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A comparison of the equivalent weights particle filter and the local ensemble transform Kalman filter in application to the barotropic vorticity equation Cover

A comparison of the equivalent weights particle filter and the local ensemble transform Kalman filter in application to the barotropic vorticity equation

Open Access
|Dec 2016

Figures & Tables

Fig. 1

Observing network diagrams.

Fig. 2

Plots of vorticity for the true state and the resulting observations using the different networks at the 6th analysis time, for a particular random seed.

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Table 1. Parameter values used in the LETKF

Observation network123
Localisation length scale, l0.0050.020.007Inflation factor, ρ0.010.010.01
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Table 2. Parameter values used in the EWPF

Observation network123
Keep proportion, κ1.01.01.0Nudging factor, σ0.70.50.7
Fig. 3

Observing network 1, every other gridpoint. The total and unobserved RMSEs are almost exactly underneath the observed RMSE plots. This is due to the widespread information from the observations effectively constraining the whole system.

Fig. 4

Observing network 2, block of dense observations.

Fig. 5

Observing network 3, tracks of observations.

Fig. 6

Trajectories of two different points in the domain when using the different assimilation methods with observing network 3 for a single experiment.

Fig. A1

Performance of the EWPF under different parameters.

Fig. B1

Performance of the EWPF with the LETKS relaxation when κ=1.0.

Fig. B2

Performance of the EWPF with the LETKS relaxation when κ=0.75.

Fig. B3

Performance of the EWPF with the LETKS relaxation when κ=0.50.

Fig. B4

Performance of the EWPF with the LETKS relaxation when κ=0.25.

Language: English
Page range: 30466 - 30466
Submitted on: Nov 20, 2015
Accepted on: Oct 2, 2016
Published on: Dec 1, 2016
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2016 Philip A. Browne, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.