Ensemble-based chemical data assimilation I: An idealized setting

TR number
TR-06-06Date
2006-03-01Author
Constantinescu, Emil M.
Sandu, Adrian
Chai, Tianfeng
Carmichael, Gregory R
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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. 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.