Are Australia’s automatic weather stations any good? Part 2: Issues affecting performance

1 Dr. Bill Johnston

Summary Said to be one of the most important weather stations in the Bureau of Meteorology’s automatic weather station (AWS) network, data for Cape Leeuwin was used to demonstrate a physically based rapid screening technique for evaluating the fitness of maximum temperature (Tmax) data for use in climate studies. Surrounded by ocean, the site is very exposed. Data were affected by ‘wind shaking’, which could reset instruments, cause breaks in thermometer mercury/alcohol columns and damage Stevenson screens. Mist, sleet, fog, rain and sea-spray blasted into screens affects measurements, which are assumed to be dry-bulb. Wind-driven rain is not measured accurately and moving the AWS to an up-draft zone close to the drop-off above the ocean in April 1999 reduced rainfall-catch by 26% and caused Tmax to be biased high relative to pre-1994 manual data. Tmax appeared to increase with rainfall and seven of 27 datapoints were identified as outliers. Non-parametric LOWESS regression visualised the shape of Tmax-rainfall relationships, which are expected to be significant, linear and negative in sign (Pearson’s linear correlation coefficient, Punc <0.05) and aided identification of out-of-range values. Tmax data for Cape Leeuwin don’t reflect the weather and are unlikely to be useful for detecting trends and changes in the climate. 1. Introduction Part 1 of this series outlined the utility of a physically based empirical reference frame for evaluating the fitness of maximum temperature data (Tmax) for detecting trends and changes in the climate. Amberley RAAF (Bureau ID 40004) was the case study and analysis of six additional widely dispersed sites showed the methodology was rigorous, statistically robust and widely applicable. Using metadata to identify non-climate changes is subjective and can be influenced a priori; for instance by making arbitrary adjustments for changepoints that make no difference or by selectively ignoring (or not documenting) those that do. In contrast, the statistical approach is straightforward, replicable and objective. Summarised from daily data, annual datasets used in the study are listed in Appendix 2 (pp. 11-16). In this Part 2 of the series the same thermodynamic principles are used for exploratory data analysis – screening individual datasets for biases and outliers, with the main focus being automatic weather stations (AWS) located in southern and eastern Australia. The overarching question is: are Australia’s automatic weather stations any good?

2. Background While some stations were automated earlier, since AWS were designated primary instruments on 1st November 1996, the Bureau has converted most of their network to automatic operation. However, like thermometers, platinum resistance probes used to monitor temperature (T-probes) may deteriorate in service. According to site summary metadata the original T-probe at Cape Leeuwin (Figure 1) operated for 11 years; Cape Otway, 17 years; Cobar AP and Double Island Point

1 Former NSW Department of Natural Resources research scientist and weather observer. 2 (Qld), 18 years; HMAS Cerberus and Deniliquin AP, 26 years; Armidale AP, 9 years; Flinders Island, 14 years and Cunderdin since June 1995 (25 years). It is concerning also that ACORN-SAT weather stations used to calculate Australia’s warming1 receive only two site visits per year for inspection and maintenance2, which is probably insufficient. Stevenson screens operating unsupervised under adverse conditions accumulate dust, grime, wind-borne salt-spray etc; paint peels off and they become damaged which affects their performance often resulting in upper-range bias on warm days. A salty sheen detected on the 60- litre screen at Cape Leeuwin in September 2015 by the Author for instance, may bias observations. Highlighted in Part 1 is that weather station documentation is mostly inadequate, out-of-date and often faulty. Site-summary metadata does not routinely show when 60-litre Stevenson screens replaced 230-litre ones for instance; for most stations site photographs are not available; positioning of T-probes in front of or behind the supporting frame within screens is important but not documented and maintenance issues such as the use of herbicide to destroy natural ground- cover in lieu of regular mowing (which is the standard) are rarely noted.

Figure 1. Looking southwest, the Almos automatic weather station at Cape Leeuwin (Western Australia) is close to the 13-metre drop-off to the ocean in an up-draft zone (left). Horizontal rain can’t be measured accurately by tipping-bucket raingauges buffeted by wind and after the site moved 30 m from the protected position between the sheds (circled right) on 14th April 1999, average rainfall declined 26%. Around 50% of days experience mist, fog, sleet, rain and sea-spray and moisture blasted into the screen by strong to gale-force winds directly off the Southern Ocean affects temperature measurements, which are assumed to be dry-bulb.

3. Site changes and metadata Being the most southwesterly point of the continent, Cape Leeuwin is one of Australia’s most iconic lighthouses. Signage proclaims the weather station (Bureau ID 09518) is “one of the most important weather stations in the AWS network” and commencing in 1907 the dataset is the longest same-locality climate record in Western Australia. According to Simon Torok3, in January 1919 the screen was a “small old observatory pattern”; in November 1926, “still old screen; thermometers 2-feet from the ground”; a new screen was installed in June 1936; by July 1964 its door (which faced south into the weather) had become

1 Australian Climate Observations Reference Network – Surface Air Temperature 2 See: Review of the Bureau of Meteorology’s Automatic Weather Stations (2017). Appendix B, Table 1 (p. 54). Bureau of Meteorology, Melbourne, 77 p. 3 Torok SJ (1996). Appendix A1, in: “The development of a high quality historical temperature data base for Australia”. PhD Thesis, School of Earth Sciences, Faculty of Science, The University of Melbourne. 3 damaged and in October 1978, “Large screen replaces small one. 250 m move towards residence” … to a more sheltered position between the sheds, which should reduce the effect of wind-shaking on instruments. As metadata is unavailable between 1926 and 1936 (10 years), 1936 and 1964 (28 years) then until the site moved 14 years later in October 1978, it is impossible to gauge the state of the site, the screen or instruments without additional research and analysis. ACORN-SAT metadata states the small screen in Figure 1 replaced (a previous) large one on 31 October 1978 and that an AWS installed on 3 February 1993 moved a small distance westward on 14 April 1999. Site-summary metadata (26 July 2020) placed the original site northwest of the lighthouse (at Latitude –34.3742, Longitude 115.1344) but failed to mention the moves in 1978 and 1999. Further, as thermometers were removed on 3 February 1993 there was no overlap between manually observed data and data provided by the rapid-sampling AWS. An undated 1950s site plan located in a file at the National Archives of Australia shows the windgauge (anemometer) about 50 m northwest of the lighthouse but places the original meteorological enclosure in a slightly more sheltered position about 40 m northeast. Looking south from the cottages, 1975 photographs (Appendix 1) show a 230-litre Stevenson screen and anemometer pole located there (not the small screen mentioned by Torok (1996)). Further, a high- level aerial photograph dated 11 November 1943 showed a concrete path leading from the lighthouse to the enclosure, a distance estimated to be 30 m (Figure 2).

