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Performance of TRMM Product in Quantifying Frequency and Intensity of Precipitation during Daytime and Nighttime across China
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Performance of TRMM Product in Quantifying Frequency and Intensity of Precipitation during Daytime and Nighttime across China
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Journal Article
Performance of TRMM Product in Quantifying Frequency and Intensity of Precipitation during Daytime and Nighttime across China
2020
Overview
The Tropical Rainfall Measurement Mission (TRMM) satellite is the first to be designed to measure precipitation, and its precipitation products have been assessed in a variety of ways. Data for its post-real-time level 2 product (3B42) performed well in terms of the precipitation amount at the monthly scale because they were corrected by a precipitation dataset that was gauged every month. However, the performance of this dataset in terms of precipitation frequency and intensity is still not ideal. To this end, TRMM 3B42 products were evaluated using precipitation data from 747 meteorological stations over mainland China in this study. The Pearson’s correlation coefficient (CC), relative bias (RB), and relative error (RE) were used to assess the capability of TRMM products in terms of estimating the frequency, intensity, and amount of precipitation for different categories of precipitation during nighttime and daytime in a multiscale analysis (including interannual variation, seasonal cycles, and spatial distribution). Our results showed the following: (1) The 3B42 products reproduced interannual trends of the frequency and amount of precipitation (except for trace precipitation) with an average correlation coefficient of 0.84. (2) 3B42 performed well at calculating the annual and monthly precipitation amount, but performed poorly for frequency and even worse for intensity. The biases in these two properties canceled out, however, which led to a better estimate of the amount. (3) 3B42 represented the distribution of the subdaily amount of precipitation over a majority of the regions in the east, but did not perform well on the Tibetan Plateau or in northwest China. The performance of 3B42, as detailed in this study, can serve as valuable guidance to data users and algorithm developers.
