Inverse modelling and combined state-source estimation for chemical weather
(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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- author
- Elbern, Hendrik ; Strunk, Achim and Nieradzik, Lars LU
- publishing date
- 2010-12-01
- 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}}, }