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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers | Copyright
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 10 Jul 2018

Research article | 10 Jul 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Discriminating Between Clouds and Aerosols in the CALIOP Version 4.1 Data Products

Zhaoyan Liu1, Jayanta Kar1,2, Shan Zeng1,2, Jason Tackett1,2, Mark Vaughan1, Melody Avery1, Jacques Pelon3, Brian Getzewich1,2, Kam-Pui Lee1,2, Brian Magill1,2, Ali Omar1, Patricia Lucker1,2, Charles Trepte1, and David Winker1 Zhaoyan Liu et al.
  • 1NASA Langley Research Center, Hampton, VA
  • 2Science Systems and Appli cations (SSAI), Hampton, VA
  • 3LATMOS, Sorbonne Université, Université de Versailles Saint Quentin, CNRS, Paris, France

Abstract. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Operations (CALIPSO) mission released version 4.1 (V4) of their lidar level 2 cloud and aerosol data products in November 2016. These new products were derived from the CALIPSO V4 lidar level 1 data, in which the calibration of the measured backscatter data at both 532nm and 1064nm was significantly improved. This paper describes updates to the V4 level 2 cloud-aerosol-discrimination (CAD) algorithm that more accurately differentiates between clouds and aerosols throughout the Earth's atmosphere. The level 2 data products are improved with new CAD probability density functions (PDFs) that were developed to accommodate the calibration changes in the level 1 data. To enable more reliable identification of aerosol layers lofted into the upper troposphere and lower stratosphere, the CAD training dataset used in the earlier data releases was expanded to include stratospheric layers and representative examples of volcanic aerosol layers. The generic stratospheric layer classification of previous versions has been eliminated in V4, and cloud-aerosol classification is now performed on all layers detected everywhere from the surface to 30km. Cloud-aerosol classification has been further extended to layers detected at single shot resolution, which were previously classified by default as clouds. In this paper, we describe the underlying rationale used in constructing the V4 PDFs and assess the performance of the V4 CAD algorithm in the troposphere and stratosphere. Previous misclassifications of lofted dust and smoke in the troposphere have been largely improved, and volcanic aerosol layers and aerosol layers in the stratosphere are now being properly classified. CAD performance for single-shot layer detections is also evaluated. Most of the single-shot layers classified as aerosol occur within the dust belt, as may be expected. Due to changes in the 532nm calibration coefficients, the V4 feature finder detects ~9.0% more features at night and ~2.5% more during the day. These features are typically weakly scattering and classified about equally as clouds and aerosols. For those tropospheric layers detected in both V3 and V4, the CAD classifications of more than 95% of all cloud and daytime aerosol layers remain unchanged, as do the classifications of ~89% of nighttime aerosol layers. Overall, the nighttime net cloud and aerosol fractions remain unchanged from V3 to V4, but the daytime net aerosol fraction is increased by about 2% and the daytime net cloud fraction is decreased by about 2%.

Zhaoyan Liu et al.
Interactive discussion
Status: open (extended)
Status: open (extended)
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Zhaoyan Liu et al.
Zhaoyan Liu et al.
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Short summary
We describe the enhancements made to the cloud-aerosol discrimination (CAD) algorithms used to produce the CALIPSO version 4 (V4) data products. Revisions to the CAD probability distribution functions have greatly improved the recognition of aerosol layers lofted into the upper troposphere, and CAD is now applied to all layers detected in the stratosphere and all layers detected at single shot resolution. Detailed comparisons show significant improvements relative to previous versions.
We describe the enhancements made to the cloud-aerosol discrimination (CAD) algorithms used to...