PrecipLSTM: A Meteorological Spatiotemporal LSTM for Precipitation Nowcasting

Abstract

Accurate and timely nowcasting precipitation has huge social and economic benefits. However, the changes of clouds including expansion, dissipation, and distortion are extremely complex, which exacerbates the difficulty of forecasting. Fortunately, it still follows certain meteorological laws, which can be explored based on spatiotemporal information but are not fully considered by previous models. In this paper, we design two modules to focus on these messages based on atmospheric characteristics. Specifically, the SLAM (Spatial Local Attention Memory) module combines local attention and memory mechanism to capture the meteorological spatial relationship, while the TDM (Time Difference Memory) module combines differential technology and memory mechanism to capture the meteorological temporal variants. We combine these two modules with PredRNN and propose PrecipLSTM to sufficiently capture the spatiotemporal dependencies of radar data. We do exhaustive experiments with five baselines on four radar datasets. It is verified that PrecipLSTM achieves state-of-the-art results with fewer parameters than the previous state-of-the-art method.