A New Strategy for Solving a Class of Constrained Nonlinear Optimization Problems Related to Weather and Climate Predictability

• Published in: Advances in atmospheric sciences Vol. 27; no. 4; pp. 741 - 749
• Main Author: 段晚锁 骆海英
• Format: Journal Article
• Language: English
• Published: Heidelberg SP Science Press 01.07.2010; Springer Nature B.V
• Subjects: Algorithms; Atmospheric Sciences; Earth and Environmental Science; Earth Sciences; Geophysics/Geodesy; Meteorology; Nonlinear systems; Optimization; Weather forecasting; 可预报性; 可预测性; 时间约束; 气候模式; 误差界; 非线性优化问题; predictability; algorithms; constrained nonlinear optimization problems
• ISSN: 0256-1530; 1861-9533
• DOI: 10.1007/s00376-009-9141-0

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 effcient 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 effcient 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 effcient. 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.