Using Chemical Transport Model and Climatology Data as Backgrounds for Aerosol Optical Depth Spatial-Temporal Optimal Interpolation

A common approach to estimate the spatial-temporal distribution of atmospheric species properties is data assimilation. Data assimilation methods provide the best estimate of the required parameter by combining observations with appropriate prior information (background) that can be model output, climatology, or some other first guess. One of the relatively simple and computationally cheap data assimilation methods is optimal interpolation (OI). It estimates a value of interest through a weighted linear combination of observational data and background that is defined only once for the whole time interval of interest. Spatial-temporal OI (STOI) utilizes both spatial and temporal observational error covariance and background error covariance. This allows for filling in not only spatial, but also temporal gaps in observations. We applied STOI to daily mean aerosol optical depth (AOD) observations obtained at the European AERONET (Aerosol Robotic Network) sites with the use of the GEOS-Chem chemical transport model simulations and the AOD climatology as backgrounds. We found that mean square errors in the estimate when using modeled data are comparable with those when using climatology. Based on these results, we merged estimates obtained using modeled data and climatology according to their mean square errors. This allows improving AOD estimates in areas where observations are limited in space and time.