Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4443591 | Atmospheric Environment | 2007 | 19 Pages |
Abstract
Data assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air quality forecasting, similar to the role it has in numerical weather prediction (NWP). Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper, we assess the performance of the ensemble Kalman filter (EnKF). Results in an idealized setting show that EnKF is promising for chemical data assimilation.
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Atmospheric Science
Authors
Emil M. Constantinescu, Adrian Sandu, Tianfeng Chai, Gregory R. Carmichael,