FullView - Renewables SAIntroduction Pimba For RenewablesSA..... 1 INTRODUCTION..... 3TIER has been retained by RenewablesSA to assess the variability and magnitude of solar irradiance,

S O L A R

FullViewSite ClimateVariability Analysis

ANALYSIS OF 10-YEAR RECORD DATE

Pimba, South Australia 23 April 2010

FOR CONTACT

RenewablesSA

[email protected]

ph: +61 3 62852658fax: +61 3 62249982

GPO Box 528, Hobart,Tasmania, Australia, 7001

NOTICECopyright c©2010 3TIER, Inc. All rights reserved. This work may be reproduced in itsentirety without charge. 3TIER claims a copyright in all proprietary and copyrightable textand graphics in this Report, the overall design of this Report, and the selection, arrangementand presentation of all materials in this Report. Partial reproduction and redistribution areprohibited without the express written permission from 3TIER. Requests for permission maybe directed to [email protected].

IntroductionPimba

For RenewablesSA

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1 INTRODUCTION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3TIER has been retained by RenewablesSA to assess the variability and magnitude of solar irradiance, wind speed andtemperature at the Pimba project site located in South Australia. This report provides a retrospective analysis of the past10 years of solar irradiance, wind speed and temperature data.

The long-term average global horizontal irradiance value at the Pimba site (Latitude: 31.236215◦S, Longitude: 136.830105◦E)is 5.301 kWh/m2/day (220.9 W/m2). The long-term average direct normal irradiance value is 5.815 kWh/m2/day (242.3W/m2), and the long-term average diffuse horizontal irradiance value is 1.591 kWh/m2/day (66.3 W/m2).

The long-term average wind speed at 10 meters above ground level (AGL) is 4.49 m/s. The long-term average temperatureat 2 meters AGL is 18.1 ◦C.

This report is accompanied by all the information used in making these summary plots and tables. A time series commaseparated value (CSV) file includes hour-ending mean data for: Global Horizontal Irradiance (W/m2), Direct NormalIrradiance (W/m2), Diffuse Irradiance (W/m2), Zenith Angle (degrees), Azimuth Angle (degrees), Wind Speed at 10m(m/s), Wind Direction at 10m (degrees), Air Temperature at 2m (Kelvin), and Relative Humidity at 2m (%). The keyvariables are also summarised in monthly and hourly diurnal tables, again provided as CSV files.

Typical Meteorological Year (TMY) files have been created in the TMY2 data format for use in existing software that isunable to process the entire time series data set. The TMY files were created using an empirical approach that selectssamples from the full time series to create a ”typical year” of data with 8760 hours, while preserving the followingcharacteristics of direct normal irradiance: monthly means, monthly cumulative distribution of daily means, diurnal cycle,and the annual mean.

No on-site observations were provided at this project location; thus, all data presented within this reportare purely processed satellite output and raw model output. If observational data become available, 3TIER canincorporate the data via additional analysis and provide statistically-corrected results.

c© 2010 3TIER, Inc.1

Table of ContentsPimba

For RenewablesSA

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Contents

1 Introduction 1

2 Explanation of Irradiance Values 32.1 Global Horizontal Irradiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2 Direct Normal Irradiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.3 Diffuse Horizontal Irradiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

3 Solar Resource Assessment 43.1 Monthly-mean Variability of Solar Irradiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43.2 Solar Irradiance Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.3 Diurnal Variability of Solar Irradiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.4 Tabular Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

4 Model simulations by 3TIER 10

5 Wind and Temperature Resource Assessment 115.1 Monthly-Mean Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115.2 Diurnal Variability of Wind Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125.3 Diurnal Variability of Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135.4 Tabular Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14


MethodologyPimba

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2 EXPLANATION OF IRRADIANCE VALUES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The irradiance values presented in this report are from 3TIER’s solar dataset. This dataset is based on the past 10years (January 1999 – March 2009) of half-hourly high-resolution (roughly 1 km) visible satellite imagery (GMS5, GOES09and AMTSAT01 using the broad-band visible wavelength channel). The satellite imagery has been processed to create10 years of hourly values of Global Horizontal Irradiance, Direct Normal Irradiance and Diffuse Horizontal Irradiance ata horizontal resolution of 2 arc minutes. To develop and validate the model, and estimate the error, 3TIER comparedthe derived irradiance values with observations from the direct surface radiation measurements contained in the BaselineSurface Radiation Network and other sites from the Australian Bureau of Meteorology.

