This study focuses on Gauteng due to its importance to the economy of South Africa and its high population density, but also due to the availability of observed meteorological data in the province. In addition it is also one of the regions for which weather forecasts are issued on a daily basis. In an attempt to understand more about the characteristics of heavy rainfall over Gauteng, observed daily rainfall data were analysed for the summer months October-March for a period of 32 years.
In this paper early summer refers to October to December and late summer to January to March. The rainfall at individual stations is investigated, but the emphasis is on the areal average rainfall over Gauteng.
One of the forecasting challenges for Gauteng is that the type of weather system responsible for precipitation, and indeed heavy rainfall, differs considerably from early to late summer. During early summer the atmosphere has a distinct extra-tropical nature when weather systems such as cut-off lows are frequent Singleton and Reason, However, in late summer January and February tropical circulation systems are much more prevalent over South Africa Dyson and Van Heerden, In this paper the emphasis is not on the weather systems responsible for the heavy rainfall but rather concentrates on rainfall statistics over Gauteng.
The results from this paper form the basis of ongoing research investigating the atmospheric variables and synoptic circulation patterns associated with heavy rainfall over Gauteng.
An example of heavy rainfall 'climatology' in the scientific literature is by Maddox et al. More recently, Brooks and Stensrud created an hourly rainfall climatology over the USA, followed by Schumacher and Johnson , who described characteristics of extreme rain events over the eastern two-thirds of the United States. They found that extreme rain events where the h precipitation total at 1 or more stations exceeds the yr recurrence amount for that location are most common in July, and that in the northern USA these events transpire almost exclusively in the warm season.
Chen et al. They found that heavy rainfall occurs with a pronounced afternoon maximum over Taiwan and that the orographic effects are important in determining the spatial distribution of heavy rainfall. A better understanding and knowledge of the climatology of heavy rainfall will facilitate the forecasting of these extreme events. The main aim of this paper is to make weather forecasters aware of how likely heavy rainfall events are over Gauteng during a particular summer month.
Understanding the spatial and temporal distribution of heavy rainfall events is a key aspect in furthering this aim. As flood-producing heavy rainfall events are infrequent, knowledge of the climatology of these events could therefore also aid inexperienced weather forecasters, by providing guidance as to how likely heavy rainfall might be during a particular time of the year.
The data used in this analysis are discussed first and some of the problems encountered in the dataset are highlighted. Information is consequently supplied about the seasonal, monthly and daily rainfall characteristics in Gauteng. Three different heavy rainfall classes are defined for average daily Gauteng rainfall and the monthly characteristics of these events are examined.
Lastly, heavy rainfall characteristics at individual stations are discussed for each of the summer months. All the rainfall stations over Gauteng were investigated for their suitability for use in this analysis. However, data from selected rainfall stations with records spanning shorter parts of the period were also included.
This was done mainly to capture data in cases where rainfall stations were replaced by new stations, with only slightly different locations, within the period under consideration. An example is the rainfall station at OR Tambo International Airport which closed on the 31 May while another station opened on 1 June at almost the same location.
Data from both these stations were then used in the analysis. Data for 5 locations were combined in this way resulting in a total number of 73 stations available for analysis over Gauteng. Not all of the rainfall stations were operational every day i.
The rainfall stations are generally spatially well-distributed throughout the province, with the exception being the north-eastern extremes where no rainfall stations were available. There is a higher concentration of rainfall stations in the major metropolitan areas of Gauteng Pretoria in the north and Johannesburg, about 50 km from Pretoria to the south. The quality of the rainfall data used, especially in such a large dataset, is of significant concern, and a considerable amount of time was spent performing quality control on the data.
Some obvious errors were easy to identify and were removed from the data set. However there were some questionable data values where it was close to impossible to determine the reliability of the observations. The raw rainfall data from SAWS include possible error information, with the daily rainfall values labelled as ' Normal' , or ' Error ' or ' Accumulated ' if accumulated over more than 1 d.
