Forecast Bias Correction: A Second Order Method

In this paper we describe a methodology for determining corrections to parameters and initial conditions in order to improve model forecasts in the presence of data. This methodology is grounded in the variational problem of minimizing the norm of the errors between forecast and data. The general method is presented, and then the method is applied to the specific example of a scalar model, the logistic equation, where it is shown that the method produces satisfactory results. Acknowledgement: We wish to record our sincere thanks to John Lewis for his interest in this work.