MSPTS-Net: Multiscale Parallel Temporal–Spatial Network for Precipitation Nowcasting

Precipitation nowcasting plays an important role in people’s daily lives, 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 letter proposes a multiscale 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 multiscale 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 multiscale 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 multiscale features across both temporal and spatial dimensions. During the training process, we use a hybrid loss function by combining mean squared error (mse) and Charbonnier loss to enhance prediction accuracy. Compared with existing methods, the proposed MSPTS-Net structure demonstrates significant advantages in performance.