Subcontract Report

NREL/SR-550-47085

December 2009 

Wind Energy Assessment Study 

for Nevada – Tall Tower 

Deployment (Stone Cabin) 

26 June 2005 – 31 December 2007 

D. Koracin, R. Reinhardt, G. McCurdy, 

M. Liddle, T. McCord, R. Vellore, T. Minor, 

B. Lyles, D. Miller, and L. Ronchetti 

Desert Research Institute 

Reno, Nevada 
National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393

303-275-3000 • www.nrel.gov

NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

Contract No. DE-AC36-08-GO28308

Subcontract Report

NREL/SR-550-47085

December 2009 

Wind Energy Assessment Study 

for Nevada – Tall Tower 

Deployment (Stone Cabin)

26 June 2005 – 31 December 2007 

D. Koracin, R. Reinhardt, G. McCurdy, 

M. Liddle, T. McCord, R. Vellore, T. Minor, 

B. Lyles, D. Miller, and L. Ronchetti 

Desert Research Institute 

Reno, Nevada 

NREL Technical Monitor: H. Thomas and M. Schwartz

Prepared under Subcontract No. NDO-5-44431-01
NOTICE

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Acknowledgements 

This work was funded by the U.S. Department of Energy. The authors are grateful to Marc 

Schwartz for guidance and helpful advice. Connie Komomua of NREL and Travis McCord of 

DRI are acknowledged for editing the manuscript. 
ii

List of Acronyms and Abbreviations

ATC American Tower Corporation

NaN not a number

NREL National Renewable Energy Laboratory

RDP relative difference product

SD squared difference 

sodar solar detection and ranging

TKE turbulence kinetic energy 
iii

Table of Contents

Acknowledgements ........................................................................................................................ i

List of Acronyms and Abbreviations .......................................................................................... ii

List of Figures ............................................................................................................................... iv

List of Tables ................................................................................................................................ iv

1 Introduction ............................................................................................................................. 1

2 Site Selection ............................................................................................................................ 1

3 Deployment and Testing of Equipment ................................................................................ 2

3.1 Stone Cabin Tower Anemometer Mounting Details ........................................................ 2

3.2 Instrument Specifications ................................................................................................. 6

3.3 Photos of Mounted Instrumentation ................................................................................. 9

4 Data Acquisition .................................................................................................................... 10

5 Database Development and Programming ......................................................................... 11

6 Maintenance .......................................................................................................................... 11

7 Turbulence, Statistics, and Analysis ................................................................................... 12

7.1 Comparison Between Sonic and Standard Anemometer Measurements ....................... 12

7.2 Sonic Anemometer Measurements of Turbulence Fluxes ............................................. 65

8 Sub-km Model Evaluation ................................................................................................. 138

8.1 Distribution Analysis for Sonic and Standard Anemometer Wind Speeds and Those 

Simulated by MM5 and WRF at Stone Cabin ........................................................................ 144

8.2 Statistical Bootstrapping .............................................................................................. 156

8.3 Spectral Analysis of Wind Speeds ............................................................................... 159

9 Results and Conclusion ...................................................................................................... 161

10 Publications and Presentations .......................................................................................... 162
iv

List of Figures

Figure 1. Location of the ATC tower at Stone Cabin ..................................................................... 2

Figure 2. Cup anemometer generalized mount configuration ........................................................ 3

Figure 3. Cup anemometer angle iron clamp detail ........................................................................ 3

Figure 4. 3-D sonic generalized tower boom mount ....................................................................... 4

Figure 5. Guy cable angle iron clamp detail ................................................................................... 5

Figure 6. Guy cable leg clamp detail .............................................................................................. 5

Figure 7. Campbell Scientific CSAT3 sonic anemometer .............................................................. 6

Figure 8. The NRG Systems #200P wind direction vane ............................................................... 7

Figure 9. The #40C cup anemometer .............................................................................................. 8

Figure 10. Photos of the mounted standard and sonic anemometers at 40, 60, and 80 m on the 

Stone Cabin tower ..................................................................................................................... 9

Figure 11. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period 9-27 February 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 

m. ............................................................................................................................................ 15

Figure 12. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period 9-27 February 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 

m. ............................................................................................................................................ 16

Figure 13. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period 9-27 February 2007 at (top) 80 m, (center) 60 m, 

and (bottom) 40 m. .................................................................................................................. 17

Figure 14. Scatterplots of 1-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period 9-27 February 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 

m. ............................................................................................................................................ 18

Figure 15. Scatterplots of hourly averaged sonic vs. standard anemometer measurements of wind 

speeds for the period 9-27 February 2007 at (top) 80 m, (center) 60m, and (bottom) 40 m. . 19

Figure 16. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period 9 February-9 March 2007 at (top) 80 m, (center) 60 m, and 

(bottom) 40 m. ........................................................................................................................ 20

Figure 17. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period 9 February-9 March 2007 at (top) 80 m, (center) 60 m, and 

(bottom) 40 m. ........................................................................................................................ 21
v 

Figure 18. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period 9 February-9 March 2007 at (top) 80 m, (center) 

60 m, and (bottom) 40 m......................................................................................................... 22

Figure 19. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period 2-29 April 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m.

................................................................................................................................................. 23

Figure 20. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period 2-29 April 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m.

................................................................................................................................................. 24

Figure 21. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period 2-29 April 2007 at (top) 80 m, (center) 60 m, 

and (bottom) 40 m. .................................................................................................................. 25

Figure 22. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period 5-30 June 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m.

................................................................................................................................................. 26

Figure 23. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period 5-30 June 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m.

................................................................................................................................................. 27

Figure 24. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period 5-30 June 2007 at (top) 80 m, (center) 60 m, and 

(bottom) 40 m. ........................................................................................................................ 28

Figure 25. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period July 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. ....... 29

Figure 26. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period July 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. ....... 30

Figure 27. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period July 2007 at (top) 80 m, (center) 60 m, and 

(bottom) 40 m. ........................................................................................................................ 31

Figure 28. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period August 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. .. 32

Figure 29. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period August 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. .. 33
vi 

Figure 30. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period August 2007 at (top) 80 m, (center) 60 m, and 

(bottom) 40 m. ........................................................................................................................ 34

Figure 31. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period 1-20 September 2007 at (top) 80 m, (center) 60 m, and (bottom) 

40 m. ....................................................................................................................................... 35

Figure 32. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period 1-20 September 2007 at (top) 80 m, (center) 60 m, and (bottom) 

40 m. ....................................................................................................................................... 36

Figure 33. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period 1-20 September 2007 at (top) 80 m, (center) 60 

m, and (bottom) 40 m.............................................................................................................. 37

Figure 34. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period February-September 2007 at (top) 80 m, (center) 60 m, and 

(bottom) 40 m. ........................................................................................................................ 38

Figure 35. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period February-September 2007 at (top) 80 m, (center) 60 m, and 

(bottom) 40 m. ........................................................................................................................ 39

Figure 36. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period February-September 2007 at (top) 80 m, (center) 

60 m, and (bottom) 40 m......................................................................................................... 40

Figure 37. Minisodar 4000 ............................................................................................................ 41

Figure 38. Graphic showing the positions and frequency of ‘good’ vs. NaN (bad data) results 

from the sodar at Tonopah at 10m AGL. Cyan bars indicate good data; red indicates NaN; 

white indicates missing data. .................................................................................................. 44

Figure 39. Same as Figure 38, but for 20 m AGL ........................................................................ 45

Figure 40. Same as Figure 38, but for 30 m AGL ........................................................................ 45

Figure 41. Same as Figure 5-38, but for 40 m AGL ..................................................................... 46

Figure 42. Same as Figure 38, but for 50 m AGL ........................................................................ 46

Figure 43. Same as Figure 38, but for 60 m AGL ........................................................................ 47

Figure 44. Same as Figure 38, but for 70 m AGL ........................................................................ 47
vii 

Figure 45. Graphic of data quality (similar to Figure 38) for the Kingston location for 10 m 

AGL ........................................................................................................................................ 48

Figure 46. Same as Figure 45, but for 20 m AGL ........................................................................ 48

Figure 47. Same as Figure 45, but for 30 m AGL ........................................................................ 49

Figure 48. Same as Figure 45, but for 40 m AGL ........................................................................ 49

Figure 49. Same as Figure 45, but for 50 m AGL ........................................................................ 50

Figure 50. Same as Figure 45, but for 60 m AGL ........................................................................ 50

Figure 51. Same as Figure 45, but for 70 m AGL ........................................................................ 51

Figure 52. Comparison of sodar- and tower-measured wind speed at Kingston at 40 m AGL for 

Julian days 166-195 (May 15-June 14, 2005) ......................................................................... 51

Figure 53. Same as Figure 52, but for 11 November-31 December 2005 .................................... 52

Figure 54. Comparison of average wind speeds by height as measured by the sodar and the tower 

anemometer, and differences, for the period 15 June-13 July 2005 at Kingston .................... 59

Figure 55. Time series of differences between daily average tower and sodar measured wind 

speeds by height at Kingston for the period 15 June-13 July 2005 ........................................ 59

Figure 56. Same as Figure 55, but for daily wind speed maxima ................................................. 60

Figure 57. Comparison of average wind speeds by height as measured by the sodar and the tower 

anemometer, and differences, for the period 11 November-31 December 2005 at Tonopah . 60

Figure 58. Time series of differences between daily average tower and sodar measured wind 

speeds by height at Tonopah for the period 11 November-31 December 2005 ..................... 61

Figure 59. Same as Figure 58, but for daily wind speed maxima ................................................. 61

Figure 60. Time series of sodar- and tower-measured daily maximum wind speeds at Kingston 

for the period 15 June-13 July 2005 ....................................................................................... 62

Figure 61. Same as Figure 60, but for daily average wind speeds ................................................ 62

Figure 62. Time series of sodar- and tower-measured daily maximum wind speeds at Tonopah 

for the period 11 November-31 December 2005 .................................................................... 63

Figure 63. Same as Figure 62, but for daily average wind speeds ................................................ 63

Figure 64. Sonic-measured turbulence kinetic energy (TKE; units in m2 s-2) at different height 

levels (averaged over 10 minute period) at Stone Cabin for the period 8 Feb 2007-19 Sept 

2007. The mean and population of the dataset are indicated (top) at 80 m, (center) at 60 m, 

and (bottom) at 40 m heights. ................................................................................................. 69

Figure 65. Same as Figure 64, but for a subset period 8 Feb-9 Mar 2007 .................................... 70
viii

Figure 66. Same as Figure 64, but for a subset period 2-29 April 2007 ....................................... 71

Figure 67. Same as Figure 64, but for a subset period 5-30 June 2007 ........................................ 72

Figure 68. Same as Figure 64, but for a subset period 1-31 July 2007 ......................................... 73

Figure 69. Same as Figure 64, but for a subset period 1-31 August 2007 .................................... 74

Figure 70. Same as Figure 64, but for a subset period 1-19 September 2007 .............................. 75

Figure 71. Sonic-measured turbulence momentum flux component u' u' (units in m2 s

-2) at 

different height levels (averaged over 10-minute period) at Stone Cabin for the period 8 Feb 

2007-19 Sept 2007. The mean and population of the dataset are indicated (top) at 80 m, 

(center) at 60 m, and (bottom) at 40 m heights. ...................................................................... 76

Figure 72. Same as Figure 71, but for a subset period 8 Feb-9 Mar 2007 .................................... 77

Figure 73. Same as Figure 71, but for a subset period 2-29 April 2007 ....................................... 78

Figure 74. Same as Figure 71, but for a subset period 5-30 June 2007 ........................................ 79

Figure 75. Same as Figure 71, but for a subset period 1-31 July 2007. ........................................ 80

Figure 76. Same as Figure 71, but for a subset period 1-31 August 2007 .................................... 81

Figure 77. Same as Figure 71, but for a subset period 1-19 September 2007 .............................. 82

Figure 78. Sonic-measured turbulence momentum flux component v' v ́ (units in m2 s

-2) at 

different height levels (averaged over 10 minute period) at Stone Cabin for the period 8 Feb 

2007-19 Sept 2007. The mean and population of the dataset are indicated (top) at 80 m, 

(center) at 60 m, and (bottom) at 40 m heights. ...................................................................... 83

Figure 79. Same as Figure 78, but for a subset period 8 Feb-9 Mar 2007 .................................... 84

Figure 80. Same as Figure 78, but for a subset period 2-29 April 2007 ....................................... 85

Figure 81. Same as Figure 78, but for a subset period 5-30 June 2007 ........................................ 86

Figure 82. Same as Figure 78, but for a subset period 1-31 July 2007 ......................................... 87

Figure 83. Same as Figure 78, but for a subset period 1-31 August 2007 .................................... 88

Figure 84. Same as Figure 78, but for a subset period 1-19 September 2007 .............................. 89

Figure 85. Sonic-measured turbulence momentum flux component w ́ w ́ (units in m2 s-2) at 

different height levels (averaged over 10 minute period) at Stone Cabin for the period 8 Feb 

2007-19 Sept 2007. The mean and population of the dataset are indicated (top) at 80 m, 

(center) at 60 m, and (bottom) at 40 m heights. ...................................................................... 90

Figure 86. Same as Figure 85, but for a subset period 8 Feb-9 Mar 2007 .................................... 91

Figure 87. Same as Figure 85, but for a subset period 2-29 April 2007 ....................................... 92

Figure 88. Same as Figure 85, but for a subset period 5-30 June 2007 ........................................ 93
ix

Figure 89. Same as Figure 85, but for a subset period 1-31 July 2007 ......................................... 94

Figure 90. Same as Figure 85, but for a subset period 1-31 August 2007 .................................... 95

Figure 91. Same as Figure 85, but for a subset period 1-19 September 2007 .............................. 96

Figure 92. Sonic-measured turbulence momentum flux component u ́ v ́ (units in m2 s

-2) at 

different height levels (averaged over 10-minute period) at Stone Cabin for the period 8 Feb 

2007-19 Sept 2007. The mean and population of the dataset are indicated (top) at 80 m, 

(center) at 60 m, and (bottom) at 40 m heights. ...................................................................... 97

Figure 93. Same as Figure 92, but for a subset period 8 Feb-9 Mar 2007 .................................... 98

Figure 94. Same as Figure 92, but for a subset period 2-29 April 2007 ....................................... 99

Figure 95. Same as Figure 92, but for a subset period 5-30 June 2007 ...................................... 100

