Applying deep learning to NASA MODIS data to create a community record of marine low-cloud mesoscale morphology

Yuan, Tianle; Song, Hua; Wood, Robert; Mohrmann, Johannes; Meyer, Kerry; Oreopoulos, Lazaros; Platnick, Steven

doi:https://doi.org/10.5194/amt-13-6989-2020

Articles | Volume 13, issue 12

https://doi.org/10.5194/amt-13-6989-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-13-6989-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 12

Research article

|

21 Dec 2020

Research article |

| 21 Dec 2020

Applying deep learning to NASA MODIS data to create a community record of marine low-cloud mesoscale morphology

Tianle Yuan, Hua Song, Robert Wood, Johannes Mohrmann, Kerry Meyer, Lazaros Oreopoulos, and Steven Platnick

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Final revised paper (published on 21 Dec 2020)
Preprint (discussion started on 31 Mar 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review comments', Anonymous Referee #1, 30 Apr 2020
- AC1: 'Response to Reviewer 1', Tianle Yuan, 22 Jun 2020
- AC3: 'Response to Reviewer 1 (revised)', Tianle Yuan, 14 Aug 2020
RC2: 'Review of Yuan et al.', Anonymous Referee #2, 04 May 2020
- AC2: 'Response to Reviewer 2', Tianle Yuan, 22 Jun 2020
- AC4: 'Response to Reviewer 2 (revised)', Tianle Yuan, 14 Aug 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Tianle Yuan on behalf of the Authors (22 Jun 2020) Author's response Manuscript

ED: Publish subject to minor revisions (review by editor) (12 Jul 2020) by Sebastian Schmidt

AR by Tianle Yuan on behalf of the Authors (16 Jul 2020)

ED: Publish as is (29 Sep 2020) by Sebastian Schmidt

AR by Tianle Yuan on behalf of the Authors (07 Oct 2020)

Short summary

We use deep transfer learning techniques to classify satellite cloud images into different morphology types. It achieves the state-of-the-art results and can automatically process a large amount of satellite data. The algorithm will help low-cloud researchers to better understand their mesoscale organizations.