Daytime precipitation identification scheme based on multiple cloud parameters retrieved from visible and infrared measurements  

作　　者：LIU XianTong LIU Qi FU YunFei LI Rui 

机构地区：[1]Key Laboratory of the Atmospheric Composition and Optical Radiation, Chinese Academy Sciences, School of Earth and Space Sciences,University of Science and Technology of China [2]Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction,China Meteorological Administration

出　　处：《Science China Earth Sciences》2014年第9期2112-2124,共13页中国科学（地球科学英文版）

基　　金：supported by the National Basic Research Program of China (Grant No. 2010CB428601);the Strategic Priority Research Program-Climate Change (Carbon Budget and Relevant Issues of the Chinese Academy of Sciences) (Grant No. XDA05100303);the Fundamental Research Funds for the Central Universities (Grant No. WK2080000024);the National Natural Science Foundation of China (Grant Nos. 41230419, 41175032 and 41075041);the Guangdong Science and Technology Plan Project (2012A061400012, 2011A032100006)

摘　　要：Visible and infrared(VIR) measurements and the retrieved cloud parameters are commonly used in precipitation identification algorithms, since the VIR observations from satellites, especially geostationary satellites, have high spatial and temporal resolutions. Combined measurements from visible/infrared scanner(VIRS) and precipitation radar(PR) aboard the Tropical Rainfall Measuring Mission(TRMM) satellite are analyzed, and three cloud parameters, i.e., cloud optical thickness(COT), effective radius(Re), and brightness temperature of VIRS channel 4(BT4), are particularly considered to characterize the cloud status. By associating the information from VIRS-derived cloud parameters with those from precipitation detected by PR, we propose a new method for discriminating precipitation in daytime called Precipitation Identification Scheme from Cloud Parameters information(PISCP). It is essentially a lookup table(LUT) approach that is deduced from the optimal equitable threat score(ETS) statistics within 3-dimensional space of the chosen cloud parameters. South and East China is selected as a typical area representing land surface, and the East China Sea and Yellow Sea is selected as typical oceanic area to assess the performance of the new scheme. It is proved that PISCP performs well in discriminating precipitation over both land and oceanic areas. Especially, over ocean, precipitating clouds(PCs) and non-precipitating clouds(N-PCs) are well distinguished by PISCP, with the probability of detection(POD) near 0.80, the probability of false detection(POFD) about 0.07, and the ETS higher than 0.43. The overall spatial distribution of PCs fraction estimated by PISCP is consistent with that by PR, implying that the precipitation data produced by PISCP have great potentials in relevant applications where radar data are unavailable.Visible and infrared （VIR） measurements and the retrieved cloud parameters are commonly used in precipitation identification algorithms, since the VIR observations from satellites, especially geostationary satellites, have high spatial and temporal reso- lutions. Combined measurements from visible/infrared scanner （VIRS） and precipitation radar （PR） aboard the Tropical Rain- fall Measuring Mission （TRMM） satellite are analyzed, and three cloud parameters, i.e., cloud optical thickness （COT）, effec- tive radius （Re）, and brightness temperature of VIRS channel 4 （BT4）, are particularly considered to characterize the cloud status. By associating the information from VIRS-derived cloud parameters with those from precipitation detected by PR, we propose a new method for discriminating precipitation in daytime called Precipitation Identification Scheme from Cloud Pa- rameters information （PISCP）. It is essentially a lookup table （LUT） approach that is deduced from the optimal equitable threat score （ETS） statistics within 3-dimensional space of the chosen cloud parameters. South and East China is selected as a typical area representing land surface, and the East China Sea and Yellow Sea is selected as typical oceanic area to assess the perfor- mance of the new scheme. It is proved that PISCP performs well in discriminating precipitation over both land and oceanic ar- eas. Especially, over ocean, precipitating clouds （PCs） and non-precipitating clouds （N-PCs） are well distinguished by PISCP, with the probability of detection （POD） near 0.80, the probability of false detection （POFD） about 0.07, and the ETS higher than 0.43. The overall spatial distribution of PCs fraction estimated by PISCP is consistent with that by PR, implying that the precipitation data produced by PISCP have great potentials in relevant applications where radar data are unavailable.

关 键 词：precipitation identification cloud parameters Tropical Rainfall Measuring Mission(TRMM) 

分 类 号：P407.8[天文地球—大气科学及气象学] P426.6