NOTES ON THE TABLES
CLIMATE AVERAGES long term weather data
Incomplete ('short') months
For most elements the program uses only months which have more than 20 days of observations, to prevent any bias. Sites which do not report on weekends will usually be included, but their percentage completeness (given at the end of the row) will be lower (about 70%) as compared to a complete unbroken record.
Extremes in 'short' months
All data were used for the Highest maximum or lowest minimum temperatures, or for the maximum wind gust as these values are not biassed by 'short' months to the same extent.
Early and very recent extremes
The temperature extremes given in this report are those values which are in the computer archive. Some sites may have had more extreme values in the 1800s or the early 1900s, which have not yet been computer entered. Extremes which have occurred within the last two to three months of this report, may also not have been entered into the computer archive.
Highest daily rainfall
The highest daily rainfall is the highest value which has been recorded. Many sites report accumulated falls at the end of a weekend or holiday, and such falls may conceal higher daily amounts than are shown.
Length of record and missing data
The last two fields in each row indicate the length of the record and how complete that record might be for each element. Together these row supply a rough indication of what data are available for the element in question
The number of 'years' of data used is simply the number of months used divided by 12, and does not mean calendar or complete years except for the rainfall decile values. It gives the rough amount of data used between the first date of occurrence and the last data of occurrence of the element.
The percentage of a complete record gives an estimate of how complete the data are for that element, where a record with no missing values between these two dates would be 100% complete. Sites with missing data will be less than 100%. The percentage complete and the number of years for one element are not necessarily related to those for another element. There will often be far more data available for rainfall than there will be for other elements.
The deciles figures were derived from 'complete' years that is years with no missing monthly totals. Thus the number of years will usually be less than for the monthly mean rainfall.
In many cases there will be no data for a particular month or element. The rows for sunshine duration, maximum wind gust or evaporation will usually not be included as these elements are only recorded by a restricted number of sites.
Median and deciles
To calculate deciles, we divide the ranked dataset into ten parts. The median is simply that value which marks the level dividing the ranked dataset in half. For example 50% of Januaries will have a total rainfall at or above the January median and 50% will have a total below. The median is also known as the 5th decile, decile 5 and the 50th percentile - they are all the same thing. Decile 9 or the 90th percentile for January, means that 90% of January totals will be at or below this figure. In other words there is a 90% chance of a January rainfall being at or below decile 9 (90th percentile), a 10% chance of it being above decile 9, and a 10% probability of it being below decile 1 (10th percentile). To get the annual decile value, you do not sum the deciles for the 12 individual months, but must calculate it separately. However it is possible for the two values to be the same by chance.
Both mean and median rainfall are included, although median is the preferred measure of 'average' rainfall from the meteorological point of view. This is because of the high variability of daily rainfall - one large fall or very small fall will over-affect the arithmetic mean, but will have less affect on the median. The median (decile 5) is therefore usually considered the more reliable indicator.
Statistics and length of record
All rainfall observations for a site that have been quality controlled were used, regardless of how many years of data there are. Users should remember that a period of less than 30 years of rainfall data may not produce reliable statistics and such information should be used with caution. As a comparison some 5-10 years of temperature data will provide a reasonable estimate of the mean, (although probably not of the extremes).
Means for specific hours and Daylight Saving
Due to the effect of Daylight Saving, these values are only nominal for most Australian sites. Daylight Saving has been used in some, but not all, states of Australia, since about 1973. The changeover occurs almost always in October and March but the exact dates vary from state to state and year to year. The averages for 9 am are hence generally a combination of 8am and 9am values, and those for 3 pm, of 2pm and 3 pm values.