Figure 96. Same as Figure 92, but for a subset period 1-31 July 2007 ....................................... 101

Figure 97. Same as Figure 92, but for a subset period 1-31 August 2007 .................................. 102

Figure 98. Same as Figure 92, but for a subset period 1-19 September 2007 ............................ 103

Figure 99. Sonic-measured turbulence momentum flux component u' w' (units in m2 s

-2) at 

different height levels (averaged over 10-minute period) at Stone Cabin for the period 8 Feb 

2007-19 Sept 2007. The mean and population of the dataset are indicated (top) at 80 m, 

(center) at 60 m, and (bottom) at 40 m heights. .................................................................... 104

Figure 100. Same as Figure 9989, but for a subset period 8 Feb-9 Mar 2007 ............................ 105

Figure 101. Same as Figure 99, but for a subset period 2-29 April 2007 ................................... 106

Figure 102. Same as Figure 99, but for a subset period 5-30 June 2007 .................................... 107

Figure 103. Same as Figure 99, but for a subset period 1-31 July 2007 ..................................... 108

Figure 104. Same as Figure 99, but for a subset period 1-31 August 2007 ................................ 109

Figure 105. Same as Figure 99, but for a subset period 1-19 September 2007 .......................... 110

Figure 106. Sonic-measured turbulence momentum flux component v' w' (units in m2 s-2) at 

different height levels (averaged over 10-minute period) at Stone Cabin for the period 8 Feb 

2007-19 Sept 2007. The mean and population of the dataset are indicated (top) at 80 m, 

(center) at 60 m, and (bottom) at 40 m heights. .................................................................... 111

Figure 107. Same as Figure 106, but for a subset period 8 Feb-9 Mar 2007 .............................. 112

Figure 108. Same as Figure 106, but for a subset period 2-29 April 2007 ................................. 113

Figure 109. Same as Figure 106, but for a subset period 5-30 June 2007 .................................. 114

Figure 110. Same as Figure 106, but for a subset period 1-31 July 2007 ................................... 115

Figure 111. Same as Figure 106, but for a subset period 1-31 August 2007 .............................. 116
x 

Figure 112. Same as Figure 106, but for a subset period 1-19 September 2007 ........................ 117

Figure 113. Sonic-measured kinematic heat flux w' T' (units in K m s-1) at different height levels 

(averaged over 10-minute period) at Stone Cabin for the period 8 Feb 2007-19 Sept 2007. 

The mean and population of the dataset are indicated (top) at 80 m, (center) at 60 m, and 

(bottom) at 40 m heights. ...................................................................................................... 118

Figure 114. Same as Figure 113, but for a subset period 8 Feb-9 Mar 2007 .............................. 119

Figure 115. Same as Figure 113, but for a subset period 2-29 April 2007 ................................. 120

Figure 116. Same as Figure 113, but for a subset period 5-30 June 2007 .................................. 121

Figure 117. Same as Figure 113, but for a subset period 1-31 July 2007 ................................... 122

Figure 118. Same as Figure 113, but for a subset period 1-31 August 2007 .............................. 123

Figure 119. Same as Figure 113, but for a subset period 1-19 September 2007 ........................ 124

Figure 120. Scatterplots of sonic-measured 10-minute averaged turbulence kinetic energy for 

the period February 8-March 9, 2007. (a) 60 m vs. 80m, (b) 40 m vs. 80 m, and (c) 40 m vs. 

60 m. ..................................................................................................................................... 125

Figure 121. Histogram of sonic-measured turbulence kinetic energy for the period February 8-

March 9, 2007 (top) 80 m, (center) 60 m, and (bottom) 40 m. ............................................. 126

Figure 122. Scatterplots of sonic-measured 10-minute averaged kinematic momentum flux 

component u'w' for the period February 8-March 9, 2007 (top) 60 m vs. 80 m, (center) 40 m 

vs. 80 m, and (bottom) 40 m vs. 60 m. ................................................................................. 127

Figure 123. Histogram of sonic-measured kinematic momentum flux component u'w' for the 

period February 8-March 9, 2007 (top) 80 m, (center) 60 m, and (bottom) 40 m. ............... 128

Figure 124. Scatterplots of sonic-measured 10-minute averaged kinematic heat flux for the 

period February 8-March 9, 2007 (top) 60 m vs. 80 m, (center) 40m vs. 80m, and (bottom) 40 

m vs. 60 m. ............................................................................................................................ 129

Figure 125. Histogram of sonic-measured kinematic heat flux for the period February 8-March 9, 

2007 (top) 80 m, (center) 60 m, and (bottom) 40 m. ............................................................ 130

Figure 126. Scatterplots of sonic-measured 10-minute averaged turbulence kinetic energy for the 

period April 2-29, 2007 (top) 60 m vs. 80m, (center) 40 m vs. 80 m, and (bottom) 40 m vs. 

60 m. ..................................................................................................................................... 131

Figure 127. Scatterplots of sonic-measured 10-minute averaged turbulence kinetic energy for the 

period June 5-30, 2007 (top) 60 m vs. 80 m, (center) 40 m vs. 80 m, and (bottom) 40 m vs. 

60 m. ..................................................................................................................................... 132
xi 

Figure 128. Scatterplots of sonic-measured 10-minute averaged turbulence kinetic energy for the 

period July 1-31, 2007 (top) 60 m vs. 80 m, (center) 40 m vs. 80 m, and (bottom) 40 m vs. 

60 m. ..................................................................................................................................... 133

Figure 129. Scatterplots of sonic-measured 10-minute averaged turbulence kinetic energy for the 

period August 1-31, 2007 (top) 60 m vs. 80 m, (center) 40 m vs. 80 m, and (bottom) 40 m vs. 

60 m. ..................................................................................................................................... 134

Figure 130. Scatterplots of sonic-measured 10-minute averaged turbulence kinetic energy for the 

period September 1-19, 2007 (top) 60 m vs. 80 m, (center) 40 m vs. 80 m, and (bottom) 40 m 

vs. 60 m. ................................................................................................................................ 135

Figure 131. Histograms of the turbulence kinetic energy for calendar seasons computed from the 

sonic anemometer data for the period of 8 February to 20 September 2007 at 80 m. .......... 136

Figure 132. Histograms of the turbulence kinetic energy for calendar seasons computed from the 

sonic anemometer data for the period of 8 February to 20 September 2007 at 60 m. .......... 137

Figure 133. Histograms of the turbulence kinetic energy for calendar seasons computed from the 

sonic anemometer data for the period of 8 February to 20 September 2007 at 40 m. .......... 138

Figure 134. MM5 and WRF 5-domain setup. Meteorological towers located in the figure are: SC 

– Stone Cabin, T - Tonopah, K – Kingston, and L5, L7 – Luning (left) Domains 1, 2, and 3. 

(right) Domains 3, 4, and 5. .................................................................................................. 140

Figure 135. Observed (sonic anemometer wind measurements) and MM5-simulated wind speeds 

at Stone Cabin for the period of Julian days 40-65, (Julian day 40.5 = 9 Feb 2007 1200 UTC) 

(top) 80m, (center) 60 m, and (bottom) 40 m heights. .......................................................... 141

Figure 136. Same as Figure 135, but for WRF ........................................................................... 142

Figure 137. Individual value plots of sonic-measured 10-minute averaged turbulence kinetic 

energy and model simulated at various horizontal grid resolutions for the period Feb 9-Mar 8, 

2007 (top) 80 m, (center) 60 m, and (bottom) 40 m. ............................................................ 144

Figure 138. Observed (sonic anemometer) and MM5-simulated turbulence kinetic energy at 

Stone Cabin for the period of Julian days 40-65. (Julian day 40.5 = 9 Feb 2007 1200 UTC) 

(top) 80 m, (center) 60 m, and (bottom) 40 m heights. ......................................................... 145

Figure 139. Frequency distribution of sonic-observed and MM5-simulated (at different 

horizontal grid resolutions 18 km, 6 km, 2 km, 0.666 km, and 0.222 km) turbulence kinetic 

energy at 80 m ....................................................................................................................... 146
xii 

Figure 140. Frequency distribution of sonic-observed and MM5/WRF-simulated (at different 

horizontal grid resolutions 18 km, 6 km, 2 km, 0.666 km, and 0.222 km) wind speeds at 80 

m. The Weibull curves are fit using Minitab 14. The shape and scale parameters are 

indicated. ............................................................................................................................... 148

Figure 141. Frequency distribution of sonic-observed and MM5/WRF-simulated (at different 

horizontal grid resolutions 18 km, 6 km, 2 km, 0.666 km, and 0.222 km) wind speeds at 60 

m. The Weibull curves are fit using Minitab 14. The shape and scale parameters are 

indicated. ............................................................................................................................... 149

Figure 142. Frequency distribution of sonic-observed and MM5/WRF-simulated (at different 

horizontal grid resolutions 18 km, 6 km, 2 km, 0.666 km, and 0.222 km) wind speeds at 

40 m. ..................................................................................................................................... 150

Figure 143. Relative Difference Product (RDP) comparison of shape and scale parameters at 

(top) 80m, (center) 60 m, and (bottom) 40 m ....................................................................... 152

Figure 144. Same as Figure 143, but for SDs ............................................................................. 153

Figure 145. Same as Figure 143, but for the SD comparison using model results against the 

Rayleigh distribution shape parameter β=2 .......................................................................... 154

Figure 146. Weibull fit of 10-minute averaged sonic (left) and standard (right) anemometer 

measured wind speeds (m s-1) at 40 m (bottom), 60 m (center), and 80 m (top) for the period 

February-September 2007. .................................................................................................... 155

Figure 147. Resampled frequency distribution of correlation coefficients for the MM5/WRFsimulated wind speeds against sonic anemometer measurements. ....................................... 157

Figure 148. Resampled frequency distribution of index of agreements for the MM5/WRFsimulated 80m wind speeds at the coarsest (18 km) and the finest (222 m) horizontal grid 

resolutions against sonic anemometer measurements. .......................................................... 158

Figure 149. Power spectrum of sonic-measured (blue circle-dash) and MM5/WRF-simulated 

wind speeds at 40, 60, and 80 m obtained from different model horizontal grid 

resolutions. ............................................................................................................................ 160

Figure 150. Power spectrum of sonic-measured (blue circle-dash) and MM5 turbulence kinetic 

energy at 40, 60, and 80 m obtained from different model horizontal grid resolutions. ...... 161
xiii

List of Tables

Table 1. List of Archived Standard Anemometer Wind Data for the Stone Cabin Tower ......................... 10

Table 2. List of Archived Sonic Anemometer Wind Data for the Stone Cabin Tower .............................. 10

Table 3. Summary of Wind Speed Data as Measured by the Sodar and the Tower Anemometer at 

Kingston for 15 June-13 July 2005 ...................................................................................................... 54

Table 4. Summary of Wind Speed Data as Measured by the Sodar and the Tower Anemometer at 

Tonopah for 11 November-31 December 2005.................................................................................... 54

Table 5. Differences in the Daily Average Wind Speeds as Measured by the Tower Anemometer and as 

Measured by the Sodar at Kingston for 15 June-13 July 2005............................................................. 55

Table 6. Same as Table 5, but for Daily Wind Speed Maxima................................................................... 56

Table 7. Differences in the Daily Average Wind Speeds as Measured by the Tower Anemometer and as 

Measured by the Sodar at Tonopah for 11 November-31 December 2005.......................................... 57

Table 8. Differences in the Daily Maximum Wind Speeds as Measured by the Tower Anemometer and as

Measured by the Sodar at Kingston for 11 November-31 December 2005 ......................................... 58

Table 9. Summary Statistics of the TKE, Momentum and Heat Fluxes Results for the Seven Different 

Time Domains...................................................................................................................................... 67

Table 10. Meteorological Towers in western Nevada. ............................................................................. 139

Table 11. Wind Speed Statistics at Heights 40, 60, and 80 m Obtained from the Observed and MM5 and 

WRF Model Simulated Results at the Stone Cabin Tower Location for the Period 9 Feb 2007 12 

UTC – 8 Mar 2007 12 UTC. .............................................................................................................. 143

Table 12. Weibull Shape and Scale Parameters for MM5, WRF, and Sonic Anemometer Data ............. 151
1 

1 Introduction

The objective of this work effort was to characterize wind shear and turbulence for representative 

wind-developable areas in Nevada. This information and the models that provide it will be useful 

in specifying the appropriate technology and helping the developer choose the optimum 

orientation and spacing for a particular landscape. A key element in this study is the 

measurement of turbulence at three levels above the ground (40, 60, and 80 m) at the existing 

tower near Tonopah (Stone Cabin). Most previous wind power density estimates are based on 

surface wind measurements and various extrapolation formulas, which are rarely verified, to 

provide estimates at hub heights. In addition, the study will provide new insight into long-term 

statistics of turbulence at multiple levels that can be used to estimate its effects on turbine 

operation and maintenance. 

2 Site Selection 

Task

Locate and secure site location for the project. Suitable site characteristics for this project include 

tall towers with heights of approximately 80 meters (m) located within 25 miles of existing or 

planned power lines and situated near developable landscapes. Additionally, the towers must be 

providing two-way, real-time communication to the Desert Research Institute.

Results and Status

Initial communications with Cellular One that were terminated due to newly initiated merger

negotiations with another telecommunications company. Subsequent searches for a viable 

alternative resulted in contact being made to American Tower Corporation (ATC) in October 

2005. Discussions with Jeffrey Deal at ATC began shortly thereafter and the Stone Cabin site in 

Nevada was chosen as a possible site location. The tower, owned by ATC, on the Stone Cabin 

site is the tallest tower in Nevada (80 meters tall). It is located approximately 31 miles east by 

northeast of Tonopah, Nevada along U.S. Route 6. Figure 1 shows its location with respect to 

the four 50 meter metrological towers, used by DRI, the environmental research arm of the 

Nevada System of Higher Education, in the Wind energy assessment study for Nevada (Phases I 

and II). Approval to investigate the site further was given by Marc Schwartz via email on 15 

Dec. 2005. 

The site was again discussed with Marc Schwartz and Mary Jane Hale at a meeting held at 

NREL’s National Wind Technology Center on 14 and 15 March 2006. The tower’s drawbacks, 

its large structural members and communication horns at the top, were also discussed. However, 

the benefits of its location, with respect to local terrain and its ability to support anemometer 

booms, became clearer after the analysis of the wind measurements by both standard and sonic 

anemometers were performed, and outweighed the drawbacks. Consequently, site selection of 

the American Tower Corporation tower at Stone Cabin, Nevada, was approved and a fully 

executable lease agreement was delivered to DRI on 2 Dec. 2006. 
2 

Figure 1. Location of the ATC tower at Stone Cabin 

3 Deployment and Testing of Equipment

Task

Purchase the necessary meteorological equipment and install and test it on site.

