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.

本研究针对从数字台风卫星图像同时预测多个ERA5气象变量这一问题，提出了一种新的方法，即条件去噪扩散概率模型（CDDPM）。与卷积神经网络（CNN）和压缩-激励网络（SENet）相比，CDDPM在生成准确和真实的气象数据方面表现最佳，显著提高了数据的质量，有望改善对台风等极端天气事件的预测能力。

通过条件去噪扩散模型从数字台风卫星图像中估计大气变量