One of the main objectives of this study is to document and compare the atmospheric environments associated with hailstorms and non hail thunderstorms during the Greek National Hail Suppression Program (GNHSP) that protects an intensively cultivated area of Central Macedonia, Greece. The analysis focuses on identifying thresholds in various thermodynamic and kinematic parameters that might provide a good discrimination between these two environments based on radiosonde measurements. Convective Available Potential Energy (CAPE), lifted condensation level (LCL), wet bulb zero height, lapse rates, relative humidity, as well as vertical wind profile parameters (such as 0-6 km bulk shear) were derived from 06 UTC radiosondes that were held at Thessaloniki’s Airport Makedonia for the operational period between March and September of 2020-2024. Thermodynamic parameters on hailstorm days have higher median values than on non hail storm days, but there is considerable overlap between the distributions. The intramonthly variation of the thermodynamic parameters in the hailstorm environment shows that their values increase from spring to summer, while this does not apply for the kinematic parameters. Discriminating between hailstorm intensity categories, it is found that values of instability and moisture parameters, with some exceptions, are higher for the severe than for the non severe category, however there is again overlap between the distributions. Hail is most likely for high CAPE and this probability increases when bulk shear increases.

How to cite: Stolaki, S.: Radiosonde observations of storm environments in Northern Greece, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-47, https://doi.org/10.5194/ecss2025-47, 2025.

Supercell storms are often associated with severe-weather phenomena such as heavy rainfall, hail, tornados or straight-line winds (downbursts or derechos), all of which can have impactful socio-economic and environmental consequences. Despite these impacts, the lack of data of supercell storms has limited their analysis and characterization of the climatology across Spain. This constraint has been addressed through a voluntary, collaborative effort to create a database of supercell events from January 2011 to December 2021. During the 11-year study period, 2,087 storms with supercell characteristics were identified. Citizen collaboration confirmed 13.7 % of the supercells detected in PPI radar images. The database also includes hail size and/or tornadoes associated with each supercell whenever such information was reported. The results reveal a spatial distribution with high supercell activity in eastern Spain, mainly in Mediterranean areas and the north-eastern part of the Iberian Peninsula.

From 2022 onward, only supercells with high socio-economic impact - specifically, those accompanied by large hail (≥ 5 cm) - have been compiled. In addition, a thorough review of the national newspaper archive is under way to identify and catalogue large-hail events from 2000 to the present. Because photographs to determine hail size are often unavailable, a proxy based on the damage reported in the press has been developed to identify these events. Currently, more than 240 large-hail events have been catalogued in Spain (Peninsula and Balearic Islands) for 2000-2024. Among them, 44 events reported very large hail (≥ 7 cm) and 7 involved giant hail (≥ 10 cm), three of which occurred in the last three years. The convective environments show substantial differences in the different large-hail categories. The trend shows a significant increase in both the number of days with hail and the number of hail events in Spain.    

How to cite: Calvo-Sancho, C., Martín, Y., González-Alemán, J. J., Azorin-Molina, C., and Martín, M. L.: Supercells and large hail in Spain, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-53, https://doi.org/10.5194/ecss2025-53, 2025.

  Hail, which causes severe property damage, is a highly localized phenomenon that is difficult to observe and predict. The Korea Meteorological Administration's Weather Radar Center has developed radar-based hail products (Potential Hail Area and Hail Risk Altitude) to support forecasters in responding to hail events. The collection of the ground-based hail observations is challenging task, and systematic management is difficult due to the two different data types: the structure data (official meteorological observations data) and unstructured data (various forms of reported data). Therefore, we developed an integrated data management system that combines both data types.

  In this study, the Hail Observation Data Collection System is a web platform developed using Python “Streamlit”. This system consists of three main components: First, the structured and the unstructured hail data is collected, separately. The structured data are automatically processed through APIs and the unstructured data are processed through the manual input of essential fields (date, time, location, reliability, source) and additional fields (detailed address, hail size, etc.). And the dataset forms of each observed hail are normalized and standardized the time of observation, location, and reliability of information, and recorded it in JSON file format. Second, this system integrates and displays to facilitate convenient analysis through the sorting of the date, time, size, and other criteria about hail cases. Third, the comprehensive analysis about spatial-temporal distribution can provide from these hail records for the long-term in Korea.

