Advancing maritime transparent cirrus detection using the advanced baseline imager “cirrus” band

Theodore M. McHardy, James R. Campbell, David A. Peterson, Simone Lolli, Richard L. Bankert, Anne Garnier, Arunas P. Kuciauskas, Melinda L. Surratt, Jared W. Marquis, Steven D. Miller, Erica K. Dolinar, Xiquan Dong

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


We describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite 16 (GOES-16) and developed with collocated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES- 16 Advanced Baseline Imager (ABI) channel-4 (1.378 µm) radiance and CALIOP 0.532-µm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378-µm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine-learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the channel-4 radiance as a function of AMF. The algorithm detects nearly 50% of subvisual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and4%of clear-sky pixels being misidentified as cirrus. A semiquantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378-mm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an overland algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.

Original languageEnglish (US)
Pages (from-to)1093-1110
Number of pages18
JournalJournal of Atmospheric and Oceanic Technology
Issue number6
StatePublished - Jun 2021


  • Algorithms
  • Cirrus clouds
  • Remote sensing

ASJC Scopus subject areas

  • Ocean Engineering
  • Atmospheric Science


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