用视频扩散模型提升台风预报精度和时长
Improving Tropical Cyclone Forecasting With Video Diffusion Models
- 引入时间层建模台风演变的长期依赖关系
- 预测误差降低19.3%，预报时效从36小时延长至50小时
- 适合气象预报与气候研究者参考
台风预报对防灾减灾至关重要。现有深度学习方法多将台风演化视为逐帧独立预测，难以捕捉长期动态。本文提出一种新型视频扩散模型应用，通过额外的时间层显式建模时序依赖，支持多帧同时生成，更准确捕捉台风演变模式。设计两阶段训练策略，显著提升单帧质量及低数据场景下的性能。实验表明，相比Nath等人方法，本方法在MAE上降低19.3%，PSNR提升16.2%，SSIM提高36.1%。最显著的是，可靠预报时长由36小时扩展至50小时。通过传统指标与弗雷歇视频距离（FVD）综合评估，证实所提方法生成结果更具时序一致性，同时保持优异的单帧质量。代码已开源：https://github.com/Ren-creater/forecast-video-diffmodels。
原文摘要 · Abstract (English)
Tropical cyclone (TC) forecasting is crucial for disaster preparedness and mitigation. While recent deep learning approaches have shown promise, existing methods often treat TC evolution as a series of independent frame-to-frame predictions, limiting their ability to capture long-term dynamics. We present a novel application of video diffusion models for TC forecasting that explicitly models temporal dependencies through additional temporal layers. Our approach enables the model to generate multiple frames simultaneously, better capturing cyclone evolution patterns. We introduce a two-stage training strategy that significantly improves individual-frame quality and performance in low-data regimes. Experimental results show our method outperforms the previous approach of Nath et al. by 19.3% in MAE, 16.2% in PSNR, and 36.1% in SSIM. Most notably, we extend the reliable forecasting horizon from 36 to 50 hours. Through comprehensive evaluation using both traditional metrics and Fréchet Video Distance (FVD), we demonstrate that our approach produces more temporally coherent forecasts while maintaining competitive single-frame quality. Code accessible at https://github.com/Ren-creater/forecast-video-diffmodels.
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