Figure 2. Joined and reoriented 1943 high-altitude aerial photographs of Cape Leeuwin (left) and a same-scaled Google Earth Pro satellite image (17 November 2017) show the lighthouse (L), the position of the windgauge (w), a met-enclosure serviced by a concrete path (1), the subsequent site between the sheds (2) and the current site atop the steep drop-down to the ocean (3). Although site-summary metadata placed the original site at ‘X’ (Latitude -34.3742, Longitude 115.1344) it was most likely to have been adjacent to the windgauge. Photographs were directly overlaid and saved at 100% and zero% opacity. Apparent shoreline differences are due to the angle of exposure and state of the tide at the time images were acquired and close inspection of individual protruding rocks and shoals showed no material change in sea level during the 74-year period between photographs. 4 The original small observatory pattern screen was most likely near the windgauge, where it was overexposed to gale-force southerly and southwesterly winds direct from the Southern Ocean. Wind-shaking noted by Torok (1996) could re-set thermometers or cause breaks in their alcohol and mercury columns. Photographs and the site plan show the site had moved east of the road before 1943 (to Latitude -34.3747, Longitude 115.1365) and that a standard 230-litre screen was used at least before 1975. That site whose coordinates were not noted in metadata was said to have moved 250 m north (more likely 195 m) in October 1978 to a 60-litre screen installed between the two sheds, with the raingauge about 3 metres to the west surrounded by lawn. ACORN-SAT metadata states the screen and raingauge relocated to their current position near the drop-off on 14 April 1999. Corresponding with the move average rainfall abruptly declined 267 mm/yr or 26% (Figure 3).

1919 Sm1926 Sc 2' 1936above new GL Sc 1964 damaged1978 move; Sc 1993 60-L 1999AWS Sc AWS move 1500 1983

1000 2000 - 1009.6 mm

-267 mm 500

Rainfall (mm)

1900 1920 1940 1960 1980 2000 2020

Figure 3. After the AWS was moved to an up-draft zone near the drop-off above the ocean in 1999, average rainfall abruptly declined by 267 mm. Dotted vertical lines indicate site changes noted by Torok (1996) and ACORN-SAT metadata. (60-L Sc refers to small screen) From 1907 to 2019 median rainfall was 970 mm received over an average of 181 wet days/year and the median wet-day rainfall was 2.5 mm. Rainfall less than or equal to 1 mm/day was recorded on 31.3% of wet days, while 84.2% of wet days received less-than or equal-to 10 mm. In contrast, 2.9% of days received >25 mm/day and rainfall exceeding 100 mm/day was recorded on only six occasions over the 113 years of record. To summarise, the place is extremely exposed and subject to strong winds and gales; about 50% of days are wet but most of the precipitation is reported as low intensity showers, mist, fog, sleet or wind-borne sea-spray. Under such extreme conditions, rainfall-catch by the gauge is likely to be considerably less than actual rainfall. Step-change analysis showed the frequency of rain-days in the 0.1 to 1 mm/day category was not homogeneous (Figure 4). Rainfall-catch changed abruptly in 1919, which suggested the site moved to a less exposed position (catch for other rainfall classes increased resulting in no change in annual rainfall). It is a paradox also that while total rainfall remained the same (Figure 3) rain in the 0-1 mm class declined in 1964. The decline could indicate that the raingauge was replaced or reoriented at the 1964 inspection; observing practices changed such as allowing light rainfalls to accumulate in the gauge or the rainfall may have changed. For temperature data, distribution of decimal fractions (i.e. x.3 where x is a whole number and 3 is the decimal fraction) of data observed consistently to a single decimal place would uniformly equal 10%/fraction/yr and the average across fractions *(x.0 + x.1 + … x.9)/10+ would be 0.45. Also, Monte Carlo simulations of 2043 random whole-degree Fahrenheit data, transform to Celsius in the ratio 54%, x.0; and 22 to 23%, x.9 and x.1oC. For data observed to the nearest 0.5oF there were no x.2 and x.3 or x.7 and x.8 transformed Celsius fractions. Further, due to there being no internal ½-degree index, Fahrenheit meteorological thermometers are difficult to observe accurately and consistently to the nearest 0.1oF. Fahrenheit thermometers were replaced across the network by 5 Celsius ones on 1 September 1972. (As the range from freezing to boiling = 180oF divisions and 100oC divisions, observed with the same degree of precision Fahrenheit is a more sensitive scale than Celsius (1 Fahrenheit degree = 0.55oC).)

1919 Sm1926 Sc 2'1936 above new GL Sc 1964 damaged1978 move; Sc 1993 60-L 1999AWS Sc AWS move 1.8

(%)) 1.6

10 1.4

(log 0-1 mm/day 1.2 1900 1920 1940 1960 1980 2000 2020

Figure 4. 31.3% of wet days recorded less than 1 mm of rainfall/day overall; however, evidenced by step-changes in 1919, 1964 and after 1993 frequency was not homogeneous. Changes in exposure to the prevailing wind partitions more (or less) incident rainfall to another intensity class or reduces rainfall-catch across all classes, which would be observable as a change in annual rainfall. After 1999, while annual rainfall declined, the frequency of falls less than 1 mm/day increased. (As data were not symmetrical about the mean, they were analysed as common logarithms.) Persistent deviations in decimal fractions and their average expose inhomogeneties in precision (Figure 5), which has implications for the quality of temperature data and temperature extremes in particular. For instance relatively precise AWS data may not be comparable with less precise historic Fahrenheit data observed in whole-degrees under the brutal conditions of a howling gale, in fog or in the middle of the night, especially when the screen was only 60 cm above ground.

0.6 1919 sm1926 Sc 2' above1936 new GL Sc 1964 damaged1972 1978metrication Sc move; 60-L1993 Sc AWS1999 AWS move 0.5 - 0.45 0.4 0.3 0.2 0.1

Av. fraction 0.0 (a) 100 80 60 x.0 40 20 x.5 Fraction (%) (b) - 10% 0 1900 1920 1940 1960 1980 2000 2020

Figure 5. Precision inhomogeneties are indicated by persistent deviations from 0.45 (a). Noting that data before 1 September 1972 are back-transformed Fahrenheit observations, changes in the frequency of selected decimal fractions in (b) - x.0, x.5 and x.3 (red squares) and x.7 (inverted blue triangles) show whole-oF were over- represented before 1919 with the proportion increasing from 1922 to 1942. Observations from 1943 to 1972 were exclusively whole degrees-F. Following metrication, observations were in whole and ½oC until 1991, while after the AWS was installed for a short period until the 17 May 1998 frequency was random around 10%. The AWS reported whole-degrees only from 18 May 1998 until 9 January 2003 after which values were again random around 10%. Quality control, measurement bias and site inconsistencies that affect measurements are major issues particularly for sites that are poorly maintained or over-exposed. Rainfall measured by the AWS tipping-bucket gauge is significantly less than prior to the move in 1999; air being monitored rises directly from the rocky west-facing drop-off which depending on cloudiness may be under the glare of the sun or not; while on days when mist, fog, rain, sleet or gale-forced sea-spray blast in from the Southern Ocean, Tmax may be measured as wet- or dry-bulb and there is no telling the difference from the data. 6

Although significantly correlated overall (Punc = 0.047) rainfall explained only 2.6% of Tmax 2 variation (R adj = 0.026) and it was noteworthy that data after 2010 were mostly outliers (Figure 6).

