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Hailstorms cause heavy losses, especially when their hailstones reach a large size. One of the European regions most affected by these severe atmospheric events is southern France, where a valuable and extensive hailpad network has been operational for more than three decades. These direct observations are extremely useful because they allow for the definitive verification of hailfall at the ground. Space-based sensors have seen increasing importance in hail monitoring. Global Precipitation Measurement (GPM) is an international mission designed to advance precipitation measurements from multispectral sensors. The GPM core satellite carries a powerful and unprecedented Dual-Frequency Precipitation Radar (DPR) for studying 3D precipitation characteristics. The objective of the present study is to evaluate the DPR sensor ability to identify hailstorms. We identified eight hailstorms over France where DPR data were coincident with ground-based observations from a hailpad network during 2014–2021. In addition, variables provided by the DPR sensor indicative of hail presence were studied and five detection algorithms were tested. This research serves as background for future work and the development of prediction algorithms based on empirical relationships with GPM data. • 2387 hailfall records between 2014 and 2021 were found. • Eight hailstorms were detected by DPR scans. • Hail forecast variables were extracted from the DPR sensor and analyzed. • Five hail-detection algorithms were tested on the storms. [ABSTRACT FROM AUTHOR]

Southwestern France is one of the European regions with higher frequency of hail and substantial losses. The Association Nationale d'Etude et de Lutte contre les Fleáux Atmosphériques (ANELFA) maintains a hailpad network there in continuous operation, creating an extensive database for the study of hail. We aimed to create a new forecast tool to improve hailfall detection and determine its spatiotemporal distribution in the region. Using a database of 92 hail days between 2001 and 2010, we obtained vertical profiles of temperature, dew point temperature, and relative humidity from WRF model simulation. After validating these by comparison with radiosonde profiles from Bordeaux at 1200 UTC, 31 stability indexes were obtained for a point representative of the entire study area. These indexes were introduced in a binary logistic regression model, which selected the most accurate ones for detecting hail days in the region, namely, the Showalter index, dew point temperature at 850 hPa, and TQ index. A logistic equation to distinguish between hail and hail-free days was constructed by combining these indexes. Results show a probability of detection (POD) of 87%, false alarm ratio (FAR) of 16.7%, and a Heidke Skill Score (HSS) of 0.696. The logistic equation was then applied to the entire study zone, and the results were introduced in a cluster analysis. Four clusters of hail days were obtained according to the spatial patterns of atmospheric thermodynamic conditions and probability of hail. The average distribution of hail for each cluster shows a strong similarity with the distribution of impacted hailpads from the ANELFA network. The average synoptic environment, monthly distribution and hail characteristics at the surface were also analyzed for each cluster. Cluster 1 (August) was the least frequent, with small and weak hailfalls. Cluster 2 (June and July) and 3 (May and September) had the most intense hail events, while cluster 3 and 4 (May) had the most extensive areas affected. The new forecast tool shows satisfactory results and complements other studies in the same region, and it can be a useful tool for operational forecasters in predicting hail days and determining the spatial distribution of hailfalls. [ABSTRACT FROM AUTHOR]

Hailstorms are meteorological phenomena of great interest to the scientific community, owing to their socioeconomic impact, which is mainly on agricultural production. With its global coverage and high spatial and temporal resolution, satellite remote sensing can contribute to monitoring of such events through the development of appropriate techniques. This paper presents an extensive validation in the south of France of a hail detection tool (HDT) developed for the Middle Ebro Valley (MEV). The HDT is based on consecutive application of two filters, a convection mask (CM) and hail mask (HM), using spectral channels of the Meteosat Second Generation (MSG) satellite. The south of France is an ideal area for studying hailstorms, because there is a robust database of hail falls recorded by an extensive network of hailpads managed by the Association Nationale d'Etude et de Lutte contre les Fleáux Atmosphériques (ANELFA). The results show noticeably poorer performance of the HDT in France relative to that in the MEV, with probability of detection (POD) 60.4% and false alarm rate (FAR) 26.6%. For this reason, a new tool to suit the characteristics of hailstorms in France has been developed. The France Hail Detection Tool (FHDT) was developed using logistic regression from channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor of the MSG. The FHDT was validated, resulting in POD 69.3% and FAR 15.4%, thus improving hail detection in the study area as compared with the previous tool. The new tool was tested in a case study with satisfactory results, supporting its future practical application. [ABSTRACT FROM AUTHOR]