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Bias correction framework for satellite precipitation products using a rain/no rain discriminative model

文献类型：期刊论文

作者Xiao, Shuai1,3; Zou, Lei3; Xia, Jun2,3; Yang, Zhizhou3; Yao, Tianci3
刊名SCIENCE OF THE TOTAL ENVIRONMENT
出版日期2022-04-20
卷号818页码:12
关键词Satellite precipitation correction IMERG R NR discriminative model ANN Hanjiang River Basin
ISSN号0048-9697
DOI10.1016/j.scitotenv.2021.151679
通讯作者Zou, Lei(zoulei@igsnrr.ac.cn)
英文摘要Despite the benefits of global coverage with high spatiotemporal resolutions, satellite precipitation products (SPPs) still suffer from inadequate accuracy in natural hazard forecasts, hydrology, and water resources management. Rain/no-rain (R/NR) detection error significantly affects the accuracy of daily SPPs, which has attracted increasing attention in recent years. This paper proposed a precipitation bias correction framework (PBCF) to improve the accuracy of daily SPPs, focusing on improving the ability of SPPs to detect the occurrence of the precipitation based on a R/NR discriminative model. Multiple land and climate variables derived from ERA5Land reanalysis dataset were used to construct the R/NR discriminative model using the artificial neural network (ANN) method. A case study on the bias correction of daily precipitation of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) over Hanjiang River Basin (HRB) was conducted for the period 2004-2018. Daily precipitation of 64 meteorological stations in HRB were spatially and randomly divided into two groups: 44 stations were used for training, validating and testing the constructed R/NR discriminative model, and the other 20 stations were used to evaluate the performance of the R/NR discriminative model in different topographic areas. The results indicate that the proposed PBCF could reduce the bias of IMERG, with the correlation coefficient (R) increased by 19.4%, the root mean square error (RMSE) and the mean absolute error (MAE) decreased by 19.0% and 29.8% on the daily scale, respectively. The constructed R/NR discriminative model could improve the ability of IMERG for detecting the precipitation occurrence, with a classification accuracy of about 86.5% and the equitable threat score (ETS) increased from 0.15 to 0.58. Further analyses showed that the proposed PBCF was more efficient than the cumulative distribution function mapping method in correcting IMERG. This study provides a novel insight for the correction of daily SPPs. (c) 2021 Elsevier B.V. All rights reserved.
资助项目National Natural Science Foundation of China[41890822] ; National Natural Science Foundation of China[42101043] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23040304]
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000789651000008
出版者ELSEVIER
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/175882]  
专题中国科学院地理科学与资源研究所
通讯作者Zou, Lei
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430000, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Xiao, Shuai,Zou, Lei,Xia, Jun,et al. Bias correction framework for satellite precipitation products using a rain/no rain discriminative model[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2022,818:12.
APA Xiao, Shuai,Zou, Lei,Xia, Jun,Yang, Zhizhou,&Yao, Tianci.(2022).Bias correction framework for satellite precipitation products using a rain/no rain discriminative model.SCIENCE OF THE TOTAL ENVIRONMENT,818,12.
MLA Xiao, Shuai,et al."Bias correction framework for satellite precipitation products using a rain/no rain discriminative model".SCIENCE OF THE TOTAL ENVIRONMENT 818(2022):12.

入库方式： OAI收割

来源：地理科学与资源研究所

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