Chemical Data Assimilation—An Overview†
AbstractChemical data assimilation is the process by which models use measurements to produce an optimal representation of the chemical composition of the atmosphere. Leveraging advances in algorithms and increases in the available computational power, the integration of numerical predictions and observations has started to play an important role in air quality modeling. This paper gives an overview of several methodologies used in chemical data assimilation. We discuss the Bayesian framework for developing data assimilation systems, the suboptimal and the ensemble Kalman filter approaches, the optimal interpolation (OI), and the three and four dimensional variational methods. Examples of assimilation real observations with CMAQ model are presented.
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Sandu, A.; Chai, T. Chemical Data Assimilation—An Overview. Atmosphere 2011, 2, 426-463.
Sandu A, Chai T. Chemical Data Assimilation—An Overview. Atmosphere. 2011; 2(3):426-463.Chicago/Turabian Style
Sandu, Adrian; Chai, Tianfeng. 2011. "Chemical Data Assimilation—An Overview." Atmosphere 2, no. 3: 426-463.