MIPRNet: Multiinformation Extraction Enhanced Perceptual Attention Networks for Precipitation Forecasting

As deep learning technology continues to advance in the field of meteorological forecasting, accurate radar echo extrapolation technology is crucial for two-hour precipitation prediction. Recent approaches commonly utilize 2D CNN and 3D CNN for feature extraction. However, the absence of high-level temporal sequence features prevents the model from acquiring sufficient information for accurate precipitation forecasting. To this end, we introduce a MIPRNet for precise precipitation forecasting. The Multi-Information Extractor (MIE), based on graph convolution and Fourier transforms, captures high-level complex temporal features of extrapolation evolution and frequency characteristics of precipitation dynamics. Meanwhile, MHPA, which utilizes the multi-head attention mechanism, aggregates and captures potential precipitation evolution patterns. In the extrapolation process, MIPRNet uses the potential evolution patterns of extrapolation obtained from the information extraction part to perform extrapolation. Experimental results on two radar meteorological datasets demonstrate that MIPRNet outperforms existing models in terms of multiple authoritative metrics.