Data assimilation with model errors
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913804436111360 |
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| author | Çibik, Aytekin Fang, Rui Layton, William Siddiqua, Farjana |
| author_facet | Çibik, Aytekin Fang, Rui Layton, William Siddiqua, Farjana |
| contents | Nudging is a data assimilation method amenable to both analysis and implementation. It also has the (reported) advantage of being insensitive to model errors compared to other assimilation methods. However, nudging behavior in the presence of model errors is little analyzed. This report gives an analysis of nudging to correct model errors. The analysis indicates that the error contribution due to the model error decays as the nudging parameter $χ\to \infty$ like $\mathcal{O}(χ^{-\frac{1}{2}})$, Theorem 3.2. Numerical tests verify the predicted convergence rates and validate the nudging correction to model errors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16291 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data assimilation with model errors Çibik, Aytekin Fang, Rui Layton, William Siddiqua, Farjana Numerical Analysis Nudging is a data assimilation method amenable to both analysis and implementation. It also has the (reported) advantage of being insensitive to model errors compared to other assimilation methods. However, nudging behavior in the presence of model errors is little analyzed. This report gives an analysis of nudging to correct model errors. The analysis indicates that the error contribution due to the model error decays as the nudging parameter $χ\to \infty$ like $\mathcal{O}(χ^{-\frac{1}{2}})$, Theorem 3.2. Numerical tests verify the predicted convergence rates and validate the nudging correction to model errors. |
| title | Data assimilation with model errors |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2504.16291 |