用卫星图预测台风气象数据，效果比传统方法更好。
Estimating Atmospheric Variables from Digital Typhoon Satellite Images via Conditional Denoising Diffusion Models
- 用条件去噪扩散模型从卫星图生成气象变量。
- PSNR达32.807，比CNN高7.9%，比SENet高5.5%。
- 适合气象数据缺失时补全或生成高质量数据。
本研究探索了扩散模型在台风领域的应用，利用数字台风卫星图像同时预测多个ERA5气象变量。研究聚焦于台风频发的台湾地区。通过对比条件去噪扩散概率模型（CDDPM）与卷积神经网络（CNN）和挤压-激励网络（SENet）的性能，结果表明CDDPM在生成准确且逼真的气象数据方面表现最佳。具体而言，CDDPM的峰值信噪比（PSNR）达到32.807，比CNN高出约7.9%，比SENet高出5.5%；均方根误差（RMSE）为0.032，较CNN改善11.1%，较SENet改善8.6%。该研究的一个关键应用是填补缺失的气象数据集，并利用卫星图像生成高质量的额外气象数据。期望研究成果能提升预报精度，减轻极端天气对脆弱地区的影响。代码已公开于https://github.com/TammyLing/Typhoon-forecasting。
原文摘要 · Abstract (English)
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.
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