Impacts of MOPITT cloud detection revisions on observation frequency and mapping of highly polluted scenes

• Published in: Remote sensing of environment 2021-09, Vol.262, p.112516, Article 112516
• Main Authors: Deeter, M.N., Mao, D., Martínez-Alonso, S., Worden, H.M., Andreae, M.O., Schlager, H.
• Format: Article
• Language: English
• Subjects: Aerosol optical depth; Aerosols; Algorithms; Biomass burning; Carbon monoxide; Case studies; Clear sky; Cloud detection; Cloudiness; Clouds; Land pollution; Meteorological satellites; MOPITT; Pollution; Pollution detection; Retrieval; Revisions; Sampling; Satellite remote sensing; Swath width; Troposphere
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2021.112516

Measurements made by the MOPITT (“Measurements of Pollution in the Troposphere”) instrument on the NASA Terra polar-orbiting platform enable the retrieval of tropospheric carbon monoxide (CO) concentrations. As determined by the Terra orbit and MOPITT swath width, the frequency of MOPITT observations at a specific location, or measurement sampling frequency, is typically about once every three to four days. However, because the MOPITT retrieval algorithm is only applicable to clear-sky scenes, MOPITT retrieval sampling frequency strongly depends on regional cloudiness and can be much smaller than the measurement sampling frequency. Moreover, highly polluted scenes, characterized by high aerosol optical depths, can be confused with cloudy scenes and thus be discarded unnecessarily by the MOPITT cloud detection algorithm. Herein are described revisions to this algorithm which substantially increase retrieval sampling over land in varying pollution conditions. The performance of the revised cloud detection algorithm is evaluated through validation, case studies, and continental-scale maps of retrieval sampling frequency. Presented case studies illustrate (1) why the current operational MOPITT cloud detection algorithm excludes extended areas of potentially valuable cloud-free MOPITT observations and (2) how, for the same scenes, improved retrieval coverage benefits analyses of regional CO variability. Maps of retrieval sampling frequency for South America and Asia exhibit well-defined improvements, especially in regions with poor sampling frequency in the current product. •The current MOPITT cloud detection algorithm is often overly conservative.•Algorithm revisions resulting in improved sampling are described and evaluated.•Case studies illustrate improved retrieval coverage in both clean and polluted scenes.