Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 13, issue 6
Articles | Volume 13, issue 6
https://doi.org/10.5194/amt-13-3447-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-3447-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 6
Research article
 | 
29 Jun 2020
Research article |  | 29 Jun 2020

Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm

Daniel J. Miller, Michal Segal-Rozenhaimer, Kirk Knobelspiesse, Jens Redemann, Brian Cairns, Mikhail Alexandrov, Bastiaan van Diedenhoven, and Andrzej Wasilewski

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Short summary
A neural network (NN) is developed and used to retrieve cloud microphysical properties from multiangular and multispectral polarimetric remote sensing observations. The NN is applied to research scanning polarimeter (RSP) observations obtained during the ORACLES field campaign and compared to other co-located remote sensing retrievals of cloud effective radius and optical thickness. A NN approach can advance more complex iterative search retrieval algorithms by providing a quick initial guess.
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