Open AccessAssessment of evolving TRMM‐based multisatellite real‐time precipitation estimation methods and their impacts on hydrologic prediction in a high latitude basin
Author(s) -
Yong Bin,
Hong Yang,
Ren LiLiang,
Gourley Jonathan J.,
Huffman George J.,
Chen Xi,
Wang Wen,
Khan Sadiq I.
Publication year - 2012
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2011jd017069
Subject(s) - environmental science , precipitation , streamflow , global precipitation measurement , climatology , quantitative precipitation estimation , satellite , latitude , snow , structural basin , rain gauge , hydrological modelling , meteorology , drainage basin , geology , geography , paleontology , cartography , geodesy , aerospace engineering , engineering
The real‐time availability of satellite‐derived precipitation estimates provides hydrologists an opportunity to improve current hydrologic prediction capability for medium to large river basins. Due to the availability of new satellite data and upgrades to the precipitation algorithms, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis real‐time estimates (TMPA‐RT) have been undergoing several important revisions over the past ten years. In this study, the changes of the relative accuracy and hydrologic potential of TMPA‐RT estimates over its three major evolving periods were evaluated and inter‐compared at daily, monthly and seasonal scales in the high‐latitude Laohahe basin in China. Assessment results show that the performance of TMPA‐RT in terms of precipitation estimation and streamflow simulation was significantly improved after 3 February 2005. Overestimation during winter months was noteworthy and consistent, which is suggested to be a consequence from interference of snow cover to the passive microwave retrievals. Rainfall estimated by the new version 6 of TMPA‐RT starting from 1 October 2008 to present has higher correlations with independent gauge observations and tends to perform better in detecting rain compared to the prior periods, although it suffers larger mean error and relative bias. After a simple bias correction, this latest data set of TMPA‐RT exhibited the best capability in capturing hydrologic response among the three tested periods. In summary, this study demonstrated that there is an increasing potential in the use of TMPA‐RT in hydrologic streamflow simulations over its three algorithm upgrade periods, but still with significant challenges during the winter snowing events.