The Patched Point Dataset (PPD) combines observations and interpolations to provide daily data for a selected set of stations (locations). The term 'patched' means that if on any day a station does not have an observation then the gap in the record is 'patched' (ie. filled) with an estimate obtained by spatial interpolation of the daily data from surrounding stations. Consequently the Patched Point Data for a given location always contain a complete data record, or in other words, there are no missing data.

SILO (Scientific Information for Land Owners) is a daily time series of meteorological data at point locations, consisting of station records which have been supplemented by interpolated estimates where observed data are missing.
Patched Point Datasets for Queensland are available free of charge. To qualify for free access, the user must first register with SILO. For further information about SILO and registration, see the SILO webpage.

SILO is a database of Australian climate data from 1889 to the present. It provides continuous, daily time-step data products in ready-to-use formats for research and operational applications. SILO's gridded datasets (in NetCDF and GeoTiff formats) are hosted on AWS Public Data. Point data (at both station and grid cell locations) are available from the SILO website. Incremental update files for mirroring point datasets at station locations are also available on AWS Public Data.

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of minimum temperature (approx. 1.2 m from ground) across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Surfaces are developed using trivariate splines (latitude, longitude and elevation) with partial dependence upon a topographic index of relative elevation and standardised night time MODIS land surface temperature. Lineage: A) Data modelling: 1. Weather station observations collected by the Australian Bureau of Meteorology were obtained via the SILO patched point dataset (https://data.qld.gov.au/dataset/silo-patched-point-datasets-for-queensland), followed by the removal of all interpolated records. 2. Climate normals representing the 1981-2010 reference period were calculated for each weather station. A regression patching procedure (Hopkinson et al. 2012) was used to correct for biases arising due to differences in record length where possible. 3. Climate normals for each month were interpolated using trivariate splines (latitude, longitude and elevation as spline variables) with partial dependence upon a topographic index of relative elevation and standardised night time MODIS land surface temperature. All models were fit and interpolated using ANUSPLIN 4.4 (Hutchinson & Xu 2013). 4. Daily anomalies were calculated by subtracting daily observations from climate normals and interpolated with full spline dependence upon latitude and longitude 5. Interpolated anomalies were added to interpolated climate normals to obtain the final daily surfaces. 6. Monthly surfaces are calculated as an aggregation of the daily product. B) Spatial data inputs: 1. Fenner School of Environment and Society and Geoscience Australia. 2008. GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3. 2. Paget, MJ, King EA. 2008. MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research. Canberra, Australia. https://doi.org/10.4225/08/585c173339358 C) Model performance (3DS-T): Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly minimum temperature RMSE = 0.59 °C Daily minimum temperature RMSE = 1.63 °C

Please refer to the linked manuscript for further details.

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of maximum temperature (approx. 1.2 m from ground) across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Surfaces are developed using trivariate splines (latitude, longitude and elevation) with partial dependence upon standardised day time MODIS land surface temperature. Lineage: A) Data modelling: 1. Weather station observations collected by the Australian Bureau of Meteorology were obtained via the SILO patched point dataset (https://data.qld.gov.au/dataset/silo-patched-point-datasets-for-queensland), followed by the removal of all interpolated records. 2. Climate normals representing the 1981-2010 reference period were calculated for each weather station. A regression patching procedure (Hopkinson et al. 2012) was used to correct for biases arising due to differences in record length where possible. 3. Climate normals for each month were interpolated using trivariate splines (latitude, longitude and elevation as spline variables) with partial dependence upon standardised day time MODIS land surface temperature. All models were fit and interpolated using ANUSPLIN 4.4 (Hutchinson & Xu 2013). 4. Daily anomalies were calculated by subtracting daily observations from climate normals and interpolated with full spline dependence upon latitude and longitude 5. Interpolated anomalies were added to interpolated climate normals to obtain the final daily surfaces. 6. Monthly surfaces are calculated as an aggregation of the daily product. B) Spatial data inputs: 1. Fenner School of Environment and Society and Geoscience Australia. 2008. GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3. 2. Paget, MJ, King EA. 2008. MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research. Canberra, Australia. https://doi.org/10.4225/08/585c173339358 C) Model performance (3DS): Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly maximum temperature RMSE = 0.48 °C Daily maximum temperature RMSE = 1.19 °C

Please refer to the linked manuscript for further details.

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of minimum temperature (approx. 1.2 m from ground) across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Surfaces are developed using bivariate splines (latitude and longitude) with partial dependence upon elevation. Lineage: A) Data modelling: 1. Weather station observations collected by the Australian Bureau of Meteorology were obtained via the SILO patched point dataset (https://data.qld.gov.au/dataset/silo-patched-point-datasets-for-queensland), followed by the removal of all interpolated records. 2. Climate normals representing the 1981-2010 reference period were calculated for each weather station. A regression patching procedure (Hopkinson et al. 2012) was used to correct for biases arising due to differences in record length where possible. 3. Climate normals for each month were interpolated using bivariate splines (latitude and longitude as spline variables) with partial dependence upon elevation. All models were fit and interpolated using ANUSPLIN 4.4 (Hutchinson & Xu 2013). 4. Daily anomalies were calculated by subtracting daily observations from climate normals and interpolated with full spline dependence upon latitude and longitude 5. Interpolated anomalies were added to interpolated climate normals to obtain the final daily surfaces. 6. Monthly surfaces are calculated as an aggregation of the daily product. B) Spatial data inputs: 1. Fenner School of Environment and Society and Geoscience Australia. 2008. GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3. C) Model performance (2DS-E): Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly minimum temperature RMSE = 0.96 °C Daily minimum temperature RMSE = 1.81 °C

Please refer to the linked manuscript for further details.

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of 9am vapour pressure across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Lineage: A) Data modelling: 1. Weather station observations collected by the Australian Bureau of Meteorology were obtained via the SILO patched point dataset (https://data.qld.gov.au/dataset/silo-patched-point-datasets-for-queensland), followed by the removal of all interpolated records. 2. Climate normals representing the 1981-2010 reference period were calculated for each weather station. A regression patching procedure (Hopkinson et al. 2012) was used to correct for biases arising due to differences in record length where possible. 3. Climate normals for each month were interpolated using trivariate splines (latitude, longitude and elevation as spline variables). All data was interpolated using ANUSPLIN 4.4 (Hutchinson & Xu 2013). 4. Daily anomalies were calculated by subtracting daily observations from climate normals and interpolated with full spline dependence upon latitude and longitude. 5. Interpolated anomalies were added to interpolated climate normals to obtain the final daily surfaces. 6. Monthly surfaces are calculated as an aggregation of the daily product. B) Spatial data inputs: 1. Fenner School of Environment and Society and Geoscience Australia. 2008. GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3. C) Model performance: Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly vapour pressure: RMSE = 0.38 hPA Daily vapour pressure: RMSE = 1.24 hPa