Improve observation-based ground-level ozone spatial distribution by compositing satellite and surface observations: A simulation experiment

Abstract

Obtaining the full spatial coverage of daily surface ozone fields is challenging because of the sparsity of the surface monitoring network and the difficulty in direct satellite retrievals of surface ozone. We propose an indirect satellite retrieval framework to utilize the information from satellite-measured column densities of tropospheric NO2 and CH2O, which are sensitive to the lower troposphere, to derive surface ozone fields. The method is applicable to upcoming geostationary satellites with high-quality NO2 and CH2O measurements. To prove the concept, we conduct a simulation experiment using a 3-D chemical transport model for July 2011 over the eastern US. The results show that a second order regression using both NO2 and CH2O column densities can be an effective predictor for daily maximum 8-h average ozone. Furthermore, this indirect retrieval approach is shown to be complementary to spatial interpolation of surface observations, especially in regions where the surface sites are sparse. Combining column observations of NO2 and CH2O with surface site measurements leads to an improved representation of surface ozone over simple kriging, increasing the R2 value from 0.53 to 0.64 at a surface site distance of 252 km. The improvements are even more significant with larger surface site distances. The simulation experiment suggests that the indirect satellite retrieval technique can potentially be a useful tool to derive the full spatial coverage of daily surface ozone fields if satellite observation uncertainty is moderate.