The Global-to-Regional ICON (GLORI) Digital Twin is a configurable on-demand global-to-regional short-range high-resolution digital twin based on ICON. It is developed in a tri-lateral cooperation between Germany, Italy and Switzerland.
GLORI provides short-range global predictions down to the storm-scale (~3 km horizontal) and on-demand high resolution (~ 500 m) predictions for selected regions, like the Alpine domain or the Italian peninsula. It includes an uncertainty estimation through ensemble forecasts for global and regional scales. The data assimilation system is ensemble-based, both for the global and the regional components. The GLORI Digital Twin aims at providing forecasts down to the application level, for a range of use cases including flood forecasting, urban heat island and urban flooding events, mineral dust predictions for energy applications and pollen predictions.
Moving to higher resolution requires improvements both in the model and in the data assimilation system. We test the ICON model in complex topography and highlight its behaviour in dependence of conditions like stable boundary layer, flow interacting with the orography, convection development, different soil textures and urban areas. In that, GLORI can also be seen as testing environment for the development of hectometric scale modeling. The research focuses also on data assimilation at higher resolution, both for the global and for the regional runs, and on the usage of high-resolution observations. The assimilation of the radar data of the three partner countries is tested over the Alpine domain, aiming at the improvement of the prediction of convective events. Dedicated studies focus on direct assimilation at 1 km resolution. This goal demands a rigorous evaluation of the entire assimilation workflow, including observation thinning, averaging strategies, localization, and observation error quantification. The impact of performing data assimilation at 2 km resolution with nesting at 1 km is then compared with direct assimilation in the 1km domain. The performance of the Digital Twin is assessed on high-impact weather events, considering in particular convective development leading to severe weather and the recent flood events.
How to cite: Marsigli, C. and the GLORI Team: The GLObal-to-Regional ICON (GLORI) Digital Twin: towards hectometric scale predictions for high-impact weather, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17981, https://doi.org/10.5194/egusphere-egu25-17981, 2025.
Forecasting convective storms is one of the most challenging tasks in Numerical Weather Prediction (NWP). Data Assimilation (DA) methods improve the initial condition and subsequent forecasts by combining observations and previous model forecasts (background). Weather radar provides a dense source of observations in storm monitoring. Therefore, assimilating radar data should significantly improve storm forecasting skills. However, extrapolating rainfall patterns (nowcasting) from radar data is often better than numerical model-based forecasting with DA in the first 2 or 3 hours (Fabry and Meunier, 2020). This is related to the fact that the radar data only provides information on the precipitation pattern and intensity in the area affected by the storm. Furthermore, it does not directly provide information on other variables that are strongly linked with the storm, such as temperature, wind, and humidity, either within the precipitation region or in the areas far from the storm. One potential solution to this problem is to use machine learning (ML) techniques to construct the DA observations operator to generate a model-equivalent of the radar data. In this approach, NWP model fields (temperature, wind components, relative humidity, precipitation) would serve as input, and radar observations would be the output of an encoder-decoder neural network. The constructed observation operator describes a non-linear relationship between the NWP model storm-related variables and radar observations, spreading radar information to other variables and potentially enhancing storm forecasting skills.
How to cite: Stefanelli, M., Zaplotnik, Z., and Skok, G.: A neural network-based observation operator for weather radar data assimilation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-420, https://doi.org/10.5194/egusphere-egu25-420, 2025.
Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation have been proposed which allow for using new input data without retraining the model. They could also dramatically accelerate the costly data assimilation process used in operational regional weather models. Here, in a central US testbed, we demonstrate the viability of score-based data assimilation in the context of realistically complex km-scale weather. We train an unconditional diffusion model to generate snapshots of a state-of-the-art km-scale analysis product, the High Resolution Rapid Refresh. Then, using score-based data assimilation to incorporate sparse weather station data, the model produces maps of precipitation and surface winds. The generated fields display physically plausible structures, such as gust fronts, and sensitivity tests confirm learnt physics through multivariate relationships. Preliminary skill analysis shows the approach already outperforms a naive baseline of the High-Resolution Rapid Refresh system itself. By incorporating observations from 40 weather stations, 10% lower RMSEs on left-out stations are attained. Despite some lingering imperfections such as insufficiently disperse ensemble DA estimates, we find the results overall an encouraging proof of concept, and the first at km-scale. It is a ripe time to explore extensions that combine increasingly ambitious regional state generators with an increasing set of in situ, ground-based, and satellite remote sensing data streams.
