For a long time, the lack of archived radar data in Germany prevented comprehensive, long-term studies of convective storms. However, the recent availability of a 20-year, homogeneous dataset based on 16, and in some years 17, single-polarization C-band radars now allows for consistent, national-scale analyses. This dataset provides a solid foundation for more precise hail statistical assessments and long-term hail frequency estimation, including potential trends. A tracking algorithm (TRACE3D), which was specifically modified to detect severe convective cells with the potential to produce hail, was used to identify 15,577 potential hail tracks (PHTs) during the summer half-year period from 2005 to 2024. Validation against building insurance data shows that the modified TRACE3D algorithm performs reasonably well and can adequately reproduce hail statistics in Germany. The spatial distribution of the PHTs reveals distinct regional patterns, including a north-to-south gradient influenced by the proximity to seas and orographic features. The highest hail frequency occurs south of Stuttgart and over the Bavarian Prealps. Most tracks are shorter than 40 km and last no more than 75 min (both at the 75th percentile). Nearly 60% of the tracks show a propagation direction from southwest to northeast, which aligns with typical mid-tropospheric conditions favoring convection. Furthermore, half of the days with PHTs are associated with atmospheric blocking regimes, such as Scandinavian, European, or Greenland blocking. Hail events in Germany are unevenly distributed in time. Sixty-three percent of days record no PHTs, and there are only occasional periods of intense hail activity with many tracks per day. While many hail days tend to be isolated (40%), under certain weather conditions, serial clustering of several hail days can form. However, such episodes rarely last more than 2 weeks and are often associated with prolonged blocking. Trend analyses show a high annual variability in PHTs with no clear trend for entire Germany. However, significant regional differences emerge: northern and central Germany show a decreasing tendency in PHT occurrence, whereas southern Germany exhibits a significant increase.

This study outlines the initial steps toward applying the Physical Climate Risk Assessment Methodology (PCRAM) to quantitatively assess and enhance resilience within the agriculture and tourism sectors, which are highly susceptible to climate change and natural disasters such as hail and other perils. Although many risk assessments and models exist globally as detailed as part of this initial review of climate risk analytics for capital in these sectors at a basic level, there exists very little analysis which integrates the direct effects of climate, engineering and socioeconomic change into the operational and capital expenditure. This gap leads to the prevalent issue of undervaluing climate adaptation in investment decisions.As part of this preliminary study, various risk assessment methods, software and frameworks, such as CLIMAAX and MYRIAD-EU, are reviewed which have been applied to the agritourism industry - given the large influence through a multitude of hazards - both climate driven and geophysical. For this preliminary framework and review the case of agritourism facilities in Northern Italy is identified as a critical pilot region due to its high-value viticulture and the increasing frequency of extreme hail events which threaten both agricultural yields and tourism infrastructure. This case study demonstrates how climate change directly impacts specialized assets such as wineries and farm-stays necessitating a detailed four-step approach.The first step identifies key assets such as farm infrastructure, wineries, accommodation and crops, and hazards within the agritourism sector. The second step, a materiality assessment, would link climate hazards to potential impacts on these assets, quantifying the severity of effects like crop damage or revenue loss and classifying them as maintenance, performance, or life-cycle costs. The third step, resilience building, identifies and evaluates both structural (e.g. hail nets, retrofitting structures for wind and earthquake) and non-structural (e.g. modified operational plans) interventions, reassessing their impact on the assets. The final step, economic and financial analysis, would compare the financial performance of the three steps to demonstrate the value of investing in resilience. This shows how an initial investment might lead to more stable revenues and a better allocation of costs over the asset's lifespan. Ultimately, this methodology may be scaled to groups of assets and transferred to other susceptible economic sectors as the research evolves.

Hailstorms cause substantial damage to buildings, crops, vehicles, and infrastructure in many regions worldwide. Despite notable progress in recent years, hail remains insufficiently understood and poorly represented in numerical weather prediction models and risk assessments. The 4th European Hail Workshop (2024) showcased advances in detection, forecasting, climatology, and impact assessment of hail, while highlighting key challenges that remain. Progress in remote sensing, weather prediction, and seamless forecasting has improved early detection of hail events, extended forecast lead times, and enhanced warning capabilities. Field campaigns and laboratory experiments are yielding new insights into hailstone characteristics, hail formation processes, and impacts. Studies of storm dynamics and microphysics emphasized the complex interactions of processes involved across a wide range of temporal and spatial scales. Finally, artificial intelligence and machine learning are opening new avenues for hail detection, prediction, and risk modeling, marking a shift toward more integrated and innovative approaches in hail research.

