A Generative Machine Learning Approach for Improving Precipitation from Earth System Models
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911928698273792 |
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| author | Hess, Philipp Boers, Niklas |
| author_facet | Hess, Philipp Boers, Niklas |
| contents | Quantifying the impacts of anthropogenic global warming requires accurate Earth system model (ESM) simulations. Statistical bias correction and downscaling can be applied to reduce errors and increase the resolution of ESMs. However, existing methods, such as quantile mapping, cannot effectively improve spatial patterns or temporal dynamics. We address this problem with a purely generative machine learning approach, combining unpaired domain translation with a super-resolution foundation model. Our results show realistic spatial patterns and temporal dynamics as well as reduced distributional biases in the processed ESM simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15026 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Generative Machine Learning Approach for Improving Precipitation from Earth System Models Hess, Philipp Boers, Niklas Geophysics Quantifying the impacts of anthropogenic global warming requires accurate Earth system model (ESM) simulations. Statistical bias correction and downscaling can be applied to reduce errors and increase the resolution of ESMs. However, existing methods, such as quantile mapping, cannot effectively improve spatial patterns or temporal dynamics. We address this problem with a purely generative machine learning approach, combining unpaired domain translation with a super-resolution foundation model. Our results show realistic spatial patterns and temporal dynamics as well as reduced distributional biases in the processed ESM simulation. |
| title | A Generative Machine Learning Approach for Improving Precipitation from Earth System Models |
| topic | Geophysics |
| url | https://arxiv.org/abs/2406.15026 |