Long-term intelligent calculation and prediction model for heavy precipitation satellite cloud Images

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.