融合多源数据的深度学习短时降水预测

Deep Learning for Short-term Precipitation Prediction Integrating Multi-source Data

针对传统降水预测方法的局限性,提出了一种融合多源数据的深度学习短时降水预测模型MSF-Net.在GPM历史降水数据的基础上融合了ERA5气象数据、雷达数据和DEM数据.利用气象特征提取模块学习多源数据的气象特征,通过注意力融合预测模块进行特征融合并实现短时降水预测.将MSF-Net的降水预测结果与多种人工智能方法进行对比,实验结果表明,MSF-Net模型的风险评分TS和偏差评分Bias最优,表明其可以在6 h的预测时效内提升数据驱动降水预测的效果.

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.