A series of multiple meteorological extreme events in close succession can lead to a substantial increase in total losses compared to randomly distributed events. In this study, different temporal clustering methods are applied to insurance loss data from southwestern Germany from 1986 to 2023 for the following hazards: windstorms, convective gusts, and hail, as well as pluvial, fluvial, and mixed flood events. We assess the timing and significance of seasonal clustering of single hazard types as well as their serial combination by use of both a simple counting algorithm and the clustering metric Ripley's K. Results show that clustering is significant only for certain hazard types compared to a random time series. However, clustering is robust for a combination of multiple hazard types, namely hail, mixed or pluvial floods, and storms. This particular combination of hazard types is also associated with higher losses compared to their isolated occurrence. Clusters of damaging hazards occur mainly during May-August and depend on the method of defining independent events (peaks-over-threshold method with flexible lengths vs. hours clause method with fixed lengths) and their resulting duration. This study demonstrates the relevance of considering multiple hazard types when evaluating clustering of meteorological hazards.

Hailstorms pose significant risks in Germany, calling for accurate forecasts and warnings. This study explores the application of a convolutional neural network (CNN) to predict daily hail-affected areas using radar-based hail footprints from 2005 to 2019. The ML model utilizes 18 thermodynamic and dynamic convection-related parameters derived from ERA5 reanalysis data. Feature selection identifies seven key predictors, with a particular emphasis on the convective available potential energy and bulk wind shear (CAPESHEAR). Model performance is assessed against climatology- and persistence-based reference forecasts, and sensitivity analyses using gradient-weighted class activation mapping (Grad-CAM) are conducted to interpret the predictions. The CNN model significantly outperforms the reference forecasts, achieving a Heidke Skill Score (HSS) of up to 0.66 for large hail-affected areas. However, lower predictive skill is observed on days with weak CAPESHEAR values or when hailstorms are isolated. Sensitivity analysis highlights CAPESHEAR as the dominant predictor influencing model decisions. These findings demonstrate the potential of ML-based hail prediction using only convective environmental parameters. Given its low computational demand once trained, this approach offers a promising tool for operational forecasting. It would be desirable to extend this approach to a more regional perspective and to include information on severity.

Based on lightning measurements in western and central Europe from 2001 to 2021 (May–August), a grid-based climatology and trend analysis of thunderstorm activity has been developed. The results indicate a significant decrease in thunderstorm activity in many regions. Extending the analysis beyond a purely grid-based approach, areas with spatio-temporal intense lightning (convective clustered events, CCEs) were identified in a second step by applying a clustering algorithm (Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise, ST-DBSCAN). For this purpose, a methodology is presented which seeks out to determine an appropriate density definition, as required by ST-DBSCAN. An analysis of the characteristics of the CCEs indicates a slight increase of smaller, more separated clusters, while larger clusters occur less frequently over time. This suggests a shift in the mesoscale organization of convective systems. Furthermore, a correlation between the North Atlantic Oscillation (NAO) and thunderstorm frequency has been identified. Notably, there was a pronounced reduction of thunderstorm activity, as well as an increased number of separated convective systems during negative NAO phases in France. This, in conjunction with a documented accumulation of years with predominantly negative NAO values between 2011 and 2020, is likely a contributing factor to the aforementioned negative trends.