970 mm 1919 sm1926 Sc 2' above1936 new GL Sc 1964 damaged1973 1978metrication Sc move; 60-L1993 Sc AWS1999 AWS move

C) 2012 2011 Tmax ~ rainfall residuals o 21 2014 1983 1983 2010 2015 2013 1949 2010 1967 2019 20 o 1935 - 19.8 C 2001 1982 1957 2015 19 1917 1917 1943 (a) 1943 P = 0.047 (b)

Temperature ( 500 1000 1500 1920 1940 1960 1980 2000 2020 Rainfall (mm) Figure 6. Although the relationship was significant, rainfall explains only 2.6% of Tmax variation (a) and residuals were not homogeneous (b). Noting that metadata from 1936 to 1964 and 1964 to 1978 was missing and conditions affecting observations would not have remained the same (the screen would have needed to be repaired, replaced or painted during those times), step-changes in residuals most likely reflect site changes not changes in the climate. (Post-1993 AWS data are highlighted and note that the AWS moved in April 1999.) Lighthouse keepers made weather observations and historical accounts1 note that the light was upgraded to vaporised kerosene in 1925 and a radio beacon was installed in 1955 near the position of the pre-1950s windgauge. The light used a manual clockwork mechanism until 1982 when it was electrified and although the reason is obscure this corresponded with a Tmax up-step. (As 1983 was the wettest year on record, a faulty thermometer was the more likely reason that Tmax was over-range.) The lighthouse was fully automated in September 1992 and shortly after, on 3 February 1993 manual temperature observations ceased. As rainfall was not a significant co-variable, Tmax data were randomised within segments to remove time-dependence and analysed as a 1-way analysis of variance problem (initially 5-blocks of varying numbers of observations, Table 1). As they were not significantly different data for the first and third segments were combined to form a single group, re-randomised and reanalysed. Table 1. Tmax means and standard deviations for data segments defined by step-change analysis in Figure 6. Means sharing the same superscripts are not statistically different and in final-round analysis their data were combined. Segment Mean Tmax SD N (oC) 1907-1934 19.65a 0.383 28 1935-1956 19.27b 0.380 22 1957-1981 19.73a 0.413 25 1982-2009 20.07c 0.341 28 2010-2019 20.50d 0.488 10

Final-round analysis found significant differences between re-grouped segment means (Figure 7, left to right (oC ±SD), 19.68 ± 0.396, N = 53; 19.27 ±0.380, N = 22; 20.06 ±0.341, N = 28; 20.50 ±0.489. N = 10). Distributions were mostly skewed-low particularly after 2010 (i.e. distance from the 1st Quartile to the median > from the median to the 3rd Quartile). Due to the numerous sources of non-climatic variation; the poor state of metadata; and possibly changes in observers and observing practices, interpretation of Cape Leeuwin data is not straightforward. Tmax and rainfall were not correlated and therefore as explained previously Tmax

1 https://lighthouses.org.au/wa/cape-leeuwin-lighthouse/ 7 cannot be representative of terrestrial temperature. While site changes only happen once, Torok (1996) made pre-1996 homogenisation adjustments in 1911 (statistical), 1936 (new screen), 1963 (statistical), and 1979 (move). ACORN-SAT v.1 made adjustments in 1936 (screen), 1971 (no reason), 1978 (move) and 1999 (move); while ACORN-SAT V.2 made adjustments in 1936 (screen), 1948 (statistical), 1967 (statistical), 1978 (move), 1993 (statistical) and 1999 (move).

C) o 21

20 - 19.8 oC

19

Temperature (

AWS 1907-1934 1935-1956 1982-2009 2010-2019 & 1957-1981 Figure 7. Box-plots present a 5-point summary of data distributions. The box depicts the upper and lower quartiles intersected by the median (solid line), whiskers are the 5th (lower) and 95th percentiles and values outside that range are outliers. So the plot displays central tendency (the median), spread in the distribution (50% of values occur within the box) and outliers to the likely range. Picking and choosing changepoints based on unreliable metadata, statistical tests that are not replicable, and applying arbitrary corrections lacks scientific merit and creates the suspicion that homogenisation adjustments were aimed to result in pre-determined trends. In contrast, linear corrections of objectively detected Tmax step-changes (Figure 6) left no residual trends, segmented trends or change attributable to the climate. The climate therefore has not changed. For instance, the step-down of -0.41oC in 1935 was cancelled by the step-up of 0.49oC in 1957 leaving no trend and no net change. But what caused the down-step? The overarching problem is that metadata is untrustworthy. Documentation was missing for a decade after 1926 and for 28 years from 1936 to 1964. Most sites maintained instrument and correspondence files and despite assurances that ACORN-SAT sites have been thoroughly researched it reflects poorly that no information was available for those periods when instruments and possibly observers would have changed.

4. AWS from 1994 Now that most Bureau sites have been automated, homogenisation based on comparisons with correlated neighbours axiomatically involves comparing ACORN-SAT AWS data with sites that also operate AWS. Also, so-called network comparisons (ACORN vs. AWAP1) are also AWS vs. AWS. Biases specific to AWS cannot be detected by comparing like with like and for Cape Leeuwin there are no overlapping thermometer data to use as a control. The Bureau claims that AWS are precise, unbiased and carefully calibrated, which may be true. It is also claimed that sites are sufficiently well maintained and that a range of other quality-control procedures (most involving inter-site comparisons) are in-place. Adding another dimension, if AWS Tmax closely reflects the weather, a statistically significant negative relationship is expected between Tmax and rainfall with fewer than 5% of values being outliers to the linear case. Furthermore, residuals are expected to be homogeneous, random and normally distributed with equal variance across the fitted data range. Sidestepping the rigid assumptions of linear regression, non-parametric LOWESS regression (LOcally Weighted Scatterplot Smoothing) fits localised subsets of data, which tracks the shape of

1 Australian Water Availability Project; https://www.cawcr.gov.au/technical-reports/CTR_013.pdf

8 the Tmax-rainfall relationship point by point. Assumptions of linearity do not apply; instead, determined by a smoothing parameter (generally in the range 0.7 to 1.0) the curve flexibly visualises relationships, highlights non-linearity and identifies outliers relative to the fit and 95% bootstrapped confidence intervals. Supported by Pearson’s linear correlation coefficient, which indicates strength, significance (that Punc <0.05) and sign (r) of relationships, LOWESS regression is conveniently undertaken using the desktop statistical application PAST1 from the University of Oslo (PAleontological STatistics; https://www.nhm.uio.no /english/research/infrastructure/past/).

