In certain cases, error covariance of a forecasting system can be considered constant or vary only slowly in time. For such cases, the Kalman gain will eventually be constant. Once such Kalman gain is available, it can be used for data assimilation, without having to solve the covariance propagation equation. This technique is especially preferable for operational systems, where computational cost is really an issue.