Estimating Atmospheric Variables from Digital Typhoon Satellite Images via Conditional Denoising Diffusion Models
This work addresses typhoon forecasting for vulnerable regions like Taiwan, offering incremental improvements in accuracy for meteorological data generation and imputation.
This study tackled the problem of predicting multiple ERA5 meteorological variables from Digital Typhoon satellite images for Taiwan, using a Conditional Denoising Diffusion Probability Model (CDDPM) which achieved a PSNR of 32.807 (7.9% higher than CNN) and an RMSE of 0.032 (11.1% improvement over CNN).
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.