AWS Tmax data were not linearly correlated (Punc = 0.17) with the sign (r = 0.28) indicating Tmax implausibly increased with rainfall. Accentuating random associations seven of the 26 data-pairs were outliers (Figure 8). Compared to the overall data centroid (of 970 mm and 19.8oC), rainfall was less and Tmax was higher than before the site moved in 1999, while data in 2011 and 2012 were implausibly over-range by more than 1oC.

742 mm 970 mm

C) 2012 2011 o 21 2014 2015 2013 2010 o 2019 - 20.2 C 20 2017 - 19.8 oC 2016 19 Punc >0.05

Temperature Temperature ( 500 1000 1500

Rainfall (mm) Figure 8. Moving the AWS towards the west-facing drop-off above the ocean on 14 April 1999 reduced rainfall- catch and caused average Tmax to increase (solid lines) relative to the overall data centroid (dotted lines). AWS Tmax was uncorrelated with rainfall (Pearson’s r (Punc) >0.05). Non-parametric LOWESS regression with 95% bootstrapped confidence intervals (dashed lines) forces a flexible, robust smooth line through localised subsets of data, which visualises the shape of the relationship between variables. Post-2010 datapoints (red squares) indicated by labels were inexplicably out-of-range to the extent that Tmax apparently increased with rainfall, which according to the First Law theorem is physically implausible. Illustrating the utility of a physically based alternative non-parametric approach to the problem of data screening; as AWS-Tmax shows a weak positive, non-significant association with rainfall and outliers considerably affect the dataset, AWS data for Cape Leeuwin are not up to the task of depicting trend and change in the climate.

5. Discussion and conclusions For weather stations examined as case studies in Part 1 of this series evaporation of rainfall removed latent heat from the environment via convection; reduced heat advection to the local atmosphere, which cooled Tmax consistent with the First Law theorem. Tmax vs. rainfall relationships were linear while site changes that affected ambient conditions caused data segments to be significantly offset relative to each other. It was found that despite it being a high- quality ACORN-SAT site used to calculate Australia’s warming, metadata for Amberley RAAF was faulty. Metadata for the six replicate sites was also faulty and in all cases changes and trends in Tmax data were due to site and instrument changes, not the climate.

1 Hammer, Ø., Harper, D.A.T., and P. D. Ryan, 2001. PAST: Paleontological Statistics Software Package for Education and Data Analysis. Palaeontologia Electronica 4(1): 9pp. 9 Poor documentation of historic changes in instruments, observers and practices at Cape Leeuwin; and factors such as over-exposure to the elements, the AWS being relocated to the turbulent up- draft zone near the drop-off to the sea, and possibly other factors that bias measurements, data could not be considered useful for detecting trend and changes in the climate. Possibly because thermometers and T-probes are frequently moist or affected by accumulated salt, Tmax data are less dynamic than for other coastal sites (within-year variance of daily data ranges from 7.8 to o 2 o 2 o 2 o 2 18.3 C vs. 19.0 to 32.1 C for Witchcliffe; 13.4 to 26.4 C , Cape Naturaliste; 18.7 to 38.1 C , Busselton; and 15.0 to 29.5oC2, Rottnest Island) and there can be little confidence that Tmax is recorded consistently as a dry-bulb estimate. Combined with Pearson’s linear correlation coefficient, which measures the strength, significance and sign of the association between Tmax and rainfall; LOWESS regression with 95% confidence intervals visualises the shape of the relationship, highlights possible non-linearly, the spread of values relative to rainfall and identifies possible outliers. As an exploratory technique the method is convenient, replicable, physically valid and versatile. In Cape Leeuwin’s case (Figure 8), Tmax increased with rainfall (which for terrestrial Tmax is implausible); AWS-data were stepped-up relative to previous data (data centroids were not the same); seven of the 26 data points were out-of-range – possibly biased by conditions that were exceptionally windy (resulting in low rainfall-catch) and moist (high relative humidity and excessive moisture entering the screen). It is concluded that if AWS Tmax data don’t reflect the weather (i.e. the expectation that Tmax is significantly inversely correlated with rainfall) they are unlikely to be useful for detecting trend and changes in the climate.

Bill Johnston 5 November 2020

Acknowledgements Methods of investigating precision inhomogeneities using decimal fractions, evolved from discussions with Chris Gillham (http://www.waclimate.net/), who helpfully reviewed the earlier draft of the paper.

Comments by David Mason-Jones and Ken Stewart (https://kenskingdom.wordpress.com/) were greatly appreciated.

Author’s note: Although this draft paper has been carefully researched and uses statistical tools, data and other evidence that is available in the public domain, no responsibility is accepted for errors of fact arising from the scarcity of information or photographs etc. Research includes intellectual property that is copyright (©).

Preferred citation: Johnston, Bill 2020. Are Australia’s automatic weather stations any good. Part 2. Issues affecting performance. http://www.bomwatch.com.au/ 17 pp. 10

11

Appendix 1 National Archives of Australia, 1975 photographs.

Cape Leeuwin lighthouse viewed from the north in 1975 showing the location of the large (230-litre) Stevenson screen and anemometer pole northeast of the lighthouse (see Figure 2). (Nation Archives of Australia Barcodes 11467624 and 11465658). The position agrees with that indicated by a 1950s plan on-file at the National Archives of Australia (NAA Barcode 1929975, p 104) 12

