Short-range error statistics in an ensemble Kalman filter
Short-range error statistics in an ensemble Kalman filter
This chapter deals with short-range error statistics in the context of the ensemble Kalman filter (EnKF). To arrive at an optimal data-assimilation system, a good description of the uncertainty in the background field is needed. Historically, different approaches, with a solid comparison against either a ground truth or observations, have been used to obtain limited descriptions. The first category includes observation system simulation experiments (OSSEs), while the second includes methods based on statistical analysis of innovations. The EnKF is a relatively new method that simulates the effect of known sources of error to arrive at a Monte Carlo estimate of flow-dependent background error statistics. It is necessary to validate the ensemble statistics–in part by comparison with results from established methods–to identify areas of improvement for the EnKF. This chapter first summarizes existing methods and then studies the properties of a research version of the Canadian global EnKF.
Keywords: ensemble Kalman filter, EnKF, uncertainty, observation system simulation experiments, OSSEs, Monte Carlo
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