Applying machine learning methods to detect convection using  Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data

Lee, Yoonjin; Kummerow, Christian D.; Ebert-Uphoff, Imme

doi:https://doi.org/10.5194/amt-14-2699-2021

Articles | Volume 14, issue 4

https://doi.org/10.5194/amt-14-2699-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-14-2699-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 4

Research article

|

08 Apr 2021

Research article |

| 08 Apr 2021

Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data

Yoonjin Lee, Christian D. Kummerow, and Imme Ebert-Uphoff

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Final revised paper (published on 08 Apr 2021)
Preprint (discussion started on 14 Nov 2020)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review of Applying machine learning methods to detect convection using GOES-16 ABI data', Gabriele Franch, 11 Jan 2021
- AC1: 'Reply on RC1', Yoonjin Lee, 11 Feb 2021
RC2: 'Applying machine learning methods to detect convection using GOES-16 ABI data', Anonymous Referee #2, 13 Jan 2021
- AC2: 'Reply on RC2', Yoonjin Lee, 11 Feb 2021

Peer-review completion

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

AR by Yoonjin Lee on behalf of the Authors (11 Feb 2021) Author's response Manuscript

ED: Referee Nomination & Report Request started (12 Feb 2021) by Gianfranco Vulpiani

RR by Anonymous Referee #2 (22 Feb 2021)

ED: Publish as is (02 Mar 2021) by Gianfranco Vulpiani

Short summary

Convective clouds are usually associated with intense rain that can cause severe damage, and thus it is important to accurately detect convective clouds. This study develops a machine learning model that can identify convective clouds from five temporal visible and infrared images as humans can point at convective regions by finding bright and bubbling areas. The results look promising when compared to radar-derived products, which are commonly used for detecting convection.