EGU General Assembly 2022  

Atmospheric particulate plays an important role in air pollution and in the climate system.  There is a strong relationship between concentrations of fine particulate matter and increased morbidity and mortality and no threshold has been determined below which no detrimental health impacts have been detected.  This has led to World Health Organisation limit guidelines being revised to 5 μg/m³ for PM2.5, representing a major challenge since reduction on the scales required are very large indeed.  Aerosol particles scatter and absorb sunlight and influence cloud properties, and hence have an impact on climate through modification of regional radiation balance.  Understanding the chemical and physical properties of particulate is essential if we are to be able to discriminate different sources, determine the processes driving the additional of particulate mass as a result of atmospheric processing, and constrain the optical properties and influence atmospheric pathways that control regional radiative properties and distribution.

Over the last 20 years there has been a transformation in the capability of instrumentation capable of determining the composition of atmospheric particulate matter.  Offline analytical capability has enabled us to achieve a much more comprehensive molecular level description of aerosol composition.  Over the same period there has been a transformation in the capability of online instrumentation for measurements of aerosol composition.  Online mass spectrometric approaches now enable chemical characterisation of particulate at the molecular level in near-real time.  Optical methods are also providing insight into fine particles, for example determining black carbon properties.  Such measurements are providing an unprecedented insight into aerosol processes in the atmosphere on a wide range of scales and offer new observational constraints on many key atmospheric processes.

This presentation will examine the development of online aerosol measurement capability and its use in air quality and regional climate research, focussing on field observations, including observations from airborne platforms. The talk will consider the source contribution of vehicle, solid-fuel and cooking to primary aerosol in urban environments, and the contribution of secondary particulate matter and its sources, considering the role of both biogenic and anthropogenic precursors. Biomass-burning is a globally important source of both organic matter and black carbon and these sources are projected to increase as climate warms.  Observations have greatly advanced our knowledge of the relationship between biomass burning aerosol composition, optical properties and effect on radiation.  Airborne observations focusing on subtropical smoke across South America and Africa and links to radiative properties and effects on climate will be discussed.  The discussion will also cover secondary inorganic aerosol contributions from sulphur and nitrogen oxidation to aerosol and cloud properties. These observations have been used to provide constraint on global model estimates of aerosol budgets and lifecycles.  The presentation will outline future challenges for observational aerosol science in the atmosphere and the role of large observation platforms given the need to reduce carbon footprint.

How to cite: Coe, H.: Aerosol composition, climate and air quality, why molecular scale observations are important and what are the future challenges, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4229, https://doi.org/10.5194/egusphere-egu22-4229, 2022.

The past decades have seen significant increases in the societal and natural damages from extreme weather events. Preventing or limiting evitable future damages requires climate change mitigation and adaptation measures. Societal adaptation to changing weather and climate extremes requires detailed knowledge on how these meteorological extremes are changing (understanding future hazard) and knowledge of the pathways in which weather impacts society (understanding vulnerability and exposure).

A full focus on meteorology is therefore misguided, as the impact of two similar meteorological events at different times or different locations will vary widely. This shows the need for explicit consideration of the entire chain of events, and how this chain results in potentially heavy societal impacts. Developments in large ensemble climate modelling, data science and storyline techniques help to identify the meteorological drivers of extreme impacts.

We will illustrate these developments through practical examples for varied ‘impacts’, e.g. hydrological extremes, renewable energy extremes, and agricultural extremes. We will provide insights into the promise and pitfalls of modern big data approaches, and discuss ways forward, including co-production efforts to increase the societal uptake and hence usefulness of our science.

How to cite: van der Wiel, K.: Linking societal impacts to changing weather, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11953, https://doi.org/10.5194/egusphere-egu22-11953, 2022.

In recent year’s southern state of India, Karnataka, has witnessed many catastrophic rainfall events. These events have caused enormous loss of life, property and crops across the State. In the year 2019, till the month of August, state was facing drought like condition because of prolonged dry spell in pre-monsoon (March-May) and south-west monsoon (June-July) season. During 06 – 10 August state has received average rainfall of 224 mm whereas some parts of the state received heavy rainfall (2493 mm) due to deep depressions over the Bay of Bengal. This study aims to evaluate the impact of lead time and three dimensional variational (3DVAR) data assimilation in simulation of heavy rainfall events during this period using Weather Research and forecasting (WRF) model. The model is configured with 3 nested-domains having high-resolution over the Karnataka State. The high resolution forecasts over Karnataka are evaluated against high resolution (~4 km) in-situ telemetric rain-gauge observations to assess model performance. These events are simulated using initial and boundary conditions from Global Forecast System (GFS) data. Lead time effect is analyzed by initializing model at 1200 UTC (12 hours prior to event day) and at 0000 UTC (event day) and the model is integrated for 48 hours duration. The impact of 3DVAR data assimilation is examine by comparing forecasts with assimilation of data from various sources like balloon, satellite, ground station and buoy (AIRS, MODIS, BUOY, TWS, ASCAT, WINDSAT, SSMIS and Radiosonde) against control experiment (without data assimilation). The results show that the model is able to capture the high intensity observed rainfall though location errors are there in many cases. It is note that model skill is sensitive to lead time and model performance for different lead time varied from case to case. Simulations with assimilation of observations in initial condition improved the forecasts compared to control simulations. The model skill (Bias Score, Threat Score and Heidke Skill Score) is better in simulations with data assimilation. 

How to cite: Bankar, A. and Vasudevan, R.: Simulation of Extreme Rainfall Events over Karnataka, Southern state in India: Impact of Lead Time and Data Assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-130, https://doi.org/10.5194/egusphere-egu22-130, 2022.

The ensembles used to produce probabilistic weather forecasts are limited by the availability of computational resources. This can lead to large sampling error and poorly resolved ensemble distributions. Furthermore, the expense of large ensembles makes it difficult to determine how many members would be needed to achieve a desired level of sampling uncertainty. A 100,000 member ensemble from a 1-dimensional idealised prediction system which replicates the key processes of convection is developed to examine how sampling error of random variables converges with ensemble size. Distributions of the three prognostic variables, evolving over 24 hours of a free-run, are found to correspond to the three categories of distribution that were identified in a study of a 1000-member NWP ensemble, indicating that the idealised model can represent key aspects of the forecast uncertainty. Bootstrap samples from the 100,000-member distributions are used to obtain widths of the 95% Confidence Interval of various sampling distributions, as function of ensemble size n. For sufficiently large ensemble size, the confidence intervals were found to decrease proportional to n^-1/2. This scaling is universal for the mean, variance, skewness, kurtosis and several quantile random variables. The sampling error depends on distribution shape and the random variable. Techniques using parameterisation and multiple small ensemble computations are also investigated as methods to allow convergence to be estimated using smaller ensembles.

How to cite: Tempest, K., Craig, G. C., and Brehmer, J. R.: Asymptotic convergence of sampling uncertainty in a 100,000 member ensemble using an idealised model of convection, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1339, https://doi.org/10.5194/egusphere-egu22-1339, 2022.

In this presentation, results will be discussed from a series of tests that were performed with the FV3-LAM model using 25, 13, and 3 km horizontal grid spacing, and two physics suites, to simulate the August 10, 2020 Midwestern Derecho, the most damaging single thunderstorm event in U.S. history. The two physics suites resemble those used in the HRRR model (referred to as RRFS, Rapid Refresh Forecast System) and the GFS model.

This derecho was poorly forecast by most models in the days and even hours before the event occurred. Only some hourly runs of the HRRR and an experimental version of the HRRR the night before correctly captured an intense bowing line of storms occurring on August 10. Therefore, experimental HRRR output from 00 UTC was used to initialize and provide lateral boundary conditions to the FV3-LAM runs. Runs were performed with and without the Grell-Freitas convective parameterizations in the RRFS suite for all grid spacings.

It was found that both the 13 km and 25 km runs that did not use convective parameterizations did a good job showing very intense convection in the correct area and time. When the convective schemes were turned on, the 25 km results were degraded, but the 13 km results did not change much. However, when grid spacing was refined to 3 km, neither runs with the RRFS or GFS physics suites simulated the derecho. The big difference from the coarser grid spacing runs was that anomalous convection formed during the night in the 3 km runs, removing the convective available potential energy, and not allowing substantial convection to form during the day on August 10. Instead, the stronger storms were well to the south and east of Iowa. Although this was a common problem with many convection-allowing models run in real time when the event occurred, this result is potentially troubling since the experimental HRRR run that provided the initial and lateral boundary conditions used the same grid spacing of 3 km, but did not produce the anomalous convection at night and thus correctly showed the intense mid-day derecho. The spurious convection in FV3-LAM seems to be due to stronger ascent prior to initiation of the spurious nocturnal convection than was present in the HRRR.  Of note, when the Grell-Freitas deep and shallow convective schemes are turned on in the 3 km FV3 run, the spurious convection is eliminated and the simulation is remarkably accurate, producing an intense derecho with over 30 m s-¹ sustained winds at 10 m, with gusts to 45 m s^-1, in the same general location at the same time as the observed event.  The use of the convective scheme results in a layer around 720 hPa with 1-2 C of warming around the time that spurious convection had formed in the 3 km run lacking the convective scheme.  This modest warming in a narrow layer is sufficient to prevent the spurious convection, completely changing the forecast of the daytime derecho from an absolute failure to a remarkable success.

How to cite: Gallus, W. and Harrold, M.: Unusual behavior in FV3-LAM simulations of the Midwestern U.S. Derecho of August 10, 2020: forecast degradation with improved resolution and a need for a convective parameterization with 3 km grid spacing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1836, https://doi.org/10.5194/egusphere-egu22-1836, 2022.

Urban-weather forecasts are necessary for well-known applications such as air pollution and urban comfort predictions. In the past few years additional uses arose such as urban air traffic by drones and helicopters. All of these applications require high-resolution numerical weather-forecasts that need to include the effect of the urban canopy. While CFD and LES methods are necessary to provide detailed information about the flow at the street level, mesoscale forecasts are needed to provide their initial and boundary conditions.

This work presents very-high resolution (500-m grid size) WRF simulations over a coastal complex terrain metropolitan area, Haifa, Israel, which is prone to high pollution events.

The simulations include three approaches to simulate the impact of the city on the simulated urban weather:

• Bulk parameterization; which corresponds to the default MODIS landuse categories of the WRF modeling system.
• Detailed local urban-canopy information for the Haifa metropolitan area derived with the help of a GIS tools was used with the two following urban canopy modules:
• The single-layer urban canopy (SLUCM) parametrization.
• The multi-level layer urban- canopy parameterization, specifically the building-effect parameterization with building energy model (BEP-BEM).

We focused on a wide variety of synoptic-scale weather conditions that, among others, can lead to or worsen high pollution events. The simulations used ERA5 reanalyses for initial and boundary conditions. We explored the sensitivity of the simulated urban flow and heat island effect to the planetary boundary layer parameterizations (YSU and Boulac), and the urban canopy modeling. Due to the lack of specific anthropogenic-heat information for the Haifa area, we used crude estimations of the timing and desired temperatures for air-conditioning usage in the BEP-BEM parameterization, and a typical diurnal cycle of anthropogenic heat for the SLUCM parameterization (with estimation of the maximal heat loads following literature for cities in similar climate zones and with similar population).

The simulations were compared to near surface observations of wind, temperature and relative humidity within and outside the urban area, and to vertical soundings at the only launching location in Israel, Beit-Dagan. Objective verification scores as well as visual verification of 2D maps of the aforementioned variables demonstrate that the simulations reproduce the different mesoscale dynamics under very different synoptic conditions. The impact of the detailed urban modeling (BEP-BEM and SLUCM) without specific information on the anthropogenic-heat, is limited in this case.  

How to cite: Rostkier-Edelstein, D., Berkovic, S., Chudnovsky, A., and Avisar, D.: Very-high resolution WRF mesoscale urban-modeling for a coastal complex terrain metropolitan area, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2424, https://doi.org/10.5194/egusphere-egu22-2424, 2022.

Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays a major role but so do potentially uncertainties arising from the representation of subgrid-scale processes, e.g. cloud microphysics. In this project, we investigate the impact of these uncertainties on the forecast of cloud properties, precipitation and hail of a selected severe convective storm over South-Eastern Germany.

Here, we focus the investigation on the effects of parametric uncertainty in a perturbed parameter ensemble, using the ICON model (with 2-moment cloud scheme, at 1 km grid spacing). A latin hypercube sampling is used to generate systematic variations of selected microphysical parameters from an eight-dimensional parameter space. Considered processes include riming, diffusional growth of ice and snow, CCN and INP activation, as well as the mass-diameter and mass-velocity relations. Isolated sensitivity experiments show distinct influences of all parameters on hail related variables, where the strongest impacts are found in simulations with reduced CCN and INP activation. We will present a detailed analysis of the simultaneous influence of parameter perturbations on the cloud microphysical evolution of the storm.

How to cite: Kuntze, P., Miltenberger, A., Hoose, C., Kunz, M., and Frey, L.: Impact of microphysical uncertainty on the evolution of a severe hailstorm, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2461, https://doi.org/10.5194/egusphere-egu22-2461, 2022.

Funded jointly by NOAA’s National Weather Service (NWS) Office of Science and Technology Integration (OSTI) and the Oceanic and Atmospheric Research (OAR) Weather Program Office (WPO), the UFS-R2O Project has made significant progress coordinating a large community of researchers, both inside and outside NOAA for integrating new research into the operational UFS applications.  The project began in July 2020 as a collaboration between the National Centers forEnvironmental Prediction (NCEP) EnvironmentalModelling Center, 8 NOAA research labs, the National Center for Atmospheric Research (NCAR), the Naval Research Lab (NRL) and 6 universities and cooperative institutes. 

The project was conceived with a focus on leveraging the nascent UFS community to build new UFS applications that will replace several existing operational modeling systems and simplify the NCEP Production Suite (NPS).  The project consists of three integrated teams covering the global Medium Range Weather/Subseasonal to Seasonal (MRW/S2S); the regional Short Range Weather/Convection Allowing Modeling (SRW/CAM); and the Hurricane applications, and are supported by seven cross-cutting development teams shown in Figure 1. The MRW/S2S team is leading the development of a six-component global coupled (atmosphere/ocean/land/sea-ice/wave/aerosol) ensemble system targeted for combining the Global Forecast System (GFS) and the Global Ensemble Forecast system (GEFS) as a single application, the SRW/CAM team is leading the development of a regional hourly-updating high-resolution and convection-allowing Rapid Refresh Forecast System (RRFS) for prediction of severe weather, and the Hurricane team developing the Hurricane Analysis and Forecast System (HAFS) for high resolution global tropical cyclone predictions.  

Some of the highlights of the progress accomplished thus far include: (1) testing and evaluation of various prototype versions of the global coupled prediction system with incremental improvements to the component models and the coupling infrastructure; (2) development of a prototype coupled data assimilation system that can update the ocean, sea-ice, atmospheric and land states; (3) development of a limited-area convective-scale short-range ensemble prediction system that formed the basis for the RRFS; and (4) development of moving nest capability within the global or regional domains for the HAFS.

This presentation highlights the outcomes of the UFS R2O Project thus far, with emphasis on results from the UFS based coupled model deterministic and ensemble prototypes targeted for medium range and sub-seasonal weather forecasts.  We will also discuss on the reanalysis and reforecast strategies for sub-seasonal to seasonal prediction capabilities, and eventual development of the Seasonal Forecast System (SFS) that will replace the existing Climate Forecast System (CFSv2) in operations.

Figure 1: Structure and composition of the UFS-R2O Project

How to cite: Tallapragada, V., Whitaker, J., and Kinter, J.: NOAA's Unified Forecast System Research to Operations (UFS-R2O) Project for accelerated transition of UFS based forecast applications into operations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3101, https://doi.org/10.5194/egusphere-egu22-3101, 2022.

Torrential rainfall is a threat in the modern society. To predict severe weather, convection resolving numerical weather prediction (NWP) is effective. This study explores a Control Simulation Experiment (CSE) aimed at controlling precipitation amount and locations to potentially prevent catastorphic disasters by simulating different scenarios of interventions of small perturbations taking advantage of the chaotic nature of dynamics. In this study, we perform a CSE using a regional model SCALE-RM for a severe rainfall event which caused catastrophic landslides and 77 fatalities in Hiroshima, Japan on August 19 and 20, 2014.

We perform a 1-km-mesh, hourly-update, 50-member observing system simulation experiment (OSSE) for this rainfall event initialized at 0000 UTC August 18. This provides the initial conditions for a 6-hour ensemble forecast initilaized at 1500 UTC Augest 19. To create small perturbations to change the nature run, we take the differences of all model variables between an ensemble member having the heaviest rain and another ensemble member having the weakest rain. Moreover, we normalize the perturbations so that the maximum wind speed is 0.1 m s-1. In this preliminary CSE, we try to control the heavy rainfall by giving the perturbations to the nature run in the OSSE at each time step from 1500 UTC to 1600 UTC on August 19, although the perturbations for all variables at all grid points are something beyond human’s engineering capability. In the nature run, 6-hour accumulated rainfall amount from 1500 UTC to 2100 UTC reaches 210 mm at the peak grid point. By contrast, the rainfall amount decreases to 118 mm in the CSE. We plan to apply limitations to the perturbations.

How to cite: Maejima, Y. and Miyoshi, T.: A Control Simulation Experiment for August 2014 Severe Rainfall Event Using a Regional Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3269, https://doi.org/10.5194/egusphere-egu22-3269, 2022.

The successful development of numerical weather prediction (NWP) helps better preparedness for extreme weather events. Weather modifications have also been explored, for example, when enhancing rainfalls by cloud seeding. However, it is generally believed that the tremendous energy involved in extreme events prevents any attempt of human interventions to avoid or to control their occurrences.

In this study, we investigate the controllability of a chaotic dynamical system by adding small perturbations to generate amplified effects and to prevent extreme events. The high sensitivity to initial conditions would ultimately lead to modifications of extreme events with infinitesimal perturbations. Based on this idea, we extend the well-known observing systems simulation experiment (OSSE) and design the control simulation experiment (CSE) with the Lorenz-96 model, a widely-used toy system in data assimilation studies. We also study the sensitivity of the control to the amplitude of the perturbation, the forecast length, the localized perturbation and the partial observations. The CSE would be applicable to other chaotic dynamical systems including realistic numerical weather prediction models.

How to cite: Sun, Q., Miyoshi, T., and Richard, S.: Control Simulation Experiments with the Lorenz-96 Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3313, https://doi.org/10.5194/egusphere-egu22-3313, 2022.

In spite of the continuous improvement of numerical weather prediction (NWP) systems, thunderstorms remain hard to predict with accuracy. This difficulty partly results from a lack of observations to describe the initial state of the atmosphere. Total lightning is a good indicator of convective activity and lightning data assimilation could improve the prediction of thunderstorms, especially in regions where storm-related observations are scarce.

The Lightning Imager (LI) onboard the Meteosat Third Generation (MTG) satellites will provide total lightning observations continuously over Europe with a spatial resolution of a few kilometers. This makes it a rich potential data source for convection-permitting NWP.

To prepare the assimilation of the flash extent accumulation (FEA) measured by LI in the French storm-scale regional AROME NWP system, a lightning observation operator is required to convert the model variables into a product comparable to the observations. Since LI FEA observations are not available yet (launch planned for the end of 2022), pseudo-LI FEA observations are generated from the records of the Météorage VLF ground-based lightning detection system (Erdmann et al., 2021).

Since neither flashes nor the electric field are predicted by the AROME model, the observation operator relies on proxy variables to link the flash observations to the prognostic variables of the model. This study focuses on the evaluation of different FEA observation operators from various proxies encountered in the literature and calculated from the outputs of 1 h AROME-France forecasts for 47 electrically active days in 2018.

Different regression techniques, linear regression as well as machine learning models, are used to relate the synthetic FEA and the modeled proxies. The data are processed as distributions over the whole domain (i.e. France) and time period since a pixel-to-pixel comparison exhibits a rather poor correlation. The training of the observation operator is performed on 44 days of the dataset and 3 days are used for the validation. The performances of each observation operator are evaluated by computing Fraction Skill Scores between synthetic FEA and proxy-based FEA. Two different proxy types emerged from the literature review: microphysical and dynamical proxies. The present study suggests that microphysical proxies seem to be more suited than the dynamical ones to model satellite lightning observations.

The performances of multivariate regression models are also evaluated by combining several proxies after a feature selection based on a principal component analysis and a proxy correlation study.

How to cite: Combarnous, P., Erdmann, F., Caumont, O., Defer, E., and Martet, M.: An observation operator for geostationary lightning imager data assimilation in storm-scale numerical weather prediction systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4254, https://doi.org/10.5194/egusphere-egu22-4254, 2022.

In the last few years, Central Europe faced a number of severe, record-breaking heatwaves. Several previous studies focused on the predictability of such heatwaves on medium-range to subseasonal time scales (5 – 30 days). However, also short-term forecasts with up to 3 days lead time can exhibit substantial errors in the prediction of maximum temperatures (Tmax), even on larger spatial scales. This study investigates the causes of such short-term forecast errors in Tmax over Central Europe for the summers of 2015–2020, with an emphasis on heatwaves. For this purpose, 3-day forecasts of the 50-member ensemble of the operational ECMWF-IFS (ECMWF-EPS) are systematically compared against 0-18h control forecasts for the respective days of interest.

In general, ECMWF-EPS shows a tendency for too cold forecasts during heatwaves, particularly in situations with stagnant air masses under clear skies and weak synoptic forcing. A pattern correlation method and a multi-variate linear regression model are used to study the relative importance of different physical processes for 72h forecast errors in Tmax. It is found that errors in downwelling short-wave radiation (SWDS), mainly due to erroneous low cloud cover, are the dominant error source, particularly in a large-scale perspective and outside of heatwaves. Moreover, Tmax forecasts errors are more strongly linked to SWDS errors on days with too warm forecasts than on days with too cold forecasts.

Within heatwaves, other error sources gain importance; averaged over all summers 2015–2020, the second most important error source is over- or underestimation of nocturnal temperatures in the residual layer. An additional Lagrangian trajectory analysis for the summers 2018–2020 (limited availability of necessary ECMWF-EPS input data) suggests that these errors may be linked to accumulating errors in previous days' diabatic heating of near-surface air masses, much more so in heatwaves than on regular summer days. Such errors in diabatic heating history, which are substantially more important in northern and western parts of Central Europe, are on average consistent with prediction errors in air mass residence time over land and cloud cover traced along trajectories. On regional scales, other physical processes can be of dominant importance, but only during heatwaves. The coastal regions are most influenced by errors in near-surface wind (ventilation by cooler maritime air) whereas errors in soil moisture are most important in some regions of southeastern Central Europe.

In summary, short-range forecasts errors of summertime maximum temperatures over Central Europe are predominantly caused by over- or underestimation of short-wave irradiance. However, the dominance of this error source diminishes substantially during heatwaves, particularly on days where ECMWF-EPS underestimates Tmax. Such days are often stable and cloud-free and a decreased importance of SWDS is therefore not unexpected due to overall lower probability for substantial misprediction. Moreover, especially in heatwaves, Tmax forecasts may suffer from accumulated errors in diabatic heating of near-surface air. Their causes may partly be attributable to errors in air mass residence time over land and/or cloud cover along the trajectory path but further research is needed.

How to cite: Lemburg, A. and Fink, A. H.: Identifying causes of short-range forecast errors in maximum temperatures during recent Central European heatwaves using the ECMWF-IFS ensemble, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4339, https://doi.org/10.5194/egusphere-egu22-4339, 2022.

The South Atlantic Convergence Zone (SACZ) has been subjectively defined as a band of cloudiness from the intense convection over the Amazon basin extending toward southeast Brazil, that is one of the main components of the South American monsoon system. The SACZ represents a region of deep convection, causing heavy precipitation events in the region for at least 4 days. The precipitation that occurs during the months of October to March is essential for maintaining the climate of the Southeast, Midwest and North of Brazil. Because of this, SACZ is an important climatological feature of the austral summer in Brazil. The representation of SACZ precipitation is complex and the need for numerical models calibrated according to the atmospheric conditions of the region to be analyzed is increasing. Thinking on this, researchers from the National Institute for Space Research (INPE) have been developing the Brazilian Global Atmospheric Model (BAM), in order to improve weather and climate forecasting simulations and climate change studies. The BAM is a semi-implicit Eulerian spectral model (BAMb-SL version, approximately 1.0° x 1.0° of horizontal resolution). With the importance of SACZ in mind and the need to improve its prediction, this study aims to analyze the climatology of SACZ through simulations of the BAM model in the period between 1992 to 2015, in which 156 SACZ event were recorded. BAM simulations will be compared with observed and reanalysis data, in order to evaluate the performance of BAM simulating ZCAS. The data that will used in this study is the BAM simulations of the variables precipitation, 200-hPa wind, outgoing longwave radiation, and 850-hPa specific humidity, daily observed precipitation data from the dataset organized and interpolated to 0.25° x 0.25° grid by Alexandre C. Xavier and available on the website https://utexas.app.box.com/v/xavier-etal-ijoc-data, outgoing longwave radiation from Climate Prediction Center do National Oceanic and Atmospheric Administration (CPC – NOAA, spatial resolution 0.75º x 0.75º) and 200-hPa wind and specific humidity at the level of 850-hPa from ERA5 of the ECMWF (spatial resolution 0.30º x 0.30º). The analyzes were obtained from statistical methods, with the mean and monthly standard deviation of the accumulated precipitation, and mean monthly of the outgoing longwave radiation, 200-hPa wind and specific humidity at the level of 850hPa, applied which data sets that were explained. Overall, the initial results showed a good agreement between the data sets. The average accumulated precipitation presented by the model simulations represented the spatial distribution of precipitation, in the central region of the Brazil were characterizing the SACZ, but these values were lower compared to those observed. The lowest OLR values presented by the reanalyses on the central region of the Brazil characterizes the SACZ position, as well as the BAM simulations. The other variables are still being analyzed. With the results obtained untill now, it is possible to say that, although the magnitude of each variable is underestimated, the simulations showed a good level of agreement between the data sets in the spatial representation of the variables analyzed in the 156 SACZ events.

How to cite: Breasciani, C., Boiaski, N., Ferraz, S., and Herdies, D.: The South Atlantic Convergence Zone Represented by the BAM Model Simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4723, https://doi.org/10.5194/egusphere-egu22-4723, 2022.

Prediction of numerical weather prediction and climate models are the basis for informed decision making and increased resilience to hydrometeorological extremes in many of today’s resource management challenges e.g. in the agricultural sector. Coupled multi-compartment models are capable of reproducing interactions and feedbacks in the geosystem, and have thereby demonstrated versatile tools in a variety of applications. The Terrestrial Systems Modelling Platform (TSMP, https://www.terrsysmp.org) is an integrated regional Earth system model that simulates processes from groundwater across the land surface to the top of the atmosphere on multiple spatio-temporal scales. TSMP consists of the atmospheric model COSMO (Consortium for Small-scale Modeling), the CLM (Community Land Model), and the ParFlow hydrologic model, coupled through OASIS3-MCT. This work presents an evaluation of daily deterministic 10-day forecasts of the atmospheric, surface, and groundwater states and fluxes for a heterogeneous mid mountain-ranges area in the German and Belgium Eifel-Ardennes region in Central Europe from TSMP in a monitoring setup. TSMP runs at convection-permitting resolution of 1km (atmosphere) and 0.5km (sub- and land surface) over an area of 150km x 150km, driven by ECMWF HRES forecasts through a one-way double nest. Data from the densely instrumented Eifel/Lower Rhine Valley observational network of Terrestrial Environmental Observatories (TERENO, https://www.tereno.net) is used for evaluation of the TSMP simulations. TSMP forecasts from July 2019 to July 2021 covering an agricultural and hydrological drought and the transition back to the climatological mean state are analyzed in detail. Despite the complex terrain and the free running TSMP, meteorological and hydrological station data are generally well represented while a certain overestimation of daily precipitation is observed.

