles over 1 September 2024 to 30 November 2025, we resampled forecasts to a 1° grid and assessed day-to-day variability (correlation and root mean square error), mean bias, variance bias, and lead-time dependence (drift) against multiple references (primarily JRA-3Q, with additional evaluation against ERA5, station data, and additionally IMERG-Late for precipitation). AIFS achieves the highest skill for temperature, precipitation, and wind at all lead times (relative humidity is unavailable from AIFS); at 3-day lead it explains, on average, 53\% more variance in daily precipitation globally than the next-best model (ICON), and 232\% more variance than GEFS in the tropics. Among the conventional systems, ICON is generally most skillful, while GEFS ranks lowest overall. Mean-bias drift is negligible across models, but variance drift is evident for several variables, most notably increasingly attenuated AIFS precipitation variability with lead time. Model correlation rankings are robust across reference datasets, although precipitation and humidity show greater reference sensitivity than temperature. We also map global predictability using 3-day lead daily temporal correlation of the locally best-performing model, showing highest predictability for temperature and wind in mid-to-high latitudes and markedly lower predictability for precipitation in the tropics. Our study provides actionable guidance on where global forecasts can be trusted and establishes a baseline for future AI and NWP model assessments. 

How to cite: Puthiyaveettil, M., Beck, H., and Ma, J.: Where Is Weather Predictable and Which Models Get It Right? Global Assessment of Conventional and AI-Based ForecastModels, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7617, https://doi.org/10.5194/egusphere-egu26-7617, 2026.

Aviation emissions contribute to climate change, one of the key contributors being contrail cirrus clouds. The importance of the impact is strongly influenced by the conditions in which they form and evolve. 

A condensation trail - or contrail - is composed of ice crystals which form behind the aircraft engine exhaust at high altitudes when local weather conditions are favorable. The formation is also influenced by the engine technology and operating conditions, and by the fuel type. The contrail persists and evolves as long as it remains in an ice supersaturated region - or ISSR-, a local atmospheric air mass characterized by a low temperature and a humidity level that is saturated versus ice. Only persistent contrails are considered as having a potential climate effect.

As part of the SESAR CICONIA project and in order to help the forecasting of ISSRs and hence persistent contrail regions, Météo France has implemented a modification to the cloud scheme of the ARPEGE (Action de Recherche Petite Echelle Grande Echelle) operational numerical weather prediction (NWP) model to enable the representation of ISSRs at cruise altitude. As part of the CICONIA project, Météo France provided Airbus with access to this modified version of ARPEGE to use operationally in forecasting areas where persistent contrails could be formed in flight tests.

During October and November 2025 the temperature and humidity from the modified ARPEGE model was used to forecast areas of potential persistent contrail formation and the test flights were performed in these identified areas. In previous test flights the Global Forecast System (GFS) had been used and was again used for these flight tests as a comparison. 

In-flight humidity and temperature measurements are compared to the ARPEGE forecast by interpolating the weather data along the flight trajectories.  The observation of persistent contrails is compared to their simulation along the trajectories using the Airbus in house model. These results support the verification and validation of the data from the modified ARPEGE model. 

In addition, for a particular day, time and area where persistent contrail coverage was forecast, the in-flight measurements from IAGOS aircraft have also been analysed to confirm where the flights were in ISSRs and if persistent contrails were formed. These results and the associated meteorological parameters were compared to the ARPEGE and GFS forecasts and the ERA5 reanalysis datasets.

The stability of the forecast, which was provided as an hourly forecast for the first 48 hours and then 3 hourly up to 72 hours will be discussed. 

The changes to the ARPEGE model to improve the ISSR forecasts, a short description of the studies and analyses of the results for the selected flights will be presented.

How to cite: Mackay, C., Crispel, P., and Arriolabengoa Zazo, S.: The prediction of Ice SuperSaturated Regions and persistent contrail formation using the modified ARPEGE weather forecast and comparison with in-flight measurements and observations., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7933, https://doi.org/10.5194/egusphere-egu26-7933, 2026.

Flood inundation damage to the socioeconomic landscape caused by short-term extreme rainfall has been a primary concern for years, particularly regarding shifts in precipitation patterns driven by climate change. To mitigate these impacts, many studies have prioritized the enhancement of flood prevention infrastructure in high-risk areas. However, the construction of permanent facilities requires rigorous scientific assessment and significant investments of time and capital. As an alternative to traditional infrastructure, weather modification strategies are being explored to reduce societal impacts. These strategies include suppressing the formation of cloud systems by altering air currents through the construction of physical obstacles, as well as removing water vapor via proactive cloud seeding. To evaluate the efficacy of seeding in reducing flood risk, we utilized simulated rainfall data from the Weather Research and Forecasting (WRF) model, which incorporates a seeding core module to test rainfall control effectiveness within the Kurokawa River basin, Kyushu, Japan. The results demonstrate that a 10% reduction in 24-hour basin-averaged rainfall led to a 20% decrease in peak discharge (excluding overflow). Correspondingly, the inundation extent was reduced by 20% across different scenarios, with the most significant improvements observed in high-depth areas. To quantify the benefits for stakeholders and the government, this study evaluated potential economic losses. The reduction in inundation successfully mitigated agricultural and property economy losses by approximately 10%. Furthermore, we assessed threats to human life by proposing critical water depth thresholds specific to elderly populations (aged over 65 years old) and younger residents based on housing types and government data. These metrics confirm that rainfall control strategies effectively safeguard the community and the broader economy against climate-driven flood disasters.

How to cite: Chang, J. and Yorozu, K.: Impact of Rainfall Control on Socio-economic Flood Risk Assessment in the Kurokawa River Basin, Japan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8827, https://doi.org/10.5194/egusphere-egu26-8827, 2026.

Reliable precipitation forecasts are crucial for water resource management and flood disaster early warning. However, numerical weather prediction (NWP) products often suffer from systematic biases, limiting their applicability across different regions. To address this issue, this study proposes a Two-step Time-dependent Enhanced Informer (TTEInformer) method for precipitation post-processing. This method employs a two-step classification and regression correction framework. It classifies precipitation into wet and dry days, then performs regression correction on samples identified as wet days, while dry day samples are maintained as zero values. To better capture temporal dependencies, TTEInformer augments the Informer model with a feature extraction module and a bidirectional gated recurrent unit (BiGRU) module. The study evaluates the proposed method over the Yalongjiang River basin upstream of the Yajiang hydrological station and compare it with multiple deep learning baselines. The results indicate that all corrected products substantially reduce forecast errors relative to the raw NWP precipitation. The proposed model demonstrates outstanding performance, achieving an R value of over 0.9 and significantly reducing forecast errors compared to other models. Moreover, the two-step correction framework effectively enhances model correction accuracy compared to traditional direct correction strategies, with notable improvements in correcting light precipitation events. The work provides a reliable post-processing method for hydrometeorological applications in precipitation forecasting.

How to cite: Xu, X., Qin, H., Yang, L., and Li, C.: A Two-step Time-dependent Enhanced Informer method for numerical weather prediction post-processing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8864, https://doi.org/10.5194/egusphere-egu26-8864, 2026.

The Portable Model for multi-scale Atmospheric Prediction (PMAP) is a numerical model currently under development at ECMWF and ETH aimed at large-eddy simulation (LES) of atmospheric flows at the weather-system scale. It builds on a non-hydrostatic, locally conserving, finite-volume, 3D semi-implicit dynamical core coupled to state-of-the-art physics parametrizations. Written entirely in Python, it leverages the GT4Py domain-specific language to achieve high performance and portability – running straightforwardly on laptops and GPU-accelerated HPC systems alike. The systematic separation of concerns between domain science (physics, numerics) and performance engineering (parallelisation, hardware optimisation) provides new avenues for model development, setup, and refinement, which we present in two related contributions.

In this first contribution, we highlight PMAPs strengths as a numerical model framework to flexibly develop and refine numerical algorithms, as well as to implement and extend diagnostics to address research questions in atmospheric sciences. This is illustrated here with an LES tracer transport experiment over complex terrain. We first demonstrate PMAPs capability to perform decametre-scale LES using a height-based terrain-following vertical coordinate in steep terrain with slopes exceeding 80°. This is possible thanks to the locally conservative advection and the 3D semi-implicit integration scheme, which ensures stability of the integration and regularization of the flow. Moreover, we exemplarily show how the Python-based model formulation facilitates evaluating and improving various aspects of flux-form semi-Lagrangian tracer transport schemes in terms of directional splitting approaches and monotonic limiters, and how these impact simulated power spectra of the tracer fields. Lastly, we present how available model diagnostics can easily be extended to perform targeted analyses of sensitivities to implementation details. 

How to cite: Papritz, L., Krieger, N., Kühnlein, C., Faghih-Naini, S., Ehrengruber, T., Ubbiali, S., Vollenweider, G., and Wernli, H.: The Portable Model for multi-scale Atmospheric Prediction (PMAP): Capabilities and development workflows , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9248, https://doi.org/10.5194/egusphere-egu26-9248, 2026.

The Portable Model for multi-scale Atmospheric Prediction (PMAP) is a numerical model currently under development at ECMWF and ETH aimed at large-eddy simulation (LES) of atmospheric flows at the weather-system scale. It builds on a non-hydrostatic, locally conserving, finite-volume, 3D semi-implicit dynamical core coupled to state-of-the-art physics parametrizations. Written entirely in Python, it leverages the GT4Py domain-specific language to achieve high performance and portability – running straightforwardly on laptops and GPU accelerated HPC systems alike. The systematic separation of concerns between domain science (physics, numerics) and performance engineering (parallelisation, hardware optimisation) provides new avenues for model development, setup, and refinement, which we present in two related contributions.

In this second contribution, we present two examples that demonstrate the benefits of resolving small-scale processes for simulating real weather and showcase how PMAP can flexibly be used as a research tool for atmospheric dynamics. (i) First, we demonstrate the benefits of sub-kilometer spatial resolution for accurately simulating the intensification of Hurricane Melissa in late October 2025 as compared to an operational km-scale model. (ii) Second, we present results from a process study of storm Éowyn, which brought record-strong winds to the British Isles in January 2025. LES of the low-level jet along the storm’s bent-back front not only accurately predicts peak wind speeds, but also resolves individual wind gusts in close agreement with observations. We highlight a range of diagnostic tools implemented in PMAP that make such analyses straightforward. Moreover, we demonstrate how the model can be employed to shed light on the atmospheric dynamical processes leading to the storm’s rapid intensification. Specifically, we show the importance of latent heat release by performing modified latent heating experiments, which in the PMAP framework are straightforward to set up, and quantitatively corroborate the crucial impact of cloud microphysical processes for the rapid intensification of Éowyn.

How to cite: Krieger, N., Papritz, L., Kühnlein, C., Hauser, S., Ubbiali, S., Vollenweider, G., and Wernli, H.: The Portable Model for multi-scale Atmospheric Prediction (PMAP): Sub-kilometre simulations of recent extreme weather, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9320, https://doi.org/10.5194/egusphere-egu26-9320, 2026.

Extreme precipitation events in India are becoming more frequent and intense, increasing the need for reliable ensemble precipitation forecasts to support early warning systems and disaster preparedness. However, current Numerical Weather Prediction models often underestimate extreme precipitation, and their forecast skill is constrained by errors in initial conditions, numerical approximations, inadequate representation of sub-grid convective processes, and coarse spatial resolution. Additionally, systematic biases in ensemble forecast distributions, such as deviations in central tendency and under- or over-dispersion, further limit the accuracy of probabilistic forecasts. Ensemble Model Output Statistics (EMOS) can reduce some of these limitations by correcting systematic biases in the ensemble mean and spread, and by partly adjusting the predicted overall distribution. Classical EMOS relies on linear transformations, limiting the ability to capture non-linear relationships between the original forecast and the corrected ensemble, and to correct asymmetric distribution errors. Moreover, it derives the corrected distribution at a given location only from the original forecast ensemble for this location. Deep learning-based distributional regression methods, such as U-Net architectures, can generalise classical EMOS by linking the original full spatial field of ensemble forecasts in a complex way to the corrected ensemble forecasts.

This study presents a U-Net based distributional regression (DRU) for daily rainfall forecasts over India, that predicts parametric marginal distributions at each forecast grid cell from the statistics of the original ensemble forecast at all grid cells. It minimises the area mean Continuous Ranked Probability Score (CRPS), while classical EMOS minimises the CRPS individually for each location. DRU is applied to postprocess forecasts with one day lead time for daily precipitation at 12-km resolution from 11 members of the National Centre for Medium Range Weather Forecasting (NCMRWF) global ensemble prediction system for the period 2018-2024. The observations for U-Net training are gridded precipitation data at 0.25° resolution from the Indian Meteorological Department and the NCMRWF forecasts were regridded to this resolution prior to DRU training. Over most of India, DRU improves local precipitation distributions, including for higher quantiles, and corrects under- or overdispersion. The forecast skill in terms of Continuous Ranked Probability Skill Score increases over large areas, particularly northern and western India, while in central and northeastern regions, there are locations where the skill decreases. For some of these, the marginal distributions are also not improved. Additionally, DRU improves the reliability for predicting the exceedance probability of various precipitation thresholds. Future endeavours will focus on optimizing DRUs for postprocessing heavy precipitation events and evaluating the forecast skill using the Brier Score.

How to cite: Thakur, C., Widmann, M., Ashrit, R., Orr, A., C. Leckebusch, G., and Geen, R.: Improving precipitation ensemble forecasts over India using a convolutional distributional regression framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9569, https://doi.org/10.5194/egusphere-egu26-9569, 2026.

Cloud-affected infrared satellites constitute a promising data source for numerical weather prediction models as they contain crucial information on atmospheric clouds and convective activity. Their sensitivity to both hydrometeor content and cloud top height, however, leads to a very non-Gaussian distribution of first-guess (FG) departures, which violates a fundamental assumption of current data assimilation schemes. To mitigate this issue, various cloud-dependent error models for normalizing the departures have been proposed (Geer and Bauer, 2011; Harnisch et al., 2016; Okamoto et al., 2014). In the current presentation, we revisit these error models and propose a refined approach that leads to a more Gaussian distribution.

We quantify the performance of these error methods in detail when applied to one-month infrared observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation and simulations from the weather forecast model AROME (Application of Research to Operations at Mesoscale) over Austria. While all methods can successfully yield approximately Gaussian FG departure distributions when normalized by the observation error, an additional quality control and a minimum error threshold is necessary for some of them.

While previously published methods estimate the observation error using the average cloud effects from the model and observation spaces, we also introduce a new method that uses the maximum values from these two spaces for observation error calculation. Results show that the new method systematically outperforms the previous methods at no additional cost. Lastly, we analyze the performance of different practical implementation choices, such as using a linear or polynomial fit.

Reference:

Geer, A. J. and Bauer, P.: Observation errors in all-sky data assimilation, Quarterly Journal of the Royal Meteorological Society, 137, 2024–2037, https://doi.org/10.1002/qj.830, 2011.

Harnisch, F., Weissmann, M., and Periáñez, Á.: Error model for the assimilation of cloud-affected infrared satellite observations in an ensemble data assimilation system, Quarterly Journal of the Royal Meteorological Society, 142, 1797–1808, https://doi.org/10.1002/qj.2776, 2016.

Okamoto, K., McNally, A., and Bell, W.: Progress towards the assimilation of all-sky infrared radiances: An evaluation of cloud effects, Quarterly Journal of the Royal Meteorological Society, 140, 1603–1614, https://doi.org/10.1002/qj.2242, 2014.

How to cite: shi, B., Griewank, P., Meier, F., Min, J., and Weissmann, M.: Revisiting error models for the assimilation of infrared satellite radiances, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9670, https://doi.org/10.5194/egusphere-egu26-9670, 2026.

Satellite observations are critical for numerical weather prediction (NWP), yet the full potential of visible and near-infrared spectral data remains to be exploited. In recent years, ECMWF has been advancing efforts to incorporate visible satellite observations into the analysis and forecasting of clouds and aerosols within the Integrated Forecasting System (IFS). These developments are now reaching operational maturity. We present visible reflectance monitoring and assimilation experiments using observations from various satellite instruments: Spinning Enhanced Visible and Infrared Imager (SEVIRI), Flexible Combined Imager (FCI), Advanced Himawari Imager (AHI), Advanced Baseline Imager (ABI), and Ocean and Land Colour Instrument (OLCI). Our study assesses the first-ever successful experimental assimilation of visible (655 nm) all-sky satellite observations in the IFS. A comprehensive evaluation of these experiments demonstrates that visible reflectance assimilation can improve the model analysis of clouds by better fitting the model trajectory to observations in visible reflectance space. We will also discuss the remaining challenges related to the future operational assimilation of visible observations, including error modelling and biases. Our findings underscore the vast potential of visible spectral observations for operational numerical weather prediction and future re-analysis products.

How to cite: Lupu, C., Necker, T., Quesada Ruiz, S., Firat, V., and Benedetti, A.: Towards operational monitoring and assimilation of visible reflectances at ECMWF, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9826, https://doi.org/10.5194/egusphere-egu26-9826, 2026.

Cyclones are frequent meteorological phenomena in Brazil, commonly associated with intense rainfall and strong winds. Most of them are extratropical systems, although subtropical and, more rarely, tropical cyclones also occur. Due to the impacts they cause, understanding the intensity and dynamics of these systems is essential for the scientific community, particularly regarding the ability of numerical models to predict their formation, associated precipitation, and wind fields. In February 2024, Cyclone Akará developed near the southeastern coast of Brazil and, as the third system classified as a tropical cyclone in the South Atlantic, represented a unique opportunity for study. With the development of the MONAN model (Model for Ocean-laNd-Atmosphere predictioN), built through a multi-institutional effort, the need arose to evaluate its performance in forecasting extreme events, especially those associated with cyclones that directly affect the country. This study aimed to analyze MONAN’s ability to represent precipitation fields and mean sea level pressure (MSLP) associated with Cyclone Akará, comparing different versions of the model from the implementation/development process. To achieve this, an object-based approach was adopted using the MODE tool (Method for Object-Based Diagnostic Evaluation). ERA5 reanalysis data (ECMWF) were used as reference to compare the cyclone’s position and intensity, while precipitation was assessed using the MERGE product (CPTEC/INPE), a high-resolution, blended rainfall dataset that combines satellite information with in situ gauge measurements to create a homogeneous daily record for South America. The evaluation focused on the intensification phase of the cyclone (February 16), considering attributes such as centroid distance, area, and total interest of the objects, in addition to traditional metrics such as RMSE, BIAS, and CSI. The results showed that the updated version performed better in forecasting intense precipitation. The detailed assessment of this event offered valuable insights for the continuous improvement of the model, contributing to the strengthening of the Brazilian numerical weather prediction system.

Keywords: Cyclone Akará, MONAN model, Precipitation, Model Evaluation, MODE.

Acknowledgment: This study was supported by the São Paulo Research Foundation - FAPESP (Process No. 2025/06119-7) and National Council for Scientific and Technological Development - CNPq (Process No. 304388/2022-0).

How to cite: Antunes Ranieri, V., Fernando Sapucci, L., Couto de Souza, D., Leite da Silva Dias, P., Khamis, E., Viezel, C., and Freitas, S.: Performance of the MONAN Model in Forecasting Cyclone Akará and Associated Precipitation Using Object-Oriented Evaluation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10950, https://doi.org/10.5194/egusphere-egu26-10950, 2026.

From 17 to 22 July 2021, Henan Province in China experienced an exceptionally severe rainfall event (hereafter referred to as the “7·21” rainstorm). In particular, record-breaking local hourly precipitation occurred in Zhengzhou on 20 July, posing an unprecedented challenge to mesoscale numerical weather prediction (NWP) systems. Uncertainty is inherent in NWP, and ensemble forecasting has increasingly become the consensus approach for quantifying such uncertainty. The use of growing initial perturbations is essential for achieving high ensemble forecast skill. To investigate the influence of initial perturbations on forecast errors during this extreme rainfall event, this study applies the orthogonal conditional nonlinear optimal perturbation method (O-CNOP-Is) to construct an ensemble perturbation strategy tailored to the Henan rainstorm. The aim is to improve the representation of extreme precipitation and its associated forecast uncertainty, thereby providing new technical support for the prediction and early warning of similar high-impact events in the future.

The O-CNOP-Is represent a set of mutually orthogonal growing initial perturbations that satisfy prescribed physical constraints and exhibit the maximum nonlinear evolution at the forecast time. In this study, an O-CNOP-Is computation framework is established using the regional mesoscale Weather Research and Forecasting (WRF) model. The Global Ensemble Forecast System (GEFS) provided by the National Centers for Environmental Prediction is used as the source ensemble of initial perturbation samples. An ensemble projection algorithm is employed to derive a set of O-CNOP-Is perturbation fields specifically targeted at the “7·21” rainfall event, fully accounting for the nonlinear evolution of the model. These O-CNOP-Is are then superimposed onto the background field to generate an ensemble whose members embody the strongest features of uncertainty growth. The resulting ensemble forecasts are compared with those from the GEFS to assess the effectiveness of CNOP-type perturbations in ensemble forecasting of extreme precipitation.

The numerical results indicate that the initial perturbations provided by O-CNOP-Is are physically reasonable for regional ensemble prediction. The perturbation amplitudes increase with time, and their spatial structures effectively reflect the baroclinic instability characteristics of the evolving atmosphere. Compared with GEFS perturbations, O-CNOP-Is contain more uncertainty information at the initial time and exhibit stronger perturbation growth at the forecast time. Throughout the forecast period, the O-CNOP-Is ensemble displays larger ensemble spread that better matches the forecast errors. Moreover, the ensemble mean forecast shows notable improvements in reproducing both the location and peak intensity of extreme precipitation centers.

Overall, the results demonstrate that the conditional nonlinear optimal perturbation approach is a highly promising method for capturing the dominant error growth modes in the Henan rainstorm. It effectively enhances ensemble forecast skill for high-impact, strongly nonlinear extreme weather events such as the “7·21” Henan rainfall, and provides a solid scientific basis and practical foundation for the development of regional mesoscale ensemble forecasting systems.

How to cite: liu, Y. and liu, J.: The Role of Orthogonal Conditional Nonlinear Optimal Perturbations (O-CNOPs) in Ensemble Forecasting of Extreme Rainfall: Improvement and Physical Perturbation Mechanisms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12835, https://doi.org/10.5194/egusphere-egu26-12835, 2026.

Coupled data assimilation (CDA) is a core method for achieving seamless forecasting with Earth system models (ESMs). It provides high-quality initial conditions for coupled models, minimizes inter-component imbalances to the greatest extent, and better utilizes observational networks. Based on whether cross-component information transfer is achieved, CDA can be classified into weakly coupled data assimilation (WCDA) and strongly coupled data assimilation (SCDA). SCDA, which incorporates cross-component information transfer, can produce more balanced and consistent analysis fields, thereby improving forecast skill. According to ECMWF (refer to the literature Coupled data assimilation at ECMWF: current status, challenges and future developments), strongly coupled data assimilation enables the transfer of observational information across different Earth system components during the assimilation phase.

This study employs the high-resolution version of the global sea-land-air-ice system model FGOALS-g3, developed by the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) at the Institute of Atmospheric Physics, Chinese Academy of Sciences, along with the Dimension-Reduced Projection Four-Dimensional Variational (DRP-4DVar) system, to conduct single-point coupled assimilation experiments. The aim is to investigate how cross-component covariance specifically regulates the transfer of observational information between Earth system components. The experimental results show that within the coupled framework, assimilating only a single-point surface pressure observation in the atmosphere not only generates analysis increments for atmospheric wind and temperature but also influences the state of the ocean surface layer. Similarly, assimilating only a single-point sea surface temperature observation not only produces analysis increments for sea surface height but also induces responses in the lower atmospheric state.

How to cite: Fu, J. and Liu, J.: Multi-Sphere Covariance Analysis for Coupled Assimilation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12842, https://doi.org/10.5194/egusphere-egu26-12842, 2026.

Starting in the early 2020s, artificial intelligence-based models (Machine Learning Weather
Prediction, MLWP) play an increasingly important role in weather forecasting. Their widely known
advantages make the use of MLWP models desirable in the future; however, it is important to have
a thorough understanding of the nature of their prediction fields and their strengths and weaknesses.
Most deterministic MLWP models have been trained using a Mean Squared Error (MSE)-
based cost function. The effective resolution of these models decreases as the forecast time
increases to avoid the double penalty occurring in the evaluation of sharp precipitation fields. It has
been shown that the effective resolution of a MLWP model forecast field is equivalent to the
ensemble average for forecast time period which had been explicitly included in the model cost
function. Since this similarity to the ensemble mean provides a strong basis for the operational
interpretation of the MLWP forecasts, it is important to verify it using model data for different
atmospheric variables.
In our work, we examined the effective resolution of a particularly important variable in
forecasting practice, the 6-hour precipitation field, by comparing the ECMWF-AIFS MLWP model,
the ECMWF HRES ensemble average, and the ERA5 reanalysis data. Effective resolution is usually
determined by examining the spectrum of model fields. However, since precipitation is a highly
localized and non-continuous atmospheric variable, we considered it appropriate to quantitatively
examine the smoothing of the fields in addition to spectral analyses. The aim of our calculations
was to determine how long the smoothing of ECMWF-AIFS precipitation forecasts follows the
ensemble average and how much they deviate from reality as represented by ERA5. We defined
smoothing as the extent to which the ERA5 precipitation field, which most accurately represents the
real field, needs to be smoothed using Gaussian filters in order for its sharpness indicators to match
those of the given forecast. Since smoothing in the precipitation field of MLWP models occurs not
only in the spatial structure but also in the reduction of extremes, we have developed a separate
method for examining sharpness that focuses exclusively on extreme values, based on the
examination of the exponential approximation of the global distribution of 6-hour precipitation
intensity.
Our results show that the agreement between the MLWP model and the ensemble mean
smoothing over the training period is only achieved in the case of extratropical precipitation
dominated by synoptic-scale processes. We did not observe such an agreement at all in tropical
areas with predominantly convective precipitation. We obtained similar results using a sharpness
metric developed for extreme values, i.e., the MLWP model's smoothing of extreme values only
matched the ensemble mean in the extratropics These results indicate that MLWP models can only
successfully predict processes on a sufficiently large scale and smooth small-scale processes such as
convection.

How to cite: András, C., Ádám, L., and Ákos, V.: Quantification of 6-hour precipitation field smoothing in deterministic Machine Learning-based Weather Forecasts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13006, https://doi.org/10.5194/egusphere-egu26-13006, 2026.

Wintertime heavy precipitation and flooding events along the US West Coast are associated with intense onshore water vapor transport by atmospheric rivers (ARs).  Although it has been widely recognized that the uncertainty in AR forecasts is a major contributor to the uncertainty in quantitative precipitation forecast (QPF) along the US West Coast, from the perspective of the atmospheric general circulation, there are multiscale contributors to the QPF uncertainty, depending on the forecast lead time and the forecast model domain size.  It remains a major forecasting and risk-management challenge to understand and quantify the multiscale interactions between uncertainties in forecasts of the upper-level jet in the North Pacific and the genesis and evolution of extratropical cyclones, whose AR-induced moisture transport directly contributes to heavy precipitation events. 

In this presentation, we leverage an ongoing effort to evaluate NOAA's newly developed AR forecast system to untangle the interactions among the multiscale controllers that contribute to the QPF uncertainty along the US West Coast.  We will show using precipitation and atmospheric analysis datasets that the QPF uncertainty along the US West Coast, dependent on the forecast lead time and the model's forecast domain size, can be linked to the uncertainties in the forecasts of convective activities in the tropical Pacific, the interaction between tropical convection and upper-level jet in the North Pacific, and the genesis and evolution of extratropical cyclones associated with the upper-level jet.

How to cite: Michelson, S., Grell, E., and Bao, J.-W.: On the Multiscale contributors to quantitative precipitation forecast uncertainty in the US West Coast, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14986, https://doi.org/10.5194/egusphere-egu26-14986, 2026.

An atmospheric river analysis and forecast system (AR-AFS) is being developed by NOAA’s Environmental Modeling Center to better understand and predict the extreme precipitation events induced by atmospheric rivers (ARs).  This system is a limited-area version of NOAA’s Unified Forecast System, with 3-km horizontal resolution.  As part of a community effort to optimize the system’s performance, we are currently evaluating the impact of different physics parameterizations on the system’s quantitative precipitation forecast (QPF) along the US West Coast. 

To a large degree, the accuracy of the precipitation forecast for a landfalling AR is determined by synoptic-scale, dynamical forcing; however, the parameterized physical processes in the model also play an important role.  The factors contributing to errors in QPF are multiscale in nature, and vary in their sensitivity to the model representation of both dynamical and physical processes.  Using precipitation observations as well as meteorological analyses, we present an evaluation of the impact of different physics parameterizations on the model performance.

How to cite: Grell, E., Bao, J.-W., Michelson, S., Bengtsson, L., and Swenson, L.: Testing and Evaluation of an Atmospheric River Prediction Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14992, https://doi.org/10.5194/egusphere-egu26-14992, 2026.

Control simulation experiments (CSEs) aim to steer model predictions toward a desired target by intervening in the model inputs. This concept is mathematically analogous to data assimilation, in which model states are adjusted to align with observations. In recent machine-learning models, automatic differentiation is used to compute gradients of a loss function and to optimize model parameters to minimize the difference between predictions and targets (e.g., reanalysis data). By optimizing input variables (initial conditions) instead of model parameters, CSEs can be formulated for differentiable models. In this study, we conduct CSEs for Typhoon Jebi (2018) using NeuralGCM, a fully differentiable global climate model. Tropospheric temperature perturbations are optimized to minimize the mismatch of 500-hPa geopotential height (Z500) over 130°E–160°E and 10°N–40°N. The results demonstrate that the predicted fields converge toward the target, indicating that CSEs can be successfully implemented and executed in a differentiable GCM framework.

How to cite: Nakano, M., Nasuno, T., and Kodama, C.: Control simulation experiments using a differentiable global climate model: A case study of Typhoon Jebi (2018), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15380, https://doi.org/10.5194/egusphere-egu26-15380, 2026.

Atmospheric inversions rely on accurately modeled atmospheric conditions, especially wind speeds, often for days in the past. Most atmospheric model products - ECMWF, GFS, and HRRR in our case - report on a scale of 0.1 - 0.5 degrees, or ~10 - 50km. These products can work quite well for inversions at coarse mesoscale resolutions, that model winds for ~100 - 1000km and beyond. However, inversions working at finer scales face significant problems in trying to model winds over a single or even a few meteorological model grid cells.

