Articles | Volume 16, issue 7
https://doi.org/10.5194/amt-16-1923-2023
https://doi.org/10.5194/amt-16-1923-2023
Research article
 | 
13 Apr 2023
Research article |  | 13 Apr 2023

Differences in MOPITT surface level CO retrievals and trends from Level 2 and Level 3 products in coastal grid boxes

Ian Ashpole and Aldona Wiacek

Related authors

Satellite-based evidence of dust emission over Northern Canada
Ian Ashpole and Aldona Wiacek
EGUsphere, https://doi.org/10.5194/egusphere-2024-3828,https://doi.org/10.5194/egusphere-2024-3828, 2025
Short summary
Impact of land–water sensitivity contrast on MOPITT retrievals and trends over a coastal city
Ian Ashpole and Aldona Wiacek
Atmos. Meas. Tech., 13, 3521–3542, https://doi.org/10.5194/amt-13-3521-2020,https://doi.org/10.5194/amt-13-3521-2020, 2020
Short summary

Related subject area

Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Robustness of atmospheric trace gas retrievals obtained from low-spectral-resolution Fourier transform infrared absorption spectra under variations of interferogram length
Bavo Langerock, Martine De Mazière, Filip Desmet, Pauli Heikkinen, Rigel Kivi, Mahesh Kumar Sha, Corinne Vigouroux, Minqiang Zhou, Gopala Krishna Darbha, and Mohmmed Talib
Atmos. Meas. Tech., 18, 2439–2446, https://doi.org/10.5194/amt-18-2439-2025,https://doi.org/10.5194/amt-18-2439-2025, 2025
Short summary
Retrieval of NO2 profiles from 3 years of Pandora MAX-DOAS measurements in Toronto, Canada
Ramina Alwarda, Kristof Bognar, Xiaoyi Zhao, Vitali Fioletov, Jonathan Davies, Sum Chi Lee, Debora Griffin, Alexandru Lupu, Udo Frieß, Alexander Cede, Yushan Su, and Kimberly Strong
Atmos. Meas. Tech., 18, 2397–2423, https://doi.org/10.5194/amt-18-2397-2025,https://doi.org/10.5194/amt-18-2397-2025, 2025
Short summary
A channel selection methodology for enhancing volcanic SO2 monitoring using FY-3E/HIRAS-II hyperspectral data
Xinyu Li, Lin Zhu, Hongfu Sun, Jun Li, Ximing Lv, Chengli Qi, and Huanhuan Yan
Atmos. Meas. Tech., 18, 2333–2352, https://doi.org/10.5194/amt-18-2333-2025,https://doi.org/10.5194/amt-18-2333-2025, 2025
Short summary
Predictions of failed satellite retrieval of air quality using machine learning
Edward Malina, Jure Brence, Jennifer Adams, Jovan Tanevski, Sašo Džeroski, Valentin Kantchev, and Kevin W. Bowman
Atmos. Meas. Tech., 18, 1689–1715, https://doi.org/10.5194/amt-18-1689-2025,https://doi.org/10.5194/amt-18-1689-2025, 2025
Short summary
Deep transfer learning method for seasonal TROPOMI XCH4 albedo correction
Alexander C. Bradley, Barbara Dix, Fergus Mackenzie, J. Pepijn Veefkind, and Joost A. de Gouw
Atmos. Meas. Tech., 18, 1675–1687, https://doi.org/10.5194/amt-18-1675-2025,https://doi.org/10.5194/amt-18-1675-2025, 2025
Short summary

Cited articles

Ashpole, I. and Wiacek, A.: Impact of land–water sensitivity contrast on MOPITT retrievals and trends over a coastal city, Atmos. Meas. Tech., 13, 3521–3542, https://doi.org/10.5194/amt-13-3521-2020, 2020. 
Ashpole, I. and Wiacek, A.: Land- and water-only Level 3 products from MOPITT TIR-NIR Version 8 CO retrievals, V1, Borealis [data set], https://doi.org/10.5683/SP3/ERCG2H, 2022. 
Buchholz, R. R., Park, M., Worden, H. M., Tang, W., Edwards, D. P., Gaubert, B., Deeter, M. N., Sullivan, T., Ru, M., Chin, M., Levy, R. C., Zheng, B., and Magzamen, S.: New seasonal pattern of pollution emerges from changing North American wildfires, Nat. Commun., 13, 2043, https://doi.org/10.1038/s41467-022-29623-8, 2022. 
Deeter, M. N., Emmons, L. K., Francis, G. L., Edwards, D. P., Gille, J. C., Warner, J. X., Khattatov, B., Ziskin, D., Lamarque, J.-F., Ho, S.-P., Yudin, V., Attié, J.-L., Packman, D., Chen, J., Mao, D., and Drummond, J. R.: Operational carbon monoxide retrieval algorithm and selected results for the MOPITT instrument, J. Geophys. Res., 108, 4399, https://doi.org/10.1029/2002JD003186, 2003. 
Download
Short summary
The MOPITT instrument has been measuring atmospheric carbon monoxide (CO) from space since 2000. Its data products are valuable for CO trend analysis. This paper compares products with different spatial resolutions to identify discrepancies in mean CO amounts and detectable trends for coastal grid boxes. It is found that CO amounts and trends differ significantly between data products for a large number of these grid boxes, essentially due to how the coarser-resolution products are created.
Share