Four-dimensional ensemble variational data assimilation and the unstable subspace
Résumé
The performance of (ensemble) Kalman filters used for data assimilation in the geosciences critically depends on the
dynamical properties of the evolution model.Akey aspect is that the error covariance matrix is asymptotically supported
by the unstable–neutral subspace only, i.e. it is spanned by the backward Lyapunov vectors with non-negative exponents.
The analytic proof of such a property for the Kalman filter error covariance has been recently given, and in particular
that of its confinement to the unstable–neutral subspace. In this paper, we first generalize those results to the case of
the Kalman smoother in a linear, Gaussian and perfect model scenario. We also provide square-root formulae for the
filter and smoother that make the connection with ensemble formulations of the Kalman filter and smoother, where the
span of the error covariance is described in terms of the ensemble deviations from the mean. We then discuss how this
neat picture is modified when the dynamics are nonlinear and chaotic, and for which analytic results are precluded or
difficult to obtain. A numerical investigation is carried out to study the approximate confinement of the anomalies for
both a deterministic ensemble Kalman filter (EnKF) and a four-dimensional ensemble variational method, the iterative
ensemble Kalman smoother (IEnKS), in a perfect model scenario. The confinement is characterized using geometrical
angles that determine the relative position of the anomalies with respect to the unstable–neutral subspace. The alignment
of the anomalies and of the unstable–neutral subspace is more pronounced when observation precision or frequency,
as well as the data assimilation window length for the IEnKS, are increased. These results also suggest that the IEnKS
and the deterministic EnKF realize in practice (albeit implicitly) the paradigm behind the approach of Anna Trevisan
and co-authors known as the assimilation in the unstable subspace.