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

Cooper, Matthew J.; Martin, Randall V.; Lyapustin, Alexei I.; McLinden, Chris A.

doi:https://doi.org/10.5194/amt-11-2983-2018

Articles | Volume 11, issue 5

https://doi.org/10.5194/amt-11-2983-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-11-2983-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 5

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. Cooper, Randall V. Martin, Alexei I. Lyapustin, and Chris A. McLinden

Data sets

IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions, Version 1 National Ice Center https://doi.org/10.7265/N52R3PMC

Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent, Version 5 M. J. Brodzik and J. S. Stewart https://doi.org/10.5067/3KB2JPLFPK3R

MODIS/Aqua Snow Cover Daily L3 Global 0.05Deg CMG, Version 6 D. Hall and G. A. Riggs https://doi.org/10.5067/MODIS/MYD10C1.006

MODIS/Terra Snow 90 Cover Daily L3 Global 0.05Deg CMG, Version 6 D. Hall and G. A. Riggs https://doi.org/10.5067/MODIS/MOD10C1.006

Short summary

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.