Articles | Volume 14, issue 4
Atmos. Meas. Tech., 14, 2699–2716, 2021
Atmos. Meas. Tech., 14, 2699–2716, 2021

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 et al.


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

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.