A novel method to account for model error due to unresolved scales in sequential data assimilation is proposed. An equation for the model error covariance required in the extended Kalman filter update is derived along with an approximation suitable for application with large scale dynamics typical in environmental modeling. The approach, referred to as Short Time Extended Kalman Filter (STEKF), is tested in the context of a low order chaotic dynamical system and it is compared with an EKF implementing a multiplicative covariance inflation, a practical procedure often used to account for model error in data assimilation. The results show that the performance of the STEKF is significantly better than that of the classical EKF with no additional computational cost and encourages the implementation of this approach in more realistic contexts.