How to cite: Iakunin, M., Wagner, N., Graf, A., Goergen, K., and Kollet, S.: Evaluation of the daily forecasts from the coupled Terrestrial Systems Modelling Platform (TSMP) over a regional-scale domain in Central Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5186, https://doi.org/10.5194/egusphere-egu22-5186, 2022.

Despite the enormous potential of precipitation forecasts to save lives and property in Africa, the generally low skill has limited their uptake. Where the forecasts have been used, the low skill makes the forecast-based decisions questionable at best. In particular, the performance of the forecasts is spatially and temporarily variable and therefore should not be generalised. To improve the performance of the models, and hence, their uptake, validation, analysis of possible sources of predictability and post-processing should continuously be carried out.

Here we evaluate the quality of reforecast from the European Centre for Medium-range Weather Forecasting over Equatorial East Africa (EEA). The reforecasts are initialised twice a week with lead time up to 45 days and are available from the subseasonal-to-seasonal (S2S) data base at a spatial (temporal) resolution of 1.5° (6-hourly). The evaluation is done using both satellite (Integrated Multi-satellite Retrieval for Global Precipitation Measurement) and ground-based (rain gauges) rainfall observations for the period 2000–2019. Both the raw and post-processed reforecasts are analysed, from daily to sub-monthly lead times and for temporal aggregations (48-hours and 120-hours total precipitation). To assess the skill of the reforecasts, an existing ensemble probabilistic climatology (EPC) derived from the observations is used as the reference forecast (Walz et al. 2021, doi: 10.1175/WAF-D-20-0233.1). First results show that there is potential of skill in the raw forecasts up to 10 days ahead particularly in the elevated areas of EEA. There is positive skill in the forecast of rainfall occurrence and the full rainfall distribution, i.e., the Brier Skill Score and the Continuous Rank Probability Skill Score, are positive in most areas, especially over land. As expected the skill decreases with lead time, vanishing completely between day 10 and 15. Aggregating the reforecasts enhances the scores further, likely due to reduction in time and temporal mismatches. The skill also varies seasonally with the long rains in March-April-May (the major dry season in June-July-August) having the best (worst) skill over most parts of the region. The raw reforecasts have a systematic bias, being overconfident at all lead-times. To correct for this bias, post-processing the reforecast using the isotonic distributional regression (IDR) method is applied and the improvement in performance will be discussed. Overall, initial results indicate that raw and postprocessed ECMWF S2S forecasts over EEA have more skill compared to findings in related studies for northern tropical Africa.

How to cite: Ageet, S., H. Fink, A., Maranan, M., Walz, E.-M., and Schulz, B.: Predictability of rainfall in Equatorial East Africa from daily to sub-monthly time scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5304, https://doi.org/10.5194/egusphere-egu22-5304, 2022.

In this study the transition from current practical predictability of midlatitude weather to its intrinsic limit is investigated. For this purpose, estimates of the current initial condition uncertainty of 12 real cases are reduced in several steps from 100% to 0.1% and propagated in time with a numerical weather prediction model (ICON at 40km resolution) that includes a stochastic convection scheme. It is found that the potential forecast improvement through initial condition perfection is 4-5 days, which can essentially be achieved with an initial condition uncertainty reduction by 90% relative to current conditions. With respect to physical processes, this reduction of the initial condition uncertainty is accompanied with a transition from rotationally-driven initial error growth to error growth dominated by latent heat release in convection and due to the divergent component of the flow. With respect to spatial scales, a transition from large-scale up-magnitude error growth to upscale error growth and an acceleration of the initial growth rate is found. Reference experiments with a deterministic convection scheme show a 5-10% longer predictability interval, but only if the initial condition uncertainty is small. These results confirm that planetary-scale predictability is intrinsically limited by latent heat release in clouds through an upscale-interaction process, while this process is unimportant on average for current amplitudes of the initial condition uncertainty.

How to cite: Selz, T., Riemer, M., and Craig, G.: The transition from practical to intrinsic predictability of midlatitude weather, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5312, https://doi.org/10.5194/egusphere-egu22-5312, 2022.

The Grell-Freitas (GF) cumulus parameterization is an aerosol-aware, scale-aware convective parameterization that has been used globally and regionally. This presentation will focus on one of the several developmental activities ongoing in GF: the continued development of its aerosol-aware capabilities and the impact on global forecast models. While it is well established that aerosols impact weather and climate, relatively little work has been done to represent their impact in medium-range forecasts and in convective parameterizations.

GF includes three aerosol related cloud processes: aerosol-influenced auto-conversion of cloud water to rain water, aerosol dependent precipitation efficiency, and aerosol wet scavenging based on memory and precipitation efficiency. Additionally, if aerosols are based on analysis or climatologies, they are allowed to slowly return to their original concentrations during precipitation-free periods.

In its most simplistic approach, aerosol pollution in GF is characterized using aerosol-optical depth (AOD). The method of our application is extremely efficient and can be adapted to use different aerosol or AOD products.  For example, other products that could be used include the aerosol climatology used by the Thompson Aerosol-Aware Microphysical Parameterization or predicted aerosols using NOAA’s aerosol prediction model, which is currently one ensemble in the Global Ensemble Forecast System – Aerosols (GEFS-Aerosols). The treatment of aerosols in GF should be most sensitive in regions with either very high or very low levels of pollution.

The impact of these changes to GF will be shown in a version of NOAA’s experimental global prediction model, with 

How to cite: Barnes, H., Grell, G., and Freitas, S.: Aerosol impacts for convective parameterizations: Applications of the Grell-Freitas Convective Parameterization, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6450, https://doi.org/10.5194/egusphere-egu22-6450, 2022.

The behavior of two eddy-diffusivity mass-flux (EDMF) planetary boundary layer (PBL) schemes used in NOAA’s Global Forecast System is examined at the level of mixing processes.  The examination is performed by comparing the two schemes in 1-D simulations of convective PBL growth using the same physics configuration and two sets of initial atmospheric states extracted from three-dimensional (3-D) GFS initial conditions.  All simulations show that the TKE-EDMF scheme mixes more and leads to less CIN and CAPE than the Hybrid-EDMF scheme.  The excessive mixing of the TKE-EDMF scheme is consistent with that seen in the 3-D GFS forecasts compared with radiosonde data.  Diagnosis using process perturbation sensitivity experiments indicates that the mass-flux term is more dominant in the TKE-EDMF than in the Hybrid-EDMF scheme.  Quantitative aspects of the local eddy diffusivity are also different between the two schemes, pointing to uncertainty in the physical partition of local and non-local mixing in the EDMF formulation of the two schemes.  Additional sensitivity experiments show essential parameters that can be optimized according to observations and/or large-eddy-simulation results that provide a more realistic partition of local and non-local mixing.

How to cite: Bao, J.-W., Grell, E., Michelson, S., and Hong, S.: Process-level differences between two PBL schemes used in NOAA’s GFS model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6531, https://doi.org/10.5194/egusphere-egu22-6531, 2022.

The Observing Systems Simulation Experiment (OSSE) is a very powerful and widely applied approach to evaluate observing systems and data assimilation methods in numerical weather prediction (NWP). In the OSSE, we generate a nature run (NR) using a model and simulate observations by sampling the NR. An independent model run with data assimilation of the simulated observations mimics an NWP system, and we compare it with the NR to evaluate the observations and data assimilation method. In this study, we extend the OSSE and design the Control Simulation Experiment (CSE), in which we add perturbations to the NR and try to modify it to a desired state. Investigating what perturbations are effective to avoid a high-impact weather event would be useful to understand the controllability of such an event. Since the weather system is chaotic, and even more so for disturbances, small differences generally lead to big differences, particularly for high-impact weather events. This suggests potentially effective control, i.e., small interventions would lead to big differences for high-impact weather events. The chaos control has been studied extensively in the field of dynamical systems theory, but taking advantage of dynamical instability to avoid certain trajectories has not been a main focus to the best of the authors’ knowledge. We first tested this idea with the Lorenz-63 3-variable model and performed an OSSE with an ensemble Kalman filter (EnKF). We extended the OSSE by adding very small perturbations (only 3% of the observation error) to the NR and found an effective approach to control the trajectory to stay in one side of the Lorenz’s butterfly attractor without shifting to the other. Following the implications and understandings from the Lorenz-63 model experiments, we tested with the Lorenz-96 40-variable model to avoid the occurrences of extreme values, mimicking to avoid extreme events in NWP. Finally, we further extended the idea to test with realistic global and regional NWP models. This presentation will summarize the concept and methodology of CSE with some proof-of-concept demonstrations with the toy models and realistic NWP models. This is an attempt to a potential paradigm change of NWP research from decades of predictability to the new era of controllability.

How to cite: Miyoshi, T., Sun, Q., Terasaki, K., and Maejima, Y.: From Predictability to Controllability: Control Simulation Experiment (CSE), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6707, https://doi.org/10.5194/egusphere-egu22-6707, 2022.

The earth’s atmosphere is a nonlinear and chaotic system. A small difference in the initial condition makes forecast different due to the chaos, the characteristics known as the “butterfly effect”. The predictability has been improved by the development of the NWP model, data assimilation, and observations for a long time. However, severe weather such as typhoon and torrential rainfall is a threat for us. Weather modification has also been investigated, such as cloud seeding and rain enhancement. It distributes cloud condensation nuclei and enhances cloud formation based on the microphysical processes. Alternatively, this study explores to control a typhoon by taking advantage of the chaotic dynamics.

The Observing System Simulation Experiment (OSSE) is a widely used approach in data assimilation research. We extend the OSSE to what we call the control simulation experiment (CSE) which changes the nature state to the desired direction by adding control signals determined by the ensemble forecasts. This study targets a typhoon, which generated over the Northwest Pacific and hit Japan. We perform CSEs to weaken the typhoon, i.e., making the central sea level pressure (SLP) higher. We apply the control only to the horizontal wind field at the first model vertical layer. Here, we limit the control signal only to reduce the kinetic energy because it would be difficult to increase kinetic energy in a real-world intervention. The CSE is found effective, i.e., we successfully weaken the typhoon when it reaches Japan. We will present the most recent results at the meeting.

How to cite: Terasaki, K. and Miyoshi, T.: Control simulation experiment for a typhoon case with a global numerical weather prediction system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7089, https://doi.org/10.5194/egusphere-egu22-7089, 2022.

In order to conduct network design experiments for a forecast system, methods are needed to evaluate the potential benefit of hypothetical observations. Ideally these methods are flexible enough to accommodate multiple observation types and forecast lead times, while being computationally fast enough to evaluate many potential observational network layouts. For ensemble forecasts, this can be achieved by assuming a linear sensitivity between the background ensemble perturbations and a forecast quantity of choice. This assumption enables estimating how much the ensemble variance of a chosen forecast quantity would be reduced for an arbitrary combination of observation locations and types, without the need to run additional forecasts. These variance reduction estimates need to take the localization used in the data assimilation framework into account, so that the estimates are consistent with the ensemble forecast system they are derived for. This localization aspect has so far received little attention.

In this presentation we compare two methods to take localization into account when estimating the benefit of hypothetical observations. One method requires inverting the background ensemble covariance matrix. The other method avoids the inversion, but needs to be provided with estimates of signal propagation over time. We use a simple linear advection toymodel to show that while both methods can function well, due to their various strengths and weaknesses they are suited to different applications.

How to cite: Griewank, P., Löhnert, U., Necker, T., Nomokonova, T., and Weissmann, M.: Accounting for localization in ensemble network design experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7393, https://doi.org/10.5194/egusphere-egu22-7393, 2022.

As the fidelity of global numerical weather prediction (NWP) models to resolve convective scale features increases with advances in computing power, high-resolution observations of clouds and precipitation are becoming increasingly important for both evaluating model performance and initialising forecasts. This talk focusses on the latter by presenting developments made to the ECMWF integrated forecast system (IFS) to allow the direct 4D-Var assimilation of spaceborne cloud radar and lidar observations. Due to the radar and lidar signal’s ability to penetrate clouds, these observations provide a unique insight to the vertical and horizontal structure of clouds. The additional information provides a fantastic opportunity to improve the model analysis of cloud and precipitation and the subsequent forecast, however extracting useful information from these observations, which are often only partially resolved by the model, pushes current data assimilation systems to their limit.

In this talk we will provide an overview of the developments to the IFS to allow the assimilation of cloud radar and lidar, including a triple-column technique to represent unresolved condensate variability in the simulated observations and the characterisation of observation error, both of which are vital to optimise the observations’ use. We will then give a thorough assessment of the impact of assimilating cloud radar and lidar on NWP forecast skill by assimilating CloudSat radar reflectivity and CALIPSO lidar backscatter on top of routinely assimilated observations. As well as showing improvements by evaluating forecasts against analyses, we will show the observations provide increases in forecast skill when verifying against independent observations, such as top-of-atmosphere radiative fluxes. Looking to the future, the upcoming ESA EarthCARE satellite mission will provide the opportunity to assimilate cloud radar and lidar observations operationally. Differences between CloudSat and CALIPSO with EarthCARE observations will be briefly discussed along with the potential for synergistic uses of other EarthCARE observations, such as Doppler velocity, cloud extinction and shortwave radiances.

How to cite: Fielding, M. and Janisková, M.: Improving NWP forecasts through the direct 4D-Var assimilation of space-borne cloud radar and lidar observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7441, https://doi.org/10.5194/egusphere-egu22-7441, 2022.

A semi-Lagrangian algorithm (SLA) is implemented in NOAA's Global Forecast System (GFS) for
simulating raindrop sedimentation in a double-moment microphysics schemes. This SLA includes
a significant improvement to its predecessor for single-moment raindrop sedimentation. It is
numerically stable and mass-conserving when used to sediment raindrops in double-moment
microphysics schemes. Numerical results from an idealized single-column model show that the
SLA overcomes an issue of mass accumulation at the cloud bottom in the case of the Eulerian
algorithm for raindrop sedimentation, which is due to the assumption of constant terminal
velocity within a time step of sedimentation. The results from the single-column model also show
that the time step in the SLA can be 10 times greater than that in the Eulerian algorithm for
sedimentation. Further numerical experiments using NOAA's GFS show that using the SLA
mitigates the numerical instability problem associated with a newly-implemented double-moment
microphysics scheme in the GFS.

How to cite: Hong, S., Li, H., Bao, J.-W., Grell, G., and Sun, R.: A Semi-Lagrangian Advection Algorithm for Falling Raindrops in aTwo-Moment Microphysics Schemes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8984, https://doi.org/10.5194/egusphere-egu22-8984, 2022.

As we enter the era of post peta-scale computing, convective-scale NWP will be performed at increasingly higher model resolutions, using more sophisticated data assimilation (DA) schemes and advanced observational datasets. Here we explore the implications for a regional-scale numerical weather prediction system that uses a unique 30-second update for a 500-m grid, using observations from an advanced multi-parameter phased array weather radar (MP-PAWR), on forecasts of convective weather systems. Experiments showed a rapid buildup in the level of atmospheric dynamical activity in the analyses from the start of cycling that promoted the initialization of spurious and often overly-strong convection in forecasts. This was found to be the consequence of substantial differences between the initial conditions and observations and the rapid updating process, which together introduced large perturbations to the analyses during early cycling, leading to the generation of an atmospheric state that was characterized by strong low-level winds and regions of high convective instability. These conditions would remain at a near constant level well after the period of initial cycling, continuing to be a strongly determining factor on the level of development of convection in the forecasts. It was subsequently demonstrated that we could reduce the level of convective activity in forecasts and so improve forecast skill by reducing the localization scale parameter to near model grid resolution, which acted to force initial conditions closer to the initial set of observations following the first update and reduce the large pertubations that caused these conditions to develop.

How to cite: taylor, J., Honda, T., Amemiya, A., otsuka, S., and Miyoshi, T.: Implications of a 30-second Update Cycle for a Convective-Scale Ensemble Radar Data Assimilation System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9244, https://doi.org/10.5194/egusphere-egu22-9244, 2022.

2021 was a standout year for ECMWF in that not one, but two major upgrades were made to the operational NWP system.

Cycle 47r2 (introduced on 11 May) increased the ensemble forecast (ENS) vertical resolution from 91 to 137 levels, bringing it into line with the high-resolution forecast (HRES). The cost of this, which is significant, was offset by running the forecast model in single precision which saved equivalent cost and is meteorologically neutral. Overall validation showed statistically significant skill improvements by the ENS forecasts, for many fields, mostly in the range 0.5-2% RMS error reduction. It also showed improvements for specific meteorological phenomena (e.g. Tropical Cyclones, Madden-Julien Oscillation).

Cycle 47r3 (introduced on 12 October) contained model, assimilation and observation usage changes. A major change, and the result of many years of research, was a complete new moist physics package. This brings significant meteorological benefit, and it this aspect users of ECMWF forecasts will see, but it also simplifies and modernizes the physics code in the IFS, and this will facilitate future improvements. This physics package includes too many changes to list here, but includes a more consistent formulation of boundary layer turbulence, shallow convection and sub-grid cloud and a new parametrized deep convection closure with an additional dependence on total advective moisture convergence. On the observation and data assimilation side the new weak constraint 4D-Var approach was applied in the Ensemble of Data Assimilations, and the all-sky observation assimilation approach was extended to a temperature sounder for the first time (AMSU-A), as well as a major update in the radiative transfer model for observation assimilation.

Cycle 47r3 validation showed significant improvements. For example, extratropical upper-air geopotential and wind in the first few days of the forecast improved by 1-2% and tropical upper-air winds throughout the medium-range improved by 1-4%. Also, tropical cyclone track errors have been reduced by 10%.

Cycle 47r3 is now being ported to the new ATOS HPC in the new ECMWF data centre in Bologna. Following the migration, the first science upgrade will be Cycle 48r1 and will contain some very important changes. The most important from a user perspective will be the ENS resolution change to TCo1279 (~9 km), hence matching the current HRES (which will remain unchanged). There will also be a large number of other changes, including the first use of the OOPS system for 4D-Var. OOPS is a modern code system that encapsulates tasks as objects, enabling both separation of concerns and more flexible interaction between components. The cycle will also see the introduction of a new multi-layer snow scheme (improving predictions of snow and of near-surface temperatures over snow), and enhancements to the use of satellite data over land. This last change represents a step on ECMWF’s strategic direction to get yet more value out of satellite data by moving from an ‘all-sky’ to an ‘all-sky, all-surface’ approach.

How to cite: Brown, A., Browne, P., English, S., Pappenberger, F., and Rabier, F.: Recent and planned NWP developments at ECMWF, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10235, https://doi.org/10.5194/egusphere-egu22-10235, 2022.

Data assimilation plays a critical role in the advancement of numerical weather prediction (NWP) via ingesting information of atmospheric observations from various platforms. As more and more observations become available, it is important to quantify the impact of assimilated observations on a forecast to help improve the use of these observations. Currently several approaches exist to estimate observational impact on the forecast skills. Ensemble Forecast Sensitivity to Observations (EFSO) is one such approach that extends upon the adjoint-based FSO method by utilizing ensemble of forecasts in replacement of an adjoint model. However, like any ensemble-based methods, EFSO also suffers from sampling error due to the use of limited-sized ensemble. This is more severe when we take ensemble-based correlations between different times. In recent years, the rapid advancement of supercomputing has facilitated the use of large number of ensemble members in NWP. Many studies have demonstrated the use of large ensembles in the context of data assimilation, however, the use of large ensemble to quantify observation impact via EFSO is yet to be explored. In this study, we implemented the EFSO method for a global atmospheric data assimilation system that consists of the Non-hydrostatic Icosahedral Atmospheric Model (NICAM) with the Local Ensemble Transform Kalman Filter (LETKF), namely the NICAM-LETKF. Using a total of 1024 ensemble members, we can examine the sensitivity of ensemble size to the accuracy of EFSO estimated error reduction via sub-sampling. We will present results from a series of EFSO experiments with the 1024-member NICAM-LETKF to conclude our findings.    

How to cite: Wu, T.-C., Terasaki, K., Kotsuki, S., and Miyoshi, T.: Examining the Sensitivity of the Accuracy of EFSO to Ensemble Size, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10704, https://doi.org/10.5194/egusphere-egu22-10704, 2022.

The ensemble data assimilation (EDA) system contains uncertainties both in initial conditions and model forecast. In general, the uncertainties are represented by the ensemble spread that is a standard deviation of background error covariance (BEC). However, this ensemble spread is usually underestimated due to insufficient ensemble size, sampling errors, and imperfect models: it often causes a filter divergence problem as the analysis ignores the observation due to insufficient model uncertainty. This phenomenon is also found in the coupled land-atmospheric modeling system, especially near the surface where the heat flux exchanges are crucial as the lower boundary conditions. Therefore, we have developed the stochastically perturbed parameterization (SPP) scheme for the Noah Land Surface Model (hereafter, SPP-Noah LSM) using the soil temperature and moisture within the coupled WRF-Noah LSM system to represent the near-surface uncertainty. It perturbs the soil variables by adding the random forcing to inflate the ensemble spread. In particular, the random forcing used in perturbation is controlled by the tuning parameters such as amplitude, time scale, and length scale, which vary depending on the target variables. To obtain the optimal random forcing parameters to the soil variables, we employed a global optimization algorithm --- the micro-genetic algorithm, which is based on the natural selection or survival of fitness to evolve the best potential solution. The optimization is conducted in each daytime and nighttime to consider the diurnal variations of soil variables. As a result, the soil temperature and moisture perturbations using the SPP-Noah LSM can indirectly inflate the ensemble BECs of temperature and water vapor mixing ratio through the heat flux changes, respectively, in the planetary boundary layer (PBL) of the EDA system. The SPP-Noah LSM with diurnal variations depicts reasonable ensemble spreads for soil variables, but the ensemble spreads for atmospheric variables from the propagation of the soil variable perturbations are less effective. Our results indicate that the inflated ensemble spread helps to produce an adequate analysis increment reducing the background error in the PBL.

How to cite: Lim, S., Park, S. K., and Cassardo, C.: Optimized Stochastically Perturbed Parameterization Scheme for the Soil Temperature and Moisture within an Ensemble Data Assimilation System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10818, https://doi.org/10.5194/egusphere-egu22-10818, 2022.

Motivated by the need to predict dust-storms, a large set of wind observations are compared to 24 h point forecasts with a high-resolution numerical model.  The cases are classified according to the dynamic nature of the winds; (a) wind over flat land, (b) enhanced winds blowing along a mountain (barrier/corner winds) and (c) downslope winds. The mean quality of the forecasts over flat land is similar to the quality of the forecasts of enhanced winds blowing along a mountain.  The quality of the forecast of the downslope winds is much poorer than the quality of the forecast of winds over flat land and winds blowing along a mountain.  In the downslope case, it is up to ten times more likely to either miss a windstorm or to forecast a windstorm that does not occur, than if the winds are not downslope.

How to cite: Ólafsson, H.: The connection between quality of wind forecasts and the dynamics of the winds, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11169, https://doi.org/10.5194/egusphere-egu22-11169, 2022.

This study investigates the medium-range predictability of persistent warm and cold extremes in Europe. To that end, deterministic ERA5 reforecasts for the period 1979-2019 are compared to the reanalysis of the respective period, thus providing a large sample for verification and bias identification. The seasonally-varying Gilbert skill score of both extreme event types reveals that cold extremes in summer exhibit particularly low predictability. A spatial variability also emerges for these scores with persistent extremes in northeastern Europe and Scandinavia generally achieving better predictability compared to other regions of Europe. Composites of basic reanalysis fields and their errors suggest that the aforementioned spatiotemporal variability in predictability is associated with differences in the typical synoptic conditions of each type of event. Moreover, it is shown that summer and winter in Europe suffer from a negative and positive bias in Rossby wave amplitude, respectively. Although the physical processes and model deficiencies involved are not straightforward to identify, we hypothesize that these biases constitute one of the factors that limit the predictability of temperature extremes at weather time scales.

How to cite: Fragkoulidis, G., Doensen, O., and Wirth, V.: Predictability of temperature extremes in Europe and biases in Rossby wave amplitude, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11665, https://doi.org/10.5194/egusphere-egu22-11665, 2022.

Temperature extremes are in general relatively difficult to forecast accurately and it is important to assess their nature and characteristics, both in numerical models and in reality.  Such extremes in Iceland have been explored and linked to two key parameters of the flow; low-level wind speed and static stability.  The results reveal very distinct distribution of cases in the space of these parameters:  Cold extremes in the winter occur only at low wind speeds, while in the summer, they occur only in low static stability.  Warm extremes in the winter occur on the other hand only at high static stability, and warm extremes in the summer occur only at low wind speeds.  This result can be summarized in The Couch Diagramme.

How to cite: Mollard, G. and Ólafsson, H.: Characteristics of extremely warm and extremely cold events in Iceland – The Couch Diagramme, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12001, https://doi.org/10.5194/egusphere-egu22-12001, 2022.

The German Weather Service (DWD) operationally runs an LETKF (Localized Ensemble Kalman Filter) assimilation scheme for the regional weather forecasts with the ICON-LAM (ICON Limited Area Mode) Numerical Weather Prediction model. We investigate the potential of using an EnVAR (Ensemble Variational data assimilation) using the kilometre-scale Ensemble Data Assimilation (KENDA) ensemble. Quality Control (QC) and Observation Aggregation (OA) are essential parts of a data assimilation system. The former ensures that the assimilated observations are likely to be "acceptable", in the sense of technical, physical and statistical properties. The latter reduces the amount of data and computations under the aspect of efficiency, and helps handling redundant or correlated observations.

We show results of assimilation experiments for KENDA and EnVAR using a similar selection of conventional observations after QC and OA, while using a fully dynamic B matrix and no variational QC. The difference of the results of the two algorithms does not only depend on the partially differing implementation of QC and OA, but also due to partially different implementations of the observation operators or even the supported observation types. Important differences to the operational global EnVAR code are e.g. the choice of suitable observation types and the interpolation specification of the first guess to the locations of the observations.

As we use the same code for the EnVAR as in the DWD's global data assimilation scheme, we can potentially assimilate many other observations systems beyond conventional observations. This includes, after some adaptations, a wide range of spaceborne observations. Additionally, it is possible to run a regional EnVAR assimilation and a deterministic forecast with a coarse resolution first guess ensemble. Re-using existing ensembles for the ensemble B matrix might be a computationally efficient way to use a variational algorithm for deterministic forecasts.

