Spatiotemporal Contextual Consistency Network for Precipitation Nowcasting

Precipitation nowcasting is forecasting rainfall in the short-term conditioned by the known meteorological parameters. Recently, deep neural networks (DNNs) have shown outstanding performance in this task. But, there are several challenges imposed by the multiple meteorological elements, including the multimodal modeling, the considerable variation in scales of precipitation region, as well as the long-tailed distribution of rainfall data. To solve these problems, this paper proposes Spatiotemporal Contextual Consistency Network (SCCN) for learning from the multi meteorological elements. Architecturally, a parameter-shared multimodal fusion CNN encoder, which dynamically exchanges features between different modalities, is used to encode the multimodal meteorological data. To improve the spatial modeling of the multiple meteorological features, we compose the multi-scale filters and deconstruction convolution to modify the gate operators in ConvLSTM to propose a spatial contextual consistency ConvLSTM (SCC-ConvLSTM). Furthermore, considering the temporal consistency in rainfall, a temporal consistency module (TCM) is designed to gear to long-tailed distribution. Under this module, different long-tailed meteorological elements are calculated to encode features and residuals fused with the previous precipitation distribution in sequence. The experimental results of precipitation nowcasting demonstrate the effectiveness of our method on the ERA5 dataset and WeatherBench dataset.