The MethaneSAT mission attempts to run an atmospheric inversion at a scale of 4km x 4km through the CORE algorithm (Described by Knapp et al at EGU2026), and in trying to extract emissions from instrument observations, we sometimes see plumes that run 30 degrees or more off of model wind directions. Wind speed and especially direction errors at this scale often lead to failed inversions, accounting for a roughly 10% - 20% loss of scenes collected by MethaneSAT, second only to cloudiness.

Luckily, many atmospheric model products are distributed as both a single estimate and as an ensemble product, containing dozens of perturbed forecasts for every time step. To minimize the CORE inference error induced by error in wind estimates, we present a technique for quickly and efficiently analyzing the wind fields in comparison to the concentration Level3 maps in order to select the most likely ensemble member. We utilize a novel Total Variation approach to quantify the expected alignment of wind fields with measured methane gradients, and select the ensemble product whose winds are most likely to produce the observed concentration map.

We demonstrate this technique on both simulated and real MethaneSAT data, and discuss the effects this has on both success rate of the inversion and on residual errors from the inversion, though the approach is not specific to methane, and is broadly applicable in any case where winds are the primary drivers of transit. Special care is taken to identify pathological cases of wind error, and how these could be addressed in the future.

How to cite: Ayvazov, S., Veness, T., Russi, M., LoFaso, N., Knapp, M., Kyzivat, E., Benmergui, J., and Wofsy, S.: Automated Selection of Meteorological Ensemble Members for Inversions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15584, https://doi.org/10.5194/egusphere-egu26-15584, 2026.

Since 1972, the US Weather Modification Reporting Act has required federal reporting of any cloud seeding activities taking place in the United States to NOAA. Leveraging these historical records, we used OpenAI’s o3 large language model to extract information about the project name, year, season, state, operator, seeding agent, apparatus used for deployment, stated purpose, target area, control area, start date and end data of all publicly reported cloud seeding activities (Donohue and Lamb, 2025). This method was validated through the performance of the data extraction pipeline on a manually labeled subset of 200 records, achieving an average accuracy of 98.38% across all fields. This structured data set, encompassing 832 distinct operational periods from 2000 – 2025, represents the first large-scale, publicly available data set of cloud seeding activities for the US that can facilitate large-scale historical analysis.

Using this data set, we use synthetic control, a statistical method to estimate the effect of an intervention, to understand whether cloud seeding is an effective strategy for augmenting snowpack and precipitation from a climatological perspective. Using the reported locations and times of historical cloud seeding operations, along with historical datasets of precipitation and snowpack, we analyze whether cloud seeding can have a climatologically significant impact in augmenting precipitation and snowpack regionally. Our approach makes it possible to rigorously evaluate cloud seeding effectiveness on a climatological scale across the US for the first time.

How to cite: Lamb, K. and Donohue, J.: Using Synthetic Control to Assess the Climatological Significance of Cloud Seeding in the Western US with a Structured Data Set of Reported Activities from 2000 – 2025, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15743, https://doi.org/10.5194/egusphere-egu26-15743, 2026.

Subseasonal forecasts, which predict weather patterns from weekly up to seasonal timescales, are crucial to minimize the adverse impacts of extreme weather events, such as heatwaves and droughts, on ecosystems and society. However, forecast skill at subseasonal lead times remains limited, as the chaotic nature of the atmosphere reduces the usefulness of the information contained in atmospheric initial conditions for increasing lead times. In contrast, land surface states, including soil moisture and vegetation anomalies, evolve more slowly and retain memory over weeks, allowing them to persist across subseasonal timescales and making them a potentially important source of predictability. Despite this, most operational weather forecast models represent only the mean seasonal cycle of land conditions, because accurately incorporating land surface anomalies remains challenging and can degrade model performance.

In order to address this situation, we develop a prototype of a hybrid weather prediction model to forecast near-surface temperature and surface soil moisture and related extremes. The model leverages the flexibility of deep learning to build on (i) satellite-based land surface observations, (ii) short-range forecasts from the Integrated Forecasting System to inform the model with physically consistent atmospheric evolution, and (iii) previous meteorological conditions sourced from reanalysis data. First results suggest that land surface anomalies exert a stronger influence during extreme conditions, when land memory persists, whereas under average conditions their influence is more evenly shared with atmospheric anomalies. Our study provides a benchmark for integrating land surface information into hybrid forecasting systems and highlights pathways to improve subseasonal prediction and early warning systems.

How to cite: Ruiz-Vásquez, M., Oh, S., Düben, P., and Orth, R.: Enhancing subseasonal forecasting skill with land observations and physics-informed deep learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15763, https://doi.org/10.5194/egusphere-egu26-15763, 2026.

How to cite: Najari, N., Potthast, R., Keller, J., Hollborn, S., and Deppisch, T.: Improving ICON-DREAM Cloud Information through AI-driven Data Assimilation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15804, https://doi.org/10.5194/egusphere-egu26-15804, 2026.

Accurate prediction of tropical cyclogenesis is fundamental to enhancing typhoon forecasting and disaster mitigation. This study investigates the impacts of Global Navigation Satellite System (GNSS) observations on cyclogenesis predictions by integrating a multi-case statistical evaluation with a targeted case study. Initially, the impact of GNSS Radio Occultation (RO) data assimilation (DA) was assessed by assimilating both conventional observations and RO data in ten tropical cyclone cases in the Northwestern Pacific from 2020 to 2022. Utilizing the WRF hybrid-3DEnVar system, the results demonstrate that incorporating RO data with a nonlocal excess phase operator improves the accuracy of cyclogenesis localization and timing, a finding further corroborated by ensemble forecasts.

Beyond RO, this research explores the potential of integrating GNSS Reflectometry (GNSS-R) data, which provides sea surface wind speed information to better capture the near-surface dynamical environment during the early stages of cyclogenesis. In a case study of Typhoon Gaemi (2024), RO data assimilation successfully captured the cyclogenesis signal that was missed in experiments without RO. Furthermore, jointly assimilating RO and GNSS-R observations (RO+R) refined the predicted genesis timing compared with RO-based experiments. These findings suggest the potential to use multi-source GNSS observations to improve the precision of tropical cyclogenesis forecasting.

How to cite: Chen, S.-Y., Pham Xuan, Q., Huang, C.-Y., Kuo, Y.-H., and Yang, S.-C.: Impact of GNSS Radio Occultation and Reflectometry Data Assimilation on Tropical Cyclogenesis Prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15881, https://doi.org/10.5194/egusphere-egu26-15881, 2026.

  As the demand for high-resolution weather forecasts continues to increase, operational numerical weather prediction centers face challenges in maintaining model consistency across horizontal scales while accurately representing region-specific physical processes. This study evaluates the KIM-Regional/Local system, a unified modeling framework employing the Korea Physics Parameterization Package (KPPP) optimized for the Korean Peninsula. The system operates on two nested scales: a 3-km regional domain covering East Asia (5-day forecasts) and a 1-km local domain covering the Korean Peninsula (2-day forecasts). A key distinction in their configuration is the treatment of convection: cumulus parameterization is applied in the 3-km regional model, whereas the 1-km local model is configured as convection-permitting, explicitly resolving deep convection without a cumulus scheme. Both domains share identical dynamical cores and other KPPP components.
  KPPP introduces several key advancements optimized to regional environmental conditions. These developments include refinements to microphysical processes, along with enhanced radiative parameterizations that account for all-sky radiation and topographic slope and shading effects. The land surface scheme includes observation-based refinements to tree and canopy height, which are expected to improve the representation of surface fluxes. To enhance initial and boundary conditions, an oceanic mixed-layer model is activated, and spatially and temporally varying Charnock coefficients are introduced for sea surface roughness calculations, replacing the previously used constant values. Together, these developments are introduced to better represent key physical processes in the model, while accounting for computational efficiency required for operational application.
  Forecast performance was evaluated for representative summer and winter periods through comprehensive verification of surface and upper-air variables against observations, alongside quantitative precipitation forecasts assessment. The most pronounced improvements are found in surface verification results, particularly in surface wind speed, where the Root Mean Square Error (RMSE) is substantially reduced compared to the current operational system. Overall enhancements are also evident in precipitation performance, with reduced biases across forecast ranges in both the 3-km and 1-km domains. These results indicate that region-specific physics development provides a robust pathway toward operationally reliable high-resolution prediction systems over regions with complex terrain.

How to cite: Jeong, S., Lee, J., Lee, E., and Lee, Y.-H.: Evaluation of KIM-Regional/Local Forecast Model Performance Based on the Korea Physics Parameterization Package, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15894, https://doi.org/10.5194/egusphere-egu26-15894, 2026.

The forecast performance of numerical weather prediction models strongly depends on the accuracy of the initial conditions, which is largely determined by the quality of observations used in data assimilation. Aircraft observations provide three-dimensional atmospheric information over data-sparse regions, such as the upper level and oceans, thereby complementing the spatial inhomogeneity of the global observations and contributing to improvements in initial conditions. However, aircraft temperature observations are known to exhibit positive biases compared to radiosonde observations, and correcting these biases is an important challenge for improving forecast performance.
The Korea Meteorological Administration (KMA) has been operating the latest version of the Korean Integrated Model (KIM) v4.0 operationally since May 2025, but aircraft temperature bias correction has not yet been applied. In this study, two experiments were conducted in July 2025 to quantitatively evaluate the impact of aircraft temperature bias correction on forecast performance using KIM v4.0 at an approximately 25 km horizontal resolution: one experiment applied bias correction globally, while the other applied it selectively over regions north of 30°N. This latitude threshold was determined based on the spatial distribution characteristics of temperature biases identified in the KIM v4.0. Aircraft observations were stratified into three vertical layers (lower: 1050–700 hPa, middle: 700–300 hPa, upper: 300–150 hPa), and aircraft ID temperature bias correction coefficients were derived and applied during the observation preprocessing step. 
The experiment applying bias correction north of 30°N showed overall improved forecast performance of temperature and geopotential height over the Northern Hemisphere and North America compared to the globally applied experiment. Additionally, performance improvements were observed in East Asia during the later forecast periods (days 4–5), with lower-level specific humidity and temperature showing improvements of 1.18% and 1.55%, respectively. These results demonstrate that selective temperature bias correction considering the spatial characteristics of aircraft observations can contribute to improving forecast performance in numerical weather prediction models.

How to cite: Lee, Y., Ha, J.-H., and Lee, Y.: Impact of Aircraft Temperature Bias Correction in the Korean Integrated Model (KIM) , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15923, https://doi.org/10.5194/egusphere-egu26-15923, 2026.

This study explores how different integration strategies for infrared (IR) and microwave (MW) brightness temperatures (TBs) impact precipitation forecasting within a 3D-Var all-sky radiance data assimilation (DA) framework. To improve heavy rainfall forecasts over the Korean Peninsula, we propose an asynchronous assimilation strategy. In this approach, when IR and MW observations overlap, we prioritize MW TBs and intentionally exclude IR TBs to minimize potential redundancies and physical inconsistencies in cloud representation. We compared this proposed method against two common synchronous strategies: one assimilating clear-sky IR with all-sky MW, and another integrating both IR and MW under all-sky conditions. Using a heavy precipitation case over Korea as a testbed, we assimilated AHI (Himawari-8), GMI (GPM), and AMSR2 (GCOM-W) data to evaluate their impacts on hydrometeor analysis and subsequent forecast accuracy. Our results indicate that the asynchronous strategy leads to a more balanced vertical distribution of solid and liquid hydrometeors, resulting in the most reliable precipitation forecasts. In contrast, the all-sky IR+MW strategy tended to overemphasize upper-level clouds while reducing low-level moisture, leading to biased localized rainfall. Meanwhile, the clear-sky IR+MW approach failed to adequately capture upper-level stratiform structures. These findings suggest that an optimized, sensor-specific integration strategy is essential for maximizing the benefits of multi-platform satellite data assimilation.

How to cite: Hwang, J. and Cha, D.-H.: Optimizing Synergistic Strategies of IR and MW Radiance in All-sky Data Assimilation for Heavy Precipitation Forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16528, https://doi.org/10.5194/egusphere-egu26-16528, 2026.

The Earth System Modelling at the Weather Scale (ESM-W) project, a collaboration between the German Weather Service (DWD) and GeoInfoDienst BW, aims to develop a coupled ocean-atmosphere forecasting system. This system utilizes the ICON-O ocean model and the ICON-NWP atmospheric model.

In this presentation, we will showcase the advancements made in the ICON-based coupled global forecasting system. Notably, a near-real-time (NRT) system has been established, comprising a time-critical weakly coupled data assimilation cycle and coupled forecasts with lead times of up to ten days. The ocean component uses a 20km resolution while the atmospheric component uses the operational 13km resolution system. The ocean component assimilates ARGO buoy data and satellite-derived sea surface observations using 3D-VAR(-FGAT) methods, while the atmosphere employs an EnVAR method leveraging ensemble information from DWD’s operational routine. This system has been generating daily analyses and forecasts since May 2025 and is improved continuously.

We will present the coupled system’s developments and performance, including evaluations for both the data assimilation system and the model components. Additionally, we show verification against non-assimilated ocean datasets, such as fixed buoys.

How to cite: Krueger, D., Bevrnja, A., Hayrapetyan, A., He, Y., Hüther, T., Schenk, N., Sprengel, M., Potthast, R., Keller, J., Hollborn, S., Schlemmer, L., and Zängl, G.: The coupled ICON Ocean-Atmosphere System and its Use for Weather-Scale Forecasts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16609, https://doi.org/10.5194/egusphere-egu26-16609, 2026.

AI-based methods have already proven their skill in weather prediction. The availability of high quality training data and ever more advanced model architectures opened the door for a new range of models providing predictions fast and at a low operational cost. Further development of such models, apart from seeking advancements in the architectural design and increased quality of the training datasets, might require a new approach. One still under-explored dimension is to tighten the link between the data-driven (often weather-system-agnostic) methods and the well established knowledge of atmospheric physics represented by NWP. 

Physics-awareness in AI model development may guide training, reduce compute requirements and improve the consistency of the predicted variables. In this talk we discuss the possible approaches to physics-informed AI model design, ranging from physics-based terms in the cost function to hybrid physical-neural architectures. We show the impact these methods have on the training process and discuss possible improvements in the forecast skill and physical consistency.

How to cite: Rognvaldsson, O. and Stanislawska, K.: Physics-informed AI systems - how the constraints from NWP can support development of better AI models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18633, https://doi.org/10.5194/egusphere-egu26-18633, 2026.

Extreme heat is emerging as one of the deadliest weather-related hazards under global warming. In response, a growing range of weather and climate interventions has been proposed to locally mitigate extreme thermal stress. The spraying of water into the air is a well-established technique for direct evaporative cooling at small scales, such as in urban or industrial environments. In this study, we assess the potential for larger-scale sea water spraying as a localized extreme heat intervention.

We first quantify the maximum evaporative cooling potential of liquid water spraying and its dependence on air temperature and relative humidity. Under hot and dry conditions, theoretical cooling exceeding 20 °C can be achieved, providing an upper bound for realistic applications. We then employ Large Eddy Simulations (LES) to investigate kilometer-scale cooling of the lower atmosphere in coastal regions experiencing sea-breeze conditions in hot and dry climates. A key innovation of this study is the introduction of sea water spraying from wind turbines, which we compare with spraying from lower infrastructures such as platforms or boats.

The resulting impacts on near-surface temperature and human thermal comfort indices are evaluated, highlighting potential cooling benefits during extreme heat events. Finally, we examine how such cooling influences plume rise, a key process for Marine Cloud Brightening. Our results demonstrate that physically bounded, localized atmospheric interventions may offer a useful tool to mitigate extreme heat in vulnerable regions, while providing insight into the effectiveness and limitations of weather- and climate interventions.

How to cite: Broerze, A., de Roode, S., and Russchenberg, H.: Cooling the air: assessing the evaporative cooling potential of sea water spraying as an extreme heat intervention, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18836, https://doi.org/10.5194/egusphere-egu26-18836, 2026.

Accuretaly representing the microphysical structure of precipitating systems remains a major challenge in Numerical Weather Prediction (NWP). Cloud and precipitation processes occur at spatial and temporal scales that are not explicitly resolved by current models and must therefore be described through simplified microphysical parameterizations. These parameterizations have a strong impact on key model outputs, such as precipitation intensity, and their improvement requires observations that are sensitive to the vertical structure and microphysical properties of hydrometeors.

Polarimetric Radio Occultations (PRO) provide a complementary observational capability to address this gap. As with standard GNSS Radio Occultations, PRO delivers high vertical resolution thermodynamic profiles under all-weather conditions. In addition, PRO is sensitive to the presence and vertical distribution of hydrometeors through the differential phase shift (ΔΦ), defined as the phase difference between horizontally and vertically polarized GNSS signals. When these signals propagate through non-spherical and/or oriented hydrometeors, differential propagation effects arise, leading to a positive differential phase shift. As a result, PRO measurements offer direct sensitivity to the microphysical structure of precipitating systems.

The accuracy of simulated PRO observables depends on the formulation of the forward operator, particularly on how the relationship between differential phase shift and hydrometeor water content is represented. Recent work has proposed a new forward operator based on a linear relationship that enables the inclusion of scattering-related information as a function of hydrometeor type. In this study, we evaluate the performance of this updated PRO forward operator under Atmospheric River (AR) conditions, which are characterized by intense moisture transport and strong precipitation. Simulations are compared with those produced using the offline forward operator currently implemented at ECMWF to assess whether the new formulation could serve as a viable replacement for operational applications.

Beyond this potential operational impact, the new forward operator enables the use of PRO as a constraint on cloud and precipitation microphysics. By exploiting the Atmospheric Radiative Transfer Simulator (ARTS) scattering database, we analyze the sensitivity of PRO observables to the scattering properties of different particle habits, providing insight into the extent to whether PRO measurements can discriminate between microphysical assumptions and improve the representation of precipitating systems in NWP models.

How to cite: Paz, A., Padullés, R., Cardellach, E., Lonitz, K., and Vidal, V.: Evaluating Polarimetric Radio Occultations for constraining precipitation microphysics in NWP, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19084, https://doi.org/10.5194/egusphere-egu26-19084, 2026.

How to cite: Žagar, N., De Chiara, G., Healy, S., Sielmann, F., and Wang, C.: Global Observing System and Tropical-Extratropical Coupling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19833, https://doi.org/10.5194/egusphere-egu26-19833, 2026.

This presentation will provide an overview of recent scientific developments at ECMWF, with focus on the upgrade of the Integrated Forecasting System (IFS) for cycle 50r1.

An upgrade to ECMWF's Integrated Forecasting System (IFS) is scheduled for operational implementation in early 2026.  IFS Cycle 50r1 brings major advances in both the forecast model and the data assimilation system, marking a significant step forward in coupled Earth system prediction. The model upgrade includes the introduction of the NEMO4-SI3 ocean and sea ice model, improved wave-ice interactions, revised vertical diffusion and gravity wave drag in the stratosphere, changes to the convection scheme, a new glacier scheme, and a revisionof the SPP scheme introduced in 2023 that reduces excessive near-surface wind spread in the ensemble. On the data assimilation side, outer-loop coupling has been introduced between the  atmosphere and ocean to provide balanced initial conditions for the coupled forecast model, while further enhancements of weak-constraint 4D-Var introduce time-varying model errors and has been extended to the boundary layer. Cost-efficiency improvements have been made through the use of single-precision trajectories, single-precision ocean model and from reducing the resolution of the first minimisation in the EDA. The system now allows humidity increments in the stratosphere, addressing longstanding issues in moisture analysis at these levels.

A major development of data-driven weather prediction models is the operational implementation (July 2025) of the Artificial Intelligence Forecasting System ensemble (AIFS-ENS). AIFS ENS is trained using a CRPS-based approach, which optimises the probabilistic scores of the ensemble forecasts. It delivers skilful weather forecasts with significantly improved speed and energy efficiency.

The 50r1 update of the ECMWF atmospheric composition forecast led to improved forecast of surface ozone and sulphur dioxide.  A further addition is the routine assimilation of aerosol optical depth retrievals from VIIRS and Sentinel 3. 

Besides the developments for the operational updates, the preparation of the production of 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) have progressed well.   

How to cite: Flemming, J., English, S., and Pappenberger, F.: Recent progress and outlook for the ECMWF Integrated Forecasting System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21470, https://doi.org/10.5194/egusphere-egu26-21470, 2026.

Extreme weather events are intensifying under climate change, yet recent advances in weather prediction operate within a forecast-only paradigm that does not directly mitigate impacts once an extreme event is anticipated. Motivated by chaos control theory, we explore whether small, instability-aware perturbations can leverage intrinsic atmospheric sensitivity to influence extreme weather evolution within an AI-based forecasting framework. We use the Aurora foundation model and identify dynamically sensitive perturbation locations using Finite-Time Lyapunov Exponent (FTLE) diagnostics. To implement a physically interpretable intervention compatible with foundation models, we introduce an idealized cloud seeding based perturbation operator that mimics condensation-driven latent heat release applied in the lower–mid troposphere. In a case study, these upstream perturbations induce coherent downstream changes in integrated vapor transport, leading to reduced peak landfall intensity and slower precipitation accumulation. These results demonstrate that instability-aware perturbations within an AI foundation model can induce dynamically meaningful downstream impacts, providing a first step toward bridging chaos control concepts and data-driven weather prediction.

How to cite: Liu, M., Huang, Q., and Lall, U.: Instability-Aware Perturbations of Extreme Events in an AI Weather Foundation Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22120, https://doi.org/10.5194/egusphere-egu26-22120, 2026.

Extreme weather events, e.g., droughts, floods, heatwaves, freezes, increasingly challenge physical, financial, and social infrastructure as population and economic growth increase exposure and vulnerability. We propose supplementing conventional disaster risk management strategies with Weather Jiu-Jitsu, an approach that leverages the chaotic dynamics of weather systems to redirect or dissipate destructive trajectories through targeted, low-energy perturbations. Coupled with deep learning models, this framework could serve as a form of nature-assisted global infrastructure to reduce catastrophic climate-extreme impacts in the 21st century. We demonstrate the potential of this strategy through successful perturbation experiments applied to tropical cyclones, atmospheric rivers, freezes, and other high-impact events.

How to cite: Huang, Q., Liu, M., and Lall, U.: Weather Jiu-Jitsu: Prospects for Atmospheric Nudging to Defuse the Impact of Catastrophic Weather Extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22129, https://doi.org/10.5194/egusphere-egu26-22129, 2026.

Data assimilation (DA) seeks to provide the most likely estimate of the true state of the atmosphere or ocean by combining a background estimate from a numerical model with observations, each weighted by their respective error covariances. For computational reasons, diagonal covariances together with variance inflation have traditionally been favoured for the observational error covariance. However, recent studies have shown that variance inflation can degrade DA performance when the correlation length scales of observational errors are comparable to, or exceed, those of the background errors—a situation frequently encountered when assimilating wind vectors derived from Atmospheric Motion Vectors. These observations are assimilated, alongside many others, in the ensemble DA system at the European Centre for Medium-Range Weather Forecasts (ECMWF). In this system, each ensemble member undergoes an independent 4D-Var minimisation after perturbing its observations and model parameters to represent observational and model uncertainties. This work presents results from efforts to explicitly account for spatial correlations in observational errors within the ensemble DA framework. In particular, it demonstrates the positive impact of introducing spatially correlated perturbations to assimilated observations on ensemble spread, offering a pathway to improved representation of uncertainty in operational forecasting.

How to cite: Pasmans, I., Holm, E., Bonavita, M., Dance, S., and Bhatt, R.: Reaching far and wide: accounting for spatially correlated observational errors in an ensemble of 4D-Vars system, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22255, https://doi.org/10.5194/egusphere-egu26-22255, 2026.

A numerical weather prediction (NWP) model is a computer program that follows a numerical recipe to discretize the governing equations of atmospheric dynamics for numerical solutions.  The center of these governing equations is the Navier-Stokes (NS) equations of fluid motion.  The closure paradox theorem (Guermond et al., 2004, J. Math. Fluid Mech.) for the numerically discretized (i.e., filtered) NS equations states that discretization (i.e., filtering) and exact subgrid closure are mutually exclusive in practice.  To feasibly solve the discretized NS equations, the subgrid closure must be inexact.  All physics parameterization schemes in an NWP model serve as a closure for the discretized governing equations of atmospheric dynamics.  Even though these schemes vary in complexity, the closure paradox theorem implies that none of them can be exact if the objective is to efficiently produce useful forecasts at the NWP model's grid resolution.  Therefore, empirical adjustment, i.e., constraining its behavior using available observations, is inevitable for any physics parameterization scheme to be feasibly used in an NWP model.

In this presentation, we will use the land-surface parameterization scheme as an example to discuss what the complexity of a physics parameterization scheme actually means, since it cannot be exact.  We will argue that the choice of complexity in a scheme is a trade-off between realism and simplicity.  We will show that a simple land-surface scheme may meet performance constraints with modest observational requirements and is computationally inexpensive enough to be practically useful.  In contrast, a more complex land-surface scheme with sounder physical foundations will yield forecasts that are acceptably more accurate only if enough observations are available to constrain its behavior.  When there are insufficient observations to constrain the complex scheme, the simple scheme should be used so that the scheme's behavior can be effectively constrained using available observations.

How to cite: Bao, J.-W., Michelson, S., and Grell, E.: What does the complexity of physics parameterization mean, since no parameterization can be exact?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22510, https://doi.org/10.5194/egusphere-egu26-22510, 2026.

Seasonal climate forecasts are critical for disaster management across the fragile Himalayan ecosystem, particularly during winter. However, these forecasts often exhibit strong spatial and temporal biases that reduce their reliability for predicting extremes at longer lead times. Traditional postprocessing methods such as quantile mapping and linear scaling assume stationarity and have limited ability to capture complex spatiotemporal error structures. To address these limitations, this study introduces Hima-Net (Himalayan-Net), a hybrid deep learning model that combines U-Net and Conv-LSTM architectures. Hima-Net is designed to improve the skill of sub-seasonal-to-seasonal (S2S) daily precipitation forecasts from the ECMWF S2S system by learning season-specific spatial and temporal patterns in forecast errors. The model is trained with a loss function that jointly emphasizes magnitude and correlation, enhancing its ability to represent the distribution and evolution of precipitation across lead times. Evaluation using metrics such as root mean square error (RMSE) and anomaly correlation coefficient (ACC) shows that Hima-Net consistently outperforms the raw forecasts across lead times over the Himalayan region. These findings demonstrate the potential of deep learning–based postprocessing to better capture and enhance spatial and temporal forecast patterns, offering a promising pathway for more accurate wintertime precipitation forecasts over the complex terrain of the Himalayas.

How to cite: Dar, J. and Ghosh, S.: Hima-Net: Deep Learning Enhancement of ECMWF S2S Winter Precipitation Forecasts over Northern India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-526, https://doi.org/10.5194/egusphere-egu26-526, 2026.

Global Navigation Satellite System Radio Occultation (GNSS RO) observations are increasingly important for improving atmospheric profiling and numerical weather prediction (NWP), especially in cloudy, moisture-rich tropical environments where other satellite observations are often degraded. This study presents two complementary advances: (1) an improved regional quality-control strategy for preserving COSMIC-2 bending-angle data in cloudy regions, and (2) an assessment of the impact of assimilating Tianmu-1 RO observations from a newly deployed 23-satellite commercial constellation on the prediction of Typhoon Gaemi (2024).

First, we show that the widely used latitude-based quality control of COSMIC-2 bending-angle data leads to excessive removal of observations between 6–8 km near the Solomon Islands, where persistent summertime altostratus frequently reach above 6 km. Despite the long-wavelength nature of RO measurements—which makes them less sensitive to clouds—these regions were incorrectly flagged as outliers. By implementing a 2.5° × 2.5° local quality-control approach, the number of discarded observations in cloudy areas is substantially reduced, yielding a more spatially uniform deviation structure relative to the local mean. This regionally adaptive method better preserves high-quality RO data in both mid-tropospheric altostratus and lower-tropospheric Intertropical Convergence Zone environments.

Second, we evaluate the impact of assimilating over 30,000 daily RO profiles from the Tianmu-1 constellation using the GSI–WRF system. Assimilating Tianmu-1 data alone—without other satellite observations—reduces 120-hour track errors of Typhoon Gaemi by 20–40%, with the largest improvements beyond 48 hours. Diagnostics show that enhanced prediction skill arises mainly from improved inner-core temperature structure and better representation of the large-scale steering flow. Remarkably, the track forecasts with Tianmu-1 assimilation are even slightly better than the operational forecasts from the NCEP Global Forecast System (GFS).

Overall, these results highlight the increasing importance of high-density GNSS RO constellations in forecasting tropical cyclone intensity and track, and emphasize the value of cloud-aware, adaptive regional quality-control techniques in preserving cloud-affected observations. Future work will extend these adaptive quality-control strategies globally and examine synergistic assimilation of COSMIC-2, Tianmu-1, and other commercial RO datasets.

How to cite: Yang, S. and Zou, X.: Positive Impacts of Tianmu-1 RO Data Assimilation on Tropical Cyclone Forecasts and the Non-negligible Influence of Altostratus Clouds on RO Data Quality, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1396, https://doi.org/10.5194/egusphere-egu26-1396, 2026.

Satellite brightness temperature (BT) observations contain rich information about the horizontal distributions of cloud and rainfall structures; while radiosonde observations provide high-vertical-resolution measurements of temperature, moisture, and wind in the atmosphere. Beyond their traditional use in assimilation and retrieval, this study demonstrates innovative quantitative uses of BT and radiosonde observations for evaluating high-resolution numerical weather prediction (NWP) simulations of tropical cyclones (TCs) and Southwest Vortices (SWVs).

First, we apply BT observations to document  the structural evolution of TCs and SWVs and to directly compare simulated hydrometeor distributions with satellite-observed cloud and precipitation features. These BT-based diagnostics provide objective constraints on model representation of convective initiation and development as well as the impact of diurnal variability.

Second, a BT-based threat-score (BT-TS) framework is introduced to assess the skill of rainfall forecasts with respect to satellite BT observations instead of rainfall observations traditionally used in TS evaluation. Using microwave humidity-sounder channels, the BT-TS metric performs well for assessing rainfall forecast in regions where precipitation observations are sparse or unavailable. The BT-TS forecast results highlight model deficiencies in timing, extent, and intensity of SWV-induced convective rainfall.