Appendix 2. Datasets. A2.1 Tmax and decimal fractions (space delimited). Note that %whole degrees before 1993 = Rnd0 + Rnd1 + Rnd9. Decimal fraction % >>>> Year Rain MaxAv MaxN MaxVar Segment AvFrac Rnd0 Rnd1 Rnd2 Rnd3 Rnd4 Rnd5 Rbd6 Rnd7 Rnd8 Rnd9 1907 1187.3 19.96 355 14.12 1 0.449 13.52 12.96 9.86 6.20 7.32 6.20 9.30 9.30 11.27 14.08 1908 912.1 19.62 362 12.41 1 0.439 17.13 11.33 8.01 8.29 6.91 7.18 6.91 7.73 11.05 15.47 1909 931.4 20.33 365 16.90 1 0.418 18.08 10.14 11.23 5.48 10.96 6.03 7.67 5.48 13.42 11.51 1910 1256.8 19.98 361 18.33 1 0.418 16.07 13.30 9.42 8.31 8.86 9.14 4.16 6.09 9.97 14.68 1911 649.3 19.80 356 13.19 1 0.407 19.10 14.61 8.15 7.58 7.87 6.74 3.93 4.49 14.33 13.20 1912 868.8 19.81 365 12.87 1 0.391 22.19 11.78 10.14 7.40 4.93 10.68 3.01 5.48 12.60 11.78 1913 1253.7 19.65 365 10.65 1 0.407 17.53 16.99 7.95 6.58 8.77 6.58 2.19 7.95 11.78 13.70 1914 737 20.09 365 10.51 1 0.359 21.37 17.81 10.14 7.40 4.93 6.85 5.21 5.75 11.51 9.04 1915 966.2 20.09 361 13.14 1 0.407 17.45 11.91 13.85 7.20 6.65 7.76 4.43 5.54 11.91 13.30 1916 920.1 19.81 366 13.90 1 0.366 18.31 14.48 13.11 8.47 7.65 9.84 5.74 3.28 9.02 10.11 1917 1337.6 18.71 365 11.99 1 0.371 24.66 13.15 7.12 4.11 7.67 15.07 6.03 1.92 6.85 13.42 1918 854.1 19.73 364 10.75 1 0.351 24.18 14.01 6.87 4.12 8.24 18.96 6.59 3.02 4.12 9.89 1919 750.8 19.23 365 9.48 1 0.384 20.00 11.51 9.86 6.85 8.22 13.15 7.12 4.93 9.32 9.04 1920 1089 19.95 366 17.33 1 0.420 19.13 7.92 8.74 6.83 11.20 11.75 5.46 6.56 9.84 12.57 1921 1301.7 19.97 365 12.79 1 0.410 21.64 12.05 4.93 6.03 6.85 13.97 6.85 4.66 8.22 14.79 1922 983.9 19.38 363 12.94 1 0.308 33.88 17.08 6.06 3.86 5.23 10.19 3.86 1.65 3.03 15.15 1923 951.1 19.27 364 11.54 1 0.302 34.62 17.86 5.49 2.47 4.95 10.16 6.59 1.37 2.47 14.01 1924 1130.6 19.41 365 10.35 1 0.299 35.62 16.16 8.22 3.56 3.84 9.59 3.01 0.55 4.93 14.52 1925 799.4 19.61 354 11.26 1 0.318 31.64 13.56 9.89 2.26 5.65 14.69 3.39 1.13 6.78 11.02 1926 1276.6 19.41 353 13.60 1 0.372 25.50 13.60 7.37 4.53 6.80 11.05 5.67 3.97 7.65 13.88 1927 1154.9 19.42 365 11.61 1 0.336 28.22 15.34 9.59 4.66 6.03 7.67 4.38 5.48 7.67 10.96 1928 1063.3 19.35 364 11.30 1 0.334 32.42 12.09 9.89 4.67 3.85 7.97 5.77 2.47 6.87 14.01 1929 1074.8 18.87 363 10.72 1 0.279 33.33 19.83 11.02 3.58 3.31 7.44 3.03 1.38 5.23 11.85 1930 982.5 19.66 364 11.39 1 0.370 25.00 15.66 10.71 4.12 3.85 9.34 2.75 4.40 6.87 17.31 1931 1013.9 19.35 364 17.25 1 0.373 23.35 12.91 9.07 4.67 5.77 14.01 6.04 5.49 7.69 10.99 1932 1029.8 19.58 366 13.99 1 0.376 24.04 12.84 10.38 2.73 4.92 16.12 3.55 3.83 8.20 13.39 1933 1130.3 20.15 363 15.68 1 0.367 27.27 11.57 7.44 2.48 8.26 13.22 6.89 3.31 6.06 13.50 1934 1174 20.01 364 11.88 1 0.379 23.63 14.01 8.24 2.75 6.87 11.81 8.79 2.75 8.79 12.36 1935 874.4 19.32 365 12.25 2 0.400 18.90 14.79 8.49 4.11 4.11 15.89 9.04 3.56 9.04 12.05 1936 833.9 19.35 366 11.45 2 0.366 22.68 13.11 9.84 6.01 5.74 13.66 7.38 3.83 6.83 10.93 1937 1106.5 19.51 363 11.71 2 0.355 23.14 17.36 7.99 5.23 7.71 9.64 5.79 4.68 7.16 11.29 1938 900.5 18.98 365 8.93 2 0.329 27.12 11.23 12.05 6.85 7.12 9.86 7.12 4.38 6.58 7.67 1939 1102.8 19.21 365 9.78 2 0.369 20.82 11.23 13.15 5.21 8.49 11.51 6.58 8.22 5.75 9.04 1940 749.6 19.29 366 8.90 2 0.389 17.76 12.02 13.66 4.37 7.10 14.21 7.65 4.64 8.20 10.38 1941 988.9 19.09 364 10.49 2 0.375 23.08 10.71 11.26 7.42 6.87 9.89 5.77 3.30 10.16 11.54 13 1942 1210.1 18.89 363 9.42 2 0.342 26.45 17.36 8.82 4.41 5.79 9.92 3.58 4.13 5.79 13.77 1943 982.4 18.64 364 7.87 2 0.271 37.09 21.98 6.04 2.20 4.12 7.14 3.57 1.10 2.20 14.56 1944 776.1 19.35 366 9.89 2 0.292 40.44 16.94 5.74 0.82 3.55 6.83 3.55 1.91 4.10 16.12 1945 950.2 19.33 364 12.57 2 0.182 56.32 26.37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 17.31 1946 935.2 18.77 365 8.81 2 0.218 55.62 22.47 0.00 0.00 0.27 0.00 0.00 0.00 0.00 21.64 1947 1027.9 18.87 363 8.30 2 0.215 51.24 26.72 0.00 0.00 0.83 1.38 0.28 0.00 0.00 19.56 1948 879.6 19.67 362 9.32 2 0.210 58.29 20.72 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20.99 