If the rainfall value was labelled anything other than Normal it was not used in the analysis for that particular day. As this research focuses on heavy rainfall it was important to have confidence in the high h rainfall values. Brooks and Stensrud explain how difficult it is to distinguish between 'rare interesting' rainfall events and 'bad data', as these often look similar. Therefore all rainfall events where h rainfall at a specific station exceeded mm were investigated for possible errors.
As will be explained later, mm was identified as a 'single very heavy rainfall event' and represents the 99 th percentile of daily maximum rainfall over Gauteng. It does happen from time to time that rainfall which was accumulated is not identified as such in the raw data set. This was relatively easy to identify in the data set when there was missing data for 1 or more days followed by a day reporting very high rainfall. This high rainfall value was then rejected only after comparison with rainfall from surrounding stations, in the process discussed below.
A 2nd set of errors removed from the data was all the cases where a station reported very heavy rainfall for several days in a row while there was no indication from surrounding stations that this did indeed occur.
When rainfall at any station over Gauteng exceeded mm on any particular day, the rainfall values at other stations over Gauteng were also analysed. If there were other stations reporting significant rainfall on that day or if there was a high percentage of rainfall stations over Gauteng reporting rainfall the value was accepted as correct. The last error check was to compare the events remaining, after the elimination of events considered by the previous checks, against other meteorological data and journals such as the SAWS newsletters and website and archived Meteosat 2 nd Generation data.
This was done in order to identify if there was a physical cause for a heavy rainfall event to occur. The real difficulty was in attempting to detect errors in the much larger number of rainfall events where the daily rainfall at a single station was more than 50 mm but did not exceed mm later defined as a 'single significant rainfall event'.
There were too many of these events to hand-check and it would be difficult to determine the accuracy as there were no other data with which to compare it. It is therefore possible that the daily rainfall dataset created as part of this research does contain some errors which may have led to some inaccuracies in the results. However, the impact of this would be limited as the research focused on the heavy rainfall events.
Using the rainfall data from the selected stations, an average daily rainfall value for Gauteng was calculated. Additionally, the percentage of rainfall stations recording more than 0 mm of rainfall was calculated and the highest rainfall measured at any of the stations was also noted. The average Gauteng rainfall was computed using a weighted average method proposed by Tennant and cited in Marx et al.
This method takes the geographical position of each station relative to the other stations into consideration. The following weighting function was applied to the daily rainfall values of all the stations:. When rainfall stations are distributed evenly over an area, this method renders results which are very close to the mathematical average the total rainfall at all the stations divided by the number of rainfall stations.
The use of the weighting method becomes important when the rainfall stations are not distributed evenly over an area, as is the case for Gauteng.
A rainfall station which is geographically distant close to other stations will have a larger smaller weight factor and will therefore contribute more less in the computation of the average rainfall. The stations with the largest weights were over the western extremes of Gauteng Fig.
Hekpoort in the north-west had a weight of 0. Other stations with weights larger than 0. These stations contributed more to the calculation of the average rainfall than the stations over central Gauteng such as Irene 0. The results from the 2 averaging methods are very similar to those of the weighted average method, generally giving slightly higher daily average values. Of the 5 d analysed, the weighted average method produced higher lower values than the mathematical average on d.
The largest difference occurred on 27 January when the weighted average was 67 mm and the mathematical average 59 mm. This was a particularly wet day as 23 stations measured more than 50 mm of rain and 11 more than mm. Stations over western Gauteng in particular measured high rainfall values, e. Randfontein mm , Krugersdorp mm and Hekpoort 66 mm.
From Fig. On 18 December the weighted average rainfall was 18 mm but the mathematical average was 23 mm. On this day there were only 4 stations with rainfall of more than 50 mm over southern Gauteng, but with mm at Viljoensdrift. Due to the isolated nature of the heavy rainfall, the weights assigned to the stations resulted in the weighted average being lower than the mathematical average; the influence of the extreme rainfall at a single station is therefore de-emphasised.
