Climate Variable Downscaling with Conditional Normalizing Flows
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910465392640000 |
|---|---|
| author | Winkler, Christina Harder, Paula Rolnick, David |
| author_facet | Winkler, Christina Harder, Paula Rolnick, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20719 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Climate Variable Downscaling with Conditional Normalizing Flows Winkler, Christina Harder, Paula Rolnick, David Artificial Intelligence Computer Vision and Pattern Recognition Atmospheric and Oceanic Physics 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. |
| title | Climate Variable Downscaling with Conditional Normalizing Flows |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2405.20719 |