The broad spectrum of possible hailstone shapes and internal structures is a product of the complex interplay between hailstone growth physics, aerodynamics, and in-storm conditions. As a result of this sensitivity, hailstone characteristics can be highly variable within a single deep convective cell. Recent progress in modeling individual hailstone trajectories and growth has benefited from new understanding of hail production processes; however, the representativeness remains uncertain. In situ observations along hail-like trajectories have only now become possible thanks to the miniaturization of radiosonde electronics, which, when packaged into a durable probe of similar shape and size to large hailstones, can survive the conditions inside thunderstorms while behaving like hailstones. Trajectory and icing data from these hail-like probes provide invaluable information for assessing the aforementioned hail growth simulations. On 24 July 2023, two Hailsondes were launched 4 min apart into a supercell during the Northern Hail Project (NHP) in Alberta, Canada, with the storm producing large hail exceeding 50 mm (1.96 in.) in maximum dimension during the flight. The vertical speed of both probes exceeded 37 m s-1 during balloon-assisted ascent, and, after the balloons detached, the probes continued to ascend to almost 8000 m above mean sea level (MSL). Despite traveling along similar trajectories, the sondes experienced different growth regimes. Investigation of polarimetric weather radar data shows changes in the probes' pathways relative to the updraft and a column of enhanced specific differential phase (KDP), indicating the first Hailsonde likely experienced a greater raindrop collection rate, contributing to the differences in icing conditions. SIGNIFICANCE STATEMENT: Recent modeling studies of how hailstones move and grow inside thunderstorm clouds have yielded exciting new insights for improving short-term forecasting; however, whether actual hailstones behave in a similar way remains unknown. Motivated by this question, this article presents the design and use of the first hail-like probe, named Hailsonde, that follows hail-like pathways while collecting measurements of in-storm conditions. The first successful flights during the Canadian Northern Hail Project revealed that simulated hail trajectories indeed resemble reality; however, icing conditions were sensitive to small changes in the probe pathway. Planned use of the Hailsonde in upcoming field experiments will continue to close this gap between simulations and reality.

Modeled hail trajectories have previously been studied in individual observed supercells or in simulated supercells with similar background environments. To explore the impact of changing updraft structure on hail formation from a different perspective, this study analyzes detailed hail trajectories in a large ensemble of time-averaged supercelllike updrafts. The updrafts are created with an idealized heat source, which allows the systematic investigation of the full range of updraft widths and intensities reported in the literature. The simulations exhibit a dominant hail trajectory pathway with a single ascent and a curved horizontal trace. However, a systematic shift in the trajectories and in their start and end locations is found with increasing updraft intensity and updraft width. Furthermore, wider updrafts but with only moderate intensity provide optimal conditions for the hail of most sizes. The exception is giant hail, which requires both wide and intense updrafts. This result is partially linked to the occurrence of an alternative trajectory pathway characterized by the recycling of hailstones (1-4 cm) in the back-sheared anvil region, which then grew to giant size after reentering the updraft.

The impact of wind shear on aerosol-cloud interactions and convective precipitation is investigated with real-case simulations using the ICOsahedral Non-hydrostatic (ICON) model over central Europe. Three days with severe convective storms have been simulated using a double-moment microphysics scheme on a 1-km grid. For each day, twenty simulations with varied initial vertical wind shear and cloud condensation nuclei (CCN) concentrations were performed. In these simulations, a higher convective potential is found for stronger wind shear. However, this is not necessarily reflected in the amount of precipitation, which shows no systematic dependency on the wind shear for the days analyzed. Changing the CCN concentration generally has a smaller impact on the precipitation amount than changing the wind shear. Even if hydrometeor amounts and microphysical process rates respond similarly to changing CCN concentrations in the different shear cases, the convective precipitation shows no systematic CCN dependency. Furthermore, it is shown that this dependency even changes for a different simulation duration. If the amount of precipitation is related to its generation processes, a systematic relationship emerges: the precipitation efficiency always increases with increasing CCN concentrations, and this increase is greater the higher the initial wind shear of the simulations is. The findings of the present paper demonstrate that the impact of wind shear on aerosol-cloud interactions is complex, and previous results from idealized simulations cannot be transferred to realistic simulations.

