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

Vaillant de Guélis, Thibault; Ancellet, Gérard; Garnier, Anne; C.-Labonnote, Laurent; Pelon, Jacques; Vaughan, Mark A.; Liu, Zhaoyan; Winker, David M.

doi:https://doi.org/10.5194/amt-15-1931-2022

Articles | Volume 15, issue 6

https://doi.org/10.5194/amt-15-1931-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-15-1931-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 6

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, Gérard Ancellet, Anne Garnier, Laurent C.-Labonnote, Jacques Pelon, Mark A. Vaughan, Zhaoyan Liu, and David M. Winker

Data sets

CALIPSO Lidar Level 2 Merge Layer Data, V4-20 NASA https://doi.org/10.5067/CALIOP/CALIPSO/LID_L2_05KMMLAY-STANDARD-V4-20

CALIPSO Infrared Imaging Radiometer (IIR) Level 2 Track, V4-20 NASA https://doi.org/10.5067/CALIOP/CALIPSO/CAL_IIR_L2_Track-Standard-V4-20

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.