Code.zip from Using spatial extreme-value theory with machine learning to model and understand spatially compounding weather extremes

When extreme weather events affect large areas, their regional to sub-continental spatial scale is important for their impacts. We propose a novel machine learning (ML) framework that integrates spatial extreme-value theory to model weather extremes and to quantify probabilities associated with the occurrence, intensity and spatial extent of these events. Our approach employs new loss functions adapted to extreme values, enabling our model to prioritize the tail rather than the bulk of the data distribution. Applied to a case study of Western European summertime heat extremes, we use daily 500-hPa geopotential height fields and local soil moisture as predictors to capture the complex interplay between local and remote physical processes. Our generative model reveals that different facets of heat extremes are influenced by individual circulation features, such as the relative position of upper-level ridges and troughs that are part of a large-scale wave pattern. This enriches our process understanding from a data-driven perspective. Our approach can extrapolate beyond the range of the data to make risk-related probabilistic statements. It applies more generally to other weather extremes and offers an alternative to traditional physical and ML-based techniques that focus less on the extremal aspects of weather data.

当极端天气事件波及大范围区域时，其从区域到次大陆级别的空间尺度对灾害影响至关重要。
本研究提出了一种融合空间极端值理论的新型机器学习（Machine Learning，ML）框架，用于对极端天气进行建模，并量化与这类事件的发生、强度及空间范围相关的概率。
该方法采用了适配极端值的新型损失函数，使得模型能够优先关注数据分布的尾部特征，而非主体部分。
本研究将该框架应用于西欧夏季极端高温的案例研究，以每日500百帕位势高度场与局地土壤湿度作为预报因子，以此捕捉局地与远程物理过程间的复杂相互作用。
我们的生成模型（Generative Model）揭示，极端高温的不同维度受到不同环流特征的影响，例如作为大尺度波列组成部分的高空脊与槽的相对位置。
这从数据驱动的视角丰富了我们对相关物理过程的认知。
本方法能够在数据分布范围之外进行外推，从而生成与风险相关的概率性结论。
该框架可更广泛地应用于其他极端天气事件，为传统物理方法以及以往较少关注天气数据极端特征的基于机器学习的技术提供了替代方案。