基于测试时训练的雷达回波外推模型，提升极端天气预报泛化能力。
REE-TTT: Highly Adaptive Radar Echo Extrapolation Based on Test-Time Training
- 引入时空自适应测试时训练机制，动态调整模型参数。
- 跨区域极端降水场景下预测精度显著优于现有方法。
- 适合需要快速适配新地区或异常天气的气象预报场景。
降水短临预报对气象预测至关重要。基于深度学习的雷达回波外推（REE）已成为主流方法，但其依赖高质量本地训练数据和固定模型参数，导致泛化能力差，难以适应不同地区和极端事件。为此，我们提出REE-TTT，一种融合自适应测试时训练（TTT）机制的新模型。核心是新设计的时空测试时训练（ST-TTT）模块，用任务特定注意力机制替代标准线性投影，增强对非平稳气象分布的适应能力，显著提升降水特征表示。在跨区域极端降水场景下的实验表明，REE-TTT在预测准确性和泛化能力上均显著优于现有最先进模型，展现出对数据分布变化的强大适应性。
原文摘要 · Abstract (English)
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.
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