A Deep Contrastive Model for Radar Echo Extrapolation
Qian Li, Jinrui Jing, Leiming Ma, Lei Chen, Shiqing Guo, Hanxing Chen, Tianying Wang, Yaming Xu · IEEE Geoscience and Remote Sensing Letters · 2024
Weather radar echo extrapolation is one of the essential means for weather nowcasting. It has been considerably inspired over the last decade by deep learning. However, the internal similarity of the echo evolution process has little been exploited. To investigate this merit, a deep contrastive model with an encoder–projector structure is proposed in this letter, which projects the subsequences sampled from the same evolution process into the neighborhood of latent space by contrastive learning. Thus, the internal evolution similarity of the input echo sequence itself can be discovered and exploited for promoting prediction. To make the training smoother, we also adopt a cumulative sampling strategy that follows a simple-to-hard manner. Experimental results on two real-world radar datasets demonstrate the superiority of our model in comparison to state-of-the-art. The effectiveness of the sampling strategy and extrapolation ability on limited input is also analyzed and verified. Training code and pretrained models are available athttps://github.com/tolearnmuch/ESCL.