National Engineering Research Center for Geographic Information
System, School of Geography and Information Engineering, China University of
Geosciences (Wuhan), Wuhan 430074, China
Nengcheng Chen
National Engineering Research Center for Geographic Information
System, School of Geography and Information Engineering, China University of
Geosciences (Wuhan), Wuhan 430074, China
State Key Laboratory of Information Engineering in Surveying, Mapping,
and Remote Sensing, Wuhan University, Wuhan 430079, China
National Engineering Research Center for Geographic Information
System, School of Geography and Information Engineering, China University of
Geosciences (Wuhan), Wuhan 430074, China
Hongchu Yu
School of Navigation, Wuhan University of Technology, Wuhan 430063,
China
Sanya Science and Education Innovation Park of Wuhan University of
Technology, Sanya, China
Zeqiang Chen
National Engineering Research Center for Geographic Information
System, School of Geography and Information Engineering, China University of
Geosciences (Wuhan), Wuhan 430074, China
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Total article views: 4,461 (including HTML, PDF, and XML)
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Total article views: 3,174 (including HTML, PDF, and XML)
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Total article views: 1,287 (including HTML, PDF, and XML)
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Precipitation forecasting has potential uncertainty due to data and model uncertainties. Here, an integrated predictive uncertainty modeling framework is proposed by jointly considering data and model uncertainties through an uncertainty propagation theorem. The results indicate an effective predictive uncertainty estimation for precipitation forecasting, indicating the great potential for uncertainty quantification of numerous predictive applications.
Precipitation forecasting has potential uncertainty due to data and model uncertainties. Here,...
