A Likelihood-Based Generative Approach for Spatially Consistent Precipitation Downscaling
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911974379487232 |
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| author | González-Abad, Jose |
| author_facet | González-Abad, Jose |
| contents | Deep learning has emerged as a promising tool for precipitation downscaling. However, current models rely on likelihood-based loss functions to properly model the precipitation distribution, leading to spatially inconsistent projections when sampling. This work explores a novel approach by fusing the strengths of likelihood-based and adversarial losses used in generative models. As a result, we propose a likelihood-based generative approach for precipitation downscaling, leveraging the benefits of both methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04724 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Likelihood-Based Generative Approach for Spatially Consistent Precipitation Downscaling González-Abad, Jose Atmospheric and Oceanic Physics Machine Learning Deep learning has emerged as a promising tool for precipitation downscaling. However, current models rely on likelihood-based loss functions to properly model the precipitation distribution, leading to spatially inconsistent projections when sampling. This work explores a novel approach by fusing the strengths of likelihood-based and adversarial losses used in generative models. As a result, we propose a likelihood-based generative approach for precipitation downscaling, leveraging the benefits of both methods. |
| title | A Likelihood-Based Generative Approach for Spatially Consistent Precipitation Downscaling |
| topic | Atmospheric and Oceanic Physics Machine Learning |
| url | https://arxiv.org/abs/2407.04724 |