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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 4 | Copyright
Nat. Hazards Earth Syst. Sci., 14, 1017-1033, 2014
https://doi.org/10.5194/nhess-14-1017-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 29 Apr 2014

Research article | 29 Apr 2014

Daytime identification of summer hailstorm cells from MSG data

A. Merino1, L. López1, J. L. Sánchez1, E. García-Ortega1, E. Cattani2, and V. Levizzani2 A. Merino et al.
  • 1Group for Atmospheric Physics, IMA, University of León, Leon, Spain
  • 2National Research Council of Italy, Institute of Atmospheric Sciences and Climate, CNR-ISAC, Bologna, Italy

Abstract. Identifying deep convection is of paramount importance, as it may be associated with extreme weather phenomena that have significant impact on the environment, property and populations. A new method, the hail detection tool (HDT), is described for identifying hail-bearing storms using multispectral Meteosat Second Generation (MSG) data. HDT was conceived as a two-phase method, in which the first step is the convective mask (CM) algorithm devised for detection of deep convection, and the second a hail mask algorithm (HM) for the identification of hail-bearing clouds among cumulonimbus systems detected by CM. Both CM and HM are based on logistic regression models trained with multispectral MSG data sets comprised of summer convective events in the middle Ebro Valley (Spain) between 2006 and 2010, and detected by the RGB (red-green-blue) visualization technique (CM) or C-band weather radar system of the University of León. By means of the logistic regression approach, the probability of identifying a cumulonimbus event with CM or a hail event with HM are computed by exploiting a proper selection of MSG wavelengths or their combination. A number of cloud physical properties (liquid water path, optical thickness and effective cloud drop radius) were used to physically interpret results of statistical models from a meteorological perspective, using a method based on these "ingredients". Finally, the HDT was applied to a new validation sample consisting of events during summer 2011. The overall probability of detection was 76.9 % and the false alarm ratio 16.7 %.

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