Rain Records

Alan Robert Clark

Last Updated: May 10, 2023

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1  Introduction

In March of 1991 I started a daily record of rainfall. We were in a drought at the time and on water restrictions, so the garden was suffering somewhat!

I have recently been asked why I collect the data, in a similar manner as if the person were asking me why I was wearing a fried egg on my head. Yes, I suppose I am mad, but I am also a keen gardener, and rain, or rather, the lack thereof, affects me greatly! :-)

It is somewhat difficult to intelligently present such a large body of data, but obviously the first thing is to establish a season, since calendar years are meaningless in the Summer rainfall areas! Hence the 2000 season runs from July 1999 to June 2000, much like SARS really :-) (I guess that now needs explaining: not Severe Acute Respiratory Syndrome, but the South African Revenue Service!!)

In all the years of recording, the split at July has let me down only a few times.

It is hard to make a LOT of sense out of the data. We supposedly have a 7 or so year dry cycle and a 7 or so year wet cycle. It is difficult to conclude that from my data. The cause of the cycle is apparently the “El-niño” effect in the Pacific. It certainly has produced widespread drought, but the trouble is: South-West is where all the “weather” comes from, and this is interrupted by it.

If this pattern were to have continued, we would be in the hands of El-niño! The Tropical Complex from the North, however descends from the North, via Botswana, and has assisted us in Egoli for many years. As a direct result, the rainfall has been quite good when, for example, the northern VryStaat has been terrible. The “Usual” pattern would have catered for the Vrystaat before us!!

The ONI (Oceanic Niño Index) is published in raw data form at https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php. Further discussion (focussed on USA effects) is at https://www.climate.gov/news-features/understanding-climate/climate-variability-oceanic-nino-index.

A positive value of ONI corresponds to a warming El Niño, which generally implies drought for us, and a negative ONI corresponds to a cooling La Niña, implying rain. Note that the ONI is expressed as C above or below a 30-year average. (Sort of kills the "its only 2 degrees" argument…)

Figure 1: Season Totals overlaid on the Oceanic Niño Index

In above figure, it can be seen that there is at least some correlation. In 1990-2, there is a strong El Niño, producing significant drought. 1996-7 was a moderate La Niña, with very wet conditions. So the “strength” of the ONI does not necessarily correlate. 2010, of course was rather damp, in the presence of a very strong El Niño…2015 onwards is a bit of a mixed bag, with 2016 particularly uncorrelated. But 2022 is once again, damp.

Lastly, most publications state that the average rainfall in JoBurg is 500–750mm. JaNee.

2  Current Season, cumulative, for the impatient :-)

Figure 2: Cumulative current season

3  Rain Data by Season

3.1  Totals by Season

Simply a plot of the annual total seasonal rainfall. Note that in 1996 we moved from Crosby to Kensington, ie west JHB to east JHB, but there shouldn’t be much of a difference :-) <— See note above, I am changing my mind :-) The average values shown do not include the effects of the current (incomplete) season, as that would artificially affect them.

Figure 3: Season totals at the Clark’s
Figure 4: Season count totals (wet-days)
Figure 5: Average amount of rain per rainy day

Notice that in very dry years, less rain falls per day (obviously for far fewer days too) and on very wet years, more falls per day. But on other years which are not extreme, but do vary quite a lot in total rainfall, the average amount per rainy day is pretty constant!! I did not really expect the consistency.

Summary Table of Summed rainfall by Month
SeasonJulAugSepOctNovDecJanFebMarAprMayJunTotal
19920.00.017.063.527.067.029.529.082.521.00.011.0347.5
19930.028.012.037.5160.0143.061.552.5104.036.00.00.0634.5
19940.06.51.0222.577.0135.0112.5108.031.080.00.00.0773.5
19950.04.06.579.048.0128.0192.075.0136.067.041.00.0776.5
19960.011.56.5147.8165.5240.5201.0248.561.081.052.07.01222.2
19970.024.55.0135.072.0191.0230.587.5363.544.5133.07.01293.5
199811.07.070.059.0138.0120.5245.571.030.022.00.00.0774.0
19990.00.029.598.0239.0155.590.589.077.565.037.08.5889.5
20001.51.017.515.077.0206.5225.5401.5307.571.539.02.01365.5
20010.09.047.0122.5150.5138.039.0135.071.022.553.53.0791.0
20020.03.0100.0114.0158.0142.0107.095.096.019.012.064.0910.0
20030.020.55.0106.027.0185.5128.085.566.57.00.018.0649.0
20040.06.04.093.094.572.0149.0177.5115.556.50.014.0782.0
200526.55.50.047.552.5199.5219.0160.088.592.54.00.0895.5
20060.00.01.554.0158.0134.0217.0259.0121.024.52.50.0971.5
20071.542.09.563.0143.5230.595.533.062.549.00.038.0768.0
20080.80.046.0170.095.0122.0199.563.5159.030.065.529.5980.8
20090.00.00.0111.0137.5105.5198.567.5107.53.532.021.0784.0
20100.024.58.5148.5149.5189.5324.0152.0114.5196.544.50.01352.0
20110.00.00.068.5122.5303.5206.593.5213.0106.024.053.51191.0
20120.06.05.090.059.0235.0185.567.5101.019.00.03.0771.0
20130.02.5121.073.5131.0133.5147.587.073.5146.011.00.0926.5
20140.09.56.0154.0152.0247.050.5124.0236.065.02.08.51054.5
20150.02.022.052.0154.5364.0167.5117.565.542.00.02.0989.0
201631.01.075.012.089.0104.0186.0210.5144.016.563.019.5951.5
201728.00.04.086.0188.5153.5170.0294.550.043.049.50.01067.0
201811.00.00.0140.5154.5207.093.0123.5195.590.025.07.01047.0
20190.00.020.070.087.595.5290.5244.557.0222.00.00.01087.0
20200.00.024.03.5160.0292.076.0180.086.5128.50.018.0968.5
20210.00.09.073.5252.0186.0119.0162.599.032.011.021.0965.0
20220.01.08.096.0211.5240.5299.0118.5140.5203.524.027.01369.5
20232.00.00.047.0272.5206.050.5394.070.544.066.50.01153.0
Avg3.56.721.389.2131.4177.3159.6144.0116.567.124.812.3946.7

