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| Titel |
Daytime identification of summer hailstorm cells from MSG data |
| VerfasserIn |
A. Merino, L. López, J. L. Sánchez, E. García-Ortega, E. Cattani, V. Levizzani |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Sciences ; 14, no. 4 ; Nr. 14, no. 4 (2014-04-29), S.1017-1033 |
| Datensatznummer |
250118403
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| Publikation (Nr.) |
 copernicus.org/nhess-14-1017-2014.pdf |
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| Zusammenfassung |
| 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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