DeepClimGAN: A High-Resolution Climate Data Generator

• URL: http://arxiv.org/abs/2011.11705v1
• Date: Mon, 23 Nov 2020 20:13:37 GMT
• Title: DeepClimGAN: A High-Resolution Climate Data Generator
• Authors: Alexandra Puchko, Robert Link, Brian Hutchinson, Ben Kravitz, Abigail Snyder
• Abstract summary: Earth system models (ESMs) are often used to generate future projections of climate change scenarios. As a compromise, emulators are substantially less expensive but may not have all of the complexity of an ESM. Here we demonstrate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator.
• Score: 60.59639064716545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Earth system models (ESMs), which simulate the physics and chemistry of the global atmosphere, land, and ocean, are often used to generate future projections of climate change scenarios. These models are far too computationally intensive to run repeatedly, but limited sets of runs are insufficient for some important applications, like adequately sampling distribution tails to characterize extreme events. As a compromise, emulators are substantially less expensive but may not have all of the complexity of an ESM. Here we demonstrate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator. In doing so, we gain the ability to produce daily weather data that is consistent with what ESM might output over any chosen scenario. In particular, the GAN is aimed at representing a joint probability distribution over space, time, and climate variables, enabling the study of correlated extreme events, such as floods, droughts, or heatwaves.

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