Hajnalka Breuer

This study presents the first application of the Weather Research and Forecasting (WRF) model for regional climate modelling in Hungary, specifically adapted for the Carpathian Basin. Simulations for the historical (1985–2010) and future (2020–2045) periods were conducted using WRF with a 10 km grid spacing, downscaling the MPI-ESM1-2-LR global climate model from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The future simulation employs the high-emission SSP5-8.5 scenario. The CARPATCLIM observational dataset was used for model evaluation and bias correction. Expected changes in mean temperature and precipitation between the future and historical periods are assessed based on the bias-corrected WRF simulations. WRF projects a spatially averaged temperature increase of 1 °C in winter, 0.7 °C in summer, and 0.6 °C in spring and autumn over Hungary. Precipitation is projected to increase by 12 percent in winter, 31 percent in spring, and 21 percent in autumn, with no significant change in summer. While future climate change assessments should always be based on multiple model simulations, this study demonstrates the utility of WRF as a regional climate model for the Carpathian region.

This study investigates the impact of assimilating Advanced Scatterometer (ASCAT) Level-2 surface soil moisture retrievals into the Application of Research to Operations at Mesoscale (AROME) model, the operational numerical weather prediction system of the Hungarian Meteorological Service. The Level-2 retrievals are geophysical soil moisture estimates derived from satellite radar backscatter observations and represent the uppermost soil layer (approximately 0–5 cm). Data assimilation is performed using a Simplified Extended Kalman Filter (SEKF) within the SURFEX surface modeling platform. In the reference configuration (REF), the same SEKF framework is applied, as used operationally for the assimilation of 2 m temperature and relative humidity observations. A second experiment (ASCAT) extends this configuration by additionally assimilating ASCAT surface soil moisture retrievals. The experimental period covers May–October 2023. The objective of the study is to quantify the added value of ASCAT soil moisture assimilation relative to the REF experiment, which does not assimilate ASCAT retrievals. Results indicate a systematic improvement in root-zone soil moisture and soil temperature, suggesting that the assimilation of surface soil moisture observations propagates beneficially to deeper soil layers. Verification against in situ and model-derived diagnostics shows a positive impact on near-surface atmospheric variables, particularly for 2 m temperature and humidity during nighttime conditions. Furthermore, precipitation verification reveals a measurable improvement, suggesting a beneficial influence of improved land–atmosphere coupling on short-range forecasts.

Supercells frequently produce hazardous weather phenomena, and their behavior is strongly influenced by the environment in which they form and propagate. Numerous previous studies, based on both observations and numerical simulations, have demonstrated that supercells often interact with external features (outflow boundaries, differential heating boundaries, storm interactions etc.) prior to hazard production. In this study, we used the Bryan Cloud Model (CM1) to conduct high-resolution supercell simulations in which an artificial cold air mass, following Fischer and Dahl (2020, 2022), was introduced into the model domain along the path of the simulated storm. A series of simulations were conducted where the storm-relative distance, depth, and cooling intensity of the artificial cold air mass were systematically varied. A total of 27 perturbed simulations and one control run were analyzed, focusing on right-moving supercells, with particular attention to surface hail production, near-surface vertical vorticity maxima (Tornado-Like Vortices, TLVs), and midlevel mesocyclone strength. The results suggest that, similar to observations, the behavior of the simulated supercell is influenced by the physical characteristics of the cold air mass, favoring either more prolific hail production or stronger near-surface vorticity generation. In some cases, however, the cold pool interaction weakens the storm intensity, highlighting the complexity of storm-scale processes.

