Hail-Detection Algorithm for the GPM Core Observatory Satellite SensorsSource: Journal of Applied Meteorology and Climatology:;2017:;volume( 056 ):;issue: 007::page 1939Author:Mroz, Kamil;Battaglia, Alessandro;Lang, Timothy J.;Cecil, Daniel J.;Tanelli, Simone;Tridon, Frederic
DOI: 10.1175/JAMC-D-16-0368.1Publisher: American Meteorological Society
Abstract: AbstractBy exploiting an abundant number of extreme storms observed simultaneously by the Global Precipitation Measurement (GPM) mission Core Observatory satellite?s suite of sensors and by the ground-based S-band Next Generation Weather Radar (NEXRAD) network over the continental United States, proxies for the identification of hail are developed from the GPM Core Observatory satellite observables. The full capabilities of the GPM Core Observatory are tested by analyzing more than 20 observables and adopting the hydrometeor classification on the basis of ground-based polarimetric measurements being truth. The proxies have been tested using the critical success index (CSI) as a verification measure. The hail-detection algorithm that is based on the mean Ku-band reflectivity in the mixed-phase layer performs the best of all considered proxies (CSI of 45%). Outside the dual-frequency precipitation radar swath, the polarization-corrected temperature at 18.7 GHz shows the greatest potential for hail detection among all GPM Microwave Imager channels (CSI of 26% at a threshold value of 261 K). When dual-variable proxies are considered, the combination involving the mixed-phase reflectivity values at both Ku and Ka bands outperforms all of the other proxies, with a CSI of 49%. The best-performing radar?radiometer algorithm is based on the mixed-phase reflectivity at Ku band and on the brightness temperature (TB) at 10.7 GHz (CSI of 46%). When only radiometric data are available, the algorithm that is based on the TBs at 36.6 and 166 GHz is the most efficient, with a CSI of 27.5%.
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contributor author | Mroz, Kamil;Battaglia, Alessandro;Lang, Timothy J.;Cecil, Daniel J.;Tanelli, Simone;Tridon, Frederic | |
date accessioned | 2018-01-03T11:01:10Z | |
date available | 2018-01-03T11:01:10Z | |
date copyright | 5/22/2017 12:00:00 AM | |
date issued | 2017 | |
identifier other | jamc-d-16-0368.1.pdf | |
identifier uri | http://138.201.223.254:8080/yetl1/handle/yetl/4246113 | |
description abstract | AbstractBy exploiting an abundant number of extreme storms observed simultaneously by the Global Precipitation Measurement (GPM) mission Core Observatory satellite?s suite of sensors and by the ground-based S-band Next Generation Weather Radar (NEXRAD) network over the continental United States, proxies for the identification of hail are developed from the GPM Core Observatory satellite observables. The full capabilities of the GPM Core Observatory are tested by analyzing more than 20 observables and adopting the hydrometeor classification on the basis of ground-based polarimetric measurements being truth. The proxies have been tested using the critical success index (CSI) as a verification measure. The hail-detection algorithm that is based on the mean Ku-band reflectivity in the mixed-phase layer performs the best of all considered proxies (CSI of 45%). Outside the dual-frequency precipitation radar swath, the polarization-corrected temperature at 18.7 GHz shows the greatest potential for hail detection among all GPM Microwave Imager channels (CSI of 26% at a threshold value of 261 K). When dual-variable proxies are considered, the combination involving the mixed-phase reflectivity values at both Ku and Ka bands outperforms all of the other proxies, with a CSI of 49%. The best-performing radar?radiometer algorithm is based on the mixed-phase reflectivity at Ku band and on the brightness temperature (TB) at 10.7 GHz (CSI of 46%). When only radiometric data are available, the algorithm that is based on the TBs at 36.6 and 166 GHz is the most efficient, with a CSI of 27.5%. | |
publisher | American Meteorological Society | |
title | Hail-Detection Algorithm for the GPM Core Observatory Satellite Sensors | |
type | Journal Paper | |
journal volume | 56 | |
journal issue | 7 | |
journal title | Journal of Applied Meteorology and Climatology | |
identifier doi | 10.1175/JAMC-D-16-0368.1 | |
journal fristpage | 1939 | |
journal lastpage | 1957 | |
tree | Journal of Applied Meteorology and Climatology:;2017:;volume( 056 ):;issue: 007 | |
contenttype | Fulltext |