Third, radiosonde profiles are used to investigate lower-tropospheric processes critical for vortex evolution, focusing on planetary boundary layer (PBL) height and vertical variability under different vertical-resolution configurations. Verification with high-vertical-resolution (~5–6 m) profiles from 119 Chinese radiosonde stations during the summers of 2021–23 shows that accurately representing PBL height and lower-tropospheric thermodynamic variability requires approximately doubling  the number of ERA5 vertical levels.

Together, these BT- and radiosonde-based diagnostics provide a comprehensive observational framework for evaluating the structural evolution of TCs and mesoscale SWVs. Future work will leverage these insights to refine cloud microphysics schemes, optimize model vertical-resolution design, and enhance the predictability of convection-permitting NWP systems.

How to cite: Zou, X.: Besides Assimilation and Retrieval: Innovative Quantitative Uses of Satellite Brightness Temperatures and Radiosonde Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1397, https://doi.org/10.5194/egusphere-egu26-1397, 2026.

Wind speed is a critical factor influencing the transport and dispersion of atmospheric pollutants in air quality models. However, numerical weather prediction (NWP) models, such as the weather research and forecasting (WRF) model, typically overestimate surface wind speeds, leading to inaccuracies in air quality predictions. To address this limitation, we developed an Artificial Intelligence (AI)-based Wind Field Correction (WFC) model aimed at improving PM_2.5 forecasts over East Asia. The WFC model was constructed using the eXtreme Gradient Boosting (XGBoost) algorithm and trained on eight years of data, incorporating WRF-simulated meteorological variables as input features and in situ, ship-based, buoy, and radiosonde observations as targets. The WFC model effectively reduced the positive bias in WRF-simulated wind speeds, achieving a 90.15% reduction at the surface level and a 94.6% reduction from the surface to 850 hPa. The bias-corrected wind fields, when incorporated into the GIST Multiscale Air Quality model (GMAQ v1.0) developed by the Gwangju Institute of Science and Technology (GIST), resulted in substantial improvements in PM_2.5 predictablity. In Central Eastern China (CEC), the wind field correction mitigated the underestimation of PM_2.5 by suppressing excessive plume dilution in the model. In South Korea (SK), the correction slowed down accelerated plume advection, leading to a closer agreement between the simulated and observed PM­_2.5 plume locations. In addition, the correction enhanced the representation of daily PM­_2.5 variability and improved statistical metrics over the capital cities of Seoul and Beijing.

How to cite: Kim, J., Yu, J., Kim, H. S., Park, S., Woo, J.-H., and Song, C. H.: Bias-correction of wind speeds to improve PM2.5 predictability in chemical transport model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2677, https://doi.org/10.5194/egusphere-egu26-2677, 2026.

Minimizing pixel-wise errors in precipitation nowcasting inherently biases models toward smooth predictions, causing failures in resolving extreme convective events. To address this, we propose IMPA-Net, a meteorology-aware framework centered on spectral consistency. The architecture integrates three innovations: a parameter-free Spatial Mixer to encode multi-variate physical interactions (e.g., terrain-wind coupling); an Integrated Multi-scale Predictive Attention (IMPA) module to capture dynamics from Meso-β to Meso-γ scales; and a Meteorology-Aware Dynamic Loss (MAD-Loss) that employs asymmetric penalties to counteract regression-to-the-mean. Experiments demonstrate a 37.3% relative improvement in HSS for severe convection (≥45 dBZ). Crucially, RAPSD analysis confirms that IMPA-Net maintains spectral energy consistency across high-frequency bands, enabling it to successfully simulate the complex "dissipation-initiation" lifecycle that existing baselines fail to capture. These findings validate that integrating domain knowledge advances the physical plausibility of data-driven forecasting.

How to cite: He, G. and Cui, H.: IMPA-Net: Meteorology-Aware Multi-Scale Fusion and Dynamic Loss for Extreme Radar-Based Precipitation Nowcasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3224, https://doi.org/10.5194/egusphere-egu26-3224, 2026.

Accurate short-term precipitation nowcasting is crucial for disaster risk reduction, flash-flood early warning, and water resource management. Conventional nowcasting approaches, such as extrapolation-based radar methods or numerical weather prediction models, often struggle to capture the nonlinear evolution of convective systems and are computationally demanding for rapid updates at high spatial and temporal resolution. The ability to provide reliable high-resolution forecasts at lead times of minutes to hours is particularly important for mitigating the societal and economic impacts of intense rainfall events. Recent developments in deep learning (DL), in combination with high-resolution radar observations, represent a compelling alternative for improving short-term precipitation forecasting. Radar-based precipitation data are particularly well suited for nowcasting applications due to their fine spatio-temporal resolution and ability to capture the dynamic structure and movement of precipitation systems. In this study, we develop and evaluate an operationally oriented DL framework for precipitation nowcasting that integrates multi-source data including high-resolution radar and satellite observations and automatic weather station measurements via the qPrec system over Slovakia. By incorporating satellite-derived forcing, the framework accounts for convection initiation and cloud development stage, providing a physical advantage over both classical extrapolation and radar-only deep learning methods. The framework leverages modern DL architectures, including convolutional encoder-decoder models such as U-Net and spatio-temporal transformer-based models (e.g., Earthformer), to learn the temporal evolution of precipitation fields inputs. The use of transformer-based models allows the network to capture long-range spatial dependencies and complex motion patterns that traditional CNNs may miss.

The proposed models generate precipitation forecasts at a spatial resolution of 1 km and a temporal resolution of 5 minutes, with lead times of up to 60 minutes. In addition to instantaneous precipitation estimates, the framework produces 15-minute accumulated precipitation for horizons up to 120 minutes. Unlike traditional methods where predictability skill remains static across resolutions, our DL approach leverages varied spatial representations to enhance predictability at these coarser temporal scales, optimizing the forecast for different hydrological requirements. These accumulated fields can be directly applied to flash-flood hazard assessment, enabling estimation of flood likelihood as a function of rainfall intensity and duration. Model performance is evaluated using standard verification metrics such as the Fractions Skill Score, and continuous ranked probability score (reducing to MAE on deterministic outputs), showing improvement over conventional radar extrapolation methods. This study demonstrates that modern DL approaches, particularly when combined with high-resolution radar observations, offer a promising path toward next-generation operational nowcasting.

How to cite: Almeida, R., Göttlich, J., Otero, N., Jurasek, M., Méri, L., Shenga, Z. D., Atencia, A., and Ma, J.: Deep Learning-Based Precipitation Nowcasting for Operational and Flash-Flood Applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8109, https://doi.org/10.5194/egusphere-egu26-8109, 2026.

Machine learning-based global weather forecasts often suffer from coarse spatial resolution, limiting their ability to capture fine-scale temperature variability in regions with complex terrain or strong urban–rural gradients. We present SR-Weather, a two-stage deep learning framework that downscales coarse 0.25° forecasts into 1 km air temperature fields. Our model is trained using ERA5 and MODIS-derived temperature data, and leverages high-resolution auxiliary inputs, including elevation, impervious surface fraction, and spatial information–normalized air temperature to enhance spatial fidelity. Applied to 7-day lead forecasts from the FuXi model, SR-Weather consistently outperforms FuXi’s own 1-day lead predictions, indicating strong capabilities in both resolution enhancement and bias correction. The model also exhibits robustness under cloud-contaminated MODIS observations by reconstructing missing temperature values using auxiliary data. While developed and validated over South Korea, SR-Weather is region-agnostic and applicable globally due to the availability of MODIS inputs and minimal reliance on localized data. These results position SR-Weather as a scalable solution for high-resolution, ML-based weather forecasting.

How to cite: Park, H., Park, S., Kang, D., and Kim, J.-H.: SR-Weather: Super-Resolution Machine Learning Weather Forecast for 1-km Air Temperature Prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9075, https://doi.org/10.5194/egusphere-egu26-9075, 2026.

Renewable energy sources have an increasingly pivotal role in global electricity generation, which poses challenges to the accurate and efficient meteorological forecasting (such as solar irradiance and hub-height wind speed). The development of AI large models has significantly shortened the time required for medium-range global weather forecast. However, their outputs typically lack high-temporal-resolution solar irradiance (e.g., provided only at 6-hour intervals or not at all), which cannot be directly applied to renewable energy forecasting.

In this work, we propose a machine learning framework to integrate the output variables from AI large models with high-resolution solar irradiance forecasting. Specifically, we train XGBoost models at 15 sites in eastern China using ERA5 reanalysis variables (2020–2023) as inputs and hourly surface solar irradiance derived from Himawari-8/9 satellite as targets. The trained models are evaluated on a 2024 test set driven by ERA5, achieving an annual mean hourly RMSE of 88.5 W m^-2.

To assess the performance of this approach in medium range forecasting, we use hourly forecasts from the GDAS-driven Pangu Weather Model during January and July 2024 as inputs. Over 20 medium-range forecast tests, our approach (Pangu-ML) yields a day-ahead (24-h lead) RMSE of 62.5 (January) /95.4 (July) W m^-2 and a 10-day lead RMSE of 92.3 (January) /110.1 (July) W m^-2. For comparison, we conduct parallel simulations using the GFS-driven WRF v4.6 model at 9-km resolution over eastern China. The WRF-based irradiance forecasts produce day-ahead and 10-day RMSEs of 78.4 (January) /107.6 (July) W m^-2 and 109.8 (January) /130.3 (July) W m^-2 across the 15 sites, demonstrating that Pangu-ML achieves comparable or even superior accuracy.

In summary, our approach takes advantage of the computational efficiency of AI large meteorological models. It enables rapid generation of solar irradiance forecasts with minimal computational cost, thereby offering a practical pathway for subsequent operational ensemble irradiance forecasting.

How to cite: Yan, M., Zhang, M., Yang, K., Shu, Z., and Shao, C.: Bridging AI Large Meteorological Models and Solar Irradiance Forecasting Through Machine Learning Approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10225, https://doi.org/10.5194/egusphere-egu26-10225, 2026.

Artificial intelligence (AI) models have demonstrated advancements in computational efficiency and forecast accuracy relative to the Numerical Weather Prediction (NWP), but they are unable to fully represent high-dimensional atmospheric dynamics. Thus, some AI-NWP coupled frameworks have been proposed, such as integrating AI-driven boundary conditions with numerical models to leverage the strengths of both approaches. However, in this coupled framework, ensemble forecasts and associated error propagation and energy dynamics remain under-explored. In this study, an AI-NWP coupled system that also uses the stochastic kinetic energy backscatter scheme (SKEBS) to generate ensemble forecasts is established. Ensemble simulations of Typhoon Yutu (2018) are carried out with the Weather Research and Forecasting (WRF) model employing Pangu-Weather and FuXi forecast data as boundary forcing. The results show that the ensemble WRF_Pangu (WRF_FuXi) improved Yutu’s track forecast by 67% (50%) compared to the traditional physics-based WRF_GFS (Global Forecast System), and reduced its intensity underestimation by about 67% relative to their AI global counterparts. Nonetheless, WRF_FuXi and WRF_Pangu exhibited limited ensemble spread and linear error growth, reflecting deterministic tendencies. Comparison of global and regional experiments show that Pangu-Weather is more physically constrained and thus better aligned with the WRF model for regional applications, while the adaptation of FuXi to the regional model is less robust. Spectral analysis revealed that AI-derived boundaries introduced excessive small-scale energy and underestimated larger-scale energy. The regional model WRF acted as a “conveyor belt”, propagating additive small-scale energy upscale, ultimately overwhelming the stochastic perturbations for ensemble generation. These findings underscore the need to incorporate more physical features into the AI-derived boundary conditions for ensemble forecasting.

How to cite: Ge, Y.: Ensemble Experiments in an AI-NWP Coupled Framework: A Typhoon Case, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10396, https://doi.org/10.5194/egusphere-egu26-10396, 2026.

Recent advancements in machine learning based weather prediction (MLWP) present novel opportunities for downstream applications like forecasting of renewable energy production from intermittent sources, like wind and solar. MLWP models guarantee shorter simulation run times and lower computational costs, allowing faster updates of downstream models and greater flexibility in the generation of weather scenarios.

Forecasting renewable energy generation critically depends on available weather forecast data at adequate temporal and spatial resolution. Using MLWP weather data in energy system modelling and forecasting has been limited by the coarse temporal resolution of the current generation of models (e.g. ECMWF’s AIFS Ensemble model runs at 6-hour time steps).

In Europe, power market participants are increasingly exposed to weather forecast inaccuracies. This is due to the combined effect of how the power price is calculated for each price area, and the recent increase in intermittent renewable installed capacities. In detail, power prices are set each day for the following day by balancing supply and demand for each Market Time Unit (MTU), which are now 15 minutes long. It is then massively important to benchmark weather forecasts on a time resolution closer to the power market MTU, to properly assess which period will potentially be oversupplied, or undersupplied from intermittent renewable sources. In this context, the 6-hour time resolution of current MLWP models becomes a significant limiting factor for their usefulness.

Using NVIDIA’s Earth2Studio framework, we demonstrate an efficient, integrated MLWP pipeline combining the [open source] AIFS model with the ModAFNO time interpolation model to provide 1-hourly time-resolution MLWP data. This interpolated data is applied to our intermittent renewable energy production models to assess the interpolation quality compared the uninterpolated AIFS data and the best-in-class numerical weather prediction data provided by ECMWF’s IFS Ensemble forecast.

How to cite: Brenna Schjønberg, H., Parviero, R., Koch, M., and Carpentieri, A.: ML-based time interpolation of AIFS Ensemble for renewable energy forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11158, https://doi.org/10.5194/egusphere-egu26-11158, 2026.

While the effects of subgrid orographic drag on large-scale circulation have been extensively studied, its influence on typhoon precipitation remains less understood. Using the Weather and Research Forecasting model, this study investigates impacts of subgrid orographic drag components (gravity wave drag (GWD), flow-blocking drag (FBD), and turbulent orographic form drag (TOFD)) on landfalling typhoon precipitation and explores their resolution sensitivity through two representative cases: Super Typhoon Lekima (2019) and Severe Typhoon In-Fa (2021). Results reveal distinct distributions of GWD and TOFD over southeastern coastal China, which significantly modulate precipitation during strong landfalls like Lekima: GWD enhances precipitation in southern land areas affected by the typhoon while suppressing it in northern regions, whereas TOFD exerts precisely opposing effects. This is mainly due to enhanced (weakened) lower-tropospheric wind speed and water vapor transport caused by GWD (TOFD). GWD is highly sensitive to horizontal resolution, exhibiting more pronounced effects on the wind, moisture, and precipitation at coarser resolutions, while TOFD remains relatively invariant to horizontal resolution changes. Resolution of subgrid orography dataset driving these parameterizations is essential for accurately simulating drag distributions and impacts. Finally, typhoon intensity modulates these effects: stronger background circulation exacerbates the precipitation impacts of both GWD and TOFD.

How to cite: Zhang, M., Yan, M., Ma, Y., Yang, K., and Shu, Z.: Impacts of subgrid-scale orographic drag on landfalling typhoon precipitation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11583, https://doi.org/10.5194/egusphere-egu26-11583, 2026.

Extreme precipitation poses significant risks to society and infrastructure, highlighting the urgent need for accurate short-term nowcasting. While deep learning models have shown promise in precipitation forecasting, they often lack integration with physical principles, leading to inconsistencies and limited skill in predicting convective evolution. In this study, we introduce RainCast—a novel generative nowcasting framework that synergistically combines deterministic physical modeling with stochastic generative networks to improve the accuracy and physical consistency of extreme rainfall forecasts.

RainCast integrates a deterministic branch based on Neural Ordinary Differential Equations (Neural ODE) to simulate large-scale advective processes and a generative branch built upon a conditional diffusion model to capture fine-scale stochastic variability. The model is guided by key physical features such as flow fields, vorticity, and divergence derived from dual-polarization radar observations, which provide essential dynamical information about convective systems. We train and evaluate the framework using vertically integrated liquid water (VIL) data from dual-polarization radars in China (GD-SPOL) and North America (SEVIR).

Quantitative assessments demonstrate that RainCast significantly outperforms existing nowcasting methods such as SimVP, SwinLSTM, and NowcastNet. On the GD-SPOL dataset, RainCast improves the Critical Success Index (CSI) for intense convection (VIL ≥ 160) by up to 14.1% at 90-minute lead times. Structural similarity metrics also show substantial gains, with reductions in Fréchet Video Distance (FVD) by 25.4% and Learned Perceptual Image Patch Similarity (LPIPS) by 44.6%. Case studies further illustrate RainCast’s ability to realistically simulate the evolution of organized convective systems, including squall lines and multicell storms, while maintaining physical coherence in wind field retrievals.

Our results underscore the value of embedding physical guidance into generative deep learning architectures for convective nowcasting. The RainCast framework represents a meaningful step toward more reliable, interpretable, and physically consistent nowcasting of extreme precipitation, with potential applications in operational meteorology and disaster preparedness.

How to cite: Pan, X. and Zhao, K.: Physics-Guided Generative Nowcasting of Extreme Precipitation with Dual-Polarization Radar and Neural ODE-Diffusion Framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11714, https://doi.org/10.5194/egusphere-egu26-11714, 2026.

Thunderstorms pose significant risks to society and the economy due to hazards such as heavy precipitation, hail, and strong winds, which is why accurate forecasts are required to mitigate their impacts. Convection-permitting numerical weather prediction (NWP) models can explicitly resolve convective processes, but predicting thunderstorms from their output remains challenging since there is no obvious state variable that directly indicates thunderstorm occurrence. Instead, many approaches rely on combining multiple convective indices, such as convective available potential energy (CAPE), which are derived from state variables like temperature, pressure, and specific humidity, and act as surrogates for thunderstorms.

In this study, we present a deep neural network model that bypasses surrogate variables and instead directly processes the vertical profiles of state variables provided by convection-permitting forecasts. Our model, SALAMA 1D, analyzes ten different NWP output fields, such as wind velocity, temperature, and ice particle mixing ratios, across the vertical dimension, to produce the corresponding probability of thunderstorm occurrence. The model’s architecture is motivated by physics-based considerations and symmetry principles, combining sparse and dense layers to produce well-calibrated, pointwise probabilities of thunderstorm occurrence, while remaining lightweight.

We trained our model on two summers of forecast data from ICON-D2-EPS, a convection-permitting ensemble weather model for Central Europe operationally run by the German Meteorological Service (DWD), using the lightning detection network LINET as the ground truth for thunderstorm occurrences. Our results demonstrate that, up to lead times of (at least) 11 hours, SALAMA 1D outperforms a comparable machine learning model that relies solely on thunderstorm surrogate variables. Additionally, a sensitivity analysis using saliency maps indicates that the patterns learnt by our model are to a considerable extent physically interpretable. Finally, we show that spatial coverage can be extended to all of Europe by retraining on ICON-EU reanalysis data. Our work advances NWP-based thunderstorm forecasting by demonstrating the potential of deep learning to extract predictive information from high-dimensional NWP data—without sacrificing model interpretability.

How to cite: Vahid Yousefnia, K., Metzl, C., and Bölle, T.: SALAMA 1D: Deep-learning-based identification of thunderstorm occurrence in NWP forecasts without relying on convective indices, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13981, https://doi.org/10.5194/egusphere-egu26-13981, 2026.

Accurate real-time prediction of heavy precipitation is essential for disaster prevention. It remains a challenge for operational meteorology, especially for sudden localized convective storms for which traditional radar and observation extrapolation methods struggle to capture their rapid vertical development, which typically originate at altitudes of 4--8 km before descending to the surface in about 10 minutes.  

In Japan, three Multi-Parameter Phased Array Weather Radars (MP-PAWR) generating 3D data every 30 seconds with high vertical resolution have been deployed. Leveraging these dense 4D observations, an AI-based model produces real-time nowcasts (very short-term forecasts) with high-resolution of 500 m and 10-minute lead time. Updated every 30 seconds, our nowcasts outperform traditional methods for predicting the onset and the dissipation of localized convective precipitation. However, performance is degraded during the mature phase of the storm when its structure becomes more complex (e.g., overlapping  convective cells in different lifecycle states, domination of horizontal motion in radar pattern changes) (Baron et al., 2025a).

Two major improvements are currently being investigated: 1) a Quantile Regression Neural Network (QRNN) technique has been integrated to assess the probability distribution of possible nowcasts and thus provide credible intervals (Baron et al., 2025b), and 2) a better representation of 3D motion is being implemented, as it plays a critical role during the mature phase of storms. The new version of the model will integrate two separate modules: one specialized for capturing 3D-motion vectors, while the second predicts rainfall intensity with motion guidance. Both modules use the current nowcast model architecture which has demonstrated solid performance. The motion module is trained using 3D motion vectors derived directly from the radar observations through a 3D Tracking Radar Echoes by Correlation (TREC) method originally designed for PAWR extrapolation (Otsuka et al., 2016).

This study will present these developments with a special focus on the motion guidance module that is being implemented. The limitations of our approach will also be discussed (e.g., QRNN vs diffusion model, TREC limitation for weak gradient cases, no information on rain precursors and mesoscale scales).

Baron et al., 2025a: “Real-time nowcasting of sudden heavy rainfall using artificial neural network and multi-parameter phased array radar”, SOLA, https://doi.org/10.2151/sola.2025-039

Baron et al., 2025b: “3D Precipitation Nowcasting from Phased Array Radar with Uncertainty Estimation Using a Quantile Regression Neural Network”, IEEE RadarConf25,  10.1109/RadarConf2559087.2025.11204931

Otsuka et al., 2016: Precipitation nowcasting with three-dimensional space–time extrapolation of dense and frequent phased-array weather radar observations. Wea. Forecasting, 31, 329–340.

How to cite: Baron, P., Otsuka, S., Amell, A., Kawamura, S., Satoh, S., and Ushio, T.: AI nowcasting of localized heavy precipitation from fast-scanning radar with probabilistic and 3D motion guided prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15508, https://doi.org/10.5194/egusphere-egu26-15508, 2026.

The development of various AI models in recent years has been very promising; the models’ ability to train from reanalysis datasets and evaluate on various metrics opened the door for a variety of new applications. However, the real stress test of any new model is its operational performance - applying predictions to data that weren't available during the model development and assessing the model’s capabilities for predicting real-world scenarios previously unseen. 

In August 2025, we deployed our first high-resolution AI-based model for Iceland and it has been providing us with continuous predictions since then. Here we evaluate forecast skill against surface observations and benchmark against NWP models from the United Weather Centres (UWC) in Denmark and Iceland and our local operational NWP model for Iceland. We analyze the model’s strengths and weaknesses in predicting various weather events and discuss how these characteristics may influence the future model design.

How to cite: Stanisławska, K. and Rögnvaldsson, Ó.: AI model in the real world - analysis of the operational performance of a high-resolution AI weather model for Iceland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18305, https://doi.org/10.5194/egusphere-egu26-18305, 2026.

The global transition toward low-carbon energy systems has increased the reliance on renewable energy sources and driven solar power to become a key component of sustainable electricity generation, thereby increasing the importance of accurate irradiance forecasting. As solar penetration grows, power system operations increasingly depend on reliable short-term forecasts to support grid balancing, reserve allocation, and real-time decision-making. Global Horizontal Irradiance (GHI) represents the integrated influence of atmospheric conditions and cloud processes on surface solar radiation and governs short-term variability in photovoltaic power output. However, rapid cloud evolution introduces strong spatiotemporal variability in GHI, making accurate prediction at sub-hourly lead times a persistent challenge for short-term solar forecasting. In this study, we develop a real-time nowcasting system to predict GHI over the western region of India at 15-minute resolution with effective lead times of up to 2 hours. The system is based on a convolutional long short-term memory (ConvLSTM) model that learns spatiotemporal cloud–radiation relationships from high-frequency geostationary satellite observations. We utilize INSAT-3DR and INSAT-3DS products obtained from the MOSDAC archive, which provide continuous monitoring of cloud evolution over the region. The nowcasting framework is implemented using routinely available satellite observations and is evaluated over a large spatial domain covering western India, a region characterized by strong seasonal variability and diverse cloud regimes associated with pre-monsoon, monsoon, and post-monsoon periods. The results demonstrate consistent performance across seasons and show that the system captures the mean diurnal evolution of GHI with stable skill during daytime solar-active periods. Evaluation results indicate mean absolute errors of approximately 60 W m-2 for 1–2 hour lead times and 72 W m-2 for 2–3 hour lead times, corresponding to about 7–12 % of typical daytime GHI under moderate to high irradiance conditions. Overall, this work demonstrates the feasibility of satellite-driven deep learning systems for real-time GHI nowcasting and highlights the potential of integrating geostationary satellite observations and spatiotemporal learning models to support renewable energy forecasting and real-time grid decision-making in regions with high and growing solar power penetration.

How to cite: Garg, S., Ghosh, S., Murtugudde, R., and Banerjee, B.: Real-Time Solar Irradiance Nowcasting for Renewable Energy Forecasting over Western India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18585, https://doi.org/10.5194/egusphere-egu26-18585, 2026.

Approximately 90% of the total precipitation in Senegal is produced by convective storms. The most intense rainfall events are associated with mesoscale convective systems (MCSs), frequently producing high-intensity rainfall that triggers pluvial flooding. Flood vulnerability is particularly high in the Greater Dakar area due to surface sealing and high population exposure. Timely and reliable short-term precipitation forecasts are therefore essential for effective early warning systems and flood risk reduction.

Precipitation nowcasting aims to describe the current atmospheric state and predict weather evolution at short lead times using real-time observations. The quality and availability of input data are key factors determining the nowcasting performance. In this study, three main data sources are employed: (i) in-situ observations from the High-resolution weather observations East of Dakar (DakE) station network, (ii) satellite-based products such as cloud-top temperature (CTT) from EUMETSAT and precipitation estimates from the Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm provided by NASA, and (iii) modeled data from the Weather Research and Forecasting (WRF) model.

The objective of this project is to identify a suitable nowcasting approach while weighing the strengths and limitations of the available data sources. Extrapolation-based methods, such as optical-flow techniques implemented in the pySTEPS library, estimate future precipitation by extrapolating observed patterns under the assumption of steady system evolution. These approaches perform well for large, long-lived convective systems, but they are unable to predict convective initiation, decay, and growth. Their applicability is further limited by the temporal resolution and detection uncertainties of the available satellite-based precipitation products identified in comparisons with station observations.

To address these limitations, a machine-learning-based nowcasting framework is developed, primarily relying on the high-temporal-resolution DakE station data to accurately capture atmospheric boundary conditions. Given the limited time span of data collection and the high predictor dimensionality, a Random Forest model was chosen as a robust approach. To mitigate challenges like zero inflation and the underestimation of extreme events, a two-step model architecture is developed: in a first step, a classification forest (I) is used to determine precipitation occurrence and the duration of the predicted event in the lead time horizon. If precipitation is expected, the model is coupled to a regression forest (II) that returns the rainfall intensity of the detected event. Future work will assess potential performance improvements from incorporating CTT-satellite and WRF-modeled data using feature importance analysis, which can also inform the placement of hypothetical new automatic weather stations.

How to cite: Berghoefer, M.-B., Haerter, J. O., and Monroy, D. L.: Random forest based precipitation nowcasting for Dakar , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18808, https://doi.org/10.5194/egusphere-egu26-18808, 2026.

Traditional weather forecasting relies on large scale numerical simulations that run on high-performance computing systems. These methods require substantial computational resources, involve complex workflows, and generate large volumes of data that often exceed individual user needs. Forecast-in-a-Box leverages advances in data-driven modelling to greatly reduce computational and energy costs while delivering tailored forecast products directly to users. Partly funded from the European Commission’s Destination Earth initiative, it packages the entire forecasting chain into a simple and user-friendly application. Built on the open-source Anemoi¹ and Earthkit² projects, it offers a reproducible and modular environment that integrates data access, model execution, and visualisation. This enables accurate forecasts that can be run locally on user desktops, on premise computing infrastructure, or in the cloud.

The approach is being evaluated through a World Meteorological Organization (WMO) Integrated Processing and Prediction System (WIPPS) pilot project led by the Norwegian Meteorological Institute (MET Norway). In this project, a fully packaged forecasting system based on affordable hardware is provided to the Malawi Department of Climate Change and Meteorological Services (DCCMS). The forecasting system is driven by Forecast-in-a-Box and leverages MET Norway’s Bris³ model (Norwegian word for “light wind), a high-resolution data driven weather forecasting model built using the Anemoi framework. The solution is designed to be largely self-contained, with the only external dependency being the retrieval of ECMWF analysis dataset for forecast initialisation.

¹https://anemoi.readthedocs.io/en/latest/

²https://earthkit.ecmwf.int

³https://lumi-supercomputer.eu/data-driven-weather-forecasting-model/

How to cite: Carton de Wiart, C., Cook, H., Tuma, V., Wong, J., Futsæter, H. A., Østvand, L., Bønes, V., Moe, B., Kristiansen, J., Hawkes, J., Sandu, I., and Quintino, T.: Forecast-in-a-Box: AI weather forecasting, easy to run and simple to deploy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20013, https://doi.org/10.5194/egusphere-egu26-20013, 2026.

The atmosphere, ocean, and sea ice components in Earth system models are coupled via boundary conditions at the sea surface. Standard coupling algorithms correspond to the first step of an iteration, so-called Schwarz waveform relaxation. Not iterating is computationally cheap but introduces a numerical coupling error, which we aim to quantify for the case of a coupled single column model: the EC-Earth AOSCM, which uses the same coupling setup and model physics as its host model, EC-Earth. To this end, we iterate until a reference solution is obtained and compare this with standard, non-iterative algorithms. Understanding the convergence behavior of the iteration, as well as the size of the coupling error, can inform model and algorithm development. Past studies demonstrated that the SWR solution eliminates phase errors, reduces ensemble spread, and can indicate whether current coupling setups are mathematically consistent. Our implementation is based on the OASIS3-MCT coupler and allows to estimate the coupling error of multi-day simulations.

