Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations

Marais, Willem J.; Holz, Robert E.; Reid, Jeffrey S.; Willett, Rebecca M.

doi:https://doi.org/10.5194/amt-13-5459-2020

Articles | Volume 13, issue 10

https://doi.org/10.5194/amt-13-5459-2020

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

https://doi.org/10.5194/amt-13-5459-2020

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

Articles | Volume 13, issue 10

Research article

|

14 Oct 2020

Research article |

| 14 Oct 2020

Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations

Willem J. Marais, Robert E. Holz, Jeffrey S. Reid, and Rebecca M. Willett

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Final revised paper (published on 14 Oct 2020)
Preprint (discussion started on 14 Apr 2020)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review on Manuscript AMT-2020-74', Anonymous Referee #2, 27 Apr 2020
- AC2: 'Response to Anonymous Referee #2', Willem Marais, 08 Jul 2020
SC1: 'Couple questions and comments', Yingxi Shi, 18 May 2020
- SC2: 'Response to Yingxi Shi', Willem Marais, 05 Jun 2020
RC2: 'Reviev of amt-2020-74', Anonymous Referee #1, 20 May 2020
- AC1: 'Response to Anonymous Referee #1', Willem Marais, 08 Jul 2020

Peer-review completion

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

AR by Willem Marais on behalf of the Authors (14 Jul 2020) Author's response Manuscript

ED: Publish as is (27 Jul 2020) by Alexander Kokhanovsky

AR by Willem Marais on behalf of the Authors (27 Jul 2020)

Short summary

Space agencies use moderate-resolution satellite imagery to study how smoke, dust, pollution (aerosols) and cloud types impact the Earth's climate; these space agencies include NASA, ESA and the China Meteorological Administration. We demonstrate in this paper that an algorithm with convolutional neural networks can greatly enhance the automated detection of aerosols and cloud types from satellite imagery. Our algorithm is an improvement on current aerosol and cloud detection algorithms.