This study explores the application of diffusion models in the field of typhoons, predicting multiple ERA5 meteorological variables simultaneously from Digital Typhoon satellite images. The focus of this study is taken to be Taiwan, an area very vulnerable to typhoons. By comparing the performance of Conditional Denoising Diffusion Probability Model (CDDPM) with Convolutional Neural Networks (CNN) and Squeeze-and-Excitation Networks (SENet), results suggest that the CDDPM performs best in generating accurate and realistic meteorological data. Specifically, CDDPM achieved a PSNR of 32.807, which is approximately 7.9% higher than CNN and 5.5% higher than SENet. Furthermore, CDDPM recorded an RMSE of 0.032, showing a 11.1% improvement over CNN and 8.6% improvement over SENet. A key application of this research can be for imputation purposes in missing meteorological datasets and generate additional high-quality meteorological data using satellite images. It is hoped that the results of this analysis will enable more robust and detailed forecasting, reducing the impact of severe weather events on vulnerable regions. Code accessible at https://github.com/TammyLing/Typhoon-forecasting.

翻译：本研究探索了扩散模型在台风领域的应用，旨在从数字台风卫星卫星图像中同时预测多个ERA5气象变量。研究重点关注台风易发区域台湾。通过比较条件去噪扩散概率模型（CDDPM）与卷积神经网络（CNN）及压缩激励网络（SENet）的性能，结果表明CDDPM在生成准确且真实的气象数据方面表现最佳。具体而言，CDDPM取得了32.807的峰值信噪比（PSNR），较CNN提升约7.9%，较SENet提升5.5%。此外，CDDPM的均方根误差（RMSE）为0.032，较CNN改善11.1%，较SENet改善8.6%。本研究的一个关键应用在于填补缺失气象数据集，并利用卫星图像生成额外的高质量气象数据。期望该分析结果能够实现更稳健、更精细的天气预报，从而降低极端天气事件对脆弱地区的影响。代码可通过https://github.com/TammyLing/Typhoon-forecasting获取。