As tropical cyclones become more intense due to climate change, the rise of Al-based modelling provides a more affordable and accessible approach compared to traditional methods based on mathematical models. This work leverages generative diffusion models to forecast cyclone trajectories and precipitation patterns by integrating satellite imaging, remote sensing, and atmospheric data. It employs a cascaded approach that incorporates three main tasks: forecasting, super-resolution, and precipitation modelling. The training dataset includes 51 cyclones from six major tropical cyclone basins from January 2019 - March 2023. Experiments demonstrate that the final forecasts from the cascaded models show accurate predictions up to a 36-hour rollout, with excellent Structural Similarity (SSIM) and Peak-To-Noise Ratio (PSNR) values exceeding 0.5 and 20 dB, respectively, for all three tasks. The 36-hour forecasts can be produced in as little as 30 mins on a single Nvidia A30/RTX 2080 Ti. This work also highlights the promising efficiency of Al methods such as diffusion models for high-performance needs in weather forecasting, such as tropical 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：随着气候变化导致热带气旋强度增强，基于人工智能的建模方法相比传统数学建模方式，提供了更经济实惠且易获取的解决方案。本研究利用生成式扩散模型，通过整合卫星影像、遥感数据与大气数据，对气旋轨迹与降水模式进行预测。研究采用级联方法整合三项核心任务：预测、超分辨率重构与降水建模。训练数据集包含2019年1月至2023年3月间来自六大主要热带气旋盆地的51个气旋样本。实验表明，级联模型生成的最终预测在36小时滚动预测窗口内保持高精度，三项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。在单张Nvidia A30/RTX 2080 Ti显卡上，36小时预测仅需不到30分钟即可完成。本研究同时揭示了扩散模型等人工智能方法在热带气旋等高精度气象预测需求中的高效潜力，在保持计算成本可控的前提下，为面临关键预测需求与财政限制的高脆弱性地区提供了理想解决方案。开源代码见 \url{https://github.com/nathzi1505/forecast-diffmodels}。