Open AccessInner-Loop Stopping Criteria for Incremental Four-Dimensional Variational Data Assimilation
Author(s) -
Amos S. Lawless,
Nancy K. Nichols
Publication year - 2006
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/mwr3242.1
Subject(s) - data assimilation , inner loop , minification , loop (graph theory) , convergence (economics) , sequence (biology) , mathematical optimization , mathematics , assimilation (phonology) , computer science , control theory (sociology) , physics , artificial intelligence , meteorology , genetics , controller (irrigation) , combinatorics , agronomy , economics , biology , economic growth , linguistics , philosophy , control (management)
Incremental four-dimensional variational data assimilation is a method that solves the assimilation problem by minimizing a sequence of approximate “inner-loop” functions. In any implementation of such a scheme a decision must be made as to how accurately to solve each of the inner minimization problems. In this paper recently developed theory is applied to derive a new stopping criterion for the inner-loop minimizations, which guarantees convergence of the outer loops. This new criterion is shown to give improved convergence compared with other commonly used inner-loop stopping criteria.