Hail-Detection Algorithm for the GPM Core Observatory Satellite Sensors
journal contributionposted on 2017-08-01, 14:42 authored by Kamil Mroz, Alessandro Battaglia, Timothy J. Lang, Daniel J. Cecil, Simone Tanelli, Frederic Tridon
By 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%.
This research used the SPECTRE High Performance Computing Facility at the University of Leicester. NEXRAD data were obtained from the National Oceanic and Atmospheric Administration via Amazon Web Services (https://aws.amazon.com/noaa-big-data/nexrad/). NEXRAD data were ingested, edited, and analyzed using the following open-source packages: Py-ART (http://arm-doe.github.io/pyart/), CSU_RadarTools (https://github.com/CSU-Radarmet/CSU_RadarTools), DualPol (https://github.com/nasa/DualPol), SkewT (https://pypi.python.org/pypi/SkewT), and ARTview (https://github.com/nguy/artview). Timothy Lang was funded by the GPM Ground Validation program, under the direction of Mathew Schwaller and Ramesh Kakar of the National Aeronautics and Space Administration. Daniel Cecil was funded by the NASA Precipitation Measurement Missions Science Team. Level-2 V04A-GPM data were downloaded from the Precipitation Processing System. The work done by A. Battaglia and F. Tridon was funded by the project Calibration and Validation Studies over the North Atlantic and UK for the Global Precipitation Measurement mission funded by the UK NERC (NE/L007169/1). The work by Simone Tanelli was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. Support from the Precipitation Measurement Missions and Cloud and Radiation Programs is gratefully acknowledged. We are grateful to Dr. Brenda Dolan for expertise that she provided regarding the hydrometeor-identification algorithm.
CitationJournal of Applied Meteorology and Climatology, 2017, 56 (7), pp. 1939-1957
Author affiliation/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Physics and Astronomy
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