论文标题
使用物理统计生成的对抗学习降低降低的极限降雨
Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning
论文作者
论文摘要
在不断变化的气候下建模极端天气事件的风险对于制定有效的适应和缓解策略至关重要。尽管可用的低分辨率气候模型捕获了不同的方案，但是对缓解和适应的准确风险评估通常需要它们通常无法解决的细节。在这里，我们开发了一种动态数据驱动的降尺度（超分辨率）方法，该方法将物理和统计数据纳入生成框架中，以学习降雨的精细空间细节。我们的方法将粗分辨率（$ 0.25^{\ circ} \ times 0.25^{\ circ} $）的气候模型输出到高分辨率（$ 0.01^{\ circ} \ times 0.01^{\ circ} $），同时有效地量化了不确定的降雨场。结果表明，降低降雨场与观察到的空间场及其风险分布非常匹配。
Modeling the risk of extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Although the available low-resolution climate models capture different scenarios, accurate risk assessment for mitigation and adaption often demands detail that they typically cannot resolve. Here, we develop a dynamic data-driven downscaling (super-resolution) method that incorporates physics and statistics in a generative framework to learn the fine-scale spatial details of rainfall. Our method transforms coarse-resolution ($0.25^{\circ} \times 0.25^{\circ}$) climate model outputs into high-resolution ($0.01^{\circ} \times 0.01^{\circ}$) rainfall fields while efficaciously quantifying uncertainty. Results indicate that the downscaled rainfall fields closely match observed spatial fields and their risk distributions.
