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Original Research Papers

A non-Gaussian Ensemble Filter for Assimilating Infrequent Noisy Observations

Authors:

John Harlim ,

Department of Mathematics and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742; Courant Institute of Mathematical Sciences, New York University, 251 Mercer st., New York, NY 10012, US
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Brian R. Hunt

Department of Mathematics and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, US
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Abstract

We present a modified ensemble Kalman filter that allows a non-Gaussian background error distribution. Using a distribution that decays more slowly than a Gaussian allows the filter to make a larger correction to the background state in cases where it deviates significantly from the truth. For high-dimensional systems, this approach can be used locally. We compare this non-Gaussian filter to its Gaussian counterpart (with multiplicative variance inflation) with the three-dimensional Lorenz-63 model, the 40-dimensional Lorenz-96 model, and Molteni’s SPEEDY model, a global model with ∼105 state variables. When observations are sufficiently infrequent and noisy, the non-Gaussian filter yields a significant improvement in analysis and forecast errors.

How to Cite: Harlim, J. and Hunt, B.R., 2007. A non-Gaussian Ensemble Filter for Assimilating Infrequent Noisy Observations. Tellus A: Dynamic Meteorology and Oceanography, 59(2), pp.225–237. DOI: http://doi.org/10.1111/j.1600-0870.2007.00225.x
  Published on 01 Jan 2007
 Accepted on 15 Dec 2006            Submitted on 3 May 2006

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