Articles | Volume 4, issue 9
Atmos. Meas. Tech., 4, 1805–1820, 2011
Atmos. Meas. Tech., 4, 1805–1820, 2011

Research article 09 Sep 2011

Research article | 09 Sep 2011

Influence of low spatial resolution a priori data on tropospheric NO2 satellite retrievals

A. Heckel2,1, S.-W. Kim4,3, G. J. Frost4,3, A. Richter1, M. Trainer3, and J. P. Burrows1 A. Heckel et al.
  • 1Institute of Environmental Physics and Institute of Remote Sensing, University of Bremen, Bremen, Germany
  • 2Department of Geography, Swansea University, Swansea, UK
  • 3NOAA Earth System Research Laboratory, Chemical Sciences Division, 325 Broadway, R/CSD4, CO 80305, Boulder, USA
  • 4Cooperative Institute for Research in Environmental Sciences, University of Colorado, CO 80309, Boulder, USA

Abstract. The retrieval of tropospheric columns of NO2 and other trace gases from satellite observations of backscattered solar radiation relies on the use of accurate a priori information. The spatial resolution of current space sensors is often significantly higher than that of the a priori datasets used, introducing uncertainties from spatial misrepresentation. In this study, the effect of spatial under-sampling of a priori data on the retrieval of NO2 columns was studied for a typical coastal area (around San Francisco). High-resolution (15 × 15 km2) NO2 a priori data from the WRF-Chem model in combination with high-resolution MODIS surface reflectance and aerosol data were used to investigate the uncertainty introduced by applying a priori data at typical global chemical transport model resolution. The results show that the relative uncertainties can be large (more than a factor of 2 if all a priori data used is at the coarsest resolution) for individual measurements, mainly due to spatial variations in NO2 profile and surface albedo, with smaller contributions from aerosols and surface height changes. Similar sensitivities are expected for other coastal regions and localised sources such as power plants, highlighting the need for high-resolution a priori data in quantitative analysis of the spatial patterns retrieved from satellite observations of tropospheric pollution.