How to cite: Burba, M., Hollborn, S., Ulbrich, S., Schraff, C., Anlauf, H., Potthast, R., and Knippertz, P.: EnVAR Quality Control and Observation Aggregation for ICON-LAM, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12132, https://doi.org/10.5194/egusphere-egu22-12132, 2022.

The WOD framework is a distributed system for:

• gathering upstream weather forecasts and observations from a wide variety of sources
• triggering scheduled or on-demand jobs
• running the WRF weather model for data-assimilation and forecasts
• processing data for long to medium-term storage
• making results available through APIs
• making data files available to custom post-processors

Much effort is put into starting processing as soon as the required data becomes available and in parallel when possible. The software is maintained in Git and can be installed on suitable hardware in a matter of hours, bringing the full flexibility and power of the WRF modelling system at your fingertips.

How to cite: Rögnvaldsson, Ó., Ragnarsson, L., and Stanisławska, K.: The WOD framework for weather forecasting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12399, https://doi.org/10.5194/egusphere-egu22-12399, 2022.

In this study, we used the Weather Research and Forecasting (WRF) version 4.2.1 model to simulate the characteristics of a rainfall-induced landslide that occurred on November 28 in Samigaluh, Kulonprogo. In addition, we investigated 22 different microphysics (MP) schemes to see how sensitive they were. The WRF model was employed with three nested domains, the innermost of which had a 1 km grid spacing and explicit convection. The model was run for 73 hours with GFS initial conditions from 00:00 UTC on November 26, 2018. We used reflectivity profiles from the Weather Radar in Yogyakarta and data from rain gauge stations in Kulonprogo to validate the simulated properties of the rainfall. Despite employing identical initial and boundary conditions and model settings, the MP approaches have significant variances in their thunderstorm simulations. To begin with, practically all of the extreme convection simulation methods over Samigaluh had the same pattern as the reported storm. For example, on November 27, radar data indicated the passage of three convective cores above Samigaluh, which the model in most MP schemes simulated. In comparison, the Ferrier_old scheme did an excellent job of simulating the convective cores' observable features. The MP schemes also had difficulties modeling the storm's updrafts. The Ferrier old scheme simulated surface rainfall distributions closer to data than the other three schemes (Goddard GCE, Morrison2, and WDM5). On the other hand, all four MP systems did an excellent job of simulating the convective variations associated with the thunderstorm. The model's generated reflectivity profiles, which showed three convective cores, were similar to the observed reflectivity profile. These characteristics match the simulated convective profiles, which peaked between 10 and 15 kilometers. The current research reveals that the microphysical systems in thunderstorm simulations have a lot of sensitivity and variability. The study also underlines the necessity for a multi-observational program such as Year of Maritime Continent (YMC) to improve the parameterization of cloud microphysics and land surface processes throughout the Indonesian region. 

How to cite: Nuryanto, D. E., Satyaningsih, R., Nuraini, T. A., Sopaheluwakan, A., Ettema, J., Jetten, V. G., Permana, D. S., Riama, N. F., and Karnawati, D.: Simulation of a heavy rainfall-induced landslide event over Kulonprogo, Yogyakarta in Indonesia using WRF: Sensitivity to cloud microphysics parameterization, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12675, https://doi.org/10.5194/egusphere-egu22-12675, 2022.

The Common Community Physics Package (CCPP) is a collection of atmospheric physical
parameterizations and a framework that couples the physics for use in Earth system models.
The CCPP Framework was developed by the U.S. Developmental Testbed Center (DTC) and is
now an integral part of the Unified Forecast System (UFS), a community-based, coupled,
comprehensive Earth modeling system designed to support research and be the source system
for NOAA‘s multi-scale operational numerical weather prediction applications.

A primary goal for this effort is to facilitate research and development of physical
parameterizations, while simultaneously offering capabilities for use in operational models. The
CCPP Framework supports configurations ranging from process studies to operational
numerical weather prediction as it enables host models to assemble the parameterizations in
flexible suites. Framework capabilities include flexibility with respect to the order in which
schemes are called, ability to group parameterizations for calls in different parts of the host
model, and ability to call some parameterizations more often than others. Furthermore, the
CCPP is distributed with a single-column model that can be used to test innovations and to
conduct hierarchical studies in which physics and dynamics are decoupled.

There are today more than 30 primary parameterizations in the CCPP, representing a wide
range of meteorological and land-surface processes. Experimental versions of the CCPP also
contain chemical schemes, making it possible to create suites that tightly couple chemistry and
meteorology.

The CCPP is developed as open-source code and has received contributions from the wide
community in the form of new schemes and innovations within existing schemes. In this poster,
we will provide an update on CCPP development and plans, as well as review existing
resources for users and developers, such as the public releases, documentation, tutorial, and
forum

How to cite: Bernardet, L., Firl, G., Heinzeller, D., Zhang, M., Trahan, S., Dudhia, J., Kavulich, M., and Ek, M.: Facilitating the development of complex models with the Common Community Physics Package and its Single-Column Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13128, https://doi.org/10.5194/egusphere-egu22-13128, 2022.

Recent research, based on remote sensing of Normalized Difference Vegetation Index (NDVI) has revealed substantial interannual variability in the maximum vegetation in Iceland.  This variability is primarily related to temperature, but also to some extent to precipitation.  Most, if not all, operational numerical weather prediction models for that region do however use climatological values for vegetation with no interannual variability.

A preliminary investigation of temperature forecasts in the highlands of Iceland indicates that high NDVI correlates with positive bias of temperature forecasts.  This is presumably associated with the impact of increased vegetation on the Bowen ratio in sparsely vegetated regions, but local circulation may also play a role.  

How to cite: Rousta, I. and Olafsson, H.: Vegetation variability and temperature forecasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13283, https://doi.org/10.5194/egusphere-egu22-13283, 2022.

The Saharan heat low (SHL) is a key component of the West African Monsoon system at the synoptic scale and a driver of summertime precipitation over the Sahel region. Therefore, accurate seasonal precipitation forecasts rely in part on a proper representation of the SHL characteristics in seasonal forecast models. This is investigated using the latest versions of two seasonal forecast systems namely the SEAS5 and MF7 systems from the European Center of Medium-Range Weather Forecasts (ECMWF) and Météo-France respectively. The SHL characteristics in the seasonal forecast models are assessed based on a comparison with the fifth ECMWF Reanalysis (ERA5) for the period 1993–2016. The analysis of the modes of variability shows that the seasonal forecast models have issues with the timing and the intensity of the SHL pulsations when compared to ERA5. SEAS5 and MF7 show a cool bias centered on the Sahara and a warm bias located in the eastern part of the Sahara respectively. Both models tend to underestimate the interannual variability in the SHL. Large discrepancies are found in the representation of extreme SHL events in the seasonal forecast models. These results are not linked to our choice of ERA5 as a reference, for we show robust coherence and high correlation between ERA5 and the Modern-Era Retrospective analysis for Research and Applications (MERRA). The use of statistical bias correction methods significantly reduces the bias in the seasonal forecast models and improves the yearly distribution of the SHL and the forecast scores. The results highlight the capacity of the models to represent the intraseasonal pulsations (the so-called east–west phases) of the SHL. We notice an overestimation of the occurrence of the SHL east phases in the models (SEAS5, MF7), while the SHL west phases are much better represented in MF7. In spite of an improvement in prediction score, the SHL-related forecast skills of the seasonal forecast models remain weak for specific variations for lead times beyond 1 month, requiring some adaptations. Moreover, the models show predictive skills at an intraseasonal timescale for shorter lead times.

How to cite: Ngoungue Langue, C. G., Lavaysse, C., Vrac, M., Peyrillé, P., and Flamant, C.: Seasonal forecasts of the Saharan heat low characteristics: a multi-model assessment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13503, https://doi.org/10.5194/egusphere-egu22-13503, 2022.

Numerical weather prediction (NWP) systems nowadays need to be capable of providing not only high-quality deterministic forecasts, but also information about forecast uncertainty.  The ensemble forecast technique is commonly used to provide an estimation of forecast uncertainty.  Since a great deal of the forecast uncertainty comes from dynamical and physical processes not resolved or explicitly represented numerically, there is a need to correctly quantify and simulate the uncertainty associated with these processes as required by the ensemble forecast technique.

To address this need, we have developed a new stochastic physics scheme for simulating the uncertainty in parameterizations in the NOAA Unified Forecast System (UFS).  This scheme is derived from the connection in mathematical physics between the Mori-Zwanzig formalism and multidimensional Langevin processes.  It follows the correspondence principle, a philosophical guideline for new theory development, such that it can be shown that the previously implemented stochastic uncertainty quantification schemes in the UFS are particular cases of this scheme.  We will show how we have used this scheme to simulate uncertainty at the process level of unresolved dynamics and physics in the UFS.  We will also present a preliminary performance comparison of previously-implemented stochastic physics schemes with the newly-developed process-level scheme in the UFS medium-range ensemble prediction

How to cite: Bao, J.-W., Michelson, S., Pegion, P., Whitaker, J., Bengtsson, L., and Penland, C.: Simulation of model uncertainty using multidimensional Langevin processes in the NOAA Unified Forecast System (UFS), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3111, https://doi.org/10.5194/egusphere-egu22-3111, 2022.

Canopies represent sub-grid scale features in earth system models and interact as such with the large-scale processes resolved numerically. The canopy is implemented with a viscosity approach, resembling a roughness parameterization. However, the idea is that high viscosity is applied locally to an obstacle area whereas free spaces are assigned low viscosity. In a first step, we test this approach on a micro-scale, using an advection-diffusion equation to solve for tracer transport around obstacles. Available wind tunnel data are used for validation of a standard finite element implementation. In a second step, this approach is combined with a multi-scale finite element approach, such that a large-scale simulation can be coupled to the micro-scale representation of a canopy. Comparison of high-resolution standard finite element and low-resolution multi-scale finite element methods will allow for quantitative error analysis. This approach has the potential to lead to better parameterizations of subgrid-scale processes in large-scale simulations.

How to cite: Patel, H., Simon, K., and Behrens, J.: Towards Canopy parameterization for Multiscale Finite Element Method, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3807, https://doi.org/10.5194/egusphere-egu22-3807, 2022.

Approximations in the moist thermodynamics of atmospheric models can often be inconsistent. Different parts of numerical models may handle the thermodynamics in different ways, or the approximations may disagree with the laws of thermodynamics. To address these problems all relevant thermodynamic quantities may be derived from a defined thermodynamic potential; approximations are then instead made to the potential itself - this guarantees self-consistency, as well as flexibility. Previous work showed that this concept is viable for vapour and liquid water mixtures in a moist atmospheric system using the Gibbs potential. However, on extension to include the ice phase an ambiguity is encountered at the triple-point. To resolve this ambiguity, here the internal energy potential is used instead. Constrained maximisation methods on the entropy can be used to solve for the system equilibrium state. However, a further extension is necessary for atmospheric systems. In the Earth’s atmosphere many important non-equilibrium processes take place; for example, freezing of super-cooled water, and evaporation into subsaturated air. To fully capture processes such as these, the equilibrium method must be reformulated to involve finite rates of approach towards equilibrium. Here the principles of non-equilibrium thermodynamics are used, beginning with a set of phenomenological equations, to show how non-equilibrium moist processes may be coupled to a semi-implicit semi-Lagrangian dynamical core. A standard bubble test case and simulations of cloudy thermals are presented to demonstrate the viability of the approach for equilibrium thermodynamics, as well as the more complex non-equilibrium regime.

How to cite: Bowen, P. and Thuburn, J.: Consistent and Flexible Thermodynamics in Atmospheric Models Using Internal Energy as a Thermodynamic Potential, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4670, https://doi.org/10.5194/egusphere-egu22-4670, 2022.

This talk introduces WAVETRISK-OCEAN, an incompressible version of the atmosphere model WAVETRISK with a free surface. This new model is built on the same wavelet-based dynamically adaptive core as WAVETRISK, which itself uses DYNAMICO’s mimetic vector-invariant multilayer rotating shallow water formulation. Both codes use a Lagrangian vertical coordinate with conservative remapping. The ocean variant solves the incompressible multi-layer shallow water equations with inhomogeneous density layers. Time integration uses barotropic–baroclinic mode splitting via a semi-implicit free surface formulation, which is about 34-44 times faster than an unsplit explicit time-stepping. The barotropic and baroclinic estimates of the free surface are reconciled at each time step using layer dilation. No slip boundary conditions at coastlines are approximated using volume penalization. The vertical eddy viscosity and diffusivity coefficients are computed from a closure model based on turbulent kinetic energy. Results are presented for a standard set of ocean model test cases adapted to the sphere (seamount, upwelling and baroclinic turbulence). An innovative feature of WAVETRISK-OCEAN is that it could be coupled easily to the WAVETRISK atmosphere model, thus providing a first building block toward an integrated Earth-system model using a consistent modelling framework with dynamic mesh adaptivity and mimetic properties.

How to cite: Kevlahan, N. and Lemarié, F.: WAVETRISK-OCEAN: an adaptive dynamical core for ocean modelling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4949, https://doi.org/10.5194/egusphere-egu22-4949, 2022.

We introduce a stochastic representation of the rotating shallow water equations and a corresponding structure preserving discretization. The stochastic flow model follows from using a stochastic transport principle and a decomposition of the fluid flow into a large-scale component and a noise term that models the unresolved flow components. Similarly to the deterministic case, this stochastic model (denoted as modeling under location uncertainty (LU)) conserves the global energy of any realization. Consequently, it permits us to generate an ensemble of physically relevant random simulations with a good trade-off between the representation of the model error and the ensemble's spread. Applying a structure-preserving discretization of the deterministic part of the equations and standard finite difference/volume approximations of the stochastic terms, the resulting stochastic scheme preserves (spatially) the total energy. To address the enstrophy accumulation at the grid scale, we augment the scheme with a scale selective (energy preserving) dissipation of enstrophy, usually required to stabilize such stochastic numerical models. We compare this setup with one that applies standard biharmonic dissipation for stabilization and we study its performance for test cases of geophysical relevance. 

How to cite: Bauer, W., Brecht, R., Li, L., and Memin, E.: Towards structure preserving discretizations of stochastic rotating shallow water equations on the sphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6267, https://doi.org/10.5194/egusphere-egu22-6267, 2022.

The gradual densification of CO₂ observation networks and CO₂ observation systems around the Earth, particularly from space, has increased the observational information available for data assimilation and atmospheric inverse modeling to all spatial scales. In particular, it makes it possible to infer surface fluxes of CO₂ over increasingly small regions.

This densification must be accompanied by a corresponding increase in the horizontal resolution of the transport models in which the observations are assimilated or which are inverted. In the latter application, the timescales involved extend over weeks, months or even years, and controlling computational speed despite increasing resolution is particularly critical. This challenge can be met by adapting transport models to new high-performance computing architectures and their new paradigms (multicore processors or accelerators based on graphics processing units). It deeply affects the structure of the codes, in particular the geometry of their mesh and the management of their inputs-outputs.

In this study, we redesign the offline transport model of the Laboratoire de Météorologie Dynamique (LMDz) Global Atmospheric General Circulation Model used in the Copernicus Atmosphere Monitoring Service inversion system (https://atmosphere.copernicus.eu/) in order to test such solutions.

First, we use a new dynamic core associated with an icosahedral-hexagonal spherical mesh, called DYNAMICO. DYNAMICO has a much better scalability than the current Cartesian grid of LMDz, while being efficiently vectorizable. Second, we use the parallel and asynchronous input-output management system called XIOS. XIOS helps damp performance losses associated with disk reads and writes.

The technical performances of the new version will be presented in the case of a regular mesh of 16,000 hexagons on the sphere, equivalent to a global resolution of about 180 km, and with 79 vertical layers, by comparison to the regular Cartesian grid. The scientific assessment is based on a large set of CO₂ observations from the ground, from airplanes and from surface remote sensing reference sites. Particular attention is paid to the skill at high latitudes where the new grid avoids the singularity of the previous version at the pole, but at the cost of a coarser resolution.

How to cite: Lloret, Z., Chevallier, F., and Cozic, A.: Scientific and technical challenges of increasing horizontal resolution in atmospheric CO2 inversion systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7353, https://doi.org/10.5194/egusphere-egu22-7353, 2022.

Successful operational weather forecasting with (semi-)implicit timestepping methods relies on obtaining an accurate solution to a very large system of equations in a timely manner. It is therefore crucial that the solver algorithm is fast and efficient, as this can account for up to a third of model runtime.

For models based on mixed finite element discretisations, the standard Schur-complement solver approach is not feasible since the Schur-complement system is dense and cannot be solved with iterative methods. To address this issue in its next-generation forecast model - codenamed LFRic - the Met Office is investigating a so called “hybridised” solver algorithm, which shows its full potential when combined with multigrid techniques.
We introduce both the hybridised discretisation and multigrid techniques on simplified problems, comparing and contrasting these with the current, non-hybridised multigrid solver algorithm used in the Met Office model. We will talk about how this is generalised to the full model and present results from this comparing several solver configurations.
Since our new hybridised multigrid solver reduces the number of global reduction operations, it is particularly promising when solving very large problems on a massively parallel computer. To explore this, we ran our code on large numbers of compute cores, and will present the results of those runs here.
The efficiency of our non-nested multigrid approach depends on the choice of the coarse level finite element space. To further improve the solver algorithm, we compare different coarse level spaces for a simplified setup in the Firedrake finite element code generation framework.

How to cite: Griffith, M., Mueller, E., and Melvin, T.: Accelerating climate- and weather-forecasts with faster multigrid solvers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10049, https://doi.org/10.5194/egusphere-egu22-10049, 2022.

The matrix model for the barotropic vorticity equation on the torus and the 2-sphere, introduced by Zeitlin, remains a reference discretization, since it provides N conserved quantities with N degrees of freedom. Modin and Vivani recently also demonstrated its relevance for the numerical study of geophysical fluid dynamics. The origins of the discretization and its connection to the Moyal bracket of quantum mechanics are, however, somewhat mysterious, hampering the prospect of generalizing the ansatz to the shallow water and primitive equations. We show how the matrix model can be understood in the framework of variational, structure preserving discretizations of fluids introduced by Pavlov and co-workers, which has recently been extended to the finite element setting by Natale and Cotter as well as Gay-Balmaz and Gawlik. Pavlov et al.’s approach is to discretely mirror the continuous theory, where the dynamics take place in the space of (divergence free) vector fields, i.e. the Lie algebra of the (volume preserving) diffeomorphism group, and the reduced Euler-Poincaré variational principle yields the dynamical equations. Specifically, one considers the representation of the group and its Lie algebra on a finite dimensional function space, i.e. through their action on scalar functions, yielding an appropriate matrix group and Lie algebra as discrete configuration space. Because of the finite dimensional setting, one has to deviate at this point from the continuous theory and introduce a non-holonomic constraint, which amounts to restricting the finite dimensional Lie algebra to elements that correspond to vector fields. The Euler-Poincaré-d’Alembert principle has consequently also to be used to obtain semi-discrete time evolution equations. A modification of this methodology is to insist on the Euler-Poincaré theory from the continuous side and modify how the Lie algebra is discretized so that it remains applicable. Specifically, one can start with the action of a symmetry group on the configuration space, e.g. SO(3) on the 2-sphere, and consider the associated infinitesimal action of the Lie algebra on functions, which corresponds to vector fields, as in the approach by Pavlov et al. When the action admits a momentum map, it can equivalently be written using the Poisson bracket and Hamiltonians linear in the Lie algebra. Building on this and requiring that a generalization of the action on functions beyond linear Hamiltonians should be consistent with the group action, one is led to the iterated action of the Poisson algebra, which is equivalent to the Moyal bracket Lie algebra for the symmetry group (through the universal enveloping algebra of the original Lie algebra). When one then fixes a finite-dimensional spectral basis to discretize functions, this corresponds to a sub-algebra of gl(n). Finally, using Euler-Poincaré theory, as in the continuous case, on this Lie sub-algebra, one obtains the matrix model by Zeitlin that retains N conserved quantities for N degrees of freedom. We hope that our rationalization of the derivation of the matrix model opens up the possibility to generalize it to other equations for geophysical fluid dynamics, and we discuss possible directions for the shallow water and primitive equations.

How to cite: Lessig, C. and da Silva, C. C.: The matrix model for the barotropic equation, connections to variational discretizations, and generalizations to the shallow water equations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11140, https://doi.org/10.5194/egusphere-egu22-11140, 2022.

Semi-Lagrangian advection schemes are accurate, efficient and retain accuracy and stability even for large Courant numbers, but are not conservative. Flux-form semi-Lagrangian schemes are conservative and used to achieve large Courant numbers. However, this is complicated and would be prohibitively expensive on grids that 
are not topologically rectangular. 

Strong winds or updrafts can lead to localised violations of Courant number restrictions which can cause a model with explicit Eulerian advection to crash. Schemes are needed that remain stable in the presence of large Courant numbers and general grids, while the accuracy in the presence of localised large Courant numbers may not be so crucial.

Implicit time stepping for advection is not popular in atmospheric science because of the cost of the global matrix solution and the phase errors for large Courant numbers. However, implicit advection is simple to implement (once appropriate matrix solvers are available) and is conservative on any grid structure and can exploit improvements in solver efficiency and parallelisation. This talk will describe an implicit version of the MPDATA advection scheme and show results of linear advection test cases. To optimise accuracy and efficiency, implicit time stepping is only used locally where needed. This makes the matrix inversion problem local rather than global. With implicit time stepping MPDATA retains positivity, smooth solutions and accuracy in space and time.

How to cite: Weller, H., Woodfield, J., Kuehnlein, C., and Smolarkiewicz, P.: Long Time Steps for Advection: MPDATA with implicit time stepping, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11370, https://doi.org/10.5194/egusphere-egu22-11370, 2022.

Thunderstorms in southeastern South America (SESA) often reach extreme intensity, duration, and vertical extension. Diverse techniques have been proposed to identify severe storm signatures in satellite images, such as overshooting tops (OTs). Previous studies have shown a large correlation between OTs and the occurrence of severe weather such as large hail, damaging winds, and tornadoes. In particular, in SESA, deep convection systems initiation is sometimes related to elevated topography such as Sierras de Córdoba and the Andes mountain range. These unique meteorological and geographical conditions motivated the RELAMPAGO-CACTI field campaign, which was conducted to study the storms in this region.

This study aims to characterize the occurrence of OTs in SESA through their spatial distribution as well as their diurnal and seasonal cycles.  An OT analysis is presented using an OT detection algorithm (known as OT-DET) applied to GOES16 satellite data from October 2018 to March 2019. OT-DET sensitivity is evaluated considering two alternatives of tropopause temperature determination and different cloud anvil temperature thresholds. OT-DET is validated against an OT occurrence database generated through an expert detection of OTs using GOES16 visible and IR images. The results of this validation as well as the OT characterization will be described at the conference. 

How to cite: Simone, I. C., Salio, P., Ruiz, J., and Vidal, L.: Study of Deep Convection with Presence of Overshooting Tops During RELAMPAGO Campaign, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-147, https://doi.org/10.5194/egusphere-egu22-147, 2022.

Various studies in the UK, Great Plains and Southeastern USA have highlighted the presence of certain radar signatures prior to the onset of or during severe convection. One type of such radar signature is a differential reflectivity (Z_DR) column, which is defined as a vertical columnar region of enhanced Z_DR that extends above the freezing level. Several field campaigns synthesising radar and in-situ measurements confirmed that such columns contain large supercooled millimetre-sized droplets lofted into convective storms and are in, or near strong updrafts. Recent work using a single research radar in Oklahoma also exploited the usefulness of detecting Z_DR columns for informing nowcasters of severe convection.

The goal of this study is to identify potential severe convective events in the UK mostly for cases over the summer season using polarimetric radar measurements. The UK Met Office has fully upgraded all 18 C-band radars since January 2018 with full dual-polarisation operational capability. From this network, we derive a 3D radar composite, which provides large coverage on the order of 1000 km for monitoring potentially hazardous weather. Environmental conditions are also investigated prior to and during the onset of convection to understand the effectiveness in Z_DR columns as precursors of severe convection depending on synoptic regime.

Using past cases, we track storm cells using maximum reflectivity in the column and identify whether the cells contain Z_DR columns, where a Z_DR column is identified based on a 3D volume thresholded by reflectivity (Z_H) and Z_DR. For nowcasting of severe storms, with Z_H > 50 dBZ, we find optimal Z_H and Z_DR thresholds of around 30 dBZ and 2.0 dB respectively existing within Z_DR columns. This agrees with past literature and physical understanding indicating a low concentration of large super-cooled water droplets within Z_DR columns explained by condensation-coalescence processes, especially during early stages of convective development. In contrast, other works may show Z_DR columns associated with areas of high Z_H, suggesting detection of such columns in more mature stages of a storm. Algorithm performance in identifying Z_DR columns for early detection of severe convection and its optimal parameters vary with synoptic regime.

How to cite: Lo, C. H. B., Stein, T. H. M., Westbrook, C. D., Scovell, R. W., Darlington, T., and Lean, H. W.: Identification of ZDR columns for early detection of severe convection in southern England, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-317, https://doi.org/10.5194/egusphere-egu22-317, 2022.

Skillful forecast of the Madden Julian Oscillation (MJO) has an important scientific interest because the MJO represents one of the most important sources of  sub-seasonal predictability. Proxies of the MJO can be derived from the first principal components of wind speed and outgoing longwave radiation (OLR) in the Tropics (RMM1 and RMM2). The challenge is to forecast these two indices. This study aims at providing ensemble forecasts MJO indices  from analogs of the atmospheric circulation, mainly the geopotential at 500 hPa (Z500) by using a stochastic weather generator. We generate an ensemble of 100 members for the amplitude and the RMMs for sub-seasonal lead times (from 2 to 4 weeks). Then we evaluate the skill of the ensemble forecast and the ensemble mean using respectively probabilistic and deterministic skill scores. We found that a reasonable forecast could reach 40 days for the different seasons. We compared our SWG forecast with other forecasts of the MJO.

How to cite: Krouma, M., Yiou, P., and Silini, R.: Ensemble forecast of the Madden Julian Oscillation using a stochastic weather generator based on analogs of  Z500, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-742, https://doi.org/10.5194/egusphere-egu22-742, 2022.

In front of determinism limitations, ensemble forecasting provides competitive advantage assessing uncertainty and helping weather information users in decision-making. Analog ensemble method (AnEn) is one of the most intuitive and computationally cheap ensemble methods that leverages a single deterministic model integration to produce probabilistic information. This method builds an ensemble forecast from a set of past observations of the target variable, neatly selected from a historical training dataset. For a given location, the most similar past forecasts to the current prediction are identified and the associated  past observations are nominated  as members of the analog ensemble forecast. However, The  AnEn forecasting quality is tightly affected by the process of skillful analogs selection in the training data which depends on predictor’s weighting among other factors. This work presents a new weighting strategy based on machine learning techniques (XGBoost, Random Forest and Linear regression) and assesses the impact of its application on the AnEn performance  for 10m wind speed  and 2m temperature forecasting over 13 Moroccan airports in the short term forecasting framework (24 hours). To achieve this, hourly forecasts from the operational mesoscale AROME model and the verifying observations covering 5 year period (2016-2020) are used.  The predictors include 2m temperature, 2m relative humidity, 10m wind speed and direction, mean sea level pressure and surface pressure,  meridonal and zonal components of 10m wind. The basic configuration of Delle Monache et al. (2013) -DM13- where all the predictor’s weights are equal to one is used here as a benchmark. The best weights are computed independently from one airport to another. Since the proposed predictor-weighting strategies can accomplish both the selection of relevant predictors as well as finding their optimal weights, and hence preserve physical meaning and correlations of the used weather variables, the AnEn performances are improved by up to 50 % for bias and by 30% for RMSE for most airports. This improvement varies as function of lead-times and seasons compared to AROME and DM13’s configuration. Results show also that AnEn performance is geographically dependent where a slight worsening is found for some airports.

