利用视频扩散模型改进热带气旋预报
Improving Tropical Cyclone Forecasting With Video Diffusion Models
AI总结：
本文提出用于热带气旋预报的视频扩散模型，通过时间层和两阶段训练同时生成多帧，显著提升预测精度并将可靠预报时效延长至50小时。
摘要：
热带气旋（TC）预报对于防灾备灾和减灾至关重要。尽管近期的深度学习方法已展现出良好前景，但现有方法通常将热带气旋演变视为一系列独立的逐帧预测，限制了其捕捉长期动力学的能力。我们提出了一种视频扩散模型在热带气旋预报中的新颖应用，该模型通过额外的时间层显式建模时间依赖关系。我们的方法使模型能够同时生成多个帧，从而更好地捕捉气旋演变模式。我们引入了一种两阶段训练策略，显著提升了单帧质量以及在低数据场景下的性能。实验结果表明，我们的方法在MAE、PSNR和SSIM上分别比Nath等人此前的方法提升19.3%、16.2%和36.1%。最值得注意的是，我们将可靠预报时效从36小时延长至50小时。通过结合传统指标和Fréchet Video Distance（FVD）进行综合评估，我们证明本方法能够生成时间一致性更强的预报，同时保持具有竞争力的单帧质量。代码可在https://github.com/Ren-creater/forecast-video-diffmodels获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