Cloud cover variability remains one of the key uncertainties in regional climate trend detection. In this study, we analyze ERA5 reanalysis data over Central and Southeastern Europe to investigate the extent to which cloud cover changes are driven by variations in synoptic-scale atmospheric circulation patterns. Building on previous research that found no clear long-term trends in atmospheric instability indicators such as LFC (Level of Free Convection) or CAPE (Convective Available Potential Energy) due to large internal variability and synoptic modulation, we shift focus to the explanatory power of Grosswetterlagen-type weather regime classifications.We apply a combination of dynamically derived weather pattern indicators—including lower- and mid-tropospheric vorticity, wind direction, and total column water vapor—to classify daily circulation patterns into six GWL clusters using hierarchical clustering. Using a gridded approach, we calculate monthly cloud type anomalies (low, medium, and high cloud cover) and assess their spatial correlation with the frequency of GWL cluster occurrences.The results show clear and spatially coherent relationships between cloud anomalies and weather type frequency. In particular, high cloud cover anomalies correlate significantly with specific GWL clusters, especially in summer months and at southern latitudes, with spatial R² values exceeding 0.6 locally.Additionally, the effect of GWL clusters on cloud anomalies varies between months, with some clusters showing consistent negative correlations across southern regions, and others influencing only specific cloud layers. Hovmöller diagrams of latitudinal cloud anomaly trends suggest periodic signals in cloud cover that are not well explained by ENSO or NAO indices alone but align well with GWL-based predictors.Our findings highlight the importance of incorporating synoptic variability when analyzing cloud cover trends. Apparent long-term trends in cloudiness can in fact result from changes in the frequency of distinct synoptic regimes. This synoptic masking likely contributes to the limited detectability of trends in traditional convective instability metrics.We conclude that variability in GWL-type weather regimes plays a crucial role in modulating monthly and seasonal cloud cover across the region, offering a mechanistic explanation for observed cloud changes and their spatial structure in ERA5. These results emphasize the need to account for synoptic-scale drivers when evaluating regional climate variability and trends.

Accurately representing land–atmosphere interactions is essential for numerical weather prediction models, as they have a significant effect on forecasted near-surface meteorological parameters. We used the SURFEX soil model, coupled with the AROME non-hydrostatic numerical weather prediction model at HungaroMet Hungarian Meteorological Service. Land data assimilation techniques are employed to provide the most accurate initial conditions for the AROME-SURFEX system. Initially, the Optimal Interpolation (OI) method was applied to determine the initial conditions for soil temperature and moisture. This study focuses on implementing the more complex and advanced Simplified Extended Kalman Filter (SEKF) for surface data assimilation. The SEKF corrects the soil temperature and soil moisture content using screen-level observations (2-m temperature and relative humidity), offering improvements over OI. We highlight the advantages of the SEKF across different seasons, noting that it is a more physically-based approach with dynamically varying Jacobians. We demonstrate how outlier Jacobians can be filtered using linearity check to handle system nonlinearity. The tuning of appropriate data assimilation parameters, such as observational and background errors, is also crucial for achieving optimal results. We evaluate the impact of the SEKF by conducting forecast verification against in situ atmospheric observations, comparing its performance with that of OI. Our results indicate a significant improvement in winter forecasts. Additionally, a moderate improvement is observed in spring, highlighting the seasonal dependency of the efficiency of the SEKF.

In discussions about climate change, the focus is usually on rising temperatures. However, it is important to understand the significant impact of climate change on the entire weather system. The cloud feedback mechanism is one of the most complex factors in the climate system. This is because clouds can have a heating and cooling effect at the same time, and this balance has a significant influence on the global radiation balance. To understand how all the different factors work together to create a complex system, we need to look closely at how these factors have changed over time.The aim of this research is to examine changes in cloud cover and convective parameters, as well as the background, causes and effects of these changes in Central Europe between 1983 and 2022. The research uses data from the ERA5 reanalysis database. Aside from the analysis of environmental conditions, an objective cyclone identifying method is used to determine regions under low- or high-pressure weather system influence. The statistical analysis shows that in general, the decrease in ERA5 low-level cloud cover is associated with an increase in cloud base. Medium- and high-level cloud cover, however, is influenced by changes in large-scale circulation systems.Low-level cloud cover decrease in the northern regions of the study area is likely due to increasing temperatures and decreasing boundary layer humidity. Though temperatures in the Mediterranean region also have risen, the increase in the frequency of negative NAO situations, and an increase in Mediterranean cyclone and low-pressure system activity - the latter of which is likely induced by the higher evaporation of the Mediterranean Sea - resulted in the increase in cloud cover over the central Mediterranean region. We have also observed an increase in the CAPE (convective area pressure energy) in the Mediterranean during the summer months, which leads to an increase in the frequency of heavy thunderstorms and extreme precipitation events in this area, contributing to the intensification of weather extremes in the region. Changes over the study area are not linear but show a region dependent 10-20 years periodical pattern which is also investigated.

