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Information constraints in variational data assimilation
Author(s) -
Kahnert Michael
Publication year - 2018
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3347
Subject(s) - data assimilation , phase space , computation , dimension (graph theory) , variational analysis , partition (number theory) , mathematics , computer science , inverse problem , algorithm , mathematical optimization , mathematical analysis , physics , meteorology , combinatorics , pure mathematics , thermodynamics
Data assimilation of indirect observations from remote‐sensing instruments often leads to highly under‐determined inverse problems. Here a formulation of the variational method is discussed in which (a) the information content of the observations is systematically analysed by methods borrowed from retrieval theory; (b) the model space is transformed into a phase space in which one can partition the model variables into those that are related to the degrees of freedom for signal and noise, respectively; and (c) the minimization routine in the variational analysis is constrained to act on the signal‐related phase‐space variables only. This is done by truncating the dimension of the phase space. A first test of the method indicates that the constrained analysis speeds up computation time by about an order of magnitude compared with the formulation without information constraints.