DMCA
A Hybrid Ensemble Kalman Filter / 3D-Variational Analysis Scheme
| Citations: | 123 - 18 self |
BibTeX
@MISC{Hamill_ahybrid,
author = {Thomas M. Hamill and Chris Snyder},
title = {A Hybrid Ensemble Kalman Filter / 3D-Variational Analysis Scheme},
year = {}
}
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Abstract
A hybrid 3-dimensional variational (3D-Var) / ensemble Kalman filter analysis scheme is demonstrated using a quasigeostrophic model under perfect-model assumptions. Four networks with differing observational densities are tested, including one network with a data void. The hybrid scheme operates by computing a set of parallel data assimilation cycles, with each member of the set receiving unique perturbed observations. The perturbed observations are generated by adding random noise consistent with observation error statistics to the control set of observations. Background error statistics for the data assimilation are estimated from a linear combination of time-invariant 3D-Var covariances and flow-dependent covariances developed from the ensemble of short-range forecasts. The hybrid scheme allows the user to weight the relative contributions of the 3D-Var and ensemble-based background covariances. The analysis scheme was cycled for 90 days, with new observations assimilated every 12 h...
Keyphrases
perfect-model assumption hybrid scheme time-invariant 3d-var covariance random noise consistent observation error statistic parallel data assimilation cycle quasigeostrophic model perturbed observation hybrid 3-dimensional variational observational density background error statistic relative contribution ensemble kalman filter analysis scheme analysis scheme ensemble-based background covariance new observation linear combination set receiving unique perturbed observation control set data void data assimilation hybrid scheme operates short-range forecast flow-dependent covariance
