824 research outputs found
Toward reliable ensemble Kalman filter estimates of CO 2 fluxes
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95036/1/jgrd18220.pd
Local ensemble transform Kalman filter, a fast non-stationary control law for adaptive optics on ELTs: theoretical aspects and first simulation results
We propose a new algorithm for an adaptive optics system control law, based
on the Linear Quadratic Gaussian approach and a Kalman Filter adaptation with
localizations. It allows to handle non-stationary behaviors, to obtain
performance close to the optimality defined with the residual phase variance
minimization criterion, and to reduce the computational burden with an
intrinsically parallel implementation on the Extremely Large Telescopes (ELTs).Comment: This paper was published in Optics Express and is made available as
an electronic reprint with the permission of OSA. The paper can be found at
the following URL on the OSA website: http://www.opticsinfobase.org/oe/ .
Systematic or multiple reproduction or distribution to multiple locations via
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Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error
The ensemble Kalman filter and its variants have shown to be robust for data assimilation in high dimensional geophysical models, with localization, using ensembles of extremely small size relative to the model dimension. However, a reduced rank representation of the estimated covariance leaves a large dimensional complementary subspace unfiltered. Utilizing the dynamical properties of the filtration for the backward Lyapunov vectors, this paper explores a previously unexplained mechanism, providing a novel theoretical interpretation for the role of covariance inflation in ensemble-based Kalman filters. Our derivation of the forecast error evolution describes the dynamic upwelling of the unfiltered error from outside of the span of the anomalies into the filtered subspace. Analytical results for linear systems explicitly describe the mechanism for the upwelling, and the associated recursive Riccati equation for the forecast error, while nonlinear approximations are explored numerically
Non-global parameter estimation using local ensemble Kalman filtering
We study parameter estimation for non-global parameters in a low-dimensional
chaotic model using the local ensemble transform Kalman filter (LETKF). By
modifying existing techniques for using observational data to estimate global
parameters, we present a methodology whereby spatially-varying parameters can
be estimated using observations only within a localized region of space. Taking
a low-dimensional nonlinear chaotic conceptual model for atmospheric dynamics
as our numerical testbed, we show that this parameter estimation methodology
accurately estimates parameters which vary in both space and time, as well as
parameters representing physics absent from the model
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