Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth

Aerosol optical depth (AOD) is one of the basic characteristics atmospheric aerosol. A global ground-based network sun and sky photometers, Robotic Network (AERONET) provides AOD data with low uncertainty. However, AERONET observations are sparse in space time. To improve density, we merged a GEOS-Chem chemical transport model prediction using an optimal interpolation (OI) method. According to OI, estimated as linear combination observational forecast, weighting coefficients chosen minimize mean-square error calculation, assuming negligible observations. obtain weight coefficients, used correlations between errors different grid points. In contrast classical where only spatial considered, developed spatial-temporal (STOI) technique for applications use temporal correlation functions. Using STOI, obtained estimates daily mean distribution over Europe. validate results, compared by STOI independent two months three sites. Compared averaged reduction root-mean-square estimate based on method about 25%. The study shows that significant improvement estimates.