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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.20228 |
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| _version_ | 1866911228084879360 |
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| author | Redondo, Yago del Valle Inclan Arriaga-Varela, Enrique Lyamzin, Dmitry Cervantes, Pablo Ramalho, Tiago |
| author_facet | Redondo, Yago del Valle Inclan Arriaga-Varela, Enrique Lyamzin, Dmitry Cervantes, Pablo Ramalho, Tiago |
| contents | We introduce SpLIIF to generate implicit neural representations and enable arbitrary downscaling of weather variables. We train a model from sparse weather stations and topography over Japan and evaluate in- and out-of-distribution accuracy predicting temperature and wind, comparing it to both an interpolation baseline and CorrDiff. We find the model to be up to 50% better than both CorrDiff and the baseline at downscaling temperature, and around 10-20% better for wind. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20228 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sparse Local Implicit Image Function for sub-km Weather Downscaling Redondo, Yago del Valle Inclan Arriaga-Varela, Enrique Lyamzin, Dmitry Cervantes, Pablo Ramalho, Tiago Machine Learning We introduce SpLIIF to generate implicit neural representations and enable arbitrary downscaling of weather variables. We train a model from sparse weather stations and topography over Japan and evaluate in- and out-of-distribution accuracy predicting temperature and wind, comparing it to both an interpolation baseline and CorrDiff. We find the model to be up to 50% better than both CorrDiff and the baseline at downscaling temperature, and around 10-20% better for wind. |
| title | Sparse Local Implicit Image Function for sub-km Weather Downscaling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.20228 |