Performance evaluation of rainfall estimates by TRMM Multi‐satellite Precipitation Analysis 3B42V6 and V7 over Brazil

DCD Melo, AC Xavier, T Bianchi… - Journal of …, 2015 - Wiley Online Library
Journal of Geophysical Research: Atmospheres, 2015Wiley Online Library
Time series precipitation data generated by the Tropical Rainfall Measuring Mission (TRMM)
have been used as a possible solution for providing rainfall information for ungauged
regions. We evaluated the quality of TRMM Multi‐satellite Precipitation Analysis (TMPA)
Version 6 (3B42V6) and Version 7 (3B42V7) products on a daily and monthly basis for a 14
year time series by comparing with gridded ground‐based rainfall data from~ 3625 rain
gauges distributed throughout Brazil. The results show that daily estimates are inaccurate for …
Abstract
Time series precipitation data generated by the Tropical Rainfall Measuring Mission (TRMM) have been used as a possible solution for providing rainfall information for ungauged regions. We evaluated the quality of TRMM Multi‐satellite Precipitation Analysis (TMPA) Version 6 (3B42V6) and Version 7 (3B42V7) products on a daily and monthly basis for a 14 year time series by comparing with gridded ground‐based rainfall data from ~3625 rain gauges distributed throughout Brazil. The results show that daily estimates are inaccurate for both Versions 6 and 7 (the refined index of agreement, dr, was less than 0.6 in most of the analyzed pixels). In general, both versions perform well on monthly basis (dr > 0.75), but no significant improvement between them could be identified with the exception of local areas. TMPA performed poorly in the northwest region, where rainfall depths are higher in Brazil; however, the quality of the ground‐based data is poor in this region because of low gauge density. Based on a seasonal analysis, we found that TMPA performed better during the dry seasons and that some improvements, although not significant, between successive versions took place over the northeast, southeast, and south regions. This study shows the value of remote sensing precipitation for providing reliable, spatiotemporally continuous precipitation at monthly timescales.
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