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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 May 2018
Research article |
| 22 May 2018
| 22 May 2018
Assessing snow extent data sets over North America to inform and improve trace gas retrievals from solar backscatter
Department of Physics and Atmospheric Science, Dalhousie University,
Halifax, Nova Scotia, Canada
Randall V. Martin
Department of Physics and Atmospheric Science, Dalhousie University,
Halifax, Nova Scotia, Canada
Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts,
USA
Alexei I. Lyapustin
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Chris A. McLinden
Air Quality Research Division, Environment and Climate Change Canada,
Toronto, Ontario, Canada
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15 citations as recorded by crossref.
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- Impact of environmental attributes on the uncertainty in MAIAC/MODIS AOD retrievals: A comparative analysis S. Falah et al. 10.1016/j.atmosenv.2021.118659
- Assessing the Impact of Corona-Virus-19 on Nitrogen Dioxide Levels over Southern Ontario, Canada D. Griffin et al. 10.3390/rs12244112
- A Long-Term Passive Microwave Snowoff Record for the Alaska Region 1988–2016 C. Pan et al. 10.3390/rs12010153
- Impacts of Soil Freeze–Thaw Process and Snow Melting Over Tibetan Plateau on Asian Summer Monsoon System: A Review and Perspective C. Wang et al. 10.3389/feart.2020.00133
- Evaluating impacts of snow, surface water, soil and vegetation on empirical vegetation and snow indices for the Utqiaġvik tundra ecosystem in Alaska with the LVS3 model Q. Zhang et al. 10.1016/j.rse.2020.111677
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- Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument M. Cooper et al. 10.1088/1748-9326/aba3a5
- Surface albedo and reflectance: Review of definitions, angular and spectral effects, and intercomparison of major data sources in support of advanced solar irradiance modeling over the Americas C. Gueymard et al. 10.1016/j.solener.2019.02.040
- A Comparison of Multi-Angle Implementation of Atmospheric Correction and MOD09 Daily Surface Reflectance Products From MODIS A. Lyapustin et al. 10.3389/frsen.2021.712093
- Sentinel-5P TROPOMI NO<sub>2</sub> retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data J. van Geffen et al. 10.5194/amt-15-2037-2022
- Ozone Monitoring Instrument (OMI) Aura nitrogen dioxide standard product version 4.0 with improved surface and cloud treatments L. Lamsal et al. 10.5194/amt-14-455-2021
- Estimation of anthropogenic and volcanic SO2 emissions from satellite data in the presence of snow/ice on the ground V. Fioletov et al. 10.5194/amt-16-5575-2023
- Calibration of Maxar Constellation Over Libya-4 Site Using MAIAC Technique M. Choi et al. 10.1109/JSTARS.2024.3367250
- Continuity between NASA MODIS Collection 6.1 and VIIRS Collection 2 land products M. Román et al. 10.1016/j.rse.2023.113963
15 citations as recorded by crossref.
- High‐Resolution Mapping of Nitrogen Dioxide With TROPOMI: First Results and Validation Over the Canadian Oil Sands D. Griffin et al. 10.1029/2018GL081095
- Impact of environmental attributes on the uncertainty in MAIAC/MODIS AOD retrievals: A comparative analysis S. Falah et al. 10.1016/j.atmosenv.2021.118659
- Assessing the Impact of Corona-Virus-19 on Nitrogen Dioxide Levels over Southern Ontario, Canada D. Griffin et al. 10.3390/rs12244112
- A Long-Term Passive Microwave Snowoff Record for the Alaska Region 1988–2016 C. Pan et al. 10.3390/rs12010153
- Impacts of Soil Freeze–Thaw Process and Snow Melting Over Tibetan Plateau on Asian Summer Monsoon System: A Review and Perspective C. Wang et al. 10.3389/feart.2020.00133
- Evaluating impacts of snow, surface water, soil and vegetation on empirical vegetation and snow indices for the Utqiaġvik tundra ecosystem in Alaska with the LVS3 model Q. Zhang et al. 10.1016/j.rse.2020.111677
- Variation of Aerosol Optical Properties over Cluj-Napoca, Romania, Based on 10 Years of AERONET Data and MODIS MAIAC AOD Product H. Ștefănie et al. 10.3390/rs15123072
- Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument M. Cooper et al. 10.1088/1748-9326/aba3a5
- Surface albedo and reflectance: Review of definitions, angular and spectral effects, and intercomparison of major data sources in support of advanced solar irradiance modeling over the Americas C. Gueymard et al. 10.1016/j.solener.2019.02.040
- A Comparison of Multi-Angle Implementation of Atmospheric Correction and MOD09 Daily Surface Reflectance Products From MODIS A. Lyapustin et al. 10.3389/frsen.2021.712093
- Sentinel-5P TROPOMI NO<sub>2</sub> retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data J. van Geffen et al. 10.5194/amt-15-2037-2022
- Ozone Monitoring Instrument (OMI) Aura nitrogen dioxide standard product version 4.0 with improved surface and cloud treatments L. Lamsal et al. 10.5194/amt-14-455-2021
- Estimation of anthropogenic and volcanic SO2 emissions from satellite data in the presence of snow/ice on the ground V. Fioletov et al. 10.5194/amt-16-5575-2023
- Calibration of Maxar Constellation Over Libya-4 Site Using MAIAC Technique M. Choi et al. 10.1109/JSTARS.2024.3367250
- Continuity between NASA MODIS Collection 6.1 and VIIRS Collection 2 land products M. Román et al. 10.1016/j.rse.2023.113963
Latest update: 14 Dec 2024
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.
To accurately infer air pollutant concentrations from satellite observations, we must first know...