In continuous regional climate simulations, the equilibrium between land-surface and atmospheric processes plays a key role both in near surface temperature and in precipitation estimations. A too strong coupling can cause the upper soil layer to dry out in larger amounts than it supposed to, which leads to reduced precipitation and temperature overestimation in summer. The amount of available soil moisture is critical both on summer heat wave strength and on precipitation formation. The strength of coupling determines the partition between sensible and latent heat fluxes which in return influences both temperature and precipitation. In this study we use 1-year long simulations at 50 km and at 5 km horizontal resolution to analyze the coupling strength between the land surface and the atmosphere with the WRF model. Both simulations refer to the same year and the initial and boundary conditions are given based on the ERA5 reanalysis dataset. The simulation is initialized based on ERA5 only at the beginning, then only the boundary conditions are updated to create continuous, regional climate modelling like environment. The 50 km resolution simulation covers most of Europe, while the 5 km simulation covers the Carpathian region. The coupling strength and the effect of surface is analyzed through changing the land cover from the USGS to the CORINE one. The study will focus on the Carpathian region as local summer precipitation formation is highly dependent on local factors rather and not on large-scale weather systems. Though land-atmosphere coupling mostly affects the surface based latent and sensible heat fluxes, the resolution of the simulations affects the amount of the coupling strength through the advection.

This study evaluates snow depth (SD) from several data sources: a combined satellite-based and in situ snow water equivalent product from the Copernicus Global Land Service (CGLS), a dataset constructed from temperature, precipitation, and relative humidity using a snow model (CARPATCLIM), two state-of-the-art reanalyses by ECMWF (ERA5 and ERA5-Land), and Weather Research and Forecasting (WRF) regional climate simulations at grid spacings of 50 km and 10 km. SD observations from weather stations are used as a reference for the pointwise comparison. The study area covers the Pannonian Basin region (part of Central and Eastern Europe). Results are presented for the 2006–2010 and 1985–2010 periods. All datasets adequately reproduce the average day-to-day variation of SD but with different error magnitudes. The ERA5 reanalysis and the CGLS product represent SD remarkably well, with correlation coefficients above 0.9 and mean errors close to zero. On the other hand, ERA5-Land and CARPATCLIM overestimate daily mean SD by 2–3 cm for some stations and display lower correlations (0.7–0.9) during the 26-year time span. The WRF simulations significantly overestimate SD in the melting period (February–March). Reduction of the grid spacing from 50 to 10 km does not improve the results. The excessive snow cover might negatively impact land–atmosphere interactions in the model and lead to biases like temperature underestimation found in previous regional climate model evaluation studies. The results indicate that even in regions where snow is not a major climatic factor, SD errors can be substantial and should be considered in model evaluation and adaptation. Over the Carpathian Mountain ranges, SD from the different data sources diverges to the extent that the sign of the monthly mean model bias changes depending on the choice of the reference dataset.

Several previous studies have shown that near-surface thermal or outflow boundaries might have a significant impact on supercell development – especially low-level mesocyclone intensification. Supercells that cross surface boundaries at an acute angle usually produce rapid and intense tornadoes on the cool side, even local tornado outbreaks, and in some cases, large hail and damaging wind gusts can occur as well. Similar interactions can be present in the Carpathian Basin as well, however, the investigation of these processes in this region is more difficult due to the complex terrain. This complexity can lead to processes on a much more localized scale. Based on observations, these local thermal baroclinic zones are typically associated with shallow surface lows over Hungary. The surface confluent flow and the equivalent potential temperature gradient zone created by preceding precipitation bears substantial horizontal vorticity near the ground that might be favorable for the low-level intensification of mesocyclones in supercells. In this research, we investigate these local baroclinic zones and their influence on supercell intensification using non-hydrostatic WRF (Weather Research and Forecasting) real and idealized simulations. We inspect how the presence of the surface baroclinic zone affected the dynamics of the simulated thunderstorm. The gained results may add significant contributions to our understanding of these processes, and support the early recognition of their patterns in the operational routine.