Ensemble-based Chemical Data Assimilation III: Filter Localization
2006) Ensemble-based Chemical Data Assimilation III: Filter Localization. Technical Report TR-06-08, Computer Science, Virginia Tech. (
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 implement and assess the performance of a localized ``perturbed observations'' ensemble Kalman filter (LEnKF). We analyze different settings of the ensemble localization, and investigate the joint assimilation of the state, emissions and boundary conditions. Results with a real model and real observations show that LEnKF is a promising approach for chemical data assimilation. The results also point to several issues on which future research is necessary.
|Item Type:||Departmental Technical Report|
|Keywords:||data assimilation, ensemble Kalman filter, chemical transport models|
|Subjects:||Computer Science > Numerical Analysis|
|Deposited By:||Constantinescu, Mr. Emil M.|
|Deposited On:||05 April 2006|