Convective storms over South America and Australia are among the most intense worldwide (e.g., Zipser 2006). However, they are less researched compared to US and Europe. This study analyses the thunderstorm climatology over South America and Australia based on over 20 years of overshooting cloud top (OT) satellite detections (Khlopenkov et al. 2021). These OTs serve as robust, horizontally homogeneous indicators of strong updrafts and hence intense thunderstorms. Furthermore, we focus on the frequency of severe storms and hail by using ERA5 Reanalysis data to exclude OTs in unfavorable environments (e.g., Punge et al. 2023).The resulting climatologies of intense thunderstorms and hail are largely consistent with existing literature, showing strong thunderstorm activity in tropical regions but more severe (e.g., hail-producing) storms in south-central South America and southeast Australia. Some notable details will also be discussed, such as the discrepancy with observational hotspots near the coast in South America and a surprisingly strong signal over northwest Australia. Furthermore, regarding a climate change signal, preliminary analysis indicates no significant trend for South America. However, the multi-year variations are strongly linked to the El Ninjo-Southern Oscillation (ENSO).

Severe thunderstorms are among the most damaging and impactful weather phenomena. In Europe, notable clusters occur in the vicinity of complex terrain. These areas not only experience frequent thunderstorms but also show a strong climate change signal with an increasing storm frequency. Despite the relevance of the subject, our understanding of severe convection in complex terrain, particularly in a changing climate, remains incomplete. This White Paper presents the current state of the research on thunderstorms in complex orography, covering storm severity, modification of pre-storm environments, convection initiation, storm-scale interactions with complex terrain, impactful hazards, numerical modeling and forecasting, climatologies and climate change signals, and innovative storm observations. Highlighting the gaps in our understanding, this review underscores the need for a coordinated European field campaign on thunderstorm intensification from mountains to plains (TIM). Initial plans for the TIM campaign, developed by the participating authors and institutions of this article, are briefly outlined. Obtaining coordinated and dense data on orographically driven storms is a key step toward improving warnings, forecasts, future climate projections, and adaptation measures.

With climate change, human exposure to heat has increased over recent decades and is expected to substantially increase in the future. This study introduces a novel metric – namely, the exponentially weighted degree-day approach – to assess population-weighted heat exposure at the national level, incorporating both static and dynamic population scenarios. Using ERA5 reanalysis and CMIP6 climate projections under the SSP2-4.5 and SSP5-8.5 scenarios, we analyze and categorize global heat exposure and its trends from 1960 until 2100. Our findings reveal a significant rise in heat exposure over past decades, disentangling the contributions of climate and demographic changes. Furthermore, a thorough analysis of biases across different datasets and model dimensions provides a global perspective based on daily maximum and daily mean temperatures. This analysis forms the basis for quantifying current and future heat exposure, together with a qualitative heat zone classification scheme. The results underscore the urgent need for targeted adaptation strategies and improved climate metrics to better assess and mitigate future heat-related risks.

The challenges associated with reliably observing and simulating hazardous hailstorms call for new approaches that combine information from different available sources, such as remote sensing instruments, observations, or numerical modelling, to improve understanding of where and when severe hail most often occurs. In this work, a proxy for hail frequency is developed by combining overshooting cloud top (OT) detections from the Meteosat Second Generation (MSG) weather satellite with convection-permitting High rEsolution ReAnalysis over Italy (SPHERA) reanalysis predictors describing hail-favourable environmental conditions. Atmospheric properties associated with ground-based reports from the European Severe Weather Database (ESWD) are considered to define specific criteria for data filtering. Five convection-related parameters from reanalysis data quantifying key ingredients for hailstorm occurrence enter the filter: most unstable convective available potential energy (CAPE), K index, surface lifted index, deep-layer shear, and freezing-level height. A hail frequency estimate over the extended summer season (April–October) in south-central Europe is presented for a test period of 5 years (2016–2020). OT-derived hail frequency peaks at around 15:00 UTC in June–July over the pre-Alpine regions and the northern Adriatic Sea. The hail proxy statistically matches with ∼63 % of confirmed ESWD reports, which is roughly 23 % more than the previous estimate over Europe coupling deterministic satellite detections with coarser global reanalysis ambient conditions. The separation of hail events according to their severity highlights the enhanced appropriateness of the method for large-hail-producing hailstorms (with hailstone diameters ≥ 3 cm). Further, signatures for missed small-hail occurrences are identified, which are characterized by lower instability and organization and warmer cloud top temperatures.