  Through the establishment of this integrated hail observation data system, the hail observation cases are systematically managed, and these data are being used to perform systematic validation and parameter improvement of radar-based hail detection technique. And the integrated spatial-temporal distribution and statistical analysis of hail data is available to support effective communication between researchers and forecasters. 

ACKNOWLEDGEMENTS
This research was supported by “Development of integrated radar analysis and customized radar technology (KMA2021-03021)” of “Development of integrated application technology for Korea weather radar” project funded by the Weather Radar Center, Korea Meteorological Administration.

How to cite: Park, J., Ye, B.-Y., and Suk, M.-K.: Establishment and Analysis of the Collection System of the Ground Hail Observation data for the verification of the Hail Detection Tech. using the High-resolution Weather Radar, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-69, https://doi.org/10.5194/ecss2025-69, 2025.

One major weather hazard is the hail produced by cloud convective systems. Hail detection from satellites plays a critical role in reducing property damage, improving severe weather forecasting and disaster preparedness, supporting agricultural resilience, and advancing climate science. Nevertheless, even nowadays, hail is difficult to timely and accurately detected, remaining a challenging topic for the scientific community to develop even more accurate methods and algorithms for hail detection. The study is an effort to examine the role of a newly established remote sensing index, namely “Hail Potential Index” (HPI) to detect cloud areas with high possibility of hail production using the Meteosat Third Generation (MTG-1) multispectral imagery. The positive impact of this index evaluated both as an independent index and as input parameter in an Artificial Neural Network (ANN) model to detect hail produced by Mesoscale Convective Systems (MCS). Using a set of hail reports on the ground as reference datasets, a set of evaluation statistics were calculated to examine its efficiency in satellite-based hail detection. Metrics such as Probability of Detection (85%), the False Alarm Ratio (11%), the Critical Success Index (81%) and correlation coefficient reaching (0.87), highlight the satisfactory results of using the HPI index to detect hail produced by organized cloud convection using exclusively the latest Meteosat imagery.

How to cite: Kolios, S.: The efficiency of a new remotely sensed index for hail detection using Meteosat Third Generation multispectral imagery, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-79, https://doi.org/10.5194/ecss2025-79, 2025.

Hailpads are a widely used method for recording hailstone impact data by capturing indentations made by hailstones during hailstorms. As a simple and low-cost approach, hailpads are often deployed in vast networks to collect spatially-distributed data. However, the subsequent process of analyzing these indentations is often long and intensive (taking up to several hours for a single hailpad), and subjective. This research presents a novel, automated approach to identify the major/minor axes and depth distributions of hailpad dents via an image processing and machine learning pipeline, aimed at reducing the time and effort required to analyze the constituent dents of a hailpad. Using high-precision 3D scans, hailpads are depth mapped and then binarized based on user-prescribed adaptive thresholding, contrast equalization, and area filtering parameters. Next, the resulting binary masks are used as input to a convolutional neural network (CNN), which separates dents in clustered and non-clustered regions via instance segmentation. Built on a training dataset of simulated hailpad binary masks, the model was evaluated with a 93.0% Intersection over Union (IoU) score on predicted dent masks. In further comparisons against manual analyses and third-party commercial 3D scan assessments, the model excels in identifying individual impacts from within densely grouped regions. However, the overall prediction distributions are hindered to varying degrees by the influence of false positives in dent detection from non-hail artifacts in the input binary masks. Overall, this automated approach demonstrates the potential to considerably expedite hailstone dent identification and lays the groundwork for extracting more physical properties in the future, such as approximations for volume, impact velocity, and accumulated impact energy.

