Ensemble Assimilation
Welcome to the SAMA's Ensemble Assimilation group page.
This theme is coordinated by
Frédéric Chevallier
(
)
The data processed with statistical assimilation methods are random variables, represented by
probability distributions. In some cases, the distributions can be described with analytical
functions, like normal or Poisson laws. For ensemble assimilations methods, these distributions are
represented with a discrete population. The number of members varies, depending on the application,
from about ten to some hundreds. Such an approach is motivated by empirical (when the analytical
law is not known) or practical reasons (when computation is simpler on a discrete population as on
an analytical law).
Ensemble Kalman Filter (EnsKF), first described by Geir Evensen in 1992 (see.
http://www.nersc.no/~geir/) is an archetypic example of
these methods. Compared to a classical Kalman Filter, the EnsKF replaces the analytical computation
of the probability distribution with a rebuilding using the finite population. The filter itself
remains analytical. This approach allows to take into account a possible nonlinearity of the
governing equations of the system, even if the optimality of the EnsKF is only demonstrated for
linear equations.
Particle Filter is another strong example of these methods. Here, the goal is to apply the Bayes
theorem to each of the ensemble members, for each successive collection of observations.
Ensemble methods are developing in an always more parallel than vectorial computing strategy.
Unlike variational methods, they do not require heavy developpments of tangent linear and adjoint
codes. On the other hand, the sampling results in a loss of precision, especially for ensembles
with few members.
Some research domains connected to ensemble assimilation: members choice, link with
predictability and robustness of members ensembles (degeneracy of the cycled ensembles is
observed).
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