Keywords : Analog Ensemble,  Machine Learning, Predictors Weighting Strategies, 2m Temperature, 10m Wind Speed, XGBoost, Linear Regression, Random Forest, Ensemble Forecasting.

How to cite: Alaoui, B., Bari, D., and Ghabbar, Y.: New AI based weighting strategy for 2m temperature and 10m wind speed forecasting over Moroccan airports  using the analog ensemble method., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2450, https://doi.org/10.5194/egusphere-egu22-2450, 2022.

Mountain lee waves are a kind of gravity waves often associated with adverse weather phenomena, such as turbulence that can affect the aviation safety. Not surprisingly, turbulence events have been related with numerous aircraft accidents reports. In this work, several mountain lee wave events in the vicinity of the Adolfo Suarez Madrid-Barajas airport (Spain) are simulated and analyzed using HARMONIE-AROME, the high-resolution numerical model linked to the international research program ACCORD-HIRLAM. Brightness temperature from the Meteosat Second Generation (MSG-SEVIRI) has been selected as observational variable to validate the HARMONIE-AROME simulations of cloudiness associated with mountain lee wave events. Subsequently, a characterization of the atmospheric variables related with the mountain lee wave formation (wind direction and speed, static stability and liquid water content) has been carried out in several grid points placed in the windward, leeward and over the summits of the mountain range close to the airport. The characterization results are used to develop a decision tree to provide a warning method to alert both mountain lee wave events and associated lenticular clouds. Both HARMONIE-AROME brightness temperature simulations and the warnings associated with mountain lee wave events were satisfactory validated using satellite observations, obtaining a probability of detection and percent correct above 60% and 70%, respectively.  

How to cite: Díaz Fernández, J., Bolgiani, P., Santos Muñoz, D., Sastre, M., Valero, F., Farrán, J. I., González Alemán, J. J., and Martín Pérez, M. L.: Characterization and warnings for mountain waves using HARMONIE-AROME, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2471, https://doi.org/10.5194/egusphere-egu22-2471, 2022.

Radar rainfall nowcasting, an observation-based rainfall forecasting technique that statistically extrapolates current observations into the future, is increasingly used for short-term forecasting (<6 hours ahead). These first hours ahead are a key time scale for e.g. (flash) flood warnings and they are generally not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models.

A recent development in nowcasting is the transition to more community-driven, open-source models. The Python library pySTEPS is an example of this. One of its main features is an efficient Python implementation of the probabilistic nowcasting scheme STEPS. pySTEPS generates an ensemble of rainfall forecasts by perturbing a deterministic extrapolation nowcast with spatially and temporally correlated stochastic noise. It considers the dynamical scaling of the rainfall predictability by decomposing the rainfall fields into a multiplicative cascade and applies different stochastic perturbations for each scale. This results in large-scale features that evolve more slowly than the small-scale features.

Despite pySTEPS' representation of the uncertainty associated with growth and decay of rainfall in the first 1-2 hours of the nowcast, it quickly loses skill after 2 hours, or even less for convective rainfall events or small radar domains. To extend the skillful lead time to the desired time scale of 6 hours or more, a blending with NWP rainfall forecasts is necessary. We have implemented an adaptive scale-dependent blending in pySTEPS based on earlier work in the STEPS scheme. In this blending implementation, the blending of the extrapolation nowcast, NWP and noise components is performed level-by-level, which means that the blending weights vary per cascade level. These scale-dependent blending weights are computed from the recent skill of the forecast components, and converge to a climatological value, which is computed from a 1-month rolling window and can be adjusted to the (operational) needs of the user. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy.

We present a validation of the blending approach in a hydrometeorological testbed using Belgian radar and NWP data for the Belgian and Dutch catchments Dommel, Geul and Vesdre. We compare the resulting ensemble rainfall and discharge forecasts of the blending implementation with ensemble nowcasts from pySTEPS, ALARO (NWP) forecasts and a linear blending strategy.

How to cite: Imhoff, R., De Cruz, L., Dewettinck, W., Velasco-Forero, C., Nerini, D., Goudenhoofdt, E., Brauer, C., van Heeringen, K.-J., Uijlenhoet, R., and Weerts, A.: Scale-dependent blending of ensemble rainfall nowcasts with NWP in the open-source pySTEPS library, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7026, https://doi.org/10.5194/egusphere-egu22-7026, 2022.

Ensemble forecasts are calculated to give insight into the range of possible future outcomes and potential risks, but it is challenging for operational forecasters to deal with large ensemble data sets and to distil pertinent information from them, especially during high-impact events where forecasts and warnings must be issued and updated quickly with a high degree of accuracy and consistency.  Therefore, it is important to streamline this process by reducing the amount of data an operational forecaster must digest while still maintaining the necessary accuracy.  To do this, a novel clustering technique has been developed for use on ensemble forecasts to extract likely scenarios in real-time.  This technique uses k-medoids clustering and the spatial separation between frontal regions in ensemble members to group similar members together.  Frontal regions are often associated with heavy rain and strong winds, common high-impact events in the UK.  A single representative member is then extracted from each cluster to present to the forecaster as a potential weather scenario.  The method is illustrated with the UK Met Office operation ensemble forecasting system, MOGREPS-G.

How to cite: Boykin, K.: Extracting likely scenarios from high resolution ensemble forecasts in real-time, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7391, https://doi.org/10.5194/egusphere-egu22-7391, 2022.

Early hydrological hazard warning demands precise weather forecasts to accurately predict the timing and the location of intense precipitation events which can cause severe floods/landslides and present risks to urban and natural environments. Extrapolation of precipitation by radar rainfall products at high space and time scales with short lead times outperforms forecasts of numerical weather prediction. Therefore, developing and improving of rainfall nowcasts systems are essential. Rainfall nowcasting is the process of forecasting precipitation field movement and evolution at high spatial and temporal resolutions with short lead times(<6h) in which the advection of the precipitation fields is estimated by extrapolating real-time remotely sensed observations. Radar rainfall nowcasting is increasingly applied because of the high potential of radar products in short-term rainfall forecasting due to their high spatiotemporal resolutions (typically, 1 km and 5 min). It consists of two procedures in tracking precipitation features to calculate the velocity from a series of consecutive radar images and propagating the most recent precipitation observation into the future using the obtained velocity. Optical flow represents one of the most used methods for tracking the motion fields from consecutive images. Deep learning techniques are those machine learning methods that utilise deep artificial neural networks. Deep learning has become one of the most popular and rapidly spreading methods in different scientific disciplines including water-related research. Deep learning applications in radar-based precipitation nowcasting is still in its early stage with many knowledge gaps and their full potential in rainfall nowcasting requires more investigation. This work evaluates the performance of a deep convolutional neural network (called rainnet) and three optical flow algorithms (called Rainymotion Sparse, Rainymotion Dense, Rainymotion DenseRotation) compared with Eulerian Persistence to assess their predictive skills in nowcasting. Synthetic precipitation scenarios have been created with different motion fields (linear and rotational motions), velocities, intensities, sizes, and locations. The models have been evaluated to forecast different precipitation processes that contribute mainly to model errors such as constant and accelerated linear and rotational motions, growth and decay in both size and intensity. Different verification metrics have been used to evaluate the skill of the forecasts.

Keywords: radar rainfall nowcasting; deep learning; optical flow; extrapolation; rainnet; rainymotion

How to cite: Abdelhalim, A., Rico-Ramirez, M., and Han, D.: Evaluation of radar rainfall nowcasting techniques to forecast synthetic storms of different processes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10595, https://doi.org/10.5194/egusphere-egu22-10595, 2022.

Rainfall plays a significant role in agricultural farming and is considered one of the major natural sources for all living things.  The increase in greenhouse emissions and change in climatic conditions have an adverse effect on the rainfall patterns. Therefore, it becomes crucial to analyze the changing patterns and to forecast rainfall  to mitigate natural disasters that could be caused by the unexpected heavy rainfalls. This paper aims to compare the performance of seven states of the art time series models namely Moving Average(MA), Naïve Forecast(NF), Simple Exponential(SE), Holt’s Linear(HL), Holt’s Linear Additive(HLA), Autoregressive Integrated Moving Average(ARIMA), Seasonal Autoregressive Integrated Moving Average(SARIMA) for the prediction of rainfall. The historical monthly rainfall data from six different stations in United Arab Emirates (UAE) was obtained to assess the performance of seven techniques. Experimental results show that ARIMA outperforms all the prediction models with a mean square error (RMSE) of 9.49 followed by Holt’s Linear model with an RMSE value of 9.91. The performance of all the models is comparable and shows promising performance in rainfall prediction. This also shows the ability of these models to predict the rainfall in arid regions like the UAE

How to cite: Baig, F., Sherif, M., Ali, L., Khan, W., and Faiz, M. A.: Predicting Rainfall using Data-Driven Time Series Approaches, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11143, https://doi.org/10.5194/egusphere-egu22-11143, 2022.

In this study, we present a deep learning-based method to provide seamless high-frequency wind speed forecasts for up to 30 hours ahead. For each selected site, our method generates an ensemble forecast with an update frequency of 10 to 15 minutes(depending on the observation site’s update-frequency). The main objective in this machine learning based post-processing method is to optimally exploit highly resolved NWP models and particularly utilize their multi-level meteorological parameters to integrate the three-dimensionality of weather processes. Further key objectives of this research are to consider different spatial and temporal resolutions and different topographic characteristics of the selected sites. We evaluate the best praxis for efficiently post-processing both the 10-meter wind speed at selected Austrian meteorological observation sites and wind speed on hub height of wind turbines in wind farms.

The method is based on an artificial neural network (ANN), particularly a long-short-term-memory (LSTM) adopted to process several differently structured inputs simultaneously (i.e., different gridded inputs along with observed time-series) and generate ensemble output. An LSTM layer models recurrent steps in the ANN and is, thus, useful for time-series, such as meteorological observations.

Our ensemble forecast method is evaluated for a case study in 2021 using several years of training, including extreme weather event for the selection of sites. The utilized data includes the meteorological observations, gridded nowcasting data as well as NWP data from ECMWF IFS and AROME at several pressure/altitude levels. Hourly runs for 12 test locations (selected TAWES sites covering different topographic situations in Austria) and two wind turbine sites in different seasons are conducted. The obtained results indicate that the model succeeds in learning from inputs while remaining computationally efficient. In most cases the ANN method yields high forecast-skills and is compared to available methods such as the raw NWP model output, climatology, and persistence.

How to cite: Schicker, I., Papazek, P., and DeWit, R.: High-frequency ensemble wind speed forecasting using deep learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11240, https://doi.org/10.5194/egusphere-egu22-11240, 2022.

Detecting and predicting heavy precipitation for the next few hours is of great importance in weather related decision-making and early warning systems. Although great progress has been achieved in convective-permitting numerical weather prediction (NWP) over the past decades, video prediction models based on deep neural networks have become increasingly popular over the last years for precipitation nowcasting where NWP models fail to capture the quickly varying precipitation patterns. However, previous video prediction studies for precipitation nowcasting showed that heavy precipitation events are barely captured. This has been attributed to the optimization on pixel-wise losses which fail to properly handle the inherent uncertainty.  Hence, we present a novel video prediction model, named CLGAN, embedding the adversarial loss is proposed in this study which aims to generate improved heavy precipitation nowcasting. The model applies a Generative Adversarial Network (GAN) as the backbone. Its generator is a u-shaped encoder decoder network (U-Net) equipped with recurrent LSTM cells and its discriminator constitutes a fully connected network with 3-D convolutional layers. The Eulerian persistence, an optical flow model DenseRotation and an advanced video prediction model PredRNN-v2 serve as baseline methods for comparison. The models performance are evaluated in terms of application-specific scores including root mean square error (RMSE), critical success index (CSI), fractions skill score (FSS) and the method of object-based diagnostic evaluation (MODE). Our model CLGAN is superior to the baseline models for dichotomous events, i.e. the CSI, with a threshold of heavy precipitation (8mm/h), is significantly higher, thus revealing improvements in accurately capturing heavy precipitation events. Besides, CLGAN outperforms in terms of spatial scores such as FSS and MODE. We conclude that the predictions of our CLGAN architecture match the stochastic properties of ground truth precipitation events better than those of previous video prediction methods. The results encourage the applications of GAN-based video prediction architectures for extreme precipitation forecasting.

How to cite: Ji, Y., Gong, B., Langguth, M., Mozaffari, A., Mache, K., Schultz, M., and Zhi, X.: GAN-based video prediction model for precipitation nowcasting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12086, https://doi.org/10.5194/egusphere-egu22-12086, 2022.

Inspired by the success of superresolution applications in computer vision, deep neural networks have recently been recognized as an appealing approach for statistical downscaling of meteorological fields. While further increasing the resolution of numerical weather prediction models is computationally very expensive, statistical downscaling models can accomplish this task much cheaper once they have been trained.

In this study, we apply a generative adversarial network (GAN) to downscale the 2m temperature over Central Europe where complex terrain introduces a high degree of spatial variability. GANs are considered superior to purely convolutional networks since the model is encouraged to generate data whose statistical properties are similar to real data. Here, the generator consists of an u-shaped encoder decoder network which is capable of extracting features on various spatial scales. As a quasi-realistic test suite, we map data from the ERA5 reanalysis dataset onto a 0.1°-grid with the help of short-range forecasts from the Integrated Forecasting System (IFS) model. To increase the complexity of the downscaling task, the ERA5 reanalysis data is coarsened beforehand onto a 0.8°-grid, thus increasing the downscaling factor to 8. We evaluate our statistical downscaling model in terms of several evaluation metrics which measure the error on grid point-level as well as the quality of the downscaled product in terms of spatial variability and produced probability function. We also investigate the importance of static and dynamic predictors such as the surface elevation and the temperature on different pressure levels, respectively. Our results motivate further development of deep neural networks for statistical downscaling of meteorological fields. This includes downscaling of other, inherently uncertain variables such as precipitation, operations on spatial resolutions at kilometer-scale and ultimately targets an operational application on output data from global NWP models.

How to cite: Langguth, M., Gong, B., Ji, Y., Amirpasha, M., Mache, K., and Schultz, M. G.: Stochastic downscaling of the 2m temperature with a generative adversarial network (GAN), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12252, https://doi.org/10.5194/egusphere-egu22-12252, 2022.

For hydrological runoff simulations in hydropower applications, accurate analyses and short-term forecasts of precipitation are of utmost importance. Traditionally, radar-based extrapolations are used for very short-term time scales (approx. 0 - 2 hours ahead). However, during recent years, convection-permitting NWP models have become better at very high spatial and temporal resolution forecasts (e.g. through radar assimilation, RUC configurations). Such models have the advantage of capturing the complex and non-linear evolution of precipitation systems like fronts or thunderstorms in a more physically accurate way than extrapolations, but they are also prone to inaccuracies in precipitation distribution. The aim of this paper is to employ machine learning to combine the strengths of the conventional radar extrapolation (localization and movement of existing storms) with the benefit of the model’s ability to predict storm evolution.  Results show that even a relatively simple sequential deep neural network is able to outperform both, the operational nowcasting and NWP model forecasts. However, the results are highly sensitive to variable selection, loss function, and localization features have a large impact on performance, which is also discussed.

How to cite: Kann, A., Atencia, A., Scheffknecht, P., and Giannakos, A.: AI-based blending of conventional nowcasting with a convection-permitting NWP model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12384, https://doi.org/10.5194/egusphere-egu22-12384, 2022.

Thanks to recent advances in multisensory observation systems and high-resolution numerical weather prediction (NWP) models, a wealth of information is available to feed and improve operational weather forecasting systems. At the same time, end users such as the renewable energy sector and hydrological services require increasingly detailed and timely weather forecasts that take into account the latest observations.

However, data assimilation in NWP models cannot yet leverage the full spatial or temporal resolution of today's observation systems. Moreover, the combined assimilation and model run takes significantly more time than an extrapolation-based nowcast, and cannot match its accuracy at short lead times. Therefore, many National Meteorological Services (NMSs) are moving towards seamless prediction systems. Seamless prediction aims to make optimal use of today’s rapidly available, high-resolution multisensory observations, nowcasting algorithms and state-of-the-art convection-permitting NWP models. This approach integrates multiple data and model sources to provide a single, frequently updating deterministic or probabilistic forecast for lead times from minutes to days.

We present the seamless ensemble prediction system of the Royal Meteorological Institute of Belgium, called Project IMA (Japanese for "now" or "soon"). It provides rapidly updating seamless forecasts for the next 5 minutes to 24 hours. The nowcasting component is based on two systems: (1) the open-source probabilistic precipitation nowcasting scheme pySTEPS, which now features a scale-dependent blending with NWP ensemble forecasts (also presented in this session) and (2) an ensemble of INCA-BE nowcasts using two different NWP models, for other meteorological variables. The short-range NWP component consists of a multimodel lagged Mini-EPS of two convection-permitting configurations of the ACCORD system: AROME and ALARO, running at 1.3km resolution. It features a 3-hourly DA cycle and provides high-frequency precipitation output to facilitate the blending of precipitation nowcasts and forecasts. The system runs robustly using our NodeRunner tool based on EcFlow, ECMWF's operational work-flow package. We will give an overview of the development (past and future), some lessons learned, and use cases for Project IMA.

How to cite: De Cruz, L., Deckmyn, A., Degrauwe, D., Dehmous, I., Delobbe, L., Dewettinck, W., Goudenhoofdt, E., Imhoff, R., Reyniers, M., Smet, G., Termonia, P., Van den Bergh, J., Van Ginderachter, M., and Vannitsem, S.: Project IMA: Building the Belgian Seamless Prediction System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12529, https://doi.org/10.5194/egusphere-egu22-12529, 2022.

Terrain with different shapes and ground surface properties has extremely complex impacts on atmospheric motion, and the forecast uncertainty and complexity caused by terrain brings great challenges to disaster prevention and mitigation. Therefore, it is essential to design a new-style model topography disturbance model for ensemble prediction system specifically to solve the prediction uncertainty caused by complex terrain. In this paper, on the basis of combing the current models and methods for dealing with different terrain uncertainty, and considering the non-uniformity of terrain gradient, the key element of describing terrain complexity, an orthogonal terrain disturbance method based on terrain gradient is designed and proposed, and the obtained high-resolution orthogonal terrain disturbance is superimposed on the static terrain height of the model to generate different ensemble members, so as to describe the uncertainty in the terrain generation process of high-resolution numerical model. At the same time, a comparative study is carried out with the ensemble forecast of model terrain disturbance between using the new-style method and using different terrain interpolation schemes or smoothing schemes. The preliminary test shows that: first of all, the ensemble dispersion of terrain height disturbance based on the new-style method is closely related to the terrain gradient. The area with small terrain gradient has smaller terrain disturbance ensemble dispersion, while the area with large terrain gradient has larger ensemble dispersion, which shows that the new scheme is more reasonable. Furthermore, compared with the model terrain disturbance schemes with different interpolation or smoothing methods, the dispersion of the new-style method is larger, and the skill of the new-style method becomes more and more obvious with the increase of model resolution. Thirdly, from the comparative study of the forecast effect of high-level and low-level weather elements, the new-style method ensemble forecast has obvious improvement on the forecast effect of low-level variables, especially in areas with complex terrain or large terrain gradient. The possible reason is that the new method can more objectively describe the terrain uncertainty. Fourthly, compared with the ensemble forecast results of different interpolation and smoothing methods, the new-style terrain disturbance scheme can improve the precipitation probability forecast skill and reduce the ensemble average root mean square error, and improve the ensemble average forecast of upper-air elements and near-surface elements. Lastly, the test of the number of ensemble members shows that the prediction effect of new-style terrain disturbance scheme with less members is equivalent or better than that of the interpolation or smoothing terrain disturbance scheme with more members. In summary, the new-style terrain perturbation theory based on terrain gradient in this paper provides a technical reference for the development of complex terrain convection-allowing scale ensemble forecast, which has important theoretical value and application prospect.

Key words: complex terrain，ensemble prediction，convection-allowing scale，topographic perturbation，topographic gradient

How to cite: Chaohui, C., Yi, L., Hongrang, H., Kan, L., and Yongqiang, J.: Preliminary study of a new-style terrain disturbance method based on gradient inhomogeneity in convection-allowing scale ensemble prediction system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13244, https://doi.org/10.5194/egusphere-egu22-13244, 2022.

To quantify the intensity of squall line in mid-latitudes, the author recently proposed a squall line intensity assessment method based on cold pool, which provides a measure of squall line intensity.

The disturbance potential temperature density is calculated by using the potential temperature, water vapor and all kinds of water condensate output from the numerical weather forecast model, and the boundary of the cold pool is judged according to the disturbance potential temperature density less than -2K. Based on the contour surface buoyancy, the high surface buoyancy is calculated according to the disturbance potential temperature density, and then the strength of the cold pool is calculated. In this method, the intensity of squall line is analyzed comprehensively by principal component analysis, combined with the weather phenomena accompanied by squall line occurrence, such as cold pool intensity, surface wind speed, ground pressure variation, surface temperature variation, simulated radar echo and so on. The above analysis is the local intensity on different grid points when the squall line occurs, and the overall squall line intensity is obtained by accumulating the local intensity in the squall line range.

The method is verified by the model output data of a squall line process occurred in northern Jiangsu on May 16, 2013. The results show that the distribution of the local squall line intensity is coupled with the surface wind field and heavy precipitation. The intensity evolution of the overall squall line reaches the peak in a short time and then decreases, which corresponds to the life history of the birth, development, maturity and dissipation of the squall line, and also reflects the characteristics of the short life history of the squall line developing rapidly and then dissipating. This method provides technical support for the forecast of squall line and the emergency plan issued by meteorological department.

Acknowledgements. This research was supported by the National Natural Science Foundation of China (Grant Nos. 41975128 and 42075053).

Keywords: squall line, intensity, assessment method, disturbance potential temperature density

How to cite: Yang, R., Jiang, Y., Chen, C., He, H., Li, Y., and Huang, H.: An Assessment Method of Squall Line Intensity Based on Cold Pool, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13532, https://doi.org/10.5194/egusphere-egu22-13532, 2022.

Non-hydrostatic km-scale weather and climate models are promising in simulating clouds, especially convective ones. However, even km-scale models need to parameterize some physical processes and are thus subject to the corresponding uncertainty of parameters. Systematic calibration has the advantage of improving model performance with transparency and reproducibility, thus benefiting model intercomparison projects, process studies, and climate-change scenario simulations. 

In this paper, the regional atmospheric climate model COSMO v6 is systematically calibrated over the Tropical South Atlantic. First, the parameters' sensitivities are evaluated with respect to a set of validation fields (outgoing longwave radiation (OLR), outgoing shortwave radiation (OSR) and latent heat flux (LHFL)). Five of the most sensitive parameters are chosen for calibration. The objective calibration then closely follows the methodology of Bellprat et al. (2016). This includes simulations considering the interaction of all pairs of parameters and the exploitation of a quadratic-form metamodel to emulate the simulations. In the current set-up with 5 parameters, 50 simulations are required to build the metamodel. Then Latin hypercube sampling is applied and the set of parameters with the best performance score is chosen as the optimal parameter set. The model is calibrated for the year 2016 and validated in 2013. And  the optimal parameter setting lead to significant improvements for both years, especially for OSR, which is closely related to low clouds. More specifically, the domain annual mean OSR bias is reduced from 40 to 13.5 Wm^-2. Moreover, when we apply the optimal setting over a larger domain with a slightly higher resolution (from 4km to 3km) in 2006, the optimal setting still works, especially for OSR and for the calibrated domain. 

The results thus show that parameter calibration is a useful and efficient tool for model improvement. We will also discuss potential limitations and highlight how the approach could be extended to global atmospheric models. Calibrating over a larger domain might help improve the overall performance, but would potentially also lead to compromises among different regions and variables, and require more computational resources.

How to cite: Liu, S., Zeman, C., Sørland, S. L., and Schär, C.: Systematic Calibration of A Convection-Resolving Model: Application over Tropical Atlantic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1214, https://doi.org/10.5194/egusphere-egu22-1214, 2022.

Eleven 40-day long integrations of five different global models with horizontal resolutions of less than 9 km are compared in terms of their global energy spectra. The method of normal-mode function decomposition is used to distinguish between balanced (Rossby wave; RW) and unbalanced (inertia-gravity wave; IGW) circulation. The simulations produce the expected canonical shape of the spectra, but their spectral slopes at mesoscales, and the zonal scale at which RW and IGW spectra intersect differ significantly. The partitioning of total wave energies into RWs an IGWs is most sensitive to the turbulence closure scheme and this partitioning is what determines the spectral crossing scale in the simulations, which differs by a factor of up to two. It implies that care must be taken when using simple spatial filtering to compare gravity wave phenomena in storm-resolving simulations, even when the model horizontal resolutions are similar. In contrast to the energy partitioning between the RWs and IGWs, changes in turbulence closure schemes do not seem to strongly affect spectral slopes, which only exhibit major differences at mesoscales. Despite their minor contribution to the global (horizontal kinetic plus potential available) energy, small scales are important for driving the global mean circulation. Our results support the conclusions of previous studies that the strength of convection is a relevant factor for explaining discrepancies in the energies at small scales. The models studied here produce the major large-scale features of tropical precipitation patterns. However, particularly at large horizontal wavenumbers, the spectra of upper tropospheric vertical velocity, which is a good indicator for the strength of deep convection, differ by factors of three or more in energy. High vertical kinetic energies at small scales are mostly found in those models that do not use any convective parameterisation.

How to cite: Stephan, C., Duras, J., Harris, L., Klocke, D., Putman, W. M., Taylor, M., Wedi, N. P., Žagar, N., and Ziemen, F.: Atmospheric energy spectra in global kilometre-scale models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1924, https://doi.org/10.5194/egusphere-egu22-1924, 2022.

Simulating diurnal cycle of rainfall is a difficult challenge for general circulation models. We developed a global unstructured mesh model, Global-to-Regional Integrated forecast SysTem (GRIST), targeting at unified weather-to-climate forecast. The performance of the model in simulating the summer precipitation over East Asia has been evaluated. Yet the performance from a global perspective remains less understood. In this study, we focus on the simulations of precipitation diurnal cycle during boreal summer, and examine four AMIP simulation results of the GRIST model. These configurations mainly differ in the horizontal resolution. Thus, they reflect the direct changes due to varying resolutions. By refining the resolution over East Asia (VR-EA) and North America (VR-NA) respectively, we analyze the similarities and differences in model behaviors in simulating diurnal cycle of precipitation over these two refinement regions. VR-EA well reproduces the nocturnal rainfall, while VR-NA fails in certain regions respectively. The underlying responses to resolution of these two models are similar. For regions dominated by nocturnal rainfall, the refined resolution significantly increases the composited precipitation intensity at night up to the magnitude of the observation but has little impact on the composite percentage. The percentage of peak rainfall within 00-06h in the model over the Southern Great Plains remains lower than the observation as the resolution refines. Given the much lower occurrence frequency, the contribution of the intense precipitation to the climatological nocturnal rainfall amounts is small in VR-NA. Over East Asia, since the precipitation frequency is comparable to the observation, VR-EA benefits from the increased precipitation intensity due to higher resolution. No apparent artificial features are observed in the transition zone of the variable-resolution mesh. The results suggest that the variable-resolution modeling is cost-effective for simulating the diurnal cycle of climatological summer precipitation.