How to cite: Manka, K., Butt, D., Miller, C., and Brimelow, J.: Automated Hailpad Dent Detection and Segmentation Using Machine Learning, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-86, https://doi.org/10.5194/ecss2025-86, 2025.

Hailstorms are among the most destructive convective weather phenomena, causing extensive damage to agriculture, infrastructure, and property. Improving hail prediction and hindcasting requires a deep understanding of the atmospheric conditions under which hail forms. This study analyzes hail occurrence across Europe using ERA5 reanalysis data and ground-based hail observations to characterize the meteorological environments conducive to hail events.

Hail-prone conditions are identified based on a dataset of key atmospheric variables - including convective parameters, moisture and temperature profiles, and dynamic indices - at 0.25° spatial resolution on a daily timescale. Feature selection is performed using statistical relevance, variance, and correlation criteria to select variables that best capture the thermodynamic and dynamic processes involved in hail formation while minimizing redundancy.

To explore patterns linking atmospheric conditions to hail events, this study applies dimensionality reduction and clustering techniques. The resulting classification reveals distinct synoptic and mesoscale regimes associated with hailstorms, highlighting spatial and seasonal variability across Europe. These clusters expose region-specific hail environments and highlight the atmospheric states most likely to generate hail.

These regime-based classifications offer direct insights for improving convective-scale numerical weather prediction. By associating each cluster with specific model physics, this framework provides guidance for optimizing convection-permitting simulations - such as those using the Weather Research and Forecasting (WRF) model - to more accurately hail events/processes. Ultimately, this work contributes to improve hail prediction capability and robust climate-scale risk assessment of hailstorms across a range of European weather regimes.

How to cite: Guerrero-Calzas, I., Baladima, F., Sanchez-Marroquin, A., Cortés, A., Hanzich, M., and Miró, J. R.: Clustering Large-Scale Atmospheric Patterns Associated with Hailstorms, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-120, https://doi.org/10.5194/ecss2025-120, 2025.

On July 24, 2023, the new European record for hail size was set in northeast (NE) Italy with a 19-cm wide hailstone, recorded during a supercell outbreak. During this event, severe storms were triggered in the Alps, moved eastward, intensified rapidly in the foothills, and generated damaging hailstorms in the plains. A detailed analysis of the available observations highlights that the second supercell developed in an atmospheric environment with an unusually weak-to-moderate potential instability. However, numerical simulations revealed a high-Θe tongue over the Adriatic Sea, which was lifted by a southerly flow above the cold pool generated by the thunderstorm outflow, associated with an initial supercell. This raised the moist layer to 1–2km above mean sea level in the area affected by the record-breaking hailstorm. The vertical profile was characterized by an intense south-westerly flow in the mid-troposphere. Additionally, there was anomalously high water-vapor transport in the layer 2–5 km above mean sea level. Consequently, high CAPE seems unnecessary for the occurrence of giant hailstorms in the region.

This hypothesis is then assessed performing a statistical analysis p for the 2018–2023 period in NE Italy, using hail reports from the Pretemp database and observed high-rez Udine soundings. The results show that hail size has a much lower correlation with potential instability compared to kinematic parameters of the mid-troposphere and water-vapor transport. While thermodynamic parameters have better skill in predicting the occurrence of hail (or hail > 2 cm), the kinematic parameters (like water-vapor transport at 600 hPa, VT600= Wind x q at 600 hPa) of the mid-troposphere are better predictors for very large (> 5 cm) and giant (> 8 cm) hail events.

How to cite: De Martin, F., Manzato, A., Carlon, N., Setvák, M., and Miglietta, M. M.: Dynamic and statistical analysis of giant hail environments in northeast Italy, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-141, https://doi.org/10.5194/ecss2025-141, 2025.

Hailstorms frequently cause severe damage to agriculture in Switzerland highlighting the need to better understand the characteristics of damaging hail events and their spatial and temporal patterns. Such knowledge is essential for improving the assessment and modeling of current and future hail risk to crops.