Results and Status 

The tower was equipped in February 2007. As proposed, the instrumentation was deployed and 

tested.

3.1 Stone Cabin Tower Anemometer Mounting Details

Wind measurement sensors were installed on the Stone Cabin tower on two tower legs at three 

elevations (40, 60 and 80 m above the land surface, respectively), totaling six instruments. At 

each elevation, cup anemometers were installed off the northern tower leg and 3-D sonic 

anemometers were installed off the southern tower leg. Sensor mounts were designed to insure 

the least amount of tower influence on the wind measurements as possible. 

3.1.1 Cup Anemometer Mount

Cup anemometers were mounted on rigid (non-guyed) booms, consisting of an aluminum pipe 

(1.32" OD x 10' long), an “off-the-shelf” pipe bracket and a fabricated clamp. The clamp was 

designed to attach to the tower’s double angle iron cross brace, and is not guyed to the tower 

(Figure 2).
3 

Figure 2. Cup anemometer generalized mount configuration 

The angle iron clamp was designed to grip the double 2 1⁄2" angle iron tower cross member 

(Figure 3). “Off-the-shelf” pipe brackets were modified to allow the boom to be retracted, for 

sensor installation and maintenance. The boom is a 10' long, 1" aluminum pipe (1.32" OD); 

however, a second clamp was used to insure a secure and stiff attachment to the tower cross 

member, so the sensor is only be 8' or so from the tower leg. 

Figure 3. Cup anemometer angle iron clamp detail 

3.1.2 3-D Sonic Anemometer Mount

The sonic anemometer measures wind speed in all three directions and, due to its short sampling 

path, is very sensitive to vertical sensor movement; therefore, these booms were designed to 

minimize sensor movement. This boom consisted of a 2"x 2"x10' aluminum tube mounted to the 

tower using the same fixture as the cup anemometers (Figure 4). This allowed the sensor arm to 

be extended to a length where the solid stabilizer arms could then be attached to the tower. The 

stabilizer arms were attached on solid pivots near the sensor mount. The pivots allowed the 

stabilizer to be adjustable to the pitch of the tower leg. The tower leg clamps (Figures 5 and 6) 

were designed to grip the tower leg without the clamp bolt being directly on the leg surface, 

through the use of the clamp plate. The use of the clamp plate allowed attachment at almost any 

point along the tower leg. This configuration allowed the instrumentation to be mounted without 

having specific engineering details about each tower mount point. Examples for the need of this 

flexibility can be seen in the photos of Section 3.3 where one of the 80 meter height struts was 

mounted to a support platform rather than the tower leg. 
4 

Figure 4. 3-D sonic generalized tower boom mount 
5 

Figure 5. Guy cable angle iron clamp detail 

Figure 6. Guy cable leg clamp detail 
6 

3.2 Instrument Specifications

3.2.1 Specifications of Sonic Anemometer CSAT3 (from www.campbellsci.com) 

 

Figure 7. Campbell Scientific CSAT3 sonic anemometer 
7 

3.2.2 Specifications of NRG #200P Wind Direction Vane and #40C Cup 

Anemometer (from www.nrgsystems.com) 

 

Figure 8. The NRG Systems #200P wind direction vane
8 

Figure 9. The #40C cup anemometer 
9 

3.3 Photos of Mounted Instrumentation

Figure 10 shows the mounted instrumentation on the Stone Cabin tower. 

Figure 10. Photos of the mounted standard and sonic anemometers at 40, 60, and 80 m on the 

Stone Cabin tower
10 

4 Data Acquisition

Task

Connect the on-site data logger to the communications equipment and ensure that all data can be 

consistently communicated and stored on-line.

Results and Status

Data collection started on February 8, 2007. The data was stored onsite and manually 

downloaded approximately once a month and recorded on DVD. Table 1 shows the current 

structure of the stored files for the standard (Table 1) and sonic (Table 2) data sets. 

Table 1. List of Archived Standard Anemometer Wind Data for the Stone Cabin Tower 

Currently Archived Standard Anemometer Data for Stone Cabin 

(40, 60, and 80 m)

File number Size Start date Start time End date End time

40 572.0 kb 02/08/07 05:22:00 PM 02/10/07 11:00:00 AM

41 188.7 kb 02/10/07 11:01:00 AM 02/11/07 12:00:00 AM

63 7.6 Mb 02/11/07 12:01:00 AM 03/05/07 12:59:00 PM

64 164.4 kb 03/05/07 01:01:00 PM 03/06/07 12:00:00 AM

92 6.3 Mb 03/06/07 12:01:00 AM 04/02/07 11:59:00 PM

95 1.3 Mb 04/03/07 12:00:00 AM 04/06/07 05:54:00 PM

120 8.1 Mb 04/06/07 05:55:00 PM 04/30/07 02:15:00 PM

156 12.4 Mb 04/30/07 02:16:00 PM 06/05/07 06:35:00 PM

203 15.8 Mb 06/05/07 06:36:00 PM 07/22/07 10:00:00 AM

234 10.6 Mb 07/22/07 10:01:00 AM 08/22/07 04:25:00 PM

262 9.8 Mb 08/22/07 04:26:00 PM 09/20/07 06:25:00 PM

Table 2. List of Archived Sonic Anemometer Wind Data for the Stone Cabin Tower 

Currently Archived Sonic Anemometer Data for Stone Cabin

Measurement Height Start date End date

40 m: 02/08/07 02/24/07

02/26/07 03/09/07

04/02/07 04/29/07

06/05/07 09/19/07

60 m: 02/08/07 03/09/07

04/02/07 04/29/07

06/05/07 09/19/07

80 m: 02/08/07 03/09/07

04/02/07 04/29/07

06/05/07 09/19/07
11 

5 Database Development and Programming

Task

Create links from Internet to database. Use existing, previously developed algorithms and 

develop new ones as needed to provide the required statistical analyses. 

Results and Status

The 20 Hz sonic anemometer data reside on our process computers. The standard anemometer 

data were archived at 1-minute intervals for all three levels and publicly accessible on the 

Internet at: 

http://www.wrcc.dri.edu/cgi-bin/rawMAIN.pl?nvwnd6

We have also processed and posted 10 minute standard anemometer data from the 50 m towers at 

five levels in western Nevada (near Tonopah), and they are also publicly available on the Internet 

at: 

http://www.wrcc.dri.edu/cgi-bin/rawMAIN.pl?nvwnd1

http://www.wrcc.dri.edu/cgi-bin/rawMAIN.pl?nvwnd2

http://www.wrcc.dri.edu/cgi-bin/rawMAIN.pl?nvwnd3

http://www.wrcc.dri.edu/cgi-bin/rawMAIN.pl?nvwnd4

Details on the 50 meter towers and their data can be found in Belu and Koracin (2009). 

6 Maintenance 

Task

Monitor the data received in real-time for evidence of a system malfunction. Generate a work

order to the equipment subcontractor or visit the site as soon as possible to resolve sensor 

problems. Otherwise, the site will be visited quarterly for general inspection and testing.

Results and Status

A detailed list of all installation and maintenance events is as follows:

Feb. 5-9, 2007 Site installation. (Greg McCurdy, Brad Lyles, Mike Betke and crew)

Feb. 10, 2007 Site visit for equipment check and data retrieval. (Greg McCurdy) 

Mar. 5, 2007 Site visit for equipment check and data retrieval. Minor programming 

adjustments to data collection computer. (Greg McCurdy) 

Apr. 3, 2007 Site visit for equipment check and data retrieval. Found data collection 

computer locked up. Operating system adjustments of virus protection 

software (seemed to be the cause of the problem). (Greg McCurdy) 

Apr. 6, 2007 Site visit to check on previous changes, verification of operation and data 

collection processes. Operation normal. (Greg McCurdy) 

Apr. 30, 2007 Site visit for equipment check and data retrieval. Operation normal. (Greg 

McCurdy) 
12 

Jun. 5, 2007 Site visit for equipment check and data retrieval. Operation normal. (Greg 

McCurdy) 

Jul. 22, 2007 Site visit for equipment check and data retrieval. Operation normal. (Greg 

McCurdy) 

Aug. 22, 2007 Site visit for equipment check and data retrieval. Operation normal. (Greg 

McCurdy) 

Sep. 20, 2007 Site visit for equipment check and data retrieval. Operation normal. (Greg 

McCurdy) 

Dec. 5, 2007 Site visit for equipment check and data retrieval. Operation normal. (Greg 

McCurdy) 

Operation normal - all sensors functioning, data collection routines checked, all data retrieved 

from collection computer, collection computer file system maintenance done, power systems 

check, and visual inspection of sensor physical condition (orientation and mounting). 

7 Turbulence, Statistics, and Analysis 

Task

The main objective is to determine the structure and temporal variability of turbulence using 

sonic anemometers at vertical levels relevant to hub heights. This section will also address the 

extent to which turbulence transfer can be inferred from profile measurements of winds and 

temperature. The study will include a time series analysis of winds and turbulence and estimation 

of their parametric and non-parametric statistical distributions; spectral analysis of winds and 

turbulence fluxes that determine expected peak energy; and determination of the strength of 

diurnal effects at high elevations. Statistics of the winds and turbulence will be performed for 

each season as well as analysis of the properties of the turbulence for various wind speed 

regimes.

We also will examine the validity of using acoustic sounder measurements to infer turbulence 

transfer through measurements of wind velocity fluctuations and the temperature-structure 

function. Seasonal and annual statistics of acoustic sounder vs. tall tower data will be compared 

and the impact on the computation of wind power density will be assessed. This assessment will 

provide insight into the feasibility and usefulness of using acoustic sounders for wind energy 

studies.

Results and Status

7.1 Comparison Between Sonic and Standard Anemometer Measurements

Sonic and standard anemometers use entirely different principles in order to measure wind speed 

and direction. Standard anemometers, such as cup and vane or propeller and vane designs, rely 

on the momentum in the wind to determine their angular momentum (or angular orientation, in 

the case of wind vanes); that is, to spin the cups or propeller. Because of this reliance on the 

angular momentum of the cups or propeller (which have angular inertia, however light they have 

been built), there is an inherent time lag for these designs to detect a change in wind speed. They 

are not generally sensitive enough to detect the wind variances that determine the turbulence flux 

quantities. 
13 

Sonic anemometers do not rely on direct measurement of the wind’s momentum. Their operating 

principle is to measure the change in the speed of sound of the air across several different paths. 

By measuring the Doppler signal, a sonic anemometer can measure turbulence fluxes. This 

aspect of sonic anemometers is illustrated in Section 4. In theory, this design should allow sonic 

anemometers to be very sensitive to changes in wind speeds.

In this section, we have assembled plots illustrating various comparisons of the sonic and 

standard anemometer data from Stone Cabin, NV. These plots are arranged by time domains in 

2007, which are as follows: 9-27 February, 2-29 April, 5-30 June, 1-31 July, 1-31 August, 1-20 

September, and the complete time domain of 9 February-20 September. In each time domain, 

there are time series, scatter plots, and histograms comparing the sonic and standard anemometer 

results at the three measurement heights: 40, 60, and 80 m AGL. 

The frequency of sampling of the sonic anemometers was 20 measurements per second. For the 

standard anemometers, it was one measurement per second. For the purpose of better comparison 

with our simulated data sets, which give hourly results, we have created and used three different 

averaging schemes for the anemometer data, which average over different subsets of each hour 

to give one value to represent the entire hour. The three subsets were averaged over the whole 

hour, averaging over the last ten minutes in each hour, and averaging over the last one minute in 

each hour. These three averaging schemes are shown in the plots of the first time domain (9-27 

February). 

Over the time scales involved here (roughly monthly), it is hard to see any significant differences 

between these averaging schemes when we look at the plots. The scatter plots of Figures 13, 15, 

and 16 show very close agreement between the different averaging schemes, with somewhat 

weaker agreement at 80 m. So, for the purpose of comparing sonic and standard anemometers, 

we have relied on the ten minute averages. 

The scatter plots over all time domains show strong agreement between the sonic and standard 

anemometers, with correlations ranging from .78 to almost unity (.99). The time series and 

histograms show an interesting difference between these anemometer types—a difference we 

would suspect from the design differences mentioned above. In the time series, we often notice 

that the sonic data seems like a green fringe above the standard data at higher wind values (for 

example, see Figure 11, top plot, on 21-23 February). Although they track each other closely, 

the sonic data seems to register slightly higher wind values during periods of strong winds. In 

addition, the standard anemometer histograms show much larger counts of observations in the 

lowest bin (0-1 m/s), see Figures 13, 18, and 21. These findings are consistent with the view 

that standard anemometers are not as sensitive as sonics at very low wind speeds, nor do they 

react quickly enough at higher wind speed to register some changes. Both of these contentions 

are supported by the fact that the mean wind speeds are consistently higher from the sonic data. 

Notice that the bias for the entire period is greatest at 80 m (0.7 m/s) and decreases to 0.5 and 0.4 

m/s at 60 and 40 m, respectively.

In some cases (Figures 19 and 34), there are sharp wind speed peaks over 20 m/s at 80 m that 

are not recorded by the sonic anemometer. This should be examined further in the future. Figure 

34 also shows that there are peaks over 15 m/s that were recorded at all levels.
14 

Figure 35 shows that the agreement between the sonic and the standard anemometer decreases 

with increasing height, possibly due to differences in measurement techniques and also due to 

tower structure and flow shadowing. 

The scatter plots, and associated correlation coefficient and bias, comparing wind speed at 80 m 

with each of the other heights (40 and 60 m) indicate that there is a certain noise in measuring 

wind speed at 80 m. This is possibly due to the large antennas at 80 m (see Figure 10). 

Consequently, the time series plots show much better agreement between the sonic and standard 

anemometer measurements at 40 m and 60 m, compared to 80 m. 

