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Geographical location of the PM 2.5 stations (red squares). The dash-dot line delimits the area covered by AURORA.

Geographical location of the PM 2.5 stations (red squares). The dash-dot line delimits the area covered by AURORA.

Source publication
Fig. 1. Geographical location of the stations measuring PM 10. The blue...
Table 1 . Performance of the data assimilation techniques when PM 10...
Fig. 2. Geographical location of the PM 2.5 stations (red squares). The...
Improving the PM10 estimates of the air quality model AURORA by using Optimal Interpolation**This work was supported by: • Research Council KUL: CoE PFV/10/002 (OPTEC), PhD/Postdoc grants • Flemish Government: iMinds Medical Information Technologies SBO 2015 • Belgian Federal Science Policy Office: IUAP P7/19 (DYSCO, Dynamical systems, control and optimization, 2012-2017).
Article
Full-text available
  • Dec 2015
This paper presents the results of using a data assimilation technique known as Optimal Interpolation (OI) for improving the PM10 estimates of the air quality model AURORA. Ground-based measurements provided by IRCEL (the Belgian Interregional Environment Agency) have been used in the data assimilation process. AURORA has been set up to cover a dom...
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... the data were provided by IRCEL, and due to the resolution of the model we only considered measurements coming from rural and urban-background stations. Fig. 2 shows the 11 stations finally ...

Citations

Using Objective Analysis for the Assimilation of Satellite-Derived Aerosol Products to Improve PM2.5 Predictions over Europe
Article
Full-text available
  • Jul 2018
We used the objective analysis method in conjunction with the successive correction method to assimilate MODerate resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) data into the Chimère model in order to improve the modeling of fine particulate matter (PM2.5) concentrations and AOD field over Europe. A data assimilation module was developed to adjust the daily initial total column aerosol concentrations based on a forecast-analysis cycling scheme. The model is then evaluated during one-month winter period to examine how such a data assimilation technique pushes the model results closer to surface observations. This comparison showed that the mean biases of both surface PM2.5 concentrations and the AOD field could be reduced from −34 to −15% and from −45 to −27%. The assimilation, however, leads to false alarms because of the difficulty in distributing AOD550 over different particle sizes. The impact of the influence radius is found to be small and depends on the density of satellite data. This work, although preliminary, is important in terms of near-real time air quality forecasting using the Chimère model and can be further developed to improve modeled PM2.5 and ozone concentrations.
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