Analysis of a localised nonlinear ensemble Kalman Bucy filter with complete and accurate observations
- Concurrent observation technologies have made high-precision real-time data available in large quantities. Data assimilation (DA) is concerned with how to combine this data with physical models to produce accurate predictions. For spatial-temporal models, the ensemble Kalman filter with proper localisation techniques is considered to be a state-of-the-art DA methodology. This article proposes and investigates a localised ensemble Kalman Bucy filter for nonlinear models with short-range interactions. We derive dimension-independent and component-wise error bounds and show the long time path-wise error only has logarithmic dependence on the time range. The theoretical results are verified through some simple numerical tests.
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| Author details: | Jana de WiljesORCiDGND, Xin T. TongORCiD |
|---|---|
| URN: | urn:nbn:de:kobv:517-opus4-540417 |
| DOI: | https://doi.org/10.25932/publishup-54041 |
| ISSN: | 1866-8372 |
| Title of parent work (German): | Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe |
| Publication series (Volume number): | Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (1221) |
| Publisher: | IOP Publ. |
| Place of publishing: | Bristol |
| Publication type: | Postprint |
| Language: | English |
| Date of first publication: | 2022/03/01 |
| Publication year: | 2020 |
| Publishing institution: | Universität Potsdam |
| Release date: | 2022/03/01 |
| Tag: | data assimilation; dimension independent bound; filter; high dimensional; localisation; nonlinear; stability and accuracy |
| Volume: | 33 |
| Issue: | 9 |
| Number of pages: | 32 |
| First page: | 4752 |
| Last Page: | 4782 |
| Source: | Nonlinearity 33 (2020) 9, 4752–4782, DOI: 10.1088/1361-6544/ab8d14 |
| Funding institution: | Deutsche Forschungsgemeinschaft (DFG)German Research Foundation (DFG); [SFB1294/1 - 318763901]; ERC Advanced Grant ACRCC [339390]; Simons CRM; Scholar-in-Residence Program; Singapore MOE AcRF Tier 1Ministry of; Education, Singapore [R-146-000-292-114] |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik |
| DDC classification: | 5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik |
| 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik | |
| Peer review: | Referiert |
| Publishing method: | Open Access / Green Open-Access |
| License (German): |  Creative Commons - Namensnennung, 3.0 Deutschland |
| External remark: | Bibliographieeintrag der Originalveröffentlichung/Quelle |