Because of this better sensitivity, and since sonic anemometers also provide turbulence flux 

information, we conclude that they might be more appropriate for measuring long term wind 

patterns than the conventional anemometers. However, we should also consider an issue of 

possible need for more frequent calibration and maintenance. 
15 

Figure 11. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period 9-27 February 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. 
16 

Figure 12. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period 9-27 February 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. 
17 

Figure 13. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period 9-27 February 2007 at (top) 80 m, (center) 60 m, and 

(bottom) 40 m. 
18 

Figure 14. Scatterplots of 1-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period 9-27 February 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. 
19 

Figure 15. Scatterplots of hourly averaged sonic vs. standard anemometer measurements of wind 

speeds for the period 9-27 February 2007 at (top) 80 m, (center) 60m, and (bottom) 40 m. 
20 

Figure 16. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period 9 February-9 March 2007 at (top) 80 m, (center) 60 m, and (bottom) 40

m. 

(The package MATLAB is used to estimate the mean values.) 
21 

Figure 17. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period 9 February-9 March 2007 at (top) 80 m, (center) 60 m, and (bottom) 40

m. 

The package MINITAB was used to estimate the statistical parameters. Wind speeds greater than 

25 m s-1 were not included. The number of sample pairs is indicated in each box. 
22 

Figure 18. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period 9 February-9 March 2007 at (top) 80 m, (center) 60 m, 

and (bottom) 40 m. 
23 

Figure 19. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period 2-29 April 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. 
24 

Figure 20. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period 2-29 April 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. 
25 

Figure 21. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period 2-29 April 2007 at (top) 80 m, (center) 60 m, and 

(bottom) 40 m. 
26 

Figure 22. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period 5-30 June 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. 
27 

Figure 23. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period 5-30 June 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. 
28 

Figure 24. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period 5-30 June 2007 at (top) 80 m, (center) 60 m, and 

(bottom) 40 m. 
29 

Figure 25. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period July 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. 
30 

Figure 26. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period July 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. 
31 

Figure 27. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period July 2007 at (top) 80 m, (center) 60 m, and (bottom) 

40 m. 
32 

Figure 28. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period August 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. 
33 

Figure 29. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period August 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. 
34 

Figure 30. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period August 2007 at (top) 80 m, (center) 60 m, and 

(bottom) 40 m. 
35 

Figure 31. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period 1-20 September 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. 
36 

Figure 32. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period 1-20 September 2007 at (top) 80 m, (center) 60 m, and (bottom) 40 m. 
37 

Figure 33. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period 1-20 September 2007 at (top) 80 m, (center) 60 m, 

and (bottom) 40 m. 
38 

Figure 34. 10-minute averaged sonic (green) and standard (blue) anemometer measurements of 

wind speeds for the period February-September 2007 at (top) 80 m, (center) 60 m, and (bottom) 40

m. 
39 

Figure 35. Scatterplots of 10-minute averaged sonic vs. standard anemometer measurements of 

wind speeds for the period February-September 2007 at (top) 80 m, (center) 60 m, and (bottom) 40

m. 
40 

Figure 36. Histograms of 10-minute averaged sonic (left) and standard (right) anemometer 

measurements of wind speeds for the period February-September 2007 at (top) 80 m, (center) 60

m, and (bottom) 40 m. 
41 

7.1.1 Sodar at Kingston and Tonopah, 2005

Introduction to Sodar

Figure 37. Minisodar 4000 

Image from ASC’s website: http://minisodar.org

Sonic Detection And Ranging (sodar) is an emerging technology for measuring the wind profile 

in the bottom of the boundary layer. It operates by sending a sonic pulse directly upwards and 

measuring the fine characteristics—especially the Doppler shift—of the return signals. Unlike a 

conventional or sonic anemometer, sodar is able to measure wind speed, direction, and 

turbulence fluxes not just at one point but upwards along a vertical line extending (depending on 

the sodar array used) from 15-20 m above the surface to 50 m or up to over 200 m above the 

surface.

Since sodar has the potential for measuring winds at the hub height of large modern wind rotors, 

it may become a feasible tool to assess the usefulness of a particular site for the production of 

wind energy, without needing to erect an expensive tall tower and mount conventional 

anemometers on. Powered by a solar array and small enough to fit into a large van, it is also 

relatively mobile.

In the data acquisition for this report, we used a Minisodar 4000 made by the Atmospheric 

Systems Corporation of Santa Clarita, California (Figure 37). The basic specifications of this 

instrument are quoted below, from the company Web site, http://minisodar.org. 

Sodar Manufacturer: Atmospheric Systems Corp. 

Sodar Model: Minisodar 4000 

Maximum Height: 200 meters

Minimum Height: 20 meters

Height Resolution: 5 meters

Frequency: 4500 Hz 

Averaging & Reporting Interval: User Selectable

Wind Speed Range: 0 to 45 m/s 

Wind Speed Accuracy: < 0.50 m/s (WS > 2 m/s) 

Wind Direction Accuracy: ± 5 degrees (WS > 2 m/s)
42 

This sodar unit was set up and operated remotely in two locations in Nevada where DRI-Western 

Regional Climate Center also had tall (50 m) wind measurement towers that recorded wind 

measurements at 10-50 m in 10 m increments. The measurements took place at Kingston, 

Nevada, from 15 June through 13 July, 2005, and then at Tonopah, Nevada, from 11 November 

through 31 December, 2005. See Figure 1 for the geographical setup of these locations. 

In this section, we assess the accuracy of the sodar by comparing it to the conventional 

anemometer measurements from the nearby tall towers. We also look at the quality of the sodar 

data as it changes with measurement height. These aspects of the sodar’s performance are 

important in light of its potential use in assessing the wind energy generating capacity at perhaps 

remote sites.

Data Quality

Description of the data used (locations/coordinates, elevation, etc.) 

Station 17 Tonopah 24NW (Wind Tower) Nevada: 

 Data begin date: 11 November 2005 

 Wind data (good wind speed and direction) 

 Begin date: 11 November 2005 

 End date: 31 December 2005 

 Data time increment: 5 minutes for sodar; 10 minutes for tower

 

 Ongoing data collection? No. 

Latitude 38° 22' 20" 

 Longitude 117° 28' 18" 

 Elevation 5035 ft. 

Station 18 Kingston 14SW (Wind Tower) Nevada:

 Data begin date: 15 June 2005 

 Wind data (good wind speed and direction) 

 Begin date: 15 June 2005 

 End date: 13 July 2005 

 Data time increment: 5 minutes for sodar; 10 minutes for tower

 Significant omitted data blocks? no 

 

Ongoing data collection? No. 

Latitude 39° 02' 44" 

 Longitude 117° 00' 03" 

 Elevation 5839 ft. 

Exact number of sodar data points: Kingston 8352 

Tonopah 13938 
43 

Generally, the sodar data at 20 m, 30 m, 40 m, and 50 m is sound at both sites, with a slight but 

progressive decrease in the quality of data with height—more and more NaNs (NaN: Not a 

Number). The NaN density plots in Figures 38 through 51 show this progression clearly. 

Outliers were not a significant problem at any height. 

The data at 10 m was quite bad (mostly NaN) at both sites. This was evidently because this sodar 

did not work well at distances very close to the system. The best data was at 20 m (as judged by 

its scarcity of NaNs and outliers), and it gradually and consistently decreases in quality with 

increasing height. The data at 60 m was marginal at both sites, with NaNs over 10% of total 

entries, and the data was comprised of almost 25% NaNs at 70 m. 

As mentioned in the description of the data section above for Tonopah, there was some data 

missing from the last third of the time period, which show in the NaN density plots as thin white 

lines. 

The time series (only the 20 m time series are shown here; the other heights show similarly) in 

Figures 52 and 53 show that at a gross level the sodar data tracked the tower data well.

See the data check summaries below for details of the data quality per height level.

Kingston Sodar Data Check Summary: 

Date File Completed? Status

7/13/2007 Kingston_sodar_DC_10 m.mat Yes bad data, 5571 NaNs, 8352 total entries

 5571/8352*100 = 66.7% NaNs > 10% limit

 

7/13/2007 Kingston_sodar_DC_20 m.mat Yes good data, 4 NaNs, 8352 total entries

 4/8352*100 = 0.05% NaNs

7/18/2007 Kingston_sodar_DC_30 m.mat Yes good data, 22 NaNs, 8352 total entries

 22/8352*100 = 0.3% NaNs

7/18/2007 Kingston_sodar_DC_40 m.mat Yes good data, 70 NaNs, 8352 total entries

 70/8352*100 = 0.8% NaNs

7/18/2007 Kingston_sodar_DC_50 m.mat Yes good data, 309 NaNs, 8352 total entries

 309/8352*100 = 3.7% NaNs

7/18/2007 Kingston_sodar_DC_60 m.mat Yes bad data, 909 NaNs, 8352 total entries

 909/8352*100 = 10.9% NaNs > 10% limit

7/18/2007 Kingston_sodar_DC_70 m.mat Yes bad data, 1998 NaNs, 8352 total entries

 1998/8352*100 = 23.9% NaNs > 10% limit
44 

Tonopah Sodar Data Check Summary: 

Date File Completed? Status

7/25/2007 Tonopah_sodar_DC_10 m.mat yes bad data, 12875 NaNs, 13931 total entries

 12875/13931*100 = 92.4% NaNs

7/25/2007 Tonopah_sodar_DC_20 m.mat yes good data, 98 NaNs, 13938 total entries

 98/13938*100 = 0.7% NaNs

7/26/2007 Tonopah_sodar_DC_30 m.mat yes good data, 257 NaNs, 13939 total entries

 257/13939*100 = 1.8% NaNs

7/26/2007 Tonopah_sodar_DC_40 m.mat yes good data, 736 NaNs, 13938 total entries

 736/13938*100 = 5.3% NaNs

7/26/2007 Tonopah_sodar_DC_50 m.mat yes good data, but at limit:

 1398 NaNs, 13938 total entries

1398/13938*100 = 10.0% NaNs

7/26/2007 Tonopah_sodar_DC_60 m.mat yes bad data, NaNs > 10% of total entries

 2355 NaNs, 13937 total entries

 2355/13937*100 = 16.9% NaNs

7/26/2007 Tonopah_sodar_DC_70 m.mat yes bad data, NaNs > 10% of total entries

 3699 NaNs, 13937 total entries

3699/13937*100 = 26.5% NaNs

NaN Density Plots—Tonopah 

Figure 38. Graphic showing the positions and frequency of ‘good’ vs. NaN (bad data) results from 

the sodar at Tonopah at 10m AGL. Cyan bars indicate good data; red indicates NaN; white 

indicates missing data.
45 

Figure 39. Same as Figure 38, but for 20 m AGL

Figure 40. Same as Figure 38, but for 30 m AGL
46 

Figure 41. Same as Figure 5-38, but for 40 m AGL

Figure 42. Same as Figure 38, but for 50 m AGL

 
47 

Figure 43. Same as Figure 38, but for 60 m AGL

Figure 44. Same as Figure 38, but for 70 m AGL 
48 

Figure 45. Graphic of data quality (similar to Figure 38) for the Kingston location for 10 m AGL

Figure 46. Same as Figure 45, but for 20 m AGL 
49 

Figure 47. Same as Figure 45, but for 30 m AGL 

Figure 48. Same as Figure 45, but for 40 m AGL 
50 

Figure 49. Same as Figure 45, but for 50 m AGL 

Figure 50. Same as Figure 45, but for 60 m AGL 
51 

Figure 51. Same as Figure 45, but for 70 m AGL 

Figure 52. Comparison of sodar- and tower-measured wind speed at Kingston at 40 m AGL for 

Julian days 166-195 (May 15-June 14, 2005)
52 

Figure 53. Same as Figure 52, but for 11 November-31 December 2005 
53 

Description of the Analysis

In MATLAB and Excel, we assembled tables of the basic statistics governing the sodar and 

tower wind speed data (Tables 3 through 8), starting with summaries like that below, then 

progressing to the tables and figures in the next section. 

Kingston 10 m Daily Wind Speed summary—example 15 June-13 July 2005 

 SODAR TOWER

Day avg max min avg max min 

1 3.33 7.54 0.51 4.15 9.60 0.27

2 3.80 8.73 0.90 6.36 12.00 0.18

3 5.61 10.67 1.41 6.72 12.70 1.08

4 3.04 5.74 0.35 3.68 7.57 0.23

5 4.45 9.88 0.99 6.28 15.61 0.18

6 4.16 6.13 2.28 8.04 16.32 0.57

7 1.90 3.49 0.80 8.35 18.57 0.29

8 3.44 5.43 1.32 7.83 15.17 0.34

9 4.38 10.83 0.83 5.59 11.41 0.72

10 4.46 9.39 0.87 5.44 11.70 0.21

11 4.36 8.86 0.54 4.98 10.92 0.90

12 2.59 5.40 0.28 3.19 7.23 0.21

13 4.65 10.14 0.71 4.43 11.03 0.18

14 2.92 7.42 0.37 4.14 8.70 0.94

15 2.20 5.69 0.08 3.66 6.85 1.47

16 2.45 5.03 0.28 3.05 5.93 0.18

17 2.53 4.87 0.37 3.14 8.26 0.22

18 2.92 6.84 0.53 3.60 10.00 0.18

19 2.64 6.89 0.13 3.46 7.98 0.24

20 2.59 5.11 0.65 3.41 6.71 0.18

21 2.20 4.30 0.16 2.85 5.21 0.30

22 2.26 5.77 0.39 2.79 6.60 0.19

23 1.70 5.29 0.20 2.76 5.90 0.18

24 2.70 5.05 0.59 4.11 8.14 0.27

25 3.95 6.79 2.00 4.65 11.46 0.51

26 3.15 6.75 0.30 3.31 8.08 0.18

27 1.61 4.93 0.05 2.94 6.56 0.18

28 2.29 5.87 0.48 4.43 10.08 0.18

29 2.26 4.96 0.70 3.77 9.37 0.22
54 

Table 3. Summary of Wind Speed Data as Measured by the Sodar and the Tower Anemometer at 

Kingston for 15 June-13 July 2005 

Kingston Sodar and Tower Data Summary

Wind Speed (m/s) for 15 June-13 July 2005

Height Data avg std avgmax N NaNs corrcoef

10 m Sodar: 3.04 1.9 6.68 4031 2677 0.546

10 m Tower: 4.55 3.23 9.85 4031 0 0.546

20 m Sodar: 4.59 3.62 11.21 4031 1 0.997

20 m Tower: 4.94 3.61 10.78 4031 0 0.997

30 m Sodar: 4.8 3.81 11.95 4031 10 0.995

30 m Tower: 5.15 3.76 11.23 4031 0 0.995

40 m Sodar: 4.98 3.8 12.17 4031 32 0.984

40 m Tower: 5.27 3.88 11.47 4031 0 0.984

50 m Sodar: 5.08 3.66 12.06 4031 130 0.96

50 m Tower: 5.42 3.97 11.81 4031 0 0.96

Table 4. Summary of Wind Speed Data as Measured by the Sodar and the Tower Anemometer at 

