When the Past Matters: FlashBack Memory for Precipitation Nowcasting
For meteorologists and disaster planners, FB offers a general memory enhancement that boosts recurrent model performance in high-resolution precipitation nowcasting.
FlashBack Memory (FB) improves precipitation nowcasting by dynamically retrieving and fusing key historical states, reducing false alarms and missed events while enhancing metrics like MSE, MAE, SSIM, and CSI across multiple datasets and models.
Accurate precipitation nowcasting is crucial for disaster mitigation and socio-economic planning, yet existing methods often struggle with false alarms, missed events, and long range dependency modeling at high spatiotemporal resolution. To address these challenges, we propose FlashBack Memory (FB), a module that dynamically retrieves key historical states and integrates them via an adaptive fusion gate, enhancing the spatiotemporal representation capability of recurrent-based models. We incorporate FB into PredRNN, PredRNNpp, MIM, MotionRNN, and PredRNN-V2, and evaluate on CIKM2017, Shanghai2020, and SEVIR datasets. Experimental results demonstrate that FB significantly improves MSE, MAE, SSIM, and CSI metrics, particularly for high-intensity rainfall and long-sequence predictions, while reducing false alarms and missed events and enhancing temporal consistency and spatial localization. The proposed method provides a general and efficient memory enhancement mechanism, improving the overall performance of recurrent-based precipitation nowcasting models.