Articles | Volume 19, issue 2
https://doi.org/10.5194/gmd-19-933-2026
https://doi.org/10.5194/gmd-19-933-2026
Methods for assessment of models
 | 
29 Jan 2026
Methods for assessment of models |  | 29 Jan 2026

Identifying sea breezes from atmospheric model output (sea_breeze v1.1)

Andrew Brown, Claire Vincent, and Ewan Short

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-4848', Anonymous Referee #1, 23 Dec 2025
    • AC1: 'Reply on RC1', Andrew Brown, 15 Jan 2026
  • RC2: 'Comment on egusphere-2025-4848', Anonymous Referee #2, 13 Jan 2026
    • AC2: 'Reply on RC2', Andrew Brown, 15 Jan 2026

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Andrew Brown on behalf of the Authors (15 Jan 2026)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (15 Jan 2026) by Nicola Bodini
AR by Andrew Brown on behalf of the Authors (16 Jan 2026)
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Short summary
We developed software to identify sea breezes from weather model output, using three different methods, and applied these to four models for a 6-month period over Australia. We tested each method using case studies and statistics of sea breeze occurrences, finding that a method that identifies atmospheric moisture fronts performs well. Some potential errors are demonstrated due to detection of other frontal systems, but this method could be useful for robustly analyzing sea breezes from models.
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