Article ID Journal Published Year Pages File Type
4443591 Atmospheric Environment 2007 19 Pages PDF
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
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