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Information constra...
Information constraints in variational data assimilation
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- Kahnert, Michael, 1968 (author)
- Chalmers tekniska högskola,Chalmers University of Technology,Sveriges meteorologiska och hydrologiska institut (SMHI),Swedish Meteorological and Hydrological Institute (SMHI)
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(creator_code:org_t)
- 2018-10-12
- 2018
- English.
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In: Quarterly Journal of the Royal Meteorological Society. - : Wiley. - 0035-9009 .- 1477-870X. ; 144:716, s. 2230-2244
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Abstract
Subject headings
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- 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)
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