How to cite: Zhou, Y., Zhang, Y., and Yu, R.: Global variable-resolution model simulation of rainfall diurnal cycle during boreal summer, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2617, https://doi.org/10.5194/egusphere-egu22-2617, 2022.

This work investigates the resolution sensitivity of an explicit dynamics-microphysics coupled system using the GRIST nonhydrostatic model, with varying uniform horizontal resolutions (120 km, 60 km, 30 km, 15 km, 5 km). The experiments follow the DYAMOND (DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains) winter protocol that covers a 40-day integration from UTC00, 20^th  to UTC00, Jan to 29^th, Feb, 2020. The five simulations did not activate parameterized convection, and no specific tuning of model physics is employed such that the direct resolution response of a fixed model system can be examined. One 120-km run with parameterized convection is done to serve as a coarse-resolution reference. Other model configurations for different runs are kept as consistent as possible except certain small differences. Results demonstrate that the model gradually improve its representation of the fine-scale features (e.g., kinetic energy spectra) as resolution increases. In terms of 40-day averaged climate, the 5-km run has an overall more realistic simulation of the rainfall distribution than lower-resolution simulations without parameterized convection (e.g., spatial distribution). Most zonally averaged climate statistics are less prone to be altered by the resolution, except those fields associated with cloud water (e.g., shortwave cloud radiative forcing). This finding was also reached by an earlier study using the ICON model. Though with better fine-scale details, the coarse-resolution averaged features of the 5-km model without parameterized convection do not necessarily (and automatically) gets better than a 120-km simulation with parameterized convection. The tropical rainfall frequency-intensity spectra become more realistic in the 5-km no-convection run, but the 120-km run with parameterized convection shows a more realistic zonally averaged mean state. This impies more development and tuning efforts are still required for global km-scale models.

How to cite: Zhang, Y., Liu, Z., and Li, J.: Resolution sensitivity of GRIST Nonhydrostatic Model During DYAMOND winter from 120 km to 5 km, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2695, https://doi.org/10.5194/egusphere-egu22-2695, 2022.

How to cite: Smith, I. H., Williams, P. D., and Schiemann, R.: Using high-resolution climate models to predict increases in atmospheric turbulence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2905, https://doi.org/10.5194/egusphere-egu22-2905, 2022.

In global storm-resolving models (SRMs), that resolve convection explicitly instead of parameterizing it, microphysical processes are now fundamentally linked to their controlling factors, i.e., the circulation. While in conventional climate models the convective parameterization is one of the main sources of uncertainties (and a popular tuning parameter), this role might be passed on to the microphysical parameterization in global SRMs. In this study, we use a global SRM with two different microphysical schemes. For each scheme we do several sensitivity runs, where in each run we vary one parameter of the applied microphysics scheme in its range of uncertainty. We find that the two microphysics schemes have distinct signatures, e.g., in how condensate is partitioned between ice and snow. In addition, perturbing single parameters of each scheme also affects condensate amounts and hence the heat budget of the tropics. Among the parameters tested, the model is particularly sensitive to the ice fall speed and the width of the raindrop size distribution, which both cause several 10s W/m2 variation in radiative fluxes. Overall, microphysical sensitivities in global SRMs are substantial and resemble inter-model differences such as in the DYAMOND ensemble. 

How to cite: Naumann, A. K. and Esch, M.: Microphysical sensitivities in global storm-resolving simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4433, https://doi.org/10.5194/egusphere-egu22-4433, 2022.

Before an off-shore wind farm is built a thorough resource assessment of all available locations for the farm needs to be performed. Since the power extraction of a wind farm depends on the cube of the wind speed even the mesoscale variability in the wind speed plays an important role in the resource assessment of a wind farm. In order to study mesoscale systems that occur in the vicinity of off-shore wind farms we've set up a convection permitting simulation in COSMO-CLM for the Kattegat sea strait. The Kattegat is particularly interesting since it is an area which features a very irregularly shaped coastline and pronounced coastal effects. Centrally located in the Kattegat lays the 400 MW Anholt wind farm. Operational data of the Anholt wind farm and scatterometer data of the Kattegat are used to validate our simulation. A relatively good agreement between observations and the model output has been found. A variety of mesoscale systems has been identified, both in unstable (e.g. a downburst) as in stable (e.g. gravity waves) conditions. The wind speed variability on temporal scales and on spatial scales over the Kattegat has been investigated. The interactions of the Anholt wind farm with these systems have been investigated using the COSMO-CLM model which incorporates the Fitch wind farm parametrisation. This research is part of a larger project aiming at developing a fast and accurate resource planning and forecasting platform for off-shore wind farms. More information about this project can be found on freewind-project.eu.

How to cite: Neirynck, J., Stoffelen, A., Meyers, J., and van Lipzig, N.: Mesoscale weather systems and their interactions with windfarms: A study for the Kattegat., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5030, https://doi.org/10.5194/egusphere-egu22-5030, 2022.

The dry subsidence regions of the tropics and subtropics play an important role in setting the Earth’s clear-sky climate sensitivity, as the clear-sky feedback in these regions is particularly sensitive to both the baseline relative humidity (RH) and small RH changes under warming. Therefore, it is crucial that climate models reliably simulate the RH and its response to warming in these regions. However, considerable inter-model differences in RH remain, also in global storm-resolving models, the newest generation of climate models with horizontal grid spacings sufficient to explicitly resolve deep convection. The goal of this study is to identify potential causes for these inter-model differences and understand the mechanisms behind it. For this we examine the effect of changes in different model parameters – including microphysical parameters and vertical grid spacing – on tropical free-tropospheric humidity in a global storm-resolving model, focusing on the dry subsidence regions. Back-trajectory calculations allow us to determine the characteristics of the last saturation points for dry tropical air masses as well as the magnitude of moisture sources and sinks during subsequent advection, and how both change in the sensitivity experiments. The trajectory analysis confirms that moisture gains and losses during advection play a secondary role in setting the RH distribution in tropical dry zones in the model, as suggested by earlier studies based on coarser models. This leaves changes in the points of last saturation, which are determined by the circulation and the temperature field, as the more likely driver of RH changes. Preliminary results from the sensitivity experiments indicate that particularly changes in the vertical grid spacing of the model can affect the RH in tropical subsidence regions. These RH changes are explained by changes in the temperature of the main outflow regions of deep convection in the upper troposphere, where most last saturation points are located. These results highlight the importance of circulation and temperature differences across global storm-resolving models in driving inter-model differences in RH.

How to cite: Lang, T., Naumann, A. K., Schmidt, H., and Buehler, S. A.: Understanding drivers of inter-model differences in tropical free-tropospheric humidity in global storm-resolving models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7657, https://doi.org/10.5194/egusphere-egu22-7657, 2022.

In the context of the Transregional Collaborative Research Center on "Arctic Amplification: Climate Relevant Atmospheric and Surface Processes, and Feedback Mechanisms”, we challenge the ICOsahedral Non-hydrostatic modelling framework ICON by performing simulations in a complex Arctic environment. Our study aims at adding a significant reference for how well ICON can perform in the Arctic and give ideas on how to improve the performance related to the microphysical parameterizations.

With the ambition to resolve the clouds directly, we used ICON in the large-eddy mode (ICON-LEM), which enables the use of a 3D Smagorinsky turbulence scheme. We further applied a two-moment microphysics scheme. The setup consists of a circular domain with 600 m resolution centred around Ny-Ålesund (Svalbard) with approx. 100 km diameter. As forcing, hourly data from a 2.4 km ICON-NWP simulation covering a limited area around the archipelago of Svalbard was used. These NWP simulations were forced with the operational global ICON forecasts. Ny-Ålesund was chosen because of its intricate topography, heterogenic surfaces and availability of observational data for comparisons.

The setup was run semi-operationally for 24 h on a daily basis for several months and therefore we were able to create statistics based on an outstandingly large data set. Using the columnar output of Ny-Ålesund we compared it to a large variety of observations (e.g. liquid water path, wind and relative humidity). This evaluation showed an astonishingly high agreement between the measurements and the simulations. For instance, the orographically influenced flow, as well as seasonal and short-range changes in humidity, are captured. Certain aspects, such as the formation of liquid vs ice in clouds, need improvement. On the whole, we could show that ICON-LEM is a useful tool to study the Arctic atmosphere and its changing climate. Further, we can continue to get a better picture of possibilities to understand the microphysical processes and improve their representation in the model.

This work was supported by the DFG funded Transregio-project TR 172 “Arctic Amplification (AC)³“.

How to cite: Kiszler, T., Chellini, G., Ebell, K., Kneifel, S., and Schemann, V.: A performance baseline for the representation of clouds and humidity for cloud-resolving ICON-LEM simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8575, https://doi.org/10.5194/egusphere-egu22-8575, 2022.

In this study, we examine the effect of the Cordillera Mountain Range (CMR) in Luzon, Philippines on Tropical Cyclone (TC) precipitation. Using the Weather Research and Forecasting model, we simulated multiple TC events with three different terrain profiles: control, reduced CMR, and enhanced CMR. We find that for most of the TC cases overland precipitation increases as mountain height increases. To further understand the interaction between TC precipitation and the mountain range, we examine the effects of relevant dynamical fields, including mountain slope, incoming perpendicular wind speed, and the moist Froude Number (F_w). We highlight that TC precipitation is strongly and positively correlated with the product of approaching wind speeds and mountain slope. It is hypothesized that stronger winds along steeper mountain slopes translate to vertical motion which in turn causes higher amounts of precipitation, especially during TC events. In contrast,  the linear relationships with other variables are less clear. It is also worth noting that a significant weakening of TCs may cause less rainfall overland, which is an indirect effect of the mountain range on TC precipitation. Understanding the interactions between TCs and mountain ranges may help in regional quantitative precipitation forecasting efforts in the mountainous regions of the Philippines.

How to cite: Racoma, B. A., Holloway, C., Schiemann, R., Feng, X., and Bagtasa, G.: The Effect of Topography on Tropical Cyclone Precipitation in the Philippines, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9043, https://doi.org/10.5194/egusphere-egu22-9043, 2022.

In global atmospheric modeling the importance of an appropriate ratio of vertical to horizontal model resolution has been emphasized earlier. Theoretical considerations for appropriate ratios have been based, e.g., on quasi-geostrophic considerations for large-scale flows and the dissipation conditions for gravity waves. In limited-area convection-permitting simulations it has been shown that in particular the simulation of shallow cloud layers depends on the vertical model resolution. 
A recent focus in global climate modeling is to increase horizontal resolutions down to a few kilometers grid spacing in order to resolve processes like convection that need to be parameterized at coarser resolutions. In these simulations, often the vertical model resolutions haven’t been changed much in comparison to traditional approaches. Questions like the following may arise: Is this appropriate? How strongly does the climate at storm-resolving horizontal scales depend on vertical resolution? Can convergence of the simulated climate be expected at a certain vertical resolution? Is it useful to invest in further increases of horizontal resolution without a refinement of the vertical grid?
To start answering these questions we have performed simulations with the ICON global atmospheric model at a horizontal resolution of 5 km with three different vertical grids comprising 55, 110, and 190 layers and corresponding vertical resolution in the troposphere of 400, 200, and 100 m, respectively, for a period of 6 weeks.  Here we will show the dependence of selected climate parameters, including the global energy budget, on the vertical resolution. 

How to cite: Schmidt, H. and Rast, S.: The dependence of the climate simulated in a global storm-resolving model on its vertical resolution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9110, https://doi.org/10.5194/egusphere-egu22-9110, 2022.

The stable water isotopes (SWIs) (δ¹⁸O and δD) are used as an indicator of the intensity of the atmospheric hydrological cycle due to their large variability in time and space. SWIs are used for investigating the model’s bias and uncertainty. In this study, we developed a new global storm-resolving model equipped with SWIs (NICAM-WISO). We applied the new model to conduct three current climate simulations using a single-moment cloud microphysics scheme, without any convection parameterization scheme: CTRL, LRES, and HRES. These simulations used the same physical process but at a different horizontal resolution (LRES, 224 km; CTRL, 56 km; HRES, 14 km). We conducted the simulations on the supercomputer Fugaku. CTRL reproduced the seasonal means of the atmospheric hydrological cycle, as well as precipitation isotopic ratios. However, all simulation results have three types of biases. First, in tropical ocean regions, the model had a negative bias in precipitation isotopic ratios; this was caused by a negative bias in vapor isotopic ratios for the middle troposphere, which resulted from excess condensation biases during upward transportation and high-frequency deep convection. Second, all simulations overestimated precipitation isotopic ratios in the East Asia summer monsoon region due to low precipitation in the region caused by a shift in the moisture convergence zone from eastern China to the western Pacific Ocean. Third, in cold continental regions such as Siberia, Greenland, and Antarctica, the model had a positive bias in precipitation isotopic ratios due to a moisture bias and a low temperature effect; these regions also had a large positive bias in terms of precipitation deuterium excess. A particularly large bias was observed in ice clouds with low ice water content, indicating uncertainties in the vapor deposition process. Together, these results suggest that stable water isotopes are helpful for identifying biases associated with cloud microphysics and the atmospheric hydrological cycle. The unique constraints of stable water isotopes revealed cloud microphysics uncertainty and biases in the hydrological simulations.

How to cite: Tanoue, M., Yashiro, H., Takano, Y., Yoshimura, K., Kodama, C., and Satoh, M.: Modelling water isotopes using a global non-hydrostatic model with explicit convection scheme, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9111, https://doi.org/10.5194/egusphere-egu22-9111, 2022.

We give an overview of the global coupled storm-resolving simulations performed so far with IFS-NEMO and IFS-FESOM2 for the H2020 Next Generation Earth Modelling Systems (NextGEMS) project. The project aims to build a new generation of eddy- and storm-resolving global coupled Earth System Models. Such models will constitute the substrate for prototype digital twins of Earth as envisioned in the EU’s ambitious Destination Earth project.

NextGEMS relies on several model development cycles, in which the models are run and improved based on feedback from the analysis of successive runs. In an initial set of storm-resolving coupled simulations, the models were integrated for 75 days, starting in January 2020. ECMWF’s Integrated Forecasting System (IFS) has been run at 9km and 4km global spatial resolution. The runs at 9km were performed with the deep convection parametrization, while at 4km, the IFS was run with and without the deep convection parametrization. So far, the underlying ocean models NEMO and FESOM2 were run on an eddy-permitting 0.25° resolution grid in a single-executable configuration with IFS. Based on the analysis by project partners during a Hackathon organised in October, several key issues were identified both in the runs with IFS, and in those run with the second storm-resolving coupled model developed in NextGEMS, ICON.

We will describe the model improvements made to IFS-NEMO/FESOM based on the lessons learned from the first runs, which will be included for the second round of simulations. These mainly consist in vastly improved conservation properties of the coupled model systems in terms of water and energy balance, which are crucial for longer climate integrations, and in a much more realistic representation of the snow and surface drag. The second round of NextGEMS simulations will also target eddy-resolving resolution in large parts of the global ocean (better than 8km) to resolve mesoscale eddies and leads in sea ice. This is thanks to a refactored FESOM2 ocean model code that allows for efficient coupled simulations in the single-executable context with IFS via hybrid parallelization with MPI and OpenMP.

How to cite: Rackow, T., Becker, T., Pedruzo Bagazgoitia, X., Sandu, I., Zampieri, L., and Ziemen, F. and the ECMWF-AWI Team: Storm-resolving simulations with IFS-NEMO/FESOM in the NextGEMS project, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10757, https://doi.org/10.5194/egusphere-egu22-10757, 2022.

The high-resolution DYAMOND simulations resolve much of the cloud relevant dynamics and cause a large improvement in the structure and diurnal cycle of clouds and precipitation. Nevertheless, from DYAMOND simulations we know that cloud properties can vary significantly even in high-resolution simulations. We focus on evaluating and if possible constraining ice cloud processes in the tropics, an area that should particularly benefit from the increased resolution because deep convection is resolved and controls the tropical upper tropospheric water budget. We analyse not only the horizontal distribution of IWP but also the cloud phase and cloud vertical structure as they are crucial to Earth’s radiation budget.

When comparing the high-resolution global simulations performed within the DYAMOND project among each other and with passive remote sensing data and ERA5 reanalysis we find that the horizontal distribution of ice water path (IWP) varies significantly. In order to understand better those differences, we analysed the connection between the simulated vertical velocity and the total IWP, and the water path of the individual hydrometeors. While the PDF of tropical vertical velocity simulated by the different models is quite similar, the total ice water path connected with those vertical velocities varies strongly. In most models, high vertical velocities are connected with significantly higher IWP than liquid water path (LWP) except in the ICON simulations which simulates similarly large increases in IWP and LWP. Most models simulate large increases in larger ice hydrometeors for large vertical velocities while FV3 simulates also large increases in ice water connected with deep convection. Differences in cloud phase e.g. when comparing NICAM and ICON simulations are connected with different vertical distributions of the condensate with NICAM IWC reaching higher atmospheric levels than the ICON IWC. We attempt to constrain the vertical distribution using active remote sensing data. 

How to cite: Corko, K. and Burkhardt, U.: Impact of tropical convection on upper tropospheric cirrus in high resolution DYAMOND simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11478, https://doi.org/10.5194/egusphere-egu22-11478, 2022.

AGRIF (Adaptive Grid Refinement In Fortran) is a package for the integration of full adaptive mesh refinement features within a multidimensional finite difference model. This library is used in ocean models like NEMO (Nucleus for European Modelling of the Ocean) to offer the possibility to run multiple levels and 2-way nested embedded zooms. Within the ESiWACE2 project, AGRIF performance have been addressed toward high resolution simulations. 
First, a simple AGRIF configuration within NEMO has been set up to simplify benchmarking, profiling and testing new optimizations ideas. We selected on purpose a configuration with small MPI sudomains to mimic simulations running on high numbers of core. Second, a profiling analysis has let us identify an important overhead. Indeed, on a zoom with a refinement of a factor 3 in both latitude and longitude covering 1/9 of the simulated domain an overhead of 46% has been observed compared with the theoretical performance. The correction of land points used in the interpolation on the zoom has been found to be a major bottlenecks. Third, we implemented an optimization concerning the correction on land point limiting as much as possible the computations and taking advantage of the specificity of each interpolation. This adjustment provided us with a reduction of 25% of the time to solution in the aforementioned configuration. For future work, we identified numerous optimizations including further optimizations of the correction of land points.

How to cite: Irrmann, G., Masson, S., Debreu, L., Guibert, D., and Raffin, E.: Optimizations of Multiscale Simulation with AGRIF, towards Exascale Applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12292, https://doi.org/10.5194/egusphere-egu22-12292, 2022.

On the morning of 10 July 2019, an intrusion of relatively cold and dry air, over the Adriatic Sea, through a "bora jet", gave rise to a frontal structure at the ground, which moved rapidly from the Northern to the Southern Adriatic. The intense thermal gradient (together with a high positive sea surface temperature anomaly), the interaction of the jet with the complex topography of Apennines  and the coastal boundary, generated a storm structure that moved parallel to the central Italy coast. In particular, between 8UTC and 12UTC, a supercell developed along the coast to the north of Pescara city (middle Adriatic), producing rainfall that reached 130 mm in 3 hours, and a violent hailstorm (estimated diameter greater than 10 cm). 

In this work, the frontal dynamics and the genesis of the thunderstorm are studied using the numerical system COAWST. Local polarimetric radar observations are also used to check the consistency of the simulations in the mature phase of the supercell. Numerical experiments are performed using a 1 km grid over central Italy, initialized using the ECMWF IFS analysis/forecasts. The sensitivity study investigates the role of the orography, the sea surface temperature (SST) and the coupling between ocean and atmosphere. Orography tests include simulations where the relevant peaks of the Apennine range  (such as Gran Sasso and Picentini) are removed as well as cases where their peaks are modified compared to their real values. In terms of SST, we employ, using an uncoupled approach, the ECMWF SST dataset, the MFS-CMEMS Copernicus dataset at 4 km, 0.01°C Satellite SST, and we investigate the role of the SST anomaly (adding +1°C and +2°C to the real field). The role of the ocean-atmosphere interaction is tested using the COAWST numerical model using an ocean model numerical grid at 1 km resolution over the whole Adriatic Sea. 

The preliminary results show that the topography and in particular  the interaction with the peaks of the Apennine range plays a fundamental role in the dynamics of the cold pool that trigger the convective system. Also, the SST anomaly is found to play an important role in the development of the supercell. In particular, we observed that the simulations forced with MFS-CMEMS SST and the COAWST model runs produce a very realistic SST, in terms of spatial and temporal distribution, but colder by about 1.5 °C in absolute value if compared to observed satellite data. This difference generates lower heat fluxes, less evaporation, weaker precipitations and smaller hail than using warmer SSTs. 

How to cite: Ferretti, R., Mazzarella, V., Marzano, F., Miglietta, M. M., Picciotti, E., Montopoli, M., Baldini, L., Vulpiani, G., Tiesi, A., Mazzà, S., and Ricchi, A.: Analysis of the development mechanisms of a large-hail storm event, on the Adriatic Sea using an atmosphere-ocean coupled model (COAWST), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12876, https://doi.org/10.5194/egusphere-egu22-12876, 2022.

Numerical simulations at cloud-resolving scales have becoming practical for both research and operational applications due to advances in computing technology.  However, deploying such capabilities beyond a limited scale (e.g., extended metropolitan region, large watershed) typically remains out of reach due to the computational cost and the complexity of the systems to support such work.  Yet such capabilities are needed to address the local impacts of precipitation events that can impact much broader areas.  In particular, convective storms driven by monsoons remain unresolved by current numerical weather prediction systems applied to sub-Saharan Africa.  To address this problem, the African Rainfall Project (ARP) was initiated to deploy the community Weather Research Forecast (WRF) model across this region at 1x1 km horizontal resolution on the World Community Grid (WCG).  WRF is configured to capture a diversity of geographic conditions in the region with appropriate boundary layer, land surface and cloud microphysics and parameterizations in addition to high vertical and temporal resolution.  WCG provides a fully distributed computational environment that crowd-sources unused computing power from volunteers’ devices and donates it to scientific projects.  As such, all computations must be embarrassingly parallel, which creates a challenge for models like WRF.  Hence, each instance of WRF must operate serially on a volunteer’s device.  To address the regional-scale simulations, sub-Saharan Africa is decomposed into individual 52 by 52 km domains at 1x1km as the third nest in two-way telescoping grids with common centroids.  The outer domains are at 3 and 9 km resolution, respectively with the same vertical resolution.  Each 48-hour simulation is done as a cold-start forced by reanalysis with output saved every 15 minutes.  The collection of these simulations will cover at least one year to capture seasonal variations.  Since there is no operational imperative, the ability of typical volunteer’s system to compute each simulation in several hours is practical.  Scaling is achieved with many thousands of systems being deployed simultaneously.  With this decomposition, over 35000 overlapping domains cover the region.  During post-processing, the individual simulations are stitched together to create a consistent, single output for over for the period of study.  Although the focus is precipitation, the simulations provide additional standard output for 2m temperature and 10m horizontal wind velocity, for example.  We will report on the results to date and validation in comparison to in situ (e.g., from TAHMO, www.tahmo.org) and remotely sensed observations as well as conventional WRF deployments for a large computational domain covering a small subset of the region.

How to cite: Treinish, L., van de Giesen, N., and Le Coz, C.: Meso-gamma-scale numerical weather simulations for sub-Saharan Africa via grid-based, distributed computing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12956, https://doi.org/10.5194/egusphere-egu22-12956, 2022.

Sudden stratospheric warmings (SSW) are major weather events in the stratosphere with a long-lasting impact on tropospheric weather conditions and, thus, offer a great potential to extend the predictability of surface weather on subseasonal time scales. However, underestimating the warming signal in the stratosphere itself hinders prediction systems to exploit this source of tropospheric predictability. In this study, hindcast experiments with the ECMWF IFS model reveal sensitivity to vertical resolution both for the amplitude and the persistence of the stratospheric warming signal and the prediction skill of surface variables. A potential mechanism for the extended and strengthened warming in the stratosphere with higher vertical resolution are better resolved gravity waves that break in the proximity of the zero-wind line in the upper stratosphere. The enhanced gravity wave drag with higher vertical resolution increases positive temperature anomalies in the middle stratosphere, consistent with anomalous subsidence over the polar cap during the SSWs. Nudging experiments confirm that the enhanced gravity wave drag results directly from increased vertical resolution, as opposed to the modified background state, and that increased surface skill and longer predictable lead times are of stratospheric origin.

How to cite: Wicker, W., Polichtchouk, I., and Domeisen, D.: The influence of resolved gravity waves in the stratosphere for subseasonal hindcasts of the troposphere during SSW events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-200, https://doi.org/10.5194/egusphere-egu22-200, 2022.

The impact of the quasi-biennial oscillation (QBO) on the surface air temperature in the Northern Hemisphere extratropics is investigated. It is found that the QBO, defined as 70-hPa zonal wind in the deep tropics, is negatively correlated with the surface air temperature over the western North Pacific in February and March. Cold temperature anomaly appears during the QBO westerly phase. Such relationship is likely mediated by the subtropical jet. During the QBO westerly phase, a horseshoe-shaped zonal wind anomaly forms in the upper troposphere and lower stratosphere and is connected to the equatorward shift of the Asia-Pacific jet. This equatorward jet shift is accompanied by a cyclonic circulation anomaly in the subtropical North Pacific and an anticyclonic circulation anomaly over northern Eurasia in the troposphere. The resultant temperature advection brings cold air to East Asia and the western North Pacific. This regional downward coupling in February and March, which is not sensitive to El Niño-Southern Oscillation, has become statistically significant in recent decades.

How to cite: Park, C.-H., Son, S.-W., Lim, Y., and Choi, J.: QBO-related Surface Air Temperature Change over the Western North Pacific in Late Winter, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-236, https://doi.org/10.5194/egusphere-egu22-236, 2022.