1949 1007.5 20.32 363 11.40 2 0.267 53.72 18.73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 27.55 1950 966.1 19.88 364 12.71 2 0.203 55.49 24.73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 19.78 1951 984.8 18.97 363 10.44 2 0.189 58.68 22.87 0.00 0.00 0.00 0.00 0.00 0.00 0.00 18.46 1952 999.8 19.43 366 10.95 2 0.193 60.11 20.77 0.00 0.00 0.00 0.00 0.00 0.00 0.00 19.13 1953 978.3 19.17 365 9.40 2 0.207 57.53 21.92 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20.55 1954 894.4 19.41 364 8.19 2 0.208 56.87 22.53 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20.60 1955 1336.2 19.02 365 11.81 2 0.239 47.95 20.55 1.92 1.37 3.29 4.93 1.64 0.55 1.37 16.44 1956 970 19.48 366 14.75 2 0.220 47.54 21.86 2.46 1.09 2.73 7.38 2.46 0.55 1.64 12.30 1957 993 19.83 365 10.10 1 0.256 47.67 15.34 3.84 1.10 3.01 7.67 3.84 0.00 1.10 16.44 1958 833.5 19.85 365 13.26 1 0.232 53.70 20.27 0.27 0.00 0.82 3.01 1.10 0.27 0.27 20.27 1959 784.1 19.93 352 8.83 1 0.257 47.73 21.59 0.57 0.85 2.27 3.41 1.99 0.28 0.28 21.02 1960 915.4 18.91 363 9.31 1 0.255 45.18 18.18 2.20 1.93 5.79 5.79 3.31 1.38 0.83 15.43 1961 1150.5 20.17 361 14.29 1 0.225 47.37 22.44 1.66 3.05 1.94 5.26 2.49 0.00 0.55 15.24 1962 970.3 20.16 365 12.44 1 0.288 44.66 17.81 3.01 1.10 1.64 4.38 3.29 0.55 2.74 20.82 1963 1265.1 20.05 363 12.76 1 0.303 30.58 13.22 12.95 8.26 5.51 5.79 3.58 4.13 6.06 9.92 1964 1136.2 19.20 366 8.93 1 0.287 36.89 15.30 8.20 6.83 4.92 4.64 3.83 1.91 2.19 15.30 1965 1120.9 19.85 363 14.20 1 0.241 42.98 16.53 9.09 5.51 3.03 4.41 2.48 0.28 2.75 12.95 1966 893.5 19.68 363 11.04 1 0.256 42.42 20.94 4.41 3.03 2.75 4.41 2.48 2.20 2.48 14.88 1967 1297.2 20.35 358 11.83 1 0.238 50.28 21.23 1.68 0.84 1.40 2.79 0.56 0.84 1.12 19.27 1968 1090.7 19.10 361 14.47 1 0.266 45.43 20.22 3.05 2.77 1.39 2.22 1.11 1.39 3.32 19.11 1969 817.3 19.86 363 11.17 1 0.277 39.12 20.66 5.23 3.86 2.20 5.79 2.48 1.10 1.65 17.91 1970 1015.2 19.82 365 11.06 1 0.298 41.10 15.62 5.48 4.66 3.56 3.01 2.19 1.37 1.92 21.10 1971 1090.9 18.90 360 10.79 1 0.311 33.89 18.06 6.94 6.39 4.44 3.89 2.78 1.94 2.78 18.89 1972 819.2 20.11 365 16.67 1 0.279 35.62 15.89 10.41 4.38 6.58 3.84 4.66 3.29 4.93 10.41 1973 1237.1 19.03 360 11.66 1 0.393 21.11 5.28 8.06 10.28 5.83 16.67 11.11 8.06 11.11 2.50 1974 1086.8 19.51 364 9.31 1 0.400 18.68 4.95 8.79 6.04 6.04 26.37 13.74 5.22 7.42 2.75 1975 987.9 19.81 361 12.71 1 0.398 21.88 4.43 9.42 5.54 3.05 27.42 5.54 8.86 11.08 2.77 1976 927.3 20.11 364 9.29 1 0.355 28.30 3.30 8.52 4.67 2.20 31.32 7.42 4.95 7.97 1.37 1977 1064.3 19.61 363 9.79 1 0.357 30.03 3.58 9.37 4.96 3.03 22.04 6.89 7.71 9.37 3.03 1978 1177.7 20.08 365 13.32 1 0.281 43.29 1.10 7.67 2.19 0.82 34.25 0.55 1.92 7.12 1.10 1979 1029.1 19.99 365 10.56 1 0.276 43.84 0.82 8.49 2.19 0.55 32.60 2.19 1.92 7.12 0.27 1980 971.2 19.81 365 10.14 1 0.288 41.37 0.82 8.22 1.10 0.55 38.63 0.55 2.47 4.93 1.37 1981 1024.8 19.53 363 10.57 1 0.257 46.56 2.48 9.09 1.38 1.65 26.45 1.38 3.58 6.89 0.55 1982 939.6 20.14 365 9.72 3 0.282 42.74 0.82 7.12 2.19 1.10 34.79 3.29 2.19 4.38 1.37 14 1983 1464.4 20.88 364 10.95 3 0.324 35.16 0.82 10.16 3.57 3.85 29.12 3.57 2.20 9.62 1.92 1984 1011.6 20.30 356 12.14 3 0.368 25.28 1.40 13.76 2.81 6.74 26.97 7.02 3.09 11.24 1.69 1985 814.4 20.27 355 8.80 3 0.381 18.03 6.76 15.77 6.20 6.20 21.13 6.20 7.04 7.32 5.35 1986 945.2 19.50 365 11.42 3 0.380 20.82 7.12 13.97 4.93 2.74 27.12 4.11 2.19 9.32 7.67 1987 818.4 19.92 365 8.17 3 0.392 21.10 6.85 12.05 4.38 6.30 18.63 7.95 6.30 10.68 5.75 1988 1196.4 20.20 366 10.52 3 0.392 20.77 4.37 10.38 4.64 2.73 34.70 5.46 5.19 6.56 5.19 1989 992.6 20.52 365 12.58 3 0.387 19.18 5.48 11.51 5.48 7.95 23.56 10.68 4.66 7.67 3.84 1990 1097.2 19.75 365 12.62 3 0.399 20.00 8.22 7.12 4.93 9.59 18.63 11.51 6.85 5.21 7.95 1991 1112 20.03 364 9.62 3 0.416 13.74 10.16 10.44 6.59 11.81 13.19 12.36 6.32 4.40 10.99 1992 1131.2 20.04 366 13.06 3 0.356 22.13 13.39 10.11 4.92 5.19 14.75 13.66 3.83 3.83 8.20 1993 851.6 