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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]

Abstract: The characterization of atmospheric conditions at different scales and their relationships to meteorological phenomena is a basic tool for improving the understanding and prediction of severe atmospheric events. Hailstorms are relatively common in southern Europe during summer, causing significant adverse impacts to property and infrastructure. This paper provides a spatiotemporal characterization of hail falls in southwestern France between 2000 and 2010, using the hail pad network operated there by the Association Nationale d'Etude et de Lutte contre les Fléaux Atmosphériques (ANELFA). This area is greatly affected by hailstorms. It was observed that the greatest incidence and severity of hail was in the central Pyrenees during May and July, with decreasing frequency and intensity toward the Atlantic coast. We selected 100 events in which severe hailstorms were reported, to study atmospheric parameters responsible for their occurrence. We performed mesoscale simulations with the WRF model, using parameterizations and fields reported in previous studies. By applying principal component analysis (PCA) and cluster analysis, we obtained three configurations to help establish relationships with the spatiotemporal incidence of hailstorms. The method and results obtained improve knowledge of the conditions favorable for hailstorms in southwestern France. This allows better hail prediction by relating atmospheric conditions with characteristics of hail precipitation on the ground. [Copyright &y& Elsevier]