Ensemble Transform Algorithms for Nonlinear Smoothing Problems

AbstractThis work investigates the characteristics of westward-propagating Rossby modes in idealized global general circulation models. Using a nonlinear smoothing algorithm to estimate the background spectrum and an objective method to extract the spectral peaks, the 4 leading meridional modes can be identified for each of the first 3 zonal wavenumbers, with frequencies close to the predictions from the Hough modes obtained by linearizing about a state of rest. Variations in peak amplitude for different modes, both within a simulation and across simulations, may be understood under the assumption that the forcing of the modes scales with the background spectrum. Surface friction affects the amplitude and width of the peaks but both remain finite as friction goes to zero, which implies that some other mechanism, arguably nonlinear, must also contribute to the damping of the modes. Although spectral peaks are also observed for the precipitation field with idealized moist physics, there is no evidence of mode enhancement by the convective heating. Subject to the same friction, the amplitude of the peaks are very similar in the dry and moist models when both are normalized by the background spectra.

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Gain Form of the Ensemble Transform Kalman Filter and Its Relevance to Satellite Data Assimilation with Model Space Ensemble Covariance Localization

Monthly Weather Review ◽

10.1175/mwr-d-17-0102.1 ◽

2017 ◽

Vol 145 (11) ◽

pp. 4575-4592 ◽

Cited By ~ 15

Author(s):

Craig H. Bishop ◽

Jeffrey S. Whitaker ◽

Lili Lei

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Satellite Data ◽

Localization Length ◽

Model Space ◽

Ensemble Size ◽

Length Scales ◽

Ensemble Transform Kalman Filter ◽

Ensemble Transform ◽

Expanded Ensemble

To ameliorate suboptimality in ensemble data assimilation, methods have been introduced that involve expanding the ensemble size. Such expansions can incorporate model space covariance localization and/or estimates of climatological or model error covariances. Model space covariance localization in the vertical overcomes problematic aspects of ensemble-based satellite data assimilation. In the case of the ensemble transform Kalman filter (ETKF), the expanded ensemble size associated with vertical covariance localization would also enable the simultaneous update of entire vertical columns of model variables from hyperspectral and multispectral satellite sounders. However, if the original formulation of the ETKF were applied to an expanded ensemble, it would produce an analysis ensemble that was the same size as the expanded forecast ensemble. This article describes a variation on the ETKF called the gain ETKF (GETKF) that takes advantage of covariances from the expanded ensemble, while producing an analysis ensemble that has the required size of the unexpanded forecast ensemble. The approach also yields an inflation factor that depends on the localization length scale that causes the GETKF to perform differently to an ensemble square root filter (EnSRF) using the same expanded ensemble. Experimentation described herein shows that the GETKF outperforms a range of alternative ETKF-based solutions to the aforementioned problems. In cycling data assimilation experiments with a newly developed storm-track version of the Lorenz-96 model, the GETKF analysis root-mean-square error (RMSE) matches the EnSRF RMSE at shorter than optimal localization length scales but is superior in that it yields smaller RMSEs for longer localization length scales.

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