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-Singal-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 https://github.com/nathzi1505/forecast-diffmodels.

翻译：随着气候变化导致热带气旋日益增强，相较于基于数学模型的传统方法，人工智能建模的兴起提供了一种更经济、更易获取的途径。本研究利用生成式扩散模型，通过整合卫星成像、遥感及大气数据，对气旋轨迹与降水模式进行预报。该方法采用级联架构，包含三项主要任务：预报、超分辨率及降水建模。训练数据集包含2019年1月至2023年3月期间来自全球六大热带气旋盆地的51个气旋。实验表明，级联模型的最终预报结果在36小时预测时长内均表现出准确的预测性能，三项任务的结构相似性指数与峰值信噪比分别超过0.5与20 dB。在单张Nvidia A30/RTX 2080 Ti显卡上，36小时预报最短仅需30分钟即可生成。本研究同时凸显了扩散模型等人工智能方法在天气预报高性能需求（如热带气旋预报）中表现出的卓越效率，且保持较低计算成本，使其特别适用于预报需求迫切但资金有限的极端脆弱区域。代码可通过 https://github.com/nathzi1505/forecast-diffmodels 获取。