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Technical Note

How Well Can Global Precipitation Measurement (GPM) Capture Hurricanes? Case Study: Hurricane Harvey

1
Department of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA
2
Coastal and Hydraulics Laboratory, U.S. Army Engineer Research and Development Center, 5825 University Research Ct suite 4001, College Park, MD 20740, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(7), 1150; https://doi.org/10.3390/rs10071150
Received: 12 June 2018 / Revised: 18 July 2018 / Accepted: 18 July 2018 / Published: 20 July 2018
(This article belongs to the Special Issue Remote Sensing of Precipitation)
Hurricanes and other severe coastal storms have become more frequent and destructive during recent years. Hurricane Harvey, one of the most extreme events in recent history, advanced as a category IV storm and brought devastating rainfall to the Houston, TX, region during 25–29 August 2017. It inflicted damage of more than $125 billion to the state of Texas infrastructure and caused multiple fatalities in a very short period of time. Rainfall totals from Harvey during the 5-day period were among the highest ever recorded in the United States. Study of this historical devastating event can lead to better preparation and effective reduction of far-reaching consequences of similar events. Precipitation products based on satellites observations can provide valuable information needed to understand the evolution of such devastating storms. In this study, the ability of recent Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM-IMERG) final-run product to capture the magnitudes and spatial (0.1° × 0.1°)/temporal (hourly) patterns of rainfall resulting from hurricane Harvey was evaluated. Hourly gridded rainfall estimates by ground radar (4 × 4 km) were used as a reference dataset. Basic and probabilistic statistical indices of the satellite rainfall products were examined. The results indicated that the performance of IMERG product was satisfactory in detecting the spatial variability of the storm. It reconstructed precipitation with nearly 62% accuracy, although it systematically under-represented rainfall in coastal areas and over-represented rainfall over the high-intensity regions. Moreover, while the correlation between IMERG and radar products was generally high, it decreased significantly at and around the storm core. View Full-Text
Keywords: hurricane Harvey; GPM satellite; IMERG; tropical storm rainfall; gridded radar precipitation hurricane Harvey; GPM satellite; IMERG; tropical storm rainfall; gridded radar precipitation
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MDPI and ACS Style

Omranian, E.; Sharif, H.O.; Tavakoly, A.A. How Well Can Global Precipitation Measurement (GPM) Capture Hurricanes? Case Study: Hurricane Harvey. Remote Sens. 2018, 10, 1150. https://doi.org/10.3390/rs10071150

AMA Style

Omranian E, Sharif HO, Tavakoly AA. How Well Can Global Precipitation Measurement (GPM) Capture Hurricanes? Case Study: Hurricane Harvey. Remote Sensing. 2018; 10(7):1150. https://doi.org/10.3390/rs10071150

Chicago/Turabian Style

Omranian, Ehsan, Hatim O. Sharif, and Ahmad A. Tavakoly. 2018. "How Well Can Global Precipitation Measurement (GPM) Capture Hurricanes? Case Study: Hurricane Harvey" Remote Sensing 10, no. 7: 1150. https://doi.org/10.3390/rs10071150

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