Interpolation issues and data codes

Accuracy

The accuracy of SILO’s interpolated datasets depends on both:

• the climate variable, and
• the number of observed values used to construct the gridded raster.

Variables such as temperature can be reliably interpolated over significant distances because air temperature is relatively stable. In contrast, rainfall is difficult to interpolate because it is typically “patchy”. In other words the rainfall received at one location can be quite different to the rainfall received at nearby location. Regardless of the climate variable, the accuracy of a gridded dataset will, in general, be strongly dependent on the number of observations used to construct it. As the number of stations reporting data for a given variable has varied considerably throughout history, the accuracy of SILO’s gridded rasters also varies depending on the period. SILO recommends users consider both the number and location of stations used to construct gridded rasters, as it can significantly affects the accuracy of interpolated estimates. (“number and location” should be a link to the new page)

Data codes

Where possible (depending on the file format), the data are supplied with codes indicating how each datum was obtained.

1. Redistribution (code 15)

Much of the data in Australia have been collected by volunteer observers at Post Offices, Police stations, etc. Some of these workplaces only operate on weekdays. Consequently the rainfall measured on Monday may be for the period since 9am on the preceding Friday. In such cases, SILO redistributes the accumulated rainfall back to the days when it probably fell according to the amount and days that rain fell at nearby stations.

2. CLIMARC interpolation method (code 35)

An anomaly interpolation technique was used to construct the gridded surfaces for maximum and minimum temperatures, radiation and vapour pressure, for all years prior to 1957. The surfaces were derived, in part, using observational data made available under the CLIMARC project. Prior to the availability of the CLIMARC datasets, there were insufficient data to construct gridded surfaces so long term mean data were typically used.

The user should note that CLIMARC interpolations:

 Are used throughout the period 1889-1956;

 Are based on relatively few observations. For example, there are data for about 60 CLIMARC stations compared to several hundred stations reporting temperature in the post-1956 period;

 Show less variation than the post-1956 data; and

 Are derived from relatively old data which in some cases contain uncorrected instrument biases.

Consequently the interpolated CLIMARC data may not be suitable for some studies e.g. climate change detection, extreme events, number of frosts etc.

We expect the interpolated CLIMARC data to be significantly better than long term averages. Furthermore, the CLIMARC data should preserve the daily relationships between different elements better than long term means. It should however be noted that in some areas and years, the CLIMARC data are no different to the previously supplied long term averages. For example, in Western Australia there are very few observations before 1907, and Ceduna didn't commence reporting until about 1940. Please consult the CLIMARC Report (PDF) for further information.