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.

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.

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.