In this paper, we discuss the dynamic coregionalization model and its capability for model selection inference and interpretation in relation to spatio- temporal dynamic calibration and mapping of daily concentration of airborne particulate matter. To do this, we consider the problem of joint modelling ground level concentration data and satellite measurements of aerosol optical thickness (AOT), which are rarely available. The maximum likelihood estimation for the large data set related to the ”padano-veneto” region, North Italy, with missing data is covered by the stable EM algorithm and implemented on a small size computer cluster.

(2010). The dynamic coregionalization model with application to air quality remote sensing [working paper]. Retrieved from http://hdl.handle.net/10446/899

The dynamic coregionalization model with application to air quality remote sensing

Fasso', Alessandro;Finazzi, Francesco
2010-01-01

Abstract

In this paper, we discuss the dynamic coregionalization model and its capability for model selection inference and interpretation in relation to spatio- temporal dynamic calibration and mapping of daily concentration of airborne particulate matter. To do this, we consider the problem of joint modelling ground level concentration data and satellite measurements of aerosol optical thickness (AOT), which are rarely available. The maximum likelihood estimation for the large data set related to the ”padano-veneto” region, North Italy, with missing data is covered by the stable EM algorithm and implemented on a small size computer cluster.
2010
Fasso', Alessandro; Finazzi, Francesco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/899
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