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- W2292650202 abstract "Filter degeneracy is the main obstacle for the implementation of particle filters in nonlinear high‐dimensional models. A new scheme, the implicit equal‐weights particle filter (IEWPF), is introduced, in which samples are drawn implicitly from proposal densities with a different covariance for each particle, such that all particle weights are equal by construction. We test and explore the properties of the new scheme using a 1000 dimensional simple linear model and the 1000 dimensional nonlinear Lorenz96 model and compare the performance of the scheme with that of a local ensemble transformed Kalman filter (LETKF). The new scheme is never degenerate and shows good and consistent performance in all experiments. The LETKF has lower root‐mean‐square errors at observed grid points, but its ensemble spread is too low at unobserved grid points, where the IEWPF performs better. Furthermore, the IEWPF has a consistent spread in all experiments. This new filter opens up a new class of particle filters that, by construction, do not suffer from the curse of dimensionality." @default.
- W2292650202 created "2016-06-24" @default.
- W2292650202 creator A5027268630 @default.
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- W2292650202 date "2016-05-03" @default.
- W2292650202 modified "2023-10-10" @default.
- W2292650202 title "Implicit equal‐weights particle filter" @default.
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- W2292650202 doi "https://doi.org/10.1002/qj.2784" @default.
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