High-resolution NO2 observations from the Airborne Compact Atmospheric Mapper:...

Lamsal, L. N., S. Janz, N. Krotkov, K. E. Pickering, R. J. D. Spurr, M. Kowalewski, C. P. Loughner, J. Crawford, W. H. Swartz, and J. R. Herman (2017), High-resolution NO2 observations from the Airborne Compact Atmospheric Mapper: Retrieval and validation, J. Geophys. Res., 122, 1953-1970, doi:10.1002/2016JD025483.

Nitrogen dioxide (NO2) is a short-lived atmospheric pollutant that serves as an air quality indicator and is itself a health concern. The Airborne Compact Atmospheric Mapper (ACAM) was flown on board the NASA UC-12 aircraft during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality Maryland field campaign in July 2011. The instrument collected hyperspectral remote sensing measurements in the 304–910 nm range, allowing daytime observations of several tropospheric pollutants, including nitrogen dioxide (NO2), at an unprecedented spatial resolution of 1.5 × 1.1 km2. Retrievals of slant column abundance are based on the differential optical absorption spectroscopy method. For the air mass factor computations needed to convert these retrievals to vertical column abundance, we include high-resolution information for the surface reflectivity by using bidirectional reflectance distribution function data from the Moderate Resolution Imaging Spectroradiometer. We use high-resolution simulated vertical distributions of NO2 from the Community Multiscale Air Quality and Global Modeling Initiative models to account for the temporal variation in atmospheric NO2 to retrieve middle and lower tropospheric NO2 columns (NO2 below the aircraft). We compare NO2 derived from ACAM measurements with in situ observations from NASA’s P-3B research aircraft, total column observations from the ground-based Pandora spectrometers, and tropospheric column observations from the space-based Ozone Monitoring Instrument. The high-resolution ACAM measurements not only give new insights into our understanding of atmospheric composition and chemistry through observation of subsampling variability in typical satellite and model resolutions, but they also provide opportunities for testing algorithm improvements for forthcoming geostationary air quality missions.

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Atmospheric Composition Modeling and Analysis Program (ACMAP)
Aura- OMI