Abstract
This study evaluated three satellite precipitation products, namely, TRMM, CMORPH, and PERSIANN, over the Three Gorges Reservoir area in China at multiple timescales. The assessment covered the following aspects: the rainfall amount, extreme precipitation, and the rainy-day detection ability. Results indicated that the CMORPH and TRMM estimates of rainfall amount were reasonably good, but the PERSIANN showed a larger bias than the other two satellite products. The data precision of CMORPH was slightly better than TRMM. All three satellite products could reproduce the diurnal cycle of rainfall, i.e., more precipitation in the morning than in the evening. The CMORPH estimates were closest to the gauge observation at 3-hourly and 12-hourly timescales. The data accuracy of CMORPH data was better during the night than in the daytime. At daily timescale, the quality of TRMM data was slightly inferior to the CMORPH, whereas the PERSIANN still differed much from the ground observation. At monthly, seasonally, and yearly timescales, the performance of TRMM was comparable to CMORPH, and both of them were obviously superior to PERSIANN. The rainy-day detection ability of CMORPH and TRMM was much better than PERSIANN. The PERSIANN data tended to overestimate the light rainy days but underestimate the heavy and torrential rainy days. The CMORPH data overestimated mainly the moderate rainy days. The TRMM data overestimated the occurrence frequency of heavy rain during the winter half year (from October to the next March). Both the CMORPH and the TRMM provided good estimates of the regional average rainy days. The data accuracy of CMORPH was slightly better than TRMM, and both were far better than the PERSIANN with respect to the rainfall amount and rainy-day detection. Nevertheless, all satellite estimates showed large biases of extreme precipitation. The CMORPH estimate of the maximum 5-day precipitation was the best of all. Both the CMORPH and TRMM data overestimated the 95th percentile of precipitation, but the PERSIANN data severely underestimated it. The PERSIANN estimates of extreme precipitation amount were the best of all during the daytime, nighttime, and the whole day. The above evaluation results could facilitate the application of satellite rainfall products and provide a reference to precipitation-related studies.
Highlights
Precipitation is one of the most important hydrometeorological variables that directly affect the social economy and people’s lives
There are more than 30 inversion methods for the satellite sounding, and more than 10 satellite rainfall estimations (SRE) are available. e representatives of SRE datasets include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) [4], the Climate Prediction Center morphing technique (CMORPH) product [5], the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) [6], and the Global Satellite Mapping of Precipitation (GSMaP) [7]
Both the TRMM and the CMORPH estimates were close to the observation regarding the location and magnitude of the rainfall maximum and minimum. e PERSIANN estimates of rainfall amount were smaller than the observation
Summary
A Multiscale Assessment of Three Satellite Precipitation Products (TRMM, CMORPH, and PERSIANN) in the Three Gorges Reservoir Area in China. Is study evaluated three satellite precipitation products, namely, TRMM, CMORPH, and PERSIANN, over the ree Gorges Reservoir area in China at multiple timescales. E TRMM data overestimated the occurrence frequency of heavy rain during the winter half year (from October to the March). Both the CMORPH and the TRMM provided good estimates of the regional average rainy days. E data accuracy of CMORPH was slightly better than TRMM, and both were far better than the PERSIANN with respect to the rainfall amount and rainy-day detection. Both the CMORPH and TRMM data overestimated the 95th percentile of precipitation, but the PERSIANN data severely underestimated it. e PERSIANN estimates of extreme precipitation amount were the best of all during the daytime, nighttime, and the whole day. e above evaluation results could facilitate the application of satellite rainfall products and provide a reference to precipitation-related studies
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