Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning

• URL: http://arxiv.org/abs/2212.01446v1
• Date: Fri, 2 Dec 2022 21:04:32 GMT
• Title: Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning
• Authors: Anamitra Saha, Sai Ravela
• Abstract summary: We develop a 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 ($0.25circ times 0.25circ$) climate model outputs into high-resolution ($0.01circ times 0.01circ$) rainfall fields while efficaciously quantifying uncertainty.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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