Reconstructing Historical Climate Fields With Deep Learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866908471546347520 |
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| author | Bochow, Nils Poltronieri, Anna Rypdal, Martin Boers, Niklas |
| author_facet | Bochow, Nils Poltronieri, Anna Rypdal, Martin Boers, Niklas |
| contents | Historical records of climate fields are often sparse due to missing measurements, especially before the introduction of large-scale satellite missions. Several statistical and model-based methods have been introduced to fill gaps and reconstruct historical records. Here, we employ a recently introduced deep-learning approach based on Fourier convolutions, trained on numerical climate model output, to reconstruct historical climate fields. Using this approach we are able to realistically reconstruct large and irregular areas of missing data, as well as reconstruct known historical events such as strong El Niño and La Niña with very little given information. Our method outperforms the widely used statistical kriging method as well as other recent machine learning approaches. The model generalizes to higher resolutions than the ones it was trained on and can be used on a variety of climate fields. Moreover, it allows inpainting of masks never seen before during the model training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_18348 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Reconstructing Historical Climate Fields With Deep Learning Bochow, Nils Poltronieri, Anna Rypdal, Martin Boers, Niklas Geophysics Machine Learning Historical records of climate fields are often sparse due to missing measurements, especially before the introduction of large-scale satellite missions. Several statistical and model-based methods have been introduced to fill gaps and reconstruct historical records. Here, we employ a recently introduced deep-learning approach based on Fourier convolutions, trained on numerical climate model output, to reconstruct historical climate fields. Using this approach we are able to realistically reconstruct large and irregular areas of missing data, as well as reconstruct known historical events such as strong El Niño and La Niña with very little given information. Our method outperforms the widely used statistical kriging method as well as other recent machine learning approaches. The model generalizes to higher resolutions than the ones it was trained on and can be used on a variety of climate fields. Moreover, it allows inpainting of masks never seen before during the model training. |
| title | Reconstructing Historical Climate Fields With Deep Learning |
| topic | Geophysics Machine Learning |
| url | https://arxiv.org/abs/2311.18348 |