期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：237
期号：2
页码：022008
DOI：10.1088/1755-1315/237/2/022008
出版社：IOP Publishing
摘要：A nonlinear roiling prediction model for satellite image has been developed based on Shapley neural network using the ensemble prediction method similar to the numerical prediction model, due to lacking of the guidance of a nonlinear prediction theory for satellite image at present. Empirical Orthogonal Function(EOF) method is applied to the samples of infrared satellite image every 6 h in heavy rainfall processes, and time coefficients extracted are used as predictands. Since the changes of precipitation cloud system are governed by the physical quantity fields in cloud cluster, the physical quantifies prediction products from numerical prediction model are used as predictors, and Shapley Neural Network Ensemble Prediction models are established for the corresponding time coefficients based on the technique of the reduction of data dimensionality for data interpretation. By integrating the predicted time coefficient and space vector, the future satellite image is obtained. Results show that the nonlinear prediction model can better forecast the main features of the development of heavy rainfall cloud cluster in future 24h.