In the absence of sea ice, SWR convergence is robust. Coupling errors for atmospheric variables can be substantial. When sea ice is present, results strongly depend on the model version. In the latest model version, coupling errors in sea ice surface and atmospheric boundary layer temperature are often large. Generally, we find that abrupt transitions between distinct physical regimes in certain parameterizations can lead to substantial coupling errors and even non-convergence of the iteration. We attribute discontinuities in the computation of atmospheric vertical turbulence and sea ice albedo as sources for these problems. We conclude the talk with some new theoretical results on analytically describing atmosphere-ocean-sea ice coupling.

How to cite: Schüller, V., Lemarié, F., Birken, P., and Blayo, E.: Quantifying Coupling Errors in Atmosphere-Ice-Ocean Coupling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-287, https://doi.org/10.5194/egusphere-egu26-287, 2026.

In some geophysical flow phenomena, such as landslide-generated tsunamis and slow earthquake-generated waves, non-hydrostatic pressure has been shown to be crucial, resulting in an effect known as the dispersive effect. One practical approach to include such an effect is by extending the Shallow Water Equations (SWE), which can be achieved by splitting the pressure terms into hydrostatic and non-hydrostatic pressure while deriving a depth-averaged form. In the end, this model requires us to solve an elliptic system of equations to make a correction to the hydrostatic SWE approximation. However, solving the elliptic problem burdens computing time significantly. Therefore, we introduce a locally adaptive non-hydrostatic model that allows us to solve the extension locally. To achieve reliable results, the corrections need to be adapted in the area where the non-hydrostatic pressure might hold a significant role. We define these areas with a simple criterion based on the hydrostatic solution. To validate our model, we apply our model to a test case that involves moving bottom-generated waves. Our result shows that we can achieve a good agreement between the local and global models, where the former approach clearly reduces the computational time.

How to cite: Firdaus, K. and Behrens, J.: Two-Dimensional Locally Adaptive Non-Hydrostatic Model for Moving Bottom-Generated Waves, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1213, https://doi.org/10.5194/egusphere-egu26-1213, 2026.

The Dynamical Core Intercomparison Project (DCMIP) is an endeavour to evaluate and compare dynamical cores through the application of idealised test cases. Previous editions of DCMIP, in 2008, 2012, and 2016, introduced many widely used cases, including baroclinic wave, tracer transport, supercell, and tropical cyclone tests. We present a new test case developed for the most recent edition of DCMIP, held in June 2025 (DCMIP2025). This case uses an easily implementable initial condition, with the addition of mountain orography, to generate nonlinear mesoscale flow phenomena. Two different orographies are considered, which lead to dynamics of a gap flow and vortex shedding. The gap flow case exhibits accelerated flow and the creation of a pair of symmetric lee vortices, whilst the vortex shedding case instigates a flow reminiscent of a von Kármán vortex street. These tests are compared in four dynamical cores: three from the United States, along with GungHo, the compatible finite element dynamical core from the UK Met Office’s new LFRic model. Contrasting simulations with each dynamical core highlights differences in the numerical designs, including the grid choice and sources of numerical diffusion.

How to cite: Andrews, T., Jablonowski, C., Hughes, O., and Bendall, T.: New mountain-generated mesoscale flow test cases from DCMIP2025, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2009, https://doi.org/10.5194/egusphere-egu26-2009, 2026.

Rayleigh-Bénard convection (RBC) — the buoyancy-driven instability that arises when a fluid is heated from below and cooled from above, leading to the formation of convective plumes — serves as a fundamental and challenging benchmark problem in geophysical fluid dynamics. Simulating RBC at high Rayleigh numbers demands extremely fine spatial resolution, which in turn requires high-performance computing (HPC) resources to achieve results within feasible runtimes. However, strong parallel scaling based on spatial domain decomposition eventually saturates due to communication overheads, and simulations involving very large numbers of time steps can still be prohibitively time-consuming, with limited scope for further speedup through additional spatial parallelism.

To overcome these limitations, time-parallel algorithms — which introduce concurrency along the temporal dimension — offer a promising approach to extend strong scaling beyond the saturation of space-only parallelization. Despite their potential, constraints imposed by causality often make these methods challenging to design, and some classes of parallel-in-time algorithms can suffer from poor parallel efficiency. In contrast, parallel-across-the-method techniques, while providing only small-scale parallelism, tend to be easier to implement and can achieve competitive efficiency. Previous efforts to develop parallel Runge-Kutta methods were only moderately successful, primarily because the associated stability restrictions were more stringent than those of their serial counterparts.

Parallel Spectral Deferred Corrections (pSDC), however, enable parallel computation of stages without significantly reducing the maximum stable time step. In this talk, we introduce pSDC and present a bespoke solver that combines pSDC with parallel Fast Fourier Transforms (FFTs) on GPUs to facilitate efficient, large-scale simulations of RBC. We show performance results obtained on the JUWELS Booster and JUPITER HPC systems at the Jülich Supercomputing Centre, showcasing how pSDC can achieve runtime reductions beyond the limits of spatial parallelization alone.

How to cite: Ruprecht, D., Saupe, T., Lunet, T., Götschel, S., and Speck, R.: Improved parallel scaling of 3D Rayleigh-Benard convection simulations by parallelization in time, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3094, https://doi.org/10.5194/egusphere-egu26-3094, 2026.

We propose a new formulation of a three-dimensional spectral model for the primitive equations, in which the spectral method is applied not only in the horizontal but also in the vertical direction. In this model, we utilize scaled Laguerre functions as the vertical basis and spherical harmonics as the horizontal basis. This formulation introduces a tunable scaling parameter, enabling the model top to be placed at high altitudes while maintaining adequate vertical resolution in the upper atmosphere. This formulation is implemented as a numerical model, and its performance is validated through a series of benchmark experiments. The results demonstrate that the numerical error of the present model decreases much faster than that of a corresponding vertical finite-difference model as the vertical degrees of freedom are increased. Furthermore, using linearized two-dimensional primitive equations, the properties of gravity waves under the proposed discretization are examined and compared with those of conventional vertical finite-difference discretizations. The results confirm that the proposed method can accurately represent upward-propagating waves. 

How to cite: Fujita, S. and ishioka, K.: Development of a Three-dimensional Spectral Atmospheric Dynamical Core using Scaled Laguerre Functions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3274, https://doi.org/10.5194/egusphere-egu26-3274, 2026.

Weather and climate models require atmospheric transport to be numerically stable, accurate, efficient, and mass-conserving. Adaptively Implicit-Explicit (AdImEx) time stepping can provide mass conservation and stability while taking long time steps for efficiency. However, challenges persist: (1) strongly reduced accuracy for large Courant numbers, and (2) a lack of preservation of uniform fields with substage time step sizes varying in space. We introduce AdHImEx, a novel AdImEx approach that resolves these issues, delivering an unconditionally stable, conservative, efficient advection scheme that preserves unity and increases temporal accuracy for large Courant numbers from first- to second-order. Remarkably, it requires only a single implicit solve per time step, performed with an iterative matrix solver, while retaining the full efficiency and third-order accuracy of the explicit time stepping in regions with small Courant numbers. Two-dimensional results will be shown.

How to cite: te Winkel, A., Weller, H., Kühnlein, C., and Kent, J.: AdHImEx: Adaptively High-order Implicit-Explicit Time Stepping for Atmospheric Transport with Long Time Steps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3905, https://doi.org/10.5194/egusphere-egu26-3905, 2026.

The choice of grid staggering has been a fundamental design decision in atmospheric dynamical core development for decades. Conventional wisdom, largely derived from low-order Von Neumann analysis, holds that the unstaggered A-Grid exhibits poor dispersion properties near the grid scale, making it unsuitable for geophysical fluid dynamics. This perception has steered the community toward staggered grid formulations (B-, C-, or D-Grid) despite the algorithmic complexity they introduce.

We present a numerical approach to Von Neumann analysis that enables rigorous evaluation of high-order schemes with complex time-stepping methods, including implicit and semi-implicit formulations such as the forward-backward scheme. By numerically solving the Fourier-transformed equations across the two-dimensional space of Courant numbers and numerical phase, this method circumvents the algebraic intractability that has limited traditional analysis to simplified low-order cases.

Our analysis reveals a critical finding: the dispersion and dissipation differences attributable to grid staggering choices diminish substantially with high-order spatial discretization. When combined with the Low Mach number Approximate Riemann Solver (LMARS), which provides implicit scale-selective diffusion matched to the dispersion characteristics, the A-Grid formulation effectively controls numerical noise while maintaining accuracy for well-resolved modes. Idealized tests with discontinuous wave packets validate these theoretical predictions and demonstrate that high-order LMARS produces significantly less numerical noise than inviscid schemes on both staggered and unstaggered grids.

These findings carry significant implications for next-generation dynamical core design. The A-Grid formulation offers compelling advantages: algorithmic simplicity facilitating GPU implementation, straightforward conservation properties, collocated variables simplifying physics-dynamics coupling, and natural compatibility with data-driven approaches in hybrid modeling. Continued adherence to conventional wisdom rooted in low-order analysis risks misguiding the development of dynamical cores optimized for modern computing architectures and emerging AI-integrated Earth system models.

How to cite: Chen, X.: Rethinking A-Grid for next-generation dynamical cores: High-order numerical analysis challenges conventional wisdom, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4623, https://doi.org/10.5194/egusphere-egu26-4623, 2026.

The pursuit of global kilometer-scale modeling in atmosphere science presents a dual challenge: comprehensive models become computationally prohibitive and analytically intractable, while idealized models lack essential physical forcing, creating a critical gap in atmospheric research. To bridge this divide, this work develops and validates the innovative ACC-LMARS-SW model, a novel theoretical framework designed for efficient, high-resolution global atmospheric dynamics studies. The model features three synergistic innovations. First, its physics kernel introduces real-world topography and a real-world-equivalent season-aware radiative forcing scheme into the shallow-water equations, significantly enhancing physical realism while retaining conceptual clarity. Second, it employs a variable-resolution stretched cubed-sphere grid that concentrates computational resources on regions of interest , achieving sub-kilometer local resolution without prohibitive global cost. Third, the dynamical core is fully redesigned for massive parallelism using the OpenACC standard, leveraging the efficiency of the Low-Mach Approximate Riemann Solver (LMARS) to harness GPU acceleration.

A suite of numerical experiments demonstrates the model's capabilities. 1) Mesh efficacy: Stretched-grid simulations show reduce error in target areas compared to uniform-resolution runs in classic tests, better resolving nonlinear eddy interactions. 2) Physical fidelity: A global simulation at ~1.5 km average resolution (with ~500 m over the South China Sea) forced by real topography and idealized radiation spontaneously generates a horizontal kinetic energy spectrum featuring distinct  k^-3 and k^-5/3 power-law segments , which is a hallmark of real atmospheric scale interactions. 3) Computational performance: GPU implementation achieves up to a 70x speedup versus estimated serial CPU execution, making global kilometer-scale theoretical experiments feasible on a single workstation.

In conclusion, ACC-LMARS-SW successfully integrates algorithmic design, mesh technology, and high-performance computing to create an efficient and physically insightful "numerical laboratory." This approach provides the high resolution and computational efficiency needed to study fundamental multi-scale dynamics, such as cross-scale energy transfer, dynamical responses to orography, and mesoscale eddy dynamics.

How to cite: Li, Z. and Chen, X.: Accelerated LMARS Shallow Water Model with a Stretched Cubed Sphere Grid at Sub-km Scales, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4665, https://doi.org/10.5194/egusphere-egu26-4665, 2026.

The multiscale finite element method (MsFEM) was originally proposed for stationary (elliptic) or quasi-stationary (parabolic) problems. It was extended to linear transport-dominated problems, utilizing a semi-Lagrangian subgrid reconstruction (SLMsR) approach. In this presentation we introduce the extension of the method to coupling non-linear systems of hyperbolic equations. To demonstrate the accuracy and applicability of SLMrS we use a shallow water model, where we discretize the momentum equation on a fine mesh and inform the coarse-mesh continuity equation of subgrid-scale features by using a multiscale basis. We show accuracy and convergence of the new method for smooth and non-smooth test cases.

How to cite: Behrens, J. and Gabsi, M.: Multiscale finite element method in a shallow water model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4907, https://doi.org/10.5194/egusphere-egu26-4907, 2026.

We introduce a new suite of idealized test cases for the dynamical cores of atmospheric General Circulation Models. The tests were developed for the 2025 Dynamical Core Model Intercomparison Project (DCMIP-2025) which took place at the National Center for Atmospheric Research (NCAR) in June 2025 alongside a summer school (https://dcmip.org). The test suite was designed to probe (a) the impact of topography on atmospheric flows at various scales (see also the EGU 2026 companion paper led by Timothy Andrews); (b) a convection (squall line) test case; and (c) idealized experiments for atmospheric machine learning emulators.

This presentation focuses on the squall line test case, which challenges nonhydrostatic dynamical cores to accurately simulate moist convection and squall line dynamics at kilometer-scale resolutions. The scenario incorporates simplified moisture feedbacks using a warm-rain Kessler parameterization and is implemented across a range of horizontal (0.25–4.0 km) and vertical (250–500 m) grid spacings on a reduced-radius sphere. We assess three leading nonhydrostatic dynamical cores: the nonhydrostatic version of the Department of Energy/NCAR ‘Spectral Element’ model (called HOMME or SE), the ‘Model for Prediction Across Scales’ (MPAS), and NOAA’s Finite-Volume cubed-sphere dynamical core FV3. These are all available within NCAR’s Community Atmosphere Model (CAM) which is the atmospheric component of the Community Earth System Model (CESM).

Our analysis examines the numerical convergence characteristics, model-to-model variability, and the influence of core-specific dissipation mechanisms on the simulated convective storms. The results demonstrate the test case’s potential as a rigorous benchmark for future core development and model intercomparison efforts. This work contributes to advancing robust evaluation frameworks for atmospheric models in the kilometer-scale, nonhydrostatic regime.

How to cite: Jablonowski, C., Androski, N., Andrews, T., and Hughes, O.: DCMIP-2025: Introducing an Idealized Squall Line Test Case for Nonhydrostatic Dynamical Cores, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8455, https://doi.org/10.5194/egusphere-egu26-8455, 2026.

We investigate the coupling of nonlinear water-wave motion to heaving buoy wave-energy dynamics [1] in the presence of an inequality constraint. Building on augmented Lagrangian variational principles (VPs) developed by Burman [2] and others, we impose constraints of the form G(q)≥0, where q involves system variables, through a Lagrange multiplier λ. The strict Kuhn–Karush–Tucker (KKT) conditions {λ G=0,G(q)≥0, λ≤0} are replaced by those smooth approximations of the involved function F(c G(q)−λ)=max(c G(q)−λ,0), with smoothing parament c>0, allowing explicit computation of the multiplier λ as (part of) a force. Our approach combines: (a) an Average Vector Field (AVF) energy-conserving time-stepping method, extended to water-wave systems with an auxiliary field, enforcing energy conservation in the discrete system; (b) a (novel) smooth relation λ(G) that regularises the KKT conditions by approximating the solution G=0 with λ≤0 and G>0 with λ=0 in the (λ,G)-plane, but leading to an implicit definition of the function F(c G(q)−λ). This framework has been implemented and tested in the finite-element environment Firedrake, leading to improved and surviving benchmarks for the problems: (i) a point particle under gravity bouncing off a rigid table, (ii) a particle moving in a rectangular (“billiard”) domain, and (iii) forced (Variational “Boussinesq” Model-type) nonlinear water waves in a horizontal channel causing buoy motion in a wave-enhancing contraction. The latter, finite-element, model supports design of a prototype wave-energy device for enhanced energy capture. More generally, this work aims to develop analytical and computational tools for finite-element coupling of nonlinear wave dynamics in fluid-structure interactions, here exemplified by the vertical (heave) motion of the buoy.

[1] O. Bokhove, A. Kalogirou and W. Zweers (2019) From Bore–Soliton–Splash to a new wave-to-wire wave-energy model. Water Waves 1, 217-258.
[2] E. Burman, P. Hansbo and M.G. Larson (2023) The augmented Lagrangian method as a framework for stabilised methods in computational mechanics. Archives of Computational Methods in Eng. 30, 2579–2604.

How to cite: Bokhove, O.: Fluid-structure interactions with numerics for a smoothed augmented Lagrangian variational principle applied to a wave-energy device, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10650, https://doi.org/10.5194/egusphere-egu26-10650, 2026.

We present a novel staggered discontinuous Galerkin method on general polytopal meshes for solving the shallow water equation on the sphere. Every polygon is decomposed into so called kite quads by joining the vertices with the midpoints of the adjacent edges and the midpoint of the polygon. The shallow water equation is solved in vector-invariant form whereby vorticity is determined diagnostically. Height and momentum are discretized on the primal respectively dual cells whereby the composed finite elements are continuous over internal edges and discontinuous over boundary edges. This results in block-diagonal mass matrices. Mass lumping can reduce the fill in of these matrices further. 
The method is implemented in Julia in the package CGDycore.jl which unifies different numerical dycores under one umbrella. Numerical results are presented for standard test cases on different spherical grids.

How to cite: Knoth, O.: Staggered finite element methods on polytopal meshes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10749, https://doi.org/10.5194/egusphere-egu26-10749, 2026.

Two-fluid magnetohydrodynamics (MHD) extends single-fluid MHD by retaining separate ion and electron thermodynamics together with an extended Ohm’s law. This enables the capture of Hall physics, dispersive waves, and fast magnetic reconnection – phenomena that are inaccessible to single-fluid MHD and are central to applications such as magnetic confinement fusion, the heliosphere, and the Earth’s magnetosphere.

In this presentation, we discuss a novel spatial discretization for the two-fluid MHD equations based on compatible finite element methods. The approach preserves important structural properties including the divergence-free constraint on the magnetic field, energy conservation, and a consistent treatment of both fluid and magnetic helicity. In particular, the ion velocity and magnetic field spaces are chosen to admit a natural discrete definition of the diagnostically determined electron velocity.

The resulting scheme is designed for low-dissipation regimes in which fluid transport dominates, a setting relevant to many scenarios of interest in the aforementioned applications. To ensure a stable field evolution, we incorporate transport stabilization for all fields while preserving the underlying structural properties of the two-fluid MHD system. The stabilization is based on interior penalty methods, extending our previous work on magnetic field transport in single-fluid resistive MHD (Wimmer, Tang, 2024). Numerical experiments demonstrate the structure preserving and stabilization properties of the method through test cases focusing on fluid helicity, magnetic helicity, and the relative decay rates of helicity and energy in the presence of dissipation.

References

Golo A. Wimmer and Xian-Zhu Tang (2024), Structure preserving transport stabilized compatible finite element methods for magnetohydrodynamics, Journal of Computational Physics, Volume 501, 112777.

How to cite: Bauer, W., Wimmer, G. A., and Tang, X.-Z.: Structure preserving, transport stabilized finite element methods for two-fluid magnetohydrodynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13095, https://doi.org/10.5194/egusphere-egu26-13095, 2026.

Radiative transfer plays a key role in the atmosphere’s thermodynamics as photons from the Sun interact with the atmosphere and with other photons being emitted by the earth itself. In climate modeling and numerical weather prediction, it is very common to simplify radiative transfer into a one-dimensional, purely vertical, phenomenon for the sake of saving computational resources. This is because solving the radiative transfer equation requires building and solving what is essentially a five-dimensional problem (three spatial dimensions and two angular dimensions). Improvements in computational resources have resulted in higher-resolution simulations for both climate and weather modeling, and this increase in resolution makes the assumption of purely vertical radiative transfer more and more difficult to justify. However, these improvements to computing power make it possible to attempt to solve a three-dimensional radiative transfer equation as part of a larger atmospheric model and in doing so take into account the three-dimensional effects that are commonly neglected. Doing this efficiently requires the use of adaptive mesh refinement, which while commonly used in the CFD community, is not a technique that is widely used in simulations of the atmosphere. In this work, Jexpresso.jl, an open source flexible general conservation law solver, is extended to solve the full equations of Radiative transfer. Adaptive mesh refinement in both space and angle is then used
to make it possible to speed up solving the three-dimensional radiative transfer equation and couple the solution to an atmospheric model which uses adaptive mesh refinement around clouds. The objective is to allow for improvements in the prediction of atmospheric flow behaviors around clouds and the resulting radiative heat fluxes, which are highly sensitive to cloud cover.

How to cite: Tissaoui, Y., Stechmann, S., Wang, H., and Marras, S.: Toward three-dimensional radiative transfer computations via adaptive mesh refinement in both space and angle, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13120, https://doi.org/10.5194/egusphere-egu26-13120, 2026.

Spectral methods have a well-established history in numerical weather prediction. However, stable formulations for high-order discontinuous Galerkin (DG) methods in both horizontal and vertical directions have only recently been developed using split-form DG (cf. Waruszewski et al. and Souza et al.).

This presentation provides an overview of the implementation of split-form DG methods for the compressible Euler equations, utilizing various formulations on both triangular and quadrangular spherical grids. Key focus areas include split-form versions on triangular grids and the linear algebra associated with HEVI-like (Horizontally Explicit, Vertically Implicit) temporal integration schemes. To manage vertically propagating sound waves implicitly, temporal integration is performed using W-Rosenbrock methods with an approximate Jacobian in the radial direction.

The framework is implemented in the Julia package CGDycore.jl, leveraging KernelAbstractions.jl and MPI for parallel execution across diverse architectures. We present numerical comparisons using the Held-Suarez test case, alongside comprehensive weak and strong scaling results for different computing environments.

How to cite: Knoth, O. and Artiano, M.: A discontinuous Galerkin weather dycore for triangular and quadrangular grids, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14161, https://doi.org/10.5194/egusphere-egu26-14161, 2026.

Various grids are being used to discretize the sphere for general circulation models of ocean and atmosphere, coupled to sea ice and land models. Depending on the numerics (or the network architecture for machine learning-based models) some grids are better suited and more widely used but no single grid meets all requirements. The HEALPix grid was originally invented for applications in cosmology and designed for spectral transform efficiency, hierarchical nested ordering and equal-area grid cells. Here, we present several variants: The OctaHEALPix grid, and the octaminimal Gaussian and Clenshaw-Curtis grids. The OctaHEALPix grid inherits all the properties of the original HEALPix grid but based on a single square matrix instead of 12 used for the base pixels (faces) of the HEALPix grid. This eliminates singularities for the 8 corner cells with 7 instead of 8 neighbouring cells. The nested hierarchy of the OctaHEALPix grid is therefore a quadtree from the coarsest to the finest zoom level. Data on the OctaHEALPix grid can be arranged as a single square matrix, representing an equal-area map projection of the sphere, allowing for interpolation-free visualisation and data storage. However, the HEALPix grids do not provide an exact quadrature in the Legendre transform, such that transform errors are higher than with Gaussian, or equi-angle latitude-based grids using the Clenshaw-Curtis quadrature. The inexact transform with the HEALPix grids does not pose any problems in simulations where other sources of error dominate. We present the grid-flexible spectral transform implemented in the atmospheric circulation model SpeedyWeather.jl that simultaneously supports all ring-based, equi-longitude grids both on CPU (including multithreading) and GPU. The OctaHEALPix grid is also favourable for machine learning-based models like diffusion models based on the UNet architecture which only require custom boundary conditions. The other HEALPix variant we present is the octaminimal Gaussian grid, which imposes Gaussian latitudes on the OctaHEALPix grid which reduces transform errors while preserving more of the HEALPix’s equal-area and hierarchy properties. Similarly, the octaminimal Clenshaw-Curtis grid uses regular latitudes with the Clenshaw-Curtis quadrature. Simulations based on these grids are presented for coupled climate simulations and implications for hybrid numerical and machine learning-based models are discussed.

How to cite: Klöwer, M., Gelbrecht, M., Leland, J., Groenke, B., and Hotta, D.: Variants of HEALPix grids for global climate modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15163, https://doi.org/10.5194/egusphere-egu26-15163, 2026.

Normal modes of hydrostatic primitive equations on the sphere decompose the linearized flow into the slow propagating Rossby, fast propagating inertio-gravity (IG), and equatorial Kelvin and MRG waves, characterized by intermediate speeds. However, this simple characterization is no longer valid in the nonlinear system, where wave-wave, wave-mean flow interactions, and adiabatic forcing can significantly alter the wave speeds.

A number of methods aimed at determining the slowly evolving component of the nonlinear flow (so called slow manifold) were proposed under the umbrella terms ”nonlinear normal mode decomposition” (NNMD) or "nonlinear normal mode initialization" (NNMI). In the classical NNMD, Rossby waves are considered slow a priory, while the slow IG part consists of the the unbalanced component of the flow slaved to the Rossby modes. More precisely, in classical NNMD, slow IG waves are those that would be stationary in a nonlinear flow consisting of the slow modes only. While this approach is very successful in suppressing high-speed gravity waves, it also suppresses slowly propagating linearly unbalanced flow and large scale tropical circulation.

We propose a new method of flow decomposition into the slow and fast components based on computing instantaneous phase speeds of normal modes in the nonlinear system. The method does not make assumptions on the composition of the slow manifold. Instead, the decomposition is obtained from a constraint optimization problem that minimizes the norm of the fast component, while requiring that the slow manifold does not contain modes propagating faster than the selected cutoff speed. We apply the method to reanalysis data, demonstrate its efficiency, and analyze the composition of the slow manifold as function of the cutoff speed. In particular, when the cutoff is chosen to be approximately equal to the fastest linear Rossby wave speed, most of the tropical circulation and significant part of unbalanced modes are retained in the slow manifold.  

How to cite: Vasylkevych, S., Chen, J., Holube, K., Zagar, N., and Lunkeit, F.: New nonlinear normal modes flow decomposition based on instantaneous phase speeds in the normal modes framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15174, https://doi.org/10.5194/egusphere-egu26-15174, 2026.

Atmospheric dynamical cores solve the equations of motion that describe the fluid flow at the resolved scales. Commonly used dynamical cores, including the Department of Energy's (DOE) ‘Higher Order Methods Modeling Environment’ (HOMME) dynamical core, also known as a ‘Spectral Element’ (SE) dynamical core, and the National Center for Atmospheric Research's (NCAR’s) ‘Model for Prediction Across Scales’ (MPAS), contained two approximations of the fluid dynamics equations. They are called the Shallow-atmosphere and Traditional (SA+T) approximations. These approximations discard the so-called Nontraditional Coriolis terms. Omitting these terms significantly biases the dynamical response to diabatic heating, inducing up to 10% relative error in the wind field as linear model results suggested. The biases induced by these approximations are expected to be most severe in tropical regions, and to become more severe at finer grid spacings. We present preliminary evidence that removing these approximations may help mitigate the double Intertropical Convergence Zone (ITCZ) bias that is present in many climate models.

We present dynamical core intercomparisons between the shallow-atmosphere version of the HOMME and MPAS dynamical cores and their deep-atmosphere variants which remove the SA+T approximations. We present an overview of the modifications made to the HOMME and MPAS dynamical cores. Aquaplanet simulations made using NCAR’s Community Earth System Model (CESM) show that for sea surface temperature distributions that produce a double ITCZ, removing the SA+T approximations induces equatorward shifts in precipitation. We will examine sensitivities to physics-dynamics coupling strategies and parametric sensitivity to the deep convection scheme. We also examine the performance of MPAS, which collocates physics columns with dynamics columns, with HOMME, which simulates physics on a sparser finite-volume grid. Our simulations are done at a nominal 1º grid spacing, providing conclusive evidence that the SA+T approximations induce climatologically significant biases at the coarser grid spacings used in workhorse climate model configurations.

How to cite: Hughes, O., Ong, H., Herrington, A., Lauritzen, P., Guba, O., Taylor, M., and Jablonowski, C.: Examining the Contribution of Deep-Atmosphere Dynamical Cores to Mitigating Double ITCZ Bias in Aquaplanets, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15662, https://doi.org/10.5194/egusphere-egu26-15662, 2026.

In the last decade, there has been growing interest in developing new dynamical cores for climate and weather simulations based on the Discontinuous Galerkin (DG) approach. To achieve accuracy, efficiency, and stability, various numerical formulations of the Euler equations are currently being explored.

In this talk, we present novel structure-preserving methods for different formulations of the Euler equations. These include models based on different thermodynamic variables, such as potential temperature, internal energy, or total energy, as well as the Exner pressure formulation and the vector-invariant form. These methods are developed within the flux-differencing DG framework, with a specific focus on the efficient implementation of split forms for both conservative and non-conservative terms.

The implementation is carried out entirely in Julia and is integrated within the Trixi.jl and CGDycore.jl ecosystems. We will discuss the different architectural approaches used in these packages and showcase numerical results, specifically focusing on the baroclinic instability test case to compare the different formulations.

How to cite: Artiano, M., Knoth, O., Spichtinger, P., and Ranocha, H.: Structure-Preserving Methods for the Euler Equations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18731, https://doi.org/10.5194/egusphere-egu26-18731, 2026.

Accurate and efficient time integration is a critical component of Numerical Weather Prediction (NWP) and climate modelling. Current approaches in the Met Office’s next‑generation dynamical core, GungHo, rely on a serial-in-time, low-order, iterative semi‑implicit timestepping scheme coupled with Flux‑Form Semi‑Lagrangian (FFSL) transport scheme. As model resolutions and computational scales increase, exploiting parallelism in the time dimension is becoming increasingly important.

This work evaluates the potential of time‑parallel Deferred Correction (DC) methods as a viable alternative to these traditional schemes. I examine two strategies for introducing temporal parallelism:

• Revisionist Integral Deferred Correction (RIDC), which parallelises across correction sweeps, and
• Spectral Deferred Correction (SDC) with time‑parallel (diagonal) preconditioners, which parallelises across collocation nodes.

Results are presented for a suite of standard dynamical core test cases for the compressible Euler equations. These experiments demonstrate how time‑parallel DC algorithms can improve temporal accuracy and offer meaningful opportunities for reducing wall‑clock time.

Together, these developments highlight the promise of DC-based integrators as a pathway toward more scalable and efficient time-stepping in next‑generation atmospheric models.

How to cite: Brown, A.: A comparison of time-parallel deferred correction methods for atmospheric modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19200, https://doi.org/10.5194/egusphere-egu26-19200, 2026.

