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Spatio-temporal optimal interpolation of aerosol optical depth observations using a chemical transport model
* 1 , 1 , 1 , 1 , 2, 3, 4 , 5, 6
1  Institute of Physics of the NAS of Belarus, 220072 Minsk, Belarus
2  Department for Atmospheric Optics and Instrumentation, Main Astronomical Observatory, 03143 Kyiv, Ukraine
3  International Center of Future Science, College of Physics, Jilin University, Changchun, China
4  Physics Faculty, Taras Shevchenko National University of Kyiv, 01601 Kyiv, Ukraine
5  Department for Atmospheric Optics and Instrumentation, Main Astronomical Observatory, Kyiv, Ukraine
6  Laboratoire d’Optique Amosphérique (LOA), Universitée des Sciences et Technologies de Lille, 59655 Villeneuve d’Ascq, France
Academic Editor: Patricia Quinn


Atmospheric aerosol has a considerable impact on air quality and climate. One of important characteristics of atmospheric aerosol is aerosol optical depth (AOD). It is a measure of the column integrated aerosol load. Global ground-based network of sun photometers AERONET provides AOD data with low uncertainty. However, AERONET observations are sparse in space and time. To obtain an estimate of the spatial and temporal distribution of AOD, data assimilation technique can be applied. One of the commonly used data assimilation methods is optimal interpolation (OI). In OI, observational data and a model forecast are linearly combined according to their relative accuracies. Weight coefficients are chosen to minimize the mean-square error in the estimate. To obtain weight coefficients, correlations between model errors in the different grid points are used. In the classical OI, only spatial correlations are considered. We use spatial and temporal correlation functions. To obtain error statistics, we use observations from AERONET sites over European region, and simulations by the global chemical transport model GEOS-Chem, assuming a negligible error of AERONET AOD observations. The estimates of the daily mean AOD distribution over Europe are obtained using proposed approach. The reduction of the root-mean-square error of the AOD estimate based on the OI method in comparison with the GEOS-Chem model results is discussed.

Keywords: data assimilation; optimal interpolation; ground-based remote sensing aerosol network AERONET; chemical transport model GEOS-Chem
Comments on this paper
Anthony Lupo
Thank you for your presentation. I enjoyed looking through the slides. This is good work!
Natallia Miatselskaya
Thank you for your opinion!