In subseasonal-to-seasonal (S2S) prediction systems, strong vortex events are found to be more predictable than sudden stratospheric warming (SSW) events. The reason for this difference in predictability between different types of events is however not resolved. To investigate this question using a larger sample size, we extend the definition of strong vortex and SSW events to wind acceleration and deceleration events due to their similar dynamics. Specifically, we use the zonal mean zonal wind at 60°N, 10hPa from ERA-interim reanalysis for the winters of 1998/99 to 2017/18 to identify wind acceleration and deceleration events, which are defined as a wind change over a 10-day window. We then assess the predictability of the identified events using the ECMWF S2S hindcasts. It is found that wind acceleration events are more predictable than deceleration events. However, when expressing the predictability of deceleration and acceleration events as a function of event magnitude, they qualitatively exhibit the same predictability behaviour; that is, events of stronger magnitude are less predictable. We explain the observed predictability dependence from two perspectives: 1) In a statistical sense, strong magnitude events lie within the tails of the climatological distribution and thus are penalised more heavily than weak magnitude events, and 2) from a dynamical perspective, extreme stratospheric events are associated with strong anomalies in precursors such as wave activity and vortex background state, and are  therefore often associated with large ensemble spread and large uncertainties. In particular, the magnitude of extremely strong wave activity is underestimated in the model for strong deceleration events. Therefore, we suggest the observed predictability difference between event types can to a large extent be explained by the difference in event magnitude between event types, i.e. the fact that wind deceleration events are associated with greater magnitudes than wind acceleration events, and that SSW events are stronger in magnitude than strong vortex events. We also suggest that a better representation of extremely strong wave activity in the prediction system can enhance the predictability of stratospheric extreme events, and by extension their impacts on surface weather and climate.

How to cite: Wu, R. W.-Y., Wu, Z., and Domeisen, D. I. V.: Understanding the Differences in the Sub-seasonal Predictability of Stratospheric Extreme Events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-253, https://doi.org/10.5194/egusphere-egu22-253, 2022.

Stratospheric dynamics have predictive skills on a subseasonal timescale for troposphere synoptic processes, which plays a crucial role in the “seamless” forecasting approach. Therefore, predicting the state of the stratospheric polar vortex (SPV) is one of the top priority tasks for modern meteorology.

Early research showed that the intensity of the vertical propagation of wave 1 over Eastern Siberia could be a predictor for an extremely strong/weak SPV in the next month during the winter season. However, this connection does not always exist. During the negative phase of the Pacific Decadal Oscillation (PDO), 70% of the variability of the SPV intensity is explained by the dynamics of wave 1 in the previous month, and during the positive phase of the PDO, there is no statistically significant connection between them. It can be concluded that the nature of the spatial propagation of planetary waves differs in different phases of the PDO.

The work aimed to confirm the effect of large-scale SST anomalies on planetary waves propagation using numerical experiments with ISCA model and to prove results of observational analysis based on JRA-55 data that showed that wave 1 is more “stationary” during the negative PDO phases than during the positive ones. Distributions of the wave 1 ridges’ location for different PDO phases are significantly different at the 8% level according to Student’s t-test.

We analyzed the differences in the vertical components of the Plumb flux for isolated large-scale SST anomalies condition corresponding to the main modes of SST variability, such as PDO, El-Nino Southern Oscillation (ENSO), and for SST anomalies in the Kara-Barents Seas region. The experiments with combined conditions were carried out as well.

How to cite: Sobaeva, D., Zyulyaeva, Y., and Gulev, S.: The effect of SST anomalies on planetary waves dynamics: numerical experiments with ISCA, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-340, https://doi.org/10.5194/egusphere-egu22-340, 2022.

The role of the midlatitude cyclone on the onset of January 2021 sudden stratospheric warming (SSW) is examined by conducting a set of numerical model experiments. The control simulation initialized on 26^th December 2020, 10 days before the SSW onset, successfully reproduces the spatio-temporal evolution of SSW. Since this event is preceded by the developing cyclone over the North Pacific, its impact is tested by initializing the model without cyclonic anomaly, over the North Pacific (20°–80°N, 110°E–160°W) from 1000 hPa to 150 hPa. The potential vorticity inversion technique is used to modify the initial condition. This perturbed simulation shows much weaker polar-vortex deceleration than the control simulation resulting in no distinct SSW onset. Such a difference is attributable to the fact that constructive linear interference between the climatological wave and the North Pacific cyclone is reduced in the perturbed simulation. It weakens the upward propagation of wavenumber one into the stratosphere, thereby reducing the breaking of the planetary-scale waves in the polar stratosphere.

How to cite: Cho, H.-O., Kang, M.-J., Son, S.-W., Hong, D.-C., and Kang, J. M.: Impact of the extratropical cyclone over the North Pacific on the onset of Sudden Stratospheric Warming: A case study of 2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-493, https://doi.org/10.5194/egusphere-egu22-493, 2022.

This study analyses long-term trends in temperature and wind climatology based on ERA5 data. We study climatology and trends separately for every decade from 1980 to 2020 and their changes during this period for winter (DJF for the NH and JJA for the SH) for 40–90°N/S . This study is focused on the pressure levels between 100–1 hPa, which essentially covers the whole stratosphere. We also analyze the impact of the sudden stratospheric warmings (SSW), North Atlantic Oscillation (NAO), El Nino Southern Oscillation (ENSO) and Quasi-biennial oscillation (QBO). This helps us to find details of climatology and trend behavior in the stratosphere in connection to these phenomena. ERA5 is one of the newest reanalysis, which is widely used for the middle atmosphere. We identify the largest differences which occur between 1990–2000 and 2000–2010 in both temperature climatology and trends. We suggest that these differences could relate to the different occurrence frequency of SSWs in 1990–2000 versus 2000–2010.

How to cite: Zajíček, R., Kozubek, M., and Laštovička, J.: Climatology and Long-Term Trends in the Stratospheric Temperature and Wind Using ERA5, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-874, https://doi.org/10.5194/egusphere-egu22-874, 2022.

The connection between the polar stratospheric vortex and the vertical component of the Eliassen–Palm flux in the lower stratosphere and upper troposphere is examined in model level data from ERA5. The particular focus of this work is on the conditions that lead to upward wave propagation between the tropopause and the bottom of the vortex near 100 hPa. The ability of four different versions of the index of refraction to capture this wave propagation is evaluated. The original Charney and Drazin index of refraction includes terms ignored by Matsuno that are shown to be critical for understanding upward wave propagation just above the tropopause both in the climatology and during extreme heat flux events. By adding these terms to the Matsuno index of refraction, it is possible to construct a useful tool that describes wave flux immediately above the tropopause and at the same time also describes the role of meridional variations within the stratosphere. It is shown that a stronger tropopause inversion layer tends to restrict upward wave propagation. It is also shown that while only 38% of extreme wave-1 Eliassen–Palm flux vertical component (F_z) at 100 hPa events are preceded by extreme F_z at 300 hPa, there are almost no extreme events at 100 hPa in which the anomaly at 300 hPa is of opposite sign or very weak. Overall, wave propagation near the tropopause is sensitive to vertical gradients in buoyancy frequency, and these vertical gradients may not be accurately captured in models or reanalysis products with lower vertical resolutions.

To better understand the role of the TIL for transmission and reflection of waves,  an analytical quasi-geostrophic planetary scale model is used to examine the role of the tropopause inversion layer (TIL) in wave propagation and reflection. The model consists of three different layers: troposphere, TIL and stratosphere. It is shown that a larger buoyancy frequency in the TIL leads to weaker upward transmission to the stratosphere and enhanced reflection back to the troposphere, and thus reflection of wave packets is sensitive not just to the zonal wind but also to the TIL’s buoyancy frequency. The vertical-zonal cross section of a wavepacket for a more prominent TIL in the analytical model is similar to the corresponding wavepacket for observational events in which the wave amplitude decays rapidly just above the tropopause. Similarly, a less prominent TIL both in the model and in reanalysis data is associated with enhanced wave transmission and a non-detectable change in wave phase above the tropopause. Models
with a poor representation of the TIL will necessarily miss all of these effects.

• Weinberger, I., C.I. Garfinkel, I.P White, and T. Birner (2021), The Efficiency of Upward Wave Propagation Near the Tropopause: importance of the form of the refractive index, JAS, https://doi.org/10.1175/JAS-D-20-0267.1.
• Weinberger, I., C.I. Garfinkel, N. Harnik, N. Paldor (under review)  Transmission and reflection of upward propagating Rossby waves in the lowermost stratosphere: Importance of the Tropopause Inversion Layer, JAS

How to cite: Garfinkel, C. and Weinberger, I.: The Efficiency of Upward Wave Propagation Near the Tropopause and Reflection from the TIL: importance of the form of the refractive index, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1022, https://doi.org/10.5194/egusphere-egu22-1022, 2022.

The dynamical mechanism by which the quasi-biennial oscillation (QBO) might influence the temperature anomaly, associated with the Madden-Julian oscillation (MJO), in the equatorial upper troposphere and lower stratosphere (UTLS) is examined by conducting a series of initial-value experiments using a dry primitive equation model. The observed temperature response to the MJO convection becomes colder and more in-phase with the convection during easterly QBO (EQBO) than westerly QBO (WQBO) phases. This QBO-dependent MJO temperature anomaly in the UTLS is qualitatively reproduced by model experiments in which EQBO or WQBO background state is artificially imposed above 250 hPa while leaving the troposphere unaltered. As in the observations, the cold anomaly in the UTLS becomes strengthened and steepened with EQBO-like background state than WQBO-like one. It turns out that the QBO zonal wind, instead of temperature, plays a major role in determining the UTLS temperature anomaly by modulating wave energy dispersion.

How to cite: Lim, Y. and Son, S.-W.: QBO wind influence on MJO-induced temperature anomalies in the upper troposphere and lower stratosphere in an idealized model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3314, https://doi.org/10.5194/egusphere-egu22-3314, 2022.

The tropical tropopause layer (TTL) is an important region where air enters from the tropical troposphere to the stratosphere. The cold point tropopause (CPT) within the TTL determines how much water vapor can enter the tropical stratosphere. The water vapor will then be transported to higher latitudes via the Brewer-Dobson circulation and further influences the stratospheric chemistry and the radiation budget around the globe. 
A dominant mode of variability in the tropical stratosphere – the quasi-biennial oscillation (QBO) – can influence the TTL and related processes through thermal wind balance and secondary circulation. The QBO consists of downward propagating easterlies and westerlies, alternating with a period of about 27–28 months. But twice since its discovery – first in 2015/16 and then again in 2019/20 – the QBO was disrupted — both in the past decade. During these anomalous years, easterlies developed at around 40–50 hPa within the westerly regime, while the westerly regime ascended and halted for about 6 months. There was also stronger tropical upwelling during QBO disruptions that favored the development of anomalous easterly wind. 
Here we focus on how the QBO disruptions can influence the TTL structure and water vapor using GPS-RO data, MLS observations, and ERA-5 reanalysis. We analyze temperature, water vapor, and tropical upwelling fields between QBO disruptions and the westerly QBO composite. We find there tends to be a colder zonal mean CPT temperature but relatively more water vapor during QBO disruptions. The increased water vapor relates to the regional pattern of the CPT temperature. During QBO disruptions, CPT temperature tends to be warmer over the western Pacific and colder over the eastern Pacific where the western Pacific is usually called the “cold trap” region and the air gets final dehydrated. Since both tropical and extratropical waves can influence the QBO and the tropical upwelling, we also investigate the roles of waves during QBO disruptions by analyzing the EP flux and its divergence and the momentum equation. We find that tropical waves and midlatitude Rossby waves both influence the zonal wind in the tropical lower stratosphere, but the stronger tropical upwelling is mainly caused by the midlatitude Rossby waves. Studying the influences of QBO on the TTL structure and roles of waves during QBO disruptions sheds light on a better understanding of the mechanisms causing QBO disruptions and their potential influences on the climate.

How to cite: Luan, L., Staten, P., Randel, W., and Kuo, Y.-H.: Tropical tropopause layer structure during QBO disruptions and the roles of waves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3329, https://doi.org/10.5194/egusphere-egu22-3329, 2022.

Sudden stratospheric warmings (SSWs) are extreme stratospheric events which can be followed by a significant impact on surface weather. Roughly two thirds of the observed SSW events are followed by an equatorward shift of the tropospheric midlatitude jet in the North Atlantic, while a third of the events generally show a poleward jet shift. However, it is not yet resolved which factors lead to the large inter-event variability in the surface impact.

Here, the sensitivity of the North Atlantic jet response to stratospheric forcing is investigated using an intermediate complexity atmospheric model. We analyze the contribution of different stratospheric and tropospheric drivers for determining the downward response, focusing on persistent anomalies in the lower stratosphere, downstream influence from the northeastern Pacific, and local tropospheric conditions in the North Atlantic at the time of the initial response. Both the model and reanalysis show that most of the variance in the tropospheric jet response after SSW events can be explained by the lower stratospheric geopotential height anomalies. To isolate the role of the stratosphere from tropospheric variability, we use model runs where the zonal mean stratospheric winds are nudged towards climatology. When stratospheric variability is suppressed, the coupling between the North Atlantic and the northeastern Pacific is found to be weaker. 

These findings shed light on the relative contribution of the stratosphere and the troposphere to the diverse downward impacts of SSW events. The implications of these results for improved long-range prediction of tropospheric jet variability the North Atlantic will be discussed.

How to cite: Gerstman, H., Jimenez-Esteve, B., and Domeisen, D. I. V.: Evaluating the relative contribution of stratospheric and tropospheric drivers for the North Atlantic jet response after sudden stratospheric warmings, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6370, https://doi.org/10.5194/egusphere-egu22-6370, 2022.

The sporadic nature of the Madden-Julian Oscillation (MJO) can influence the extratropical circulation response. However, there are differences in the extratropical response depending on the propagation speed of the MJO in the tropics. Here, we define slow (fast) MJO events as events that take more (less) than 20 (10) days to propagate from the Indian Ocean (phase 3) to the Pacific Ocean (phase 6). The slowly propagating MJO episodes lead to a positive North Atlantic Oscillation (NAO) response at a lag of 10 days following phase 4 of the MJO, whereas fast MJO episodes lead to a development of a positive NAO response 10-15 days following phase 2-3. The slowly propagating MJO episodes can lead to a stronger positive (negative) NAO response after a lag of 10 days following phase 4 (7-8).

In addition to this tropospheric pathway, the MJO can also impact the stratospheric circulation, which in turn can impact the NAO via downward coupling. The stronger impact on the NAO during slow MJO episodes suggests that the stratosphere plays a role in the teleconnection of the MJO to the North Atlantic region. This is evident from the zonal wind response within the stratospheric polar vortex at 60^oN and 10hPa and the geopotential height response at 500 hPa and 100 hPa. In this study, we discuss the stratospheric pathways during fast and slow MJO episodes using ERA-Interim reanalysis with respect to the strength of the Northern Hemisphere stratospheric polar vortex and for stratosphere-troposphere coupling.

How to cite: Yadav, P., Domeisen, D., and Garfinkel, C.: The role of the stratosphere in tropical-extratropical interactions arising from slow MJO episodes., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6381, https://doi.org/10.5194/egusphere-egu22-6381, 2022.

The quasi-biennial oscillation (QBO), describing alternate easterly and westerly winds in the tropical stratosphere, originally shows downward phase propagation with time. However, in February 2016 and January 2020, downward-propagating westerly winds were split into two with one propagating upward and the other propagating downward, so-called a QBO disruption. Previous studies have mainly focused on the cause of the localized negative momentum forcing initiating the QBO disruption. However, the upward displacement of the westerly QBO followed by the negative momentum forcing, clearly seen in 2015/16 but not in 2019/20, has not been investigated in detail. Here, we show that the distinct upward propagation of the westerly winds in 2015/16 can be explained by the stronger Brewer-Dobson circulation (BDC) using MERRA-2 global reanalysis data. We found that strong Rossby waves with wavenumbers 1 and 2 propagating from the troposphere mainly induce the strong BDC in 2015/16. Potential contributions of El Niño and Barents–Kara sea ice reduction to wavenumber 1–2 Rossby waves are also discussed.

How to cite: Kang, M.-J., Son, S.-W., and Chun, H.-Y.: Distinct Upward Propagation of the Westerly QBO in Winter 2015/16 Compared to 2019/20 and its Relationship with Brewer-Dobson Circulation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6662, https://doi.org/10.5194/egusphere-egu22-6662, 2022.

Modes of climate variability that remotely alter the northern hemisphere stratospheric polar vortex state are explored using the Hadley Centre Climate Model (HadGEM3). Experiments are performed that sample combinations of El Niño—Southern Oscillation (ENSO) and quasi-biennial oscillation (QBO) states. These modes were chosen as El Niño and QBO easterly phases are known to weaken the polar vortex.

The El Niño induced weakening of the polar vortex is found to be more pronounced during QBO easterly than QBO westerly. Likewise, the polar vortex weakening caused by QBO easterly is stronger during El Niño than during neutral ENSO conditions.

It is also found that El Niño induces a change to the QBO itself, namely an increase in the descent rate of the QBO, but this is not large enough to explain the nonlinear response of the polar vortex. Other possible mechanisms are investigated, such as whether the QBO and ENSO teleconnections to the polar vortex are sensitive to the prior state of the polar vortex. Impacts of this nonlinearity on the surface response are also explored.

How to cite: Walsh, A., Screen, J., Scaife, A., and Smith, D.: Non-linearity in the extratropical teleconnection to ENSO and the QBO, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7446, https://doi.org/10.5194/egusphere-egu22-7446, 2022.

Antarctic ozone has been regarded as a major driver of the Southern Hemisphere (SH) circulation change in the recent past. Here, we show that Antarctic ozone can also affect the subseasonal-to-seasonal (S2S) prediction during the SH spring. Its impact is quantified by conducting two reforecast experiments with the Global Seasonal Forecasting System 5 (GloSea5). Both reforecasts are initialized on September 1st of each year from 2004 to 2020 but with different stratospheric ozone: one with climatological ozone and the other with year to-year varying ozone. The reforecast with climatological ozone, which is common in the operational S2S prediction, shows the skill re-emergence in October after a couple of weeks of no prediction skill in the troposphere. This skill re-emergence, mostly due to the stratosphere-troposphere dynamical coupling, becomes stronger in the reforecast with year to-year varying ozone. The surface prediction skill also increases over Australia. This result  suggests that a more realistic stratospheric ozone could lead to improved S2S prediction in  the SH spring.

How to cite: Oh, J., Son, S.-W., Choi, J., Lim, E.-P., Carfinkel, C., Hendon, H., Kim, Y., and Kang, H.-S.: Impact of Stratospheric Ozone on the Subseasonal Prediction in the Southern Hemisphere Spring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8995, https://doi.org/10.5194/egusphere-egu22-8995, 2022.

The tropical Madden-Julian oscillation (MJO) is the strongest of the intraseasonal climate oscillations.  It generates a Rossby wave train that can be associated with high-impact weather events at northern midlatitudes in winter and spring.  Here, we investigate using 41 years of ECMWF reanalysis data (1979-2019) why static stabilities in the tropical lower stratosphere are unusually low under easterly QBO and solar minimum conditions, leading to stronger MJO episodes.  Results indicate an important role for extratropical wave forcing events, including stratospheric warmings, occurring preferentially in late fall and early winter during QBOE and SMIN.  This increases the tropical upwelling rate beyond that caused by the QBO induced meridional circulation alone, further reducing lower stratospheric temperatures and static stability during northern winter.  In many but not all years, major sudden stratospheric warmings (SSWs) contribute significantly to the results obtained here.  Of the 11 clear QBOE years in the study period, six had SSWs in early winter prior to Jan. 15.  Of the 12 clear QBOW years, none had early winter SSWs while six had SSWs in late winter after Jan. 15.  There are two main implications of these results: (1) Observations of wave forcing and tropical static stabilities in late fall / early winter, combined with the known QBO and solar phases, may provide a means of projecting the likely strength of the MJO in a given winter; (2) A necessary prerequisite for a successful simulation of the QBO/solar - MJO connection in a global climate model may be the ability to simulate a preferred occurrence of extratropical wave forcing events, including SSWs, in early winter under QBOE and SMIN conditions. 

How to cite: Hood, L. and Galarneau, Jr., T.: QBO/Solar Modulation of the Boreal Winter Madden-Julian Oscillation: The Role of Extratropical Wave Forcing in Late Fall / Early Winter, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9785, https://doi.org/10.5194/egusphere-egu22-9785, 2022.

Historically, retrieving the detailed structure of atmospheric temperature trends from observations has been demanding. For decades, observations of upper-air temperature have either lacked the necessary vertical resolution, or the horizontal coverage. This has resulted in limited knowledge about the important transition zone around the tropopause. Recent advances in satellite measurement techniques provide new insight into the thermal structure of the upper troposphere/lower stratosphere region. This is a prerequisite for understanding the complex processes of this part of the atmosphere. With unprecedented resolution, latest climate observations from GPS Radio Occultation satellites reveal a significant warming of the atmosphere. The tropical upper troposphere has already warmed about 1 K in the 21^st century alone, and the stratospheric trend structure indicates a possible change in stratospheric circulation.

How to cite: Ladstädter, F., Steiner, A. K., and Gleisner, H.: Observational evidence of large changes of Earth's atmospheric thermal structure in the 21st century, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9823, https://doi.org/10.5194/egusphere-egu22-9823, 2022.

The aim of this study is to comprehensively assess the boreal winter climatology of the European Consortium Earth-system model (EC-EARTH), specifically the contributing version to CMIP6, v3.3. To identify model biases, the climatological stratospheric circulation of a 100-year long simulation with prescribed climatological boundary conditions and fixed radiative forcing, representative of present-day climate, is compared to reanalysis data. An important issue is found in the vertical distribution of stratospheric temperature from the tropics to mid-latitudes in EC-EARTH, which is seemingly linked to radiative processes of ozone, leading to a biased warm middle-upper stratosphere. Consistent with this bias, the Brewer-Dobson circulation at middle/lower levels is weaker than reanalysis while the polar vortex in EC-EARTH is stronger at the upper-stratosphere. The amplitude of Planetary waves is overall underestimated, but the magnitude of the background wave injection from the troposphere into the stratosphere is overestimated in relation to a weaker polar vortex at lower-stratospheric levels and thus less effective wave filtering. The overestimation of the background wave driving is maximum in early-winter and consistent with an increase of sudden stratospheric warmings at this time, as compared to reanalysis. When the wave injection climatology is decomposed spatially, a distinctive role of the planetary waves is revealed: while large-scale waves (wavenumbers 1-2) dominate the eddy heat flux over the North Pacific, small-scale waves (wavenumbers 3-4) are responsible for the doubled-lobe structure of the eddy heat flux over Eurasia. EC-EARTH properly simulates this climatological feature, although overestimates its amplitude over central Eurasia.

How to cite: Palmeiro, F. M., García-Serrano, J., Rodrigo, M., Abalos, M., Christiansen, B., and Yang, S.: Wintertime biases in the EC-EARTH stratosphere: CMIP6 version, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10569, https://doi.org/10.5194/egusphere-egu22-10569, 2022.

February-March 2020 was marked by highly anomalous large-scale circulations in the Northern extratropical troposphere and stratosphere. The Atlantic jet reached extreme strength, linked to some of the strongest and most persistent positive values of the Arctic Oscillation index on record, which provided conditions for extreme windstorms hitting Europe. Likewise, the stratospheric polar vortex reached extreme strength that persisted for an unusually long period. Past research indicated that such circulation extremes occurring throughout the troposphere-stratosphere system are dynamically coupled, although the nature of this coupling is still not fully understood and generally difficult to quantify. 

We employ sets of numerical ensemble simulations to statistically characterize the mutual coupling of the early 2020 extremes. We find the extreme vortex strength to be linked to the reflection of upward propagating planetary waves and the occurrence of this reflection to be sensitive to the details of the vortex structure. Our results show an overall robust coupling between tropospheric and stratospheric anomalies: ensemble members with polar vortex exceeding a certain strength tend to exhibit a stronger tropospheric jet and vice versa. Moreover, members exhibiting a breakdown of the stratospheric circulation (e.g. a sudden stratospheric warming) tend to lack periods of persistently enhanced tropospheric circulation. Despite indications for vertical coupling, our simulations underline the role of internal variability within each atmospheric layer. The circulation extremes during early 2020 may be viewed as resulting from a fortuitous alignment of dynamical evolutions within the troposphere and stratosphere, aided by each layer's modification of the other layer's boundary condition.

How to cite: Rupp, P., Loeffel, S., Garny, H., Chen, X., Pinto, J., and Birner, T.: Potential links between tropospheric and stratospheric circulation extremes during early 2020, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11634, https://doi.org/10.5194/egusphere-egu22-11634, 2022.

Streamer events are induced by breaking of planetary waves near the tropopause. Streamers are significant transient disturbances to the seasonal circulation patterns in the tropopause-stratosphere region at mid latitudes. They modify dynamics of the polar jet stream and of the lower stratosphere.  At streamers’ flanks, strong wind shear occurs and gravity waves can be excited.  Western Europe and the surrounding regions of the North Atlantic are typical regions where streamer events develop.

Long range infrasound propagation is mainly controlled by temperature and wind fields in the atmosphere. Zonal winds in the stratosphere and jet stream near the tropopause belong to key factors that drive infrasound propagation.

A feasibility study on utilisation of ground infrasound measurements in research of streamer events was performed under the ESA’s Aeolus+Inovation project Lidar Measurements to Identify Streamers and Analyse Atmospheric Waves. Three western stations of the Central and Eastern European Infrasound Network WBCI (50.25°N 12.44°E), PVCI (50.53°N 14.57°E), and PSZI (47.92°N 19.89°E) were included in the study of streamer events from February 2020 to March 2021. WBCI is a large aperture array used for observations of low frequency infrasound in the frequency range of 0.0033-0.4 Hz. The stations PVCI and PSZI operate in the infrasound band of 0.05-5 Hz. We focused on statistical comparison of infrasound arrival parameters in periods influenced by streamer events and on calm days.

The presented analysis of the data of the three infrasound stations located in Central Europe did not identify significant first order phenomena related to streamer events. Considering further streamer events and including more stations is necessary to find out if ground infrasound observations could serve for monitoring of streamer events.

How to cite: Sindelarova, T., Kozubek, M., Podolska, K., Bondar, I., Pasztor, M., and Kuchelbacher, L.: Can ground infrasound measurements be a useful complementary technology in studies of streamer events?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-751, https://doi.org/10.5194/egusphere-egu22-751, 2022.

Infrasound waves are generated by large range of natural and anthropogenic sources. Natural sources include earthquakes, volcanic eruptions, bolides, storms and lightning, tornadoes, avalanches, tsunamis. Anthropogenic sources consist of nuclear explosions, chemical and accidental explosions, quarry blasts, aircraft activity, industrial, oil and gas refinery flares, hydroelectric dams etc.

In the military field, the infrasound generated by the military technique are important, both for moving vehicles and for shooting. They represent a way of activity revealing, and can be used only if the acoustic spectrum is well known, in order to be able to make a clear discrimination between the multiple possible sources. Therefore, the infrasound data characterized by frequency (Hz), maximum observed amplitude (Pa) and maximum estimated detection distance (km) are collected for the possible sources. At the same time, once an event is identified, the signal is processed to compute the direction (back azimuth) and speed.

Thus, in the framework of the PN-III-P2-2.1-PED-2019-0100 project, we aim to develop a system for rapid localization of the position of the guns position in firing fields. Multiple tests were performed using different types of portable recording equipment with sampling rates between 1 and 50,000 SPS using different sensors (MEMS microphones, Chaparral M25 sensors, geophones, pressure microphones). By calculating the azimuth and the distance, testing sources could be identified. Methods for identification and alarming on the infrasonic events generated by weapons in belligerent areas based on the data provided by the pilot installation will be further developed in the framework of the mentioned project.

