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arXiv · 2601.01605

REE-TTT: Highly Adaptive Radar Echo Extrapolation Based on Test-Time Training

Xin Di·Xinglin Piao·Fei Wang·Guodong Jing·Yong Zhang
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Abstract

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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Xin Di, Xinglin Piao, Fei Wang, Guodong Jing, Yong Zhang. 2026-01-04. REE-TTT: Highly Adaptive Radar Echo Extrapolation Based on Test-Time Training. https://arxiv.org/abs/2601.01605

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