Observational Data-Driven Surface Concentration Derived from Satellite Columns

Satellite remote sensing in the ultraviolet, visible, and infrared spectral ranges have provided excellent spatiotemporal coverage of the Earth’s atmosphere and have tremendously advanced our understandings on the emissions, distributions, and trends of atmospheric compositions. However, these satellite observations are in general only sensitive to the total column abundance. It is a major challenge to accurately infer the correspondence between the observed atmospheric column and surface concentrations, which have important implications for human and ecosystem health, crop yield, and air quality. Typically, atmospheric chemical transport models are used to estimate the vertical distribution of trace gases that is strongly affected by PBL processes and to derive surface concentrations from satellite columns, and this adds additional uncertainty.

We will present an observational-driven framework to infer PBL and surface concentrations of reactive trace gases from satellite column measurements through the incorporation of spaceborne and suborbital remote sensing, existing in situ aircraft profile observations, and measurements from commercial airlines. This approach involves (1) deriving representative, planet boundary layer height (PBLH)-based vertical profiles for reactive trace gases such as formaldehyde, ammonia, and NO2 from aircraft measurements, (2) constraining PBLH from (re)analysis using LIDAR and aircraft profiles and constructing spatiotemporally resolved PBLH field, and (3) deriving satellite-based surface concentration data, merging multiple datasets to a high-resolution, common grid, and validating the results using ground-based networks.