MSPTS-Net:Multi-Scale Parallel Temporal-Spatial Network for Precipitation Nowcasting

Precipitation Nowcasting plays an important role in people’s daily life, yet current prediction methods still encounter some challenges. Most radar echo extrapolation methods rely on a Single-Input-Single-Output (SISO) structure, which can accumulate errors over time, resulting in a decline in prediction accuracy for longer forecasts. Furthermore, the radar echoes exhibit complex characteristics, making it difficult to capture underlying dynamics solely through the extraction of features at a single scale, especially for high-intensity rainfall events. To overcome these difficulties, this paper proposes a Multi-Scale Parallel Temporal-Spatial Network (MSPTS-Net) based on a Multi-Input-Multi-Output (MIMO) architecture, which reduces the accumulation error for longer predictions. Specifically, we employ a multi-scale temporal feature extraction module to capture both long-term and short-term temporal evolution patterns. In the spatial dimension, we adopt a global to local multi-scale feature extraction module to model and represent the characteristics of radar echoes across different scales. For better representation of temporal spatial characteristics, the self-attention mechanism is employed to integrate multi-scale features across both temporal and spatial dimensions. During the training process, we use a hybrid loss function by combining MSE and Charbonnier loss to enhance prediction accuracy. Compared with existing methods, the proposed MSPTS-Net structure demonstrates significant advantages in performance.