So far, approaches to assess crop hail damage have included radar, hail pad, and satellite-based methods, however they are often restricted to specific crops or hampered by the limited availability of exposure data and damage reports. Current challenges further include the uncertainty of radar-derived hail size estimates, which are often used as a proxy for hail intensity, as well as the uncertain relationship between hail size and actual crop damage.

To address these challenges and improve the link between observed hail characteristics and agricultural damage, this study combines various radar-based hailstorm indicators, such as the probability of hail (POH), maximum expected severe hail size (MESHS) and thunderstorm radar tracking data, with extensive and high-resolution exposure and damage data provided by the crop insurer Swiss Hail. This includes the policies insured at municipality level as well as damage reports for various crop types from 2014 to 2024. To evaluate which hazard indicators are most relevant for crop damage prediction, a random forest regression approach with feature selection techniques is applied.

First results indicate that combining multiple indicators can achieve robust model performance, particularly when including both municipality-scale hail characteristics (i.e. hail area and duration indicators, MESHS, velocity and mean reflectivity) as well as larger-scale information about the probability of hail and number of storms over a day. The most informative indicator is a proxy integrating the spatial extent and duration of hail occurrence within a municipality.

Based on these findings we perform a risk assessment of potential damage development under prospective future climatological conditions. To do so, we consider recent scientific findings on expected changes in hailstorm frequency and severity through a storyline-scenario approach. The scenarios are built through sampling characteristic hail days from the storm-object-based stochastic hail event set HailStoRe developed for the Swiss Hail Climatology.

How to cite: Philipps, J., Feyen, H., and Schröer, K.: Risk assessment for crop hail damage in Switzerland under current and potential future scenarios, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-168, https://doi.org/10.5194/ecss2025-168, 2025.

Large hail can cause considerable damage (e.g. crop losses in agriculture or damage to house roofs) and so the predictioon of its maximum size is of particluar interest to the population. Our aim is to provide an estimation of the size of large hailstones at the surface from the forecasts of the Rapid Update Cycle ICON-RUC. The ICON-RUC produces hourly updated forecasts for Germany with a two-moment microphysics scheme, which particularly improves the prediction of severe convective storms. The prognostic variables of the two-moment scheme are the mass and the number concentrations of each hydrometeor, including hail. This allows a direct calculation of the mass-weighted mean diameter for hail, which can serve as an indication of the larger particles in the distribution.

Unfortunately, there are no area-wide hail observations over Germany that could be taken as the real truth.  However, the DWD's weather warning app (WarnWetter) offers the option of submitting reports on the current weather situation. This also includes hail. Although the accuracy of the individual reports cannot be determined, the data as a whole can still be a good help to evaluate the predicted hail size in the model. In addition, there are estimates of hail size from radar reflectivities, which can also be used to determine the adjustment for estimating the maximum hailstone size on the surface.

Days from the 2024 convective season are evaluated. These are matched with the hailsizes from the radar database for quality control and then compared to  model forecasts from the ICON-RUC. This provides information on how accurate the hail forecast is (location and time errors) and whether the estimate of the (average) hail size is realistic. In addition, we investigate how to correct the mean averaged hail size in order to obtain a realistic maximum possible diameter on the ground. In the end, this should help to improve the overall hazard assessment of a thunderstorm and thus minimize the hazard risk.

How to cite: Löbel, S., Blahak, U., Schultze, M., and de Lozar, A.: Evaluation of the hailstone size of certain hail events over Germany in 2024, comparison with ICON-RUC forecasts and estimation of the maximum hail size., 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-189, https://doi.org/10.5194/ecss2025-189, 2025.

How to cite: Vurakaranam, A., Berndt, C., Lengfeld, K., Josipovic, L., Schultze, M., and Schröer, K.: Exploring Tree-Based Machine Learning Methods for Estimation of Hail Sizes, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-229, https://doi.org/10.5194/ecss2025-229, 2025.

