Preprints
https://doi.org/10.5194/amt-2021-419
https://doi.org/10.5194/amt-2021-419

  15 Dec 2021

15 Dec 2021

Review status: this preprint is currently under review for the journal AMT.

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

Thibault Vaillant de Guélis1,2,3, Gérard Ancellet1, Anne Garnier2,3, Laurent C.-Labonnote4, Jacques Pelon1, Mark A. Vaughan3, Zhaoyan Liu3, and David M. Winker3 Thibault Vaillant de Guélis et al.
  • 1LATMOS/IPSL, CNRS, Sorbonne Université, UVSQ, 75252 Paris, France
  • 2Science Systems and Applications, Inc., Hampton, VA 23666, USA
  • 3NASA Langley Research Center, Hampton, VA 23681, USA
  • 4LOA, Université de Lille, 59655 Villeneuve d’Ascq, France

Abstract. The features detected in monolayer atmospheric columns sounded by the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP) and classified as cloud or aerosol layers by the CALIOP version 4 (V4) cloud and aerosol discrimination (CAD) algorithm are reassessed using perfectly collocated brightness temperatures measured by the Imaging Infrared Radiometer (IIR) onboard the same satellite. Using the IIR’s three wavelength measurements of layers that are confidently classified by the CALIOP CAD algorithm, we calculate two-dimensional (2-D) probability distribution functions (PDFs) of IIR brightness temperature differences (BTDs) for different cloud and aerosol types. We then compare these PDFs with 1-D radiative transfer simulations for ice and water clouds and dust and marine aerosols. Using these IIR 2-D BTD signature PDFs, we develop and deploy a new IIR-based CAD algorithm and compare the classifications obtained to the results reported by the CALIOP-only V4 CAD algorithm. IIR observations are shown to be able to identify clouds with a good accuracy. The IIR cloud identifications agree very well with layers classified as confident clouds by the V4 CAD algorithm (88 %). More importantly, simultaneous use of IIR information reduces the ambiguity in a notable fraction of "not confident" V4 cloud classifications. 28 % and 14 % of the ambiguous V4 cloud classifications are confirmed thanks to the IIR observations in the tropics and in the midlatitudes respectively. IIR observations are of relatively little help in deriving high confidence classifications for most aerosols, as the low altitudes and small optical depths of aerosol layers yield IIR signatures that are similar to those from clear skies. However, misclassifications of aerosol layers, such as dense dust or elevated smoke layers, by the V4 CAD algorithm can be corrected to cloud layer classification by including IIR information. 10 %, 16 %, and 6 % of the ambiguous V4 dust, polluted dust, and tropospheric elevated smoke respectively are found to be misclassified cloud layers by the IIR measurements.

Thibault Vaillant de Guélis et al.

Status: open (until 26 Jan 2022)

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Thibault Vaillant de Guélis et al.

Thibault Vaillant de Guélis et al.

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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 CALIOP version 4 CAD algorithm. IIR classifications agree with the majority of V4 cloud identifications, reduces 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.