Improving Global Precipitation in Numerical Weather Prediction Systems based on Deep Learning Techniques

Accurate weather forecasts are crucial for many aspects of people's lives. Traditional short-term precipitation forecasting methods mainly rely on numerical weather predictions based on meteorological dynamics modelling. However, numerical weather predictions based on high-resolution physical models require hours of computation on supercomputers. Deep learning networks possess strong feature extraction capabilities, directly mapping images, meteorological data, and other information to high-dimensional feature spaces. Neurons extract abstract features, enabling continuous training and learning for predictions. Therefore, deep learning provides a new solution for short-term precipitation forecasting. In the past, deep learning has been used for precipitation forecasting by employing radar echo extension methods; however, these methods are limited to short-term forecasts and do not utilize other meteorological data, resulting in inaccurate predictions. This study proposes a model containing an attention mechanism, a convolutional extended short-term neural network (Att-PredRNN), based on 5.625° (32×64 grid points) resolution ECMWF reanalysis data and three years of global meteorological and daily precipitation data. The network uses multiple meteorological fields as input factors to predict spatial precipitation distribution for 24-hour precipitation forecasting. The model significantly improves the capture of spatial and temporal correlations through the attention matrices focusing on different parts of the input sequence. Experimental results show that the proposed Att-PredRNN model outperforms other comparison models in terms of RMSE, CSI, POD, and FAR overall and dramatically enhances the accuracy of short-term precipitation forecasts. Our code and data are publicly available.