Tonopah for 11 November-31 December 2005 

Tonopah Sodar Data Summary

Wind Speed (m/s) for 11 November-31 December 2005

Height Data avg std avgmax N NaNs corrcoef

10 m Sodar: 5.64 2.82 NaN 480 5764 0.446

10 m Tower: 4.05 2.5 8.43 6244 0 0.446

20 m Sodar: 2.8 2.07 6.58 6196 46 0.977

20 m Tower: 4.36 2.66 8.82 6242 0 0.977

30 m Sodar: 3.21 2.43 7.27 6129 112 0.989

30 m Tower: 4.42 2.71 8.67 6241 0 0.989

40 m Sodar: 3.55 2.71 7.91 5917 325 0.99

40 m Tower: 4.65 2.85 9.09 6242 0 0.99

50 m Sodar: 3.78 2.88 8.67 5649 593 0.988

50 m Tower: 4.84 2.96 9.49 6242 0 0.988
55 

Table 5. Differences in the Daily Average Wind Speeds as Measured by the Tower Anemometer 

and as Measured by the Sodar at Kingston for 15 June-13 July 2005 

Kingston Tower minus Sodar difference of daily averages

Wind Speed (m/s) for 15 June-13 July 2005

day 10 m 20 m 30 m 40 m 50 m 

1 0.82 0.31 0.27 0.12 0.03

2 2.56 0.2 0.14 -0.02 -0.05

3 1.11 0.17 0.05 -0.11 -0.05

4 0.64 0.25 0.23 0.05 0.16

5 1.83 0.24 0.12 0.09 0.1

6 3.88 0.2 0.13 0.35 1.05

7 6.45 0.46 0.95 2.05 3.34

8 4.39 0.36 0.28 0.33 0.81

9 1.21 0.27 0.21 0.02 -0.05

10 0.98 0.35 0.21 0.02 0.08

11 0.62 0.27 0.27 0.16 0.19

12 0.6 0.46 0.47 0.28 0.12

13 -0.22 0.21 0.13 0.06 -0.14

14 1.22 0.32 0.34 0.26 0.17

15 1.46 0.19 0.23 0.08 0.31

16 0.6 0.33 0.34 0.24 0.35

17 0.61 0.18 0.23 0.12 0.07

18 0.68 0.35 0.42 0.32 0.25

19 0.82 0.37 0.46 0.3 0.22

20 0.82 0.31 0.3 0.17 0.27

21 0.65 0.25 0.3 0.2 0.22

22 0.53 0.24 0.24 0.11 -0.05

23 1.06 0.5 0.52 0.31 0.08

24 1.41 0.58 0.54 0.23 -0.02

25 0.7 0.76 0.65 0.48 0.4

26 0.16 0.49 0.51 0.44 0.32

27 1.33 0.59 0.59 0.44 0.36

28 2.14 0.6 0.5 0.42 0.46

29 1.51 0.5 0.52 0.32 0.25

averages: 1.40 0.36 0.35 0.27 0.32
56 

Table 6. Same as Table 5, but for Daily Wind Speed Maxima 

Kingston Tower minus Sodar difference of daily maxima

Wind Speed (m/s) for 15 June-13 July 2005

day 10 m 20 m 30 m 40 m 50 m 

1 2.06 -0.31 -0.06 -0.5 0.05

2 3.27 -0.91 -1.41 -0.73 -0.91

3 2.03 -0.28 -0.24 -0.97 -1.04

4 1.83 -0.48 -1.61 -1.48 0.62

5 5.73 -0.12 -1.81 -2.2 -0.04

6 10.19 -2.12 -2.69 -2.42 0.95

7 15.08 -1.02 -1.73 -0.44 5.13

8 9.74 -3.77 -2.91 -1.87 -2.6

9 0.58 -1.31 -2.05 -2.07 -2.54

10 2.31 -0.29 -2.65 -2.67 -3.61

11 2.06 -0.46 -0.53 -0.74 -0.26

12 1.83 1.59 0.72 0.43 -0.19

13 0.89 -0.08 -0.48 -0.34 -0.88

14 1.28 -0.44 0.08 -0.23 -0.53

15 1.16 -0.4 -0.86 -1.35 0.99

16 0.9 -0.53 0.03 -0.72 0.24

17 3.39 -0.01 0.07 -0.1 -0.42

18 3.16 0.93 1.05 0.89 1.03

19 1.09 -0.75 -0.9 -0.65 -0.32

20 1.6 0.07 0.05 -0.45 0.31

21 0.91 -0.83 -0.77 -0.54 -0.69

22 0.83 0.1 -0.22 -0.76 -0.52

23 0.61 0.93 0.31 -0.4 -0.58

24 3.09 -1.01 0.08 0.25 -0.41

25 4.67 -0.9 -1.4 -0.6 0.26

26 1.33 0.55 0.37 0.72 0.46

27 1.63 0.79 1.27 0.94 0.51

28 4.21 -0.51 -1.33 -0.56 -0.73

29 4.41 -0.74 -1.29 -0.66 -1.56

averages: 3.17 -0.42 -0.72 -0.70 -0.25
57 

Table 7. Differences in the Daily Average Wind Speeds as Measured by the Tower Anemometer 

and as Measured by the Sodar at Tonopah for 11 November-31 December 2005 

Tonopah Tower minus Sodar difference of daily averages 

Wind Speed (m/s) for 11 November-31 December 2005

day 10 m 20 m 30 m 40 m 50 m 

1 -1.49 1.46 1.1 1.04 1.02

2 1.17 1.61 1.16 0.99 0.99

3 4.33 1.6 1.08 0.99 0.83

4 0.46 1.81 1.16 1.16 1.21

5 -3.23 2.42 1.79 1.47 1.32

6 -3.52 1.18 1.11 1.01 0.88

7 -0.05 2 1.55 1.48 1.54 

8 -1.47 1.44 1.24 1.16 1

9 0.95 1.39 1.27 1.14 1.16

10 -3.92 1.41 1.22 0.97 0.99 

11 -0.33 1.36 1.13 0.97 0.9

12 -1.3 1.15 1.01 0.9 0.94

13 -2.27 1.29 1.22 1.07 1.01

14 -2.21 1.35 1.19 1.14 1.03

15 2.31 1.21 1.01 1.26

16 1.12 3.44 2.22 1.92 1.75

17 0.34 2.43 1.76 1.63 1.3 

18 -0.94 1.45 1.27 1.38 1.5

19 0.99 0.68 0.54 0.29

20 0.38 1.29 0.79 0.75 0.77 

21 -1.8 1.26 0.93 0.96 1.03

22 -2.77 2.03 1.38 1.26 1.34

23 -0.95 2.13 1.62 1.28 1.18

24 -2.88 1.76 1.42 1.24 1.22

25 -0.19 1.79 1.53 1.36 1.38

26 2.03 2.04 1.72 1.41 1.21

27 1.06 1.03 1.16 1.03

28 -2.02 1.64 1.23 1.33 1.27

29 -0.83 2.52 1.79 1.33 1.37

30 -2.28 2.33 1.68 1.32 1.28 

31 -1.29 1.07 1.16 1.04 1.06

32 -4.72 1.3 1.14 1.18 1.15

33 1.02 1.12 1.04 0.97

34 -5.12 1.98 1.58 1.71 1.54

35 1.56 1.27 1.23 1.13

36 -3.32 1.69 1.35 1.32 1.24

37 -3.99 0.88 0.71 0.75 0.72 

38 1.21 0.63 0.49 0.95

39 0.61 0.74 0.93 0.91

40 0.91 0.83 0.91 0.97 

41 -0.26 0.72 0.8 0.94 1

42 0.99 1.08 1.07 1.08

43 -2.78 1.05 0.98 0.98 1.04

44 1.36 1.12 1.1 1.11

45 1.35 1.37 1.2 1.33

46 -0.87 1.59 1.19 1.2 1.36

47 0.85 0.97 0.97 1.03

48 1.37 0.94 0.91 0.99

49 -2.94 1.45 1.13 1.04 1.03

50 0.79 0.71 0.82 0.93 

51 1.63 1.62 2.28 2.83

average: -1.36 1.52 1.21 1.15 1.14
58 

Table 8. Differences in the Daily Maximum Wind Speeds as Measured by the Tower Anemometer 

and as Measured by the Sodar at Kingston for 11 November-31 December 2005 

Kingston Tower minus Sodar difference of daily maxima 

Wind Speed (m/s) for 11 November-31 December 2005

day 10 m 20 m 30 m 40 m 50 m

1 3.01 1.16 1.79 1.72 1.91 

2 1.57 1.74 2.16 1.62 1.45 

3 5.32 1.16 0.12 0.31 0.13

4 4.8 1.59 0.95 1.19 0.8 

5 0.3 3.16 2.02 1.22 1.02 

6 -4.05 1.56 1.6 1.63 1.06

7 -0.34 3.15 1.88 1.46 0.79 

8 -2.69 1.07 0.92 1.06 -6.9 

9 -1.46 2.05 2.4 -0.27 -0.08

10 -2.02 2.07 1.15 -1.31 1.35 

11 -2.99 1.46 1.46 1.06 0.54 

12 -1.64 1.03 1.1 0.45 1.09

13 -2.13 1.53 1 0.85 1.32 

14 -2.01 1.65 1.61 1.24 1.3 

15 3.97 3.37 2.6 2.5

16 7.98 2.91 1.94 2.01 3.55 

17 2.66 4.23 0.91 0.78 0.08 

18 -2.84 -0.11 -1.18 1.38 1.88

19 0.93 0.43 0.5 0.77 

20 5.69 1.82 1.07 0.8 0.72 

21 2.53 0.64 0.38 0.4 -0.91

22 0.99 3.17 2.13 1.13 1.34 

23 -0.86 1.87 1.21 0.81 -6.58 

24 -2.43 1.98 1.16 0.55 0.46 

25 0.79 2.95 1.82 1.83 0.75 

26 6.84 4.83 3.47 1.13 -0.88 

27 2.52 2.31 2.03 1.5 

28 0.5 1.77 0.99 0.41 0.48 

29 1.89 3.82 2.78 1.53 0.6 

30 0.51 1.97 2.2 0.92 0.51

31 -2.78 2.11 2.26 1.66 1.94 

32 -1.05 1.62 0.62 1.25 1.2 

33 0.97 0.99 1.48 1.32 

34 -3.75 2.31 2.12 1.17 0.59

35 2.27 2.02 1.69 1.02 

36 0.56 2.12 1.85 1.09 0.09 

37 -0.98 1.89 1.62 0.09 0.55 

38 -0.95 -1.59 -1.92 -2.78 

39 1.14 -2.17 0.86 0.93

40 1.29 1.03 1.01 1.13 

41 1.07 1.03 0.6 0.97 1.17 

42 2.06 1.3 1.5 1.46 

43 -0.37 1.25 0.74 0.96 0.84

44 1.84 1.21 1.22 1.55

45 1.53 1.58 1.4 1.62 

46 20.78 7.13 9.03 10.51 

47 1.4 1.68 1.11 0.91 

48 1.34 0.93 0.47 0.21 

49 1.1 1.94 1.17 0.8 0.56 

50 0.72 -0.45 0.62 1.17

51 2.04 1.51 2.72 3.35 

average: 1.44 2.24 1.40 1.18 0.82
59 

3.04

incomplete

sodar data

at 10m

4.55

1.51

4.59

4.94

0.35

4.8

5.15

0.35

4.98

5.27

0.29

5.08

5.42

0.34

0

1

2

3

4

5

6

Avg Wind Speed (m/s)

10m 20m 30m 40m 50m

Height AGL

Average Wind Speeds by Height AGL for Kingston 14SW, Sodar vs Tower

15 June-13 July 2005

Sodar

Tower

T-S difference

Figure 54. Comparison of average wind speeds by height as measured by the sodar and the tower 

anemometer, and differences, for the period 15 June-13 July 2005 at Kingston

Kingston 14SW Tower minus Sodar Difference of Daily Wind Speed Averages

15 June-13 July 2005

-1

0

1

2

3

4

5

6

7

123456789

1011121314151617181920212223242526272829

averages:

Day of Operation

WS Difference (m/s)

10m 20m 30m 40m 50m

Figure 55. Time series of differences between daily average tower and sodar measured wind 

speeds by height at Kingston for the period 15 June-13 July 2005
60 

Kingston 14SW Tower minus Sodar Difference of Daily Wind Speed Maxima

15 June-13 July 2005

-5

0

5

10

15

20

123456789

101112131415161718

19

2021

22

23242526272829

averages:

Day of operation

WS Difference (m/s)

10m 20m 30m 40m 50m

Figure 56. Same as Figure 55, but for daily wind speed maxima

5.64

incomplete

sodar data

at 10m

4.05

-1.59

2.8

4.36

1.56

3.21

4.42

1.21

3.55

4.65

1.1

3.78

4.84

1.06

-2

-1

0

1

2

3

4

5

6

Avg Wind Speed (m/s)

10m 20m 30m 40m 50m

Height AGL

Average Wind Speeds by Height AGL for Tonopah 24NW, Sodar vs Tower

11 November-31 December 2005

Sodar

Tower

T-S difference

Figure 57. Comparison of average wind speeds by height as measured by the sodar and the tower 

anemometer, and differences, for the period 11 November-31 December 2005 at Tonopah
61 

Tonopah 24NW Tower minus Sodar Difference of Daily Wind Speed Averages

11 November-31 December 2005

-6

-4

-2

0

2

4

6

13579

1113151719212325272931333537394143454749

51

Day of operation

WS difference (m/s)

10m 20m 30m 40m 50m

Figure 58. Time series of differences between daily average tower and sodar measured wind 

speeds by height at Tonopah for the period 11 November-31 December 2005

Tonopah 24NW Tower minus Sodar Difference of Daily Wind Speed Maxima

11 November-31 December 2005

-10

-5

0

5

10

15

20

25

13579

111315171921232527293133353739414345474951

Day of operation

WS difference (m/s)

10m 20m 30m 40m 50m

Figure 59. Same as Figure 58, but for daily wind speed maxima
62 

Figure 60. Time series of sodar- and tower-measured daily maximum wind speeds at Kingston for 

the period 15 June-13 July 2005

Figure 61. Same as Figure 60, but for daily average wind speeds
63 

Figure 62. Time series of sodar- and tower-measured daily maximum wind speeds at Tonopah for 

the period 11 November-31 December 2005

Figure 63. Same as Figure 62, but for daily average wind speeds
64 

Conclusions

We have been looking for patterns in the differences between the sodar data and the tower data, 

both spatially and temporally, in order to assess the accuracy and sensitivity of the sodar wind 

speed measurements relative to the towers’ anemometers. In doing this, we have assumed the 

conventional anemometer data to be the standard for wind speed measurement—in effect, to be 

“truth.”

