Learning Dependence Structures in High-Dimensional Climate Data for Extreme Event Simulation

In this article, we explore the use of generative adversarial networks (GANs) to model and simulate spatial extremes in climate data. We combine GANs with extreme value theory to create a more accurate and efficient method for analyzing high-dimensional climate data. This approach allows us to generate a large number of synthetic compound events, which are combinations of multiple climate variables that can lead to extreme impacts.
To understand how GANs work, imagine a game between two AI systems: the generator and the discriminator. The generator creates new data samples, while the discriminator tries to distinguish between real and fake data. Through this competition, the generator improves over time, generating more realistic data that can be used for climate modeling.
The article applies this methodology to a dataset of daily maximum wind speed, significant wave height, and precipitation from 2013 to 2022 over the Bay of Bengal. The resulting model efficiently generates synthetic compound events, which can help scientists better understand and prepare for extreme climate events.
Univariate risk assessments, which consider only one variable at a time, have been shown to significantly underestimate true hazard levels. By combining GANs with extreme value theory, we can create more accurate and efficient methods for modeling spatial dependence structures in high-dimensional climate data. This is particularly important for successful climate adaptation policies, as it allows scientists to better understand and prepare for extreme climate events.
In summary, this article demonstrates the power of GANs in combination with extreme value theory for modeling and simulating spatial extremes in climate data. The resulting models can help scientists better understand and prepare for extreme climate events, leading to more accurate and efficient climate adaptation policies.