这篇论文用LSTM和CNN补热带气旋数据里的Rmax缺失值，发现时序模型少用10倍数据效果还更好，做沿海灾害评估的值得看看。
该研究评估了用1DCNN、LSTM和传统机器学习方法填补热带气旋最大风速半径（Rmax）缺失值的效果。加入34节风速半径（R34）作为输入显著提升了所有模型的性能。时序模型在样本量少约一个数量级的情况下，平均相关性仍高于非时序模型，说明其能更好保留Rmax的跨风暴变异性。迁移学习未带来性能提升，因为合成数据集（RAFT、STORM）的Rmax分布比IBTrACS观测数据更低且变异性更小。研究展示了时序深度学习在重建不完整热带气旋记录方面的潜力。
Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data
Probabilistic coastal hazard assessments require accurate characterization of tropical cyclone (TC) parameters, yet datasets often contain missing records for the radius of maximum winds (Rmax), a key variable in Joint Probability Method analyses. This study evaluates data-driven approaches for Rmax imputation, including one-dimensional Convolutional Neural Networks (1DCNNs), Long Short-Term Memory (LSTM) networks, and conventional machine learning models. We examine physics-informed input augmentation, temporal modeling, and transfer learning using synthetic RAFT and STORM datasets for pre-training and observational IBTrACS data for fine-tuning. Including the radius of 34-knot winds (R34) substantially improves performance across all model types. Temporal models achieve higher average correlations than non-temporal models despite using approximately an order of magnitude fewer samples, indicating better preservation of relative Rmax variability across storms. This advantage is more pronounced when R34 is unavailable, suggesting temporal information can partially compensate for missing storm-size predictors. Transfer learning does not improve performance, likely because synthetic datasets have lower and less variable Rmax distributions than IBTrACS. These findings demonstrate the potential of temporal deep learning for reconstructing incomplete TC records and highlight the importance of physics-informed inputs, observational data availability, and distributional consistency in coastal hazard assessment.