How to cite: Manshausen, P., Cohen, Y., Pathak, J., Pritchard, M., Garg, P., Mardani, M., Kashinath, K., Byrne, S., and Brenowitz, N.: Diffusion Model Data Assimilation of Sparse Weather Station Observations at Kilometer Scales, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10945, https://doi.org/10.5194/egusphere-egu25-10945, 2025.
The current study utilizes the operational data assimilation system of Deutscher Wetterdienst (DWD) to investigate the impacts of improved radar forward operator. First of all, it is shown that for experiments in which both conventional and radar data are assimilated and the latent heat nudging (LHN) is applied, the one with improved operator (i.e., with improved Mie-scattering scheme and accounting for beam broadening effect and etc.) exhibits neutral impacts on short-term forecasts. However, in subsequent experiments, in which conventional data are not assimilated and the LHN is switched off, the one with improved operator not just reduces representation error during data assimilation cycles but also enhances the short-term forecast skills. In addition, it is found that the subsequent experiments result in much shorter observation error correlation length scales for radar reflectivity data, indicating that the application of the LHN or assimilation of conventional data may increase the length scales.
How to cite: Zeng, Y., de Lozar, A., Feng, Y., Blahak, U., Khosravianghadikolaei, K., and Janjic, T.: Study on representation error of radar data in convective scale data assimilation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8227, https://doi.org/10.5194/egusphere-egu25-8227, 2025.
New constellations to provide high-resolution atmospheric observations from microwave sounders operating in low-earth orbit are now coming online and are demonstrating the potential to provide operationally useful data. The first of these missions, the NASA TROPICS (Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats) Earth Venture (EVI-3) mission, was successfully launched into orbit on May 8 and May 25, 2023 (two CubeSats in each of the two launches). TROPICS is now providing nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. TROPICS is providing rapid-refresh microwave measurements (median refresh rate of better than 60 minutes early in the mission with four functional CubeSats, and now approximately 70-90 minutes with three functional CubeSats) over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm lifecycle. Hundreds of high-resolution images of tropical cyclones have been captured thus far by the TROPICS mission, revealing detailed structure of the eyewall and surrounding rain bands. The new 205-GHz channel in particular (together with a traditional channel near 92 GHz) is providing new information on the inner storm structure, and, coupled with the relatively frequent revisit and low downlink latency, is already informing tropical cyclone analysis at operational centers. A neural network algorithm to retrieve the atmospheric temperature and moisture vertical profiles has recently been developed and validated, with retrieval uncertainties approaching those of state-of-the-art microwave sounders, but with much better revisit rate. In this presentation, we highlight the use of these high-revisit thermodynamic data from TROPICS to better characterize storm structure and environmental conditions over a variety of cases over the nearly two-year mission lifetime to date.
How to cite: Blackwell, W. and the TROPICS Science Team: Investigations of Tropical Cyclone Thermodynamic Structure using NASA TROPICS Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7546, https://doi.org/10.5194/egusphere-egu25-7546, 2025.
Satellite data assimilation is progressing beyond the conventional “clear-sky” approach towards the “all-sky” approach. While the former eliminates observations affected by clouds, the latter assimilates all observations including clear-sky, cloudy and precipitation conditions. The exploitation of cloud affected radiances is a promising endeavour as these observations are directly related to particularly challenging weather phenomena (e. g. convection, frontal systems, low stratus, and fog). This study focuses on the assimilation of clear and cloud affected (all-sky) radiances, from the 6.2 μm and 7.3 μm channel sensitive to water vapour in the upper troposphere using satellite data from SEVIRI, an instrument onboard Meteosat-10. The goal is to describe the improvements in short range forecasts in the high-resolution limited area Numerical Weather Prediction Model (NWP), AROME (Application of Research to Operations at MEsoscale) used at GeoSphere Austria. 3D-Var data assimilation experiments were performed to study the impact of all-sky vs clear-sky. A significant challenge is accurately representing observation errors, which are influenced by the complex and variable nature of clouds. This work implements an observation error model that dynamically adjusts error values based on cloud amount. The model addresses the increased uncertainties in cloud-dense regions by assigning higher observation errors, while clearer areas receive lower error values, in alignment with the need for spatially adaptive error characterization in all-sky conditions. Results demonstrate that the cloud-dependent error model leads to more Gaussian departures which can be expected to improve the assimilation of cloud-affected radiances, leading to better initial conditions and refined representations of atmospheric states and consequently the forecast.
How to cite: Neduncheran, A., Meier, F., Wittmann, C., Weissmann, M., and Griewank, P.: Optimizing all-sky infrared radiance assimilation with dynamic cloud-dependent error modeling , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18449, https://doi.org/10.5194/egusphere-egu25-18449, 2025.