A nationwide 3D spatio-temporal homogeneous radar composite for Germany has been created using 16 single-polarization C-band radars, covering now a period of 20 years. This dataset enables a detailed analysis of hail statistics, including regional differences, as well as preliminary estimates of long-term changes and emerging trends.

The TRACE3D tracking algorithm for severe convective cells with potential hail signals (> 55 dBz) was used to identify 13,324 potential hail tracks (PHTs) during the summer half-year (2005 – 2024). The spatial distribution of these PHTs reveals distinct regional patterns, including a north-south gradient influenced by the proximity of northern Germany to the North Atlantic and by orographic features. The highest hail frequency occurs south of Stuttgart, in the Neckar Valley, over the Swabian Jura, and over the foothills of the Alps. Most tracks are shorter than 45 km and last less than 75 minutes (both 75th percentile). Around 50% of the tracks follow a flow direction between south and west, aligning with typical mid-tropospheric conditions that favor convection. Furthermore, half of the days with PHTs are associated with atmospheric blocking regimes, such as Scandinavian, European, and Greenland blocking.

Temporally, hail events in Germany are unevenly distributed, with 63 % of days recording no PHTs and only infrequent periods of intense hail activity with many tracks on a day. Although many hail days usually tend to be isolated (~ 43 %), clustering of hail days can occur under specific synoptic conditions; however, such sequences rarely extend beyond two weeks.

Trend analyses reveal a high annual variability in the number of PHTs, with no clear nationwide trend over the past 20 years. However, significant regional differences emerge: In northern and central Germany, there are generally no statistically significant trends, although a slight downward tendency can be observed. In contrast, southern Germany exhibits a significant increase in the number of days with PHTs.

How to cite: Mohr, S., Tonn, M., Augenstein, M., and Kunz, M.: A 20-year spatio-temporal analysis of 3D radar-based hail tracks in Germany: Trends and regional differences, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-237, https://doi.org/10.5194/ecss2025-237, 2025.

Large hail is a hazardous convective phenomenon that causes significant socioeconomic damage. Although climate change has led to an increase in severe convective events in several Russian regions, the statistics of large hail occurrences in Russia remain understudied. For the first time, this work compiles a comprehensive database of hail events in Russia from 1906 to 2024. Sources include ESWD data, routing meteorological observations, news and social network information, and existing scientific literature. The database contains over 12,000 hail reports, including more than 4,000 reports of large hail. 

A comparative analysis of hail events and lightning activity, based on WWLLN data from 2016 to 2024, reveals a statistically significant difference in the characteristics of lightning between hail and non-hail cases. The skill score analysis enables the determination of the optimal frequency of lightning flashes for identifying severe hail occurrences. Lightning data were used to specify the time and place for some hail cases in the presented database.

Analysis of the collected database revealed an exponential distribution of hail sizes, with a median diameter of 2 cm and a 95th percentile of 5 cm. The spatial distribution of hail generally reflects population density, with the highest frequencies (including hail larger than 10 cm in diameter) occurring in southern and central European Russia and southern Western Siberia. The temporal distribution of hail reports revealed a pronounced annual cycle, with a peak in June, as well as a diurnal cycle, with a peak around 16:00 local time.

To evaluate the atmospheric conditions associated with hail formation, thermodynamic and dynamic indices were calculated for hail events during the warm season (May–August) between 2010 and 2020, using ERA5 reanalysis data at an hourly temporal resolution and a 25 km spatial resolution. Statistically significant relationships were found between index values and the occurrence and size of hail. The most informative indices and their threshold values were identified for diagnosing hail of various sizes.

The study was supported by the Russian Science Foundation (grant no 24-17-00357).

How to cite: Bugrimov, A., Chernokulsky, A., Davletshin, S., Pustovalov, K., Shikhov, A., and Sprygin, A.: Climatology and formation environments of large hail in Russia, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-240, https://doi.org/10.5194/ecss2025-240, 2025.

