Bluestein, H. B.: Observations and Theory of Weather Systems, Vol. 2, Synoptic-Dynamic Meteorology in Midlatitudes, Oxford University Press, ISBN 9780195062687, 1993. a
Brown, A., Short, E., Green, S., and Vincent, C.: sea_breeze: v1.1, Zenodo [code],
https://doi.org/10.5281/zenodo.17220916, 2025.
a,
b,
c
Bureau of Meteorology: Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia – Version 2 (BARRA2), National Computational Infrastructure Data Catalogue [data set],
https://doi.org/10.25914/1x6g-2v48, 2024.
a
Cafaro, C., Frame, T. H. A., Methven, J., Roberts, N., and Bröcker, J.: The added value of convection‐permitting ensemble forecasts of sea breeze compared to a Bayesian forecast driven by the global ensemble, Quarterly Journal of the Royal Meteorological Society, 145, 1780–1798,
https://doi.org/10.1002/qj.3531, 2019.
a,
b,
c
Carver, R. W. and Merose, A.: ARCO-ERA5: An Analysis-Ready Cloud-Optimized Reanalysis Dataset, 22nd Conf. on AI for Env. Science, Denver, CO, Amer. Meteo. Soc, 4A.1,
https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/415842 (last access: 23 January 2026), 2023. a
Clarke, R. H.: Fair weather nocturnal inland wind surges and atmospheric bores: Part I Nocturnal wind surges, Australian Meteorological and Oceanographic Journal, 31, 133–145, 1983.
a,
b
Coceal, O., Bohnenstengel, S. I., and Kotthaus, S.: Detection of sea-breeze events around London using a fuzzy-logic algorithm, Atmospheric Science Letters, 19,
https://doi.org/10.1002/asl.846, 2018.
a,
b,
c,
d,
e,
f,
g
Dao, T. L., Vincent, C. L., Huang, Y., Peatman, S. C., Soderholm, J. S., Birch, C. E., and Roberts, D. S.: Joint modulation of coastal rainfall in Northeast Australia by local and large-scale forcings, Quarterly Journal of the Royal Meteorological Society, e70027,
https://doi.org/10.1002/qj.70027, 2025.
a
Du, Y. and Rotunno, R.: Thermally driven diurnally periodic wind signals offthe east coast of China, Journal of the Atmospheric Sciences, 72, 2806–2821,
https://doi.org/10.1175/JAS-D-14-0339.1, 2015.
a
Grant, L. D. and Van Den Heever, S. C.: Aerosol-cloud-land surface interactions within tropical sea breeze convection, Journal of Geophysical Research, 119, 8340–8361,
https://doi.org/10.1002/2014JD021912, 2014.
a
Hallgren, C., Körnich, H., Ivanell, S., and Sahlée, E.: A Single-Column Method to Identify Sea and Land Breezes in Mesoscale-Resolving NWP Models, Weather and Forecasting, 38, 1025–1039,
https://doi.org/10.1175/WAF-D-22-0163.1, 2023.
a,
b,
c,
d,
e,
f,
g,
h,
i
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
a
Huang, Y., Arblaster, J., and Lane, T.: AUS2200: A High-Resolution Regional Atmospheric Modeling Dataset, National Computational Infrastructure Data Catalogue [data set],
https://doi.org/10.25914/w95d-q328, 2025a.
a,
b
Huang, Y., Li, S., Zhu, Y., Liu, Y., Hong, Y., Chen, X., Deng, W., Xi, X., Lu, X., and Fan, Q.: Increasing Sea-Land Breeze Frequencies Over Coastal Areas of China in the Past Five Decades, Geophysical Research Letters, 52,
https://doi.org/10.1029/2024GL112480, 2025b.
a
Jenkner, J., Sprenger, M., Schwenk, I., Schwierz, C., Dierer, S., and Leuenberger, D.: Detection and climatology of fronts in a high‐resolution model reanalysis over the Alps, Meteorological Applications, 17, 1–18,
https://doi.org/10.1002/met.142, 2010.
a,
b
Kraus, H., Hacker, J. M., and Hartmann, J.: An observational aircraft-based study of sea-breeze frontogenesis, Boundary-Layer Meteorology, 53, 223–265,
https://doi.org/10.1007/BF00154443, 1990.
a
Mackallah, C., Chamberlain, M. A., Law, R. M., Dix, M., Ziehn, T., Bi, D., Bodman, R., Brown, J. R., Dobrohotoff, P., Druken, K., Evans, B., Harman, I. N., Hayashida, H., Holmes, R., Kiss, A. E., Lenton, A., Liu, Y., Marsland, S., Meissner, K., Menviel, L., O'farrell, S., Rashid, H. A., Ridzwan, S., Savita, A., Srbinovsky, J., Sullivan, A., Trenham, C., Vohralik, P. F., Wang, Y. P., Williams, G., Woodhouse, M. T., and Yeung, N.: ACCESS datasets for CMIP6: Methodology and idealised experiments, Journal of Southern Hemisphere Earth Systems Science, 72, 93–116,
https://doi.org/10.1071/ES21031, 2022.