How to cite: Ionescu, C., Ghica, D. V., Toader, V., Marmureanu, A., Neagoe, C., and Predoi, C.: Studies for development of a system for rapid localization of the guns position in firing fields, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1305, https://doi.org/10.5194/egusphere-egu22-1305, 2022.

Infrasound event catalogs that span long durations are useful in identifying repeating sources from a common location, which can provide ground truth for studying the time varying nature of the atmosphere as well as quantifying event characteristics. We focus on producing a regional infrasound bulletin for the Korean peninsula region for 1999 to 2021. We use data from six South Korean infrasound arrays that are cooperatively operated by SMU and KIGAM. The detection procedure uses an adaptive F-detector (Arrowsmith et al., 2008) that inputs arrival time and backazimuth into the Bayesian Infrasonic Source Location (Modrak et al., 2010) procedure. The bulletin consists of 16,417 events over 22 years with repeated events from many locations and with source types that include shallow-depth earthquakes, limestone mines and quarries. We show that the majority of these events occur during working hours and days, suggesting a human cause. Installations of additional infrasound arrays in South Korea and the IMS infrasound arrays in Russia and Japan increase the number of infrasound events while improving location accuracy. Events that have associated signals at a large number of arrays are reviewed and evaluated to assess their quality. Infrasound amplitudes from the events are normalized for propagation effects to estimate source size. Ray tracing using the G2S atmospheric model generally correctly predicts the arrivals when strong stratospheric winds exist. Local weather data which captures small-scale variations in the wind velocity can, in some cases, explain observations that are not predicted by the G2S model.

How to cite: Park, J., Arrowsmith, S., Che, I.-Y., Hayward, C., and Stump, B.: Infrasound Detection and Location of Sources in and around the Korean Peninsula, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1536, https://doi.org/10.5194/egusphere-egu22-1536, 2022.

Every day, around one thousand thunderstorms occur around the world producing about 45 lightning flashes per second. One prominent infrasound station of the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty Organization for studying lightning activity is IS17 in Ivory Coast where the lightning rate is relatively high. Infrasound is defined as acoustic waves with frequencies below 20 Hz, the lower limit of human hearing. Statistical results are presented in this paper based on infrasound measurements from 2004 to 2019. One-to-one association between infrasound detections from 0.5 to 5 Hz and lightning flashes detected by the World Wide Lightning Location Network within 500 km from the infrasound station is systematically investigated. Most of the infrasound signals detected at IS17 in this frequency band are due to thunder, even if the thunderstorms are located up to 500 km away from the station. A decay of the thunder amplitude with the flash distance, d, is found to scale as d to the power of -0.717 for flashes within 100 km from the station, which holds for direct and tropospheric waveguide propagation. Interestingly, the stratospheric detections reflect a pattern in the annual azimuth variation, which is consistent with the equatorial stratospheric Semi-Annual Oscillation.

How to cite: Farges, T., Hupe, P., Le Pichon, A., Ceranna, L., and Diawara, A.: Infrasound thunder detections across 15 years over Ivory Coast: localization, propagation, and link with the stratospheric semi-annual oscillation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1620, https://doi.org/10.5194/egusphere-egu22-1620, 2022.

We are presenting a new and novel approach to the detection and parameter estimation of infrasonic signals. Our approach is based on the likelihood function derived from a multi-sensor stochastic model expressed in different frequency channels. Using the likelihood function, we determine, for the detection problem, the Generalized Likelihood Ratio Test (GLRT) and, for the estimation of the slowness vector, the Maximum Likelihood Estimation (MLE). We establish new asymptotic results (i) for the GLRT under the null hypothesis leading to the computation of the corresponding p-value and (ii) for the MLE by focusing on the two wave parameters back-azimuth and horizontal trace velocity. The Multi-Channel Maximum-Likelihood (MCML) detection and estimation method is implemented in the time-frequency domain in order to avoid the presence of interfering signals. Extensive simulations with synthetic signals show that MCML outperforms the state-of-the-art multi-channel correlation detector algorithms like the Progressive Multi-Channel Correlation (PMCC) in terms of detection probability and false alarm rate in poor signal-to-noise ratio scenarios. We also illustrate the use of the MCML on real data from the International Monitoring System (IMS) and show how the improved performances of this new method lead to a refined analysis of events in accordance with expert knowledge.

How to cite: Poste, B., Charbit, M., Le Pichon, A., Listowski, C., Roueff, F., and Vergoz, J.: The Multi-Channel Maximum-Likelihood (MCML) method: a new approach for infrasound detection and wave parameter estimation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1651, https://doi.org/10.5194/egusphere-egu22-1651, 2022.

Global scale infrasound observations confirm that the detection capability of the International Monitoring System (IMS) deployed to monitor compliance with the Comprehensive Nuclear-Test ban Treaty (CTBT) is highly variable in space and time. Previous studies estimated the radiated source energy from remote observations using empirical yield-scaling relations accounting for the along-path stratospheric winds. However, these relations simplified the complexities of infrasound propagation as the wind correction applied does not account for an accurate description of the middle atmosphere along the propagation path. In order to reduce the variance in the calculated transmission loss, massive frequency and range-dependent full-wave propagation simulations are carried out, exploring a wide range of realistic atmospheric scenarios. Model predictions are further enhanced by incorporating fine-scale atmospheric structures derived from a two-dimensional horizontal wave number spectrum model. A cost-effective approach is proposed to estimate the transmission losses at distances up to 8,000 km along with uncertainties derived from multiple gravity wave realizations. In the context of the future verification of the CTBT, this approach helps advance the development of network performance simulations in higher resolution and the evaluation of middle atmospheric models at a global scale with limited computational resources.

How to cite: Le Pichon, A., Ceranna, L., and Listowski, C.: Evaluating long range middle atmospheric variability for global infrasound monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1879, https://doi.org/10.5194/egusphere-egu22-1879, 2022.

The International Monitoring System (IMS) was established to monitor for nuclear explosions, and is capable of detecting many different signals of interest (e.g. volcanoes, earthquakes, atmospheric convection etc.) embedded in the station specific ambient noise. The ambient noise can be separated into coherent noise (e.g. microbaroms) and incoherent noise (e.g. wind turbulence). The analysis of the coherent ambient noise was expanded through the use of updated IMS data-sets up to the end of 2020 for all 53 currently certified IMS stations. Monthly reference curves will be presented, which provide a means to determine the deviation from nominal monthly behavior. An example of this use is through the Ambient Noise Stationarity (ANS) factor created for this paper, which provides quick references to the data quality compared to the nominal situations allowing for the identification of either poor data quality, or instances of strong abnormal signals of interest. Further investigation, through use of information about the number of detections can be used to distinguish between poor data quality and strong abnormal signals of interest.

How to cite: Kristoffersen, S., Le Pichon, A., Hupe, P., and Matoza, R.: Updated global reference models of broadband infrasound signals for atmospheric studies and civilian applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1902, https://doi.org/10.5194/egusphere-egu22-1902, 2022.

The International Monitoring System (IMS), which has been established for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) verification since the late 1990s, is supposed to detect every explosion of at least 1 kt TNT equivalent worldwide. Pressure waves in the infrasound range (between ~0.01 and 20 Hz) can efficiently propagate over long distances, depending on the winds near the stratopause. Therefore, the IMS verification technology monitoring the atmosphere comprises a global infrasound network consisting of up to 60 stations, 53 of which have already been certified. Moreover, research studies and projects have suggested infrasound observations of repeating or persistent sources for probing the winds in the middle atmosphere, where numerical weather prediction models suffer from the lack of continuous observation technologies for data assimilation. One type of repetitive source is active volcanoes. In turn, this natural hazard for civil security can be monitored using infrasound, and prototypes of applications for the release of early volcanic eruption warnings have been established. However, access to raw infrasound data or products of the IMS is limited to specific user groups, which might hinder the utilization of infrasound observations.

In this study, we present IMS infrasound open-access data products for atmospheric studies and civilian applications. For this purpose, 18 years of raw infrasound data (2003-2020) were reprocessed using the Progressive Multi-Channel Correlation method with a one-third octave frequency band configuration between 0.01 and 4 Hz. From the comprehensive detection lists of 53 stations, four products were derived that differ in frequency range and temporal resolution. These are (i) low-frequency infrasound events (0.02-0.07 Hz, 30 min), detections in the microbarom frequency range – in both (ii) a lower (0.15-0.35 Hz) and (iii) a higher (0.45-0.65 Hz) frequency spectrum (both 15 min) – and (iv) observations with relatively high centre frequencies of between 1 and 3 Hz (5 min). Along with several detection parameters, calculated quantities for assessing the relative quality of the products are provided. All four products are provided per station and include detections of volcanic eruptions, while the microbarom products best reflect the middle atmosphere dynamics. The data products are demonstrated by historical and recent examples of natural events that produced infrasound detected at IMS stations. Global compilations of the products highlight the stratospheric circulation effect in the microbarom detections.

How to cite: Hupe, P., Ceranna, L., Le Pichon, A., Matoza, R. S., and Mialle, P.: Infrasound Broadband Bulletin Products of the IMS for Atmospheric Studies and Civilian Applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2667, https://doi.org/10.5194/egusphere-egu22-2667, 2022.

To fill the gap in infrasound network coverage, the Central and Eastern European Infrasound Network (CEEIN) has been established in 2018 with the collaboration of the Zentralanstalt für Meteorologie and Geodynamik (ZAMG), Vienna, Austria; the Institute of Atmospheric Physics of the Czech Academy of Sciences (CAS IAP), Prague, Czech Republic; the Research Centre for Astronomy and Earth Sciences of the Eötvös Loránd Research Network (ELKH CSFK), Budapest, Hungary; and the National Institute for Earth Physics (NIEP), Magurele, Romania. The Main Centre of Special Monitoring National Center for Control and Testing of Space Facilities, State Agency of Ukraine joined CEEIN in 2019. We present the first CEEIN bulletin (2017-2020) of infrasound-only and seismo-acoustic events, and using ground truth events, we demonstrate how adding infrasound observations to seismic data in the location algorithm improves location accuracy. We show how the CEEIN infrasound arrays improve the detection capability of the European infrasound network and identify coherent noise sources observed at CEEIN stations.

How to cite: Bondár, I., Šindelářová, T., Ghica, D., Mitterbauer, U., Liashchuk, A., Baše, J., Chum, J., Czanik, C., Ionescu, C., Neagoe, C., Pásztor, M., Kouba, D., and Le Pichon, A.: The Central and Eastern European Infrasound Network Bulletin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3503, https://doi.org/10.5194/egusphere-egu22-3503, 2022.

Gravity Waves (GW) alter the propagation path of acoustic energy in the middle atmospheric waveguide and complexify the large scale picture where infrasound (IS) propagation is mainly driven by the seasonal changes in stratospheric winds. Thus, GW affect the detection capability of the IS station network of the International Monitoring System (IMS) established to monitor the compliance with the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Atmospheric models explicitly resolving a part of the GW spectrum are relevant tools to be considered for investigating the effect of GW on infrasound propagation, given increasing computing means made available by HPC facilities. Parabolic equation simulations allow accounting for the partial reflections induced by GW. They can be used to quantify the impact of GW on infrasound transmission loss, for instance. Here, we use atmospheric specification fields obtained in the framework of the Dynamics of the Atmospheric General Circulation Modeled on Nonhydrostatic Domains (DYAMOND). DYAMOND is an international project, initiated by the Max Planck Institute for Meteorology (MPIM) and the University of Tokyo. It describes a framework for the intercomparison of high-resolution global models. It mainly focuses on the troposphere, but some models were run with a high enough top so that GW are resolved up to the stratosphere. Lidar observations are used to validate the model at Observatoire de Haute Provence (France) and we investigate the potential energy of GW activity across the IMS. By filtering out small-scale perturbations (GW) in atmospheric specifications and comparing parabolic equation simulations with and without GW, respectively, we quantify the impact of GW on the main atmospheric waveguide. We focus on the transmission loss derived at the surface, and more particularly in the shadow zones, for different national or IMS infrasound stations during the (northern hemisphere) winter.

How to cite: Listowski, C., Stephan, C., Le Pichon, A., Hauchecorne, A., Kim, Y.-H., Achatz, U., and Bölöni, G.: Quantifying the impact of gravity waves on infrasound propagation using high-resolution global models for atmospheric specifications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3619, https://doi.org/10.5194/egusphere-egu22-3619, 2022.

Mediterranean hurricanes, or medicanes, are tropical-like cyclones forming once or twice per year essentially over the waters of Mediterranean Sea. These mesocyclones pose a serious threat to coastal infrastructures and lives, because of their strong winds and intense rainfalls. Infrasound technology has already been employed to investigate acoustic signatures of severe weather events. In order to characterize medicane infrasound detections, we use data from the International Monitoring System (IS48 infrasound station, Tunisia), processed with a multi-channel correlation algorithm. For four different medicanes, high and/or low frequency detections are corresponding to these events, and non-detected cases are also discussed. These detections are discussed by considering other datasets such as satellite observations, a surface lightning detection network, and products mapping the intensity of the swell. While convective systems and lightning seem to be the main sources of detections above 1 Hz, hotspots of swell related to the medicanes are evidenced in the 0.1-0.5 Hz range.

How to cite: Forestier, E., Listowski, C., Dafis, S., Le Pichon, A., Farges, T., De Carlo, M., Vergoz, J., and Claud, C.: Infrasound Signatures of Mediterranean Hurricanes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3907, https://doi.org/10.5194/egusphere-egu22-3907, 2022.

In order to contribute to the improvement of the confidence and the quality of measurements produced by regional and international infrasound monitoring networks, this work investigates a methodology of uncertainty evaluation associated with on-site measurement systems. The proposed approach is applied to infrasound signals processed with TDOA (Time Difference of Arrival) propagation model which takes as inputs the wave parameters of the incoming signals (e.g. back-azimuth, horizontal trace velocity) recorded at the array elements. On one hand, relevant input uncertainties are investigated for propagation targeting the incoming signals (loss of coherence, noise), the instrumentation (microbarometers, calibration system, wind noise reduction system, environmental sensitivity) and the propagation model (sampling frequency and frequency band). On the other hand, relevant advanced output quantities of interest based on TDOA outputs are proposed. Statistical tools are then derived to evaluate the main contributions to the uncertainty associated with the advanced output quantities.

How to cite: Demeyer, S., Kristoffersen, S., Le Pichon, A., Fischer, N., and Larsonnier, F.: Contribution to uncertainty evaluation associated with on-site infrasound monitoring systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4253, https://doi.org/10.5194/egusphere-egu22-4253, 2022.

The infrasound array in Hungary has been operational since May 2017 at Piszkés-tető. Since then, it has collected over a million PMCC detections from various known sources such as microbaroms from the Northern Atlantic, quarry blasts and mine explosions, eruptions of Etna, storms, airplanes and so on. The goal of this study is to train, test, validate and compare machine learning models such as Random Forest and Support Vector Machine, for identification and separation of infrasound signals from storms and quarry blasts. The dataset contains identified signals from previous studies and from the Hungarian Seismo-Acoustic Bulletins. The features for training are extracted both from the time and frequency domains of the signals.

How to cite: Pásztor, M. and Bondár, I.: Identification and separation of infrasound signals from storms and quarry blasts via machine learning algorithms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5143, https://doi.org/10.5194/egusphere-egu22-5143, 2022.

NIEP operates BURAR-BURARI seismo-acoustic array deployed in northern Romania under a joint effort with AFTAC (USA). Currently, the 6 infrasonic elements are distributed over a 0.7 km aperture, whilst the 9 SP borehole seismometers are distributed over an area of 5 km².

Impulsive and short-duration signals, generated by repeating sources confined in certain directions, are frequently detected during daytime both by seismic and infrasonic sensors. As a number of active quarries are located in the local to near-regional distance ranges, we assumed that many of the seismo-acoustic signals, characterized with PMCC algorithm (for infrasound), and with f-k analysis (for seismic), are generated by the surface blasts conducted in these sites.

Two cases are addressed in this study:

(1) The location of the local/near-field source is unknown: An empirical method for identification of near-field quarries, based on associating the seismic signal with the infrasonic arrival, is presented. The method is the most effective in the distance range of fastest infrasonic phases (direct or tropospheric), i.e., within 5 – 50 km of the infrasound array. The shorter distance and impulsive signals, with quite large SNR, indicate the direct waves arrivals. Seismic surface type waves (Rayleigh and Love) are propagating along the Earth surface. Source location is based upon phase identification and characteristics (back azimuth, arrival time and apparent velocity) from both seismic and acoustic data. The seismo-acoustic signals are characterized by short duration (2-4 s on the waveform), high frequency content, stable azimuth, and quite stable trace velocity. Depending on the atmospheric conditions, the method can still be applied to the analysis of more distant events as well.

(2) The location of the local or near-regional source is listed in the updated Romanian seismic catalogue (ROMPLUS): Since artificial blasting can produce seismic and acoustic signals simultaneously, analysis of seismo-acoustic records is applied to discriminate between anthropogenic events and earthquakes. In the distance range of interest (up to 350 km), the infrasonic array records both tropospheric and stratospheric phases. Signals recorded at distances over 200 km show longer duration, travel time analysis indicating stratospheric path. For the most infrasonic arrivals generated by the near-surface blasts, the apparent acoustic speed is close to the sound speed at the array site. The apparent velocity of the seismic phases increases with the epicentral distance. Infrasonic signals detected by BURARI were investigated in order to associate them with seismic events recorded in the ROMPLUS catalogue, and to identify quarry blasts. Based on the InfraGA 2D ray tracer and NRL-G2S atmospheric models, the ducting conditions towards the station are highlighted in order to explain the recordings. Ray tracing predictions are consistent with the infrasound detections at station for near-regional sources.

Joint analysis of the seismic and acoustic recordings has proven to be a useful tool for identifying and locating quarry blasting sources.

This presentation has been accomplished in the framework of the National Core-Programme MULTIRISC project (contract 31N/2019), PN 19080101.

How to cite: Ghica, D.: Use of a seismo-acoustic array for local to near-regional quarry blasts monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5342, https://doi.org/10.5194/egusphere-egu22-5342, 2022.

Detecting and notifying ongoing volcanic explosive eruptions can support the activities of the Volcanic Ash Advisory Centres (VAAC) in their contribution to the International Airways Volcano Watch. However, local monitoring systems are missing on many active volcanoes. Here, the use of a global monitoring that, even with lower reliability, can allow a fast response. Many studies have shown so far the utility and potential of long-range infrasound monitoring for this aim, but still open questions remain concerning the real efficiency and reliability of such a system.

In this study we investigate the potential of the infrasound network of the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) to detect volcanic explosive eruptions at large distances. We apply a procedure based on the Infrasound Parameter (IP) calculated from a single array to selected volcanoes by accounting for realistic infrasound propagation conditions.

The procedure was applied to data recorded by the I06AU infrasound array (Cocos Island) between January 2012 and December 2019 and targeting Indonesian volcanoes at source-to-receiver distances ranging between 1000 and 2000 km, where activity from 11 volcanoes was reported in the period of analysis with an energy spanning from mild explosions to VEI4 eruptions.

The system reliability was evaluated from the ratio between real ones and the total number of notifications provided from I06AU array for each volcano.

The IP was calculated following previous studies and improved with new constraints accounting for the source strength and signal persistency. These allowed us to improve significantly the system reliability for events VEI3 or greater and strongly reduce the number of false alerts. Still, undetected explosive events remain due to unfavorable propagation conditions and unresolved ambiguity due to short spacing among volcanoes with respect to the array. We propose to solve this last issue by considering volcanic sectors rather than single volcanic edifices. Instead of a notification for a single volcano, an alert for an area of interest could be issued to draw the attention and trigger further analysis of satellite images by the VAACs.

This study is performed to improve the Volcanic Information System (VIS) proposed and developed in the framework of FP7 and H2020 ARISE projects.

How to cite: Gheri, D., Marchetti, E., Belli, G., Le Pichon, A., Ceranna, L., Hupe, P., Mialle, P., and Hereil, P.: Monitoring of Indonesian Volcanoes with I06AU infrasound array, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5380, https://doi.org/10.5194/egusphere-egu22-5380, 2022.

Mesospheric temperature inversions are subject to investigations due to the links with multiscale dynamics such as planetary wave and gravity waves. Knowing the impact on climatological inversions also requires understanding the phenomena occurring before, through, and after a mesospheric inversion. We use data obtained during a measurement campaign over Maïdo observatory in La Réunion Island and focus on a specific event occurring in the night between the 9^th and the 10^th of October 2017. Among the several observations available, LIDAR measurements provided vertical profiles of temperature and gravity waves potential energy completed by high vertical resolution radiosoundings. The airglow layer observed by an InGaAs camera shows the evolution of gravity wave structures at about 87 km between 0.9 and 1.7 µm. Gravity wave parameters such as horizontal wavelengths or intensity emission variations are extracted, along with potential energy compared with LIDAR data. We use atmospheric models (ERA5, WACCM, WRF) and specific tools (NEMO, GROGRAT) to add supplementary information about the night selected. We present here the first results related to the gravity waves and energy exchanges in the frame of the temperature inversion.

How to cite: Hauchecorne, A., Bellisario, C., Chane-Ming, F., Keckhut, P., Simoneau, P., Trémoulou, S., Litowski, C., Berthet, G., Jégou, F., and Khaykin, S.: Case study of a mesospheric inversion over Maïdo observatory through a multi-instrumental observation., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5593, https://doi.org/10.5194/egusphere-egu22-5593, 2022.

How to cite: Amezcua, J., Näsholm, S. P., and Vera-Rodriguez, I.: Constraining middle and upper atmospheric variables by assimilating measurements from infrasound propagation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6803, https://doi.org/10.5194/egusphere-egu22-6803, 2022.

Between 0.1 and 0.6 Hz, the coherent ambient infrasound noise is dominated worldwide by signals originating from the ocean, the so-called microbaroms. With an energy peaking around 0.2 Hz, microbaroms signals are generated by second order non linear interactions between wind-waves at the ocean surface and are able to propagate all around the globe through the stratosphere and thermosphere. Monitoring these signals allows characterizing the source activity and probing the properties of their propagation medium, the middle atmosphere, assuming that the source is well modelled. A new theoretical description of the mechanism signal generation connecting the amplitude of the pressure signal to the height and frequency wave oscillation has been proposed by De Carlo et al. (2020). This model has been evaluated quantitatively through systematic comparisons with worldwide observations (De Carlo et al., 2021). This model has been implemented by the Laboratoire d’Océanographie Physique et Spatiale (LOPS) research unit of IFREMER in the DATARMOR HPC center (11088 cores - 426 Tflops) which allows big data hosting and intensive computation. We present a technical overview of the ARROW product and its implementation framework for both hindcast and real-time production. In the context of the future verification of the Comprehensive nuclear Test Ban Treaty (CTBT), ARROW offers an opportunity to target infrasonic signals of specific interest interfering with the global ambient coherent noise. This product, based on a state-of-the-art numerical wave model, paves the way for improved medium-range weather forecasting, by building a global and time-dependent reference database used as input to develop innovative remote sensing methods. 

How to cite: De Carlo, M., Accensi, M., Ardhuin, F., and Le Pichon, A.: ARROW (AtmospheRic InfRasound by Ocean Waves): a new real-time product for global ambient noise monitoring., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7564, https://doi.org/10.5194/egusphere-egu22-7564, 2022.

Multistatic meteor radar observations offer the possibility to investigate the short-term variability at the mesosphere and lower thermosphere on regional scales. Here we present preliminary results of spatially resolved 3D winds and their corresponding horizontal wavelength spectra using the Nordic Meteor Radar Cluster and CONDOR in Chile with a recently developed 3DVAR+div retrieval. The new retrieval provides for the first time a physically consistent solution for the vertical winds that conform the continuity equation. Based on these spectra we can separate the spatial scales that are driven by rotational modes from those dominated by divergent gravity waves. Furthermore, we present the first results of momentum flux spectra derived from these observations on a daily basis.

How to cite: Stober, G., Liu, A., Kozlovsky, A., Qiao, Z., Tsutsumi, M., Gulbrandsen, N., Nozawa, S., Lester, M., Kero, J., Belova, E., and Mitchell, N.: Multistatic meteor radar observations and tomographic retrievals to assess the spatial and temporal variability of 3D winds on regional scales at the mesosphere and lower thermosphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8039, https://doi.org/10.5194/egusphere-egu22-8039, 2022.

The mobile Infrasound Array of the Austrian National Data Center which is a part of the Central and Eastern European Infrasound Network (CEEIN) was installed early 2021 at the Trafelberg in Lower Austria. The array aperture is approximately 1000 m. All sites are equipped with Hyperion IFS 3000 sensors and sara® dataloggers. Power is supplied by a fuelcell and solar panels. The data is locally saved and stored on USB sticks, as well it is transferred in real-time to the Headquarter of ZAMG in Vienna. The data is recorded in miniseed format and processed and analyzed manually by using the dtkGPMCC- and dtkDIVA-Software, developed by CEA/DASE (Commissariat à l'Énergie Atomique/Département analyse, surveillance, environment, France). Several challenges occured due to failures of sensors as well of dataloggers. In frame of the SWOT analysis strengths, weaknesses, opportunities and threats of the new installed array were compiled. Results of the analysis will be shown in the presentation.

How to cite: Mitterbauer, U.: Challenges of the Infrasound Array in Austria, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9401, https://doi.org/10.5194/egusphere-egu22-9401, 2022.

Infrasound observations and complimentary numerical simulations have shown that infrasound propagation is strongly influenced by topography within approximately 10 km from the source. Recent computational efforts using ray theory have shown that topographic influence extends over hundreds of km and is especially strong when considering propagation through the troposphere. Wind and temperature gradients also have a strong influence on propagation at these distances, which suggests that both topography and 3-D atmospheric structure need to be accounted for in long range waveform modeling. Here we show preliminary results from numerical simulations of linear acoustic propagation through a moving, inhomogeneous atmosphere using an in-development 3-D finite difference time-domain (FDTD) propagation code. We compare our synthetic waveforms in two and three dimensions with existing community infrasound propagation codes and discuss future developments, including open source licensing. Lastly, we present preliminary results from applying this code to the Humming Roadrunner experiments and similar data sets.

How to cite: Bishop, J. W., Blom, P. S., and Fee, D.: Infrasound Propagation with Realistic Terrain and Atmospheres Using a Three-Dimensional Finite-Difference Time-Domain Method, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13054, https://doi.org/10.5194/egusphere-egu22-13054, 2022.

The rate of diapycnal mixing, largely due to internal-wave breaking, is a key ingredient to understanding upwelling and horizontal circulation in the ocean. Here, we show a first-principles quantification of the downscale energy flux in the internal wavefield, that ultimately feeds the wave-breaking, shear-instability energy sink responsible for mixing. The approach is based on the wave kinetic equation that describes the inter-scale energy transfers via 3-wave nonlinear resonant interactions. Our results compare favorably with the phenomenological ‘Finescale Parameterization’ formula, by which deep ocean mixing is commonly implemented in the global models, and provide novel insights in the complex problem of oceanic energy transfers.

How to cite: Dematteis, G., Polzin, K., and Lvov, Y.: Energy flux quantification in the oceanic internal wavefield, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-698, https://doi.org/10.5194/egusphere-egu22-698, 2022.

