Information constraints in variational data assimilation
Journal article, 2018

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.

data assimilation

remote sensing

inverse problems

chemical transport modelling


Michael Kahnert

Chalmers, Space, Earth and Environment, Microwave and Optical Remote Sensing


Quarterly Journal of the Royal Meteorological Society

0035-9009 (ISSN) 1477-870X (eISSN)

Vol. 144 716 2230-2244

Subject Categories

Computational Mathematics

Other Physics Topics

Control Engineering



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