Climate Variable Downscaling with Conditional Normalizing Flows

• URL: http://arxiv.org/abs/2405.20719v1
• Date: Fri, 31 May 2024 09:20:33 GMT
• Title: Climate Variable Downscaling with Conditional Normalizing Flows
• Authors: Christina Winkler, Paula Harder, David Rolnick,
• Abstract summary: We apply conditional normalizing flows to the task of climate variable downscaling. We show that the method allows us to assess the predictive uncertainty in terms of standard deviation from the fitted conditional distribution mean.
• Score: 21.2670980628433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predictions of global climate models typically operate on coarse spatial scales due to the large computational costs of climate simulations. This has led to a considerable interest in methods for statistical downscaling, a similar process to super-resolution in the computer vision context, to provide more local and regional climate information. In this work, we apply conditional normalizing flows to the task of climate variable downscaling. We showcase its successful performance on an ERA5 water content dataset for different upsampling factors. Additionally, we show that the method allows us to assess the predictive uncertainty in terms of standard deviation from the fitted conditional distribution mean.

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