Abstract
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.
Highlights
Precipitation is one of the key components of the water cycle that is crucial to study the hydrological balance, water resources management, drought monitoring, flood forecasting, as well as critical social and climatological issues [1]
The MD and unconditional bias (UB) metrics indicated if the Global Satellite-based Precipitation Estimates (GSPEs) under- or over-valued the in-situ rain gauge measurements
The UB and MD metrics were consistent in the case of Integrated Multi-satellitE Retrievals for the GPM (IMERG)-F, Global Satellite Mapping of Precipitation (GSMaP)-G, and GSMaP-S with values of 1.75, 1.84, and 2.08 and 0.64 mm/day, 0.71 mm/day, and 0.92 mm/day, respectively (Table 4)
Summary
Precipitation is one of the key components of the water cycle that is crucial to study the hydrological balance, water resources management, drought monitoring, flood forecasting, as well as critical social and climatological issues [1]. The ground rainfall gauges are used to measure rainfall flux directly and determine its rate in a small area [3] They can capture continuous measurements at high temporal frequencies. The operation of rain gauges is costly, and in most cases, they are sparsely distributed or unavailable in remote areas due to difficulties of access for installation and maintenance [5]. Ground weather Radars can gain information about the internal structure of storms and provide real-time high-resolution monitoring of precipitation over vast areas [6]. They are unavailable or not dense enough over most regions of the world
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