PPNet: A Precipitation Nowcasting Model Based on Pre-Prediction
Precipitation nowcasting has always been a hot research topic in weather forecasting. Traditional forecasting methods are based on numerical weather prediction. But recently the radar extrapolation-based methods using deep learning have attracted many researchers' attentions. Among them, the temporal prediction network cannot be calculated in parallel, which causes it to take too long time and has the problem of gradient explosion. The fully convolutional networks can solve the above two problems, but it does not have the ability to extract temporal information. Therefore, based on Taylor frozen hypothesis, a 2D fully convolutional Pre-predicted Precipitation nowcasting Network (PPNet) with a pre-prediction auxiliary inference structure is proposed. The network firstly extracts coarse-grained temporal and spatial information, and then uses the fully convolution structure to refine the feature granularity thereby effectively remitting the drawback that 2-D convolutional networks cannot extract temporal information. In addition, the paper provides a temporal features constraint structure to constrain the pre-predicted features and the structure makes the extracted features more realistic.The ablation experiments prove that the proposed pre-prediction auxiliary inference structure and temporal features constraint structure have excellent ability to extract temporal features and improve the sensitivity of the network to temporal features. Compared with the current best rainfall prediction algorithms and video prediction algorithms, the paper's network achieves better prediction results, especially in the rainstorm area.
万方数据
维普
