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arXiv · 1908.10580

Analysis of a localised nonlinear Ensemble Kalman Bucy Filter with complete and accurate observations

Jana de Wiljes·Xin T. Tong
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Abstract

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 localization techniques is considered to be a state-of-the-art DA methodology. This article proposes and investigates a localized Ensemble Kalman Bucy Filter (l-EnKBF) 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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Jana de Wiljes, Xin T. Tong. 2019-08-28. Analysis of a localised nonlinear Ensemble Kalman Bucy Filter with complete and accurate observations. https://doi.org/10.1088/1361-6544/ab8d14

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