The error estimates were derived comparing the model data with observations that were not used in training or tuningthe modelling system. Australia has few available observations, but the indications are that the results in Australia weresimilar to those in North America (even though the satellites and therefore the tuning algorithms were different). In fact,the Australian results may be marginally more accurate than the North American results, but to provide conservativeestimates of accuracy 3TIER has applied the same error estimates to this work as were derived from the larger numberof comparisons available in the USA. For more information on 3TIER’s validation procedures, including validation whitepapers, please visit: http://www.3tier.com/en/support/solar-prospecting-tools/what-were-3tiers-solar-prospecting-data-validation-procedures/.

2.1 Global Horizontal Irradiance

Global Horizontal Irradiance is the quantity of the total solar radiation per unit area that is intercepted by a flat, horizontalsurface. This value is of particular interest to photovoltaic installations. It includes both direct beam radiation (radiationthat comes from the direction of the sun) and diffuse radiation (radiation that has been scattered by the atmosphere andwhich comes from all directions of the sky). The estimate has a standard error of 10%.

2.2 Direct Normal Irradiance

Direct Normal Irradiance is the quantity of direct beam solar radiation per unit area that is intercepted by a flat surface thatis at all times pointed in the direction of the sun. This quantity is of particular interest to concentrating solar installationsand installations that track the position of the sun. The estimate has a standard error of 16%.

2.3 Diffuse Horizontal Irradiance

Diffuse Horizontal Irradiance is the quantity of diffuse solar radiation per unit area that is intercepted by a flat, horizontalsurface that is not subject to any shade or shadow and does not arrive on a direct path from the sun. The estimate has astandard error of 10%.


Solar Site AnalysisPimba

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3 SOLAR RESOURCE ASSESSMENT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

This section provides a retrospective analysis of the past 10 years of solar irradiance data at the Pimba project site(Latitude: 31.236215◦S, Longitude: 136.830105◦E). All irradiance data presented within this section are valid only for thisparticular location.

3.1 Monthly-mean Variability of Solar Irradiance

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

100

200

300

W /

m2

Global Horizontal Irradiance

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec100

200

300

400

W /

m2

Direct Normal Irradiance

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecMonth

40

60

80

100

W /

m2

Diffuse Horizontal Irradiance

Figure 1: Variability of monthly-mean Global Horizontal [top], Direct Normal [middle], and Diffuse Horizontal [bot-tom] irradiance. Long-term monthly-mean values are denoted by coloured circles. Upper and lower boundariesof the dark shading correspond to the 75% and 25% quartiles, while the light shading denotes the maximumand minimum monthly-mean irradiance values. Please note that the vertical scale varies between the plots.



For RenewablesSA

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3.2 Solar Irradiance Distributions

0 100 200 300 400 500 600 700 800 900 10000

4

8

12Fr

eque

ncy

(%)

0 100 200 300 400 500 600 700 800 900 1000


0 100 200 300 400 500 600 700 800 900 10000

4

8

12

16

Freq

uenc

y (%

)

0 100 200 300 400 500 600 700 800 900 1000


0 100 200 300 400 500 600 700 800 900 1000W / m2

0

4

8

12

16

20

24

28

Freq

uenc

y (%

)

0 100 200 300 400 500 600 700 800 900 1000W / m2


Figure 2: Distribution of hourly Global Horizontal [top], Direct Normal [middle] and Diffuse Horizontal [bottom]daylight irradiance values using 50 W/m2 bins. (0 W/m2 bin contains only values ≤ 25.) Each vertical barrepresents the frequency of irradiance values occurring within each bin. For example, a vertical bar centered on200 W/m2 reaching up to 10% means that one-tenth of all daytime values are between 175 and 225 W/m2.Please note that the vertical scale varies between the plots.



