An adaptive quality control procedure for data assimilation

Abstract

We describe a simple adaptive quality control procedure that limits the impact of individual observations likely to be inconsistent with the state of the data assimilation system. It smoothly increases the observation error variance depending on the projected increment, state error variance and so-called K-factor so that the resulting increment does not exceed the estimated state error times K. Because an estimate of the state error is readily available in the Kalman filter (KF), the method is particularly suitable for the KF, ensemble Kalman filter (EnKF), or ensemble optimal interpolation systems. The tests show that setting K to about 1.5–2 or above has no detrimental effect for performance of nearly optimal systems; at the same time it still makes it possible to make use of observations that might otherwise be discarded by the background check. The technique is successfully used in the EnKF codes TOPAZ and EnKF-C.

Keywords

• data assimilation
• observation quality control