A Rapid Optimization Algorithm for GPS Data Assimilation

Global Positioning System (GPS) meteorology data variational assimilation can be reduced to theproblem of a large-scale unconstrained optimization. Because the dimension of this problem is too large,most optimal algorithms cannot be performed. In order to make GPS/MET data assimilation able tosatisfy the demand of numerical weather prediction, finding an algorithm with a great convergence rateof iteration will be the most important thing. A new method is presented that dynamically combines thelimited memory BFGS (L-BFGS) method with the Hessian-free Newton(HFN) method, and it has a goodrate of convergence in iteration. The numerical tests indicate that the computational efficiency of themethod is better than the L-BFGS and HFN methods.