Temperature Map Information

Air temperature

The air temperature analyses and associated maps use data contained in the Bureau of Meteorology climate database, the Australian Data Archive for Meteorology (ADAM). The analyses are initially produced automatically from real-time data with limited quality control. They are intended to provide a general overview of air temperature across Australia as quickly as possible after the observations are received.

What is air temperature?

According to the American Meteorological Society's Glossary of Meteorology, temperature is the quantity measured by a thermometer. In gaseous fluid dynamics, temperature represents molecular kinetic energy, which is then consistent with the equation of state and with definitions of pressure as the average force of molecular impacts and density as the total mass of molecules in a volume. Air temperature is measured in a shaded enclosure (most often a Stevenson Screen) at a height of approximately 1.2 m above the ground. Maximum and minimum temperatures for the previous 24 hours are nominally recorded at 9 am local clock time. Minimum temperature is recorded against the day of observation, and the maximum temperature against the previous day.

Mean maximum temperature (°C)

The average daily maximum air temperature, for each month and as an annual statistic, calculated over all years of record.

Highest temperature (°C)

The highest (by month and overall) maximum air temperature observed at the site.

Lowest maximum temperature (°C)

The lowest (by month and overall) maximum air temperature observed at the site.

Decile 1 maximum (or minimum) temperature (°C)

Monthly or annual decile 1 (10th percentile) of maximum or minimum air temperature. The annual decile value must be calculated from yearly data, and cannot be obtained by adding together the monthly deciles. Decile values are used to give an indication of the spread of the observations over the period of record; in this case, daily observations within a month. To determine decile 1of a series of observations, they are first arranged in order from lowest to highest, and then divided into 10 equal groups. Decile 1 is the value at the top of the 1st grouping. Over the long term about one day in ten can be expected to have a (maximum or minimum) temperature below the decile 1 value.

Decile 9 maximum (or minimum) temperature (°C)

Monthly decile 9 (90th percentile) of maximum or minimum air temperature. The annual decile value must be calculated from yearly data, and cannot be obtained by adding together the monthly deciles. Decile values are used to give an indication of the spread of the observations over the period of record; in this case, daily observations within a month. To determine decile 9 of a series of observations, the data are first arranged in order from lowest to highest, and then divided into 10 equal groups. Decile 9 is the value at the top of the 9th grouping. Over the long term about one day in ten can be expected to have a (maximum or minimum) temperature exceed the decile 9 value.

Mean minimum temperature (°C)

The long-term average daily minimum air temperature observed during a calendar month and over the year.

Lowest temperature (°C)

The lowest recorded temperature observed at the site, calculated over all years of record.

Highest minimum temperature (°C)

The highest recorded minimum temperature observed at the site, calculated over all years of record.

Daily air temperature maps

At about 1:30 am each day, the daily maximum/minimum air temperature values for the previous day are analysed. Temperatures are measured directly at about 750 sites across the country, and stored in the climate database maintained by the National Climate Centre. These station data are then analysed onto 0.25x0.25 and 0.05x0.05 degree grids.

The national map shown on the web is based on the 0.05x0.05 degree grid, sub-sampled at every fifth point to give an effective resolution of 0.25x0.25 degrees. The regional maps are based directly on the 0.05x0.05 degree grids, so there may be some differences in the fine detail between the national map and the regional maps.

All analyses and maps are progressively updated over the following six months, as new data becomes available and as the data in the climate database are improved through quality control. The schedule of updates is available here. Subsequent versions will tend to be more accurate, as they will be based on larger quality-controlled input datasets. A date stamp at the bottom right-hand corner of each map indicates when the analysis was produced.

Monthly and multiple-monthly maximum and minimum temperature maps

Monthly maximum and minimum temperatures are calculated as the averages of the corresponding daily maximum or minimum temperature.

The latest maximum and minimum temperature maps, for periods of one or more months, are usually produced on the first day of the following month, with further updates according to the schedule of updates available here. Subsequent versions will be more accurate, as they will be based on larger and more accurate input datasets. A date stamp at the bottom right-hand corner of each map indicates when the analysis was produced.

Analyses over 3, 6 and 12 months are based on the average of the one-month grids which comprise the period in question.

The anomaly maps show the departure from the long-term climate average calculated over the period 1971-2000, with the daily anomalies calculated with respect to the monthly average for the relevant month. There would normally be some correlation between rainfall anomalies and maximum and minimum temperature anomalies, with excessive rainfall coinciding with above average maximum and minimum temperature values (positive anomalies) and drought conditions coinciding with negative anomalies. During droughts, soil and vegetation become drier thereby reducing the amount of water available for evaporation and transpiration.

Analysis technique

The analyses are computer generated using a sophisticated analysis technique described here. This method uses an optimised Barnes successive correction technique that applies a weighted averaging process to the station data. Topographical information is included by the use of anomalies (departures from average) in the analysis process. On the maps each gridpoint represents an approximately square area with sides of about 5 kilometres (0.05 degrees). The size of the grids is limited by the data density across Australia.

This gridpoint analysis technique provides an objective average for each grid square and enables useful estimates in data-sparse areas such as central Australia. However, in data-rich areas such as southeast Australia or in regions with strong gradients, "data smoothing" will occur resulting in gridpoint values that may differ slightly from the exact maximum and minimum temperatures measured at the contributing stations.

Map formats

Most of these maximum and minimum temperature maps are produced as both colour and black/white GIF images, with low and high resolution versions available in each case. The low resolution colour GIF images are the ones usually displayed, with links to the other three types placed under the main image. Place names are generally to be found on the high resolution versions. Portable Document Format (PDF) versions of the images are also generated for high-quality printing. Please note however that the PDF version is not archived for reasons of space. PDF version of older maps may be obtained from the National Climate Centre (webclim@bom.gov.au), but charges may be imposed for their provision.

Quality control

The analyses use data collected through electronic and paper communication channels. These data have been screened for errors, using an automated technique, and make use of quality control which has been undertaken on the climate database. Full quality control is completed some weeks to months after the end of the most recent month when (a) extreme values are confirmed by written reports, and (b) data more generally are compared with those of nearby stations so that values and dates of occurrences are similar.

Occasionally in the data-sparse areas, observational errors may enter the analyses because they cannot be detected by comparison with other reports. In these instances, the erroneous maps will be amended as soon as is practicable.