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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