Advancing Maritime Transparent Cirrus Detection Using the Advanced Baseline Imager “Cirrus” Band

Author:

McHardy Theodore M.1,Campbell James R.2,Peterson David A.2,Lolli Simone3,Bankert Richard L.2,Garnier Anne4,Kuciauskas Arunas P.2,Surratt Melinda L.2,Marquis Jared W.5,Miller Steven D.6,Dolinar Erica K.2,Dong Xiquan1

Affiliation:

1. 1 Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA

2. 2 Naval Research Laboratory, 7 Grace Hopper Avenue, Monterey, CA 93943, USA

3. 3 CNR-IMAA, Istituto di Metodologie per l’Analisi Ambientale, Tito Scalo, Italy

4. 4 Science Systems Applications Inc., 1 Enterprise Pkwy, Hampton, VA, 23666, U.S.

5. 5 Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota

6. 6 Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA

Abstract

AbstractWe 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 Ch. 4 radiance as a function of AMF. The algorithm detects nearly 50% of sub-visual 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 and 4% of clear-sky pixels being misidentified as cirrus. A semi-quantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378 μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an over-land 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.

Publisher

American Meteorological Society

Subject

Atmospheric Science,Ocean Engineering

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