Inverse modelling and combined state-source estimation for chemical weather

Summary, in English

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).

• Author: Hendrik Elbern; Achim Strunk; Lars Nieradzik
• Publishing year: 2010-12-01
• Language: English
• Pages: 491-513
• Publication/Series: Data Assimilation : Making Sense of Observations
• Links: http://dx.doi.org/10.1007/978-3-540-74703-1_19
• Document type: Book chapter
• Publisher: Springer
• Topic: Bioinformatics (Computational Biology)
• Status: Published
• ISBN/ISSN/Other: ISBN: 9783540747031; ISBN: 9783540747024