A novel algorithm is described for the assimilation of time-averaged observations. A demonstration of this algorithm in an ideal model using an ensemble Kalman filter technique suggests the potential for resolving dynamical features that have a characteristic time-scale longer than the averaging time of the observations. This technique may offer new perspectives in climate reconstruction and in the assimilation of integrated meteorological quantities such as accumulated precipitation.