Abstract
Two versions of Global Satellite Mapping of Precipitation (GSMaP) products (GSMaP-V4 and GSMaP-V5) are validated both in a single grid scale and in contiguous China by comparing to gauge-based rainfall analysis dataset. GSMaP products can capture spatial patterns and magnitude of rainfall in daily mean precipitation. They perform better in summer than in winter over the Chinese Mainland. They also have better estimation over the southeast than over the northwest of the Chinese Mainland. An apparent system underestimate is detected in both GSMaP products. The underestimation existing in the GSMaP-V4 has been largely improved in GSMaP-V5. The impacts of snow cover and vegetation fraction are also investigated. The result indicates that snow cover deeply impacts the POD and FAR of GSMaP products. NDVI may result in overestimated precipitation in sparse vegetation regions. These results implicate that it is useful to use some auxiliary data from other sensors (e.g., MODIS) to improve the quality of precipitation product.
Highlights
Over the past decade, satellite meteorology has made great contributions to improving the understanding of the global water cycle and its effects on the large-scale dynamics of general atmospheric circulation
These daily climatology fields are adjusted by the Parameter-Elevation Regression on Independent Slopes Model (PRISM) monthly precipitation climatology of Daly et al [34] to correct the bias caused by orographic effects
mean error (ME) indicates that the Global Satellite Mapping of Precipitation (GSMaP) product underestimates precipitation by about 0.53 mm/day for GSMaP-V4 and 0.31 mm/day for GSMaP-V5
Summary
Satellite meteorology has made great contributions to improving the understanding of the global water cycle and its effects on the large-scale dynamics of general atmospheric circulation. These precipitation estimates include TRMM-TMPA, PERSIANN, CMORPH, and GSMaP [3,4,5,6] These products combine precipitation information from multiple sensors (e.g., passive microwave (PMW) sensors and infrared (IR) radiometers) and multiple algorithms [7,8,9,10,11] to produce estimates of rainfall over the globe at a spatial resolution of 0.25∘ latitude/longitude (or finer) and 3-hour temporal resolution (or less). To understand the strengths and weaknesses of global rainfall products retrieved from remote sensing data, the International Precipitation Working Group (IPWG) established a program for validation of daily rainfall against rainfall measurements from rain gauges and radars [12] Such satellite precipitation datasets have been extensively evaluated all over the world in recent years [13,14,15,16,17]
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