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.

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twostage filter equivalent weights particle filter ewpf weight collapse highdimensional systems assimilation methods local ensemble transform kalman smoother barotropic vorticity equation root meansquared errors letkf nudging term geosciences meteorology oceanography climate science

Philip A. Browne,.A comparison of the equivalent weights particle filter and the local ensemble transform Kalman filter in application to the barotropic vorticity equation. 68 (0),.

Philip A. Browne,"A comparison of the equivalent weights particle filter and the local ensemble transform Kalman filter in application to the barotropic vorticity equation" 68

Philip A. Browne,"A comparison of the equivalent weights particle filter and the local ensemble transform Kalman filter in application to the barotropic vorticity equation"