TY - JOUR
T1 - A non-Gaussian ensemble filter for assimilating infrequent noisy observations
AU - Harlim, John
AU - Hunt, Brian R.
PY - 2007/3
Y1 - 2007/3
N2 - 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.
AB - 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.
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U2 - 10.1111/j.1600-0870.2007.00225.x
DO - 10.1111/j.1600-0870.2007.00225.x
M3 - Article
AN - SCOPUS:34247587126
SN - 0280-6495
VL - 59
SP - 225
EP - 237
JO - Tellus, Series A: Dynamic Meteorology and Oceanography
JF - Tellus, Series A: Dynamic Meteorology and Oceanography
IS - 2
ER -