Articles | Volume 15, issue 6
Atmos. Meas. Tech., 15, 1931–1956, 2022
Atmos. Meas. Tech., 15, 1931–1956, 2022
Research article
30 Mar 2022
Research article | 30 Mar 2022

Assessing the benefits of Imaging Infrared Radiometer observations for the CALIOP version 4 cloud and aerosol discrimination algorithm

Thibault Vaillant de Guélis et al.

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Revised manuscript accepted for AMT
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Cited articles

Ackerman, S. A.: Remote sensing aerosols using satellite infrared observations, J. Geophys. Res.-Atmos., 102, 17069–17079,, 1997. a, b
Ackerman, S. A., Smith, W. L., Revercomb, H. E., and Spinhirne, J. D.: The 27–28 October 1986 FIRE IFO Cirrus Case Study: Spectral Properties of Cirrus Clouds in the 8–12 µm Window, Mon. Weather Rev., 118, 2377–2388,<2377:TOFICC>2.0.CO;2, 1990. a
AERIS/ICARE:, last access: 21 March 2022. a
Ashpole, I. and Washington, R.: An automated dust detection using SEVIRI: A multiyear climatology of summertime dustiness in the central and western Sahara, J. Geophys. Res.-Atmos., 117, D08202,, 2012. a
Avery, M. A., Ryan, R. A., Getzewich, B. J., Vaughan, M. A., Winker, D. M., Hu, Y., Garnier, A., Pelon, J., and Verhappen, C. A.: CALIOP V4 cloud thermodynamic phase assignment and the impact of near-nadir viewing angles, Atmos. Meas. Tech., 13, 4539–4563,, 2020. a, b
Short summary
A new IIR-based cloud and aerosol discrimination (CAD) algorithm is developed using the IIR brightness temperature differences for cloud and aerosol features confidently identified by the CALIOP version 4 CAD algorithm. IIR classifications agree with the majority of V4 cloud identifications, reduce the ambiguity in a notable fraction of not confident V4 cloud classifications, and correct a few V4 misclassifications of cloud layers identified as dense dust or elevated smoke layers by CALIOP.