Accurate representation of atmospheric dynamics at convection scale still represents a major challenge for numerical models and a critical aspect in operational weather predictions. Reliable forecast initial conditions, generated by the data assimilation cycle using new observations coming from different platforms, are crucial to improve the forecast accuracy in deep convection environments. In this work, the ICOsahedral Non-hydrostatic (ICON) model is run at convection-permitting scale over the Italian domain, in combination with the Kilometre-scale Ensemble Data Assimilation (KENDA) system, to test the model performance on a poorly-predicted extreme convective storm in the Marche region, Italy, on 15 September 2022. We show here the positive impact of data assimilation at convection scale on the forecast of this event, which allows to improve the localization and the intensity of the storm although substantial underestimation of precipitation still persists. The relative impact of different observations datasets is evaluated, starting from conventional and radar data operationally assimilated for numerical weather predictions over Italy. After pointing out the importance of low-level moisture convergence in the process of convection initiation and the significant undersampling of humidity field in conventional data, the added value of humidity-sensitive microwave radiances from polar satellites is analyzed. Observations sensitive to mid-lower tropospheric humidity in clear-sky conditions are employed, taken from the Microwave Humidity Sounder instrument, still little investigated in limited-area models at many numerical weather prediction centers. In order to better exploit the information content of microwave satellite observations, the preliminary development towards all-sky assimilation is presented.
How to cite: Grenzi, M., Gastaldo, T., Poli, V., Marsigli, C., Janjic, T., Cacciamani, C., and Carrassi, A.: Assimilating multi-platform observations to improve severe convection forecasting in the ICON model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10874, https://doi.org/10.5194/egusphere-egu25-10874, 2025.
Forecasting monsoon precipitation over Arizona is challenging due to its complex terrain since the model grid structure may misrepresent topographic details and the sparse observation network is insufficient for initialization of the model at the scale of the topography, particularly the spatial distribution of moisture. Our study aims to evaluate the monsoon precipitation forecast skill over Arizona by conducting an Observing System Experiment (OSE), or “data denial study” using the Data Assimilation Research Testbed (DART) to assimilate Global Positioning System precipitable water vapor (GPS-PWV) into the advanced version of the Weather Research and Forecast (WRF) model. The High-Resolution Rapid Refresh (HRRR) model is used as the initial and boundary conditions. The hourly GPS-PWV data were collected from 30 sites across Arizona characterized by a very nonuniform distribution with clusters of observations separated by large spatial gaps.
The precipitation event of interest occurred on 16 August 2021 with convective initiation developing over the Mogollon Rim in the afternoon and precipitation occurring over Flagstaff, Sedona, and Prescott as the increasingly well-organized mesoscale convective system propagated southwestward to the Arizona-California border. The amount of total precipitation recorded by NOAA’s MRMS (Multi Radar – Multi Sensor) system was 25 - 60 mm within the 12- hour period of 00 UTC 16 August to 12 UTC 16 August. In this study we initiated the forecast at 06 UTC 15 August with 40 ensemble members and assimilated the hourly GPS-PWV data over the 6h period from 1200-1800 UTC, after which we ran a deterministic forecast using the mean ensemble data assimilation analysis at 18 UTC as the initial condition for this “free forecast”.
We discovered that this forecast and assimilation system was sensitive to the specification of the initial state of the atmosphere, the radius of influence in the Ensemble Kalman Filter data assimilation system, and the model physics. Therefore, we tested the simulation using a variety of horizontal and vertical localizations and microphysics schemes to find a configuration resulting in the least-bias PWV. We used this optimal configuration to forecast 24 other precipitation events occurring in the 2021 monsoon season.
Our results show: 1) GPS-PWV data assimilation reduced forecast PWV errors across the model domain. 2) GPS-PWV data assimilation increased instability (due to moistening) of the pre-convective atmosphere over the Mogollon Rim and southeastern Arizona by as much as 1000 J/kg. 3) GPS-PWV data assimilation maintained these more favorable atmospheric conditions for convection and improved precipitation forecasts for at least 6 hours into the free forecast period but became too moist afterward. 4) The results revealed a surprising dry bias of 3-4 mm PWV in the HRRR model (used for initial conditions) compared to the actual GPS-PWV values, and this bias was maintained in the WRF model control run without GPS-PWV data assimilation for at least 18h.
How to cite: Risanto, C. B., Arellano, Jr., A. F., Koch, S., Castro, C. L., Shohan, S., and Adams, D. K.: Impacts of Assimilating GPS-PWV in Convective-permitting Model on Forecasting Monsoon Precipitation over Arizona Complex Terrain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-100, https://doi.org/10.5194/egusphere-egu25-100, 2025.
