Possibility of Skill Forecast Based on the Finite-Time Dominant Linear Solutions for a Primitive Equation Regional Forecast Model

Abstract

The possibility of using forecast errors originating from the finite-time dominant linear modes for the prediction of forecast skill for a primitive equation regional forecast model is studied. This is similar to the method for skill prediction suggested by several other authors using simplified models. Two main problems associated with a sophisticated forecast model not considered in these other studies are investigated. 1) The number of degrees of freedom is typically too large for the evaluation of the spectrum of dominant modes associated with the linear error evolution equation, and 2) many different, physically meaningful, error measures may be used for this model and the dominant linear modes may be sensitive to the selection of the error measure. It is shown first that the finite-time dominant linear solutions can be computed with sufficient accuracy for the complex forecast model using standard ?power? method with a small number of iterations for all error measures considered in this study. The forecast skill is then estimated using the nonlinear forecast errors originating from the initial errors that are defined by these measure-dependent dominant solutions. These results show that the estimated forecast skill is very sensitive to the choice of error measure used for the computation of the finite-time dominant modes.