Articles | Volume 14, issue 6
Research article
04 Jun 2021
Research article |  | 04 Jun 2021

Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model

Meng Gao, Bryan A. Franz, Kirk Knobelspiesse, Peng-Wang Zhai, Vanderlei Martins, Sharon Burton, Brian Cairns, Richard Ferrare, Joel Gales, Otto Hasekamp, Yongxiang Hu, Amir Ibrahim, Brent McBride, Anin Puthukkudy, P. Jeremy Werdell, and Xiaoguang Xu


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2020-507', Yingxi Shi, 23 Feb 2021
    • AC1: 'Reply on RC1', Meng Gao, 23 Feb 2021
      • RC2: 'Reply on AC1', Yingxi Shi, 23 Feb 2021
        • AC3: 'Reply on RC2', Meng Gao, 28 Apr 2021
  • RC3: 'Comment on amt-2020-507', William Espinosa, 12 Mar 2021
    • AC2: 'Reply on RC3', Meng Gao, 26 Apr 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Meng Gao on behalf of the Authors (28 Apr 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (30 Apr 2021) by Alexander Kokhanovsky
AR by Meng Gao on behalf of the Authors (30 Apr 2021)
Short summary
Multi-angle polarimetric measurements can retrieve accurate aerosol properties over complex atmosphere and ocean systems; however, most retrieval algorithms require high computational costs. We propose a deep neural network (NN) forward model to represent the radiative transfer simulation of coupled atmosphere and ocean systems and then conduct simultaneous aerosol and ocean color retrievals on AirHARP measurements. The computational acceleration is 103 times with CPU or 104 times with GPU.