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3.3 Diurnal Variability of Solar Irradiance

January

0 4 8 12 16 20 240

200400600800

1000W

/ m

2February

0 4 8 12 16 20 24

March

0 4 8 12 16 20 24

April

0 4 8 12 16 20 240

200400600800

1000

W /

m2

May

0 4 8 12 16 20 24

June

0 4 8 12 16 20 24

July

0 4 8 12 16 20 240

200400600800

1000

W /

m2

August

0 4 8 12 16 20 24

September

0 4 8 12 16 20 24

October

0 4 8 12 16 20 24Hour of Day (ACST)

0200400600800

1000

W /

m2

November

0 4 8 12 16 20 24Hour of Day (ACST)

Global Horizontal Direct Normal Diffuse Horizontal

December

0 4 8 12 16 20 24Hour of Day (ACST)

Figure 3: Diurnal cycle of Global Horizontal (black), Direct Normal (orange) and Diffuse Horizontal (blue) irradiancefor each month of the year. The horizontal axis is Australian Central Standard Time (ACST). Figures 4, 5, and 6show the diurnal cycle of Global Horizontal, Direct Normal, and Diffuse Horizontal solar irradiance, respectively,for each calendar month as a ”12 X 24” table.



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3.4 Tabular Data


Hour

of D

ay (A

CST)

0:301:302:303:304:305:306:307:308:309:3010:3011:3012:3013:3014:3015:3016:3017:3018:3019:3020:3021:3022:3023:30Avg

Jan

Jan

Feb

Feb

Mar

Mar

Apr

Apr

May

May

Jun

Jun

Jly

Jly

Aug

Aug

Sep

Sep

Oct

Oct

Nov

Nov

Dec

Dec

Avg

Avg0.00.00.00.00.00.034.4

185.0379.6564.7722.6843.2915.5914.5842.5731.1576.4387.6191.845.50.00.00.00.0

305.9

0.00.00.00.00.00.07.9

115.3302.8498.4662.3788.6862.0859.8798.2681.3518.7332.2146.820.30.00.00.00.0

274.8

0.00.00.00.00.00.00.567.8239.4428.6594.9722.2778.9774.6712.0595.7436.6247.777.71.60.00.00.00.0

236.6

0.00.00.00.00.00.00.027.4

169.4351.7505.5611.3653.0645.3583.6462.5304.4129.213.10.00.00.00.00.0

185.7

0.00.00.00.00.00.00.08.0

111.0267.0415.1517.4553.2531.3466.0360.5212.867.00.50.00.00.00.00.0

146.2

0.00.00.00.00.00.00.01.068.1207.8345.9442.3487.2469.1415.4313.0177.245.80.00.00.00.00.00.0

123.9

0.00.00.00.00.00.00.01.271.4211.7352.4458.8509.2500.8443.4346.2210.972.20.80.00.00.00.00.0

132.5

0.00.00.00.00.00.00.016.2143.2318.6475.2578.7618.9610.7555.2446.8297.9127.7

9.40.00.00.00.00.0

174.9

0.00.00.00.00.00.01.675.9250.5436.0590.7688.1729.6716.3656.4532.9363.5173.828.00.00.00.00.00.0

218.5

0.00.00.00.00.00.026.9171.8365.2550.6700.3798.0827.9802.7718.8583.4406.9221.563.70.30.00.00.00.0

259.9

0.00.00.00.00.00.968.7234.5425.4591.2729.5823.2852.2822.2740.8615.2458.0272.9103.4

7.10.00.00.00.0

281.0

0.00.00.00.00.01.270.3

230.6418.1589.7730.2831.3872.6869.3805.1682.6526.7338.4155.730.10.00.00.00.0

298.0

0.00.00.00.00.00.2

17.595.2246.6419.6570.5677.4724.3712.5647.5532.0376.9203.867.49.10.00.00.00.0

220.9

0 200 400 600 800 1000

W/m2

Figure 4: Hourly-mean Global Horizontal Irradiance values in W/m2. The vertical axis is Australian Central StandardTime (ACST). Time series graph of the diurnal variability is shown in Figure 3.



