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.

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