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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 5
Atmos. Meas. Tech., 11, 2983-2994, 2018
https://doi.org/10.5194/amt-11-2983-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 11, 2983-2994, 2018
https://doi.org/10.5194/amt-11-2983-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 22 May 2018

Research article | 22 May 2018

Assessing snow extent data sets over North America to inform and improve trace gas retrievals from solar backscatter

Matthew J. Cooper1, Randall V. Martin1,2, Alexei I. Lyapustin3, and Chris A. McLinden4 Matthew J. Cooper et al.
  • 1Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
  • 2Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts, USA
  • 3NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
  • 4Air Quality Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada

Abstract. Accurate representation of surface reflectivity is essential to tropospheric trace gas retrievals from solar backscatter observations. Surface snow cover presents a significant challenge due to its variability and thus snow-covered scenes are often omitted from retrieval data sets; however, the high reflectance of snow is potentially advantageous for trace gas retrievals. We first examine the implications of surface snow on retrievals from the upcoming TEMPO geostationary instrument for North America. We use a radiative transfer model to examine how an increase in surface reflectivity due to snow cover changes the sensitivity of satellite retrievals to NO2 in the lower troposphere. We find that a substantial fraction (>50%) of the TEMPO field of regard can be snow covered in January and that the average sensitivity to the tropospheric NO2 column substantially increases (doubles) when the surface is snow covered.

We then evaluate seven existing satellite-derived or reanalysis snow extent products against ground station observations over North America to assess their capability of informing surface conditions for TEMPO retrievals. The Interactive Multisensor Snow and Ice Mapping System (IMS) had the best agreement with ground observations (accuracy of 93%, precision of 87%, recall of 83%). Multiangle Implementation of Atmospheric Correction (MAIAC) retrievals of MODIS-observed radiances had high precision (90% for Aqua and Terra), but underestimated the presence of snow (recall of 74% for Aqua, 75% for Terra). MAIAC generally outperforms the standard MODIS products (precision of 51%, recall of 43% for Aqua; precision of 69%, recall of 45% for Terra). The Near-real-time Ice and Snow Extent (NISE) product had good precision (83%) but missed a significant number of snow-covered pixels (recall of 45%). The Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data set had strong performance metrics (accuracy of 91%, precision of 79%, recall of 82%). We use the Fscore, which balances precision and recall, to determine overall product performance (F = 85%, 82 (82)%, 81%, 58%, 46 (54)% for IMS, MAIAC Aqua (Terra), CMC, NISE, MODIS Aqua (Terra), respectively) for providing snow cover information for TEMPO retrievals from solar backscatter observations. We find that using IMS to identify snow cover and enable inclusion of snow-covered scenes in clear-sky conditions across North America in January can increase both the number of observations by a factor of 2.1 and the average sensitivity to the tropospheric NO2 column by a factor of 2.7.

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To accurately infer air pollutant concentrations from satellite observations, we must first know the reflectivity of the Earth’s surface. Using a model, we show that satellite observations are better able to observe NO2 near the surface if snow is present. However, knowing when snow is present is difficult due to its variability. We test seven existing snow cover data sets to assess their ability to inform future satellite observations and find that the IMS data set is best suited for this task.
To accurately infer air pollutant concentrations from satellite observations, we must first know...
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