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Hour

of D

ay (A

CST)

0:301:302:303:304:305:306:307:308:309:3010:3011:3012:3013:3014:3015:3016:3017:3018:3019:3020:3021:3022:3023:30Avg

Jan

Jan

Feb

Feb

Mar

Mar

Apr

Apr

May

May

Jun

Jun

Jly

Jly

Aug

Aug

Sep

Sep

Oct

Oct

Nov

Nov

Dec

Dec

Avg

Avg0.00.00.00.00.00.057.9

300.3488.1587.1642.6690.5727.7727.9693.0654.2593.4491.4312.086.10.00.00.00.0

294.1

0.00.00.00.00.00.011.2189.0415.1545.6621.6675.5704.2693.8663.7617.4542.1427.1239.336.00.00.00.00.0

265.9

0.00.00.00.00.00.00.0

126.8386.2538.6622.8674.9677.0672.8653.8612.2540.4394.7147.5

1.60.00.00.00.0

252.1

0.00.00.00.00.00.00.053.9

310.7506.1596.8632.9627.3626.1614.7555.5458.6236.124.30.00.00.00.00.0

218.5

0.00.00.00.00.00.00.016.6237.7459.8587.1646.9634.6607.3576.3536.8415.1152.2

0.20.00.00.00.00.0

202.9

0.00.00.00.00.00.00.00.2

139.3400.5542.4600.8614.4585.7565.4506.9369.9115.2

0.00.00.00.00.00.0

185.0

0.00.00.00.00.00.00.00.6

148.9399.7535.5601.0616.0601.1567.0525.6416.9164.1

0.60.00.00.00.00.0

190.7

0.00.00.00.00.00.00.044.8331.3553.8651.4673.2654.8640.2631.8609.4532.8299.824.20.00.00.00.00.0

235.3

0.00.00.00.00.00.01.7

148.6419.2573.4647.8665.1664.0665.9654.5607.1516.3320.861.50.00.00.00.00.0

247.8

0.00.00.00.00.00.049.6299.8495.2600.8659.2689.1689.2674.6641.0584.7493.7354.1121.1

0.00.00.00.00.0

264.7

0.00.00.00.00.00.0

120.4358.8508.5575.0619.6652.6654.0634.1598.8551.5496.9375.9171.910.20.00.00.00.0

263.7

0.00.00.00.00.00.0

118.0348.8492.3569.8619.4655.1669.9677.0656.2613.5545.4425.9249.454.60.00.00.00.0

279.0

0.00.00.00.00.00.0

29.9158.4365.8526.6612.6655.4662.0651.6627.3582.4495.0315.8115.116.30.00.00.00.0

242.3

0 200 400 600 800

W/m2

Figure 5: Hourly-mean Direct Normal Irradiance values in W/m2. The vertical axis is Australian Central StandardTime (ACST). Time series graph of the diurnal variability is shown in Figure 3.



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Hour

of D

ay (A

CST)

0:301:302:303:304:305:306:307:308:309:3010:3011:3012:3013:3014:3015:3016:3017:3018:3019:3020:3021:3022:3023:30Avg

Jan

Jan

Feb

Feb

Mar

Mar

Apr

Apr

May

May

Jun

Jun

Jly

Jly

Aug

Aug

Sep

Sep

Oct

Oct

Nov

Nov

Dec

Dec

Avg

Avg0.00.00.00.00.00.025.290.0

139.7173.4200.9210.7209.1207.0203.9195.2174.8140.792.128.90.00.00.00.087.2

0.00.00.00.00.00.06.765.4126.4167.1189.8199.8205.0208.5207.5195.0170.1133.978.314.80.00.00.00.082.0

