Open AccessResearch on the selection method of FY-3D/MWHTS clear sky observation data based on neural network
Author(s) -
Qianyu He,
Xiao Guo,
Deguang Li,
Yanling Jin,
Lanjie Zhang,
Ruanyu Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1656/1/012007
Subject(s) - sky , artificial neural network , remote sensing , computer science , cloud computing , brightness temperature , inversion (geology) , brightness , data assimilation , satellite , radiative transfer , meteorology , environmental science , microwave , artificial intelligence , geography , geology , engineering , telecommunications , paleontology , physics , structural basin , quantum mechanics , aerospace engineering , optics , operating system
The selection of clear sky data in space-borne remote sensing data is very important for its data application. For FY-3D satellite microwave humidity and temperature sounder (MWHTS), an inversion system of atmospheric cloud water content by MWHTS is established based on neural network. The cloud water content inversion value is used to select clear sky data from MWHTS observation data. The experimental results show that FY-3D/MWHTS clear sky data selection method based on neural network can effectively select MWHTS observation data, thus improving the simulation brightness temperatures accuracy of MWHTS by radiative transfer model. This method can be used to select clear sky data by using space-borne observation data itself. It is easy to operate and has important practical value for climate change research, numerical weather forecast, etc., based on space-borne observation data.