A quantile-conserving ensemble filter based on kernel-density estimation

Ensemble Kalman filters are an efficient class of algorithms for large-scale ensemble data assimilation, but their performance is limited by their underlying Gaussian approximation. A two-step framework for ensemble data assimilation allows this approximation to be relaxed: The first step updates the ensemble in observation space, while the second step regresses the observation state update back to the state variables. This paper develops a new quantile-conserving ensemble filter based on kernel-density estimation and quadrature for the scalar first step of the two-step framework. It is shown to perform well in idealized non-Gaussian problems, as well as in an idealized model of assimilating observations of sea-ice concentration.

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Author Grooms, I.
Riedel, Christopher P.
Publisher UCAR/NCAR - Library
Publication Date 2024-06-28T00:00:00
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Metadata Date 2025-07-10T20:01:01.931679
Metadata Record Identifier edu.ucar.opensky::articles:27371
Metadata Language eng; USA
Suggested Citation Grooms, I., Riedel, Christopher P.. (2024). A quantile-conserving ensemble filter based on kernel-density estimation. UCAR/NCAR - Library. https://n2t.org/ark:/85065/d7571h7t. Accessed 04 August 2025.

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