Spatio-temporal optimal interpolation of aerosol optical depth observations using a chemical transport model

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.