0.00.00.00.00.00.00.541.4102.2139.9165.6182.6198.5197.3186.9170.5143.0103.443.81.50.00.00.00.069.9

0.00.00.00.00.00.00.019.580.9

123.3151.0172.0186.3183.6167.7149.0115.467.39.80.00.00.00.00.059.4

0.00.00.00.00.00.00.06.459.5100.5123.5136.2150.7151.4141.0118.184.935.90.50.00.00.00.00.046.2

0.00.00.00.00.00.00.01.043.884.9110.3124.8134.0135.8126.1106.777.326.00.00.00.00.00.00.040.4

0.00.00.00.00.00.00.01.244.885.5111.7129.2139.5141.1134.2113.983.738.80.80.00.00.00.00.042.7

0.00.00.00.00.00.00.011.063.897.4121.6145.3162.2166.0152.1124.995.559.67.10.00.00.00.00.050.3

0.00.00.00.00.00.01.444.0100.4131.4153.8173.6184.7178.1171.0155.2124.780.818.20.00.00.00.00.063.2

0.00.00.00.00.00.019.681.4128.8158.9180.1192.2196.6198.4192.0176.4143.799.036.80.30.00.00.00.075.2

0.00.00.00.00.00.943.6102.6146.5179.4203.5213.1218.1220.3215.8198.9162.2117.160.66.00.00.00.00.087.0

0.00.00.00.00.01.245.6103.0150.2183.5205.1215.5214.4211.6207.7191.6170.9135.281.620.90.00.00.00.089.1

0.00.00.00.00.00.2

11.947.699.3

135.9160.2175.0183.6183.6175.9158.4129.587.236.56.20.00.00.00.0

66.3

0 50 100 150 200 250

W/m2

Figure 6: Hourly-mean Diffuse Horizontal Irradiance values in W/m2. The vertical axis is Australian Central StandardTime (ACST). Time series graph of the diurnal variability is shown in Figure 3.


Model SimulationsPimba

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4 MODEL SIMULATIONS BY 3TIER. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The assessment of the wind resource at the Pimba project site presented in this report is based on 10+ years of simulateddata (January 1999 – March 2009). The simulated data set is constructed using a state-of-the-art Numerical WeatherPrediction (NWP) model that processes coarse-resolution historic gridded data and high resolution topographical andsurface data to generate the meteorological time series data.

The NWP model simulated data set is constructed from two separate model runs: a 10+ year 15km resolution simulationand a 1-year 5km resolution simulation. Some details of the NWP model configuration are shown below in Table 1. Theextent of the coarsest grid was selected to capture the effect of synoptic weather events on the wind resource at the site,as well as to allow the model to develop regional, thermally-driven circulations. The increasingly fine 45km, 15km, and5km grids were selected to model the effect of local terrain and local scale atmospheric circulations.

Parameter ValueMesoscale numerical weather prediction model WRFHorizontal resolution of valid study area 5.0kmNumber of vertical levels 31Elevation data base 3 second SRTMVegetation data base 30 second USGSSurface parameterisation Monin-Obukhov similarity modelBoundary layer parameterisation YSU model (MRF with entrainment)Land surface scheme 5-layer soil diffusivity model

Table 1: Numerical weather prediction model configuration.

c© 2010 3TIER, Inc.10

Wind and Temperature Site AnalysisPimba

For RenewablesSA

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5 WIND AND TEMPERATURE RESOURCE ASSESSMENT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

This section provides a retrospective analysis of the past 10 years of wind and temperature data, derived from the NWPmodel, at the Pimba project site (Latitude: 31.236215◦S, Longitude: 136.830105◦E). All data presented within this sectionare valid only for this particular location.

