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

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      Remote Sensing
      MDPI AG

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          Abstract

          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.

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          Most cited references51

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          Analyses of Global Monthly Precipitation Using Gauge Observations, Satellite Estimates, and Numerical Model Predictions

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                Author and article information

                Journal
                Remote Sensing
                Remote Sensing
                MDPI AG
                2072-4292
                July 2018
                July 20 2018
                : 10
                : 7
                : 1150
                Article
                10.3390/rs10071150
                c4c4c86c-0ba5-4d68-a341-543ad1124081
                © 2018

                https://creativecommons.org/licenses/by/4.0/

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