Abstract
The goal of atmospheric data assimilation is to determine the most accurate representation of the signal from the available observations. The optimality of a data assimilation scheme measures how much information has been extracted from the observations. It is possible to quantify the optimality of the scheme using on-line performance diagnostics. Such a diagnostic is the proposed lagged innovation covariance procedure. This diagnostic has been developed from Kalman filter theory. Its characteristics are examined using a simple scalar model, a univariate one-dimensional linear advection model, and a linear quasigeostrophic model. The model results are compared with actual lagged innovation covariances derived from the innovation sequences of an operational data assimilation system.