5.1 Monthly-Mean Variability


3

4

5

6

Win

d Sp

eed

(m/s

)

Figure 7: Variability of monthly-mean wind speed at 10m AGL at Pimba. Long-term monthly-mean values are denotedby coloured circles. Upper and lower boundaries of the dark shading correspond to the 75% and 25% quartiles,while the light shading denotes the maximum and minimum monthly-mean wind speeds.


5

10

15

20

25

30

Tem

pera

ture

(o C)

Figure 8: Variability of monthly-mean temperature at 2m AGL at Pimba. Long-term monthly-mean values are denotedby coloured circles. Upper and lower boundaries of the dark shading correspond to the 75% and 25% quartiles,while the light shading denotes the maximum and minimum monthly-mean temperature.

c© 2010 3TIER, Inc.11


For RenewablesSA

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5.2 Diurnal Variability of Wind Speed

January

0 4 8 12 16 20 243

4

5

6

7W

ind

Spee

d (m

/s)

February

0 4 8 12 16 20 243

4

5

6

7March

0 4 8 12 16 20 243

4

5

6

7

April

0 4 8 12 16 20 243

4

5

6

7

Win

d Sp

eed

(m/s

)

May

0 4 8 12 16 20 243

4

5

6

7June

0 4 8 12 16 20 243

4

5

6

7

July

0 4 8 12 16 20 243

4

5

6

7

Win

d Sp

eed

(m/s

)

August

0 4 8 12 16 20 243

4

5

6

7September

0 4 8 12 16 20 243

4

5

6

7

October

0 4 8 12 16 20 24Hour of Day (ACST)

3

4

5

6

7

Win

d Sp

eed

(m/s

)

November

0 4 8 12 16 20 24Hour of Day (ACST)

3

4

5

6

7December

0 4 8 12 16 20 24Hour of Day (ACST)

3

4

5

6

7

Figure 9: Diurnal cycle of wind speed at 10m AGL for each month of the year. The horizontal axis is in Australian CentralStandard Time (ACST). Figure 11 shows the diurnal cycle of wind speed for each calendar month as a ‘12 X24’ table.

c© 2010 3TIER, Inc.12


For RenewablesSA

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5.3 Diurnal Variability of Temperature

January

0 4 8 12 16 20 245

101520253035

Tem

pera

ture

(o C)

February

0 4 8 12 16 20 245

101520253035

March

0 4 8 12 16 20 245

101520253035

April

0 4 8 12 16 20 245

101520253035

Tem

pera

ture

(o C)

May

0 4 8 12 16 20 245

101520253035

June

0 4 8 12 16 20 245

101520253035

July

0 4 8 12 16 20 245

101520253035

Tem

pera

ture

(o C)

August

0 4 8 12 16 20 245

101520253035

September

0 4 8 12 16 20 245

101520253035

October

0 4 8 12 16 20 24Hour of Day (ACST)

5101520253035

Tem

pera

ture

(o C)

November

0 4 8 12 16 20 24Hour of Day (ACST)

5101520253035

December

0 4 8 12 16 20 24Hour of Day (ACST)

5101520253035

Figure 10: Diurnal cycle of temperature at 2m AGL for each month of the year. The horizontal axis is in AustralianCentral Standard Time (ACST). Figure 12 shows the diurnal cycle of temperature for each calendar month asa ‘12 X 24’ table.

c© 2010 3TIER, Inc.13


For RenewablesSA

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5.4 Tabular DataHo

ur o

f Day

(ACS

T)

0:301:302:303:304:305:306:307:308:309:3010:3011:3012:3013:3014:3015:3016:3017:3018:3019:3020:3021:3022:3023:30Avg

Jan

Jan

Feb

Feb

Mar

Mar

Apr

Apr

May

May

Jun

Jun

Jly

Jly

Aug

Aug

Sep

Sep

Oct

Oct

Nov

Nov

Dec

Dec

Avg

Avg5.054.844.654.444.274.214.264.594.584.574.394.134.013.984.074.174.354.414.214.334.795.095.245.214.49

5.094.844.624.424.274.174.154.504.594.494.374.114.014.034.184.314.394.414.094.394.865.115.265.254.50

