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Open AccessArticle

Performance Assessment of Sub-Daily and Daily Precipitation Estimates Derived from GPM and GSMaP Products over an Arid Environment

1
Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr. NW, Calgary, Alberta, AB T2N 1N4, Canada
2
Department of Geology, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
3
Department of Electrical Engineering, Faculty of Engineering, Port Said University, Port Fouad 42523, Egypt
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2840; https://doi.org/10.3390/rs11232840
Received: 3 November 2019 / Revised: 19 November 2019 / Accepted: 27 November 2019 / Published: 29 November 2019
(This article belongs to the Special Issue Environmental Modelling and Remote Sensing)
Precipitation is a critical variable for comprehending various climate-related research, such as water resources management, flash flood monitoring and forecasting, climatic analyses, and hydrogeological studies, etc. Here, our objective was to evaluate the rainfall estimates obtained from Global Precipitation Mission (GPM), and Global Satellite Mapping of Precipitation (GSMaP) constellation over an arid environment like the Sultanate of Oman that is characterized by a complex topography and extremely variable rainfall patterns. Global Satellite-based Precipitation Estimates (GSPEs) can provide wide coverage and high spatial and temporal resolutions, but evaluating their accuracy is a mandatory step before involving them in different hydrological applications. In this paper, the reliability of the Integrated Multi-satellitE Retrievals for the GPM (IMERG) V04 and GSMaP V06 products were evaluated using the reference in-situ rain gauges at sub-daily (e.g., 6, 12, and 18 h) and daily time scales during the period of March 2014–December 2016. A set of continuous difference statistical indices (e.g., mean absolute difference, root mean square error, mean difference, and unconditional bias), and categorical metrics (e.g., probability of detection, critical success index, false alarm ratio, and frequency bias index) were used to evaluate recorded precipitation occurrences. The results showed that the five GSPEs could generally delineate the spatial and temporal patterns of rainfall while they might have over- and under-estimations of in-situ gauge measurements. The overall quality of the GSMaP runs was superior to the IMERG products; however, it also encountered an exaggeration in case of light rain and an underestimation for heavy rain. The effects of the gauge calibration algorithm (GCA) used in the final IMERG (IMERG-F) were investigated by comparison with early and late runs. The IMERG-F V04 product did not show a significant improvement over the early (i.e., after 4 h of rainfall observations) and late (i.e., after 12 h of rainfall observations) products. The results indicated that GCA could not reduce the missed precipitation records considerably. View Full-Text
Keywords: Dry environment; hydrology; Integrated Multi-satellitE Retrievals for the GPM; rain gauge records; satellite-based precipitation; statistical evaluation; Sultanate of Oman Dry environment; hydrology; Integrated Multi-satellitE Retrievals for the GPM; rain gauge records; satellite-based precipitation; statistical evaluation; Sultanate of Oman
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MDPI and ACS Style

Shawky, M.; Moussa, A.; Hassan, Q.K.; El-Sheimy, N. Performance Assessment of Sub-Daily and Daily Precipitation Estimates Derived from GPM and GSMaP Products over an Arid Environment. Remote Sens. 2019, 11, 2840.

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