Data-driven algorithms provide an exciting opportunity to represent unresolved, and therefore parameterised, processes in a way that matches available data without the need to `hand-tune' the parameterisation. However, on their own they suffer from issues with accurate prediction of extreme events and long-term trends, at least in part due to their lack of any representation of physical constraints. Hybrid weather and climate prediction models address this issue by combining data-driven algorithms with more traditional algorithms based on solving the partial differential equations (PDEs) that govern atmospheric flow. However, training the data-driven component separately from the PDE solver can introduce issues with stability and still does not ensure that the combined model will not drift over long times. Online training addresses this issue by more tightly coupling the two model components during training. This requires that the PDE model is differentiable so that during training backpropagation can be performed on multiple timesteps of both the PDE solver and the data-driven component. In this work we introduce a compatible finite element dynamical core that is automatically differentiable since it is built using the Firedrake finite element library. Here we will show how this can be coupled to PyTorch to perform end-to-end training on idealised test cases.

How to cite: Shipton, J. and Hartney, N.: Investigating the impact of model formulation on the accuracy andefficiency of a hybrid dynamical core with online training., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20183, https://doi.org/10.5194/egusphere-egu26-20183, 2026.

Intraseasonal variation of winter climate in China has been remarkable in recent years, such as: reversed or alternating extreme cold and extreme warm events in different months or in different stages of the winter. There are many challenges in climate prediction in winter because the intraseasonal climate variation is often within the seasonal mean variation. It is therefore urgent to understand the intraseasonal variation of winter climate in China, to identify its predictability and predictive sources, and to propose effective prediction methods and prediction models for it. The author reviews progress in research during the last five years on the main characteristics, physical processes, mechanisms, predictability, and prediction of intraseasonal variation of winter climate in China, considering several related systems including the winter monsoon, Siberian high, and stratospheric polar vortex. 

How to cite: Fan, K.: Intraseasonal variation of winter climate in China and climate prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1041, https://doi.org/10.5194/egusphere-egu26-1041, 2026.

How to cite: Keloth Methal, A., Raj, P., Sukumaran, S., and Kodamana, H.: Capturing Long-Range Dependencies for Improved MISO Prediction via Deep Learning , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1176, https://doi.org/10.5194/egusphere-egu26-1176, 2026.

Improving subseasonal predictions of heatwave (HW) onset is crucial for early warning systems. While soil moisture (SM) is recognized as a key initial land surface condition, the impact of its three-dimensional (3D) error structure on HW onset prediction uncertainty, and strategies to mitigate this uncertainty, remain insufficiently explored.

This two-part study addresses these gaps, focusing on predictions with a three-week lead time for eight HW onsets over the Middle and Lower Reaches of the Yangtze River (MLYR) region. First, the Conditional Nonlinear Optimal Perturbation (CNOP) method was employed to identify the 3D-structured initial SM errors that maximize uncertainty in subseasonal HW onset predictions. Results show that these structured CNOP-type errors, characterized primarily by negative anomalies with coherent vertical patterns, intensify HW magnitude and advance onset timing. They exert greater impact than spatial random errors by altering surface energy partitioning: reducing latent heat and enhancing sensible heat primarily through vegetation-related processes, while also modulating net longwave radiation via the Stefan-Boltzmann law. Further experiments revealed the importance of deep-layer SM errors and nonlinear synergistic effects across soil layers.

Building on this, the second part evaluates whether targeted observations of initial SM in CNOP-identified sensitive areas (SAs) can enhance prediction skill. Observing System Simulation Experiments (OSSEs) for eight HW events demonstrate that initializing with more realistic SM over SAs consistently outperforms improvements over non-sensitive areas. This targeted approach improves predictions for an average of 86% of ensemble members per case and reduces the mean error in area-averaged maximum temperature during HW onset by 43%. The improvement is attributed to more accurate initial SM conditions, leading to a better representation of surface heat fluxes.

Collectively, these studies systematically highlight the error structure of initial SM field as a key source of subseasonal HW prediction uncertainty and demonstrate the practical potential of CNOP-based targeted observation strategies to improve HW onset predictions.

How to cite: Liu, H., Sun, G., Mu, M., Zhang, Q., and Chen, B.: From Error Identification to Targeted Observations: The Role of 3D Soil Moisture Errors in Improving Subseasonal Heatwave Onset Predictions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2229, https://doi.org/10.5194/egusphere-egu26-2229, 2026.

Unlike traditional droughts that evolve gradually, flash droughts (FD) are characterized by rapid intensification, leading to sustained dry conditions with disproportionately high impacts on ecosystems. Despite substantial progress in short- to medium-range weather forecasting, predicting these events remains a significant hurdle for both early warning systems and physically-based subseasonal-to-seasonal (S2S) prediction frameworks.

To address this challenge, we present a deep learning framework leveraging a Vision Transformer with explicit temporal attention for the prediction of soil moisture anomalies (SMA) over Europe. The model employs a dual-stream attention mechanism that disentangles temporal dynamics from spatial dependencies: temporal self-attention with rotary positional embeddings captures lead-time-dependent evolution at each location, while spatial attention encodes cross-regional relationships. This architecture enables to learn multi-scale representations, ranging from synoptic variability to persistent anomaly patterns. Furthermore, the model supports probabilistic forecasting, estimating the full conditional distribution of soil moisture anomalies to provide principled uncertainty quantification, a critical requirement for operational early warning systems. 

Additionally, the framework employs a multitask learning approach that exploits the relationship between continuous soil moisture anomalies and discrete flash drought characteristics derived from the Flash Drought Intensity Index (FDII). This index integrates both the rate of soil moisture decline and drought severity into a unified indicator.  A shared encoder learns representations that capture the coupled dynamics of soil moisture evolution and flash drought emergence, while task-specific prediction heads accommodate the distinct statistical properties of each target variable.

The results indicate that our approach achieves predictive skill competitive with more complex spatio-temporal models while maintaining computational efficiency suitable for operational deployment. Evaluation against standard baselines, including climatology and persistence, as well as state-of-the-art deep learning models, demonstrates the framework’s ability to resolve the rapid intensification dynamics typical of flash drought onset. This work lays the foundation for interpretable, scalable, and probabilistic prediction of rapid-onset drought events at S2S timescales.

How to cite: Otero, N., Fernández-Torres, M. Á., Özer, A., and Ma, J.: Towards Sub-Seasonal Flash Drought Prediction Using a Vision Transformer Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3518, https://doi.org/10.5194/egusphere-egu26-3518, 2026.

Recent advances in machine learning (ML) illustrate the potential for significant improvements in weather predictive skill. At the same time, ML-based technologies have broadened the range of organisations capable of delivering skilful atmospheric forecasts. Given these developments, the ECMWF AI Weather Quest was designed as an open and transparent international competition, enabling a range of organisations to submit ML-based subseasonal forecasts. With participation from more than 40 competing teams spanning academia, public institutions and private companies, the Quest provides a unique framework for systematically evaluating and comparing multiple ML-based subseasonal forecasting systems.    

Participants of the AI Weather Quest submit global probabilistic quintile forecasts of near-surface temperature, mean sea level pressure, and precipitation at either a 3- or 4-week lead time in an operational-style forecasting environment. This set-up has encouraged model development whilst challenging participants to develop forecasting systems that operate in realistic settings and deliver actionable forecast parameters.

In this presentation we will provide an overview of the AI Weather Quest design and compare subseasonal forecast skill of both ML-based and dynamical prediction systems. Additionally, we will highlight emerging approaches in ML-based post-processing and fully data-driven forecasting. During the first three-month competitive season, ML-based post-processing of dynamical forecasts achieved the highest skill, highlighting contrasting performance across approaches and underscoring the need for further development in both dynamical and ML-based forecasting. We will also share opportunities for wider engagement and discuss future developments planned for the ECMWF AI Weather Quest.

How to cite: Talib, J., Loegel, O., Vitart, F., Hoffmann, J., and Chantry, M.: Insights from the AI Weather Quest: An international machine-learning competition for sub-seasonal prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3570, https://doi.org/10.5194/egusphere-egu26-3570, 2026.

Forecasting extratropical weather on subseasonal timescales continues to be a challenge. One possible source of atmospheric predictability on these timescales are slowly evolving components of the climate system, most notably tropical modes of variability such as the Madden–Julian Oscillation (MJO) and tropical waves. These sources of predictability are not fully exploited because of systematic errors in numerical weather prediction (NWP) models. In particular, forecast errors that develop in the tropics at lead times of several days grow up-scale, propagate and degrade forecast skill in the extratropics on subseasonal timescales. Regions of forecast errors that exert the strongest influence on extratropical forecast skill remain poorly identified. Relaxation experiments using NWP models provide a means to isolate these regions, but such experiments are computationally demanding. In this study, we employ machine learning–based weather prediction models to perform relaxation experiments across multiple tropical regions. Probabilistic forecasts are generated using perturbed initial conditions from the Ensemble of Data Assimilations (EDA) of the European Centre for Medium-Range Weather Forecasts (ECMWF). Reforecasts covering a five-year period (2020--2024) are used to systematically assess the impact of relaxation strategies and the role of tropical variability, with a particular focus on the MJO, on extratropical subseasonal forecast skill. A key-finding is that predictions of the negative phase of the North Atlantic Oscillation improve when relaxation is applied following MJO phases 6 and 7 at initial time. Rossby wave source diagnostics are examined to investigate the dynamical processes leading to improvements in extratropical forecasts. The results demonstrate the value of relaxation experiment as a diagnostic tool when integrated with emerging machine learning–based prediction systems.

How to cite: Li, S., Namdev, P., and Quinting, J.: Impact of Systematic Relaxation Experiments on Subseasonal Forecast Skill in Machine Learning Weather Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4190, https://doi.org/10.5194/egusphere-egu26-4190, 2026.

A multi-scale coupled framework has been implemented in the Central Weather Administration’s Global Ensemble Prediction System Version 3 (CWA GEPSv3) to improve extended-range forecasts over Taiwan. Reforecasts for January from 2001 to 2020 show skillful Madden–Julian Oscillation (MJO) predictions, with an average lead time of 17 days and a maximum of 33 days. The model realistically captures the eastward propagation of the MJO from the Indian Ocean to the Maritime Continent (MC) but fails to sustain its intensity beyond the MC due to a boundary-layer dry bias emerging 5–10 days before the convection center’s arrival.

Event-based analysis reveals that accurate MJO forecasts are more common during La Niña years, whereas poor forecasts occur more often during El Niño years. The low-frequency moisture field mitigates the dry bias over the MC during La Niñas, but amplifies it during El Niños. Ocean–atmosphere coupling enhances forecast skill at 20–30 lead days, and it is only pronounced for the good-prediction cases.

The boundary-layer dry bias over the MC primarily results from weak upward motion linked to insufficient meridional convergence. A modified dynamical core enhances the simulation of horizontal convergence, yielding clearer eastward propagation of MJO signals. These results elucidate the physical processes underlying model biases in GEPSv3 and provide practical guidance for improving subseasonal-to-seasonal forecasting.

How to cite: Tseng, Y., Lee, C.-W., Shao, Y.-C., Liu, P.-Y., Tseng, H.-H., and Chen, J.-H.: MJO Prediction in the CWA GEPSv3: Model Performance and Physical Processes Underlying Simulation Errors, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4276, https://doi.org/10.5194/egusphere-egu26-4276, 2026.

Data-driven models have achieved significant progress in sub-seasonal prediction, but predicting the initiation of the Madden-Julian Oscillation (MJO) remains a critical challenge, largely due to initial uncertainties from sparse observations over tropical oceans and complex multiscale interactions. Therefore, identifying sensitive areas in initial conditions is crucial to both reveal the underlying error growth mechanisms and provide guidance for target observations. Here, the FuXi-S2S model is applied to explore the initial sensitivity and instability modes of MJO initiation. First, the evaluation of prediction skill identifies the initiation of primary MJO events at a 3-pentad lead time as a critical bottleneck. Simulations initialized with optimized initial conditions within analysis uncertainty closely reproduce the observed MJO evolution, thereby validating the high initial sensitivity during the first 4 pentads. Subsequently, the conditional nonlinear optimal perturbation (CNOP) method is utilized to identify the optimally growing initial errors (OGIEs) and optimal precursors (OPRs). Analysis of OGIEs reveals three dominant types of error modes causing the largest forecast errors, indicating that the rapid growth of OGIEs is driven by the coupling of local low-level thermodynamic instability (temperature and moisture) and upstream upper-level dynamic forcing (wind). Moreover, the spatial structure and perturbation evolution of OPRs exhibit high consistency with OGIEs. The identification of these shared instability modes provides a theoretical foundation for target observations, suggesting that additional observations in sensitive areas can simultaneously reduce initial errors and capture precursors.

How to cite: Peng, Z., Mu, M., and Li, H.: Predictability of MJO Initiation in Data-Driven Models: Shared Instability Modes of Optimally Growing Initial Errors and Optimal Precursors, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4347, https://doi.org/10.5194/egusphere-egu26-4347, 2026.

Sudden stratospheric warming (SSW) is identified as key sources of skill in winter subseasonal-to-seasonal (S2S) forecasts because of their surface impacts lasting up to 30–60 days through stratosphere troposphere coupling, despite their typical prediction being limited about 10 days. A better understanding of the predictability of the SSW itself, thus, is fundamental. Most of the previous studies investigate the predictability of SSW events using linear approaches, which are insufficient given the inherently chaotic and nonlinear nature of SSWs. In the study, we apply a nonlinear method—Backward Searching for the Initial Condition (BaSIC)—to quantify the local predictability limit the 2021 SSW event, which caused cold extremes across East Asia and North America. Using ERA5 reanalysis and the S2S reforecasts data, BaSIC estimates the maximum prediction lead time of this 2021 SSW event to be 17 days. To explore sensitive region of forecast uncertainty, we identify regions of fastest error growth via error tracking in S2S systems using BaSIC method. Forecast errors during the SSW event are small across the polar stratosphere after initiation but grow gradually over two weeks, accelerating rapidly over central Eurasia (30◦-60◦E) and spreading across the continent. This points to central Eurasia at high altitudes as a critical region for SSW forecast error development.

How to cite: li, X.: Quantifying the practical local predictability of the 2021 sudden stratospheric warming event using a nonlinear method, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5219, https://doi.org/10.5194/egusphere-egu26-5219, 2026.

Heatwaves are among the most impactful climate-related hazards in Europe, with increasing frequency, duration, and severity under climate change. Currently, heatwave warnings across Europe are typically issued 2–7 days in advance. Extending these lead times could substantially enhance preparedness by enabling earlier adaptive actions and more effective resource allocation.

On sub-seasonal time scales, extended-range weather forecasts (approximately 2 weeks to 1 month) have been shown to exhibit higher skill for warm extremes than for average temperature conditions over Europe. In particular, the persistence of prolonged heat waves seems to have a higher-than-average level of predictability even at a 3-week lead time. Recent verification studies have demonstrated statistically significant probabilistic skill of ECMWF extended-range ensemble reforecasts for European heatwaves defined using 5-day mean temperatures. Together, these findings indicate that extended-range ensemble forecasts can provide early warning information not only on the likelihood of heatwave occurrence, but also on the potential persistence and severity of extreme heat events.

Building on this demonstrated predictability, we take the next step towards practical early warning applications by developing a forecast and alert logic for extended-range heatwave prediction. We post-process ECMWF extended-range ensemble forecasts to produce probabilistic heatwave forecasts using a previously developed and verified methodology based on 5-day mean temperature thresholds. Building on this framework, we develop a rule-based alert logic that translates probabilistic forecasts into actionable early warning information. The alert logic combines forecasted heatwave probabilities with indicators of forecast reliability and flow-dependent predictability, including ensemble spread, the heatwave life cycle state at forecast initialization, and North Atlantic sea surface temperature anomalies at the time of forecast initialization. These components are used to define multiple alert levels, ranging from no-action conditions to high-impact heatwave risk.

The proposed framework provides a practical pathway from extended-range probabilistic forecasts to impact-oriented early warning.

How to cite: Korhonen, N. and Gregow, H.: Development of Forecast and Alert Logic for Extended-Range Heatwave Early Warning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5602, https://doi.org/10.5194/egusphere-egu26-5602, 2026.

For more than half a century, meteorology has accepted a fundamental limit: weather cannot be predicted beyond two weeks. This boundary, rooted in the chaotic nature of the atmosphere, has shaped generations of forecasting science, defining the boundary between weather and climate forecasting and constraining our ability to anticipate high-impact extremes. At the same time, extreme heat has emerged as the deadliest climate-related hazard worldwide, underscoring the urgent need for reliable early warnings at lead times relevant for public health, energy systems, and disaster risk reduction. Recent advances in deep learning have produced a new class of global weather models accelerating progress in forecasting, raising the question of whether the traditional two-week limit is beginning to shift.

Here we evaluate six state-of-the-art deep learning weather emulators — Pangu-Weather, FuXi, ArchesWeather, AIFS, GraphCast and Aurora — alongside leading dynamical approaches and statistical baselines in forecasting global surface temperature and extreme heat events at a 14-day lead time. Models are evaluated using a suite of metrics, considering global temperature and extreme heat forecasting in both regression and classification settings. Several emulators rival or even surpass physics-based forecasts for temperature, but struggle to balance deterministic skill with realistic spectral properties. While all models display predictive skill for extreme heat, their predictions are deterministic and often inaccurate, offering little insight into uncertainty and limiting their reliability. Overall, results demonstrate that deep learning is starting to extend the frontiers of deterministic predictability. However, key limitations remain that constrain their applicability for operational early-warning systems, highlighting the need for reliable probabilistic approaches in a rapidly warming climate.

How to cite: Decancq, C., Mortier, T., Keune, J., and Miralles, D.: Weather Emulators Push the Frontier of Heat Extremes Forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6412, https://doi.org/10.5194/egusphere-egu26-6412, 2026.

Intraseasonal variations of sea surface temperature (SST) in the South China Sea (SCS) during winter are investigated by using atmospheric and oceanic reanalysis in this study. The dominant pattern of SST variations within the 10–90-day timescale is derived by empirical orthogonal function analysis, which features a basin-wide warming or cooling spatial pattern, with a cycle period of 30–35 days. Composite analysis and mixed-layer heat budget analysis are conducted to investigate the physical process controlling SST variability. The formation of intraseasonal SST variations is primarily attributable to the forcing of wind-related latent heat flux and shortwave radiation flux changes. During the warming (cooling) period, anomalous southerly (northerly) winds tend to weaken (enhance) climatological northerly winds. This, in turn, results in a weakening (enhancement) of wind speed, favoring a reduction (increase) in latent heat flux from the ocean into atmosphere, accompanied by an increase (decrease) in shortwave radiation flux. In addition to surface heat flux forcings, ocean zonal advection is the second most significant contributing factor, exerting a negative effect. Finally, the effect of the Madden–Julian Oscillation (MJO) on the SST is studied. The variations in wind anomalies and surface heat flux changes associated with SST intraseasonal variability are significantly related to the MJO activities. The anomalous anticyclone (cyclone) in the northwest Pacific Ocean is induced by MJO, with enhanced (depressed) convection occurring in the Indian Ocean and depressed (enhanced) convection over the Maritime Continent and western Pacific Ocean, which accounts for the anomalous southerly (northerly) winds and enhanced (depressed) shortwave radiation observed. 

How to cite: Mingyue, Q., Ren, X., Wu, X., and Wang, G.: Intraseasonal SST variations in the South China Sea during the borealwinter and the impacts of Madden-Julian Oscillation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6531, https://doi.org/10.5194/egusphere-egu26-6531, 2026.

Based on the hindcasts from five subseasonal-to-seasonal (S2S) models participating in the S2S Prediction Project,
this study evaluates the performance of the multimodel ensemble (MME) approach in predicting the subseasonal
precipitation anomalies during summer in China and reveals the contributions of possible driving factors. The results
suggest that while single-model ensembles (SMEs) exhibit constrained predictive skills within a limited forecast lead time
of three pentads, the MME illustrates an enhanced predictive skill at a lead time of up to four pentads, and even six pentads,
in southern China. Based on both deterministic and probabilistic verification metrics, the MME consistently outperforms
SMEs, with a more evident advantage observed in probabilistic forecasting. The superior performance of the MME is
primarily attributable to the increase in ensemble size, and the enhanced model diversity is also a contributing factor. The
reliability of probabilistic skill is largely improved due to the increase in ensemble members, while the resolution term does
not exhibit consistent improvement. Furthermore, the Madden–Julian Oscillation (MJO) is revealed as the primary driving
factor for the successful prediction of summer precipitation in China using the MME. The improvement by the MME is not
solely attributable to the enhancement in the inherent predictive capacity of the MJO itself, but derives from its capability in
capturing the more realistic relationship between the MJO and subseasonal precipitation anomalies in China. This study
establishes a scientific foundation for acknowledging the advantageous predictive capability of the MME approach in
subseasonal predictions of summer precipitation in China, and sheds light on further improving S2S predictions.

How to cite: Guo, L.: Advantages of the Multimodel Ensemble Approach forSubseasonal Precipitation Prediction in Chinaand the Driving Factor of the MJO, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9223, https://doi.org/10.5194/egusphere-egu26-9223, 2026.

The Madden-Julian Oscillation (MJO) is the dominant and most influential intraseasonal oscillation in the tropics, as well as a prominent source of subseasonal-to-seasonal predictability. Its maintenance and propagation, particularly across the Maritime Continent, remain a challenge for theory, simulation and forecasting. In modern MJO theories, moisture plays a central role. As the primary source of moisture, sea surface latent heat flux has received significant attention. The sea surface latent heat flux can be induced by both base-state winds and wind bursts, but the different effects of these two types of latent heat flux are not well understood.

This study addresses this issue by analyzing CESM hindcasts of a strong MJO event in April 2009 that crossed the Maritime Continent with little decay. The control simulation reproduces the observed propagation characteristics and intensity of this event. We then impose an upper limit on wind speed in the bulk formula used by the model to calculate sea surface latent heat flux. By lowering this limit in different simulations, we reduce the latent heat flux from strong winds first, followed by the flux from base-state winds. Based on frequency distributions over the Indian Ocean and the Maritime Continent, the peaks for base-state winds and wind bursts occur at 3 m/s and 10 m/s respectively.

Results indicate that wind-burst-induced latent heat flux is essential to maintaining MJO amplitude over the Maritime Continent, due to the region’s lower sea fraction. As convection over land tends to disrupt the coherent organization of the MJO convection envelope, lower sea fraction increases the sensitive of MJO amplitude to sea surface latent heat flux. On the other hand, the base-state surface latent heat flux modulates MJO propagation speed due to its effectiveness in moistening the atmosphere. As the base-state latent heat flux is reduced, the atmosphere dries, moisture advection decreases, and the MJO slows down. Additional simulations confirm these findings in other MJO cases. This study underscores the importance of accurately simulating strong winds for maintaining MJO amplitude over the Maritime Continent and overcoming the barrier effect.

How to cite: Weng, L. and Lin, Y.: Distinct Roles of Base-State and Wind-Burst-Induced Sea Surface Latent Heat Flux in MJO Maintenance and Propagation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9451, https://doi.org/10.5194/egusphere-egu26-9451, 2026.

A set of relaxation experiments with the European Centre for Medium-range Weather Forecasts (ECMWF) model is used to explore the influence of tropical and stratospheric teleconnections on forecast skill, variability of forecast ensemble mean (EM) and ensemble spread (ES) in the wintertime Northern Hemisphere at sub-seasonal timescales. The influence is diagnosed by comparing the relaxation experiments, which relax the temperature and wind fields in specific regions to observed values, with the free running (control) experiment. During weeks 3–6 the tropical relaxation increases the forecast skill for sea level pressure (SLP) mostly south of 50°N but also over the North Atlantic, Northern Europe and eastern Canada. Skill improvements occur via both stratospheric and tropospheric pathways. The stratospheric relaxation improves the skill mostly in high latitudes, over Europe, and North Atlantic. Skill improvements are smaller for surface temperature and total precipitation, suggesting a smaller role of the teleconnections in their predictability.

The increases in skill are generally associated with increased variability of EM, considered to represent the predictable signal, and reduced ES representing noise. However, this does not happen in all areas where the skill is increased. In high latitudes, where the stratospheric impacts are strongest, the EM variability does not increase in the stratospheric relaxation experiments consistently with increases in skill, implying that EM does not reflect well the predictable signal. We suggest that the ensemble size available in the experiments (11 members) is not always enough to make it possible to fully extract signal from noise, and that larger ensembles (20–50 members or even more depending on area and variable) would be beneficial for studies of sub-seasonal predictability associated with the teleconnections in mid- and high latitudes, including windows for forecast opportunities.

How to cite: Karpechko, A., Butler, A., and Vitart, F.: Signal, noise and skill in sub-seasonal forecasts: the role of tropical teleconnections and stratosphere-troposphere coupling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11008, https://doi.org/10.5194/egusphere-egu26-11008, 2026.

Energy demand, especially for residential heating, is largely driven by temperature.  High-quality subseasonal-to-seasonsl (S2S) forecasts of temperature are therefore valuable for risk management and energy trading, yet the use of these forecasts is often limited by their complexity, by difficulties in combining different forecast types, and by the relatively weak probabilistic skill they produce. Sequential learning algorithms (SLA), offer a means to overcome many of these difficulties.  SLAs optimally combine information from multiple ‘experts’ or predictors using weights and reduce forecast bias by continuously learning over time from each forecast verification.  The information from these ‘experts’, which can both be statistical or numerical forecasts, are used as SLA inputs and blend into a single information stream through dynamical updating of the weights. Here, ‘experts’ are defined as quantiles of raw ECMWF S2S 2 m temperatures (T2m) forecasts without bias adjustment and ERA5 T2m climatology. The SLA produces probabilistic forecasts of Great Britain-averaged T2m at lead times of 1-4 weeks for the period 2004-2023.  Results show positive anomaly correlation co-efficient and rank probability skill scores for SLA T2m forecasts across all weeks compared to both the raw S2S and climatological forecast.  Analysis of the weight evolution shows that SLA relies heavily on the raw forecast experts at weeks 1-2 but shifts towards climatological experts in the later weeks, with a clear seasonal evolution to the weight profile. It is also confirmed that this online-learning approach with adaptive weights outperforms the most optimal static weight combination even though the latter is permitted the benefit of perfect foresight.

How to cite: Das, A., Brayshaw, D., Methven, J., Frame, T., O'Reilly, C., Sharma, S., Mammatt, J., and Fox, S.: Using machine learning to enhance skill of subseasonal-to-seasonal (S2S) temperature forecasts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11985, https://doi.org/10.5194/egusphere-egu26-11985, 2026.

Subseasonal weather prediction (2–6 weeks lead time) represents a critical "predictability desert" where the influence of atmospheric initial conditions diminishes and boundary forcings have not yet become dominant. Despite its inherent difficulty, skillful subseasonal forecasting is vital for decision-making in agriculture, water resource management, public health and disaster preparedness. While recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have revolutionized synoptic-scale weather forecasting, these gains have not yet fully translated to the subseasonal regime, where systematic biases in both dynamical and data-driven models remain a primary bottleneck.

In this work, we present a novel Probabilistic Bias Correction (PBC) framework that leverages ML to systematically identify and correct errors in global model forecasts. Our approach is model-agnostic, as we demonstrate by showing it can enhance both traditional physics-based dynamical ensembles and emerging AI-based forecasting systems. By training on historical reanalysis and model forecast datasets, the PBC framework significantly reduces systematic errors that typically degrade raw model skill at subseasonal lead times.

We evaluate the performance of our PBC algorithms against several high-standard benchmarks, including climatology, multi-model super-ensembles from major operational centers, and state-of-the-art AI models. Notably, our framework was benchmarked within the context of the AI Weather Quest (sponsored by ECMWF). Results demonstrate that our PBC forecasts outperform all participating dynamical and ML models, including the ECMWF Integrated Forecasting System (IFS) and Artificial Intelligence Integrated Forecasting System (AIFS), in predicting 2-meter temperature, precipitation and mean sea level pressure.

To demonstrate the real-world utility of this system for early warning capabilities, we present case studies of extreme winter weather events in the Eastern United States and Europe. Our model successfully predicted these high-impact events several weeks in advance, with forecasts disseminated in real-time to stakeholders via social media. Our findings suggest that while AI-based models like FuXi-S2S offer a strong alternative to dynamical systems, the integration of probabilistic post-processing is critical to maximize predictive skill and provide reliable, sector-specific decision support in a changing climate.

How to cite: Mouatadid, S., Weyn, J., Guan, H., Orenstein, P., Cohen, J., Mackey, L., Lu, A., Flaspohler, G., Ni, Z., and Dong, H.: Bridging the "Predictability Desert": A Probabilistic Bias Correction Framework for AI and Dynamical Subseasonal Forecasts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14077, https://doi.org/10.5194/egusphere-egu26-14077, 2026.

Organized mesoscale convective systems (MCSs) over the Arabian Peninsula (AP) are a major driver of extreme precipitation and flash flooding in the cool season (October - April). Existing criteria for MCS tracking methods do not capture this phenomenon over the AP, including several record-breaking MCS-driven extreme precipitation events that caused significant socioeconomic losses in the Kingdom of Saudi Arabia (KSA). In this study, we evaluate the MCS tracking capability and calibrate regional tracking criteria for the AP.

Based on several MCS-driven precipitation events over the past 20 years, AP MCS criteria are updated as follows: size over 20,000 km², cloud-top temperature less than 230 K, and merging/splitting duration over 3 hours. The AP MCS tracking criteria are also updated specifically for application to convective-permitting Weather Research and Forecasting model (WRF) output. WRF MCS size and durations are similar to observed MCSs, but the core cloud temperature threshold is lowered to 218 K.

The MCS tracking algorithm is then applied to a 20-year MERGIR brightness temperature (Tb) dataset and a corresponding 20-year subseasonal WRF (4-km grid spacing) ensemble reforecast product. The WRF subseasonal ensemble reforecasts are available at 1-week to 4-week lead times. Forecast skill is assessed using categorical statistics such as the Critical Success Index, combined with a neighborhood verification method to reduce double-penalty effects.

The AP MCS tracking results based on WRF subseasonal ensembles exhibit robust tracking capability in both early and late cool season, with respect to seasonal climatology and extreme convective case studies. The convective-permitting reforecast demonstrates subseasonal forecast skill and the potential to enhance early warning capabilities for public safety and disaster risk mitigation.

How to cite: Chang, H.-I., Risanto, C. B., Castro, C. L., Luong, T., and Hoteit, I.: Developing tracking capability for mesoscale convective systems in the Arabian Peninsula through observations and model-based subseasonal reforecasts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14647, https://doi.org/10.5194/egusphere-egu26-14647, 2026.