The data check summaries show the quick decrease in sodar data quality above 50 m, by the 

rapid increase in NaNs. The good data (i.e., mean data with NaNs and outliers numbering less 

than 10% of the total number of data entries) is confined for both locations to the 20, 30, 40, and 

50 m heights above ground level. 

Looking at Figures 54 and 57, one can notice that there is no strong correlation between height 

above the ground and the tower minus sodar average wind speed differences. (Throughout this 

analysis we are neglecting the 10 m sodar data as being too incomplete to tell us anything other 

than that this sodar system doesn’t work well at 10 meters. For our purposes—that is, our interest 

in what is happening at rotor hub height—this is not a problem. However, the 10 m data were 

included for completeness.). In Figure 54 (Kingston), the differences stay pretty close to 

constant. In Figure 57 (Tonopah), these differences show a small decreasing trend with height. 

In both figures it is not completely clear that the sodar becomes significantly more or less 

accurate with height when compared to the tower data. 

Figures 55, 56, 58, and 59 are plots of the tower minus sodar differences for the daily wind 

speed averages and maxima. These are included because they show that there is no increase or 

decrease in the accuracy of the sodar over the time periods considered. From this, we can infer 

that the calibration of the sodar does not get worse with time. 

Figures 60 and 61 show the daily wind speed maxima and averages at 20 m for Kingston, while 

Figures 62 and 63 show the same for Tonopah. These plots are representative of the results at 

the different heights. If the sodar were resolving higher wind speeds differently than low wind 

speeds when compared to the towers, we might see a different pattern in the maxima plots versus 

how the daily sodar wind speed averages track the tower daily wind speed data. For instance, 

since the sodar was taking measurements every five minutes to the towers’ ten, there is a greater 

chance for the sodar to catch a short-lived high wind speed result that the tower might miss, and 

the results should therefore trend slightly higher than the daily averages. In fact the plots suggest 

this may be the case. Although the daily averages are lower for the sodar results, the daily 

maxima are relatively greater (in the case of the Kingston 20 m maxima, they are absolutely 

greater). This is, however, only a slight trend. Otherwise the maxima track quite similarly to the 

daily averages, suggesting a similar level of measurement sensitivity toward the higher winds.

Regarding the statistical analysis of the comparison between the sodar and tower data (Figures 

54 to 59) and neglecting the 10 m level, daily average differences between the wind speeds 

measured by the tower and sodar are less than 0.4 m/s (and positive) at the Kingston location and 

less than 1.6 ms

-1(negative) at the Tonopah location. Average differences between the maximum 

daily wind speeds measured by the tower and sodar are less than 0.8 ms

-1 (and negative) at the 

Kingston location and less than 2.3 ms

-1 (and positive) at the Tonopah location
65 

These results, when taken together, make a good case that the primary problems with this sodar 

usage were two: the initial calibration of the unit and its inability to extend its measurement 

reach much above 50 m. Some of the differences could be also related to calibration of the 

anemometers. We have seen that the sodar was sensitive to wind changes between 20 and 50 m 

and tracked well with the tower data. From these results, it seems likely that if the sodar were 

well calibrated it would yield measurements coming close to the accuracy and reliability of the 

tower anemometers over that range of measurement heights.

Nor do we see any basis in these results for distinguishing between the sodar performances at 

Kingston and Tonopah. Both sites show similar average wind speeds on the period of action 

(Kingston slightly larger), and both are at similar elevations. Considering how the sodar works, it 

is a pertinent question whether the amount of particulate matter in the air affects the sodar’s 

accuracy, and whether there was a difference in the air quality in this respect between the two 

sites. However, the data collected for these sites does not include any data types useful for 

investigating this question. 

Because this particular sodar was a small portable unit, we should not expect its measuring reach 

to extend very high into the boundary layer. It is hoped that a larger and more powerful unit 

should be able to resolve wind speeds up to the hub height of modern large rotors—80, 90, or 

over 100 m height. However, powering such a unit in a remote location may become a challenge.

7.2 Sonic Anemometer Measurements of Turbulence Fluxes

We have obtained Stone Cabin 20-Hz sonic anemometer data stored on DVDs for the period 

from 8 February to 20 September 2007 for 40, 60, and 80m levels. Programs to read in data and 

compute turbulence fluxes were completed. For 10-min periods, the following turbulence fluxes 

were computed:

22 2

22 2

', ', '

' ', ' ', ' '

0.5 ' ' '

uvw

uv uw vw

TKE u v w = ++    

Time series plots (Figures 64 through 121) of turbulence kinetic energy (TKE; m2s

-2), 

momentum fluxes (u'w', v'w'; m2s

-2) and heat fluxes (w'T'; K m s-1) for the complete domain time 

domain (8 February 2007 – 20 September 2007) obtained from the high frequency (20 Hz) sonic 

anemometer measurements. For clarity, the plots are further arranged by the month (8 February – 

9 March, 2-29 April, 5-30 June, 1-31 July, 1-31 August, and 1-20 September 2007). 

While examining the time series (Figures 64 through 70) and statistics (Table 9) of the TKE, it 

is apparent that there are many sharp peaks of TKE at all levels. Some of them are greater than 

60 m2s

-2 (Figure 71). Some of the large peaks that occurred generally at only one level were 

excluded from the statistic analysis (Table 9). Since these are 10-min averages each including 

12000 samples, these values need to be considered in this first report. Some of these large values 

could be associated with the system malfunctions. Average values for the entire period are 

relatively reasonable (below 2 m2

s

-2) and indicate increase of the TKE with height. Scatter plots 

of the TKE (Figures 120 through 130) indicate that there is a possible problem with 
66 

measurements at 80 m since the scatter plots of the TKE between 40 and 60 m show pretty close 

agreement, while any combination with 80 m worsens the comparison. Similarly as in the case 

with the wind speed analysis, this could be related to the obstruction antennas at 80 m (see 

Figure 10). 

A time series of the u'u' variance (Figure 70) shows large peaks (especially at 40 m) that 

contribute to the large values of observed TKE. Some of the peaks coincide at all levels, but 

many of them (especially at the beginning of the period) could be related to problems with the 

measurement technique. 

A time series of v'v' variance (Figure 78) indicates more uniform behavior (especially at 40 m) 

compared to the u'u' plot. 

Since the plot of w'w' variance (Figure 85) shows much smaller values compared to u'u' and v'v' 

plots, one can conclude that the most contribution to large TKE peaks are coming from u'u' 

component. This could be related to the mounting orientation and the tower’s flow shadowing 

effects.

Although a time series of u'v' (Figure 92) indicates sharp peaks, notice that most of them are 

coinciding at all levels. The values of this parameter appear to be similar at all levels. 

Statistics of the u'w' kinematic momentum flux component shows that the average values are 

negative (as expected) (Table 9); however, time series of the same parameter is quite noisy at 40 

m with a significant number of sharp peaks. 

In contrast to u'w' behavior, a time evolution of v'w' (Figure 106) is more uniform at all levels 

with more noise (or realistic variability) at higher levels.

Plots of w'T' have sharp isolated peaks of several order of magnitude larger than most of the 

values, especially at 40 m. Since the peaks generally do not coincide at various levels, there 

might be some problems with detecting this parameter. These peaks could be removed in further 

analysis .
67 

Table 9. Summary Statistics of the TKE, Momentum and Heat Fluxes Results for the Seven 

Different Time Domainsa
68 

Table 9, Continued

a Time domains include the complete time domain for this project—8 Feb-20 Sept 2007—as well as the monthly 

results: 8 Feb-9 Mar, 2-29 Apr, 5-30 June, 1-31 July, 1-31 Aug, and 1-20 Sept. (N = number of samples; std = 

standard deviation, max, min = maximum and minimum values in the time series.)
69 

Figure 64. Sonic-measured turbulence kinetic energy (TKE; units in m2 s-2) at different height 

levels (averaged over 10 minute period) at Stone Cabin for the period 8 Feb 2007-19 Sept 2007. 

The mean and population of the dataset are indicated (top) at 80 m, (center) at 60 m, and (bottom) 

at 40 m heights.
70 

Figure 65. Same as Figure 64, but for a subset period 8 Feb-9 Mar 2007
71 

Figure 66. Same as Figure 64, but for a subset period 2-29 April 2007
72 

Figure 67. Same as Figure 64, but for a subset period 5-30 June 2007
73 

Figure 68. Same as Figure 64, but for a subset period 1-31 July 2007
74 

Figure 69. Same as Figure 64, but for a subset period 1-31 August 2007
75 

Figure 70. Same as Figure 64, but for a subset period 1-19 September 2007
76 

Figure 71. Sonic-measured turbulence momentum flux component u' u' (units in m2 s-2) at different 

height levels (averaged over 10-minute period) at Stone Cabin for the period 8 Feb 2007-19 Sept 

2007. The mean and population of the dataset are indicated (top) at 80 m, (center) at 60 m, and 

(bottom) at 40 m heights.
77 

Figure 72. Same as Figure 71, but for a subset period 8 Feb-9 Mar 2007
78 

Figure 73. Same as Figure 71, but for a subset period 2-29 April 2007
79 

Figure 74. Same as Figure 71, but for a subset period 5-30 June 2007
80 

Figure 75. Same as Figure 71, but for a subset period 1-31 July 2007.
81 

Figure 76. Same as Figure 71, but for a subset period 1-31 August 2007
82 

Figure 77. Same as Figure 71, but for a subset period 1-19 September 2007
83 

Figure 78. Sonic-measured turbulence momentum flux component v' v ́ (units in m2 s-2) at different 

height levels (averaged over 10 minute period) at Stone Cabin for the period 8 Feb 2007-19 Sept 

2007. The mean and population of the dataset are indicated (top) at 80 m, (center) at 60 m, and 

(bottom) at 40 m heights.
84 

Figure 79. Same as Figure 78, but for a subset period 8 Feb-9 Mar 2007
85 

Figure 80. Same as Figure 78, but for a subset period 2-29 April 2007
86 

Figure 81. Same as Figure 78, but for a subset period 5-30 June 2007
87 

Figure 82. Same as Figure 78, but for a subset period 1-31 July 2007
88 

Figure 83. Same as Figure 78, but for a subset period 1-31 August 2007
89 

Figure 84. Same as Figure 78, but for a subset period 1-19 September 2007
90 

Figure 85. Sonic-measured turbulence momentum flux component w ́ w ́ (units in m2 s-2) at 

different height levels (averaged over 10 minute period) at Stone Cabin for the period 8 Feb 2007-

19 Sept 2007. The mean and population of the dataset are indicated (top) at 80 m, (center) at 60 m, 

and (bottom) at 40 m heights.
91 

Figure 86. Same as Figure 85, but for a subset period 8 Feb-9 Mar 2007
92 

Figure 87. Same as Figure 85, but for a subset period 2-29 April 2007
93 

Figure 88. Same as Figure 85, but for a subset period 5-30 June 2007
94 

Figure 89. Same as Figure 85, but for a subset period 1-31 July 2007
95 

Figure 90. Same as Figure 85, but for a subset period 1-31 August 2007
96 

Figure 91. Same as Figure 85, but for a subset period 1-19 September 2007
97 

Figure 92. Sonic-measured turbulence momentum flux component u ́ v ́ (units in m2 s-2) at 

different height levels (averaged over 10-minute period) at Stone Cabin for the period 8 Feb 2007-

19 Sept 2007. The mean and population of the dataset are indicated (top) at 80 m, (center) at 60 m, 

and (bottom) at 40 m heights.
98 

Figure 93. Same as Figure 92, but for a subset period 8 Feb-9 Mar 2007
99 

Figure 94. Same as Figure 92, but for a subset period 2-29 April 2007
100 

Figure 95. Same as Figure 92, but for a subset period 5-30 June 2007
101 

Figure 96. Same as Figure 92, but for a subset period 1-31 July 2007
102 

Figure 97. Same as Figure 92, but for a subset period 1-31 August 2007
103 

Figure 98. Same as Figure 92, but for a subset period 1-19 September 2007
104 

Figure 99. Sonic-measured turbulence momentum flux component u' w' (units in m2 s-2) at 

different height levels (averaged over 10-minute period) at Stone Cabin for the period 8 Feb 2007-

19 Sept 2007. The mean and population of the dataset are indicated (top) at 80 m, (center) at 60 m, 

and (bottom) at 40 m heights.
105 

Figure 100. Same as Figure 9989, but for a subset period 8 Feb-9 Mar 2007
106 

Figure 101. Same as Figure 99, but for a subset period 2-29 April 2007
107 

Figure 102. Same as Figure 99, but for a subset period 5-30 June 2007
108 

Figure 103. Same as Figure 99, but for a subset period 1-31 July 2007
109 

Figure 104. Same as Figure 99, but for a subset period 1-31 August 2007
110 

Figure 105. Same as Figure 99, but for a subset period 1-19 September 2007
111 

Figure 106. Sonic-measured turbulence momentum flux component v' w' (units in m2 s-2) at 

different height levels (averaged over 10-minute period) at Stone Cabin for the period 8 Feb 2007-

19 Sept 2007. The mean and population of the dataset are indicated (top) at 80 m, (center) at 60 m, 

and (bottom) at 40 m heights.
112 

Figure 107. Same as Figure 106, but for a subset period 8 Feb-9 Mar 2007
113 

Figure 108. Same as Figure 106, but for a subset period 2-29 April 2007
114 

 Figure 109. Same as Figure 106, but for a subset period 5-30 June 2007
115 

Figure 110. Same as Figure 106, but for a subset period 1-31 July 2007
116 

Figure 111. Same as Figure 106, but for a subset period 1-31 August 2007
117 

Figure 112. Same as Figure 106, but for a subset period 1-19 September 2007
118 

Figure 113. Sonic-measured kinematic heat flux w' T' (units in K m s-1) at different height levels 

(averaged over 10-minute period) at Stone Cabin for the period 8 Feb 2007-19 Sept 2007. The mean 

and population of the dataset are indicated (top) at 80 m, (center) at 60 m, and (bottom) at 40 m 

heights. 
119 

Figure 114. Same as Figure 113, but for a subset period 8 Feb-9 Mar 2007
120 

Figure 115. Same as Figure 113, but for a subset period 2-29 April 2007
121 

Figure 116. Same as Figure 113, but for a subset period 5-30 June 2007
122 

Figure 117. Same as Figure 113, but for a subset period 1-31 July 2007
123 

Figure 118. Same as Figure 113, but for a subset period 1-31 August 2007
124 

Figure 119. Same as Figure 113, but for a subset period 1-19 September 2007
125 

Figure 120. Scatterplots of sonic-measured 10-minute averaged turbulence kinetic energy for the 

period February 8-March 9, 2007. (a) 60 m vs. 80m, (b) 40 m vs. 80 m, and (c) 40 m vs. 60 m. 

