Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12313/1733
Title: Assimilation of ozone measurements in the air quality model AURORA by using the Ensemble Kalman Filter
Authors: Barrero Mendoza, Oscar
Keywords: Atmospheric modeling
Boundary conditions
Data assimilation
Computational modeling
Stochastic processes
Kalman filters
Vectors
Issue Date: 12-Dec-2011
Publisher: 2011 50th IEEE Conference on Decision and Control and European Control Conference
Citation: O. M. Agudelo, O. Barrero, V. Peter and B. De Moor, "Assimilation of ozone measurements in the air quality model AURORA by using the Ensemble Kalman Filter," 2011 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, 2011, pp. 4430-4435.
Abstract: This paper presents the results of using the Ensemble Kalman Filter (EnKF) for improving the ozone estimations of the air quality model AURORA. The EnKF is built around a stochastic formulation of the model, where some of its parameters are assumed to be uncertain. These uncertainties turn out to be the main reason behind the differences between the model predictions and the real measurements. The filter estimates these parameters as well as the ozone concentration field by using ground-based measurements from the Airbase database. The assimilation experiments are carried out over a region that consists of Belgium, Luxembourg, and some small parts of Germany, France and the Netherlands. The simulations results show that the EnKF significantly reduces the error of the ozone estimations.
URI: https://ieeexplore.ieee.org/document/6160444
ISSN: 0191-2216
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