To fulfill the requirements of the 2008/50 Directive, which allows member states and regional authorities to use a combination of measurement and modeling to monitor air pollution concentration, a key approach to be properly developed and tested is the data assimilation one. In this paper, with a focus on regional domains, a comparison between optimal interpolation and Ensemble Kalman Filter is shown, to stress pros and drawbacks of the two techniques. These approaches can be used to implement a more accurate monitoring of the long-term pollution trends on a geographical domain, through an optimal combination of all the available sources of data.

A comparison of reanalysis techniques: Applying optimal interpolation and Ensemble Kalman Filtering to improve air quality monitoring at mesoscale

Candiani Gabriele;
2013

Abstract

To fulfill the requirements of the 2008/50 Directive, which allows member states and regional authorities to use a combination of measurement and modeling to monitor air pollution concentration, a key approach to be properly developed and tested is the data assimilation one. In this paper, with a focus on regional domains, a comparison between optimal interpolation and Ensemble Kalman Filter is shown, to stress pros and drawbacks of the two techniques. These approaches can be used to implement a more accurate monitoring of the long-term pollution trends on a geographical domain, through an optimal combination of all the available sources of data.
2013
Data assimilation
Optimal interpolation
Ensemble Kalman Filter
Particulate matter
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/259785
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