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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
DOI:https://doi.org/10.1088/1361-6544/ab8d14
ISSN:0951-7715
ISSN:1361-6544
Title of parent work (English):Nonlinearity
Publisher:IOP Publ.
Place of publishing:Bristol
Publication type:Article
Language:English
Date of first publication:2020/07/28
Publication year:2020
Release date:2023/12/14
Tag:data assimilation; dimension independent bound; filter; high dimensional; localisation; nonlinear; stability and accuracy
Volume:33
Issue:9
Number of pages:31
First page:4752
Last Page:4782
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 / Hybrid Open-Access
License (German):License LogoCreative Commons - Namensnennung, 3.0 Deutschland
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