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 filter (KF).
Keywords
- Kalman Filter
- Data Assimilation
- Aerosol Optical Depth
- Error Covariance Matrix
- Variational Data Assimilation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgments
The authors are indebted to the BERLIOZ and VERTIKO project members for measurement data, and to Dr. A. Richter (IFE University of Bremen) and Dr. H. Eskes (KNMI) for satellite retrievals. The work was mainly supported from the German Ministry for Research and Technology in the frame of the AFO2000 project SATEC4D. Computing facilities were granted by ZAM, the Research Centre Jülich, on a Cray T3E and IBM Power 4.
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Elbern, H., Strunk, A., Nieradzik, L. (2010). Inverse Modelling and Combined State-Source Estimation for Chemical Weather. In: Lahoz, W., Khattatov, B., Menard, R. (eds) Data Assimilation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74703-1_19
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