Quantitative hail monitoring in an alpine area: 35-year climatology and links with atmospheric variables

Authors

  • Emanuele Eccel,

    Corresponding author
    1. IASMA Research and Innovation Centre - Fondazione Edmund Mach - Environment and Natural Resources Area Via Mach, 1—38010 San Michele all'Adige, Italy
    • FEM Via Mach, 1-38010 S. Michele, Italy.
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  • Piero Cau,

    1. IASMA Research and Innovation Centre - Fondazione Edmund Mach - Environment and Natural Resources Area Via Mach, 1—38010 San Michele all'Adige, Italy
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  • Kathrin Riemann-Campe,

    1. International Max Planck Research School of Earth System Modelling (IMPRS), Meteorologisches Institut, Universität Hamburg, Bundesstrasse 55, 20146 Hamburg, Germany
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  • Franco Biasioli

    1. IASMA Research and Innovation Centre - Fondazione Edmund Mach - Food Quality and Nutrition Area Via Mach, 1—38010 San Michele all'Adige, Italy
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Abstract

Hail climatology is usually weakly known and unsatisfactorily standardised in measurement techniques. Trentino, in the Italian Alps, boasts a 271-hailpad network operated since 1974, covering nearly the whole of the regional agricultural area. Many hail indices, concerning both extensive and energetic features of hailstorms, were investigated in a 35-year period, seeking for climatic trends. The results show that, despite a slight, non-significant trend of decrease in the number of events and in the hit surfaces, most energetic indices, which are directly correlated to the damage to crops, have increased in the period, some at considerable rates. Particularly, indices referring to extreme values show the clearest trends. The correlation with atmospheric variables from ECMWF's reanalysis (ERA-40) was considered. Data were processed at six gridpoints, to calculate three instability-related indices. Other ten variables were considered, either integrated over the atmospheric column or at separate atmospheric levels. Correlations between seasonally averaged single atmospheric predictors and hail indices show a varied record of cases, where only some pairs of atmospheric predictors and hail indices give positive results. Statistical links were also sought using a multivariate method involving principal component regression (PCR) and partial least square regression (PSLR) techniques. Despite the more general approach allowed by these methods, only some hail indices respond to the attempt of setting up satisfactory statistical models. The principal-component predictors are built with many atmospheric variables, warning against a simplified use of correlations of some hail indices with few atmospheric predictors. Particularly, it is shown that the number of events is not a useful index for assessing general climatic features of hailstorms, and that the use of one instability index alone—like convective available potential energy (CAPE)—does not allow a thorough description of the links between atmospheric precursors of hail and its real occurrence. Copyright © 2010 Royal Meteorological Society

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