False monsoon onsets involve an early-season wet spell followed by a prolonged dry spell, often resulting in agricultural losses when sowing is initiated during premature rains and farmers are unprepared for the dry conditions. Despite its importance for risk reduction for hundreds of millions of farmers in the tropics, the predictability of these pre-monsoonal wet–to–dry events remains largely unexplored. Here, we benchmark six state-of-the-art artificial intelligence weather prediction (AIWP) models (AIFS, FuXi, FuXi-S2S, GraphCast, GenCast, NeuralGCM) and a numerical weather prediction (NWP) model (IFS) against novel, decision-relevant historical reference forecasts to assess the ability to predict the false monsoon onset at lead times up to 30 days. We find that both AIWP and NWP models exhibit positive predictive skill in the core monsoon zone of India, with ensemble-based probabilistic models retaining positive predictive value relative to these reference forecasts across all lead times. Deterministic skills vary strongly with regions, with good short-lead predictability (0-10 days) and a decrease in skills at longer lead times (11-30 days). We further evaluated the models using well-documented canonical false onset events from the literature and found that skillful forecasts are associated with the ability to reproduce the large-scale circulation evolution characteristic of false onsets, in particular the progression from a transient monsoon-like state to a subsequent circulation collapse that produces a dry spell.

We use agriculturally relevant thresholds to define monsoon onset, wet spells, and dry spells. To enable a meaningful assessment of model skill, the reference forecast is constructed from 124 years of gridded rain-gauge observations and quantifies the baseline probability of false monsoon onsets within a decision-relevant framework. We first calibrate model-specific event-definition wet- and dry-spell thresholds using quantile mapping within a leave-one-year-out cross-validation framework, rather than applying bias correction directly to rainfall fields. Forecast performance is evaluated using deterministic and probabilistic metrics, including probability of detection, false alarm ratio, critical success index, and Brier score. Reliability diagrams show systematic overconfidence at higher forecast probabilities, indicating the need for additional calibration and post-processing. Together, this framework establishes a decision-relevant benchmark and evaluates current AI-based and physics-based forecast systems for the sub-seasonal early warning of false onsets involving dry spells. 

How to cite: Gupta, M., Aitken, C., Masiwal, R., Marchakitus, A., Boos, W., Kowal, K., Jina, A., and Hassanzadeh, P.: Operational benchmarking of AI and NWP models for false monsoon onset prediction in India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17228, https://doi.org/10.5194/egusphere-egu26-17228, 2026.

Midlatitude synoptic storms are a major source of forecast uncertainty, yet the mechanisms linking storm track variability, ensemble spread and extreme event predictability remain insufficiently understood. Differences in storm track intensity between the Pacific and the Atlantic sectors span multiple timescales, from daily to seasonal and decadal, and have been linked to basin characteristics, strength of the subtropical jet, the latitudinal position of the jet stream, and to the changes in the cyclone life-cycles. Yet, the relevance of these dynamical processes for forecast uncertainty remains unclear.

This study investigates how the occurrence and intensity of synoptic storms modulate ensemble forecast spread related to the storm track in the North Pacific and North Atlantic, with the goal of identifying sources of forecast uncertainty for midlatitude weather at subseasonal lead times (2–6 weeks lead time). We use ECMWF reforecasts from the Subseasonal to Seasonal (S2S) Prediction Project database, verified against EAR5 reanalysis,  to investigate ensemble forecast distributions of the upper-troposphere westerly jet and various storm activity metrics based on eddy kinetic energy (EKE). Our analysis involves a systematic assessment of spread-mean relationships for EKE and other dynamical variables for different midlatitude regions and lead times. 

A key result is a robust linear, positive relationship between ensemble mean and spread of EKE in the Atlantic sector, suggesting the contribution of synoptic-scale storms for reliable forecasts. Furthermore, this spread-mean coupling implies that periods of enhanced storm activity, such as wintertime storminess over the North Atlantic, are associated with systematically larger EKE ensemble spread. In contrast, in the Pacific the relationship seems inherently different, indicating a non-trivial role of synoptic storms in forming forecast uncertainty. Large-scale variation of the storm track - such as those associated with teleconnections - are expected to modulate ensemble spread and thereby induce flow-dependent variations in predictability.

The findings highlight the relevance of storm track diagnostics and EKE-based spread metrics as promising tools to improve forecast accuracy and enhance early warning capabilities for high-impact midlatitude storms.

How to cite: Gerstman, H., Rupp, P., Wu, R. W.-Y., Spaeth, J., Vitart, F., and Romppainen-Martius, O.: Linking storm track activity and subseasonal forecast uncertainty in the North Pacific and North Atlantic, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17748, https://doi.org/10.5194/egusphere-egu26-17748, 2026.

The Blue Nile Basin, located in the Greater Horn of Africa, is highly vulnerable to climate variability, where hydrometeorological extremes have severe socio-economic consequences. Reliable prediction on sub-seasonal to seasonal (S2S) timescales is therefore critical for preparedness in sectors such as agriculture, water and dam management. However, S2S prediction in the region remains particularly challenging due to complex orography, the influence of large water bodies, various climate zones, and the interaction of multiple large-scale circulation modes, including ENSO, the Indian Ocean Dipole, and the Madden–Julian Oscillation.

While global forecasting systems provide valuable large-scale climate information, their direct application at regional scale is limited. In heterogeneous regions such as the Blue Nile Basin, global products are often insufficient in spatial resolution and show systematic biases that reduce their usability for regional and sector-specific applications. This requires targeted post-processing to correct errors and regionally enhance the forecasts.

In this study, we evaluate state-of-the-art seasonal and sub-seasonal forecasting products from the ECMWF, focusing on the SEAS5 seasonal forecasting system (lead times up to 215 days) and the ECMWF sub-seasonal range forecasts (lead times up to 46 days). Forecast skill is assessed against ERA5 and ERA5-Land reanalyses, as well as a composite observational dataset combining satellite and station measurements (CHIRPS). To enhance the raw forecasts, we apply an established statistical post-processing technique, namely bias correction and spatial disaggregation (BCSD), alongside advanced deep learning approaches. The latter include Seasonal AFNO-based models and ProS2St, which has previously been developed and tested at global scale within the ECMWF AI Weather Quest Challenge.

Our results demonstrate that post-processing methods significantly improve raw forecast performance over the Blue Nile Basin. Despite these improvements, outperforming climatology remains challenging for meteorological variables alone. However, we show that when the enhanced forecasts are used as input for subsequent impact models, such as hydrological models, they provide added value compared to climatological forcing.

This work highlights the potential of regionally enhanced meteorological forecasts as a foundation for sub-seasonal to seasonal prediction systems. By coupling post-processed meteorological forecasts with hydrological and crop models, we enable S2S forecasts that support improved decision-making in specific sectors. The focused evaluation of S2S forecasting products and post-processing methods for the Blue Nile Basin, together with their integration into downstream impact models, represents a novel contribution toward operational and application-oriented prediction systems in the region.

How to cite: Wiegels, R., Chwala, C., Polz, J., Glawion, L., Lorenz, C., Weber, J. N., Hageltom, Y., Sawadogo, W., Schober, T. C., Janner, S., Zargar, M., Bronstert, A., and Kunstmann, H.: Toward a Seamless Sub-Seasonal to Seasonal Prediction System for the Blue Nile Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18144, https://doi.org/10.5194/egusphere-egu26-18144, 2026.

Forecast skill on subseasonal-to-seasonal timescales varies strongly with the large-scale atmospheric state, creating intermittent “windows of opportunity” for skillful prediction. Here we evaluate state-dependent predictability of 2m temperature in subseasonal hindcasts with CESM and in a perfect modelling framework. Skill is quantified as a function of the Pacific-North American pattern, the phase of the El Nino Southern Oscillation, the Madden-Julian Oscillation, the North Atlantic Oscillation and the soil state. Both models exhibit regionally significantly enhanced subseasonal skill during dynamically organized flow regimes like the PNA, El Niño or La Nina, and certain MJO phases, when tropical forcing projects onto an amplified winter jet and supports coherent Rossby wave propagation. The resulting predictability is modulated by the seasonality of the background flow. Our findings demonstrate that regional S2S forecast skill may be higher than suggested by spatial averages. It is investigated if AI generated forecasts can capture this state-dependent predictability.

How to cite: Berner, J., Jaye, A., Richter, J., Mayer, K., and Fowler, M.: On the state-dependent predictability horizon in dynamical and AI forecasts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21897, https://doi.org/10.5194/egusphere-egu26-21897, 2026.

How to cite: Nemani, R. S., Bannister, R. N., Lawless, A. S., Wei, H., and Thomas, C.: Machine Learning-Driven Background Error Covariances for High-Resolution Data Assimilation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1685, https://doi.org/10.5194/egusphere-egu26-1685, 2026.

A multiradar mosaic is a key solution to the insufficient detection range of a single weather radar. In the traditional grid-preprocessed mosaicking method (GPM), radar polar coordinate data are interpolated into Cartesian grids to compensate for vertically undersampled regions in radar volume scans. However, such interpolation fails to accurately reconstruct the polarization parameters in these regions. Therefore, this study presentss a polar coordinate direct-mosaicking method (PDM) for the high-density radar network in South China, which directly operates on polar coordinate data and avoids initial interpolation. Based on typical precipitation cases from May to August 2021, three key issues in the PDM are addressed: First, horizontal reflectivity (Z_H) biases and differential reflectivity (Z_DR) offsets are corrected; second, the number of radars in the mosaicking process is evaluated, with five radars determined to be optimal; and third, the weights of different radar data are optimized by considering vertical and horizontal distances, along with the melting layer position. Compared with the GPM, the PDM yields a more accurate representation of the melting layer, with a smaller mean height error (192 m compared with 470 m) and a more realistic estimation of thickness (661 m compared with 1507 m). It also improves the continuity of polarimetric parameters within convective core regions. The case studies indicate that the PDM enables earlier identification of Z_DR columns and more accurate estimation of their heights. These advancements provide high-quality observational constraints for cloud microphysical research and offer potential for improving convective-scale data assimilation.

How to cite: Li, Z., Wu, C., Liu, L., and Zhang, Y.: Enhancing Convective-scale Polarimetric Signatures through a Polar Coordinate Direct-Mosaicking Method for High-density Radar Networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2320, https://doi.org/10.5194/egusphere-egu26-2320, 2026.

Assimilation of column-integrated observations such as precipitable water vapor (PWV) remains challenging when vertical moisture profile constraints (e.g., radiosondes) are unavailable. Ensemble data assimilation systems typically employ Gaspari–Cohn (GC) localization (e.g., in DART) to limit the spatial influence of observations and reduce spurious correlations arising from finite ensemble size. However, GC assumes homogeneous and isotropic correlations and does not represent physically driven vertical inhomogeneity, such as transient moisture structures associated with convection or moisture transport. Consequently, vertically displaced but dynamically sensitive layers may be underrepresented during PWV assimilation. The generalized Gaspari–Cohn (GenGC) localization function introduced by Gilpin et al. (2023) relaxes these assumptions by allowing localization parameters to vary spatially, enabling inhomogeneous and anisotropic correlation structures. This flexibility is particularly relevant for PWV assimilation, where the vertical distribution of moisture sensitivity can vary substantially with atmospheric state.

Sensitivity of water vapor mixing ratio (qvapor) to PWV was analyzed for Tucson, Flagstaff, Albuquerque, and Santa Teresa at 12 UTC during July–September 2021. These sensitivities were used to estimate the appropriate vertical influence of PWV assimilation and to construct a vertically varying GenGC localization. The performance of GenGC was evaluated relative to a standard GC localization with a fixed vertical radius of 3.5 km. These four locations in the Southwest US are chosen since they are impacted by the North American monsoon. To date, forecasting the monsoon precipitation is still challenging even with convective-permitting models coupled with data assimilation (Risanto et al. 2026 – in review).

Global Positioning System PWV (GPS-PWV) observations from Tucson and Flagstaff for three summer days in 2021 were assimilated into a convective-permitting (1.8 km) WRF ensemble with 40 members. HRRRv4 provided initial and boundary conditions. PWV was assimilated at 12 UTC using both GC and GenGC vertical localization, and radiosonde observations are used for independent verification.

Initial results indicate that qvapor exhibits strongest sensitivity to PWV from the surface up to approximately 6 km MSL, motivating a GenGC localization that extends vertical influence on this level. PWV analyses produced using GenGC were generally closer to observations than those using GC, reflecting the inclusion of moisture adjustments above 3.5 km MSL. In some cases, GenGC also improved near-surface qvapor relative to GC; however, larger positive qvapor biases were found above ~5 km MSL. Further investigation is required to assess the robustness of these results.

Future work will expand the number of cases and implement GenGC directly within the DART framework to evaluate its broader applicability for assimilating PWV and related datasets.

How to cite: Risanto, C. B., Gilpin, S., and Arellano, A.: Investigating the Application of Generalized Gaspari-Cohn Correlation Function in Vertical Localization, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2699, https://doi.org/10.5194/egusphere-egu26-2699, 2026.

A convective-scale ensemble data assimilation (EDA) system has been developed in Taiwan to improve very short-term heavy rainfall prediction. The ground-based GNSS Zenith Total Delay (ZTD) data provides fast moisture information, which captures the precursor of convection initialization over complex terrain. Focusing on thunderstorm prediction in the Taipei Basin, previous studies have shown that assimilating ZTD data from the Central Weather Administration (CWA) operated stations provides effective moisture adjustment. Incorporating the surface 10-meter wind further exploits the benefit of ZTD assimilation in very short-term precipitation prediction. Including non-CWA-operated stations, there are more than 400 GNSS stations in Taiwan, forming a uniquely dense GNSS observation network. In addition to ZTD observation, the tropospheric gradient (TG) measurement provides spatial moisture variations in the low troposphere. Based on a severe afternoon thunderstorm on 24 June 2022 in the Taipei Basin, we conducted rapid-update data assimilation experiments to investigate the impact of the ground-based GNSS data. Data assimilation was performed over a three-hour period at 30-minute interval to predict a heavy rainfall event lasting two hours.

Compared to standard ZTD assimilation using CWA-operated stations, the assimilation of dense ZTD observations improves the moisture representation near the Taipei Basin, which is critical for the timing of convection initialization. For this case, TG observation reveals a strong moisture gradient into an inland river valley upstream of the Basin. Additional TG assimilation enhances moisture, facilitating the rapid convection development and the merging of the convection cells. Consequently, assimilating both dense ZTD and TG leads to significant improvements in the forecasted intensity and location of heavy rain, as well as the forecast performance at a longer lead time. Notably, the impact of TG assimilation is more pronounced when combined with dense ZTD data.

How to cite: Yang, S.-C., Chang, Y.-P., Yeh, T.-K., Zus, F., Thundathil, R., and Wickert, J.: Improving Afternoon Thunderstorm Prediction in Taiwan: Insights from Dense Ground-based GNSS Assimilation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5447, https://doi.org/10.5194/egusphere-egu26-5447, 2026.

Ground-based profilers provide continuous information on atmospheric boundary-layer (ABL) temperature and humidity, but zenith-only observations suffer from large representation errors in heterogeneous environments. This contribution explores the potential of scanning Microwave Radiometer (MWR) brightness temperatures (TBs) to better constrain ABL water vapor and to reduce representation error relevant for convection-permitting data assimilation. It especially aims to eventually evaluate the synergy of scanning MWR humidity observations with the already planned Differential Absorption Lidar (DIAL) network LIDIA by DWD.

As a proof of concept, radiosonde profiles are combined with co-located ground-based HATPRO MWR observations from recent field campaigns in Germany, including FESSTVaL (2021), Socles (2021–2022), and Vital I (2024). For each radiosonde launch, temporally matched MWR measurements are extracted for several viewing geometries. The evaluation by TB forward modeled from radiosondes gives first promising results.

The presentation highlights how low elevation azimuth scan TB information, especially combined with the upcoming LIDIA network can provide additional constraints on horizontal gradients and boundary layer humidity. The next steps are: assimilation experiments with data from the upcoming Vital II campaign (summer 2026), where combined zenith-pointing DIAL and scanning MWR observations will be assimilated into ICON-D2 to quantify their impact on short-range forecasts of humidity and convective initiation on convection-permitting resolution.

How to cite: Pschera, A., Toporov, M., Löhnert, U., and Schomburg, A.: A Proof of Concept for Boundary-Layer Moisture Data Assimilation Using Scanning Microwave Radiometer Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5510, https://doi.org/10.5194/egusphere-egu26-5510, 2026.

Bayesian model calibration with data assimilation methods receives continued interest for climate models, where turbulence-resolving large eddy simulations (LES) often serve as the ground truth for cloud processes. This study extends the calibration hierarchy towards the LES themselves, which are in turn validated against measurement data. Ensemble data assimilation for turbulent simulations is approached from a smoother perspective by calibrating a semi-idealistic LES simulation against averaged measurements with an ensemble Kalman smoother (EnKS).

This work re-visits the well-known test case simulating marine stratocumulus clouds (DYCOMS-II), which has been used extensively for forward model validation. Sub-grid scale (SGS) turbulence parameters are calibrated alongside the parameterized initial condition and forcing, aiming at a wholistic uncertainty representation. For the PyCLES model (dx=35 m), the calibrated setup achieves an improvement over the default model configuration used in previous studies. Experiments with different advection schemes reveal how the calibration result varies for implicit and explicit SGS modeling. For some of the tested schemes, consistent model errors on some observations require manual specification of larger data uncertainties in order to stabilize the calibration.

Through the analysis of partial increments, the EnKS methodology provides insights to parametric model sensitivities, and a means to explore a large parameter space. However, the performance is hindered by weak nonlinearities in the parameter-to-data map, which includes a nonlinear normalizing parameter transform. The practical application of EnKS-based calibration for LES models is facilitated by relying directly on a perturbed physics ensemble, which is commonly used for sensitivity studies. The results also invite to re-interpret biases found in previous model intercomparison studies on the DYCOMS-II case, as well as to consider the influence of uncertainties in initial condition and forcing when assessing parametric sensitivities in LES simulations.

How to cite: Grund, D., Mishra, S., and Schemm, S.: Bayesian Calibration of a Large-Eddy Resolving Model towards Campaign Measurements with an Ensemble Kalman Smoother, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5745, https://doi.org/10.5194/egusphere-egu26-5745, 2026.

How to cite: Singh, I., Bukowski, J., Marinescu, P., Dolan, B., Posselt, D., Schulte, R., Grant, L., Leung, G., Lewis, J., Prasanth, S., Rasmussen, K., Schumacher, C., Storer, R., and van den Heever, S. C.: High-resolution Numerical Simulations of Tropical and Subtropical Convection for the NASA INCUS Mission, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5970, https://doi.org/10.5194/egusphere-egu26-5970, 2026.

The demand for accurate weather forecasts is increasing because of, for example, high-impact events becoming more frequent and more extreme. However, convection-permitting numerical weather prediction systems still lack precise initial conditions due to observational gaps. In order to improve the predictions, filling those gaps is of great importance.

Ground-based water vapor profiling networks could contribute to that using broadband differential absorption light detection and ranging systems (DIALs). In this work, the impact of different hypothetical configurations of such networks on the convection-permitting Icosahedral Non-hydrostatic (ICON) D2 model operationally used by the German Weather Service is assessed. For that, an ensemble sensitivity analysis (ESA) is employed. Using an ensemble-based sensitivity, the ESA method quantifies the change of variance in a forecast metric ensemble due to the assimilation of additional data. Compared to other observational impact assessment (OIA) methods, it benefits from not requiring forecast validation, actual measurements, or additional model runs.

First results indicate that for the default network configuration and two summer afternoon cases, the additional observations decrease the spread of an ensemble of the model on average by 5.5 to 7 percent (%) depending on the specific water vapor forecast metric used. The forecast metrics respond as expected to changes in the network parameters, that is, the spread decreases further for smaller instrumental errors and larger vertical ranges of the DIALs. Assimilating point measurements leads to a 70% smaller spread reduction relative to the one obtained for water vapor profiles up to a height of 1200 meters.

On the one hand, the results of these case studies confirm the expected benefit of a DIAL network, while also indicating what to focus on for further improvements. On the other hand, they demonstrate the applicability of the flexible and computationally cheap ESA method to this kind of evaluation. With the results being comparable to those obtained by other OIA methods, an assessment of how realistic the ESA results here are could be conducted. That could help to judge whether the method could and should be applied in OIA more broadly.

How to cite: Raabe, N., Toporov, M., Griewank, P., Weissmann, M., Matsunobu, T., and Löhnert, U.: Observational impact of ground-based water vapor profiling networks on convection-permitting numerical weather prediction – an ensemble sensitivity analysis case study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7077, https://doi.org/10.5194/egusphere-egu26-7077, 2026.

Quantifying the evolution of uncertainty is critical to both probabilistic forecasting and data assimilation (DA) in numerical weather prediction (NWP). In this study, we investigate the applicability of conformal prediction (CP), a recent machine learning (ML) method, to quantify uncertainty in a controlled, idealized setting. We use a toy model, designed to mimic convective process. The CP provides a set of possible outcomes with a chosen confidence level. Here, we compare and evaluate the average empirical coverage, the average interval length and average interval score loss (AISL) for the three variants of CP i.e., a) Standard CP, b) Normalized CP and c) Conformalized Quantile Regression. We also combine DA with the CP estimates of uncertainty and quantify the significance of improvement. Our results highlight the strengths and limitations of each approach, providing insights into the effectiveness of CP to complement ensemble-based uncertainty quantification in simplified atmospheric models.

How to cite: George, C., Janjic, T., Javanmardi, A., and Hüllermeier, E.: Uncertainty quantification via conformal prediction in data assimilation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7371, https://doi.org/10.5194/egusphere-egu26-7371, 2026.

How to cite: Neduncheran, A., Meier, F., Scheffknecht, P., Wittmann, C., Weissmann, M., and Griewank, P.: From MSG SEVIRI to MTG FCI: Extending All-Sky IR radiance assimilation in the regional high-resolution ensemble prediction system of GeoSphere Austria , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9329, https://doi.org/10.5194/egusphere-egu26-9329, 2026.

An accurate forecast of T2m in complex terrain is essential for a wide range of economic and societal applications. However, improper assimilation of T2m observations can degrade forecast quality. 
The 3DVar scheme relies on a climatological error covariance matrix, which can produce unrealistic analysis increments and consequently inaccurate forecasts, particularly over sloped terrain. For example, an observation from a valley station may still generate increments at the mountaintop, even though the valley observation does not adequately represent the atmospheric conditions at the mountaintop. In contrast, 3DEnVar utilizes flow-dependent error covariances that are representative of the recent atmospheric flow regime. Hybrid-3DEnVar, on the other hand, employs an error covariance with 50\% weight assigned to the climatological covariance matrix and 50\% to the flow-dependent covariance matrix. Hybrid-3DEnVar has been tested in AROME-Austria and improved the initial conditions of AROME-Austria compared to 3DVar.  
Our study compares the T2m forecast of  Hybrid-3DEnVar against the operational 3DVar scheme of AROME-Austria in complex terrain.
For this purpose, we utilize the convective-scale limited-area ensemble forecast system (C-LAEF) Alpe Adria based on the numerical weather prediction model AROME, which operates at a horizontal grid space of 1 km. The 32-member ensemble of C-LAEF Alpe Adria (with a 1 km horizontal resolution) provides the flow-dependent error covariances. 
We conduct three sets of data denial experiments in which we run a 3-hourly assimilation cycle over 10 days, once with all observations and once with the T2m measurements removed. The first set of experiments uses 3DVar, the second 3DEnVar, and the third Hybrid-3DEnVar. We present first-guess departure statistics and forecast verification for T2m over the Alpine terrain.

How to cite: Jyoti, K., Griewank, P., Meier, F., and Weissmann, M.: Can AROME-Austria Hybrid-3DEnVar improve the forecast of 2-meter temperature (T2m) in complex Alpine terrain?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9782, https://doi.org/10.5194/egusphere-egu26-9782, 2026.

In recent years, with the development of computing power, kilometer-scale resolution has become possible in regional numerical weather prediction (NWP) and climate simulation. While the refined numerical mesh allows for a more detailed representation of weather and climate, it also moves atmospheric modeling into gray zones, where the parameterization of turbulence and convection becomes a challenge in NWP. The newly developed machine learning (ML) methods would be a better choice to address this challenge. Previous ML weather prediction models primarily focus on global mesoscale forecasting. This study develops a purely data-driven three-dimensional Fourier neural operator (FNO) model for simulating the idealized convective boundary layer (CBL) at 800-m grid spacing, which is a resolution in the gray zone. The filtered large-eddy simulation (LES) data of the CBL are used for training the FNO models. The FNO models can accurately predict the instantaneous spatial structures and flow statistics of the boundary layer. The structures of vertical velocity near the surface predicted by the FNO models are consistent with those of the filtered LES, overcoming the issue of overly large cell structures predicted by traditional numerical simulations. The FNO models perform better than the gray-zone CM1 simulations in predicting profiles of flow statistics. Furthermore, the temperature and velocity spectra predicted by the FNO models are close to the results of filtered LES. The FNO models, trained using data for a few surface heat flux values Q_s from 0.14 to 0.26 K m s^-1, demonstrate certain generalization capabilities for other Q_s within and out of this range. Overall, the FNO model is a promising ML method for fast and accurate weather prediction in the gray zone.

How to cite: Luo, T., Shi, X., Li, Z., Wang, J., Chan, P. W., and Wu, N.: Prediction of Convective Boundary Layer in the Gray Zone Using Fourier Neural Operator, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10136, https://doi.org/10.5194/egusphere-egu26-10136, 2026.

Radar data assimilation (DA) is critical for convective-scale forecasting, as it provides real-time, high-resolution information on precipitation, wind, and convective system dynamics that is not captured by surface observations or satellite data. Polarimetric radar observations complement conventional reflectivity (Zh) and radial velocity (Vr) measurements by enabling the determination of hydrometeor types and particle size characteristics. Differential reflectivity (ZDR) is one of the key polarimetric radar variables, defined as the ratio between horizontal and vertical reflectivity, that provides direct information on hydrometeor shape and size. Despite its strong potential to better constrain storm microphysics and improve convective-scale forecasts, the assimilation of ZDR remains challenging. Challenges associated with observation operators, error characterisation, and data quality underscore the need for further research in this area.

The current study investigates the direct assimilation of ZDR in an observing system simulation experiment (OSSE) of a long-lived supercell. The OSSE is conducted using the ICOsahedral Nonhydrostatic (ICON) model with a two-moment microphysics scheme and the Local Ensemble Transform Kalman Filter (LETKF), employing both hydrometeor mixing ratios and number concentrations as analysis variables. Synthetic observations are generated using the polarimetric radar forward operator EMVORADO developed at the Deutscher Wetterdienst. The synthetic ZDR observations are assimilated in addition to the non-polarimetric variables, namely Zh and Vr, while a reference experiment assimilating only non-polarimetric synthetic observations was conducted for comparison. A series of sensitivity experiments are performed to assess the impact of DA settings on assimilation performance, for varying observation error, localisation radius, and ensemble size. In addition, appropriate thresholds and no-reflectivity (clear air) equivalents for ZDR observations are examined.

How to cite: Bardachova, T., Janjic, T., Carrassi, A., de Lozar, A., and Mendrok, J.: Direct assimilation of differential reflectivity in an idealised setup , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10222, https://doi.org/10.5194/egusphere-egu26-10222, 2026.

To improve the forecast quality of numerical weather prediction (NWP), the German Meteorological Service (Deutscher Wetterdienst, DWD) has initiated a project aimed at assessing data quality and assimilation of observations from ground-based remote sensing instruments that have not yet been exploited operationally.

The objective of this initiative is to fill the observational gap in the atmospheric boundary layer, especially with respect to short time scales, by providing continuous, high-temporal-resolution profiles of thermodynamic variables, wind, and cloud properties. These observations are expected to be especially beneficial for weather forecasting applications. The DWD is evaluating various remote sensing systems with regard to the continuous data supply, their operational use, and their impact on NWP.  

In this contribution, we present first results of the assimilation of two ground-based remote sensing instruments into the kilometer-scale ensemble data assimilation system (KENDA): water vapour mixing ratio from a Differential Absorption Lidar (DIAL) and radar reflectivity from a cloud radar. For the integration of the DIAL observations into the data assimilation code environment, only small adjustments were necessary. In contrast, the cloud radar data required an adaptation of the complex forward operator EMVORADO (Efficient Modular Volume scan Radar Operator), which was originally developed and previously used only for precipitation radars.

In an initial step, single observation data assimilation experiments and their observation minus first guess statistics have been shown to produce promising results. To assess the impact in an operational setting, dedicated data assimilation experiments were conducted and compared to reference experiments without these additional observations. Based on the successful data assimilation cycling experiments, first forecast experiments including the DIAL have been performed. Current results indicate a neutral to positive impact on humidity, temperature, and wind forecasts. The impact of cloud radar data in such experiments is currently under investigation by testing different settings.

Our findings suggest that ground-based remote sensing data can provide valuable additional information for convective-scale data assimilation, and justify more extensive impact studies in the context of NWP.

How to cite: Pruschke, J., Schomburg, A., Mendrok, J., Stephan, K., Görsdorf, U., Löffler, M., Knist, C., and Schraff, C.: First Steps Towards Data Assimilation of Differential Absorption Lidar and Cloud Radar Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11124, https://doi.org/10.5194/egusphere-egu26-11124, 2026.

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 into 550-km orbits with approximately 33-degree inclination). Over the course of the mission, TROPICS provided more spaceborne microwave soundings than any operational program, and the combined forecast impact was larger and more spatially coherent than that of any individual passive microwave platform, illustrating the benefit of constellation-based temporal sampling for constraining rapidly evolving tropical convection. Prior to the deorbit of the last TROPICS spacecraft in December 2025, observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution were used to conduct high-value science investigations of tropical cyclones. TROPICS has provided rapid-refresh microwave measurements (median refresh rate of better than 60 minutes early in the mission with four functional CubeSats) in twelve channels spanning 92 to 205 GHz 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. Thousands of high-resolution images of tropical cyclones have been captured 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) has provided new information on the inner storm structure, and, coupled with the relatively frequent revisit and low downlink latency, has informed tropical cyclone analysis at operational centers. The suite of TROPICS products is publicly available with much improved median revisit rates and were provided with data latencies that are sufficient to enable their use in operational tropical cyclone forecasting applications. 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 30-month mission lifetime.

