Hail-Detection Algorithm for the GPM Core Observatory Satellite Sensors

Hail-Detection Algorithm for the GPM Core Observatory Satellite Sensors 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%. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Meteorology and Climatology American Meteorological Society

Hail-Detection Algorithm for the GPM Core Observatory Satellite Sensors

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1558-8432
eISSN
1558-8432
D.O.I.
10.1175/JAMC-D-16-0368.1
Publisher site
See Article on Publisher Site

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%.

Journal

Journal of Applied Meteorology and ClimatologyAmerican Meteorological Society

Published: Jul 18, 2017

References

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