This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.
SILO is a Queensland Government database containing continuous daily climate data for Australia from 1889 to present. Gridded datasets are constructed by spatially interpolating the observed point data. Continuous point datasets are constructed by supplementing the available point data with interpolated estimates when observed data are missing.
SILO provides climate datasets that are ready to use. Raw observational data typically contain missing data and are only available at the location of meteorological recording stations. SILO provides point datasets with no missing data and gridded datasets which cover mainland Australia and some islands.
(A) Processing System Version History
* Prior to 2001
The interpolation system used the algorithm detailed in Jeffrey et al.1
The normalisation procedure was modified. Observational rainfall, when accumulated over a sufficient period and raised to an appropriate fractional power, is (to a reasonable approximation) normally distributed. In the original procedure the fractional power was fixed at 0.5 and a normal distribution was fitted to the transformed data using a maximum likelihood technique. A Kolmogorov-Smirnov test was used to test the goodness of fit, with a threshold value of 0.8. In 2001 the procedure was modified to allow the fractional power to vary between 0.4 and 0.6. The normalisation parameters (fractional power, mean and standard deviation) at each station were spatially interpolated using a thin plate smoothing spline.
The normalisation procedure was modified. The Kolmogorov-Smirnov test was removed, enabling normalisation parameters to be computed for all stations having sufficient data. Previously parameters were only computed for those stations having data that were adequately modelled by a normal distribution, as determined by the Kolmogorov-Smirnov test.
* January 2012 - November 2012
The normalisation procedure was modified:
o The Kolmogorov-Smirnoff test was reintroduced, with a threshold value of 0.1.
o Data from Bellenden Ker Top station were included in the computation of normalisation parameters. The station was previously omitted on the basis of having insufficient data. It was forcibly included to ensure the steep rainfall gradient in the region was reflected in the normalisation parameters.
o The elevation data used when interpolating normalisation parameters were modified. Previously a mean elevation was assigned to each station, taken from the nearest grid cell in a 0.05° 0.05° digital elevation model. The procedure was modified to use the actual station elevation instead of the mean. In mountainous regions the discrepancy was substantial and cross validation tests showed a significant improvement in error statistics.
o The station data are normalised using: (i) a power parameter extracted from the nearest pixel in the gridded power surface. The surface was obtained by interpolating the power parameters fitted at station locations using a maximum likelihood algorithm; and (ii) mean and standard deviation parameters which had been fitted at station locations using a smoothing spline. Mean and standard deviation parameters were fitted at the subset of stations having at least 40 years of data, using a maximum likelihood algorithm. The fitted data were then spatially interpolated to construct: (a) gridded mean and standard deviation surfaces (for use in a subsequent de-normalisation procedure); and (b) interpolated estimates of the parameters at all station locations (not just the subset having long data records). The parameters fitted using maximum likelihood (at the subset of stations having long data records) may differ from those fitted by the interpolation algorithm, owing to the smoothing nature of the spline algorithm which was used. Previously, station data were normalised using mean and standard deviation parameters which were taken from the nearest pixel in the respective mean and standard deviation surfaces.
* November 2012 - May 2013
The algorithm used for selecting monthly rainfall data for interpolation was modified. Prior to November 2012, the system was as follows:
o Accumulated monthly rainfall was computed by the Bureau of Meteorology;
o Rainfall accumulations spanning the end of a month were assigned to the last month included in the accumulation period;
o A monthly rainfall value was provided for all stations which submitted at least one daily report. Zero rainfall was assumed for all missing values; and
o SILO imposed a complex set of ad-hoc rules which aimed to identify stations which had ceased reporting in real time. In such cases it would not be appropriate to assume zero rainfall for days when a report was not available. The rules were only applied when processing data for January 2001 and onwards.
In November 2012 a modified algorithm was implemented:
o SILO computed the accumulated monthly rainfall by summing the daily reports;
o Rainfall accumulations spanning the end of a month were discarded;
o A monthly rainfall value was not computed for a given station if any day throughout the month was not accounted for - either through a daily report or an accumulation; and
o The SILO ad-hoc rules were not applied.
* May 2013 - current
The algorithm used for selecting monthly rainfall data for interpolation was modified. The modified algorithm is only applied to datasets for the period October 2001 - current and is as follows:
o SILO computes the accumulated monthly rainfall by summing the daily reports;
o Rainfall accumulations spanning the end of a month are pro-rata distributed onto the two months included in the accumulation period;
o A monthly rainfall value is computed for all stations which have at least 21 days accounted for throughout the month. Zero rainfall is assumed for all missing values; and
o The SILO ad-hoc rules are applied when processing data for January 2001 and onwards.
Datasets for the period January 1889-September 2001 are prepared using the system that was in effect prior to November 2012.
(A) Processing System Version History
No changes have been made to the processing system since SILO's inception.
(B) Major Historical Data Updates
* All observational data and station coordinates were updated in 2009.
* Station coordinates were updated on 26 January 2012.
The observed data are interpolated using a tri-variate thin plate smoothing spline, with latitude, longitude and elevation as independent variables.4 A two-pass interpolation system is used. All available observational data are interpolated in the first pass and residuals computed for all data points. The residual is the difference between the observed and interpolated values. Data points with high residuals may be indicative of erroneous data and are excluded from a subsequent interpolation which generates the ﬁnal gridded surface. The surface covers the region 112˚E - 154˚E, 10˚S - 44˚S on a regular 0.05˚ × 0.05˚grid and is restricted to land areas on mainland Australia and some islands.
Gridded datasets for the period 1957-current are obtained by interpolation of the raw data.
Gridded datasets for the period 1957-current are obtained by interpolation of the raw data. Gridded datasets for the period 1889-1956 were constructed using an anomaly interpolation technique. The daily departure from the long term mean is interpolated, and the gridded dataset is constructed by adding the gridded anomaly to the gridded long term mean. The long term means were constructed using data from the period 1957-2001. The anomaly interpolation technique is described in Rayner et al.6
The observed and interpolated datasets evolve as new data becomes available and the existing data are improved through quality control procedures. Modifications gradually decrease over time, with most datasets undergoing little change 12 months after the date of observation.
"Queensland Department of Science, Information Technology, Innovation and the Arts" (2013) SILO Patched Point data for Narrabri (54120) and Gunnedah (55023) stations in the Namoi subregion. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/0a018b43-58d3-4b9e-b339-4dae8fd54ce8.
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