Data

Hourly near-surface air temperature grids for Australia

Commonwealth Scientific and Industrial Research Organisation
Stewart, Stephen ; Cai, Dejun ; McVicar, Tim ; Van Niel, Tom
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/1sgg-b556&rft.title=Hourly near-surface air temperature grids for Australia&rft.identifier=https://doi.org/10.25919/1sgg-b556&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=This collection provides hourly near-surface (1.5 m) air temperature grids for Australia with a spatial resolution of 1 km. These data are spatially interpolated using quart-variate thin plate splines (full spline dependence on easting, northing, elevation, and time-varying coastal distance index), and include two key products: i) a long term climatology for every hour on every 5th day of the year (representing 1990-2019), and ii) a time-series of hourly air temperature data beginning on 01/Jan/2015. \n\nLineage: The original station data were supplied by the METAR data stream from BoM from January 1990 to March 2023 for 621 stations across Australia. A direct spatial interpolation approach (i.e., models are re-fitted for each time step using all available observations and model covariates) was applied to generate air temperature grids across all Australia using the ANUSPLIN v4.4. software (Hutchinson and Xu, 2013). Two key data products are included in this collection:\n\n1.\tHourly air temperature climatologies (every 5th DOY, 1990-2019)\nHourly air temperature climatologies were interpolated with quart-variate thin plate splines as a function of easting, northing, elevation, and a time-varying coastal distance index. Elevation (m) was exaggerated by a factor of 100 to represent the differences in horizontal and vertical synoptic scales typical for spline-based climate interpolation (Hutchinson et al. 2009). The coastal distance index is calculated as a limiting transformation of the generalised distance to coast (Hutchinson et al. 2021), and corresponds to sea breeze dynamics, particularly during spring and summer in the afternoon and evening hours. Observations of the same hour within +/- 5 DOYs (i.e., 5 DOYs before and 5 DOYs after the target DOY for each hour) are included to increase the number of values available for calculating stable climatologies. A total of 505 stations were used for interpolating climatologies.\n\n2.\tHourly air temperature (every hour, Jan/2015 to Mar/2023)\nHourly air temperature was directly interpolated with quart-variate thin plate splines as a function of easting, northing, elevation, and a time-varying coastal distance index (i.e., following the same approach as the climatologies, but for every hour). A total of 621 stations were used for interpolating anomalies. \n\n\nVersion history:\n\nv2: All interpolations now use a direct spatial interpolation method, 80–95 % of the data points as knots, and include a time-varying coastal distance index that allows the models to represent the effects of sea breeze dynamics on air temperature. These changes reduced cross-validated root mean squared error (RMSE) by 14 % for air temperature climatologies (v2 RMSE = 0.75 °C) and 7 % for hourly interpolations (v2 RMSE = 1.56 °C) when pooled across all available times and stations. \n\n\n\n\n&rft.creator=Stewart, Stephen &rft.creator=Cai, Dejun &rft.creator=McVicar, Tim &rft.creator=Van Niel, Tom &rft.date=2024&rft.edition=v3&rft.coverage=westlimit=106.0816486111111; southlimit=-45.235774722222224; eastlimit=161.69075527777778; northlimit=-7.000851388888889; projection=WGS84&rft_rights=Creative Commons Attribution-ShareAlike 4.0 International Licence https://creativecommons.org/licenses/by-sa/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2023.&rft_subject=near-surface air temperature&rft_subject=hourly&rft_subject=spatial interpolation&rft_subject=temporal interpolation&rft_subject=climate&rft_subject=weather&rft_subject=Atmospheric dynamics&rft_subject=Atmospheric sciences&rft_subject=EARTH SCIENCES&rft_subject=Climatology&rft_subject=Climate change science&rft.type=dataset&rft.language=English Access the data

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Creative Commons Attribution-ShareAlike 4.0 International Licence
https://creativecommons.org/licenses/by-sa/4.0/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO 2023.

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Brief description

This collection provides hourly near-surface (1.5 m) air temperature grids for Australia with a spatial resolution of 1 km. These data are spatially interpolated using quart-variate thin plate splines (full spline dependence on easting, northing, elevation, and time-varying coastal distance index), and include two key products: i) a long term climatology for every hour on every 5th day of the year (representing 1990-2019), and ii) a time-series of hourly air temperature data beginning on 01/Jan/2015.

Lineage: The original station data were supplied by the METAR data stream from BoM from January 1990 to March 2023 for 621 stations across Australia. A direct spatial interpolation approach (i.e., models are re-fitted for each time step using all available observations and model covariates) was applied to generate air temperature grids across all Australia using the ANUSPLIN v4.4. software (Hutchinson and Xu, 2013). Two key data products are included in this collection:

1.\tHourly air temperature climatologies (every 5th DOY, 1990-2019)
Hourly air temperature climatologies were interpolated with quart-variate thin plate splines as a function of easting, northing, elevation, and a time-varying coastal distance index. Elevation (m) was exaggerated by a factor of 100 to represent the differences in horizontal and vertical synoptic scales typical for spline-based climate interpolation (Hutchinson et al. 2009). The coastal distance index is calculated as a limiting transformation of the generalised distance to coast (Hutchinson et al. 2021), and corresponds to sea breeze dynamics, particularly during spring and summer in the afternoon and evening hours. Observations of the same hour within +/- 5 DOYs (i.e., 5 DOYs before and 5 DOYs after the target DOY for each hour) are included to increase the number of values available for calculating stable climatologies. A total of 505 stations were used for interpolating climatologies.

2.\tHourly air temperature (every hour, Jan/2015 to Mar/2023)
Hourly air temperature was directly interpolated with quart-variate thin plate splines as a function of easting, northing, elevation, and a time-varying coastal distance index (i.e., following the same approach as the climatologies, but for every hour). A total of 621 stations were used for interpolating anomalies.


Version history:

v2: All interpolations now use a direct spatial interpolation method, 80–95 % of the data points as knots, and include a time-varying coastal distance index that allows the models to represent the effects of sea breeze dynamics on air temperature. These changes reduced cross-validated root mean squared error (RMSE) by 14 % for air temperature climatologies (v2 RMSE = 0.75 °C) and 7 % for hourly interpolations (v2 RMSE = 1.56 °C) when pooled across all available times and stations.




Available: 2024-05-13

Data time period: 2015-01-01 to ..

This dataset is part of a larger collection

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161.69076,-7.00085 161.69076,-45.23577 106.08165,-45.23577 106.08165,-7.00085 161.69076,-7.00085

133.88620194444,-26.118313055555

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