A comparative study of 4D-VAR and a 4D Ensemble Kalman Filter: perfect model simulations with Lorenz-96

By Elana J. Fertig, John Harlim, Brian R. Hunt. 2007

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ABSTRACT We formulate a four-dimensional Ensemble Kalman Filter (4D-LETKF) that minimizes a cost function similar to that in a 4D-VAR method. Using perfect model experiments with the Lorenz-96 model, we compare assimilation of simulated asynchronous observations with 4D-VAR and 4D-LETKF. We find that both schemes have comparable error when 4D-LETKF is performed sufficiently frequently and when 4D-VAR is performed over a sufficiently long analysis time window.We explore how the error depends on the time between analyses for 4D-LETKF and the analysis time window for 4D-VAR.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/1182a6d61a8a88b17e73ac998b64b79768027dc8

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Citations in the corpus

Adaptive covariance relaxation methods for ensemble data assimilation: experiments in the real atmosphere (2017, methodology): “The LETKF can consider temporally distributed observations over a 6 h time window centred at the analysis time (also known as 4D-LETKF: Fertig et al., 2007; Miyoshi et al., 2010).”
Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter (2005, methodology): “Indeed, in a perfect model scenario [11], we obtained similar results with both methods when we used a sufficiently long time window for 4D-Var and when we used enough ensemble members and performed the analysis sufficiently frequently in a 4D version (described in Section 4 of this article) of our ”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW) (2009, methodology): “Ensemble-based retrospective data assimilation algorithms (Zupanski 2005; Hunt et al. 2004; Fertig et al. 2007; Liu et al. 2008) are still subject to sampling errors and need to be tested further in real atmospheric data assimilation systems.”
Asynchronous data assimilation with the EnKF (2010, methodology): “D Lorentz model in Fertig et al. (2007), and with a global circulation model in Harlim and Hunt (2007).”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test (2008, methodology): “MLEF (Zupanski 2005) or 4DEnKF (Hunt et al. 2004; Fertig et al. 2007 ), our approach (En4DVAR) adopts the incremental and preconditional scheme in the variational algorithm so that it can be more easily incorporated into many of the operational centers that using variational assimilation as their o”
An Approach to Generating Mutually Independent Initial Perturbations for Ensemble Forecasts: Orthogonal Conditional Nonlinear Optimal Perturbations (2016, methodology): “For example, it has been applied in studies of data assimilation (Fertig et al. 2007; Whitaker and Hamill 2002; Hunt et al. 2004; Ott et al. 2004) and adaptive observation (Khare and Anderson 2006).”