1Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
2Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
3Atomic and Molecular Physics Division, Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts, USA
4Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
5Fluid Dynamics Lab, Eindhoven University of Technology, Eindhoven, the Netherlands
6RT Solutions Inc., Cambridge, Massachusetts 02138, USA
7IAP/CAS, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
8BIRA-IASB, Belgian Institute for Space Aeronomy, Brussels, Belgium
9Instituut voor sterrenkunde, KU Leuven, Celestijnenlaan 200D, 3001 Heverlee, Belgium
10Center for Environmental Remote Sensing, Chiba University, 1–33 Yayoicho, Inage-ku, Chiba 263-8522, Japan
Abstract. Retrievals of tropospheric nitrogen dioxide (NO2) from the Ozone Monitoring Instrument (OMI) are subject to errors in the treatments of aerosols, surface reflectance anisotropy, and vertical profile of NO2. Here we quantify the influences over China via an improved retrieval process. We explicitly account for aerosol optical effects (simulated by nested GEOS-Chem at 0.667° long. × 0.5° lat. and constrained by aerosol measurements), surface reflectance anisotropy, and high-resolution vertical profiles of NO2 (simulated by GEOS-Chem). Prior to the NO2 retrieval, we derive the cloud information using consistent ancillary assumptions.
We compare our retrieval to the widely used DOMINO v2 product, using MAX-DOAS measurements at three urban/suburban sites in East China as reference and focusing the analysis on the 127 OMI pixels (in 30 days) closest to the MAX-DOAS sites. We find that our retrieval reduces the interference of aerosols on the retrieved cloud properties, thus enhancing the number of valid OMI pixels by about 25%. Compared to DOMINO v2, our retrieval better captures the day-to-day variability in MAX-DOAS NO2 data (R2 = 0.96 versus 0.72), due to pixel-specific radiative transfer calculations rather than the use of a look-up table, explicit inclusion of aerosols, and consideration of surface reflectance anisotropy. Our retrieved NO2 columns are 54% of the MAX-DOAS data on average, reflecting the inevitable spatial inconsistency between the two types of measurement, errors in MAX-DOAS data, and uncertainties in our OMI retrieval related to aerosols and vertical profile of NO2.
Sensitivity tests show that excluding aerosol optical effects can either increase or decrease the retrieved NO2 for individual OMI pixels with an average increase by 14%. Excluding aerosols also complexly affects the retrievals of cloud fraction and particularly cloud pressure. Employing various surface albedo data sets slightly affects the retrieved NO2 on average (within 10%). The retrieved NO2 columns increase when the NO2 profiles are taken from MAX-DOAS retrievals (by 19% on average) or TM4 simulations (by 13%) instead of GEOS-Chem simulations. Our findings are also relevant to retrievals of other pollutants (e.g., sulfur dioxide, ormaldehyde, glyoxal) from UV–visible backscatter satellite instruments.