4.504.364.224.023.883.823.773.924.124.104.103.963.863.893.994.084.153.993.563.914.264.474.564.524.08

4.023.943.863.743.653.633.583.593.994.174.254.284.264.214.204.214.163.583.443.553.733.984.114.083.93

3.753.733.683.643.613.623.643.643.884.164.344.524.654.694.694.654.433.613.543.573.633.703.753.773.95

4.013.973.943.893.863.883.913.903.984.204.384.524.674.814.944.944.614.014.044.044.024.034.034.044.19

4.114.064.014.014.044.074.114.124.294.564.805.125.325.445.475.435.174.344.374.314.264.244.224.194.50

4.704.614.504.434.404.364.354.374.725.015.305.615.785.875.925.945.754.994.734.694.684.724.784.794.96

5.054.964.884.774.684.604.564.755.175.435.786.046.136.156.176.206.105.725.014.964.985.115.075.105.31

4.984.894.804.694.544.474.444.845.195.275.385.365.385.415.465.475.455.264.604.594.724.864.934.975.00

4.904.664.464.324.174.004.034.284.444.544.484.384.384.404.534.624.634.634.264.274.644.995.105.074.51

4.884.724.494.264.164.054.154.344.414.484.394.344.324.424.424.514.624.574.414.184.544.845.014.964.48

4.594.474.344.224.134.074.084.244.454.584.664.684.724.764.824.864.814.464.184.234.434.604.684.674.49

3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5Wind Speed

m/s

Figure 11: Hourly-mean wind speed values at 10m AGL in m/s. The vertical axis is in Australian Central Standard Time(ACST). Time series graph of the diurnal variability for each month is shown in Figure 9.

c© 2010 3TIER, Inc.14


For RenewablesSA

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Hour

of D

ay (A

CST)

0:301:302:303:304:305:306:307:308:309:3010:3011:3012:3013:3014:3015:3016:3017:3018:3019:3020:3021:3022:3023:30Avg

Jan

Jan

Feb

Feb

Mar

Mar

Apr

Apr

May

May

Jun

Jun

Jly

Jly

Aug

Aug

Sep

Sep

Oct

Oct

Nov

Nov

Dec

Dec

Avg

Avg22.221.520.820.219.719.220.022.224.727.329.431.232.533.333.733.733.232.330.727.926.425.224.123.126.4

21.921.220.620.019.519.119.221.423.826.428.630.231.532.232.632.532.031.129.126.925.624.423.422.525.7

19.018.417.817.316.816.416.217.820.222.625.126.828.128.929.329.228.627.524.823.222.121.220.319.622.4

15.515.014.514.013.613.212.913.315.517.519.821.622.923.624.024.023.522.220.119.018.117.416.716.018.1

11.210.710.310.09.69.49.18.910.111.112.914.816.417.417.918.017.516.014.813.913.212.612.011.512.9

7.57.26.86.56.36.15.95.76.37.28.610.111.712.913.613.713.311.610.69.89.28.78.37.89.0

7.77.26.96.66.36.05.85.66.57.69.110.912.513.614.214.313.812.311.210.49.79.28.78.29.3

9.59.08.68.17.87.47.27.28.810.212.114.015.416.316.716.816.415.213.712.712.011.310.710.111.5

13.212.712.211.811.411.010.711.613.415.217.218.820.020.721.221.321.020.218.417.216.315.414.714.015.8

15.114.413.913.312.912.512.414.216.318.420.422.023.224.124.624.724.423.521.719.818.617.616.715.918.4

18.417.717.016.415.915.516.218.421.023.525.627.228.329.129.429.328.928.026.323.822.321.220.119.222.4

20.019.318.618.017.517.118.120.222.625.027.128.729.930.731.131.130.729.928.425.724.223.021.920.924.2

15.214.614.113.613.212.912.914.015.917.819.821.522.823.724.224.223.822.620.919.318.317.416.615.818.1

