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

Author:

Shen Zhehui123ORCID,Yong Bin23,Wu Hao234

Affiliation:

1. a College of Civil Engineering, Nanjing Forestry University, Nanjing, China

2. b National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, China

3. c Cooperative Innovation Center for Water Safety and Hydro-Science, Hohai University, Nanjing, China

4. d School of Geographic Information and Tourism, Chuzhou University, Chuzhou, China

Abstract

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.

Funder

National Natural Science Foundation of China

Publisher

American Meteorological Society

Reference69 articles.

1. Evaluation of satellite-retrieved extreme precipitation rates across the central United States;AghaKouchak, A.,2011

2. Systematic and random error components in satellite precipitation data sets;AghaKouchak, A.,2012

3. Benchmarking high-resolution global satellite rainfall products to radar and rain-gauge rainfall estimates;Anagnostou, E. N.,2010

4. Bolvin, D. T., and G. J. Huffman, 2015: Transition of 3B42/3B43 research product from monthly to climatological calibration/adjustment. NASA Tech. Rep., 11 pp., https://gpm.nasa.gov/sites/default/files/document_files/3B42_3B43_TMPA_restart.pdf.

5. Similarity and difference of the two successive V6 and V7 TRMM multisatellite precipitation analysis performance over China;Chen, S.,2013

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