# Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter

@article{Hunt2005EfficientDA, title={Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter}, author={Brian R. Hunt and Eric Kostelich and Istvan Szunyogh}, journal={Physica D: Nonlinear Phenomena}, year={2005}, volume={230}, pages={112-126} }

Abstract Data assimilation is an iterative approach to the problem of estimating the state of a dynamical system using both current and past observations of the system together with a model for the system’s time evolution. Rather than solving the problem from scratch each time new observations become available, one uses the model to “forecast” the current state, using a prior state estimate (which incorporates information from past data) as the initial condition, then uses current data to…

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