There are three common types of predictability problems
in weather and climate, which each involve different constrained nonlinear
optimization problems: the lower bound of maximum predictable time, the
upper bound of maximum prediction error, and the lower bound of maximum
allowable initial error and parameter error. Highly efficient algorithms
have been developed to solve the second optimization problem. And this
optimization problem can be used in realistic models for weather and climate
to study the upper bound of the maximum prediction error. Although a
filtering strategy has been adopted to solve the other two problems, direct
solutions are very time-consuming even for a very simple model, which
therefore limits the applicability of these two predictability problems in
realistic models. In this paper, a new strategy is designed to solve these
problems, involving the use of the existing highly efficient algorithms for
the second predictability problem in particular. Furthermore, a series of
comparisons between the older filtering strategy and the new method are
performed. It is demonstrated that the new strategy not only outputs the
same results as the old one, but is also more computationally efficient.
This would suggest that it is possible to study the predictability problems
associated with these two nonlinear optimization problems in realistic
forecast models of weather or climate. 相似文献