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.

Highlights

  • Since its launch in 2006, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Operations (CALIPSO) mission (Winker et al, 2010) has provided vertically-resolved measurements of aerosols and clouds between 81.8° S and 81.8° N thanks to its primary instrument: the two-wavelength (532 and 1064 nm) Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP)

  • 5.1 Infrared Radiometer (IIR) cloud and aerosol discrimination (CAD) score vs version 4 (V4) CAD score

  • This paper describes how the IIR brightness temperature observations can be used to discriminate aerosol from cloud monolayers. 1-D radiative transfer simulations have been performed first to gain insight into how the IIR brightness temperature differences (BTDs) signature evolves with increasing optical depth and altitude of clouds and aerosols

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Summary

Introduction

Since its launch in 2006, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Operations (CALIPSO) mission (Winker et al, 2010) has provided vertically-resolved measurements of aerosols and clouds between 81.8° S and 81.8° N thanks to its primary instrument: the two-wavelength (532 and 1064 nm) Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP). Using a split-window technique (Inoue, 1985), dust or volcanic aerosols can be detected using channels centered in the atmospheric window (e.g., Ackerman, 1997; Pierangelo et al, 2004; Ashpole and Washington, 2012; Prata and Prata, 2012; Capelle et al, 2018) These aerosol layers can be distinguished from ice clouds (Ackerman et al, 1990) through the analysis of the sign and amplitude of inter-channel brightness temperature differences (BTDs), because clouds and aerosols such as volcanic ash or dust exhibit different spectral variations of their respective complex refractive indices. This technique has proven to be useful in reducing the frequency of dense dust misclassified as cloud in the V3 CAD algorithm using on-board satellite infrared spectroradiometers (Chen et al, 2010; Naeger et al, 2013a, b).

IIR observations
CALIOP observations
Observed clear-sky IIR signature uncertainty
Aerosols
Clouds
IIR CAD score
IIR CAD score masks
IIR CAD score vs V4 CAD score
Cirrus fringes
Example of cloud misclassified as dust by the V4
Conclusions
455 Acknowledgements
Full Text
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