Short‐Term Precipitation Prediction for Contiguous United States Using Deep Learning
Guoxing Chen, Wei‐Chyung Wang
Abstract
Abstract Accurate short‐term weather prediction, essential for many aspects of life, relies mainly on forecasts from numerical weather models. Here, we report results supporting strongly deep learning as a viable, alternative approach. A 3D convolutional neural network, which uses a single frame of meteorology fields as input to predict the precipitation spatial distribution, is developed based on 39‐years (1980–2018) data of meteorology and daily precipitation over the contiguous United States. Results show that the trained network outperforms the state‐of‐the‐art weather models in predicting daily total precipitation, and the superiority of the network extends to forecast leads up to 5 days. Combining the network predictions with the weather‐model forecasts significantly improves the accuracy of model forecasts, especially for heavy‐precipitation events. Furthermore, the millisecond‐scale inference time of the network facilitates large ensemble predictions for extra accuracy improvement. These results demonstrate the promising prospects of deep learning in short‐term weather predictions.