19.54 362 10.33 3 0.420 11.05 10.50 12.71 10.77 9.12 10.22 11.05 7.73 7.46 9.39 1994 846.6 20.43 362 10.03 3 0.464 9.67 7.18 10.77 11.33 9.94 9.12 9.94 10.22 11.60 10.22 1995 828.8 20.06 361 10.61 3 0.491 7.48 6.93 11.91 8.31 7.76 12.74 11.08 8.31 13.85 11.63 1996 1009.2 20.48 357 10.88 3 0.469 10.08 8.12 8.96 10.92 10.08 8.96 10.36 9.24 11.20 12.04 1997 981.6 20.12 359 10.22 3 0.441 12.81 7.80 9.75 11.70 6.96 10.03 12.26 10.58 10.03 8.08 1998 944 20.03 365 10.60 3 0.166 67.40 3.56 3.29 4.38 1.92 3.84 5.21 2.74 3.56 4.11 1999 972 20.51 365 10.69 3 0.019 96.16 0.00 0.82 0.27 0.82 0.27 0.82 0.27 0.27 0.27 2000 842.4 20.31 364 11.35 3 0.008 98.63 0.00 0.00 0.27 0.00 0.55 0.00 0.27 0.00 0.27 2001 530.8 19.62 363 8.83 3 0.000 99.72 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2002 668.2 19.70 365 9.89 3 0.000 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2003 772 19.88 365 10.87 3 0.458 10.68 11.23 7.67 10.41 8.49 9.32 8.49 12.05 10.14 11.51 2004 648 19.87 366 11.47 3 0.458 9.02 8.47 9.56 9.56 12.84 12.30 10.38 7.38 9.29 11.20 2005 716.6 19.39 365 9.45 3 0.438 12.05 10.68 10.68 8.49 9.59 10.68 7.40 10.14 9.59 10.68 2006 680.2 20.04 365 7.81 3 0.438 12.33 10.68 10.96 7.40 9.04 11.51 8.49 7.67 12.60 9.32 2007 666.2 20.01 365 9.88 3 0.461 11.78 7.40 9.32 7.95 11.23 12.88 6.85 11.51 9.32 11.78 2008 877.6 20.14 366 8.74 3 0.480 9.02 9.56 7.38 11.48 7.10 10.93 9.84 11.20 10.93 12.57 2009 719.2 20.07 363 10.98 3 0.465 9.92 11.29 10.47 7.99 7.16 8.26 11.29 9.37 12.67 11.57 2010 625 20.53 363 11.36 4 0.442 9.09 13.77 9.09 9.09 10.47 10.47 7.16 11.29 9.37 10.19 2011 869.6 21.21 363 13.84 4 0.468 8.54 9.37 6.34 11.29 11.02 13.22 9.64 12.40 6.61 11.57 2012 770 21.13 366 12.98 4 0.460 9.84 10.38 10.93 7.65 10.66 7.65 10.11 9.29 12.84 10.66 2013 982.8 20.75 365 11.65 4 0.440 10.96 9.32 9.59 9.32 10.41 13.42 10.14 9.04 9.86 7.95 2014 680.4 20.69 365 8.93 4 0.442 8.77 11.78 12.88 9.59 7.67 8.77 9.32 13.97 7.95 9.32 2015 648.2 20.62 365 9.26 4 0.479 9.59 7.67 7.67 8.77 11.51 11.23 9.59 12.60 10.14 11.23 2016 856 19.68 366 11.26 4 0.458 10.93 10.11 7.65 10.66 9.84 10.38 8.47 8.74 11.20 12.02 2017 866.2 19.98 354 9.04 4 0.426 10.45 14.97 9.32 9.89 10.17 8.47 7.34 9.60 9.89 9.89 2018 736 20.10 364 8.78 4 0.454 10.71 9.89 9.07 10.44 8.24 9.34 10.44 12.64 9.62 9.62 2019 694.6 20.31 364 8.95 4 0.462 8.24 9.07 9.07 10.99 11.54 11.81 8.79 10.16 10.16 10.16 15 A2.2 Daily wet-day rainfall frequency (% of days within classes >0 mm/day) (Tab delineated) Year rain WetDays Nilto1mmPc NilTo10mmPc Nil1To10mmPc Nil1To25mmPc Nil10To20mmPc Over25Pc Over100mmPc 1907 1187.3 204 32.4 81.9 53.9 62.7 11.8 4.9 0.0 1908 912.1 192 41.1 84.9 51.0 56.3 11.5 2.6 0.0 1909 931.4 204 37.7 86.3 55.4 59.8 10.3 2.5 0.0 1910 1256.8 180 23.9 76.1 59.4 71.7 13.3 4.4 0.0 1911 649.3 194 43.8 91.2 52.6 55.2 7.2 1.0 0.0 1912 868.8 186 37.1 83.9 57.0 60.8 10.8 2.2 0.0 1913 1253.7 196 37.2 83.2 51.5 57.7 8.2 5.1 0.0 1914 737 182 41.2 89.6 57.1 56.6 5.5 2.2 0.0 1915 966.2 204 31.9 87.3 65.2 65.7 8.3 2.5 0.0 1916 920.1 198 34.3 85.4 56.1 65.2 10.6 0.5 0.0 1917 1337.6 216 31.9 83.3 56.9 63.9 10.2 4.2 0.5 1918 854.1 190 37.9 88.4 61.1 58.9 6.3 3.2 0.0 1919 750.8 177 33.9 88.1 59.9 63.8 8.5 2.3 0.0 1920 1089 187 25.7 82.4 65.2 71.7 9.6 2.7 0.0 1921 1301.7 191 24.6 82.2 63.9 70.2 8.9 5.2 0.5 1922 983.9 190 26.3 86.3 65.3 71.6 10.5 2.1 0.5 1923 951.1 193 29.0 81.9 59.6 69.4 15.0 1.6 0.0 1924 1130.6 186 25.8 82.8 60.2 68.8 9.7 5.4 0.0 1925 799.4 174 33.3 84.5 56.9 64.4 12.6 2.3 0.0 1926 1276.6 205 25.4 78.5 56.6 70.7 13.7 3.9 0.0 1927 1154.9 197 29.4 83.2 58.4 66.5 10.7 4.1 0.0 1928 1063.3 196 29.6 82.1 56.1 67.9 12.2 2.6 0.0 1929 1074.8 182 26.9 81.3 61.0 69.2 12.1 3.8 0.0 1930 982.5 189 26.5 84.1 68.3 71.4 11.6 2.1 0.0 1931 1013.9 180 32.2 85.6 63.3 63.3 7.8 4.4 0.0 1932 1029.8 166 30.7 78.3 51.2 63.9 13.9 5.4 0.0 1933 1130.3 173 27.7 78.6 57.8 67.6 12.7 4.6 0.0 1934 1174 187 28.3 81.8 61.0 66.8 10.7 4.8 0.0 1935 874.4 182 29.7 85.2 62.6 68.7 10.4 1.6 0.0 1936 833.9 180 33.9 