In an effort to enhance storm-scale data assimilation and prediction, we have recently updated the atmospheric Model for Prediction Across Scales (MPAS-A; Skamarock et al. 2012), coupled to the Ensemble Kalman Filter (EnKF) Data Assimilation Research Testbed (DART) system (Ha et al., 2017), for regional analysis using variable-resolution capabilities. In this talk, we will introduce unique features of the interface, leveraging the model's native coordinate both horizontally (e.g., unstructured meshes) and vertically (e.g., terrain-following height), and demonstrate its suitability for storm-scale DA. As its robustness was demonstrated in the U. S. National Severe Storms Laboratory (NSSL)'s Warn-On-Forecast framework during tornado watches in 2024, the performance of regional ensemble analysis incorporating storm-scale data assimilation using radars and cloud water path from the GOES-R satellite will be presented.
How to cite: Ha, S. and Park, J.: Convective-scale ensemble data assimilation using unstructured meshes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12280, https://doi.org/10.5194/egusphere-egu25-12280, 2025.
The accuracy of numerical weather prediction models is highly dependent on the precision of the initial conditions, especially for forecasting storms and convective-scale weather events. Radars, with their ability to capture the internal structure and important microphysical and dynamical processes within convective systems, play a crucial role in improving weather forecasts at convective scales. Unlike conventional single-polarization radar, dual-polarization radar additionally provides information on the types and sizes of hydrometeor particles. As a result, polarimetric radar data (PRD) is a valuable data source for data assimilation (DA). Despite its potential, PRD is not yet directly assimilated into operational convection-permitting numerical models. This limitation arises from several challenges, including the highly non-linear nature of observation operators for polarimetric variables and the difficulty of estimating model error at convective scales, which require further research.
Our study primarily aims to directly assimilate PRD within an idealized setup. To accomplish this, Observation System Simulation Experiments (OSSEs) were conducted to simulate the evolution of a long-lived supercell using the ICOsahedral Nonhydrostatic (ICON) model with a two-moment microphysics scheme. For the assimilation of PRD data, the Kilometer-scale Ensemble Data Assimilation (KENDA) system was utilized, which incorporates the Local Ensemble Transform Kalman Filter (LETKF), along with the polarimetric radar forward operator EMVORADO-POL developed at the Deutscher Wetterdienst (DWD). In the current idealized setup, two types of DA experiments were conducted: a reference experiment that assimilated only non-polarimetric variables, such as reflectivity and radial velocity, and an experiment that assimilated differential reflectivity (ZDR) in addition to the non-polarimetric variables. The results from both experiments were compared, and appropriate thresholds and equivalents of noreflectivity data for polarimetric data were examined. Additionally, the sensitivity to DA settings, such as localization radius and the number of ensemble members, was also tested.
How to cite: Bardachova, T., Ramezani Ziarani, M., and Janjic, T.: Direct assimilation of dual-polarization radar data in the idealized setup, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4163, https://doi.org/10.5194/egusphere-egu25-4163, 2025.
Forecasting precipitation in tropical regions is challenging because of substantial errors in both the models and the initial conditions. The interaction between tropical waves and convection suggests possible predictability. Therefore, an accurate representation of these waves in the models and initial conditions is important for increasing the accuracy of precipitation forecasts. This study intends to improve the predictive reliability of the ICON (Icosahedral Nonhydrostatic) global model for tropical weather events, such as tropical waves and the Madden-Julian Oscillation (MJO). We initially analyze the ability of data assimilation (DA) to conserve total energy, enstrophy, moist static energy, and other physical properties. Then, we implement an advanced DA technique, the Quadratic Programming Ensemble (QPEns), with a moist static energy constraint. Preliminary findings show that the moist static energy constraint, together with accurate wind and humidity data, decreases forecast errors and improves tropical wave representation. This induces more reliable long-term precipitation forecasts.
How to cite: Ramezani Ziarani, M., Ruckstuhl, Y., and Janjic, T.: Enhancing Tropical Weather Forecasts with Constrained Data Assimilation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4313, https://doi.org/10.5194/egusphere-egu25-4313, 2025.
Surface observations can provide crucial information for NWP models. If not assimilated carefully, however, they can degrade forecast accuracy, especially in complex terrains like the Alps. The horizontal and vertical covariances of climatological background error covariances used in the three-dimensional variational (3DVar) data assimilation (DA) method can produce unrealistic increments over sloped terrain. For instance, an observation from a valley station can still generate increments at the mountaintop, even though the valley observation may not accurately represent the mountaintop's weather conditions.
