An Algorithm for Retrieving Precipitable Water Vapor over Land Based on Passive Microwave Satellite Data

被引:19
|
作者
Zhou, Fang-Cheng [1 ,2 ]
Song, Xiaoning [2 ]
Leng, Pei [3 ]
Wu, Hua [1 ,4 ]
Tang, Bo-Hui [1 ,4 ]
机构
[1] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] Chinese Acad Agr Sci, Minist Agr, Key Lab Agriinformat, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China
[4] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
SURFACE-TEMPERATURE; SUN PHOTOMETER; RAMAN LIDAR; GPS; RADIOMETER; EMISSION; NETWORK; AERONET; MODEL; SOIL;
D O I
10.1155/2016/4126393
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Precipitable water vapor (PWV) is one of the most variable components of the atmosphere in both space and time. In this study, a passive microwave-based retrieval algorithm for PWV over land without land surface temperature (LST) data was developed. To build the algorithm, two assumptions exist: (1) land surface emissivities (LSE) at two adjacent frequencies are equal and (2) there are simple parameterizations that relate transmittance, atmospheric effective radiating temperature, and PWV. Error analyses were performed using radiosonde sounding observations from Zhangye, China, and CE318 measurements of Dalanzadgad (43 degrees 34'37 '' N, 104 degrees 25'8 '' E) and Singapore (1 degrees 17'52 '' N, 103 degrees 46'48 '' E) sites from Aerosol Robotic Network (AERONET), respectively. In Zhangye, the algorithm had a Root Mean Square Error (RMSE) of 4.39mm and a bias of 0.36 mm on cloud-free days, while on cloudy days there was an RMSE of 4.84 mm and a bias of 0.52 mm because of the effect of liquid water in clouds. The validations in Dalanzadgad and Singapore sites showed that the retrieval algorithm had an RMSE of 4.73 mm and a bias of 0.84 mm and the bigger errors appeared when the water vapor was very dry or very moist.
引用
收藏
页数:11
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