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Abstract: Many thermodynamic parameters and indices are currently being used as thunderstorm predictors because of their high correlations with the beginning and development of convection. Many of these indices have been developed for one specific area and their forecasting accuracy has generally been assessed in that zone and not in others. It is a highly intriguing question whether there are parameters or indices that may function adequately as thunderstorm predictors, as far as the Probability of Detection is concerned, irrespective of the latitude of the study zone. In order to approach this issue the present study focuses on data from 1692 sounding days in León (Spain), Zaragoza (Spain), Bordeaux (France) and Mendoza (Argentina). Specific discriminant models have already been developed for these areas. When comparing the results found by the different models constructed for each of the four study zones it may be noticed that there are no indices that function extremely well in all of the zones. Rather, a common ingredient pattern is observed for the beginning of convection): atmospheric instability and moist layers in the low atmosphere. It may also be concluded that sounding data alone are not enough to detect accurately the triggering mechanism, which is the third ingredient necessary for convection. The aim of this paper is to build a logistic equation integrating the four study zones. The stepwise method was employed with this purpose because it allows for the gradual inclusion of variables in the final equation according to their discriminating power. The results obtained suggest that Showalter Index and 850 hPa Dew Point Temperature are the variables that best characterize preconvective conditions irrespective of the geographic area considered. The values for POD (Probability of Detection) and FAR (False Alarm Ratio) are acceptable, but they are clearly lower than the ones obtained by each of the models in the study zone for which they were developed. [Copyright &y& Elsevier]

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]

Abstract: Predicting the occurrence of hailstorms is one of the most complicated tasks in weather forecasting because of the small area of land that is usually affected, and because of the short time hail events last. Today there are a number of parameters available that may be used to characterize preconvective conditions and predict the beginning of convection. However, forecast models based on stability indices should be developed separately for each geographic area. The databases available were used in this study with two aims in mind: determining which meteorological variables or indices obtained from a radiosonde in the study zone are more relevant in characterizing preconvective conditions; and, secondly, setting up an objective short-term forecast model for storms on the basis of one or more meteorological parameters. The logistic regression was used to establish the dichotomy risk/no risk of storms. A function was developed combining seven meteorological variables. The results show that the forecast model has a Probability of Detection of 0.87 and a False Alarm Ratio of 0.18. [Copyright &y& Elsevier]