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• W2542676986 abstract "Data assimilation methods that work in high-dimensional systems are crucial to many areas of the geosciences: meteorology, oceanography, climate science and so on. The equivalent weights particle filter (EWPF) has been designed for, and recently shown to scale to, problems that are of use to these communities. This article performs a systematic comparison of the EWPF with the established and widely used local ensemble transform Kalman filter (LETKF). Both methods are applied to the barotropic vorticity equation for different networks of observations. In all cases, it was found that the LETKF produced lower root mean–squared errors than the EWPF. The performance of the EWPF is shown to depend strongly on the form of nudging used, and a nudging term based on the local ensemble transform Kalman smoother is shown to improve the performance of the filter. This indicates that the EWPF must be considered as a truly two-stage filter and not only by its final step which avoids weight collapse." @default.
• W2542676986 created "2016-11-04" @default.
• W2542676986 creator A5030856303 @default.
• W2542676986 date "2016-12-01" @default.
• W2542676986 modified "2023-09-25" @default.
• W2542676986 title "A comparison of the equivalent weights particle filter and the local ensemble transform Kalman filter in application to the barotropic vorticity equation" @default.
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• W2542676986 doi "https://doi.org/10.3402/tellusa.v68.30466" @default.
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