26 Jan 2021

26 Jan 2021

Review status: this preprint is currently under review for the journal AMT.

Assessing sub-grid variability within satellite pixels using airborne mapping spectrometer measurements

Wenfu Tang1,2, David P. Edwards2, Louisa K. Emmons2, Helen M. Worden2, Laura M. Judd3, Lok N. Lamsal4,5, Jassim A. Al-Saadi3, Scott J. Janz4, James H. Crawford3, Merritt N. Deeter2, Gabriele Pfister2, Rebecca R. Buchholz2, Benjamin Gaubert2, and Caroline R. Nowlan6 Wenfu Tang et al.
  • 1Advanced Study Program, National Center for Atmospheric Research, Boulder, CO, USA
  • 2Atmospheric Chemistry Observations and Modeling, National Center for Atmospheric Research, Boulder, CO, USA
  • 3NASA Langley Research Center, Hampton, VA 23681, USA
  • 4NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
  • 5Universities Space Research Association, Columbia, MD 21046, USA
  • 6Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA

Abstract. Sub-grid variability (SGV) of atmospheric trace gases within satellite pixels is a key issue in satellite design, and interpretation and validation of retrieval products. However, characterizing this variability is challenging due to the lack of independent high-resolution measurements. Here we use tropospheric NO2 vertical column (VC) measurements from the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument with a spatial resolution of about 250 m 250 m to quantify the normalized SGV (i.e., the standard deviation of the sub-grid GeoTASO values within the sampled satellite pixel divided by their mean of the sub-grid GeoTASO values within the sampled satellite pixel) for different satellite pixel sizes. We use the GeoTASO measurements over the Seoul Metropolitan Area (SMA) and Busan region of South Korea during the 2016 KORUS‐AQ field campaign, and over the Los Angeles Basin, USA during the 2017 SARP field campaign. We find that the normalized SGV of NO2 VC increases with increasing satellite pixel sizes (from ~10 % for 0.5 km × 0.5 km pixel size to ~35 % for 25 km × 25 km pixel size), and this relationship holds for the three study regions, which are also within the domains of upcoming geostationary satellite air quality missions. We also quantify the temporal variability of the retrieved NO2 VC within the same satellite pixels (represented by the difference of retrieved values at two different times of a day). For a given satellite pixel size, the temporal variability within the same satellite pixels increases with the sampling time difference over SMA. For a given small (e.g., <= 4 hours) sampling time difference within the same satellite pixels, the temporal variability of the retrieved NO2 VC increases with the increasing spatial resolution over the SMA, Busan region, and the Los Angeles basin.

The results of this study have implications for future satellite design and retrieval interpretation, and validation when comparing pixel data with local observations. In addition, the analyses presented in this study are equally applicable in model evaluation when comparing model grid values to local observations. Results from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model indicate that the normalized satellite SGV of tropospheric NO2 VC calculated in this study could serve as an upper bound to the satellite SGV of other species (e.g., CO and SO2) that share common source(s) with NO2 but have relatively longer lifetime.

Wenfu Tang et al.

Status: open (until 23 Mar 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Wenfu Tang et al.


Total article views: 295 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
220 69 6 295 21 2 1
  • HTML: 220
  • PDF: 69
  • XML: 6
  • Total: 295
  • Supplement: 21
  • BibTeX: 2
  • EndNote: 1
Views and downloads (calculated since 26 Jan 2021)
Cumulative views and downloads (calculated since 26 Jan 2021)

Viewed (geographical distribution)

Total article views: 277 (including HTML, PDF, and XML) Thereof 273 with geography defined and 4 with unknown origin.
Country # Views %
  • 1
Latest update: 02 Mar 2021