  The difference between a model forecast and actual observations is called
forecast bias. This bias is due to either incomplete model assumptions and/or
poorly known parameter values and initial/boundary conditions. In this paper we
discuss a method for estimating corrections to parameters and initial
conditions that would account for the forecast bias. A set of simple
experiments with the logistic ordinary differential equation is performed using
an iterative version of a first order version of our method to compare with the
second order version of the method.
