Articles | Volume 13, issue 6
https://doi.org/10.5194/amt-13-3447-2020
https://doi.org/10.5194/amt-13-3447-2020
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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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Daniel Miller on behalf of the Authors (09 Mar 2020)  Manuscript 
ED: Referee Nomination & Report Request started (28 Mar 2020) by Paquita Zuidema
RR by Anonymous Referee #3 (07 Apr 2020)
RR by Anonymous Referee #1 (16 Apr 2020)
ED: Publish subject to minor revisions (review by editor) (16 Apr 2020) by Paquita Zuidema
AR by Daniel Miller on behalf of the Authors (05 May 2020)  Author's response   Manuscript 
ED: Publish as is (10 May 2020) by Paquita Zuidema
AR by Daniel Miller on behalf of the Authors (20 May 2020)  Manuscript 
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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.