Improving the Accuracy of Daily Satellite-Derived Ground-Level Fine Aerosol Concentration Estimates for North America
We improve the accuracy of daily ground-level fine particulate matter concentrations (PM2.5) derived from satellite observations (MODIS and MISR) of aerosol optical depth (AOD) and chemical transport model (GEOS-Chem) calculations of the relationship between AOD and PM2.5. This improvement is achieved by (1) applying climatological ground-based regional bias-correction factors based upon comparison with in situ PM2.5, and (2) applying spatial smoothing to reduce random uncertainty and extend coverage. Initial daily 1-σ mean uncertainties are reduced across the United States and southern Canada from ± (1 ?g/m3 + 67%) to ± (1 ?g/m3 + 54%) by applying the climatological ground-based regional scaling factors. Spatial interpolation increases the coverage of satellite-derived PM2.5 estimates without increased uncertainty when in close proximity to direct AOD retrievals. Spatial smoothing further reduces the daily 1-σ uncertainty to ±(1 ?g/m3 + 42%) by limiting the random component of uncertainty. We additionally find similar performance for climatological relationships of AOD to PM2.5 as compared to day-specific relationships.