The supercell storm that occurred in southwestern Germany on June 23, 2021, had an exceptionally long lifetime of 7.5 hr, travelled a distance of nearly 190 km, and produced large amounts of hail. During that summer, the Swabian MOSES field campaign was held in that area, and several hydro-meteorological measurements are available, as the storm passed directly over the main observation site. We present hindcasts of this event with the Icosahedral Non-hydrostatic model using two horizontal grid spacings (i.e., 2 km, 1 km) with a single-moment and an advanced double-moment microphysics scheme. Numerical results show that all 2 km model realizations do not simulate convective precipitation at the correct location and time. For the 1 km grid spacing, changes in aerosol concentration resulted in large changes in convective precipitation. Only the 1 km run assuming a low cloud condensation nuclei (CCNs) concentration is able to realistically capture the storm, whereas no supercell is simulated in the more polluted scenarios. Observed aerosol particle concentrations indicate that CCNs values were the lowest of the month, which suggests that the low aerosol concentration is a reasonable assumption. The thermodynamic structure of the pre-convective environment, as well as other observations, showed the best agreement to this model run as well, indicating that the good representation of the supercell was obtained for the right reason. The automatic tracking of individual clouds revealed that more convective cells with longer lifetimes are simulated at finer resolution. We also find a negative aerosol-precipitation effect that is not only due to a reduced collision-coalescence process, but also to weaker cold-rain processes. These findings demonstrate the benefits of using an aerosol-aware double-moment microphysics scheme for convective-scale predictability and that the use of different CCNs concentrations can determine whether a supercell is successfully simulated or not. The supercell storm that occurred in southwestern Germany on June 23, 2021, during the Swabian MOSES field campaign had an exceptionally long lifetime of 7.5 hr, travelled a distance of nearly 190 km, and produced large amounts of hail. Hindcasts of this event with the Icosahedral Non-hydrostatic model show that only the combination of high grid spacing and low cloud condensation nuclei concentration results in a realistic representation of the storm. These findings demonstrate the benefits of using an aerosol-aware double-moment microphysics scheme for convective-scale predictability and that the use of different cloud condensation nuclei concentrations can determine whether a supercell is successfully simulated or not. image

Heavy precipitation over western Germany and neighboring countries in July 2021 led to widespread floods, with the Ahr and Erft river catchments being particularly affected. Following the event characterization and process analysis in Part 1, here we put the 2021 event in the historical context regarding precipitation and discharge records and in terms of the temporal transformation of the valley morphology. Furthermore, we evaluated the role of ongoing and future climate change on the modification of rainfall totals and the associated flood hazard, as well as implications for flood management. The event was among the five heaviest precipitation events of the past 70 years in Germany. However, consideration of the large LAERTES-EU regional climate model (RCM) ensemble revealed a substantial underestimation of both return levels and periods based on extreme value statistics using only observations. An analysis of homogeneous hydrological data of the last 70 years demonstrated that the event discharges exceeded by far the statistical 100-year return levels. Nevertheless, the flood peaks at the Ahr river were comparable to the reconstructed major historical events of 1804 and 1910, which were not included in the flood risk assessment so far. A comparison between the 2021 and past events showed differences in terms of the observed hydro-morphodynamic processes which enhanced the flood risk due to changes in the landscape organization and occupation. The role of climate change and how the 2021 event would unfold under warmer or colder conditions (within a −2 to +3 K range) was considered based on both a pseudo global warming (PGW) model experiments and the analysis of an RCM ensemble. The PGW experiments showed that the spatial mean precipitation scales with the theoretical Clausius–Clapeyron (CC) relation, predicting a 7 % to 9 % increase per degree of warming. Using the PGW rainfall simulations as input to a hydrological model of the Ahr river basin revealed a strong and non-linear effect on flood peaks: for the +2 K scenario, the 18 % increase in areal rainfall led to a 39 % increase of the flood peak at gauge Altenahr. The analysis of the high-resolution convection-permitting KIT-KLIWA RCM ensemble confirmed the CC scaling for moderate spatial mean precipitation but showed a super CC scaling of up to 10 % for higher intensities. Moreover, the spatial extent of such precipitation events is also expected to increase.

