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 Fr\'echet 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.

翻译：热带气旋预报对防灾减灾至关重要。尽管近期的深度学习方法已展现出潜力，但现有方法通常将气旋演变视为一系列独立的帧间预测，限制了其捕捉长期动态的能力。本文提出一种用于热带气旋预报的视频扩散模型新应用，该模型通过附加的时间层显式建模时间依赖性。我们的方法使模型能够同时生成多帧图像，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及低数据条件下的性能。实验结果表明，我们的方法在平均绝对误差上优于Nath等人的先前方法19.3%，在峰值信噪比上提升16.2%，在结构相似性指标上提升36.1%。最显著的是，我们将可靠预报时长从36小时延长至50小时。通过综合使用传统指标和弗雷歇视频距离进行评估，我们证明该方法能生成时间一致性更强的预报结果，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。