Contrastive Learning for Climate Model Bias Correction and
Super-Resolution
- URL: http://arxiv.org/abs/2211.07555v1
- Date: Thu, 10 Nov 2022 19:45:17 GMT
- Title: Contrastive Learning for Climate Model Bias Correction and
Super-Resolution
- Authors: Tristan Ballard, Gopal Erinjippurath
- Abstract summary: Post-processing is needed to make accurate estimates of local climate risk.
Here we propose an alternative approach to this challenge based on a combination of image super resolution (SR) and contrastive learning generative adversarial networks (GANs)
We find that our model successfully reaches a spatial resolution double that of NASA's product while also achieving comparable or improved levels of bias correction in both daily precipitation and temperature.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Climate models often require post-processing in order to make accurate
estimates of local climate risk. The most common post-processing applied is
bias-correction and spatial resolution enhancement. However, the statistical
methods typically used for this not only are incapable of capturing
multivariate spatial correlation information but are also reliant on rich
observational data often not available outside of developed countries, limiting
their potential. Here we propose an alternative approach to this challenge
based on a combination of image super resolution (SR) and contrastive learning
generative adversarial networks (GANs). We benchmark performance against NASA's
flagship post-processed CMIP6 climate model product, NEX-GDDP. We find that our
model successfully reaches a spatial resolution double that of NASA's product
while also achieving comparable or improved levels of bias correction in both
daily precipitation and temperature. The resulting higher fidelity simulations
of present and forward-looking climate can enable more local, accurate models
of hazards like flooding, drought, and heatwaves.
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