用生成模型模拟英国风灾，提升气象风险评估能力
Using Generative Models to Produce Realistic Populations of UK Windstorms
- 用四种生成模型基于历史数据模拟英国风灾空间特征
- 扩散-生成对抗网络表现最优，但极端事件略高估
- 多模型融合可提升可靠性，适合气象与灾害研究
本研究评估了基于历史ERA5再分析数据训练的生成模型在模拟英国风灾方面的潜力。对比了四种模型：标准GAN、WGAN-GP、U-net扩散模型和扩散-GAN。标准GAN虽具较广变异性，但在主成分方向对齐不足；WGAN-GP表现均衡，但偶有极端事件误判；U-net扩散模型生成高质量空间模式，但持续低估强度；扩散-GAN总体表现更优，但对极端事件存在高估。结合各模型优势的集成方法或可提升整体可靠性。该研究为生成模型在气象学中的应用奠定基础，有望用于风灾分析与风险评估。
原文摘要 · Abstract (English)
This study evaluates the potential of generative models, trained on historical ERA5 reanalysis data, for simulating windstorms over the UK. Four generative models, including a standard GAN, a WGAN-GP, a U-net diffusion model, and a diffusion-GAN were assessed based on their ability to replicate spatial and statistical characteristics of windstorms. Different models have distinct strengths and limitations. The standard GAN displayed broader variability and limited alignment on the PCA dimensions. The WGAN-GP had a more balanced performance but occasionally misrepresented extreme events. The U-net diffusion model produced high-quality spatial patterns but consistently underestimated windstorm intensities. The diffusion-GAN performed better than the other models in general but overestimated extremes. An ensemble approach combining the strengths of these models could potentially improve their overall reliability. This study provides a foundation for such generative models in meteorological research and could potentially be applied in windstorm analysis and risk assessment.
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