Which Is Better, an Ensemble of Positive–Negative Pairs or a Centered Spherical Simplex Ensemble?

By Xuguang Wang, C. Bishop, Simon Justin Julier. 2004

238 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/a315abe97a31faf3e8ab391e8312d7631dc81bb5

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Citations in the corpus

Ensemble Transform Kalman Filter-based ensemble perturbations in an operational global prediction system at NCEP (2006, methodology): “In another comparison experiment, we carried out in Wei and Toth (2003), it was clearly shown that the PECA (perturbation versus error correlation analysis) value increases with the number of ensemble members in both NCEP and ECMWF operational paired systems.”
A comparative study of 4D-VAR and a 4D Ensemble Kalman Filter: perfect model simulations with Lorenz-96 (2007, methodology): “This covariance matrix is similar to that used for the constant error covariance for OI by Wang et al. (2006). In this scenario, it provides similar analysis errors to using a covariance matrix generated with the NMC method (Parrish and Derber, 1992). For 4D-LETKF, we obtain the analysis ensemble at”
Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter (2005, unknown): “But to formulate the analysis more explicitly, we now linearize H about the background ensemble mean x̄b.”
The local ETKF and SKEB: Upgrades to the MOGREPS short‐range ensemble prediction system (2009, methodology): “The perturbations thus generated will be orthogonal to each other in the space of the observations (Wang et al., 2004), and have the appropriate variance, as dictated by the Kalman filter formulation.”
Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office (2013, methodology): “The global component of this system (MOGREPSG) is based on the ensemble transform Kalman filter (ETKF) of Wang et al. (2004), and for the experiments described in this paper included the horizontal localization scheme described in Bowler et al. (2009), the stochastic kinetic energy backscatter…”
Estimating surface CO 2 fluxes from space-borne CO 2 dry air mole fraction observations using an ensemble Kalman Filter (2008, methodology): “We use the revised, unbiased Ensemble Transform Kalman Filter (ETKF) algorithm (Wang et al., 2004; Livings et al., 2008) to determine the analysis ensemble ∆X and the a posteriori error covariance, P: ∆Xa = ∆Xf T, (8) and 15 Pa = ∆Xf T(∆Xf T)T .”
Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system (2008, methodology): “It is desirable that all initial perturbations are centred around the best possible analysis field in order to get better ensemble mean performance (Toth and Kalnay, 1997; Buizza et al., 2005; Wang et al., 2004; W06).”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, background): “It was shown (Wang et al., 2004; Sakov and Oke, 2008b; Livings et al., 2008) that the symmetric square root preserves the zero mean in the updated perturbations.”
Improvement of ensemble reliability with a new dressing kernel (2005, methodology): “For details on the construction of the spherical simplex ETKF ensemble, please refer to previous experiments in Wang and Bishop (2003) and Wang et al. (2004).”
A Hybrid ETKF-3DVAR Data Assimilation Scheme for the WRF Model. Part I: Observing System Simulation Experiment (2008, background): “——, ——, and S. J. Julier, 2004: Which is better, an ensemble of positive–negative pairs or a centered spherical simplex ensemble?”
A deterministic formulation of the ensemble Kalman filter: an alternative to ensemble square root filters (2008, methodology): “(Bishop et al., 2001; Wang et al., 2004), underestimates the analysed error covariance because of the alternating signs in the expansion and therefore is not as robust as the DEnKF in regard to filter convergence.”