We used a hybrid three-dimensional ensemble variational (Hybrid-3DEnVar) DA method to address this issue, incorporating a 50-member convection-permitting ensemble. This method was recently tested in Geosphere Austria's convective scale limited-area NWP model AROME at a 2.5 km horizontal resolution. We assimilated 2-meter temperature, 2-meter relative humidity, geopotential, and 10-meter wind components from 680 surface stations, including the Austrian TAWES network and SYNOP observations from neighbouring countries. 400 stations were actively assimilated from the observation dataset, and the rest were used to verify the analysis.
Our results present the effectiveness of this newly tested Hybrid-3DEnVar against GeoSphere Austria's operational 3DVar in assimilating surface observations over complex Alpine terrain.
How to cite: Jyoti, K., Griewank, P., Meier, F., and Weissmann, M.: Comparison of Hybrid-3DEnVar against 3DVar for the assimilation of surface observations over the Alpine terrain in AROME-Austria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11180, https://doi.org/10.5194/egusphere-egu25-11180, 2025.
Hydrometeor formation and cloud processes occur at very small spatial scales and cannot be explicitly resolved on the numerical grids of weather prediction models. Parameterizations of these processes are a necessary component of forecast models and are known to be a major source of forecast error. Two main challenges arise. First, the mismatch between the effective resolution of prediction models and the resolution of satellite observations leads to representativeness errors. Second, sub-optimal parameterizations induce systematic errors in hydrometeor fields and cloud properties. In the presence of such model biases, the assimilation of cloud-scale observations can be detrimental to the analysis. It remains an open question how to properly account for the scale mismatch and for systematic errors when assimilating small-scale observations into a larger-scale numerical model.
In this work, we compare different approaches for assimilating radiance observations that contain unresolved scales, such as data thinning, use of superobservations, and a multi-scale decomposition of the two-dimensional cloud field. We study the problem using observing system simulation experiments (OSSEs) performed with the WRF model and the DART EAKF assimilation system. The nature run is a high-resolution large eddy simulation (dx=250 m) of deep moist convection developing in moderate wind shear, which supports organization into multicell storms. Synthetic satellite imagery is computed from the nature run using operators available through RTTOV. Several 40-member km-scale ensemble experiments (dx=2 km) evaluate the impact of assimilating thinned, averaged, or multi-scale radiance observations.
How to cite: Kugler, L., Serafin, S., and Weissmann, M.: Dealing with unresolved scales of motion and systematic errors in the assimilation of cloud-affected satellite radiances, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10275, https://doi.org/10.5194/egusphere-egu25-10275, 2025.
Assimilating visible satellite observations has become an increasingly active research topic and has been shown to provide valuable information for improving weather forecasts. The assimilation of these observations, however, is challenging due to operator deficiencies and model deficiencies in the representation of clouds. Our work focuses on evaluating the potential of the RTTOV observation operator to simulate visible satellite images in the convection-permitting AROME-Austria model, which is operational at Geosphere Austria. Specifically, we examine the systematic deviations caused by operator and model errors in all-sky conditions. In cloudy conditions, we build on findings from preceding studies and conduct sensitivity tests to evaluate model equivalents with modified operator settings. In clear-sky conditions, we aim to evaluate and mitigate the systematic deviations caused by orographic shadowing and high-albedo surfaces with the help of an advanced visible operator developed by DWD. A summer month of 3-hourly forecasts from 6 UTC to 18 UTC provides the basis for this analysis. Our findings aim to address operator deficiencies and model inconsistencies, laying the groundwork for integrating visible observations as a new observation type into the AROME-Austria model.
How to cite: Chkeir, S., Griewank, P., Scheck, L., Meier, F., Wittmann, C., and Weissmann, M.: Evaluation of visible satellite images from AROME-Austria as preparation for assimilating visible observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12711, https://doi.org/10.5194/egusphere-egu25-12711, 2025.
This study investigates sensitivity of convection to coincident atmospheric environment at meso-gamma spatial scales. The data used for the study comprise a large ensemble of three-dimensional high-resolution local domains that were extracted from cloud-resolving model simulations of different cases of subtropical and tropical convection over land and ocean. The simulations were produces as part of the NASA (National Aeronautics and Space Administration) INCUS (Investigation of Convective Updrafts) mission (van den Heever, 2021). The simulations include a diverse set of convective morphologies associated with different synoptic environments (Marinesku et al., 2024 ). Each neighborhood domain of dimensions 25.6 x 25.6 x 18 km, respectively in latitudinal, longitudinal and vertical direction, is centered on a deep convection core vertical profile that was selected for tracking convective cloud evolutions for the purpose of investigations within the INCUS mission (Sokolowsky et al. 2024 ). The ensemble of neighborhoods used in this study therefore represent a distribution of local convective states and the associated environments embedded in a wide range of larger scales environments.