Hailstorms are among the most damaging convective weather hazards in Europe, causing significant losses to infrastructure, vehicles, agriculture, and energy systems. However, reliably detecting and characterizing hail in real time remains a major challenge.

This study aims to improve existing hail detection approaches by combining polarimetric radar observations and environmental data using machine learning techniques. We utilize dual-polarization radar data from the C-band radar operated at the Karlsruhe Institute of Technology (KIT), which provides key variables, such as reflectivity, differential reflectivity, specific differential phase, and correlation coefficient. Convective cells are identified and tracked using the storm tracking algorithm TRACE3D, which performs object-based tracking and incorporates morphological analyses and pattern recognition to extract features indicative of severe convection, including size, shape, echotop height, temporal development, and motion characteristics. These cell objects are linked to environmental predictors derived from numerical weather prediction models, including Convective Available Potential Energy (CAPE), Lifted Index (LI), and storm-relative helicity (SRH), which are known to strongly influence hail formation. Ground-truth verification is supported by hail size reports from the European Severe Weather Database (ESWD), crowd-sourced observations via the DWD WarnWetter app, and insurance claim records. For the estimation of hail probability and hailstone size, we apply machine learning models such as logistic regression, random forest, and convolutional neural networks (CNNs), using combined radar and environmental features as input.

At the time of the conference, we anticipate presenting first results on model performance, predictor relevance, and detection accuracy. The object-based approach of our study enables integration into operational radar systems and supports more accurate, timely hail warnings for improved risk mitigation in weather-sensitive sectors.

How to cite: Kavil Kambrath, G. and Kunz, M.: Near-real-time probabilistic Hail Detection based on polarimetric radar quantities and environmental conditions using machine learning methods, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-255, https://doi.org/10.5194/ecss2025-255, 2025.

Detailed ground observations of hailstones are historically rare, particularly as it relates to properties that describe hail beyond its maximum single axis diameter, or in sampling the hail swath in substantive detail. To address this gap the In Situ Collaborative Experiment for the Collection of Hail in the Plains (ICECHIP) campaign was conducted between May 15 and June 28th of 2025. Active periods of convection persisted throughout the campaign yielding over 20 intensive observation periods. These included measurement of hail in numerous storms producing 50 mm or greater hail, with both in situ measurement platforms and post-storm transects of the hailswaths sampled close to time of fall in cooled environments and regularly thereafter  at horizontal resolutions in the hundreds of meters. 

Five types of instruments focused on direct hail capture: impact disdrometers with video cameras and hailpads formed the bulk of the sensing array, deployed ahead of the storm. These were complemented by mesonet pods for near-storm environment and SUMHOs, (Super Mobile Hail Observatory) deployable instrumented supersites that funnel hail into cooled storage with colocated hailpad, and measure hailstone fall speed using vertical pointing radar. High resolution and speed video cameras additionally captured hail fall speed and orientation. These were operated in a range of array configurations to best sample storm evolution or the swath at impressive resolution. Post storm sampling accumulated thousands of hailstones, and performed 2-3 axial dimensional measurements along with mass, and for a subset of stones exceeding 2cm, compressive strength testing via crushing.

This presentation will focus on three exemplary cases, a supercell in northeast Colorado producing a 14 mile-wide swath with hail diameters reaching 90 mm, a merging supercell that produced giant hailstones measuring as large as 150mm, and a non-supercell case producing extremely soft accumulating hail. Hailstone size and sphericity distributions, compressive strength properties and mass will be compared across the respective swaths to provide preliminary insights into the variability of hail and its potential for damage under different classes of storms.

How to cite: Allen, J., Giammanco, I., Adams-Selin, R., Meisenzahl, B., Sorber, J., Brimelow, J., Kennedy, A., Vagasky, H., Dawson, D., Servey, S., Brooks, K., Clover, K., Richer, M., Gartner, M., Kurtz, T., and Schultz, T.: Ground Observations from the In Situ Collaborative Experiment for the Collection of Hail in the Plains, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-312, https://doi.org/10.5194/ecss2025-312, 2025.