How to cite: Blackwell, W. and the TROPICS Science Team: A Summary of the New Capabilities for Observing Tropical Cyclone Thermodynamic Structure Provided by the NASA TROPICS Mission, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12774, https://doi.org/10.5194/egusphere-egu26-12774, 2026.

Partial analysis increments (PAI) are an efficient diagnostic to determine the influence individual observations or groups of observations on the analysis (Diefenbach et al.). Addittionally, the diagnostic can be used to approximate the influence that observations would have had for different assimilation settings. We use PAI to investigate whether observations could be more beneficial if they were assimilated using different localization settings. Localization is an essential component of any ensemble-based data-assimilation system, necessary to mitigate the effects of a limited ensemble size and to reduce the computational cost. Localization for satellite observations, which lack a constant or well-defined observation location, is particularly challenging, and numerous approaches have been proposed. Using PAI, we can estimate the performance of many different localization approaches without needing to rerun experiments and determine settings that lead to an optimal analsis using independent observations for verification. In this poster, we present results from a one-month-long cycled forecast of the regional modelling system of Deutscher Wetterdienst, in which the PAIs are compared against non-assimilated radiosondes. PAI is used to optimise and understand localisation by (1) considering a range of localisation functions over a normalised metric; (2) studying different combinations of parameters for a Gaspari-Cohn localization function and (3) optimising localization over a set of idealised functions. The current settings of DWD for vertical localisation of satellite radiances in the visible 0.6µm and infrared 6.2µm 7.3µm channels - which were originally designed to improve estimates of cloud-related variables - perform well against our metrics but could be improved upon by using a multi-peaked localisation function.

How to cite: Griewank, P., Diefenback, T., Necker, T., Parker, M., Schomburg, A., and Weissmann, M.: Optimizing localization for ensemble data assimilation using partial analysis increments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13080, https://doi.org/10.5194/egusphere-egu26-13080, 2026.

While understanding convective processes is critical for prediction of severe weather and cloud properties, observing convective mass flux at convective-scales is difficult using traditional techniques. The NASA INvestigation of Convective UpdraftS (INCUS) mission will quantify convective mass flux from a convoy of three Ka-band radars using a unique time-differencing approach. In order to develop the necessary observational approaches to calibrate and validate the INCUS products, preliminary testing was performed using data collected under the umbrella of the Testing of INCUS Measurements Experiment - Suborbital preLaunch Investigation of Convective Evolution (TIME-SLICE) campaigns. The focus of TIME-SLICE is to leverage existing and low-cost instruments to experiment with rapid sampling of ground assets to derive vertical motions and reflectivity time differences.

TIME-SLICE Colorado (TIME-SLICE-CO) was conducted in the northern Front Range of Colorado during the summer of 2024. During the two months of intensive operations, the Colorado State University CHIVO C-band radar scanned convection using the Multisensor Agile Adaptive Sampling (MAAS) framework, which uses ancillary information to select and follow targets of interest and then scan with RHIs with 30 s frequency. Additionally, a site with multiple frequencies of vertically pointing radars and ground instruments collected continuous data. Lessons learned from this preliminary testing highlighted the need for both priority sampling over the ground site by MAAS as well as larger coverage of convection by multiple scanning radars to broaden the coverage of vertical velocity retrievals and account for horizontal advection.

Building on the lessons from TIME-SLICE-CO, a follow-on campaign was held in North Alabama during summer 2025. TIME-SLICE Alabama (TIME-SLICE-AL) extends the objectives of TIME-SLICE to the variety of convection in the Alabama region while employing novel phased-array radar (PAR) sampling in concert with a high concentration of pre-existing instruments. In TIME-SLICE-AL, two X-band PARs, the Stony Brook University (SBU) SKYLER-2 radar and the SBU ROARS radar, were positioned alongside The University of Alabama in Huntsville (UAH) C-band ARMOR radar, operating in a rapid (30-second) RHI mode, all coordinated within the MAAS to autonomously sample a large number of convective clouds. Additionally, novel 3D observations of updrafts were collected by tilting the SKYLER PAR vertically. These innovative new sampling techniques and combined observations allow for rapid quantification of radar reflectivity time differences coincident with Doppler-derived vertical motion estimates, and the development of a large, rich case database. Further, existing assets deployed at the UAH Severe Weather Institute and Radar & Lightning Laboratories site, including  a disdrometer and the new Ka Profiling Radar (KaPR), and at the nearby US Department of Energy ARM Mobile Facility 3 in the Bankhead National Forest, provide additional context to the primary radar observations. In this presentation, we will provide an overview of the TIME-SLICE-CO and -AL campaigns, present several of the cases captured, highlight some of the early novel science results, and apply the knowledge gained to the validation efforts for the INCUS mission.

How to cite: Dolan, B., Freeman, S., Kollias, P., Rasmussen, K., Gatlin, P., Luke, E., Chandrasekar, V., Amiot, C., Ebbert, E., Knupp, K., Kwinta, C., Pangle, P., Petersen, W., Schumacher, C., Tanelli, S., Treserras, B., van den Heever, S., Williams, C., and Wolff, D.:  TIME-SLICE: Developing observation techniques for estimating convective mass flux through rapid, adaptive sampling , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14526, https://doi.org/10.5194/egusphere-egu26-14526, 2026.

NASA's upcoming INCUS mission will observe tropical deep convection at unprecedented spatiotemporal resolution (∼3 km horizontal sampling at intervals of 30 s, 90 s, and 120 s). INCUS will observe a wide variety of deep convective environments and tropical storms at different stages of their life cycle, producing many distinct convective outcomes. How do we distinguish these outcomes? Which differences are associated with genuinely different local environmental states, and which aspects vary even when the local environment is effectively the same?

We use Kernel Flows, a nonlinear machine learning estimator, to separate aspects of local deep convection that are constrained by their environmental state from those that are not. We first define the environmental state as a vector X formed from variables always present in the troposphere describing background thermodynamic and kinematic conditions, independent of whether convection is active (temperature, pressure, water vapor, horizontal wind fields). We define convective variables as vertical velocity, condensed water mass, and convective mass flux, which arise only where convection is present and correspond to quantities INCUS algorithms are designed to retrieve.

The analysis uses a database of high-resolution simulations (100 m horizontal grid spacing) across tropical and subtropical regions in anticipation of INCUS. From these simulations, we extract 25 km × 25 km neighborhoods containing a mixture of deep moist convective updrafts and surrounding non-updrafts. Within each neighborhood, we coarsen the environmental and convective variables to approximately 3 km resolution to match anticipated INCUS radar resolution. We represent the environmental state using principal components and define 19 scalar descriptors (Y_i) to characterize various aspects of convection.

Using Kernel Flows with no assumptions on Gaussian conditional distributions, we learn the functional relationship between each convective descriptor (Y_i) and environmental state (X) independently. We quantify how much variability in each descriptor is associated with environmental state differences by comparing the estimator's error variance to the prior variance of Y_i.

We find that aggregate convective descriptors exhibit variability almost entirely associated with environmental state differences. Total vertical velocity and total convective mass flux over the neighborhood, along with neighborhood-mean column maxima of these quantities, achieve R² ≥ 0.97 with at least 84% of standard deviation explained by the environmental state. These convective descriptors act as invariants of the local environment: differences in these metrics reflect environmental state (X) differences, rather than natural variability within an equivalent environmental state. Conversely, convective descriptors emphasizing horizontal and vertical organization of updrafts, such as how total updraft area is divided among horizontally contiguous clusters and maximum heights of vertical velocity and convective mass flux, have R² values typically below 0.5–0.55 and retain substantial variability even when X does not vary significantly. For these descriptors, only a modest fraction of variance is associated with environmental state differences, indicating that most observed differences reflect variability not captured by the local environmental state, potentially from smaller-scale dynamics or stochastic processes.

Our findings identify which aspects of deep convection are almost entirely tied to their local environmental state at spatiotemporal scales commensurate with INCUS observations.

How to cite: Prasanth, S., Haddad, Z., Susiluoto, J., Marinescu, P., Bukowski, J., Singh, I., Grant, L., and van den Heever, S.: Using machine learning to unearth the aspects of deep convection that are robustly predictable from the local environmental state, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15811, https://doi.org/10.5194/egusphere-egu26-15811, 2026.

The atmospheric coherence length is a key parameter for quantitatively describing the strength of optical turbulence and is crucial for laser propagation, astronomical observation, and turbulence-degraded image restoration. Although existing studies have widely used convolutional neural networks (CNNs) to retrieve this parameter from single-frame distorted images, they fail to fully utilize the dynamic characteristics of turbulence. To address this, this study proposes a CNN-based inversion model incorporating dynamic optical flow features. By integrating the optical flow method with CNNs, the model captures and utilizes the turbulent motion information between consecutive image frames. The input to the model is the optical flow displacement field calculated from two successive image frames, and the angle-of-arrival fluctuation variance derived from the optical flow is incorporated into the loss function as a physical constraint. This design significantly enhances sensitivity to subtle image distortions induced by weak turbulence, effectively overcoming the performance bottleneck of traditional static single-frame input models under weak turbulence conditions.

The model was trained on a simulated dataset and validated using measured data obtained on June 13-14, 2022, at Science Island in Hefei, Anhui Province, China. The measured data were collected synchronously by an atmospheric coherence length monitor and an imaging device over a 500-meter horizontal near-ground propagation path. The study systematically compared the performance of four classic CNN architectures (AlexNet, VGGNet, EfficientNet, CVTStegoNet) with and without the incorporation of TV-L1 optical flow features. The results show that the introduction of optical flow features universally and significantly improves the inversion accuracy and robustness of the models under different turbulence strengths. Specifically, the method achieved faster training convergence and superior generalization performance across all tested models. On a test set comprising 4,500 training samples and 500 independent validation samples, the model with integrated optical flow features reduced the root mean square error (RMSE) and mean relative error (MRE) by an average of approximately 40%, while the coefficient of determination (R²) was generally above 0.99. Among the four models, the fused model based on AlexNet achieved the best overall performance.

This work demonstrates the critical role of utilizing turbulent dynamic features in enhancing the accuracy of inversion models, offering a novel and practical deep learning solution for high-precision, real-time detection of the atmospheric coherence length.

How to cite: Wang, Y., Wang, P., Huang, Y., and Mei, H.: Inversion of Atmospheric Coherence Length Using Optical Flow-based Convolutional Neural Networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16922, https://doi.org/10.5194/egusphere-egu26-16922, 2026.

How to cite: Chen, H. and Zhao, K.: Assimilation of Dual-Polarization Radar Data Based on Hydrometeor Classification for Improving the Short-Term Prediction of Convective Storms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17383, https://doi.org/10.5194/egusphere-egu26-17383, 2026.

Convective precipitation is the primary source of freshwater and groundwater recharge in the Arabian Peninsula (AP), occurring as sporadic, localised events. Understanding the Lagrangian properties of these convective systems and how they are influenced by land surface characteristics, topography, and climate change requires convection-permitting modelling, which can be computationally inefficient in such an arid region. However, we trained three machine learning (ML) models -Random Forest (RF), Extreme Gradient Boosting (XGB), and Deep Learning (DL)- to establish relationships between convective environments and precipitation. These models can be applied to any numerical model output (e.g., CMIP6) to infer the probability of convective precipitation, thereby identifying when convection-permitting simulations should be performed. Constrained by CMIP6 temporal (6h) and spatial (~1°) resolutions, we aggregated IMERG V07 precipitation to 6h and averaged over 1 °. We derived 102 features from ERA5 describing moisture, lift, instability, and location to characterise the atmospheric profile. A profile is labelled convective if the accumulated precipitation exceeds the local climatological median, thereby reducing the problem to a binary classification task. The ML classifiers show high skill in identifying convective environments over the northern AP during cold months (Oct-Apr), with a Heidke Skill Score (HSS) of approximately 0.65. However, over the southern AP during warm months (May-Sep), the HSS values drop to around 0.35. These results support the established finding that convective systems over the AP in cold months are linked to large-scale atmospheric patterns. In contrast, in warm months they are localised and/or orographically influenced. Findings also demonstrate that the ML models learned convective environment patterns across the AP. Furthermore, the dirnual performance of ML models remains comparable (HSS: ~ 0.55), except at 12 UTC (HSS: ~ 0.48), when the convection is relatively localised. The SHapley Additive exPlanations (SHAP) method enables the interpretation of each feature’s contribution to the ML models’ prediction. The top three features identified by SHAP differ across ML models. Equivalent potential temperature at 850 hPa, humidity index, and lightning potential index are most important for RF. XGB emphasizes precipitable water vapour, relative humidity at 1000 hPa, and most unstable CAPE. In DL, precipitable water vapour, relative humidity at 700 hPa, and 2m dew-point temperature are the key contributors. The current warming over the AP is +1.22 °C relative to pre-industrial levels. We aim to analyse periods with comparable warming across 10 CMIP6 historical simulations to evaluate the models' ability to reproduce the spatiotemporal distribution and characteristics of convective environments. To assess the effect of climate change, we will analyse two future periods with changes of +2.22 and +3.22 °C from the SSP5-8.5 scenario. SHAP can help evaluate whether the model‑inferred importance ranking of mechanisms controlling convective precipitation changes between present and future simulations. In-depth analysis is undergoing.

How to cite: Homoudi, A., Rust, H. W., Barfus, K., Bernhofer, C., and Mauder, M.: Convective Environments over the Arabian Peninsula in Current and Future Climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1893, https://doi.org/10.5194/egusphere-egu26-1893, 2026.

This study investigates the morphological structure, propagation, and evolution of Mesoscale Convective Systems (MCSs) associated with four historical extreme rainstorm events in Ningxia (August 21, 2016; July 22, 2018; August 9, 2022; and August 24, 2024). The analysis utilizes high-resolution observational data, including the China Severe Weather Automatic Nowcasting(SWAN) radar mosaic, three local C-band radars, and 1020 regional automatic weather stations. The results classify these extreme precipitation MCSs into two distinct categories: (1) Topography-dominated Back-Building/Quasi-Stationary (BB/QS) systems characterized by a single rainstorm center (e.g., the "8·21" and "7·22" events); and (2) Composite Multi-MCS types (Training Line/Adjoining Stratiform, TL/AS and Embedded Lines, EL) characterized by dual rainstorm centers (e.g., the "8·9" and "8·24" events). The BB/QS MCS concentrates heavy rainfall along the eastern foothills of the Helan Mountains. Driven by orographic lifting and cold pool dynamics, new convective cells initiate on the southern flank and propagate northward (back-building), resulting in a quasi-stationary system. These systems exhibit deep convection with 40 dBZ echoes reaching 12 km and the 60 dBZ centroid located around 9 km (above the 0°C level). This "high-centroid, strong ice-phase" structure yields high precipitation efficiency, producing accumulated rainfall exceeding 240 mm. In contrast, the TL/AS MCS affects the eastern banks of the Yellow River. Here, convective cell motion is highly parallel to the orientation of the convective line, leading to a significant "training effect" where cells continuously regenerate upstream and propagate downstream. This mode features a lower convective centroid, with the 60 dBZ center located approximately at 4 km (below the 0°C level), indicating a typical "low-centroid, warm-rain" process that results in accumulations exceeding 200 mm. Furthermore, the EL MCS in the arid northwest region is notably modulated by the dry low-level environment, causing a marked contraction of the stratiform cloud region. Strong echoes (>40 dBZ) are generally confined below 6 km. Due to weaker updrafts, the EL phase itself produces limited rainfall (about 20 mm); its primary disaster risk stems from Nonlinear (NL) convective systems triggered during the system's evolution. Based on these findings, a conceptual evolution model for extreme rainstorm MCSs in Ningxia is established, providing a theoretical basis for monitoring and early warning of extreme precipitation in the arid regions of Northwest China.

How to cite: Li, A. and Chen, Y.: Evolution Laws and Conceptual Models of MCS for Different Types of Extreme Rainstorms in the Arid Region of Northwest China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2165, https://doi.org/10.5194/egusphere-egu26-2165, 2026.

This study investigates the response of convectively coupled Kelvin waves (CCKWs) under two warming scenarios using the ICOsahedral Non‐hydrostatic model (ICON) in its global storm-resolving configuration.  A control simulation (CTRL) is conducted using prescribed historical (1979–1997) sea surface temperatures (SSTs). Taking CTRL as reference, a simulation with a homogenously SST increase of 4 K (+4K) and another with a 4-fold increase in atmospheric CO₂ concentration (4×CO₂) are conducted.  Results show that CCKWs are substantially strengthened in the +4K experiment, exhibiting enhanced spectral power, faster phase speeds, and more active spatial activity, whereas nearly no change is found in the 4×CO₂ experiment. Under uniform SST warming, enhanced surface energy input-dominated by increased latent heat flux-supports stronger and deeper tropical convection, increasing the strength and coherence of convection-circulation coupling. We hypothesize that this enhanced coherence may shorten the adjustment timescale between convective heating and wave-related circulation, resulting in faster eastward propagation and enhanced wave variability. These results highlight the critical role of surface-driven flux enhancement in modulating convectively coupled tropical wave activity under warming.

How to cite: Ji, X., Segura Cajachagua, H. M., Ortega Arango, S., and Feng, T.: Response of convectively coupled Kelvin waves under warming scenarios in a global storm-resolving model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3016, https://doi.org/10.5194/egusphere-egu26-3016, 2026.

Rain processes are often underrepresented in our understanding of the trade-wind layer, despite trade cumuli precipitating ~30% of the time. The vertical structure of the main building blocks of precipitating convection, namely in-cloud updrafts and downdrafts, is poorly characterized due to a lack of suitable observations. Lidars cannot penetrate deeply into clouds, and hydrometeor fall speeds dominate the mean Doppler velocity from cloud-profiling radars. Here, we retrieve the vertical air motion inside precipitating clouds by making use of the Doppler velocity spectrum from the ground-based Ka-band radars at the Barbados Cloud Observatory (BCO), using methods previously developed for stratocumuli. The resulting dataset spans more than five years (2019-2025) at high (2s) resolution and is validated against available lidar measurements. We resolve circulations at the cloud scale. Shallow precipitating cumuli feature a narrow updraft at the cloud front that develops up to cloud top. The wider precipitation downdraft is triggered slightly below cloud top, where the rain content is large enough, and extends down to the surface. We show that deeper clouds are associated with stronger updrafts and downdrafts. Faster downdrafts are also associated with higher cloud reflectivity, suggesting that microphysical processes play a large role in determining their strength. The long record also lets us ascertain seasonal variability. Shallow precipitating cumuli feature faster updrafts in the summer trades, leading to larger and faster downdrafts than in winter. We show that the total mass flux of shallow precipitating cumuli is highly variable. Both the updraft and the downdraft mass fluxes mainly depend on the cloud fraction, but their balance hinges on the downdraft intensity. These observations can improve our understanding of tropical convection and shed light on the assumptions behind convective parametrizations and constrain cloud-resolving simulations.

How to cite: Poydenot, F., Robbins-Blanch, N., Zhu, Z., and Vogel, R.: Observations of vertical motion inside precipitating trade cumuli, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5727, https://doi.org/10.5194/egusphere-egu26-5727, 2026.

The organization of deep convection into squall lines is not represented in global climate models, despite being among the biggest storms on Earth and having a huge impact on precipitation and extremes in tropical regions such as the Sahel or the Amazon [1, 2]. The overarching goal of this project is to parameterize the occurrence of squall lines in the general circulation model LMDZ, and their impact on their environment. Since the interaction between wind shear and cold pools is essential in squall line formation and maintenance [3], we plan to take advantage of the cold pool scheme [4] already implemented in LMDZ.

Regarding the occurrence of squall lines, we hypothesize that it can be predicted based on the domain-mean wind profile and the cold pool properties, diagnosed from the cold pool scheme.

Regarding the impact of squall line in their environment, we hypothesize that squall lines impact the vertical profile of diabatic heating [5], either through entrainment in convective updrafts [6] or through the rate of rain evaporation [7]. We also hypothesize that the larger rate of rain evaporation strengthens cold pools favoring more likely and more intense convection [8]. Entrainment and rate of rain evaporation can be varied in the convective scheme of LMDZ [9] through tunable parameters.

To test these hypotheses, we analyse simulations from the cloud-resolving model SAM in radiative-convective equilibrium configuration with various wind shear and large-scale ascent conditions.

Ultimately, the representation of squall lines should improve the simulation of precipitation distribution, variability and extremes in tropical regions such as the Sahel or the Amazon.

[1] R. Roca, J. Aublanc, P. Chambon, T. Fiolleau, N. Viltard, J. Clim. 27, 4952–4958 (2014).

[2] R. Roca, T. Fiolleau, Com. Earth & Env. 1, 18 (2020).

[3] R. Rotunno, J.B. Klemp, M.L. Weisman, J. Atmos. Sci., 45(3), 463-485 (1988).

[4] J.-Y. Grandpeix, J.-P. Lafore, J. Atmos. Sci., 67, 881–897 (2010).

[5] U. Anber, S. Wang, A. Sobel, J. Atmos. Sci., 71, 2976–2993 (2014).

[6] T. Becker, C. S. Bretherton, C. Hoehenegger, B. Stevens, Geophys. Research Lett. 45, 455–462 (2018).

[7] J. P. Lafore, J. L. Redelsperger, G. J. Jaubert, Atmos. Sci., 45(22), 3483-3500 (1988).

[8] G. G. Rooney, A. J. Stirling, R. A. Stratton, M. Whitall, Quart. J. Royal. Meteoro. Soc. 148, 962–980 (2021).

[9] K. A. Emanuel, J. Atmos. Sci. 48, 2313–2329 (1991).

How to cite: Segueni, M., Risi, C., and Rochetin, N.: Organization of deep convection into squall lines, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5758, https://doi.org/10.5194/egusphere-egu26-5758, 2026.

Convective storms can develop rapidly, creating hazards to local populations via intense precipitation, strong winds and lightning. The large-scale environment in which thunderstorms develop is often well-captured in forecast systems yet predicting where individual storms will initiate remains a fundamental challenge. It is known that differential heating driven by soil moisture patterns creates atmospheric circulations which favour convective initiation over drier soils, whilst wind shear between low and mid-levels can enhance upscale storm growth.

Here we show that the most explosive initiations are especially favoured over soil moisture contrasts via an interaction with wind shear. Analysing 2.2 million afternoon convective initiations across Sub-Saharan Africa identified from Meteosat Second Generation imagery for the period 2004-2023, we find that stronger low-level directional wind shear systematically enhances the sensitivity of convective initiations to underlying soil moisture gradients (as identified by combining ERA5 wind fields, MSG land surface temperature and Advanced Scatterometer soil moisture). We detect 68% more initiations classed as extreme given favourable (versus unfavourable) soil conditions, with the most rapidly deepening clouds occurring where soil moisture-induced circulations oppose the direction of mid-level cloud displacement. We propose this configuration promotes wider, more resilient updraughts capable of overcoming shear-enhanced entrainment. Furthermore, where mid-level wind opposes the low-level flow, we find subsequent rainfall to be strongly correlated with locally drier soils as developing rainy clouds follow the mid-level wind direction. Whilst such shear conditions are particularly common over Tropical North Africa, the effect favours negative soil moisture-precipitation feedbacks globally. The combination of soil moisture heterogeneity and wind shear provides a potentially important source of predictability for where deep convection develops, particularly for the most rapidly-developing thunderstorms.

How to cite: Klein, C., Taylor, C., Barton, E., Hahn, S., and Wagner, W.: Wind shear enhances soil moisture influence on rapid thunderstorm growth, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5873, https://doi.org/10.5194/egusphere-egu26-5873, 2026.

Understanding the life cycle of cumulus clouds remains challenging due to limited knowledge of the factors governing initiation and growth processes. Cloud-cloud interactions—specifically how neighboring clouds influence each other—are a critical but underexplored aspect of evolution of cloud populations. Our key innovation lies in, using both realistic and idealized large-eddy simulations (LES), to identify and quantify competitive relationships among neighbouring  clouds as a fundamental driver of cloud population dynamics.

Realistic LES over land reveals a statistically significant pattern: growing clouds suppress the development of their immediate neighbors, suggesting competition for moisture among neighbouring cloudy updrafts. Idealized LES further uncovers that this competition is strongest during the decaying stage of clouds. During this stage, cloud-cloud  interactions reduce cloud depths but expand their horizontal extent through environmental moistening—a feedback that effectively reduces cloud spacing and intensifies competition. We also systematically establish the controlling factors of competitive strength by conducting multiple idealized LES simulations by varying: aerosol loading, inter-cloud distance, and surface forcing heterogeneity.

This work establishes cloud-cloud competition as a previously missing process in cumulus evolution theory. By mechanistically resolving how interactions redistribute moisture and energy, we provide a framework to understand population-scale organization. Our findings offer direct pathways to improve cumulus parameterizations in Earth system models, particularly in representing convective clustering and cloud lifetime effects. 

How to cite: Chen, J., Xu, C., Lu, C., Hagos, S., Xiao, H., Feng, Z., and Fast, J.: Cloud-Cloud Interactions and Their Roles in the Development of Convective Cloud Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6472, https://doi.org/10.5194/egusphere-egu26-6472, 2026.

Observations show a bimodal frequency distribution in total column vapour (TCV) over tropical oceans, with convective rainfall predominantly produced on the moist side of the frequency minimum between two modal peaks. Here we show a km-scale model of the tropics with explicit convection produces a bimodal TCV distribution, whereas the same model with parameterized convection does not. The parameterized model also fails to realistically confine rainfall to a moist mode. Using concepts from statistical mechanics we relate TCV frequency and tendency, and isolate process contributions to tendency in TCV phase-space. Where bimodality is lacking, we find an incorrect relationship between moisture flux convergence and TCV in environments with little or no rainfall. The resulting lack of a strong gradient in TCV tendency with respect to TCV is inconsistent with that expected to maintain a TCV frequency minimum.  Our results demonstrate value in the TCV probability distribution as a process diagnostic for the upscale impacts of convection, and as a test for realism in model moisture-dynamics coupling.

How to cite: Bassford, J., Maybee, B., Marsham, J., and Parker, D. J.: Tropical TCV as a process diagnostic: connecting probability to convective processes in km-scale models via moisture budget statistics , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6857, https://doi.org/10.5194/egusphere-egu26-6857, 2026.

Using the aerosol--aware Weather Research and Forecasting (WRF) model in an idealized tropical island framework, we examine how boundary layer  moisture modulates the convective response to cloud condensation nuclei (CCN). Simulations across CCN concentrations of 30--2400 per cubic cm and varying relative humidity,  CAPE and surface–flux regimes, show that convection consistently weakens with increasing CCN, but only when the boundary layer is sufficiently moist. In such a setting, fewer CCN yield larger droplets that rapidly convert to rain and reduce evaporation at mid-levels (between 2 and 4km), both of which  warm and dry the layer and thereby weakening shallow convection. This limits vertical transport of moist static energy (MSE), allowing near-surface MSE and Convective Available Potential Energy (CAPE) to build up. As a result, subsequent deep convection in clean cases exhibits stronger updrafts, greater graupel production, and enhanced convective and mass fluxes. In contrast, humid but polluted environments yield numerous small drops which remain lofted and suppress warm rain, enhancing the evaporative source of vapor at mid-levels, strengthening  shallow convection, limiting CAPE growth, and ultimately producing weaker convection.However, when the boundary layer is dry, both CAPE and convection show little sensitivity to CCN concentration, highlighting the role of moisture preconditioning. Satellite composites of TRMM  convective intensity (measured by 40 dBZ echo top height) and MODIS droplet number (Nd) over tropical islands tentatively appear to support this mechanism. Higher Nd values are associated with lower CAPE and weaker convective vigor, with the strength of the trend being proportional to the near-surface relative humidity - consistent with simulations. Together, these results suggest that in a tropical island-setting, aerosols impact convection primarily when the boudary layer  is preconditioned with sufficient moisture, and that under these conditions increased aerosol loading tends to suppress rather than invigorate deep convection.

How to cite: Robinson, F., Storelvmo, T., Sherwood, S., and Kirshbaum, D.: Humidity-Dependent Sensitivity of Tropical Island Deep Convection to Aerosols, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6908, https://doi.org/10.5194/egusphere-egu26-6908, 2026.

Convective parameterizations used in atmospheric models to represent the effects of unresolved shallow and deep convection on the large-scale flow are traditionally formulated in a diagnostic manner that assumes an instantaneous adjustment of convection to the resolved-scale environment. Prognostic parameterizations, on the other hand, can represent the time evolution and memory of moist atmospheric convection, leading to more realistic interactions between convection and large-scale atmospheric circulation, especially if far from convective quasi-equilibrium. Several such prognostic formulations for the mass flux have been proposed by relaxing the quasi-equilibrium assumption introduced by Arakawa and Schubert [1], based on assumed relations between the convective mass flux and the convective kinetic energy [2,3]. In this work, we develop a new prognostic formulation for shallow and deep convection with cloud cover and convective velocity both being treated as prognostic variables. Interactions between shallow and deep convection are represented in our formulation due to their differing effects on the large-scale environment, but also due to direct cloud-updraft interactions. In radiative-convective equilibrium (RCE), our model predicts that the cumulus cloud cover is proportional to the radiative cooling rate and that the convective velocity depends only on the relative humidity and the tropospheric depth, in agreement with numerical experiments. In addition, our model predicts that convective available potential energy decreases in RCE with the increase of the radiative cooling rate due to the cloud-updraft interaction. Moreover, we show that the inclusion of the cloud-updraft interaction and the cold pools feedback is required for a realistic representation of the diurnal cycle of shallow and deep convection.

[1] Arakawa, A., & Schubert, W. H. (1974). Interaction of a cumulus cloud ensemble with the large-scale environment, Part I. Journal of Atmospheric Sciences, 31(3), 674-701.

[2] Pan, D. M., & Randall, D. D. (1998). A cumulus parameterization with a prognostic closure. Quarterly Journal of the Royal Meteorological Society, 124(547), 949-981.

[3] Yano, J. I., & Plant, R. (2012). Finite departure from convective quasi‐equilibrium: Periodic cycle and discharge–recharge mechanism. Quarterly Journal of the Royal Meteorological Society, 138(664), 626-637.

How to cite: Vraciu, C. and Plant, R.: A prognostic cumulus parameterization with cloud-updraft interaction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6921, https://doi.org/10.5194/egusphere-egu26-6921, 2026.