The sample density and correlation coefficients are indicated.
126 

Figure 121. Histogram of sonic-measured turbulence kinetic energy for the period February 8-

March 9, 2007 (top) 80 m, (center) 60 m, and (bottom) 40 m.

The statistics of the distribution are indicated. 
127 

Figure 122. Scatterplots of sonic-measured 10-minute averaged kinematic momentum flux 

component u'w' for the period February 8-March 9, 2007 (top) 60 m vs. 80 m, (center) 40 m vs. 

80 m, and (bottom) 40 m vs. 60 m. 

The sample density and correlation coefficients are indicated.
128 

Figure 123. Histogram of sonic-measured kinematic momentum flux component u'w' for the 

period February 8-March 9, 2007 (top) 80 m, (center) 60 m, and (bottom) 40 m. 

The statistics of the distribution are indicated. 
129 

Figure 124. Scatterplots of sonic-measured 10-minute averaged kinematic heat flux for the period 

February 8-March 9, 2007 (top) 60 m vs. 80 m, (center) 40m vs. 80m, and (bottom) 40 m vs. 60 m. 

The sample density and correlation coefficients are indicated.
130 

Figure 125. Histogram of sonic-measured kinematic heat flux for the period February 8-March 9, 

2007 (top) 80 m, (center) 60 m, and (bottom) 40 m. 

The statistics of the distribution are indicated.
131 

Figure 126. Scatterplots of sonic-measured 10-minute averaged turbulence kinetic energy for the 

period April 2-29, 2007 (top) 60 m vs. 80m, (center) 40 m vs. 80 m, and (bottom) 40 m vs. 60 m. 

The sample density and correlation coefficients are indicated.
132 

Figure 127. Scatterplots of sonic-measured 10-minute averaged turbulence kinetic energy for the 

period June 5-30, 2007 (top) 60 m vs. 80 m, (center) 40 m vs. 80 m, and (bottom) 40 m vs. 60 m. 

The sample density and correlation coefficients are indicated.
133 

Figure 128. Scatterplots of sonic-measured 10-minute averaged turbulence kinetic energy for the 

period July 1-31, 2007 (top) 60 m vs. 80 m, (center) 40 m vs. 80 m, and (bottom) 40 m vs. 60 m. 

The sample density and correlation coefficients are indicated.
134 

Figure 129. Scatterplots of sonic-measured 10-minute averaged turbulence kinetic energy for the 

period August 1-31, 2007 (top) 60 m vs. 80 m, (center) 40 m vs. 80 m, and (bottom) 40 m vs. 60 m. 

The sample density and correlation coefficients are indicated.
135 

Figure 130. Scatterplots of sonic-measured 10-minute averaged turbulence kinetic energy for the 

period September 1-19, 2007 (top) 60 m vs. 80 m, (center) 40 m vs. 80 m, and (bottom) 40 m vs. 60

m. 

The sample density and correlation coefficients are indicated.
136 

7.2.1 Turbulence Kinetic Energy by Season

Most of the seasonal differences in the turbulence kinetic energy (TKE) are caused by convective 

activity in the spring and summer. While the results at 40 and 60 m are quite similar, the TKE at 

80 m is noticeably greater where full-developed eddies could be expected. Notice a large “tail”

(values greater than 5 m2

s

-2) in all seasons.

Figure 131. Histograms of the turbulence kinetic energy for calendar seasons computed from the 

sonic anemometer data for the period of 8 February to 20 September 2007 at 80 m.
137 

Figure 132. Histograms of the turbulence kinetic energy for calendar seasons computed from the 

sonic anemometer data for the period of 8 February to 20 September 2007 at 60 m.
138 

Figure 133. Histograms of the turbulence kinetic energy for calendar seasons computed from the 

sonic anemometer data for the period of 8 February to 20 September 2007 at 40 m.

8 Sub-km Evaluation 

Task

In this section, we examine the effect of horizontal resolution on the accuracy of 

regional/mesoscale model predictions for the near surface (standard) height as well as hub 

heights. Data from tall towers will be used for the model evaluation. 

Results and Status

Two mesoscale models, MM5 and WRF, were used for this task. The model setup consisted of 4 

domains nested into a parent domain. The parent domain (Domain 1) consisted of 103×103 grid 

points in the horizontal with a grid resolution of 18 km. As for the nested domains, domain 2 

consisted of 79×79 grid points in with a grid resolution of 6 km, domain 3 consisted of 121×112 

grid points with a grid resolution of 2 km, domain 4 consisted of 148 ×100 grid points with a 

grid resolution of 666m, and domain 5 consisted of 34×34 grid points with a grid resolution of 

222 m. Domains 1, 2, and 3 encompassed all of the meteorological tower locations listed in 

Table 10; domains 4 and 5 encompassed only the Tonopah and Stone Cabin wind towers, 
139 

respectively. The domain setup is shown in Figure 134. The atmosphere was divided into 40 

unequally spaced layers. About half of them resolve the boundary layer processes in the lowest 

kilometer, of which the 10 layers from the ground were arranged at about 10 meter intervals in 

accordance with the meteorological tower measurements for verification. 

Table 10. Meteorological Towers in Nevadaa. 

Tower location Coordinates

Latitude; Longitude Station Elevation (m)

Tonopah 24W [T] 38.3722o N; 117.4717o W 1535

Stone Cabin [SC] 38.1114o N; 116.7394o W 2004

Kingston 14 SE [K] 39.0455o N; 117.0008o W 1780

Luning 5 N [L5]

Luning 7W [L7]

38.5725o N; 118.1755o W

38.54083o N; 118.2942o W

1523

1354 a Winds are monitored at 10, 20, 30, 40, and 50 m at T, K, L5, L7 and at 40, 60, and 80 m at SC. SC has sonic 

anemometers installed.

For the initial and boundary conditions, which were composed of available synoptic observations 

ingested into the first-guess model fields archived, we used the Eta model outputs. The model 

physics were chosen accordingly to study the evolution of meteorological fields in the study 

region after a series of numerical experimentation. Similar physics options were chosen for MM5 

and WRF. Briefly, the physics choices are: Gayno-Seaman scheme for boundary layer processes, 

Kain-Fritsch scheme and Reisner’s scheme for convective and cloud microphysical processes, 

and Rapid Radiative Transfer Model calculations for radiative processes.

Model simulations with the same domain setup as shown in Figure 134 have been carried out 

using MM5 and WRF for the period starting from 9 February 2007 at 1200 UTC to 11 March 

2007 at 1200 UTC, and the modeled wind estimates and turbulence kinetic energy were

compared against the meteorological tower observations at Stone Cabin. A preliminary 

sensitivity examination suggested that downscaling of meteorological variables from the parent 

domain into the nests at a later time (using an optional module known as ‘NESTDOWN’ in 

MM5) impacts the accuracy of the simulated winds over the complex terrain. In lieu of this, the 

simulations in all the domains have been started simultaneously. Also, a pre-forecast period of at 

least 12-24 hours was necessary for better accuracy in the high-resolution wind simulations. 

The observed and MM5-simulated wind speed at 40, 60, and 80 m at the Stone Cabin tower 

location are shown in Figures 135 and 136. A first glance at the figures shows that there are 

significant differences in the results obtained on various grid resolutions. Moreover, there are 

also significant differences in the results using two mesoscale models. Notice that there are 

smaller differences among the various WRF grids compared to the MM5 grids. The significant 

high wind episodes were better simulated at sub-kilometer grids. Statistics derived using RMSE 

and index of agreement (IOA) (Willmott 1982; Wilks 1995) showed that, on average, the RMSE 

(Table 11) was about 4 m s-1, and the skill of WRF simulated wind predictions is generally 

superior to MM5 by displaying a consistent trend of skill improvement and index of agreement 

as high as 0.65 (index of agreement 0 – worst and 1 is the best) at sub-kilometer grids. It is 

important to mention that the WRF results show improvement in the RMSE and IOA while using 

higher resolution (Table 11). The MM5 results do not show clearly this trend (Table 11).
140 

Although the mesoscale models were not able to reproduce the high peaks of the TKE as 

computed from the sonic data (Figures 138 and 139), Figure 137 indicates that the increased 

resolution had a tendency to generate few of the larger values compared to coarser grid 

resolution results. 

Figure 134. MM5 and WRF 5-domain setup. Meteorological towers located in the figure are: SC – 

Stone Cabin, T - Tonopah, K – Kingston, and L5, L7 – Luning (left) Domains 1, 2, and 3. (right) 

Domains 3, 4, and 5.

The frequency distribution simulated 40, 60, and 80-m wind speeds (Figures 137 through 139) 

is generally comparable with the observed distribution, following the generic Weibull 

distribution of scale and shape parameters (shown in Table 12) in the range 4.0-6.0 and about 

1.7. 
141 

Figure 135. Observed (sonic anemometer wind measurements) and MM5-simulated wind speeds 

at Stone Cabin for the period of Julian days 40-65, (Julian day 40.5 = 9 Feb 2007 1200 UTC) (top) 

80m, (center) 60 m, and (bottom) 40 m heights.
142 

Figure 136. Same as Figure 135, but for WRF
143 

Table 11. Wind Speed Statistics at Heights 40, 60, and 80 m Obtained from the Observed and MM5 

and WRF Model Simulated Results at the Stone Cabin Tower Location for the Period 9 Feb 2007 12 

UTC – 8 Mar 2007 12 UTC.a

 

Model grid 

resolution

MM5

(RMSE)

Height=40 m

MM5

(Index of 

Agreement)

Height = 40m

WRF

(RMSE)

Height = 40 m 

WRF

(Index of 

agreement)

Height = 40m

18km 3.198 0.647 3.589 0.454

6 km 3.496 0.576 3.720 0.461

2 km 3.760 0.559 3.603 0.472

0.666 km 3.653 0.587 3.679 0.474

0.222 km 4.498 0.448 3.638 0.494

Model grid 

resolution

MM5

(RMSE)

Height = 60m 

MM5

(Index of 

Agreement)

Height = 60m

WRF

(RMSE)

Height = 60m 

WRF

(Index of 

agreement)

Height = 60m

18km 3.619 0.530 3.736 0.490

6 km 4.006 0.450 3.748 0.539

2 km 4.252 0.434 3.628 0.533

0.666 km 4.169 0.451 3.643 0.564

0.222 km 4.336 0.457 3.656 0.567

Model grid 

resolution

MM5

(RMSE)

Height = 80m 

MM5

(Index of 

Agreement)

Height = 80m

WRF

(RMSE)

Height = 80m 

WRF

(Index of 

agreement)

Height = 80m

18km 3.542 0.460 3.293 0.536

6 km 4.069 0.420 3.318 0.587

2 km 4.399 0.381 3.201 0.604

0.666 km 4.321 0.358 3.131 0.643

0.222 km 4.005 0.582 3.128 0.650

a The index of agreement indicates the quantitative measure of model performance (= 0, worst skill; = 1, best skill). 

RMSE = Root-mean square error.
144 

8.1 Distribution Analysis for Sonic and Standard Anemometer Wind Speeds and 

Those Simulated by MM5 and WRF at Stone Cabin

Figure 137. Individual value plots of sonic-measured 10-minute averaged turbulence kinetic 

energy and model simulated at various horizontal grid resolutions for the period Feb 9-Mar 8, 2007 

(top) 80 m, (center) 60 m, and (bottom) 40 m. 
145 

Figure 138. Observed (sonic anemometer) and MM5-simulated turbulence kinetic energy at Stone 

Cabin for the period of Julian days 40-65. (Julian day 40.5 = 9 Feb 2007 1200 UTC) (top) 80 m, 

(center) 60 m, and (bottom) 40 m heights.
146 

Figure 139. Frequency distribution of sonic-observed and MM5-simulated (at different horizontal 

grid resolutions 18 km, 6 km, 2 km, 0.666 km, and 0.222 km) turbulence kinetic energy at 80 m 
147 

8.1.1 Application of the Weibull Distribution

Weibull distributions often describe the distribution of instantaneous wind speed measurements 

over a period of time. The generic form of a Weibull distribution is a continuous probability 

distribution with probability density function f’ as follows:

 where x greater than or equal to zero, and f (x; α, β) = 0 for x < 0. α is the scale parameter and β 

is the shape parameter of the distribution. β generally varies between 1 and 3 (mathematically, β 

could assume any value for describing wind speed distributions, however this narrow interval is 

of practical significance). Typically β indicates the dominance of the wind regimes, i.e., β = 1 (β 

= 3) indicates a low (high) wind speed regime. For wind analysis, the Rayleigh distribution (a 

special case of Weibull distribution where β = 2) typically represents moderate wind regimes. 

The scale parameter α stretches or contracts the distribution along the x-axis in accordance with 

β. Higher values of α indicate that the wind speeds are less tightly clustered or well spread about 

the mean. Thus, the shape and scale parameters together give a statistical spectrum of wind 

speeds over a period of time. 

Weibull curves were fit to hourly wind speed data sets simulated by MM5 and WRF, and 

measured by the sonic anemometers at Stone Cabin over for the time period of 9 February 2007 

at 12 UTC to 8 March 2007 at 12 UTC. The model data sets were obtained from various 

horizontal grid resolutions (Δx = Δy =18 km, 6 km, 2 km, 0.666 km, and 0.222 km). Figures 140 

to 142 show the frequency distribution of the wind speed data with Weibull curves fit to them. 

