Cloud Layer and Precipitation Forecasting via Multi‐Scale Gated Temporal and Spatial Attention Network

ABSTRACT Cloud layer and precipitation forecasting play a crucial role in daily life and decision‐making. Most existing deep learning models extract features at a single scale and ignore the correlation between features at different scales in the cloud layer and precipitation data. This hinders the ability to extract multi‐scale cloud layer features and precipitation features and further constrains the predictive accuracy of the model. To address these challenges, we propose the multi‐scale gated temporal and spatial attention network (MGTSA‐Net). This network is designed to capture multi‐scale spatiotemporal features in the cloud layer and precipitation data more effectively. As a result, it can improve the accuracy of cloud layer and precipitation forecasting. The core component is the multi‐scale temporal gated (MTG) module, which integrates multi‐scale convolutions and gated recurrent unit (GRU). To further enhance the model's capability of spatial feature extraction, we integrate a multi‐scale spatial attention (MSA) module into the encoder. Experimental evaluations on the WeatherBench dataset demonstrate that the MGTSA‐Net outperforms state‐of‐the‐art models in predictive accuracy and computational efficiency.