REE-TTT: Highly Adaptive Radar Echo Extrapolation Based on Test-Time Training
This addresses the challenge of applying precipitation nowcasting models across diverse regions and extreme events for meteorological forecasting, representing a strong specific gain rather than a broad breakthrough.
The paper tackled the problem of poor generalization in deep learning-based radar echo extrapolation for precipitation nowcasting by proposing REE-TTT with a test-time training mechanism, resulting in substantial outperformance of state-of-the-art models in prediction accuracy and generalization under cross-regional extreme precipitation scenarios.
Precipitation nowcasting is critically important for meteorological forecasting. Deep learning-based Radar Echo Extrapolation (REE) has become a predominant nowcasting approach, yet it suffers from poor generalization due to its reliance on high-quality local training data and static model parameters, limiting its applicability across diverse regions and extreme events. To overcome this, we propose REE-TTT, a novel model that incorporates an adaptive Test-Time Training (TTT) mechanism. The core of our model lies in the newly designed Spatio-temporal Test-Time Training (ST-TTT) block, which replaces the standard linear projections in TTT layers with task-specific attention mechanisms, enabling robust adaptation to non-stationary meteorological distributions and thereby significantly enhancing the feature representation of precipitation. Experiments under cross-regional extreme precipitation scenarios demonstrate that REE-TTT substantially outperforms state-of-the-art baseline models in prediction accuracy and generalization, exhibiting remarkable adaptability to data distribution shifts.