To capture relationships between convection and environment states over a range of spatial scales contained within the neighborhoods in a concise manner, the analysis was performed in a phase space of two-dimensional horizontal scales spectral powers that are associated with leading vertical principal components of the physical variables representing the convection and environment. The convection state was represented by vertical velocity and total condensate, and the environment by temperature, humidity, divergence and vorticity.
The main finding is that the variability of vertical velocity and total condensate states at the convective scales (< 10 km) exhibits high insensitivity to the variability of the neighborhood domain average environment states. In contrast, notable co-variance was found between the vertical velocity and environment states at the convective scales, especially with the temperature mid-to-upper tropospheric warming variability and the variability of divergence in mid-troposphere and above 10 km. For the total condensate, significant co-variance was exhibited also between its neighborhood domain average and the the convection scale environment. This relationship reflects impact of the convective processed on the environment states including coupling between the convection dynamics and microphysics.
In the context of convective scale data assimilation the findings suggest that for representation of the convection state variability at the convective scales would be weekly constrained in the absence of convective scale observations of the environment states.
References
Marinescu, P.J., van den Heever, S.C., Grant, L.D., Bukowski, J. and Singh, I., 2024. How Much Convective Environment Subgrid Spatial Variability Is Missing Within Atmospheric Reanalysis Data Sets?. Geophysical Research Letters, 51(24), p.e2024GL111856.
Sokolowsky, G.A., Freeman, S.W., Jones, W.K., Kukulies, J., Senf, F., Marinescu, P.J., Heikenfeld, M., Brunner, K.N., Bruning, E.C., Collis, S.M. and Jackson, R.C., 2024. tobac v1. 5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena. Geoscientific Model Development, 17(13), pp.5309-5330.
van den Heever, S. C. (2021). NASA selects new mission to study storms, impacts on climate models. NASA Earth (https://www.nasa.gov/press‐release/nasa‐selects‐new‐mission‐to‐study‐storms‐impacts‐on‐climate‐models)
How to cite: Vukicevic, T., Prasanth, S., Haddad, Z., Posselt, D., and Hristova-Veleva, S.: Sensitivity of convection to environment within local neighborhoods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16093, https://doi.org/10.5194/egusphere-egu25-16093, 2025.
Localization is essential for any ensemble-based data-assimilation system for numerical weather prediction, and most localization approaches are distance-based. For example, in the observation-space localization used by the Deutscher Wetterdienst (DWD), the localization is a function of the distance between a model grid point and an observation location. Observation-space localization for satellite observations is especially challenging because they do not have a constant or well-defined observation location. Instead, the observed signal may originate from various vertical levels and is affected by the presence of clouds. We derive an optimal localization for all-sky visible and infrared satellite observations over Germany by minimizing the difference between the DWD operational analysis and radiosonde profiles in a 1-month cycled assimilation experiment that excluded radiosondes. We use reconstructed partial analysis increments (PAI) to approximate a wide range of localization settings without needing to rerun the costly month-long experiment. We find that visible satellite observations require no localization, but that infrared observations deteriorate the analysis if they are not localized carefully.
How to cite: Griewank, P., Parker, M., Necker, T., Miyoshi, T., Schomburg, A., Diefenbach, T., and Weissmann, M.: Optimal vertical localization for the assimilation of cloud-affected satellite observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16990, https://doi.org/10.5194/egusphere-egu25-16990, 2025.
Abstract
To enhance the accuracy of rainfall estimation using remote sensing data, such as radar and satellite, it is crucial to improve the accuracy of the estimation relationships. Rainfall estimation is influenced by various factors, including rainfall type, geographical characteristics (e.g., inland and oceanic rainfall), and orographic rainfall features. Developing estimation formulas that account for variations in rainfall characteristics based on topography (elevation) and seasonal temperature changes is essential. Ensuring the reliability of observation data used in deriving these formulas is a top priority for achieving accurate rainfall estimation. This study evaluates the effectiveness of utilizing rain gauge data under varying wet-bulb temperature conditions to improve the reliability of rainfall analysis. The analysis employed disdrometer data collected over five years (2020–2024), applying channel-based particle diameter information and number concentration-based variable calculation methods to enhance the generalizability of the findings. Quantitative comparisons of rain gauge observation accuracy under different wet-bulb temperature conditions were conducted, alongside an analysis of the temperature ranges in which two types of rain gauges (tipping-bucket and weighing gauges) could be effectively utilized. Furthermore, we assessed the quality management of rain gauge data preprocessing for raindrops across various temperature conditions. The results indicate that when the wet-bulb temperature exceeded 2°C, the difference (RMSE) in rainfall between disdrometer and rain gauge observation data was less than 0.2 mm. However, this difference increased significantly to over 0.4 mm when the wet-bulb temperature was below 2°C, with particularly large differences exceeding 1.0 mm when disdrometer data were not preprocessed. These discrepancies reflect variations in hydrometeor characteristics and particle fall velocities due to temperature changes. This study underscores the necessity of establishing meteorological conditions for rainfall analysis.
