Deep Learning for Short-term Precipitation Prediction Integrating Multi-source Data
This study proposes a deep learning model for short-term precipitation forecasting,called MSF-Net,to address the limitations of traditional methods.This model integrates multi-source data,including GPM historical precipitation data,ERA5 meteorological data,radar data,and DEM data.A meteorological feature extraction module is employed to learn the meteorological features of the multi-source data.An attention fusion prediction module is used to achieve feature fusion and short-term precipitation forecasting.The precipitation forecasting results of MSF-Net are compared with those of various artificial intelligence methods.Experimental results indicate that MSF-Net achieves optimal threat score(TS)and bias score(Bias).This suggests that it can enhance the effectiveness of data-driven precipitation forecasting within a 6 h prediction horizon.
deep learningshort-term precipitation predictionattention mechanismdata fusiondata-driven
NETL
NSTL
万方数据
