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Inverse modelling and combined state-source estimation for chemical weather

Elbern, Hendrik ; Strunk, Achim and Nieradzik, Lars LU orcid (2010) p.491-513
Abstract

Air quality data assimilation aims to find a best estimate of the control parameters (see theory chapter) for those processes of the atmosphere which govern the chemical evolution of biologically relevant height levels, typically located in the the lowermost atmosphere. As in data assimilation (see theory chapters), we have to resort to numerical models to complement usually sparse observation networks; these models serve as system constraints. Several research groups are developing data assimilation methods similar to those applied to meteorological applications. Techniques range from nudging to advanced spatio-temporal methods such as four-dimensional variational (4D-Var) data assimilation and various simplifications of the Kalman... (More)

Air quality data assimilation aims to find a best estimate of the control parameters (see theory chapter) for those processes of the atmosphere which govern the chemical evolution of biologically relevant height levels, typically located in the the lowermost atmosphere. As in data assimilation (see theory chapters), we have to resort to numerical models to complement usually sparse observation networks; these models serve as system constraints. Several research groups are developing data assimilation methods similar to those applied to meteorological applications. Techniques range from nudging to advanced spatio-temporal methods such as four-dimensional variational (4D-Var) data assimilation and various simplifications of the Kalman filter (KF).

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Please use this url to cite or link to this publication:
author
; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Data Assimilation : Making Sense of Observations - Making Sense of Observations
pages
23 pages
publisher
Springer
external identifiers
  • scopus:80054069135
ISBN
9783540747031
9783540747024
DOI
10.1007/978-3-540-74703-1_19
language
English
LU publication?
no
id
2fd86ce8-4b1f-4445-8ae5-8c0e407aa896
date added to LUP
2018-11-27 09:29:40
date last changed
2024-09-17 08:30:44
@inbook{2fd86ce8-4b1f-4445-8ae5-8c0e407aa896,
  abstract     = {{<p>Air quality data assimilation aims to find a best estimate of the control parameters (see theory chapter) for those processes of the atmosphere which govern the chemical evolution of biologically relevant height levels, typically located in the the lowermost atmosphere. As in data assimilation (see theory chapters), we have to resort to numerical models to complement usually sparse observation networks; these models serve as system constraints. Several research groups are developing data assimilation methods similar to those applied to meteorological applications. Techniques range from nudging to advanced spatio-temporal methods such as four-dimensional variational (4D-Var) data assimilation and various simplifications of the Kalman filter (KF).</p>}},
  author       = {{Elbern, Hendrik and Strunk, Achim and Nieradzik, Lars}},
  booktitle    = {{Data Assimilation : Making Sense of Observations}},
  isbn         = {{9783540747031}},
  language     = {{eng}},
  month        = {{12}},
  pages        = {{491--513}},
  publisher    = {{Springer}},
  title        = {{Inverse modelling and combined state-source estimation for chemical weather}},
  url          = {{http://dx.doi.org/10.1007/978-3-540-74703-1_19}},
  doi          = {{10.1007/978-3-540-74703-1_19}},
  year         = {{2010}},
}