The July 2021 flood in central Europe was one of the five costliest disasters in Europe in the last half century, with an estimated total damage of EUR 32 billion. The aim of this study is to analyze and assess the flood within an interdisciplinary approach along its entire process chain: the synoptic setting of the atmospheric pressure fields, the processes causing the high rainfall totals, the extraordinary streamflows and water levels in the affected catchments, the hydro-morphological effects, and the impacts on infrastructure and society. In addition, we address the question of what measures are possible to generate added value to early response management in the immediate aftermath of a disaster. The superposition of several factors resulted in widespread extreme precipitation totals and water levels well beyond a 100-year event: slow propagation of the low pressure system Bernd, convection embedded in a mesoscale precipitation field, unusually moist air masses associated with a significant positive anomaly in sea surface temperature over the Baltic Sea, wet soils, and steep terrain in the affected catchments. Various hydro-morphodynamic processes as well as changes in valley morphology observed during the event exacerbated the impact of the flood. Relevant effects included, among many others, the occurrence of extreme landscape erosion, rapidly evolving erosion and scour processes in the channel network and urban space, recruitment of debris from the natural and urban landscape, and deposition and clogging of bottlenecks in the channel network with eventual collapse. The estimation of inundation areas as well as the derived damage assessments were carried out during or directly after the flood and show the potential of near-real-time forensic disaster analyses for crisis management, emergency personnel on-site, and the provision of relief supplies. This study is part one of a two-paper series. The second part (Ludwig et al., 2022) puts the July 2021 flood into a historical context and into the context of climate change.

<p>Information about the hail threat of a thunderstorm is typically limited to rather indirect data from remote sensing or reanalysis. Ground observations of maximum hail diameters provide a more accurate assessment but often suffer from other problems, such as limited or non-uniform coverage. Despite these shortcomings, the above data sources are commonly used for nowcasting of hail producing storms and in hail climatologies. However, only very few studies have actually compared the skill of these different proxies. One reason for this is the lack of ground truth data which could be used to verify whether damaging hail was falling. This is because of the described issues with hail reports, and the fact that insurance datasets, which could provide a more reliable confirmation of hail damage, are usually not made available for research.</p> <p>To fill this gap, this study uses a 5-year dataset of crop damage claims of a German agricultural insurance. These insurance claims cover large parts of Germany and most accurately reflect hail damage during the growing season of crops from May to August, which is also the time of year with the strongest thunderstorm activity. The damage claims are used to verify and compare common proxies for hail, which include the radar-based (I) column maximum reflectivity and (II) hail tracks using the more refined TRACE3D tracking algorithm, (III) lightning density, (IV) European Severe Weather Database hail reports, (V) observed overshooting tops from geostationary satellites, and (VI) microwave imager hail signatures from polar-orbiting satellites. Their skill in predicting damaging hail is assessed by categorical verification with probability of detection, false-alarm rate, and Heidke Skill Score, including a sensitivity analysis to varying thresholds.</p> <p>Preliminary results based on 30 events in 2014 indicate that none of the proxies alone can predict hail damage with high accuracy. However, all of them show at least some skill, except for the microwave imager. The radar-based predictors show the largest skill on average. If these findings can be confirmed over more cases, while also including null cases, this would support the use of proxies I-V for hail climatologies but discourage nowcasts of hail for an individual thunderstorm with one of the proxies alone.</p>

