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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/amt-2020-369
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-369
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  29 Sep 2020

29 Sep 2020

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This preprint is currently under review for the journal AMT.

Two-dimensional and multi-channel feature detection algorithm for the CALIPSO lidar measurements

Thibault Vaillant de Guélis1,2, Mark A. Vaughan3, David M. Winker3, and Zhaoyan Liu3 Thibault Vaillant de Guélis et al.
  • 1NASA Postdoctoral Program Fellow, NASA, Langley Research Center, Hampton, VA 23681, USA
  • 2Science Systems and Applications, Inc., Hampton, VA 23666, USA
  • 3NASA Langley Research Center, Hampton, VA 23681, USA

Abstract. In this paper we describe a new two-dimensional and multi-channel feature detection algorithm (2D-McDA) and demonstrate its application to lidar backscatter measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. Unlike previous layer detection schemes, this context sensitive feature finder algorithm is applied to a 2D lidar scene i.e., to the image formed by many successive lidar profiles. Features are identified when an extended and contiguous 2D region of enhanced backscatter signal rises significantly above the expected clear air value. Using an iterated 2D feature detection algorithm dramatically improves the fine details of feature shapes and can accurately identify previously undetected layers (e.g., subvisible cirrus) that are very thin vertically but horizontally persistent. Because the algorithm looks for consistent 2D patterns, it potentially offers improved discrimination of juxtaposed cloud and aerosol layers. Moreover, the 2D detection algorithm uses the backscatter signals from all available channels: 532 nm parallel, 532 nm perpendicular, and 1064 nm total. Since the backscatter from some aerosol or cloud particle types can be more pronounced in one channel than another, simultaneously assessing the signals from all channels greatly improves the layer detection. For example, ice particles in subvisible cirrus strongly depolarize the lidar signal and, consequently, are easier to detect in the 532 nm perpendicular channel. Use of the 1064 nm channel greatly improves the detection of dense smoke layers, because smoke extinction at 532 nm is much larger than at 1064 nm, and hence the range-dependent reduction in lidar signals due to attenuation occurs much faster at 532 nm than at 1064 nm. Moreover, the photomultiplier tubes used at 532 nm are known to generate artifacts in an extended area below highly reflective liquid clouds, introducing false detections that artificially lower the apparent cloud base altitude, i.e. the cloud base when the cloud is transparent or the level of complete attenuation of the lidar signal when it is opaque. By adding the information available in the 1064 nm channel, this new algorithm can better identify the true apparent cloud base altitudes of such clouds.

Thibault Vaillant de Guélis et al.

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Thibault Vaillant de Guélis et al.

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Short summary
We introduce a new lidar feature detection algorithm that dramatically improves the fine details of layers identified in the CALIOP data. By applying our two-dimensional scanning technique to the measurements in all three channels, we minimize false positives while accurately identifying previously undetected features such as subvisible cirrus and the full vertical extent of dense smoke plumes. Multiple comparisons to version 4.2 CALIOP retrievals illustrate the scope of the improvements made.
We introduce a new lidar feature detection algorithm that dramatically improves the fine details...
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