5 10 15 20 25 30 35Temperature

oC

Figure 12: Hourly-mean temperature values at 2m AGL in degrees Celsius. The vertical axis is in Australian CentralStandard Time (ACST). Time series graph of the diurnal variability for each month is shown in Figure 10.

c© 2010 3TIER, Inc.15

ADDENDUM ON MODELLING APPROACH AND POTENTIAL SOURCES OF DIFFERENCE There may be differences in the modelling results between 3TIER and alternative information providers. This statement underlines some of the potential sources of difference. General Modelling Approach There are several methods to achieve solar modelling. Some approaches rely primarily on interpolation between ground-based observations, although this is impractical in locations where the observations are sparse. Although other approaches exist, it has been found that modelling from satellite-based observations is the most accurate approach to obtaining solar information beyond ~25km from a well-maintained observation station. 3TIER uses satellite-based modelling to produce its solar data. However, there are also variations in the satellite modelling approach that may cause differences in the modelled irradiance. 3TIER’s Modelling Approach 3TIER uses satellite imagery to determine the cloud index for each hour. This is combined with turbidity information, terrain data and snow cover information to calculate the Global Horizontal Irradiance (GHI). After calculating the GHI, the Direct Normal Irradiance (DNI) and Diffuse Irradiance (DIF) are calculated using a methodology based upon the work of Richard Perez at the State University of New York (SUNY). Some of the features of 3TIER’s modelling which may be different to other approaches include: Turbidity Index 3TIER understands that the conversion from GHI to DNI and DIF is very sensitive to the turbidity index. After considering several approaches, 3TIER has decided that the best approach available at this stage is to use the Linke Turbidity Index with the monthly values calculated using from J. Remund et al1.

1 Remund J., Wald L., Lefevre M., Ranchin T., Page J., 2003. Worldwide Linke turbidity information. Proceedings of ISES Solar World Congress, 16-19 June 2003, Göteborg, Sweden. Also described at: http://www.helioclim.net/linke/linke_helioserve.html

This data set is not ideal over Australia and has some spatial anomalies. For this reason 3TIER are very careful in checking the turbidity index in all locations where our data is applied to ensure that it is not in a region affected by the unusual spatial effects in the turbidity index. To date 3TIER has not received a request that fell in a region where the Linke Turbidity Index did not seem reliable. The approach has been validated against on-ground observations. The Turbidity Index values used were: Site name Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Neuroodla 4.2

5 4.35

3.8 3.85

3.45

3.3 3.25

3.15

3.8 3.85

4.05

4.2

Northwest Bend

3.3 3.5 3.25

3.05

3.15

3.1 2.75

3.1 3.45

3.25

3.4 3.5

Pimba 4.35

4.35

3.95

3.9 3.4 3.35

3.3 3.1 3.8 3.95

4.15

4.3

Pt Augusta 3.85

3.95

3.55

3.6 3.25

3.25

3.05

3.1 3.75

3.55

3.85

3.95

Note that the Turbidity Index values for Northwest Bend are the lowest of all sites, meaning that given similar GHI values, it would be expected to have the highest DNI. Not all providers of solar information use turbidity information and instead may try to adjust the end results to observations rather than including the information in the initial processing. Application of the GHI>DNI and GHI>DIF Conversion There are different ways of calculating DNI and DIF from GHI. 3TIER uses the work2 of Richard Perez from SUNY. 3TIER has implemented the work from section 3.3 of this paper as it provides better information on a global scale. There are other potential sources of difference in approach and model tuning as well – but the most likely sources of difference have been covered in the above statements.

2 Perez, R., P. Ineichen, K. Moore, M. Kmiecik, C. Chain, R. George, and F. Vignola, 2002: A new operational model for satellite-derived irradiances: Description and validation. Solar Energy, 73, 307-317

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FullView - Renewables SAIntroduction Pimba For RenewablesSA..... 1 INTRODUCTION..... 3TIER has been retained by RenewablesSA to assess the variability and magnitude of solar irradiance,