Keywords: Disdrometer, Wet-bulb temperature, Long-term observation, Hydrometeor
Acknowledgement
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (RS-2022-NR071182).
How to cite: Kim, H.-J., Suh, S.-H., Byun, J., and Jun, C.: Validation of Rainfall Data Analysis Using Disdrometer Data Under Wet-Bulb Temperature Conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2686, https://doi.org/10.5194/egusphere-egu25-2686, 2025.
In the wake of the continuous expansion and refinement of the ground automatic meteorological observation network in China, the development of an effective quality control system for ground automatic station observation data has become an urgent task of great significance in the field of meteorology. Although extensive research has been conducted on quality control techniques for traditional ground observation meteorological variables such as precipitation, temperature, and pressure both domestically and abroad, the exploration of quality control strategies for precipitation phase observation data remains relatively limited.This research endeavor undertakes the utilization of upper-air and manual ground observation datasets covering the period from 2000 to 2014. Through a comprehensive analysis and selection process, meteorological factors that exert a pronounced influence on precipitation phase are identified and optimized. Subsequently, the random forest algorithm is applied to establish a quality control model for the automatic observation data of three primary precipitation phases: rain, snow, and sleet. Employing this meticulously constructed quality control model, an in-depth quality assessment is carried out on the ground automatic precipitation phase observation data collected during the period from 2015 to 2023, after the discontinuation of manual observations. A total of 15,806 station-records are flagged as suspicious or incorrect. It is observed that the stations with such data anomalies are preponderantly located in regions with sparse human habitation and challenging maintenance conditions, such as the Qinghai-Tibet Plateau, the Tianshan Mountains, and the mountainous areas in northern Heilongjiang. In contrast, regions like Guangdong, Guangxi, Yunnan, Fujian, and Hainan exhibit relatively high data quality, with the eastern regions generally outperforming the western ones (Figure 1).For the identified suspicious data, a rigorous manual verification procedure is implemented. For example, at 14:00 on January 31, 2019, the quality control results for Wuqia, Akto, and Kashgar stations in Xinjiang indicated snowfall, yet the automatic observations registered precipitation. With the ground temperatures of these stations being -10°C, -6°C, and -6°C respectively, it is meteorologically implausible for rain to occur in Xinjiang during winter. Hence, the automatic precipitation observations at these stations are deemed incorrect. After conducting a substantial amount of manual verification on other suspicious and incorrect data, it is determined that the identification accuracy rate of this quality control method surpasses 98.5%. Presently, this research outcome has been successfully incorporated into the operational quality control framework for ground automatic precipitation phase observation.
Figure 1 Frequency Diagram of Stations with Suspected or Incorrect Precipitation Phase Quality Control from 2015 to 2023
How to cite: Ou, X., Lin, H., and Huang, X.: Research on Quality Control Methodology of Automatic Precipitation Phase Observation Data Based on Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3384, https://doi.org/10.5194/egusphere-egu25-3384, 2025.
Variational data assimilation theoretically assumes Gaussian-distributed observational errors, yet actual data often deviates from this assumption. Traditional quality control methods have limitations when dealing with nonlinear and non-Gaussian-distributed data. To address this issue, our study innovatively applies two advanced machine learning (ML) based quality control (QC) methods, Minimum Covariance Determinant (MCD), and Isolation Forest to process precipitable water (PW) data derived from satellite FengYun-2E (FY2E). We assimilated the ML QC-processed TPW data using the Gridpoint Statistical Interpolation (GSI) system and evaluated its impact on heavy precipitation forecasts with the Weather Research and Forecasting (WRF) v4.2 model. Both methods notably enhanced data quality, leading to more Gaussian-like distributions and marked improvements in the model’s simulation of precipitation intensity, spatial distribution, and large-scale circulation structures. During key precipitation phases, the Fraction Skill Score (FSS) for moderate to heavy rainfall generally increased to above 0.4. Quantitative analysis showed that both methods substantially reduced Root Mean Square Error (RMSE) and bias in precipitation forecasting, with the MCD method achieving RMSE reductions of up to 58% in early forecast hours. Notably, the MCD method improved forecasts of heavy and extremely heavy rainfall, whereas the Isolation Forest method demonstrated superior performance in predicting moderate to heavy rainfall intensities. This research not only provides a basis for method selection in forecasting various precipitation intensities, but also offers an innovative solution for enhancing the accuracy of extreme weather event predictions.
