As cyclones become more intense due to climate change, the rise of AI-based modelling provides a more affordable and accessible approach compared to traditional methods based on mathematical models. This work leverages diffusion models to forecast cyclone trajectories and precipitation patterns by integrating satellite imaging, remote sensing, and atmospheric data, employing a cascaded approach that incorporates forecasting, super-resolution, and precipitation modelling, with training on a dataset of 51 cyclones from six major basins. Experiments demonstrate that the final forecasts from the cascaded models show accurate predictions up to a 36-hour rollout, with SSIM and PSNR values exceeding 0.5 and 20 dB, respectively, for all three tasks. This work also highlights the promising efficiency of AI methods such as diffusion models for high-performance needs, such as cyclone forecasting, while remaining computationally affordable, making them ideal for highly vulnerable regions with critical forecasting needs and financial limitations. Code accessible at \url{https://github.com/nathzi1505/forecast-diffmodels}.

翻译：随着气候变化导致气旋强度增加，基于人工智能的建模方法相较于传统数学模型的预测手段，提供了更经济且易获取的解决方案。本研究利用扩散模型，通过整合卫星成像、遥感与大气数据，采用包含预报、超分辨率及降水建模的级联框架，基于六大主要洋盆51个气旋数据集进行训练，实现气旋路径与降水模式的预测。实验表明，级联模型最终预报在长达36小时的滚动预测中保持精准，三个任务的结构相似性指数(SSIM)均超过0.5，峰值信噪比(PSNR)均超过20分贝。本研究同时揭示了扩散模型等人工智能方法在满足高性能需求（如气旋预报）时展现的显著效率优势，其计算成本可控的特性使其特别适用于预报需求迫切但资金有限的脆弱地区。代码开源地址：\url{https://github.com/nathzi1505/forecast-diffmodels}