86.1 57.8 64.4 10.6 1.7 0.0 1937 1106.5 181 28.7 78.5 54.1 68.5 17.7 2.8 0.0 1938 900.5 180 28.3 83.9 61.1 70.6 11.7 1.1 0.0 1939 1102.8 186 25.8 82.8 62.4 70.4 10.2 3.8 0.0 1940 749.6 170 28.8 88.8 69.4 68.8 7.6 2.4 0.0 1941 988.9 184 25.5 84.8 67.4 70.7 10.9 3.8 0.0 1942 1210.1 202 22.3 84.7 67.8 73.8 8.4 4.0 0.0 1943 982.4 177 22.6 84.7 64.4 75.1 10.7 2.3 0.0 1944 776.1 181 35.4 86.2 57.5 62.4 9.4 2.2 0.0 1945 950.2 192 28.6 85.9 62.5 68.8 8.9 2.6 0.0 1946 935.2 182 31.3 84.6 58.2 67.0 9.9 1.6 0.0 1947 1027.9 207 25.6 86.0 67.6 72.0 10.6 2.4 0.0 16 1948 879.6 183 32.8 90.2 62.8 64.5 6.0 2.7 0.0 1949 1007.5 172 26.7 84.9 62.2 69.2 8.7 4.1 0.0 1950 966.1 181 27.1 84.5 64.1 70.2 12.2 2.8 0.0 1951 984.8 192 31.3 84.9 60.4 66.1 11.5 2.6 0.0 1952 999.8 188 25.0 85.1 67.6 72.3 10.1 2.7 0.0 1953 978.3 190 29.5 85.3 60.5 68.4 12.1 2.1 0.0 1954 894.4 187 26.7 85.0 62.6 71.7 11.8 1.6 0.0 1955 1336.2 191 24.1 74.9 55.0 71.7 18.8 4.2 0.0 1956 970 176 34.1 83.5 56.8 64.2 13.1 1.7 0.0 1957 993 173 28.3 82.1 62.4 69.4 14.5 2.3 0.0 1958 833.5 181 27.6 87.8 66.3 70.2 6.6 2.2 0.0 1959 784.1 160 31.9 85.6 61.3 66.9 11.3 1.3 0.0 1960 915.4 187 32.6 89.8 65.2 62.6 4.3 4.8 0.0 1961 1150.5 192 32.3 82.8 59.4 63.5 10.9 4.2 0.0 1962 970.3 194 28.9 86.6 59.3 67.0 6.7 4.1 0.0 1963 1265.1 178 19.1 77.0 60.7 76.4 16.3 4.5 0.0 1964 1136.2 202 24.3 80.2 58.9 72.8 14.4 3.0 0.0 1965 1120.9 184 21.2 79.3 62.0 76.6 16.8 2.2 0.0 1966 893.5 188 31.9 85.6 60.6 66.0 10.1 2.1 0.0 1967 1297.2 166 21.1 78.3 65.1 72.3 12.0 6.6 0.6 1968 1090.7 202 18.8 82.7 65.8 79.2 13.4 2.0 0.0 1969 817.3 157 31.8 86.6 60.5 65.0 8.3 3.2 0.0 1970 1015.2 177 26.6 85.3 67.8 70.6 9.6 2.8 0.0 1971 1090.9 197 28.4 86.3 65.0 67.5 8.6 4.1 0.0 1972 819.2 152 30.3 80.3 58.6 67.1 15.8 2.6 0.0 1973 1237.1 201 23.4 79.1 58.2 74.6 16.4 2.0 0.0 1974 1086.8 164 26.2 76.8 53.7 69.5 18.9 4.9 0.0 1975 987.9 178 25.3 83.7 64.6 71.3 13.5 3.4 0.0 1976 927.3 147 26.5 81.0 61.2 69.4 10.9 4.8 0.0 1977 1064.3 155 22.6 79.4 62.6 73.5 15.5 3.9 0.0 1978 1177.7 170 29.4 75.3 52.9 67.6 18.2 2.9 0.0 1979 1029.1 177 20.9 80.8 67.2 76.8 14.7 2.3 0.0 1980 971.2 184 25.0 86.4 66.8 71.7 10.9 3.3 0.0 1981 1024.8 174 25.3 83.3 66.7 71.8 13.8 2.9 0.0 1982 939.6 172 30.2 82.0 61.0 66.9 15.1 2.9 0.0 1983 1464.4 171 22.8 73.1 57.9 70.2 17.0 7.0 0.6 1984 1011.6 184 27.2 85.3 66.3 70.1 11.4 2.7 0.0 1985 814.4 170 32.4 86.5 68.2 67.6 10.6 0.0 0.0 1986 945.2 163 26.4 83.4 66.3 70.6 11.0 3.1 0.0 1987 818.4 159 27.7 85.5 69.2 69.2 10.7 3.1 0.0 1988 1196.4 183 25.7 81.4 65.6 69.9 14.2 4.4 0.0 1989 992.6 185 22.7 83.2 66.5 76.2 15.1 1.1 0.0 1990 1097.2 200 29.5 83.5 60.5 67.5 12.0 3.0 0.0 1991 1112 176 27.8 78.4 55.1 69.3 15.3 2.8 0.0 17 1992 1131.2 191 26.7 83.2 63.4 70.2 13.1 3.1 0.0 1993 851.6 159 30.2 86.2 59.7 66.7 8.2 3.1 0.0 1994 846.6 155 41.3 89.7 56.1 54.8 5.8 3.9 0.6 1995 828.8 158 28.5 82.3 54.4 68.4 12.0 3.2 0.0 1996 1009.2 159 28.3 79.9 53.5 67.9 12.6 3.8 0.0 1997 981.6 149 41.6 79.9 41.6 52.3 12.1 6.0 0.0 1998 944 150 30.0 84.0 62.7 65.3 12.7 4.7 0.0 1999 972 160 35.6 81.3 51.3 60.6 15.6 3.8 0.0 2000 842.4 159 36.5 84.9 60.4 62.3 15.1 1.3 0.0 2001 530.8 167 53.9 93.4 52.1 45.5 7.8 0.6 0.0 2002 668.2 170 52.4 89.4 48.8 47.1 8.8 0.6 0.0 2003 772 174 46.0 87.9 46.6 51.7 8.6 2.3 0.0 2004 648 168 36.3 89.3 54.2 62.5 7.7 1.2 0.0 2005 716.6 185 43.8 90.3 50.8 52.4 5.4 3.8 0.0 2006 680.2 156 43.6 86.5 46.2 53.2 8.3 3.2 0.0 2007 666.2 183 39.9 87.4 50.8 60.1 12.0 0.0 0.0 2008 877.6 193 37.8 85.5 49.7 60.6 11.4 1.6 0.0 2009 719.2 164 40.2 85.4 48.8 59.1 12.8 0.6 0.0 2010 625 166 44.6 92.2 53.6 54.2 6.0 1.2 0.0 2011 869.6 178 36.0 87.1 52.8 61.8 10.1 2.2 0.0 2012 770 177 42.4 88.7 49.7 55.4 7.3 2.3 0.0 2013 982.8 172 31.4 83.1 54.7 63.4 10.5 5.2 0.0 2014 680.4 164 42.7 89.6 53.7 55.5 6.7 1.8 0.0 2015 648.2 144 36.1 88.2 53.5 61.8 6.9 2.1 0.0 2016 856 183 37.2 85.8 51.4 61.2 9.8 1.6 0.0 2017 866.2 184 38.0 87.0 52.7 59.8 8.2 2.2 0.0 2018 736 186 41.9 88.2 50.0 56.5 8.1 1.6 0.0 2019 694.6 145 51.7 86.9 38.6 44.8 7.6 3.4 0.0