A comparative study of 4D-VAR and a 4D Ensemble Kalman Filter: perfect model simulations with Lorenz-96
نویسندگان
چکیده
We formulate a four-dimensional Ensemble Kalman Filter (4D-LETKF) that minimizes a cost function similar to that in a 4D-VAR method. Using perfect model experiments with the Lorenz-96 model, we compare assimilation of simulated asynchronous observations with 4D-VAR and 4D-LETKF. We find that both schemes have comparable error when 4D-LETKF is performed sufficiently frequently and when 4D-VAR is performed over a sufficiently long analysis time window. We explore how the error depends on the time between analyses for 4D-LETKF and the analysis time window for 4D-VAR.
منابع مشابه
Some Ideas for Ensemble Kalman Filtering
In this seminar we show clean comparisons between EnKF and 4D-Var made in Environment Canada, briefly describe the Local Ensemble Transform Kalman Filter (LETKF) as a representative prototype of Ensemble Kalman Filter, and give several examples of how advanced properties and applications that have been developed and explored for 4D-Var can be adapted to the LETKF without requiring an adjoint mo...
متن کاملJoint state and parameter estimation with an iterative ensemble Kalman smoother
Both ensemble filtering and variational data assimilation methods have proven useful in the joint estimation of state variables and parameters of geophysical models. Yet, their respective benefits and drawbacks in this task are distinct. An ensemble variational method, known as the iterative ensemble Kalman smoother (IEnKS) has recently been introduced. It is based on an adjoint model-free vari...
متن کاملEnsemble Kalman Filter: Current Status and Potential
In this chapter we give an introduction to different types of Ensemble Kalman filter, describe the Local Ensemble Transform Kalman Filter (LETKF) as a representative prototype of these methods, and several examples of how advanced properties and applications that have been developed and explored for 4D-Var (four-dimensional variational assimilation) can be adapted to the LETKF without requiring...
متن کاملOn the Equivalence Between Suboptimal 4D-Var and EnKF
Two families of methods are widely used in data assimilation: the four dimensional variational (4D-Var) approach, and the ensemble Kalman filter (EnKF) approach. The two families have been developed largely through parallel research efforts, and each method has its advantages and disadvantages. It is of interest to combine the two approaches and develop hybrid data assimilation algorithms. This...
متن کاملThe Iterative Ensemble Kalman Smoother: the Best of Both Worlds?
Data assimilation seeks a mathematically optimal compromise between outcomes of a numerical model that simulates a physical system and observations of that system. It has been successfully used for twenty years in operational meteorology to perform the best forecast, and is now being used or tested in many geoscience fields. Two main classes of methods have taken the lead. Firstly, 4D-Var is a ...
متن کامل