How to cite: Shen, W., Chen, S., Xu, J., Zhang, Y., Liang, X., and Zhang, Y.: Enhancing Extreme Precipitation Forecasts through Machine Learning Quality Control of Precipitable Water Data from Satellite FengYun-2E: A Comparative Study of Minimum Covariance Determinant and Isolation Forest Methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3931, https://doi.org/10.5194/egusphere-egu25-3931, 2025.
This research investigates the dynamics of the Sea Breeze Front (SBF) in the southwestern Caspian Sea, specifically focusing on Bandar Anzali, Iran. Utilizing two years of observational data alongside Weather Research and Forecasting (WRF) model simulations, the study examines the meteorological characteristics and behaviors associated with SBF events. SBF days were identified by analyzing land-sea temperature contrasts, supported by wind shifts, temperature decreases, increases in humidity, and cloud formation.
In-depth analysis reveals consistent atmospheric patterns during SBF events, such as temperature variations and notable wind shifts. The intensity of the land-sea thermal contrast is influenced by both local topography and atmospheric stability. A detailed case study of March 4, 2022, highlighted key meteorological changes, including temperature drops and wind direction shifts. While the WRF model accurately captured temperature and pressure variations, it slightly underestimated humidity and dew point.
Machine learning techniques, particularly K-means clustering, were employed to classify distinct atmospheric regimes linked to SBF occurrences. The clustering analysis identified two primary atmospheric patterns: cold, humid air masses favorable to SBF development, emphasizing the significant role of land-sea temperature gradients and local wind dynamics.
This study highlights the value of combining observational data, numerical simulations, and machine learning techniques to better understand coastal mesoscale processes. The findings provide fresh insights into SBF behavior in the Caspian region, with implications for enhancing coastal weather forecasting and management. Future work should focus on improving the accuracy of WRF model simulations and further examining the impact of regional topography on SBF dynamics.
Keywords: Sea Breeze Front, Machine Learning, WRF, K-means Clustering, Temperature Gradient, Caspian Sea,.
How to cite: Sepehri, J.: Exploring the Dynamics of Sea Breeze Fronts in the Southwestern Caspian Sea: Analysis Using Observational Data, WRF Simulations, and Machine Learning Approaches, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7413, https://doi.org/10.5194/egusphere-egu25-7413, 2025.
Extreme weather events associated with deep moist convection pose significant social risks, requiring advanced technologies for anticipatory measures. Numerical forecasting, particularly at convection-resolving scales, relies heavily on high-quality initial conditions obtained through the assimilation of complex remote-sensing-based observations. Integrating these observations into assimilation systems presents challenges due to the nonlinear relationships between observed quantities and model variables. This research explores an iterative implementation of the Local Ensemble Transform Kalman Filter based on the tempering of the observation likelihood (tempered LETKF), which can partially handle these non-linearities.
In this work, we use an N-variable Lorenz model for its simplicity and low computational cost to evaluate the performance of the method against the traditional implementation of the LETKF. We conducted comparisons under various levels of uncertainty concerning both the model and the observations. Additionally, we tested the behavior of the system for different ensemble sizes and for varying degrees of tempering. The initial findings show notable enhancements in the estimation of initial conditions and the stability of the data assimilation cycle, indicating potential benefits in more realistic model applications. The encouraging results motivate further research on tempering methods in mesoscale modeling systems, especially for predicting severe weather events linked to deep moist convection.
How to cite: Gacitua Gutierrez, J., Ruiz, J. J., Pulido, M., Dillon, M. E., García Skabar, Y., Maldonado, P., Otsuka, S., Amemiya, A., Miyoshi, T., and Pajarola, R.: Tempered local ensemble transform kalmann filter: simple model experiments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12910, https://doi.org/10.5194/egusphere-egu25-12910, 2025.
Additional speakers
- Tijana Janjic, KU Eichstätt Ingolstadt, Germany
- Tomislava Vukicevic, University of Belgrade, Serbia