Effective Actions for Ensemble Data Assimilation
arXiv:0908.2045 · doi:10.1016/j.physleta.2009.08.072
Abstract
Ensemble data assimilation is a problem in determining the most likely phase space trajectory of a model of an observed dynamical sys- tem as it receives inputs from measurements passing information to the model. Using methods developed in statistical physics, we present effective actions and equations of motion for the mean orbits associ- ated with the temporal development of a dynamical model when it has errors, there is uncertainty in its initial state, and it receives informa- tion from measurements. If there are correlations among errors in the measurements they are naturally included in this approach.
10 pages
Cited by in corpus (7)
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- Data Assimilation using a GPU Accelerated Path Integral Monte Carlo Approach
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- Nonlinear system identification employing automatic differentiation
- Coarse-grained sensitivity for multiscale data assimilation
- Self-Consistent Stochastic Model Errors in Data Assimilation