Marine shallow clouds are highly abundant over subtropical oceans and play a key role in regulating Earth’s radiative balance, exerting a significant net cooling effect. However, their representation in current climate models remains incomplete, contributing substantially to uncertainties in climate projections and cloud feedback mechanisms. Marine shallow clouds have conventionally been classified into closed cells, open cells, and shallow cumulus clouds. Recent advances in deep learning have expanded this classification by enabling the automatic detection of additional pattern types. Yet, even within these pattern classes lies a spectrum of finer patterns that have not been systematically identified, leaving their underlying dynamics and radiative impacts underexplored.
We present an AI architecture for the classification of marine shallow cloud satellite images into newly defined fine pattern classes. We focus specifically on marine shallow cloud regimes with large cloud fraction values (above 0.9), which we partition into four classes: closed cells, closed cloud streets (rolls), stratiform clouds and unorganized convection. This finer partitioning enables the extraction of richer information from large cloud fraction conditions and a more detailed investigation of physical processes governing their organization. The training dataset was labeled by the Weizmann Cloud Physics Group members. Explainable AI tools are used to analyze the model’s internal representations and learn how it differentiates between the new classes. Applying the trained model to a large number of satellite images enables us to construct a novel, comprehensive and systematically classified database of cloud patterns. The availability of this extensive dataset allows the use of remote sensing cloud properties to characterize the unique features of each regime, and radiative flux data to assess their distinct radiative behaviors, despite their similarly high cloud fraction values. This work provides a clearer understanding of marine shallow cloud patterns and offers insight into the relationships between cloud morphology, underlying dynamics, and radiative effects.

How to cite: Roth, H., Dror, T., Sarafian, R., Dekel, G., Altaratz, O., and Koren, I.: Detailed classification of marine shallow clouds with large cloud fraction using artificial intelligence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6979, https://doi.org/10.5194/egusphere-egu26-6979, 2026.

Convective aggregation, which often leads to organized convective systems, is ubiquitous over tropical oceans and contributes substantially to both total and extreme precipitation in these regions. Cold pools, crucial processes linked to convection, are known to exert both suppressing and triggering effects on convective initiation, but their role in convective aggregation remains debated. As inspired by the previous study of Biagioli and Tompkins (2023), herea simple two-layer stochastic dynamic model for convective aggregation (see the figure below) is developed by coupling boundary-layer cold pool dynamics with free-troposphere moisture dynamics. The novelty of this model lies in two key aspects: the incorporation of compensating subsidence as a dynamic mechanism to maintain the total number of individual convective cells, and the coupling of the boundary-layer moist static energy (MSE) budget to represent both suppressing and triggering effects of cold pools. The results show that the suppressing effect of cold pools is essential for keeping individual convective cells separated, consistent with observations, while their triggering effect promotes larger cluster sizes and shorter aggregation timescales. The model is then applied to investigate how convective aggregation responds to changes in mean convection lifetime and mean boundary-layer MSE under warming. The parameter sensitivity experiments confirm the robustness of the results and provide insight into phase transitions across different parameter regimes. The model is expected to serve as a theoretical tool for investigating the impact of various physical processes on convective aggregation and may potentially serve as a prototype for convection parameterization that incorporates cold pools and convective organization.

How to cite: Yang, Q. and Li, Y.: The Role of Cold Pools in Convective Aggregation over Tropical Oceans: A Two-Layer Stochastic Dynamic Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7045, https://doi.org/10.5194/egusphere-egu26-7045, 2026.

The maximum vertical velocity within deep convective updrafts (w_max) is a key control on precipitation intensity and lightning, yet the physical processes that set its magnitude remain unclear. Here we use data-driven equation discovery to identify the dominant controls on w_max in idealized deep convection. We analyze a set of radiative-convective equilibrium simulations spanning a wide range of sea surface temperatures (290-310 K) and imposed radiative cooling rates (0.75-3 K/day), tracking individual clouds and diagnosing their pre-storm environment and in-cloud properties. Treating the pre-storm environment and in-cloud processes separately, for each we identify  a small number of predictors from a broad set of physically motivated variables that robustly explain variations in w_max across simulation regimes. Interpretable equations derived via symbolic regression from the in-cloud variables indicate that latent heating provides the primary acceleration of updrafts, while pressure perturbations act as a leading-order decelerating mechanism that limits peak velocities. Pre-storm predictors such as CAPE and triggering strength constrain the range of possible w_max values, whereas in-cloud condensate loading and pressure effects determine the realized extremes. This work provides a physically interpretable framework for understanding convective updraft intensity, using data-driven analysis informed by existing physical knowledge.

How to cite: Pechacova, A., Casallas, A., Agasthya, L., Beucler, T., and Muller, C.: Data-driven equation discovery of the maximum vertical velocity in idealized deep convection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7300, https://doi.org/10.5194/egusphere-egu26-7300, 2026.

Changes in tropical precipitation, particularly extremes, are closely linked to mesoscale deep convective systems (DCSs). While previous work has largely focused on precipitation characteristics, much less is known about how the DCSs which produce extreme rainfall respond to warming. Recent studies showed that the Lagrangian perspective offered by storm tracking in satellite imaging was promising. Here, we exploit two models (SAM and MesoNH) of idealised tropical convection from the RCEMIP project, on which the TOOCAN DCS tracking algorithm has been applied, to study DCSs life cycles in an idealised setup. Focusing on their onset rate, lifetime, area and precipitation intensity, we show that despite the relatively small increase in domain-mean precipitation (+2.5 %/K), the characteristics of DCSs change much more with warming, but their respective responses mostly compensate. Mean DCS precipitation intensity increases in both SAM (+10 %/K) and MesoNH (+2.3 %/K). However, the two models predict strong but opposite responses in DCS area and onset rate—increased area (+8.0 %/K) and lower onset rate (–13 %/K) in SAM, the opposite in MesoNH (–4.2 %/K and +7.4 %/K, respectively). This may be related to their different organisation response to warming. Yet, we find that the probability density functions (PDFs) of DCS lifetime, area and precipitation intensity normalised by their respective ensemble average over all DCSs, are climate and model invariant: the PDF of each of these variables is identical in both the colder and the warmer simulation, in both SAM and MesoNH. If confirmed, such a climate invariance could fuel further research about the physical mechanisms of extreme storms response to warming.

How to cite: Clavier, N., Bao, J., and Muller, C.: Invariance in convective storms with warming: A Lagrangian view, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7943, https://doi.org/10.5194/egusphere-egu26-7943, 2026.

The impacts of the anthropogenic heat (AH) effect on the evolution of a merger-formation bow echo over the Guangdong-Hong Kong-Macao Greater Bay Area are documented. The utilization of radar data assimilation greatly improves the simulated results comparing against observations, strengthening the robustness of analyses in this work. The simulation with AH effect produces the most accurate results compared to observations, exhibiting approximately 62% larger spatial extent of heavy rainfall (> 30 mm) and twice the area of strong winds (> 10.8 m s^-1) compared to the non-AH simulation. Additionally, the top 1% rain rates and surface winds from the AH-included simulation are about 25% stronger and 23% greater, respectively, relative to the non-AH counterpart. On the one hand, higher AH flux tends to enhance the values of CAPE and vertical wind shear within urban areas on average, providing favorable thermodynamic environmental conditions for convective development. On the other hand, greater AH effect triggers stronger convective cell, leading to more intense merged system. This cell plays a crucial role in the merger process and the formation of bow echo, but it does not persist sufficiently in the non-AH simulation. A third sensitivity simulation, excluding the urban land cover, produces results comparable to those of the non-AH simulation. This study quantifies the relative contribution of the AH effect to the evolution of convective systems and the associated weather-related hazards over the Greater Bay Area, underscoring the significant impacts of AH forcing on the regional flow patterns and the corresponding convection dynamics.

How to cite: Zhou, A. and Zhao, K.: The Impact of Anthropogenic Heat Effect on the Evolution of a Merge - Formation Bow Echo in the Greater Bay Area of China , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8801, https://doi.org/10.5194/egusphere-egu26-8801, 2026.

Self-aggregation of deep moist convection has been widely studied in idealized radiative-convective equilibrium. While its relevance to the real tropical climate remains debated, one potential link is zonal convective aggregation within tropical rain belt. However, the mechanisms controlling zonal aggregation are still not well understood. Here, to investigate how convection interacts with the large-scale environment in a zonally symmetric setting, we conduct a series of idealized cloud-resolving simulations in which a meridionally varying, sinusoidal sea surface temperature (SST) distribution is systematically controlled. In particular, we vary the SST amplitude and SST maximum value.

We find that the system selects either a zonally uniform or a zonally aggregated state depending on the following SST parameters: zonal aggregation occurs when both the SST amplitude and the SST maximum are large. We explain this behavior as follows.

For large SST amplitude but low SST maximum, a narrow convergence zone can be maintained by the strong meridional pressure gradient and the associated circulation. In contrast, for large SST amplitude and high SST maximum, the narrow convergence zone cannot be sustained because the circulation weakens as free-tropospheric static stability increases, consistent with convection sitting on a warmer (and more stable) moist adiabat by warmer SST. As a result, convection over the high-SST region cannot maintain surface convergence solely via the meridional overturning circulation driven by subsidence over the low-SST region. Instead, it selects a zonally aggregated state that also extends the circulation in the zonal direction.

How to cite: Yanase, T. and Hohenegger, C.: Controls of Zonal Convective Self-Aggregation in an Idealized Tropical Rain Belt, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9423, https://doi.org/10.5194/egusphere-egu26-9423, 2026.

In the tropics, heavy daily accumulated precipitation has been shown to be associated with the most organized convective systems. Over the oceans, a simple scaling of the extreme precipitation explains the changes of the high percentiles with SST at a rate close to that predicted from thermodynamics and Clausius Clapeyron. This study aims at clarifying the sensitivity of the deep convective systems relevant to extreme precipitation with respect to SST and to reconcile the object-oriented and the grid box perspectives. For that purpose, two satellite-based precipitation products (IMERG, and GIRAFE) are used together with SST from OSTIA and the deep convective cloud properties from CACATOES. The CACATOES database, a Level-3 product derived from TOOCAN, is available for the 9-year period (2012–2020) over the entire tropical belt (30°S–30°N) on a 1° daily grid.

Our results show that the increase in extreme precipitation fraction is associated with longer lived, larger and slower propagating DCS as the SST warms from 300K to 302.5K. As a consequence, the residence time of the mean system over extreme precipitation grid boxes increases with SST. While it may qualitatively explain the increased accumulated precipitation extreme, akin to the slowdown trend of landing hurricanes, it remains to be shown whether this hypothesis is up to quantitative analysis.

Preliminary investigations of the scaling of the precipitation fraction with SST nevertheless reveals a product-dependence that needs to be explored further using a larger ensemble of satellite precipitation products. The physical robustness of the hypothesis is also further analysed by looking at the thermodynamical and dynamical environment, using ERA-5, of the Deep Convective System relevant to the extreme precipitation grid boxes. A moistening of the environment, consistent with the precipitation increase is found. The changes in the dynamical environment will be further discussed at the conference.

How to cite: Rajasekharan Sujatha, A. and Roca, R.: Are increases in extreme accumulated precipitation over tropical oceans with SST warming due to the slowing of deep convective systems?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10223, https://doi.org/10.5194/egusphere-egu26-10223, 2026.

How to cite: Mequignon, F., Chaboureau, J.-P., and Richard, J.: Diversity of Convective Updrafts during 5 Airborne Campaigns: Insights from a Large Dataset of km-Scale Simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11105, https://doi.org/10.5194/egusphere-egu26-11105, 2026.

Measuring vertical velocity is crucial for advancing our understanding of deep convection. The Convective Core Observations through MicrOwave Derivatives in the trOpics (C2OMODO) mission aims to retrieve vertical velocity using ice-sensitive microwave measurements taken at two closely spaced time intervals. In preparation for the mission, it is essential to investigate the relationship between vertical velocity and satellite measurements. However, vertical velocity within convective cores is rarely measured, and no comprehensive dataset currently exists. To address this, it is necessary to create datasets that combine vertical velocity with corresponding synthetic satellite observations. The recent porting of the Meso-NH non-hydrostatic mesoscale model to GPU architectures enables the efficient generation of such high-resolution datasets. We present the RASTA (RAdar SysTem Airborne) collection, a dataset of Meso-NH kilometer-scale simulations developed for the C2OMODO project. It is based on five airborne field campaigns during which the RASTA radar was deployed: CADDIWA, EXAEDRE, HAIC Cayenne, HAIC Darwin and MAESTRO. The dataset comprises 13 billion atmospheric columns from 53 simulations, validated against RASTA radar data and satellite observations. The exploration of the convective cores variability in the dataset is carried out using formal concept analysis (FCA), a mathematical framework that links objects and attributes through a Galois connection. FCA is used to classify convective cores according to the environmental factors that most influence them. The resulting concept lattices reveal which combinations of conditions favor convection. They also highlight common patterns and specific characteristics in different meteorological environments.

How to cite: Richard, J. and Chaboureau, J.-P.: Variability of Convective and Ice Cloud Structures in the RASTA Collection of Kilometer-Scale Meso-NH Simulations for C2OMODO, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11838, https://doi.org/10.5194/egusphere-egu26-11838, 2026.

The space-borne C³IEL (Cluster for Cloud evolution, ClImatE and Lightning) mission,  developed jointly by the French and the Israeli space agencies, aims at providing new insights on convective clouds, at high spatial and temporal resolutions, close to the scales of the individual convective eddies. The mission will simultaneously characterize the convective cloud dynamics, the interactions of clouds with the surrounding water vapor, and the lightning activity.

The C³IEL mission consists in a short-baseline (~150 km) train of 2 synchronized small satellites. Each satellite carries a visible camera (670 nm) for cloud imagery at a spatial resolution of ~20 meters, near-infrared water vapor imagers (1.04, 1.13 et 1.37 µm ; ~125 m at nadir) measuring in and near the water vapor absorption bands, a lightning imager (777.4 nm ; ~140 m at nadir; 10 ms time resolution) and three photometers (337, 391 and 777.4 nm; 50 μs time resolution).

The scientific objectives of the C³IEL mission will be first reminded. They consist in documenting the convective cloud development through their 3D evolution and their environment with the retrieval of water vapor surroundings the clouds. In addition, the lightning activitiy created by such clouds will be observed. The presentation will first remind the satellite train configuration, the different sensors of the mission and the innovative and different observational strategies that will be applied during daytime and nighttime. We will then provide an update on the expected observations and products.

Figure : Artist view of the C³IEL, Cluster for Cloud evolution, ClImatE and Lightning, mission. © CNES - Olivier Satteler.

How to cite: Cornet, C., Rosenfeld, D., Aviad, S., Cheymol, C., Defer, E., Deschamps, A., Frid, A., Gillot, L., Holodovsky, V., Kaidar, A., Navas, J.-B., Penide, G., Price, C., Ricard, D., Rimboud, A., Schechner, Y., Truffier, A., Yair, Y., and Zemb, A.: C3IEL, the Cluster for Cloud evolution ClImatE and Lightning Mission to Study Convective Clouds at High Spatial and Temporal Resolutions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11929, https://doi.org/10.5194/egusphere-egu26-11929, 2026.

Entrainment and detrainment processes remain one of the biggest challenges in convective cloud dynamics and need to be parameterized in mass-flux-based convection schemes. A common assumption in convection parameterizations is that the fate of a mixture between cloud and environmental air is determined by the "buoyancy sorting" hypothesis, and further that the net effect of entrainment and detrainment is to reduce the vertical momentum in the cloud. In this study, Lagrangian trajectories are investigated to understand the entrainment and detrainment processes in maritime shallow cumulus clouds and to examine hypotheses in convection parameterization schemes. Analysis of vertical momentum in cumulus clouds using Lagrangian trajectories and using a bulk budget approach both indicate that the overall impact of entrainment and detrainment on momentum is to accelerate the cloud updraft, rather than acting as a drag force. Following the trajectories, it is found that the entrained air has larger mean vertical velocity than the detrained air, in contradiction with the typical assumption in the mass-flux based plume models. This finding indicates the necessity for a careful treatment of the dynamical properties in the near cloud environment. Investigating the buoyancy of entraining and detraining trajectories, we find that the widely accepted "buoyancy sorting" hypothesis is not able to correctly describe both entrainment and detrainment processes, regardless of how the cloud objects are defined. Instead, whether a mixed parcel is likely to be entrained into or detrained out of the cloud depends on its vertical acceleration. More specifically, vertically accelerated parcels near cloud edge are more likely to be entrained and vertically decelerated parcels are more likely to be detrained. Thus, the "acceleration sorting" hypothesis is proposed. Decomposition of the vertical momentum budget for the entrained and detrained trajectories shows that it is the pressure gradient acceleration, especially the dynamical pressure gradient acceleration associated with the flow structure and the effective buoyancy, rather than the buoyancy alone, that dominate the "acceleration sorting". Our results suggest that the flow structure of cloud thermals might be a potential candidate responsible for "acceleration sorting" processes during the entrainment and detrainment. These findings provide new insights to the understanding of the physical processes during both entrainment and detrainment.

How to cite: Gu, J.-F., Plant, R., Holloway, C., Clark, P., and Stirling, A.: Understanding the Entrainment and Detrainment Processes in Maritime Shallow Cumulus Clouds using Lagrangian Trajectories: Buoyancy Sorting or Acceleration Sorting?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11967, https://doi.org/10.5194/egusphere-egu26-11967, 2026.

In the tropics, the spatio-temporal aggregation of deep convective systems (DCS) has strong implications for the regional and global energy balance, for cloud-circulation interactions, and for the production of heavy precipitation on various scales. Yet, no ubiquitous definition or tracking algorithm exists for identifying cloud "aggregates", or cloud "clusters" in an objective way. Current cloud tracking tools are either developed for detecting individual cloud entities, or for identifying cloud clusters faithful to the original definition of mesoscale convective systems. They thus exclude a wide variety of DCS ensembles that organize in space and time without always merging into contiguous structures.

This work introduces a methodology to define and track coherent aggregates of deep convective systems using infrared brightness temperature (Tb) retrievals from geostationary satellites intercalibrated across the tropical band. The novelty lies in the combination between a spatiotemporal clustering and a region-growing method: starting with the coldest and largest cloud cores, we gradually attribute to preexisting aggregates the new neighboring systems that appear when the Tb threshold is increased to warmer values, until reaching the warmer outer edge of anvil clouds. Using a few criteria to measure organizational properties of the segmentation, we tune the algorithm parameters to maximize the realism of final cloud aggregates, minimize split-and-merge issues, and ensure that each aggregate is not intertwined with its neighbors whenever possible.

We demonstrate the good performance of this algorithm through a variety of realistic modes for deep convective organisation. The example case studies chosen include mesoscale convective complexes and cyclones over tropical oceans, cloud clusters embedded in African Easterly Waves, and the upscale merging of DCS in the course of the diurnal cycle of convection over tropical continents. A few composite properties of aggregates are shown for the entire tropics, to illustrate the potential of this dataset in providing new insights on the variety of organized convective patterns and on their associated multiscale interactions. The dynamics of individual systems can now be explored as an integral component of larger convective aggregates, across the diversity of cluster morphologies that populate the current tropics.

How to cite: Fildier, B. and Saba, O.: Tracking aggregates of deep convective systems in the tropics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12752, https://doi.org/10.5194/egusphere-egu26-12752, 2026.

    Titan, the largest moon of Saturn, is four times smaller than Earth but has a slightly higher surface pressure (1.5 bar), due to its very extended atmosphere. Titan's atmosphere is mainly composed of nitrogen, with a few percents of methane. The pressure and temperature (~94 K at the surface) conditions there enable a full "hydrological" cycle of methane, with lakes stable on the surface, evaporation, clouds, rain, and rivers.
    In this work, we focus on the methane clouds in Titan's troposphere, and in particular on the convective ones.
    Titan's methane clouds are monitored from Earth-based telescopes since the end on the 1990's, and have been observed from close by the Cassini spacecraft during half a Titan year (2004 - 2017). Some of the methane clouds in Titan's troposphere are thought to exhibit convective dynamics (e.g. Griffith 2005, 2009, Schaller 2009, Lemmon 2019, Rannou 2021). To understand the processes driving the clouds formation and evolution, modeling is also used, from global scale to regional scale models.

    Here we use a regional scale model (160x160 km), with kilometric horizontal resolution. The model is a coupling between the physics of the Titan Planetary Climate Model (Titan PCM, de Batz de Trenquelléon et al. 2025 a,b) and the dynamics of the Weather Research and Forecast model (WRFv4, Skamarock et al. 2019).
    We perform idealized simulations with several initial perturbations, at several seasons and locations, in order to constrain in which conditions methane convection appears on Titan.

    We obtain condensation for a diversity of setups, with in some cases the triggering of deep convection. We discuss the environments where we find deep convection in our model, and compare them to what has been observed on Titan. We also study convective ascents and the convective available potential energy (CAPE) obtained in our simulations, and compare it to Earth's storms.

References

de Batz de Trenquelléon et al. a, 2025. The New Titan Planetary Climate Model. I. Seasonal Variations of the Thermal Structure and Circulation in the Stratosphere. Planet. Sci. J. 6, 78. https://doi.org/10.3847/PSJ/adbbe7

de Batz de Trenquelléon et al. b, 2025. The New Titan Planetary Climate Model. II. Titan’s Haze and Cloud Cycles. Planet. Sci. J. 6, 79. https://doi.org/10.3847/PSJ/adbb6c

Griffith et al. 2005. The Evolution of Titan’s Mid-Latitude Clouds. Science 310, 474–477. https://doi.org/10.1126/science.1117702

Griffith et al. 2009. CHARACTERIZATION OF CLOUDS IN TITAN’S TROPICAL ATMOSPHERE. ApJ 702, L105. https://doi.org/10.1088/0004-637X/702/2/L105

Lemmon et al. 2019. Large-scale, sub-tropical cloud activity near Titan’s 1995 equinox. Icarus 331, 1–14. https://doi.org/10.1016/j.icarus.2019.03.042

Rannou et al. 2021. Convection behind the Humidification of Titan’s Stratosphere. ApJ 922, 239. https://doi.org/10.3847/1538-4357/ac2904

Schaller, E.L., Roe, H.G., Schneider, T., Brown, M.E., 2009. Storms in the tropics of Titan. Nature 460, 873–875. https://doi.org/10.1038/nature08193

Skamarock et al., 2019. A description of the advanced research WRF version 4. NCAR tech. note ncar/tn-556+ str 145.

How to cite: Moisan, E., Spiga, A., and Chatain, A.: Modeling convective methane clouds on Titan with a kilometer-scale regional model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13288, https://doi.org/10.5194/egusphere-egu26-13288, 2026.

Rising thermals are a fundamental component of atmospheric convection. Representing small parcels of air with relatively low density compared to their environment, thermals are known to carry a significant part of the vertical mixing in convective layers. Coherent structures in various modes of convection have been observed to consist of sets of thermals, in the form of vertical chains but also exhibiting more complex spatial structures. The recent focus on the spatial organization of convection has renewed interest in this topic, with meteorological field experiments providing new insights into thermal behavior. However, key aspects of thermal life cycle and population statistics remain unknown. To fill this data gap, this study tracks thermals in large-eddy simulations of a diurnal cycle of convection over land. Based on measurements on 30 August 2016 during the Hi-SCALE field campaign at the ARM SGP site, the case features a shallow convective boundary layer transitioning into deep convection during the late afternoon. Adopting previously proposed tracking algorithms, hundreds of thousands of thermals are thus identified and analyzed. Apart from investigating thermal life-cycle statistics and their diurnal evolution, a key research objective is to gain insight into what controls the birth rate of such thermals. Good scaling of thermal birth rates with various integrated buoyancy scales is reported, distinguishing between various layers and various thermal classes. The birth rate of dry thermals in the sub-cloud layer scales well with the surface-driven Deardorff convective velocity scale. Thermal presence in the cloud layer is found to be partially driven by local buoyancy scales, but is significantly boosted by thermals rising into the cloud layer during the shallow convective phase. This surface coupling disappears during the transition to deeper convection in the late afternoon, after which thermal birth rates are generally lower but scale well with the cloud layer buoyancy flux. The implications and potential use of these results for the conceptual modeling of convective organization and its representation in larger-scale circulation models are briefly discussed.

How to cite: Neggers, R. and Bartolomé García, I.: On the birth rate of thermals in convective layers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14071, https://doi.org/10.5194/egusphere-egu26-14071, 2026.

Convection in the tropics is driven by large-scale instability and the buoyancy of convective plumes, but has a variety of representations in Earth system models (ESM). In model convective schemes, subgrid parameterizations approximate the processes that regulate plume buoyancy, leading to substantial spread in simulated tropical thermodynamic structure. While entrainment is known to strongly influence plume buoyancy, recent work highlights an important role for cloud microphysical processes—particularly mixed-phase water physics and precipitation efficiency—in shaping buoyancy during ascent (Emmenegger et al. 2024). The influence of these microphysical processes on tropical buoyancy, and how they should be constrained in convective schemes, is explored here.

Using a perturbed-parameter ensemble (PPE) of the Community Atmosphere Model version 6 (Eidhammer et al. 2024), together with numerical single-column simulations, we construct process-oriented diagnostics to identify controls on convective sensitivity over the tropical western Pacific. Across the ensemble, variability in the convection sensitivity metric is dominated by deep convective parameters, while shallow convective and microphysical parameters exert weaker but systematic influence, revealing a clear hierarchy of physical controls. This hierarchy provides a useful way to organize the role of cloud processes, but does not explain how these parameter sensitivities arise or interact across scales.

To interpret the ensemble behavior, we use a simple bulk plume framework that isolates the effects of entrainment and key microphysical processes on plume buoyancy. Observations from the US Department of Energy’s Atmospheric Radiation Measurement field campaigns together with theoretical considerations of plume buoyancy are used to derive constraints for model representation of these processes. Together, the PPE diagnostics, observational constraints, and bulk plume framework clarify how microphysical and dynamical processes interact to shape simulated tropical convective instability. This approach provides a physics-informed basis for interpreting parameter sensitivity and improving the representation of convection in coarse resolution ESMs.

How to cite: Emmenegger, T., McCluskey, C., Truesdale, J., Neelin, J. D., and Kuo, Y.-H.: Hierarchies of dynamical and microphysical control on tropical convective instability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14512, https://doi.org/10.5194/egusphere-egu26-14512, 2026.

More of Earth’s surface is covered by stratocumulus clouds (Sc) than by any other cloud type, making Sc particularly important for Earth’s energy balance, primarily through the reflection of incoming solar radiation. However, representing Sc and their radiative impacts remains one of the greatest challenges for global climate modeling as models cannot resolve the length scale of the processes involved in its formation and evolution. For this reason, Sc represent a major source of uncertainty in climate projections (Wood 2012).

The challenge becomes more complicated due to the organizational complexity exhibited by Sc across a broad range of spatial scales. In particular, marine Sc fields display characteristic mesoscale patterns that can exhibit both organized and disorganized structures. Among these morphological regimes, cellular convection has received particular attention as cloud decks can self-organize into semi-regular tessellations composed of closed and open convective cellular fields.

Here we analyze satellite imagery of Sc organizing into low-reflectivity regions of open cells embedded within closed cellular cloud fields, known as "pockets of open cells" (POCs) (Stevens et al. 2005). We first track POCs from the time they emerge to when they dissipate. Second, a cell-scale analysis is performed for convective fields both inside and outside the POC boundaries to characterize the interface between POC and non-POC regions.

We propose a segmentation, tracking, and morphological analysis of cell geometry and dynamics in both closed and open cellular fields, with particular emphasis on the interactions between these cell types during POC development. A statistical analysis of multiple POCs is conducted to characterize the temporal and spatial contributions of cellular structural and kinetic changes to POC evolution, incorporating the local dynamics between individual cells (Farrell et al. 2017).

Using this framework, key differences between open and closed cellular regimes are identified based on velocity dynamics and morphological evolution. 
Whereas closed cells exhibit relatively slow dynamics, open cells continuously rearrange, changing both size and shape, and display significant cellular mobility, revealing motion flows within the POC region.

Finally, shallow cold pools within POCs are identified based on their lifetime, area expansion, and interactions between neighboring cells. These cold pools, which result from stratiform precipitation from open cells, play a dominant role in the dynamics of open cell fields.

The primary result of this analysis reveals a previously unreported regime of collective cellular dynamics, in which the emergence and evolution of convective organization is strongly influenced by cold pools, exhibiting structural and dynamical behaviors not observed in other known cellular systems.

How to cite: Monroy Merida, D. L. and Haerter, J.: Morphological cellular analysis of Pockets of Open Cells on Marine Stratocumulus fields , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14944, https://doi.org/10.5194/egusphere-egu26-14944, 2026.

Operational forecasts of monsoon heavy rainfall suffer from the inadequate representation of convective lifecycle and three-dimensional organization of moisture. This study introduces a novel trajectory-based lifecycle framework that synchronizes atmospheric profiles with precipitation stages for dynamic tracking of convective. Using 16 years (2005–2020) of ERA5 reanalysis and rain-gauge data over the subtropical monsoon region, Hong Kong, we diagnose the coevolution of Integrated Relative Humidity (IRH), Moist Static Energy (MSE), and dynamical fields across rainfall intensities.  Results show that rainfall intensity correlates with the moist layer depth: organized deep convection (e.g., heavy rain) is coupled with full-tropospheric saturation (IRH ≥0.88 and ≥0.85 at the lower- and mid-upper tropospheric layers) and robust ascent, whereas disorganized shallow convection (e.g., light rain) is confined to the lower troposphere. Critically, the framework reveals an IRH trajectory of an upward-expanding moistening pattern synchronized with peaking MSE gradients (>10 kJ kg⁻¹) and strengthening low-level convergence, underscoring a coupled energy-dynamic sequence fundamental to the lifecycle of organized convective systems. Moreover, IRH anomalies from seasonal baselines are more consistent predictors of intense events than absolute thresholds, highlighting the importance of environmental preconditioning. This trajectory-based approach provides physical insights into convective organization in subtropical monsoons and a process-oriented tool for evaluating and improving the representation of convection in models.

How to cite: Wan, M., Su, H., and Chan, P. W.: A Trajectory-Based Framework for Diagnosing Convective Lifecycle and Organization in Monsoon Rainfall: Coupled Evolution of Moisture, Energy, and Dynamics , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15571, https://doi.org/10.5194/egusphere-egu26-15571, 2026.