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Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm

文献类型：期刊论文

作者Lao, Ping1; Liu, Qi1; Ding, Yuhao1; Wang, Yu2,3; Li, Yuan1; Li, Meng1
刊名REMOTE SENSING
出版日期2021-08-01
卷号13
关键词rainrate estimation mesoscale convective system machine learning algorithm cloud top temperature FY-4A meteorological satellite
DOI10.3390/rs13163273
通讯作者Liu, Qi(qliu7@ustc.edu.cn)
英文摘要Satellite rainrate estimation is a great challenge, especially in mesoscale convective systems (MCSs), which is mainly due to the absence of a direct physical connection between observable cloud parameters and surface rainrate. The machine learning technique was employed in this study to estimate rainrate in the MCS domain via using cloud top temperature (CTT) derived from a geostationary satellite. Five kinds of machine learning models were investigated, i.e., polynomial regression, support vector machine, decision tree, random forest, and multilayer perceptron, and the precipitation of Climate Prediction Center morphing technique (CMORPH) was used as the reference. A total of 31 CTT related features were designed to be the potential inputs for training an algorithm, and they were all proved to have a positive contribution in modulating the algorithm. Random forest (RF) shows the best performance among the five kinds of models. By combining the classification and regression schemes of the RF model, an RF-based hybrid algorithm was proposed first to discriminate the rainy pixel and then estimate its rainrate. For the MCS samples considered in this study, such an algorithm generates the best estimation, and its accuracy is definitely higher than the operational precipitation product of FY-4A. These results demonstrate the promising feasibility of applying a machine learning technique to solve the satellite precipitation retrieval problem.
WOS关键词GEOSTATIONARY SATELLITE ; DEEP CONVECTION ; EAST CHINA ; RAINFALL ; PREDICTION ; VICINITY ; CMORPH ; MCSS
资助项目National Key Research and Development Program of China[2018YFC1507401] ; National Natural Science Foundation of China[41875030] ; National Natural Science Foundation of China[42075075] ; Fundamental Research Funds for the Central Universities
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000689791500001
出版者MDPI
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/125219]  
专题中国科学院合肥物质科学研究院
通讯作者Liu, Qi
作者单位1.Univ Sci & Technol China, Sch Earth & Space Sci, Hefei 230026, Peoples R China
2.Univ Sci & Technol China, Sch Environm Sci & Optoelect Technol, Hefei 230026, Peoples R China
3.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Atmospher Opt, HFIPS, Hefei 230031, Peoples R China
推荐引用方式
GB/T 7714		 Lao, Ping,Liu, Qi,Ding, Yuhao,et al. Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm[J]. REMOTE SENSING,2021,13.
APA Lao, Ping,Liu, Qi,Ding, Yuhao,Wang, Yu,Li, Yuan,&Li, Meng.(2021).Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm.REMOTE SENSING,13.
MLA Lao, Ping,et al."Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm".REMOTE SENSING 13(2021).

入库方式： OAI收割

来源：合肥物质科学研究院

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