Bottom right is the overall average.

3.2  Monthly Totals by Wet and Dry Months

It is difficult to present the monthly data, as there are now too many years of it, and we are running out of graph space :-)

It is to be noted that the really wet months are a tad random!! And yes, Feb 2000 was a reasonably damp month. Note too that May ’97 in the “dry” months was rather special, leading to the pretty good seasonal total, even so late in the year.

Figure 6: “Wet” part of the season—monthly totals
Figure 7: “Dry” part of the season—monthly totals

3.3  Lies, Damned lies, and Statistics

Another way of looking at this is the min/mean/max type of graph per month. I have also added the First Quartile, Median, Third Quartile. I think that the month of February shows beautifully the classic question so often asked by newcomers to statistics: “What is the difference between a mean and a median”. The difference between min/max and 1st and 3rd quartiles is even more marked. The difference is caused by a once-off 30mm in 1992, and a once-off 401.5mm in 2000.

Figure 8: Min,mean,max and 1st quartile,median,3rd quartile by month.

Also of interest is a similar graph of the number of rainy days by month.

Figure 9: Min,mean,max and 1st quartile,median,3rd quartile rainy days by month.

4  Rain Data detail

For those oddballs that really like detailed stuff, here is the data on a yearly and a monthly basis.

4.1  Per Year

Figure 10: 1992 Season
Figure 11: 1993 Season
Figure 12: 1994 Season
Figure 13: 1995 Season
Figure 14: 1996 Season
Figure 15: 1997 Season
Figure 16: 1998 Season
Figure 17: 1999 Season
Figure 18: 2000 Season
Figure 19: 2001 Season
Figure 20: 2002 Season
Figure 21: 2003 Season
Figure 22: 2004 Season
Figure 23: 2005 Season
Figure 24: 2006 Season
Figure 25: 2007 Season
Figure 26: 2008 Season
Figure 27: 2009 Season
Figure 28: 2010 Season
Figure 29: 2011 Season
Figure 30: 2012 Season
Figure 31: 2013 Season
Figure 32: 2014 Season
Figure 33: 2015 Season
Figure 34: 2016 Season
Figure 35: 2017 Season
Figure 36: 2018 Season
Figure 37: 2019 Season
Figure 38: 2020 Season
Figure 39: 2021 Season
Figure 40: 2022 Season
Figure 41: 2023 Season

4.2  Per Year Cumulative

The cumulative graphs show very nicely “when” the season “kicks-in”. Some “good” seasons are awful to begin with, some start off with a bang, but fizzle dismally.

Figure 42: 1992 Season
Figure 43: 1993 Season
Figure 44: 1994 Season
Figure 45: 1995 Season
Figure 46: 1996 Season
Figure 47: 1997 Season
Figure 48: 1998 Season
Figure 49: 1999 Season
Figure 50: 2000 Season
Figure 51: 2001 Season
Figure 52: 2002 Season
Figure 53: 2003 Season
Figure 54: 2004 Season
Figure 55: 2005 Season
Figure 56: 2006 Season
Figure 57: 2007 Season
Figure 58: 2008 Season
Figure 59: 2009 Season
Figure 60: 2010 Season
Figure 61: 2011 Season
Figure 62: 2012 Season
Figure 63: 2013 Season
Figure 64: 2014 Season
Figure 65: 2015 Season
Figure 66: 2016 Season
Figure 67: 2017 Season
Figure 68: 2018 Season
Figure 69: 2019 Season
Figure 70: 2020 Season
Figure 71: 2021 Season
Figure 72: 2022 Season
Figure 73: 2023 Season

4.3  Per Month

Note the scale changes: “Wet” and “Dry” months have different maxima on the yranges, but are consistent within those classifications for comparison purposes.

Figure 74: July
Figure 75: August
Figure 76: September
Figure 77: October
Figure 78: November
Figure 79: December
Figure 80: January
Figure 81: February
Figure 82: March
Figure 83: April
Figure 84: May
Figure 85: June

The online version is "http://ytdp.ee.wits.ac.za/rain.html"


This document was translated from LATEX by HEVEA.