Articles | Volume 15, issue 17
Research article
14 Sep 2022
Research article |  | 14 Sep 2022

Segmentation-based multi-pixel cloud optical thickness retrieval using a convolutional neural network

Vikas Nataraja, Sebastian Schmidt, Hong Chen, Takanobu Yamaguchi, Jan Kazil, Graham Feingold, Kevin Wolf, and Hironobu Iwabuchi


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-45', Anonymous Referee #1, 04 Mar 2022
    • AC1: 'Reply on RC1', Vikas Nataraja, 20 Apr 2022
  • RC2: 'Comment on amt-2022-45', Anonymous Referee #2, 05 May 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Vikas Nataraja on behalf of the Authors (30 Jun 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (01 Jul 2022) by Jing Wei
RR by Anonymous Referee #3 (14 Aug 2022)
ED: Publish as is (14 Aug 2022) by Jing Wei
AR by Vikas Nataraja on behalf of the Authors (23 Aug 2022)
Short summary
A convolutional neural network (CNN) is introduced to retrieve cloud optical thickness (COT) from passive cloud imagery. The CNN, trained on large eddy simulations from the Sulu Sea, learns from spatial information at multiple scales to reduce cloud inhomogeneity effects. By considering the spatial context of a pixel, the CNN outperforms the traditional independent pixel approximation (IPA) across several cloud morphology metrics.