Asynchronous data assimilation with the EnKF

By Pavel Sakov, Geir Evensen, Laurent Bertino. 2010

161 citations recorded by the source corpus.

Abstract This study revisits the problem of assimilation of asynchronous observations, or four-dimensional data assimilation, with the ensemble Kalman filter (EnKF). We show that for a system with perfect model and linear dynamics the ensemble Kalman smoother (EnKS) provides a simple and efficient solution for the problem: one just needs to use the ensemble observations (that is, the forecast observations for each ensemble member) from the time of observation during the update, for each assimilated observation. This recipe can be used for assimilating both past and future data; in the context of assimilating generic asynchronous observations we refer to it as the asynchronous EnKF. The asynchronous EnKF is essentially equivalent to the four-dimensional variational data assimilation (4D-Var). It requires only one forward integration of the system to obtain and store the data necessary for the analysis, and therefore is feasible for large-scale applications. Unlike 4D-Var, the asynchronous EnKF requires no tangent linear or adjoint model.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/a0256cb880707392624971375e397ca66ae37542

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Citations in the corpus

Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, methodology): “The altimeter and the sea ice drift data are assimilated asynchronously (Sakov et al., 2010), accounting for time-delayed covariances.”
The Twentieth Century Reanalysis Project (2009, methodology): “Technically, this makes our implementation a light Ensemble Kalman Smoother, rather than an Ensemble Kalman Filter, but the equations are unaltered (Sakov et al., 2010).”
An Iterative EnKF for Strongly Nonlinear Systems (2012, background): “…EnKF can be easily adopted to assimilate asynchronous observations by simply using in the analysis the ensemble observations H(E) estimated at the time of each observation (Hunt et al. 2007), provided that propagation of the ensemble anomalies about the truth is mainly linear (Sakov et al. 2010).”
Ensemble Randomized Maximum Likelihood Method as an Iterative Ensemble Smoother (2011, methodology): “The batch update of the ES is similar to the asynchronous EnKF in Sakov et al. (2010) in which the EnKF is used to assimilate data that are not all collected at the same time.”