a
Masouleh, Z. P., Walker, D. J., and Crowther, J. M. C.: A long-term study of sea-breeze characteristics: A case study of the coastal city of Adelaide, Journal of Applied Meteorology and Climatology, 58, 385–400,
https://doi.org/10.1175/JAMC-D-17-0251.1, 2019.
a
Masselink, G. and Pattiaratchi, C. B.: Characteristics of the Sea Breeze System in Perth, Western Australia, and Its Effect on the Nearshore Wave Climate, Journal of Coastal Research, 17, 173–187, 2001.
a,
b
May, R. M., Arms, S. C., Marsh, P., Bruning, E., Leeman, J. R., Goebbert, K., Thielen, J. E., and Bruck, Z.: MetPy: A Python Package for Meteorological Data, UCAR/NSF Unidata [code],
https://doi.org/10.5065/D6WW7G29, 2019.
a,
b
Miller, S. T., Keim, B. D., Talbot, R. W., and Mao, H.: Sea breeze: Structure, forecasting, and impacts, Reviews of Geophysics, 41,
https://doi.org/10.1029/2003RG000124, 2003.
a
Oliphant, A. J., Sturman, A. P., and Tapper, N. J.: The evolution and structure of a tropical island sea/land-breeze system, northern Australia, Meteorology and Atmospheric Physics, 78, 45–59,
https://doi.org/10.1007/s007030170005, 2001.
a
Pettersen, S.: Weather Analysis and Forecasting, Vol. 1, Motion and Motion Systems, McGraw-Hill, 2nd Edn., 1956. a
Planchon, O., Dubreuil, V., and Gouery, P.: A method of identifying and locating sea-breeze fronts in north-eastern Brazil by remote sensing, Meteorological Applications, 13, 225–234,
https://doi.org/10.1017/S1350482706002283, 2006.
a
Qian, T., Epifanio, C. C., and Zhang, F.: Linear theory calculations for the sea breeze in a background wind: The equatorial case, Journal of the Atmospheric Sciences, 66, 1749–1763,
https://doi.org/10.1175/2008JAS2851.1, 2009.
a
Soderholm, J., McGowan, H., Richter, H., Walsh, K., Weckwerth, T., and Coleman, M.: The coastal convective interactions experiment (CCIE): Understanding the role of sea breezes for hailstorm hotspots in Eastern Australia, Bulletin of the American Meteorological Society, 97, 1687–1698,
https://doi.org/10.1175/BAMS-D-14-00212.1, 2016.
a
Steele, C. J., Dorling, S. R., Von Glasow, R., and Bacon, J.: Modelling sea-breeze climatologies and interactions on coasts in the southern North Sea: Implications for offshore wind energy, Quarterly Journal of the Royal Meteorological Society, 141, 1821–1835,
https://doi.org/10.1002/qj.2484, 2015.
a
Su, C.-H., Dharssi, I., Le Marshall, J., Le, T., Rennie, S., Smith, A., Stassen, C., Steinle, P., Torrance, J., Wang, C., and Warren, R. A.: BARRA2: Development of the next-generation Australian regional atmospheric reanalysis, Tech. rep., Australian Bureau of Meteorology, ISBN 9781925738964, 2022. a
Su, C.-H., Rennie, S., Torrance, J., Howard, E., Stassen, C., Lipson, M., Warren, R., Pepler, A., Dharssi, I., and Franklin, C.: BARRA-C2: Development of the kilometre-scale downscaled atmospheric reanalysis over Australia, Tech. rep., Australian Bureau of Meteorology, ISBN 9781925738834, 2024. a
Thomas, C. M. and Schultz, D. M.: What are the best thermodynamic quantity and function to define a front in gridded model output?, Bulletin of the American Meteorological Society, 100, 873–896,
https://doi.org/10.1175/BAMS-D-18-0137.1, 2019.
a
Van Der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., and Yu, T.: Scikit-image: Image processing in python, PeerJ, 2014,
https://doi.org/10.7717/peerj.453, 2014.
a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, I., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nature Methods, 17, 261–272,
https://doi.org/10.1038/s41592-019-0686-2, 2020.
a
Xia, G., Draxl, C., Optis, M., and Redfern, S.: Detecting and characterizing simulated sea breezes over the US northeastern coast with implications for offshore wind energy, Wind Energy Science, 7, 815–829,
https://doi.org/10.5194/wes-7-815-2022, 2022.
a
Zhou, Y., Guan, H., Gharib, S., Batelaan, O., and Simmons, C. T.: Cooling power of sea breezes and its inland penetration in dry-summer Adelaide, Australia, Atmospheric Research, 250,
https://doi.org/10.1016/j.atmosres.2020.105409, 2021.
a
