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ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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A dataset of sea breeze objects over Australia (1979-2024)

Authors: Brown, Andrew; Vincent, Claire;

A dataset of sea breeze objects over Australia (1979-2024)

Abstract

Overview This record contains a dataset of sea breeze objects derived from the Bureau of Meteorology Atmospheric Regional Reanalysis for Australia, version 2, with convective-scale configuration (BARRA-C2, Su et al., 2025), over a 1979-2024 period. BARRA2 is made available by the Bureau of Meteorology under a CC-BY 4.0 license via the Australian National Comptutational Infrastructure Sea breeze objects are defined on a time-latitude-longitude grid, at around 4.4 km horizontal spacing at hourly intervals. The objects are provided as a binary mask on a grid in .zarr format. Both hourly and daily masks are provided here and compressed into .zip archives. sea_breeze_mask_hourly.zip: Contains 1,978 .zarr directories of hourly sea breeze objects, with each directory containing no more than 10 days of objects. sea_breeze_mask_daily.zip: Contains three .zarr directories with daily sea breeze masks over the whole 1979-2024 period. Object identification method and software Sea breeze obects are defined using a moisture frontogenesis approach based on surface winds and specific humidity, with further filtering of objects using a set of morphological and physical characteristics. Filtering settings are provided in the .zarr metadata. Details on the methods can be found in Brown et al. (2025a). Code to produce the objects, and accomanying documentation, can be found in Brown et al. (2025b) or on GitHub. Daily data resampling The daily mask provided here has been resampled at each grid point from hourly data, by considering if a sea breeze is present on a given day. A day is either defined by UTC, Australian Eastern Standard Time (AEST, UTC + 10), or Australian Western Standard Time (AWST, UTC + 8), and each are provided as separate .zarr directories: UTC: sea_breeze_mask_utc.zarr AEST: sea_breeze_mask_aest.zarr AWST: sea_breeze_mask_awst.zarr For the AEST and AWST data, sea breeze objects occurring before 0900 or after 2100 are not included in the resampling. Opening the data After unzipping, the .zarr directories can be loaded into Python using xarray following the code below. This was tested with the following python packages: xarray version 2023.2.0 zarr version 2.13.3 import xarray as xr f = xr.open_dataset("sea_breeze_mask_utc.zarr", engine="zarr") Additional details: Spatial extent: 108.02E to 158.50E, 45.69S to 6.93S Temporal extent: 1979-01-01 to 2024-12-31 Time frequency: Daily and hourly

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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