The average Gauteng daily rainfall was henceforth used to calculate the average Gauteng monthly rainfall and the data standardised in order to identify wet and dry months. Moreover, the rainfall at the individual rainfall stations was investigated in order to identify those locations in Gauteng where heavy rainfall occurs most frequently. This was done by dividing Gauteng into eight 0. An extreme precipitation event is usually defined by using a daily amount exceeding a certain threshold Zhang et al.
However, different threshold values apply for different parts of the world. One approach is to define heavy rainfall by considering when the areal average rainfall exceeds a particular threshold.
For example, Houze et al. In a South African study, Poolman contributing to Dyson et al. The Gauteng Province is approximately 16 km 2 in size. When the average daily rainfall is at least 25 mm over Gauteng it would fall into the major rain event definition provided by Houze et al. Over the yr period the daily average rainfall exceeded 25 mm on only 65 occasions. A further classification is made, with a 'heavy rainfall event' defined as the 95 th percentile, in this case 13 mm, and a 'very heavy rainfall event', similar to Houze's major rain event, when average daily rainfall exceeds 26 mm.
However as these thresholds may be applied in an operational environment they were adjusted slightly to fall in line with thresholds commonly used in the forecasting offices. Therefore a 'significant rainfall event' is classified as rainfall exceeding 10 mm, a 'heavy rainfall event' when the rainfall exceeds 15 mm and a 'very heavy rainfall event' when the rainfall exceeds 25 mm.
Extreme precipitation events are often defined by referring to the rainfall from individual stations. Bradley and Smith define extreme rainstorms as a 'major rain event' when the daily rainfall accumulation is at least mm at 1 or more rainfall stations. However, Chen et al. In a recent study Fragoso and Tildes Gomes identified an extreme rainfall event over southern Portugal when 40 mm occurred in 24 h. Zhang et al.
They also discuss the characteristics of heavy rainfall by examining the 90 th percentile of daily rainfall and the yr return values. Extreme rain events in the U. This spatially-varying threshold is most relevant to identify truly extreme events for this location Schumacher and Johnson, The maximum daily rainfall which occurred at any station over Gauteng was identified for all summer months. Following the definition for the areal average rainfall the 90 th 59 mm , 95 th 75 mm and 99 th mm percentile of these values was used to identify a heavy rainfall event at an individual station.
However, the forecasters at SAWS issue advisories and warnings for heavy rainfall when more than 50 mm of rain is expected at any location Rae, Therefore a 'single significant rainfall' event is defined when the rainfall at any rainfall station exceeds 50 mm. A 'single heavy rainfall' event is when the rainfall exceeds 75 mm at a single station and a 'single very heavy rainfall event' when the rainfall at a single station exceeds mm.
This value is close to the mm used by Bradley and Smith and the mm used by Chen et al. Two additional heavy rainfall classes are defined which combined the areal average rainfall and rainfall at individual rainfall stations.
A 'major rain event' is defined when the average daily rainfall over Gauteng exceeds 10 mm with at least 50 mm at a single station and an 'extreme rain event' is defined when the average Gauteng rainfall exceeds 15 mm with more than 75 mm at a single station.
The average Gauteng monthly rainfall was calculated for each summer month from to The monthly values were used to calculate an average rainfall value for early summer October to December and late summer January to March as depicted in Table 1.
The average summer rainfall October to March over Gauteng for this yr period was mm. The totals depicted in Table 1 were standardised with respect to the long-term average and standard deviation for early and late summer rainfall and the results are depicted in Fig.
The yr average early summer rainfall was mm and late summer rainfall mm. The early summer with the highest average rainfall was in when mm occurred over Gauteng. The early summer rainfall was less than mm on 5 occasions in the past 32 years. Three of these very dry early summers occurred in the past decade , and Also note from Fig. This had not transpired before during this yr period, although the early 80s had 3 consecutive dry years.
It is no surprise to find that the wettest late summer over Gauteng was in , with mm of rain, as tropical cyclone Eline invaded southern Africa in February Dyson and Van Heerden, and was responsible for widespread heavy rainfall over the entire sub-continent, including Gauteng.
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