Near-inertial waves (NIWs) are of major relevance to the global ocean circulation as they inject wind energy from the surface to the ocean interior and represent a primary source  of energy to the internal wave continuum. Eddies and fronts play a significant role in the downward penetration of NIW energy (from generation to propagation) and subsurface dissipation. Much of our understanding of NIW interactions with submeso- and mesoscale flows comes from limited observations as well as idealized theoretical and numerical processes, but these do not typically consider the presence of temporally evolving larger-scale flows. On the other hand, more realistic and time-evolving eddy fields from submesoscale-resolving Ocean General Circulation Models (OGCMs) forced with winds show truncated spectra at the subsurface due to the lack of vertical resolution -the subgrid vertical scale is 1-2 orders of magnitude larger than the scale at which dissipation occurs.  Since OGCMs are indeed very attractive tools to quantify global-regional impacts of small-scale phenomena, we propose to gain understanding of their biases in terms of wave-eddy interactions by using a novel approach.

This approach consists of nesting a non-hydrostatic Boussinesq model (Flow_Solve) into an OGCM configuration (NEMO-GLAZUR64) for the Gulf of Lion with O(1 km) horizontal and  O(30 m) vertical resolution. Preliminary analysis of NEMO-GLAZUR64 output reveals a highly energetic NIW field with intriguing distribution patterns relative to the eddies. We zoom into these patterns by following eddies with our nesting approach. The Boussinesq model provides a magnifying glass into dynamical processes that are either parameterized or fully unresolved in the OGCM. Wave energy budgets inferred from high-resolution process studies with Flow_Solve and NEMO-GLAZUR64 are then compared in order to better constrain model uncertainty in OGCMs due to NIW dynamics. 

How to cite: Claret, M., Lelong, M.-P., Winters, K. B., and Ourmières, Y.: Wave-eddy interactions in the Gulf of Lion: Bridging ocean general circulation models and process ocean simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2813, https://doi.org/10.5194/egusphere-egu22-2813, 2022.

The scattering of short inertia-gravity waves by large-scale geostrophic turbulence in the atmosphere and ocean can be described as a diffusion of wave action in wavenumber space. When the time dependence of the turbulent flow is neglected, waves conserve their frequency, which restricts the diffusion of energy to the constant-frequency cone. We relax the assumption of time independence and consider scattering by a flow that evolves slowly compared with the wave periods, consistent with a small Rossby number. The weak diffusion across the constant-frequency cone introduced by time dependence leads to a stationary energy spectrum that remains localised around the cone (specifically decaying as 1/σ⁵ with σ the angular deviation from the cone) corresponding to a small frequency broadening. We contrast our results with unbounded frequency broadening that arises for surface- or shallow-water waves.

How to cite: Cox, M., Vanneste, J., and Kafiabad, H.: Inertia-gravity wave diffusion by geostrophic turbulence: the impact of flow time dependence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3214, https://doi.org/10.5194/egusphere-egu22-3214, 2022.

Terrestrial atmosphere supports propagation of various wave types. An important component of the dynamics especially in the middle atmosphere are the internal gravity waves (GWs) that are incessantly being generated from initial perturbations in a stably stratified atmosphere. Horizontal GW wavelengths range from a few to thousands of kilometres. Together with a wide range of temporal and vertical scales, this complicates their global observations and modeling, requiring high resolution model simulations. Subsequent analyses, nevertheless, contain a significant margin of uncertainty originating in the separation of GWs from the background flow, which is often performed by statistical means. In our work, we explore properties of a Gaussian high-pass filter method, using a deep WRF simulation with the horizontal resolution of 3 km in the region of the Drake Passage. Due to the revealed sensitivity of momentum flux and drag estimates to a filter cutoff parameter, we propose a new method that sets the value of the parameter on the basis of the horizontal spectra of horizontal kinetic energy.

How to cite: Procházková, Z., Kruse, C., Kuchař, A., Pišoft, P., and Šácha, P.: Detection of internal gravity waves by high-pass filtering, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3884, https://doi.org/10.5194/egusphere-egu22-3884, 2022.

The infrared emission lines observed between 80 and 100 km known as nightglow allow the investigation of dynamic phenomena such as gravity waves. These perturbations act on local temperature and density. However, the observation of the local perturbations in the nightglow layer is mainly performed by spectrally broad cameras. Swenson and Gardner (1998) introduced the cancellation factor linking relative variations of intensity with relative variations of temperature. The cancellation factor is a function of the perturbation vertical wavelength estimated from simulation that do not include spectral variations. In this study, we intend to estimate the spectral variability of the cancellation factor, in particular within the range 0.9-1.7 µm corresponding to infrared InGaAs camera, used during measurement campaigns. We describe briefly the model that resolves the vibrational states of the nightglow main source (OH). Then vertically propagating gravity waves are applied on a 1D scheme and the cancellation factor is computed based on the impact on both temperature and intensity. Spectral variations of the cancellation factor are observed and compared along the variation of the vertical wavelength.

How to cite: Bellisario, C., Simoneau, P., Hauchecorne, A., Keckhut, P., Chane-Ming, F., and Listowski, C.: Spectral variations of the cancellation factor for temperature investigation in the mesospheric nightglow layer, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4192, https://doi.org/10.5194/egusphere-egu22-4192, 2022.

Resolving inertia-gravity (IG, or gravity) waves poses a real challenge for the formulation of numerical schemes for numerical weather prediction (NWP) and climate models due to different time scales of Rossby wave dynamics and fast-propagating IG waves. With ever increasing emphasize placed on high-resolution simulations, the importance of the issue is growing due to the implications of Courant-Friedrich-Levy (CFL) stability criterium. It is especially prominent in the tropical atmosphere, where a significant part of variability is associated with divergence-dominated dynamics. Detangling gravity and Rossby wave dynamics in the tropics is a challenging problem due to a lack of sepaartion between the Rossby and gravity regime that is present in the extra-tropics.   

TIGAR (Transient Inertia Gravity and Rossby wave dynamics) targets this problem by employing the eigensolutions of the linearized primitive equations on the sphere as the basis functions for the numerical representation of dynamical variables. This leads to the description of dynamics in terms of physically identifiable structures, i.e. the Rossby and gravity waves, which are fully dynamically separated at the linearization level. The benefits of such approach can be reaped on analytical, modelling and computational sides. As a research tool, TIGAR allows to study wave-wave interections directly in the model, without the need of intermediate software for wave filtering. Simplified models aimed at particular dynamical regime can be obtained from a full model with a simple configuration change. For instance, retaining only the Rossby modes in the spectral expansion will result in the quasi-geostrophic model, while additionally keeping the Kelvin and mixed Rossby-gravity waves will reproduce essential features of tropical circulation. 

Numerically, high precision computation is achieved in TIGAR through the use of higher order exponential time-differencing schemes, which take advantage of the normal modes framework, leading to the major increase in computational efficiency and stability. The comparison with classical time-stepping schemes in the horizontal component of the model shows accuracy improvements of several orders of magnitude at the same computational cost. In our testing on multiscale flows, the stability gains associated with the enhanced representation of gravity wave dynamics raise CFL time-step bound for explicit schemes by a factor of 4-6. 

We present TIGAR solutions of some classical steady and time-dependent problems including barotropic and baroclinic instability tests.

How to cite: Vasylkevych, S. and Žagar, N.: TIGAR - a new global atmospheric model for the simulation of Transient Inertia-Gravity And Rossby wave dynamics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6185, https://doi.org/10.5194/egusphere-egu22-6185, 2022.

Gravity waves impact the large scale circulation, and increasing our understanding of them is important to improve weather and climate models. This presentation focusses on atmospheric gravity waves in the stratosphere using data from the ECMWF ERA5 reanalysis, AIRS (Atmospheric Infrared Sounder) on NASA’s Aqua satellite and a high resolution run of the IFS operated at a km-scale spatial resolution. Data was examined during the first 2 weeks of November, as the high resolution model was initialized on the 1^st of this month. Asia and surrounding regions are investigated, because preliminary studies of AIRS data suggested strong gravity wave activity in this region during this time period. Waves can also be seen in the ERA5 data at the same times and locations. The high resolution model also shows significant gravity wave activity in similar areas to where it is seen in the AIRS data, particularly over Russia. The 2D+1 S-Transform was used to find wave amplitudes, horizontal and vertical wavelengths and momentum flux for all three datasets. Weather models are advancing rapidly and km scales such as the experimental IFS run could become operational in next decade. At these grid scales, gravity waves must be resolved instead of parameterized so the models need to be tested to see if they do this correctly. This work provides information on how a cutting edge model resolves gravity waves compared to observations.

How to cite: Lear, E., Wright, C., Hindley, N., and Polichtchouk, I.: Comparing gravity waves in a kilometer scale run of the IFS to AIRS satellite observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6323, https://doi.org/10.5194/egusphere-egu22-6323, 2022.

Observations with high vertical resolution have shown that vertical wavenumber (m) power spectra of horizontal wind and temperature fluctuations have a universal shape with a steep slope that is roughly proportional to ~m^–3. Several theoretical models explaining the universal spectra were proposed based on the assumption of gravity wave (GW) saturation. However, it has not yet been sufficiently confirmed that such characteristic spectra are fully composed of GWs. Thus, in the present study, we examine whether the m^–3 spectra are due to GWs, using a GW-permitting general circulation model with a high top in the lower thermosphere. The model-simulated spectra have steep spectral slopes, which is consistent with observations. GWs are extracted as fluctuations having total horizontal wavenumbers of 21–639. From the comparison between spectra of the GWs and those of all simulated fluctuations, it is shown that GWs are dominant only at high ms, while disturbances other than the GWs largely contribute to the spectra at low ms even in the m^–3 range. In addition, we examine vertical and geographical distributions of the characteristic wavenumbers, slopes, and amplitudes of GW spectra. The slopes of GW spectra are particularly steep near the eastward and westward jets in the middle atmosphere. It is theoretically shown that strong vertical shear below the jets is responsible for the formation of steep GW spectral slopes.

Reference:

Okui, H., Sato, K., and Watanabe, S., Contribution of gravity waves to the universal vertical wavenumber (m^–3) spectra revealed by a gravity-wave permitting general circulation model, submitted to Journal of Geophysical Research Atmospheres.

How to cite: Okui, H., Sato, K., and Watanabe, S.: Contribution of gravity waves to the universal vertical wavenumber (m–3) spectra revealed by a gravity-wave permitting general circulation model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6694, https://doi.org/10.5194/egusphere-egu22-6694, 2022.

We investigate the influence of a barotropic geostrophic current on
the internal wave (IW) generation over a shelf slope.
It is well known that most of the energy in the tide-topography
generated waves lies in waves with tidal frequency $\sigma_T$. 
Here we restrict our attention on the frequencies other than the dominant frequency $\sigma_T$. 
The current $V_g(x)$ is modeled as an idealized Gaussian function centered at
$x_0$ with width $x_r$ and maximum velocity $V_{max}$.
The bathymetry is modelled as a linear slope with smoothed corners.
Since the center of the current lies on the slope, there will always
be a region on the slope where the effective frequency $f_{eff}$ is
greater than the Coriolis parameter $f$ and another region where
$f_{eff} < f$. Parametric subharmonic instability (PSI) occurs where
waves with approximately half of the primary wave frequency, in this
case $\sigma_T/2$, are generated. In the presence of a large current,
PSI can occur where $f_{eff} < \sigma_T/2 < f$. This could not
happen without a current, i.e. $f_{eff} = f > \sigma_T/2$. Other interesting
interactions, including interharmonics and strong tidal harmonics, are also observed.

How to cite: He, Y. and Lamb, K.: Tide-topography interactions: the influence of an along-shelf current on the internal wave spectrum, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6715, https://doi.org/10.5194/egusphere-egu22-6715, 2022.

An experimental study of the focused internal waves generated by a horizontally oscillating torus in a linearly stratified fluid is presented for a large range of Stokes numbers from 100 to 6000. For low Stokes number the waves are unimodal, i.e. in each propagation direction they diffuse to form a single wave beam, after their emission at the critical locations where the wave rays are tangential to the torus boundary. In that regime, the waves amplify in amplitude in a single focal zone. With increasing Stokes number the waves become bimodal, forming dual wave beams in each propagation direction and focusing in four zones of amplitude amplification.

Comparison of the experimental results at small oscillation amplitude with an original linear theory gives excellent agreement over the entire Stokes number range. As the oscillation amplitude increases the wave amplitude saturates in the focal zone. This saturation only appears at large oscillation amplitude for low Stokes number and is present already at moderate oscillation amplitude for high Stokes number.

Fourier analysis reveals triadic interactions of the fundamental wave with two subharmonic waves owing to focusing. This triadic resonance is visible only at large oscillation amplitude when viscous effects are high, i.e. for low Stokes number, but with increasing Stokes number it manifests itself at smaller oscillation amplitude. For high Stokes numbers, above 1800, and large oscillation amplitudes, greater than or equal to the minor radius of the torus, wave turbulence is observed.

The Stokes drift, calculated theoretically, appears as the key to understand the generation of vertical mean flow in the focal zone. At low and moderate Stokes numbers the mean flow is almost exactly opposed by the Stokes drift, while for higher Stokes numbers perturbations of this flow start to appear with time, possibly due to the generation of subharmonics.

How to cite: Shmakova, N., Voisin, B., Sommeria, J., and Flor, J.-B.: Effects of viscosity on internal wave focusing by an oscillating torus., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6834, https://doi.org/10.5194/egusphere-egu22-6834, 2022.

Observations and high resolution models suggest a high potential for gravity waves (GW) to propagate horizontally, which is usually not considered in current parameterizations of general circulation models (GCM). For a quantification of the oblique propagation of orographic GWs and their transport of momentum throughout the atmosphere, we present a mountain wave model (MWM) that describes the terrain induced GW sources, propagation and momentum flux. Being aware of horizontal wind gradients, the model also allows for GW refraction which leads to a turning of the wave vector.

The MWM we present here is a simplified model identifying orographic GW sources from topography data. It is similar to the one presented in Bacmeister et.al. (1994). First, the topography is smoothed using a Gaussian bandpass filter, which allows to consider the different scales of generated MWs separately. This smoothed topography is afterwards reduced to the inherent ridge structure (i.e. to the arêtes of mountains) by employing edge and line detection algorithms from computer vision. Using this underlying arête structure in combination with a fit of idealized Gaussian-shaped mountain ridges to the topography gives us a straightforward way of determining MW parameters for launching a ray, i.e. source location, orientation and size of the wave vector as well as the displacement amplitude. These parameters are then used to calculate the propagation in space and time in given atmospheric backgrounds (determined from smoothed ERA5 (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation) data) with the ray tracer GROGRAT. The results can then be binned in terms of momentum flux and drag or used for a reconstruction of 3D temperature perturbations for a given time.

The MWM presented here has been validated against global satellite data as well as local measurements to a new quality compared to previous studies. The validation has been performed by applying an instrument-specific observational filter to the model data before considering global maps of momentum flux distributions and horizontal cross-sections of temperature perturbations. Comparisons of these to satellite data and limb measurement retrievals respectively will be shown in this presentation.

How to cite: Rhode, S., Preusse, P., Ern, M., Krasauskas, L., Geldenhuys, M., and Riese, M.: Quantification of oblique orographic gravity wave propagation deduced from a mountain wave model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6993, https://doi.org/10.5194/egusphere-egu22-6993, 2022.

How to cite: Neduhal, V., Žagar, N., and Zaplotnik, Ž.: Zonal wavenumber spectra of the vertical velocity and horizontal wind divergence associated with the Rossby and non-Rossby waves in the tropics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8251, https://doi.org/10.5194/egusphere-egu22-8251, 2022.

With an aim of understanding the role of internal waves to oceanic mixing, various mechanisms have been cited as a possible explanation for how they transfer energy across the wavenumber and frequency spectra and eventually contribute to small-scale turbulence. Triadic Resonance Instability (TRI) has become increasingly recognised as potentially one of these mechanisms. This talk will summarise experimental work that examines the long-term temporal and spatial evolution of this instability in the more realistic scenario of a finite-width internal wave beam. Experiments have been conducted using a new generation of wave maker, featuring a flexible horizontal boundary driven by an array of independently controlled actuators. We present experimental results exploring the role the finite-width of a wave beam has on the evolution of TRI. Experimentally, we find that the approach to a saturated equilibrium state for the three triadic waves is not monotonic, rather their amplitudes continue to oscillate without reaching a steady equilibrium. A detailed study into the structure of the secondary waves shows that this behaviour is also witnessed in Fourier space. We show how a spectrum of these resonant frequencies in the flow ‘beat’ to cause interference patterns which manifest throughout the instability as slow amplitude modulations.

How to cite: Grayson, K., Dalziel, S., and Lawrie, A.: Experimental Investigation into the long-term spatial and temporal development of Triadic Resonance Instability in a finite-width internal wave beam, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8254, https://doi.org/10.5194/egusphere-egu22-8254, 2022.

Convection, both observed and modeled, generates gravity waves (GWs) that significantly impact large-scale circulations in the stratosphere and above. However, models that permit convection and resolve the GWs they generate cannot reproduce the timing, location, and intensity of the actual convective cells that generate the observed convective GWs. This issue prevents comparison of observed and modeled convective GWs and model validation/evaluation. 

Here, convective latent heating is predicted based on radar observations and provided to an idealized version of WRF, allowing WRF’s dynamics to generate convective updrafts/downdrafts and generated convective GWs both mechanically and diabatically. Two methods are used to predict convective latent heating: the composited lookup table of Bramberger et al. 2020 and neural networks (NNs) using the same, and additional, input variables. Offline performance of the NN-predicted latent heating can be improved over the previous method when more input variables are used. Preliminary comparisons of modeled and observed (via superpressure-balloon and satellite) convective GWs will be presented. 

How to cite: Kruse, C., Alexander, M. J., Bramberger, M., Hassanzadeh, P., Chattopadhyay, A., Green, B., and Grimsdell, A.: Simulating Convective GWs forced by Radar-Based, Neural-Network-Predicted Diabatic Heating, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10138, https://doi.org/10.5194/egusphere-egu22-10138, 2022.

At around 04:14 UTC on the 15th January 2022, a major volcanic eruption began beneath the Tongan islands of Hunga Tonga and Hunga Ha’apai (175.4W, 20.5S). Located under only a shallow depth of water, the volcano rapidly launched a plume of super-heated ash and vapourised water upwards into the atmosphere. Over the next few hours, satellite observations reveal unprecedented large-scale concentric waves in the mid-stratosphere (near 40km altitude) radiating away from the eruption across the entire Pacific Ocean. In this presentation, we show brightness temperature perturbations in the 4.3 micron bands of the AIRS/Aqua, CrIS/Suomi-NPP and CrIS/JPSS-1 instruments that reveal three groups of atmospheric waves of special interest. First, an initial concentric wave is found travelling near the stratospheric speed of sound, likely to be an acoustic compression wave. There then follows a gap, which corresponds to phase speeds not permitted by theory, then a second group of waves likely to be gravity waves. These gravity waves are shown to be travelling near the maximum phase speed permitted, and there is a suggestion that some may travel the whole way around the globe in the tropics. Third, we observe small-scale gravity waves that pervade many thousands of kilometres across almost the entire Pacific Ocean, suggesting an extremely consistent heating source. All three of these wave observations are unprecedented in more than 20 years of stratospheric satellite observations, and this eruption may potentially have produced the first observations of an acoustic wave in the mid-stratosphere that can be measured from space. Now that we have space-borne instruments to observe it, this volcanic eruption provides a unique test of theoretical predictions of atmospheric wave phase speeds on some of the largest scales possible.

How to cite: Hindley, N., Hoffmann, L., Alexander, M. J., Mitchell, C., Osprey, S., Randall, C., Wright, C., and Yue, J.: The global reach of gravity waves at the stratospheric speed limit from the 2022 Hunga Tonga volcanic eruption, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10645, https://doi.org/10.5194/egusphere-egu22-10645, 2022.

As the first Doppler wind lidar in space, ESA’s flagship Aeolus satellite provides us with a unique opportunity to study the propagation of gravity waves (GWs) from near the surface to the tropopause and UTLS. Existing space-based measurements of GWs in this altitude range are spatially limited and, where available, use temperature as a proxy for wind perturbations. Recent research confirms Aeolus’ ability to measure GWs, and so this and future spaceborne wind lidars have the potential to transform our understanding of these critically-important dynamical processes.

Here, we present results from a special campaign onboard Aeolus, involving a change to the satellite’s range-bin settings designed to allow better observations of orographic GWs over the Southern Andes during winter 2021. In line with recent research, we expect to see GW wind structures extending down to near the wave sources, enabling detailed measurements of vertical and horizontal wavelength, pseudo-momentum flux and wave intermittency. Such parameters will feed into the next generation of NWP and global circulation models, which will resolve waves at higher resolutions and employ more advanced parametrization schemes.

How to cite: Banyard, T., Wright, C., Hindley, N., and Halloran, G.: Atmospheric Gravity Wave Observations from a Special Aeolus Campaign over the Southern Andes during Winter 2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10667, https://doi.org/10.5194/egusphere-egu22-10667, 2022.

Atmospheric waves in the tropical tropopause layer are recognized as a significant influence on processes that impact global climate. For example, waves drive the quasi-biennial oscillation (QBO) in equatorial stratospheric winds and modulate occurrences of cirrus clouds. However, the QBO in the lower stratosphere and thin cirrus have continued to elude accurate simulation in state-of-the-art climate models and seasonal forecast systems. We use first-of-their-kind profile measurements deployed beneath a long-duration balloon to provide new insights into impacts of fine-scale waves on equatorial cirrus clouds and the QBO just above the tropopause. Analysis of these balloon-borne measurements reveals previously uncharacterized fine-vertical-scale waves (<1km) with large horizontal extent (>1000km) and multiday periods. These waves affect cirrus clouds and QBO winds in ways that could explain current climate model shortcomings in representing these stratospheric influences on climate. Accurately simulating these fine-vertical-scale processes thus has the potential to improve sub-seasonal to near-term climate prediction.

How to cite: Bramberger, M., Alexander, M. J., Davis, S. M., Podglajen, A., Hertzog, A., Kalnajs, L., Deshler, T., Goetz, J. D., and Khaykin, S.: First measurements of fine-vertical-scale wave impacts on the tropical lower stratosphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13062, https://doi.org/10.5194/egusphere-egu22-13062, 2022.

The current orographic gravity wave drag parameterisation in the vicinity of low-level blocking is inadequate. Reducing the gravity wave amplitude (and thereby reducing the gravity wave drag) by assuming an effective mountain height dependent on the blocking depth is not realistic, yet this is implemented in most orographic gravity wave drag parameterisation schemes. The blocking layer acts as a sloped dynamic barrier that uplifts the air similarly to the mountain slope. Through a variety of mechanisms low-level blocking can induce more gravity waves or gravity waves with a higher momentum flux (compared to the current representation by parameterisation schemes). One possible solution is to modify the parameterisation scheme to not reduce the gravity wave momentum flux by the blocking depth. More realistic parameterisation schemes are likely to improve the models' performance.

How to cite: Geldenhuys, M.: On gravity wave parameterisation in vicinity of low-level blocking..., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13076, https://doi.org/10.5194/egusphere-egu22-13076, 2022.

• The generation of internal gravity waves from an initially geostrophically balanced flow is diagnosed in non-hydrostatic numerical simulations of shear instabilities for varied dynamical regimes. A non-linear decomposition method up to third order in the Rossby number Ro is used as the diagnostic tool for a consistent separation of the balanced and unbalanced motions in the presence of their non-linear coupling. Wave emission is investigated in an Eady-like and a jet-like flow. For the jet-like case, geostrophic and ageostrophic unstable modes are used to initialize the flow in different simulations. Gravity wave emission is in general very weak over a range of values for Ro. At sufficiently high Ro, however, when the condition for symmetric instability is satisfied with negative values of local potential vorticity, significant wave emission is detected even at the lowest order. This is related to the occurrence of fast ageostrophic instability modes, generating a wide spectrum of waves. Thus, gravity waves are excited from the instability of the balanced mode to lowest order only if the condition of symmetric instability is satisfied and ageostrophic unstable modes obtain finite growth rates.

How to cite: Chouksey, M., Eden, C., and Olbers, D.: Gravity wave generation by shear instability of balanced flow, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13505, https://doi.org/10.5194/egusphere-egu22-13505, 2022.

Atmospheric rivers (ARs) are generally considered to be transient and concurrent with an extratropical cyclone (Ralph et al. 2018). However, this is not necessarily the case for the ARs in the East Asian summer monsoon (EASM). Despite several climatological surveys on the EASM ARs in recent years (e.g., Park et al. 2021a), through what processes they develop is still unclear because of the complex interplay between monsoonal and extratropical circulations in the region (Horinouchi 2014; Park et al. 2021b).

In this talk, we demonstrate that the EASM ARs have different “flavors” in terms of moisture transport characteristics. By quantifying the relative contribution of high- and low-frequency components of the integrated water vapor transport anomaly (IVTA) for each AR, it is found that both components are important in East Asia summer, in contrast to the ARs in the U.S. west coast where the high-frequency component is predominant.

To investigate the synoptic condition governing the high- and low-frequency IVTA, the EASM ARs are classified into the three categories—1) transient, 2) quasi-stationary and 3) intermediate ARs—depending on the fractional contribution of high-frequency IVTA to total IVTA. While the transient ARs are driven by an extratropical cyclone in an analogy of classical ARs, the quasi-stationary ARs are associated with an anomalously enhanced monsoon flow. The intermediate ARs, which are a majority of summertime ARs in East Asia, show the confounding features of the two types. We suggest that the concept of “transient” and “quasi-stationary” AR flavors offer an important foundation in understanding the EASM ARs with a variety of underlying dynamics. Further implications and possible future works will be also discussed.

References:

Horinouchi, T., 2014: Influence of upper tropospheric disturbances on the synoptic variability of precipitation and moisture transport over summertime East Asia and the northwestern Pacific. J. Meteor. Soc. Japan, 92, 519–541, https://doi.org/10.2151/jmsj.2014-602.

Park, C., S.-W. Son, and H. Kim, 2021a: Distinct features of atmospheric rivers in the early versus late EASM and their impacts on monsoon rainfall. J. Geophys. Res. Atmos., 126, e2020JD033537, https://doi.org/10.1029/2020JD033537.

Park, C., S.-W. Son, and J.-H. Kim, 2021b: Role of baroclinic trough in triggering vertical motion during summertime heavy rainfall events in Korea. J. Atmos. Sci., 78, 1687–1702, https://doi.org/10.1175/JAS-D-20-0216.1.

Ralph, F. M., M. D. Dettinger, M. M. Cairns, T. J. Galarneau, and J. Eylander, 2018: Defining “atmospheric river”: How the glossary of meteorology helped resolve a debate. Bull. Amer. Meteor. Soc., 99, 837–839. https://doi.org/10.1175/BAMS-D-17-0157.1.

How to cite: Park, C. and Son, S.-W.: Transient versus quasi-stationary flavors of atmospheric rivers during East Asian summer monsoon, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-242, https://doi.org/10.5194/egusphere-egu22-242, 2022.