Information constraints in variational data assimilation

• 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.

Subject headings

NATURVETENSKAP  -- Matematik -- Beräkningsmatematik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Computational Mathematics (hsv//eng)

NATURVETENSKAP  -- Fysik -- Annan fysik (hsv//swe)

NATURAL SCIENCES  -- Physical Sciences -- Other Physics Topics (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Keyword

data assimilation

remote sensing

inverse problems

chemical transport modelling

Publication and Content Type

art (subject category)

ref (subject category)