The values of α and β obtained for the simulated time series of wind speeds correspond to the 

values obtained for sonic measurements. On average, the shape parameters of the observed and 

simulated wind regimes for the considered time period (Table 12) follow very close to the 

Rayleigh distribution. The scale parameters obtained from WRF simulated wind speeds were 

significantly smaller than the obtained from MM5; however, MM5 is closer to the sonic scale 

parameter than WRF. The higher wind regimes as seen in sonic measurements were mostly 

better captured by the model’s finest grid resolution than from the coarse grid resolutions. 

Two different metrics (at each of the three heights) have been devised to delve further into the 

differences between the MM5, WRF, and sonic wind speed data. The “relative difference 

product” (RDP) uses both shape and scale differences from the corresponding sonic shape and 

scale parameters to indicate a cumulative difference of model simulated and observed. The 

“squared difference” (SD) focuses on the difference in shape parameter of each model to a 

standard shape parameter. In Figure 144, the standard shape parameter is the corresponding 

sonic shape parameter; in Figure 145 the standard shape parameter is the Rayleigh shape 

parameter (β = 2). Both sets of figures yield to a general finding that MM5 agrees with the sonic 

data better than the agreement of WRF with the sonic data at 40 m and 60 m, and also that the 

situation is reversed (the agreement of WRF is better) at 80 m. Figure 143 illustrates that RDP 

shows substantial better overall (α and β) skill by MM5 at 40 and 60 m, and somewhat less skill 

at 80 m. Figure 144, which illustrates SD, mimics this finding, with not as big a gap in skill 

between the models. Comparison by SD with the Rayleigh shape parameter (Figure 145) show 

that, while the trends are very similar to the previous three figures, the differences are smaller, 

showing that both of the models’ results tend towards the Rayleigh distribution more than the 
148 

sonic data does. There is a slight trend towards increased skill in these metrics at finer horizontal 

grid resolutions, which is also evidenced in other skill measure such as the index of agreement. 

Figure 146 shows a comparison of data from the two anemometer types using Weibull fitting 

(for a more detailed comparison of sonic versus standard anemometers, please see Section 4). 

One item of interest stands out clearly. Because standard anemometers need to overcome their 

own inertia to start spinning, they register more zero ms-1 readings: they do not have the 

sensitivity to pick up very low wind speeds. This lack of low wind speed sensitivity is reflected 

in the differences between the shape parameters. The high number of zero wind speed readings 

of the standard anemometers moves the Weibull shape to the left—that is, towards lower values, 

or a lower wind speed regime. At each height, this same difference is illustrated. The sonic 

anemometers pick up the lowest wind speeds while the standard anemometer cannot. Hence in 

this case the Weibull curve generated by the sonic data gives a better description of the overall 

wind regime than that from the standard anemometer data.

Figure 140. Frequency distribution of sonic-observed and MM5/WRF-simulated (at different 

horizontal grid resolutions 18 km, 6 km, 2 km, 0.666 km, and 0.222 km) wind speeds at 80 m. The 

Weibull curves are fit using Minitab 14. The shape and scale parameters are indicated.
149 

Figure 141. Frequency distribution of sonic-observed and MM5/WRF-simulated (at different 

horizontal grid resolutions 18 km, 6 km, 2 km, 0.666 km, and 0.222 km) wind speeds at 60 m. The 

Weibull curves are fit using Minitab 14. The shape and scale parameters are indicated.
150 

Figure 142. Frequency distribution of sonic-observed and MM5/WRF-simulated (at different 

horizontal grid resolutions 18 km, 6 km, 2 km, 0.666 km, and 0.222 km) wind speeds at 40 m. 

The Weibull curves are fit using Minitab 14. The shape and scale parameters are indicated.
151 

Table 12. Weibull Shape and Scale Parameters for MM5, WRF, and Sonic Anemometer Data

Weibull fitted Shape and Horizontal Scale Parameters

For MM5, WRF, and sonic anemometer wind speed data

(grid resolutions used in MM5/WRF = 18 km, 6 km, 2 km, 0.666 km, 0.222 km)

at 40 m, Stone Cabin, NV, 9 Feb-8 Mar 2007

shape scale shape scale

Sonic-40 m 1.711 5.748 Sonic-40 m 1.711 5.748

MM5 18 km 1.993 5.647 WRF 18 km 2.433 4.342

MM5 6 km 1.947 5.433 WRF 6 km 1.966 4.318

MM5 2 km 1.837 5.764 WRF 2 km 2.193 4.734

MM5 0.666 km 1.789 5.665 WRF 0.666 km 2.124 4.534

MM5 0.222 km 2.014 6.475 WRF 0.222 km 2.061 4.502

Mean (MM5) 1.916 5.797 Mean (WRF) 2.155 4.486

at 60 m

shape scale shape scale

Sonic-60 m 1.779 6.199 Sonic-60 m 1.779 6.199

MM5 18 km 2.113 5.742 WRF 18 km 2.285 4.511

MM5 6 km 2.077 5.933 WRF 6 km 1.874 4.441

MM5 2 km 1.998 6.263 WRF 2 km 2.078 4.794

MM5 0.666 km 1.97 6.112 WRF 0.666 km 1.89 4.649

MM5 0.222 km 1.893 6.652 WRF 0.222 km 1.877 4.673

Mean (MM5) 2.010 6.140 Mean (WRF) 2.001 4.614

at 80 m

shape scale shape scale

Sonic-80 m 1.636 5.371 Sonic-80 m 1.636 5.371

MM5 18 km 2.426 5.563 WRF 18 km 2.219 4.705

MM5 6 km 2.247 6.392 WRF 6 km 1.819 4.423

MM5 2 km 2.223 6.654 WRF 2 km 2 4.733

MM5 0.666 km 2.198 6.6424 WRF 0.666 km 1.84 4.643

MM5 0.222 km 1.789 6.553 WRF 0.222 km 1.809 4.644

Mean (MM5) 2.177 6.361 Mean (WRF) 1.937 4.630
152 

Figure 143. Relative Difference Product (RDP) comparison of shape and scale parameters at (top) 

80m, (center) 60 m, and (bottom) 40 m 
153 

Figure 144. Same as Figure 143, but for SDs 
154 

Figure 145. Same as Figure 143, but for the SD comparison using model results against the 

Rayleigh distribution shape parameter β=2
155 

Figure 146. Weibull fit of 10-minute averaged sonic (left) and standard (right) anemometer 

measured wind speeds (m s-1) at 40 m (bottom), 60 m (center), and 80 m (top) for the period 

February-September 2007. 

N = number of samples used. The shape and scale parameters are indicated in each of the boxes.
156 

8.2 Statistical Bootstrapping

The statistical bootstrap (also referred to as the resampling procedure) is one of the methods that 

can be used to calculate estimates of a certain number of unknown parameters of a random 

process or a signal observed in noise, based on a random sample. Such situations are common in 

signal processing and the bootstrap is especially useful when only a small sample is available or 

an analytical analysis is too cumbersome or even impossible. With the bootstrap, the random 

pairs of observations and model simulated results of the time series are reassigned, and estimates 

are recomputed. These are done thousands of times and treated as repeated experiments. The 

Bootstrap Toolbox is a set of Matlab functions consisting of procedures for resampling, 

hypothesis testing, and confidence interval estimation. In this study, the estimates are correlation 

coefficients and index of agreement were computed using random experiments for a sample pair 

size of 642. The repeated experiments were conducted for correlation coefficients for 20000 

times, and for 1000 times for index of agreement. 

Bootstrapped frequency distribution of correlation coefficients is plotted for sonic measurements 

against MM5 and WRF simulated wind speeds at various horizontal grid resolutions at heights 

40, 60 and 80 m above ground level at the Stone cabin location. This is shown in Figure 147. 

The correlation coefficients show a consistent trend of improvement with height as well as for 

decreasing the grid resolutions for WRF simulated wind speeds as compared to MM5. Better 

correlation coefficients were in the range 0.3-0.4 at 80 m. Another random experiment was 

conducted using the estimator index of agreement at 80 m (Figure 148) showed significant 

improvement of skill by grid refinement at sub-kilometer grid resolutions. The skill of MM5 

(WRF) is improved from 0.45 to 0.6 (0.5 to 0.65) by grid refinement from 18 km to 222 m.
157 

Figure 147. Resampled frequency distribution of correlation coefficients for the MM5/WRFsimulated wind speeds against sonic anemometer measurements. 

MM5 (a: 80 m, b: 60 m, and c: 40 m) and WRF (d: 80 m, e: 60 m, and f: 40 m). The number of 

pairs at each of the heights is 642, and the resampling size is 20,000. The mean of the 

distribution is indicated in each of the boxes. 
158 

Figure 148. Resampled frequency distribution of index of agreements for the MM5/WRF-simulated 

80m wind speeds at the coarsest (18 km) and the finest (222 m) horizontal grid resolutions against 

sonic anemometer measurements. 

The size of the pairs is 642, and the resampling size is 1,000. 
159 

8.3 Spectral Analysis of Wind Speeds

A time series record often exhibits multiple periodic components. According to Parseval's 

Theorem, the total power computed for the time series in the time domain must be equal the total 

power computed in the frequency domain. The power spectrum gives the power in the signal at 

each frequency. The power of the time series is computed by simply applying the discrete 

Fourier transform to the given time series and, by convention, the transform has elements equally 

spaced in frequency, with the first element corresponding to zero frequency (theoretically infinite 

period), then up to the Nyquist frequency (the critical frequency; the frequency greater than half 

of the sampling frequency) at the middle of the record. Our interest is to see whether the 

observed and simulated time series of wind speeds at the height of the measurements exhibit 

periodicity for the diurnal cycle.

The power spectrum was computed for sonic observed and model simulated wind speeds at 

various horizontal grid resolutions, and is shown in Figure 149. The sampling period was during 

9 February 2007-8 March 2007. The spectrum is normalized by the maximum value of the power 

spectrum so that the range lies between 0 and 1. The x-axis in Figure 6-16 represents the period 

in hours/cycles by inverting the frequency. The power spectrum shows a periodicity at 24 hours 

indicating that the observed and modeled time series exhibit a diurnal cycle distinctly. The WRF 

results on all grid resolutions better track periodicity computed from the measurements compared 

to the MM5 results. Notice that MM5 significantly overestimated mid-range values (between 15 

and 24 hours), especially on high-resolution grids at 40 m. 

Figure 150 shows a power spectrum of the turbulence kinetic energy (TKE) computed from the 

MM5 results and sonic data. Notice that this version of WRF did not provide direct output of the 

TKE. Although the model was able to reproduce daily TKE maximum, some of the results 

obtained on higher resolution grids overestimated sub-daily periodicity (15-24 hrs) and 

underestimated periodicity for larger periods.
160 

Figure 149. Power spectrum of sonic-measured (blue circle-dash) and MM5/WRF-simulated wind 

speeds at 40, 60, and 80 m obtained from different model horizontal grid resolutions. 

MM5 (a: 80m, b: 60 m, and c: 40 m), and WRF (d: 80 m, e: 60 m, and f: 40 m). The power 

spectrum is normalized by the maximum value. 
161 

Figure 150. Power spectrum of sonic-measured (blue circle-dash) and MM5 turbulence kinetic 

energy at 40, 60, and 80 m obtained from different model horizontal grid resolutions. 

MM5 (a: 80 m, b: 60 m, and c: 40 m). The power spectrum is normalized by the maximum 

value. 

9 Results and Conclusion 

The study results showed that both community models (MM5 and WRF) are capable of 

capturing basic flow properties. A spectrum of simultaneous horizontal model resolutions from 

18km to 222m indicates that there is no firm conclusion that higher resolution automatically 

yields better results. This is mainly due to the model complexity in physical parameterizations, 

which might not be appropriate for very high model resolutions. Long-term (7 months) 

measurements by sonic anemometer showed much higher peaks in the turbulence kinetic energy 

(TKE) at all three levels than reported in the literature. Simulated TKE is noticeably 

underestimated compared to sonic measurements, which might have a significant impact for 

turbine deployment at these elevations. The empirical formulas to estimate winds at higher 

elevations based on the available standard heights (6 or 10 m) have large errors compared with 

actual tower measurements. The evaluated model results can provide guidance on possible errors 

and uncertainties while estimating wind maps in this and other areas. 
162 

10 Publications and Presentations 

Belu, R., and Koracin, D. 2009: Wind characteristics and wind energy potential in western 

Nevada. Renewable Energy, 34, 2246-2251. 

Koracin, D.; Reinhardt, R. “Wind energy assessment for Nevada: Observations and modeling.”

Presented at the 61st American Chemical Society Northwest Regional Meeting (invited talk), June 

25-28, 2006, Reno, NV. 

Koracin, D.; Reinhardt, R.L. Liddle, M.; McCord, T.; Podnar, D.; Minor, T. “Assessment of 

wind energy for Nevada using towers and mesoscale modeling.” Energy Sustainability 2007, 

June 27-30, 2007, Long Beach, CA. 

Koracin, D.; Reinhardt, R.L. Liddle, M.; McCord, T.; Podnar, D.; Minor, T. “Assessment of 

wind energy for Nevada using towers and mesoscale modeling.” Presented at the 2007 Annual 

Energy Symposium (invited talk), August 15-16, 2007, UNLV, Las Vegas, NV.

Reinhardt, R.; Koracin, D.; Minor, T.; Pepper, D.; Hess, R.; Osborn, D. “Wind energy assessment 

for Nevada and the Southwest: Observations and modeling.” Wind Power Conference and 

Exhibition 2006, June 4-7, 2006, Pittsburg, PA.
F1146-E(10/2008) 

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December 2009

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26 June 2005 – 31 December 2007

4. TITLE AND SUBTITLE

Wind Energy Assessment Study for Nevada – Tall Tower 

Deployment (Stone Cabin): 26 June 2005 – 31 December 2007

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DE-AC36-08-GO28308

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6. AUTHOR(S)

D. Koracin, R. Reinhardt, G. McCurdy, M. Liddle, T. McCord, R. 

Vellore, T. Minor, B. Lyles, D. Miller, and L. Ronchetti

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Desert Research Institute

2215 Raggio Parkway

Reno, NV 89512

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14. ABSTRACT (Maximum 200 Words)

The objective of this work effort was to characterize wind shear and turbulence for representative wind-developable 

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Tonopah (Stone Cabin); wind shear; turbulence; tall towers; Desert Research Institute; National Renewable Energy 

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