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

Brown, Andrew; Vincent, Claire; Short, Ewan

doi:10.5194/gmd-19-933-2026

Articles | Volume 19, issue 2

https://doi.org/10.5194/gmd-19-933-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-19-933-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 2

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

Supplement

https://doi.org/10.5194/gmd-19-933-2026-supplement

Data sets

AUS2200: A High-Resolution Regional Atmospheric Modeling Dataset Y. Huang et al. https://doi.org/10.25914/w95d-q328

Bureau of Meteorology Satellite Observations (Collection) Bureau of Meteorology https://doi.org/10.25914/61a609f9e7ffa

Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia – Version 2 (BARRA2) Bureau of Meteorology https://doi.org/10.25914/1x6g-2v48

Model code and software

sea_breeze: v1.1 Andrew Brown et al. https://doi.org/10.5281/zenodo.17220916

andrewbrown31/sea_breeze_analysis: v1.0 Andrew Brown https://doi.org/10.5281/zenodo.17230239

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.