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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References

SHOWING 1-10 OF 91 REFERENCES
An Ensemble Adjustment Kalman Filter for Data Assimilation
Abstract A theory for estimating the probability distribution of the state of a model given a set of observations exists. This nonlinear filtering theory unifies the data assimilation and ensemble
A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts
Abstract Knowledge of the probability distribution of initial conditions is central to almost all practical studies of predictability and to improvements in stochastic prediction of the atmosphere.
A Local Ensemble Kalman Filter for Atmospheric Data Assimilation
TLDR
A new, local formulation of the ensemble Kalman Filter approach for atmospheric data assimilation based on the hypothesis that, when the Earth's surface is divided up into local regions of moderate size, vectors of the forecast uncertainties in such regions tend to lie in a subspace of much lower dimension than that of the full atmospheric state vector of such a region.
Data Assimilation Using an Ensemble Kalman Filter Technique
The possibility of performing data assimilation using the flow-dependent statistics calculated from an ensemble of short-range forecasts (a technique referred to as ensemble Kalman filtering) is
A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation
An ensemble Kalman filter may be considered for the 4D assimilation of atmospheric data. In this paper, an efficient implementation of the analysis step of the filter is proposed. It employs a Schur
Atmospheric Data Assimilation with an Ensemble Kalman Filter: Results with Real Observations
Abstract An ensemble Kalman filter (EnKF) has been implemented for atmospheric data assimilation. It assimilates observations from a fairly complete observational network with a forecast model that
Four-dimensional local ensemble transform Kalman filter : numerical experiments with a global circulation model
We present a four-dimensional ensemble Kalman filter (4D-LETKF) that approximately and efficiently solves a variational problem similar to that solved by 4D-VAR, and report numerical results with the
Assessing a local ensemble Kalman filter : perfect model experiments with the National Centers for Environmental Prediction global model
The accuracy and computational efficiency of the recently proposed local ensemble Kalman filter (LEKF) data assimilation scheme is investigated on a state-of-the-art operational numerical weather
Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics
A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and
Data Assimilation into a Primitive-Equation Model with a Parallel Ensemble Kalman Filter
TLDR
The algorithm is applied to the assimilation of synthetic altimetry data in the context of an imperfect model and known representation-error statistics and the error estimates obtained are compared to the actual errors.
...
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