<p>We investigate microphysical uncertainties in hailstorms using statistical emulation in a single model framework with the objective to disentangle the relative contributions from aerosols, microphysical parameters and environmental conditions to the uncertainty in cloud-, precipitation- and hail-related parameters.</p> <p>Our selected case study is the Andreas hailstorm on 28 July 2013 in the Neckar Valley and over the Swabian Jura in Southwest Germany. We perform model simulations on cloud-resolving scale with the numerical weather prediction model ICON coupled with the aerosol module ART (ICON-ART). We use a two-moment cloud <span dir="ltr" role="presentation">microphysics</span> scheme with a representation of ice nucleation by dust aerosols.<br />We generated a perturbed parameter ensemble (PPE) to sample uncertainties in cloud-, precipitation- and hail related parameters. Six parameters from the categories aerosols, microphysics and environmental conditions were jointly perturbed, namely the cloud condensation nuclei (CCN) and ice nuclei (IN) concentrations, the riming efficiency of graupel and hail, the convective available potential energy (CAPE) and vertical wind shear. The defined parameter ranges are based on forecast analysis and literature. We used the maximin Latin hypercube algorithm to distribute the parameters well-spaced in the six-dimensional parameter uncertainty space. For these six parameters, an ensemble of 90 members was generated and in addition a smaller independent ensemble of 45 members serves for validation.</p> <p>We used the Gaussian process emulation and developed emulators for hail- and precipitation related output variables. To quantify contributions to the uncertainty in the output variables from the perturbed parameters individually as well as interactions between them, a variance-based sensitivity analysis was performed. We will present first results, which reveal the importance of the CCN concentration for controlling the number concentration of hail particles as well as the CCN concentration and environmental conditions for controlling the amount of hail and precipitation in the model. The geographical distribution of hail and precipitation shows a large variety among the ensemble members, with storm tracks shifted further to the north or south compared to the reference simulation. The path of the storm track is thereby mainly controlled by CAPE and the vertical wind shear, however, aerosol parameters seem to be important for the development of multiple storm tracks. </p>

Hail is one of the most dangerous hazards for agriculture, infrastructures, and buildings of all severe-weather phenomena linked to deep moist convection. Due to global warming, the frequency and severity of hailstorms are expected to increase throughout Europe. It is, therefore, increasingly urgent to improve our understanding of hail hazard and risk. However, the intrinsic difficulties in systematically observing and simulating hail are still a major hurdle. While direct hail observations are heterogeneous, temporally-limited, and scarce, numerical simulations lack sufficient detail to represent the localized and rapidly-evolving hailstorm dynamics. A possible way to indirectly assess hail likelihood is through remote-sensing detections combined with numerical dynamical and thermodynamical characteristics of convective environments prone to hailstorm formation. Indeed, multiple data sources are necessary as, to date, more than a singular proxy is required to characterize hail. The new high-resolution reanalysis dataset SPHERA (High rEsolution ReAnalysis over Italy), developed at ARPAE, is considered for investigating hail-favoring environments over Italy and nearby countries. SPHERA is dynamically downscaled from ERA5, driven by the convection-permitting model COSMO (at 2.2 km grid spacing), providing meteorological data at hourly frequency. A set of parameters is extracted from SPHERA and combined with Overshooting cloud Top (OT) satellite detections, constituting a reliable proxy for hail. A probabilistic algorithm recently developed at NASA automatically detects OTs from geostationary Meteosat Second Generation SEVIRI infrared images. However, not all OTs are associated with hail formation in the storm. Hence, a filter is developed to retain only those occurrences linked to potential hailstorm formation based on the surrounding environmental conditions. To do so, ESWD (European Severe Weather Database) crowdsourced reports, representing the most reliable hail observational dataset in Europe, are coupled with SPHERA proxies to investigate convective environments nearby hailstorm events. The procedure is applied over five years (2016-2020). Hence, the primary purpose is to present the methodology rather than a sound hail frequency estimate. The resulting hailstorm characterization reveals a non-hailing OTs removal exceeding a quarter, mainly over the Mediterranean sea and complex-topography areas. The maximum hail likelihood is observed over pre-Alpine regions and the northern Adriatic sea around 15 UTC in June-July, in agreement with recent European hail climatologies. The validation against ESWD reports provides a hit rate exceeding 60%, roughly 20% more than the previous non-probabilistic OT detections coupling with ERA-Interim over Europe. Separating hit/miss reports and small/large hail reveals different characteristics of hail-prone environments. Most hit reports pertain to large hailstones (diameters ≥ 3 cm), suggesting an inclination of the method to perform better in cases of severe hail occurrences. Investigating the interrelations among SPHERA parameters quantifying the main mechanisms needed for hail formation (i.e., atmospheric instability, freezing level height, and storm organization), the leading role of thermal and thermodynamical factors over the kinetics of the storm emerges. These results suggest promising opportunities to enhance hail dynamics comprehension owing to the advancements in remote-sensing techniques and the development of convection-permitting numerical simulations. In addition, further extension of the analysis should shed more light on the climatology of hailstorms.