Which Error Components in TRMM-Based Multisatellite Precipitation Estimates Reduce over Chinese Mainland after Official Bias Adjustments: Systematic or Random?

Zhehui Shen aCollege of Civil Engineering, Nanjing Forestry University, Nanjing, China
bNational Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, China
cCooperative Innovation Center for Water Safety and Hydro-Science, Hohai University, Nanjing, China

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Bin Yong bNational Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, China
cCooperative Innovation Center for Water Safety and Hydro-Science, Hohai University, Nanjing, China

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Hao Wu bNational Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, China
cCooperative Innovation Center for Water Safety and Hydro-Science, Hohai University, Nanjing, China
dSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, China

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Abstract

Climatological calibration algorithm (CCA) and satellite–gauge combination (SG) are two official bias adjustments for satellite precipitation estimates (SPE) in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA). The CCA is designed for the near-real-time SPEs, while the SG procedure is a final step to merge pure SPEs with gauge observations. This study explored the impacts of CCA and SG on the systematic and random errors of TMPA SPEs. The errors of TMPA version-7 near-real-time products before and after CCA (RT_UC, RT_C), and the research product TMPA 3B42 (V7), were decomposed into systematic and random components, benchmarked by the China Gauge-based Daily Precipitation Analysis (CGDPA). After being calibrated by CCA, RT_C reduced the systematic errors relative to RT_UC over the Chinese mainland, except in the Tibetan Plateau and Tianshan Mountains. However, CCA did not aid in reducing random errors; instead, it even exacerbated the random errors. On the other hand, the SG merging is more effective in reducing systematic errors of SPEs than CCA calibration because of the direct inclusion of simultaneous gauge data from the Global Precipitation Climatology Centre (GPCC). We also found that SG merging reduced the random errors of pure SPEs over regions with relatively higher elevations. Despite lower random errors in V7 over the complex terrain region, the SG unfavorably increased the random errors over southeastern China. The results reported here may offer valuable insights into the effects of CCA and SG techniques drawn from TMPA, with the potential to advance the development of bias-adjusting algorithms for SPEs in the future.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bin Yong, yongbin@hhu.edu.cn

Abstract

Climatological calibration algorithm (CCA) and satellite–gauge combination (SG) are two official bias adjustments for satellite precipitation estimates (SPE) in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA). The CCA is designed for the near-real-time SPEs, while the SG procedure is a final step to merge pure SPEs with gauge observations. This study explored the impacts of CCA and SG on the systematic and random errors of TMPA SPEs. The errors of TMPA version-7 near-real-time products before and after CCA (RT_UC, RT_C), and the research product TMPA 3B42 (V7), were decomposed into systematic and random components, benchmarked by the China Gauge-based Daily Precipitation Analysis (CGDPA). After being calibrated by CCA, RT_C reduced the systematic errors relative to RT_UC over the Chinese mainland, except in the Tibetan Plateau and Tianshan Mountains. However, CCA did not aid in reducing random errors; instead, it even exacerbated the random errors. On the other hand, the SG merging is more effective in reducing systematic errors of SPEs than CCA calibration because of the direct inclusion of simultaneous gauge data from the Global Precipitation Climatology Centre (GPCC). We also found that SG merging reduced the random errors of pure SPEs over regions with relatively higher elevations. Despite lower random errors in V7 over the complex terrain region, the SG unfavorably increased the random errors over southeastern China. The results reported here may offer valuable insights into the effects of CCA and SG techniques drawn from TMPA, with the potential to advance the development of bias-adjusting algorithms for SPEs in the future.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bin Yong, yongbin@hhu.edu.cn
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