Rainfall
The rainfall 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 rainfall across Australia as quickly as possible after the observations are received. Subsequent analysis in following days make use of late-arriving observations and more comprehensive quality control.
What is rainfall?
According to the American Meteorological Society's Glossary of Meteorology, rainfall is the amount of precipitation of any type (including the liquid equivalent of frozen hydrometeors such as hail and snow); usually taken as that amount measured by means of a rain gauge in millimeters of liquid water depth over a specified period of time. For Australia, rainfall is typically measured for a 24-hour period ending 9am local time.
A more accurate term would be precipitation or precipitation amount. However, the broad use of 'rainfall' is firmly established in meteorology.
Daily and monthly rainfall maps
Daily rainfall for the current day is the 24-hour total rainfall from 9am (local time) the day before, to 9am of the current day. Each day at about 1:30pm AEST, rainfall values from up to 3000 sites across the country are analysed onto 0.25x0.25 and 0.05x0.05 degree grids with limited quality control. Monthly analyses are carried out on the last day of the month, across a similar number of sites, using accumulated daily rainfall data for the month.
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 twelve months, as new data become available and as the data in the climate database are improved through quality control. The schedule of updates provides an overview of how frequent each data type is updated, although additional unscheduled updates may occur from time to time. Subsequent versions will tend to be more accurate, as they will be based on larger quality-controlled input datasets and will contain more data from non-real-time reporting sites. Approximately 80% of the site data is available after 40 days, reaching to around 97% after one year. Current and historical site data used in each of the daily or monthly analyses can be obtained through Climate Data Online or Weather station directory. A date stamp at the bottom right-hand corner of each map indicates when the analysis was produced.
An additional set of daily rainfall analyses and maps is also available, the 'recalibrated' daily rainfall products. The ordinary daily and monthly rainfall analyses (see below) use different spatial scales, and in most cases sums of daily rainfall grids are only approximately equal to the corresponding monthly analyses. The 'recalibrated' daily rainfall analyses are derived from grids which have been scaled so that they sum to equal the corresponding monthly rainfall analyses. Accordingly, the 'recalibrated' daily rainfall maps are only generated once the complete monthly rainfall analysis becomes available at the end of the month.
The daily rainfall gauge network has varying density across the country, and is in some places not dense enough to support a daily rainfall analysis. In some of the daily rainfall maps, these data 'voids' are shown in grey shading. As the daily rainfall gauge network varies over time, the shaded data void regions likewise change over time.
Analyses over 3, 6 and 12 months or longer are based on the summation of the one-month grids which comprise the period in question.
Anomaly maps
Anomaly maps are used to compare the departure of the observations from a climate normal (or reference) period. The reference period is used to calculate a climate average using similar months associated with the observation period. The WMO defines climatological standard normals for use by the international community in order to maintain consistency in the calculation of climate statistics across the world and can be used as an indication of conditions most likely to be experienced in a given location. The reference period for supporting long-term climate change assessments is 1961 to 1990, and is commonly used in our climate maps, climate statistics and is the base period for most climate change studies. The next standard reference period is expected to be 1991 to 2020.
For instance, the anomaly map for the period December 2018 to February 2019 (summer), would be making reference to the monthly December, January and February grids over the 1961 to 1990 standard reference period.
Decile maps
Decile maps are used to give an indication of how the observation values for that period sit relative to the full history of record. The maps show if an observation is average (middle decile bands 4 to 7), below average (decile bands 2 to 3), very much below average (decile band 1), above average (decile bands 8 to 9) or very much above average (decile band 10). The extreme ends of the distribution are the lowest on record and highest on record, and each can also be considered within decile bands 1 and 10 respectively.
To determine which decile band an observation over a particular time period (e.g. monthly, annual or any multi-month period) falls into, the following calculation is carried out. First all data since the beginning of the record are collated over similar time periods, then values at each grid point are sorted in order from lowest to highest, and divided into ten equal groups, with the threshold value for each of the eleven decile band divisions used to categorise the relative rank for the current observation period.
For example, if a calculation on 2019 annual temperature data was performed during 2020, using the full record of the 110 years of sorted (or ranked) data since 1910, there would be 10 equal groups of 11 years within each. The lowest on record would be the smallest (first) value. Temperatures from that value to the top of the first grouping would be classified as decile 1 (or very much below average). This continues through to the last grouping of years, and temperatures between the top of the ninth grouping and the highest value would be decile 10 (or very much above average).
Historical decile maps do need to be recomputed from year-to-year, as addition of recent years changes the reference period of the full history of record, and perhaps some parts of a map that were highest on record in one historical period may have since been exceeded with the most recent data in the latest year. Due to the lengthy computation time required to re-run the historical decile analysis, the historical maps will not be updated in real-time.
Analysis technique
The analyses are computer generated using a sophisticated analysis technique described in Jones et al. (2009). 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 rainfall ratio (actual rainfall divided by monthly 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 rainfall amount measured at the contributing stations.
Map formats
Maps on the website are low resolution .gif or .png files. Some map selections have further format options for download including:
- higher resolution versions with place names
- Portable Document Format (PDF) for the current issue of some popular selections
- Custom versions with different resolutions, formats or map areas, are available by request via the Bureau feedback form. Cost recovery charges may apply
Map Projections
The map projections used are either Cylindrical Equidistant (CE) or Lambert Conformal (LC). The Lambert Conformal projection takes three parameters; the central longitude (in degrees east of the Greenwich Meridian) and two standard parallels of latitude (in degrees south of the equator).
| Region | Aus. | Qld | NSW | Vic. | Tas. | SA | WA | NT |
|---|---|---|---|---|---|---|---|---|
| Map projection | LC 134° 10°, 40° | CE | CE | LC 140.8° 10°, 40° | LC 146.5° 10°, 44° | CE | CE | CE |
The Victoria and Tasmania maps are based on a finer resolution analysis than the remaining maps. Consequently there may be slight inconsistencies in the detail represented on the Vic./Tas. maps as compared against the Aus./SA/NSW maps.
Grid format
Daily, weekly and monthly rainfall totals grids may be downloaded from the Bureau's website. These grids are in an ASCII format suitable for ingesting into geographic information systems (GISs), compressed using the UNIX compress utility. The ASCII grids have appended to them their original AIFS ASCII grid header (a Bureau of Meteorology grid format), to provide additional grid metadata. Note that some GISs may require the user to change the grid file extension from '.grid' to '.txt', prior to ingestion into the GIS.
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, 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.
