A national laboratory of the U.S. Department of Energy

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National Renewable Energy Laboratory 

Innovation for Our Energy Future 

The Great Plains Turbulence 

Environment: Its Origins, Impact 

and Simulation 

N.D. Kelley, B.J. Jonkman, and G.N. Scott 

National Renewable Energy Laboratory 

Presented at AWEA’s 2006 WindPower Conference 

Pittsburgh, Pennsylvania 

June 4–7, 2006 

Conference Paper 

NREL/CP-500-40176 

December 2006 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
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The Great Plains Turbulence Environment: Its Origins, Impact, and Simulation 

Neil D. Kelley, Bonnie J. Jonkman, and George N. Scott 

National Wind Technology Center 

National Renewable Energy Laboratory 

Golden, Colorado 80401 

Abstract 

Research conducted over the past 15 years has demonstrated that organized or coherent 

turbulence structures in turbine inflows are often responsible for increased structural loading and 

fatigue damage as well as the loss of availability during turbine operations. Turbine heights and 

dimensions, including the turbine design, siting, and operations, continue to increase. Thus, the 

role of site specificity of the turbulent inflow environment, particularly at night, has become very 

important in all phases of wind plant development including the turbine design, siting, and 

operations. In this paper we summarize the known impacts of nocturnal turbulence on wind 

turbine performance and operations. We discuss our progress in numerically simulated coherent 

inflow turbulent conditions generated by atmospheric instabilities that are frequently associated 

with a Great Plains nocturnal low-level jet stream. Finally, we compare the general 

characteristics of nocturnal turbulent flows that are associated with wind farms in or near 

complex terrain with those installed over more homogenous landforms but in the presence of 

low-level jet streams. 

Introduction 

The Great Plains, particularly its western extent, is the largest region of high wind resource in the 

United States. It is characterized by frequent, strong, high-level winds during the nighttime 

hours. The same region has frequent flows of more energetic winds, called low-level jet streams

(LLJs), that form in the lowest 500 m above the ground, particularly at night. These LLJs are 

often responsible for the substantially greater wind resource associated with increasing height 

that is characteristic of the Great Plains. The availability of this resource is driving wind turbine 

designs to ever-increasing hub heights and rotor diameters. To capture a better resource in more 

numerous but less energetic wind regimes closer to population centers, the Low-Wind Speed 

Turbine (LWST) Project of the U.S. Department of Energy (DOE) envisions taking advantage of 

stronger winds that frequently occur at higher altitudes in such areas by looking at turbine 

designs with nameplate capacities as large as (and possibly larger than) 5 MW. Such designs 

will necessitate larger diameter rotors that operate farther from the ground and extend deeper into 

the atmospheric boundary layer. 

Background 

The wind energy technologies that can take advantage of increased wind speeds at higher 

altitudes require larger diameter rotors that rise deeper into the atmospheric boundary layer. 

These rotors and the large support structures necessary to support them have been accompanied 

by an increase in structural flexibility that is often further exacerbated by the need to reduce 

weight. The result has been that the large turbines currently being installed are also more 

1
dynamically active. In [1] we discussed the potential role of nocturnal LLJs that are prevalent in 

the areas targeted for potential LWST installations such as the Great Plains. In [2] we discussed 

that, although nocturnal LLJs provide the benefit of an increased wind resource with height, they 

also supply the intense vertical wind shears and temperature gradients below the height of the 

LLJ maximum velocities that feed the development of atmospheric instabilities such as KelvinHelmholtz Instability (KHI). As discussed in both [1] and [2], KHI is responsible for creating 

intense bursts of coherent turbulence in the nighttime (statically stable) boundary layer. During 

the day, the boundary layer is usually statically unstable or convective and will not support KHI. 

Convective motions typically have spatial dimensions that are much larger than the largest wind 

turbine, whereas coherent motions associated with KHI in a stable boundary layer can be the 

same size as the largest turbine rotors or much smaller. KHI occurs when the vertical gradients 

of temperature and wind speed allow atmospheric wave motions called billows or KH waves to 

develop. The KH billows actually represent a form of atmospheric resonance in the turbulent 

wind field that is controlled by stability (vertical motions being damped by negative buoyancy). 

In [2] we reviewed our research in determining the role of coherent or organized turbulence in 

the aeroelastic and structural response of wind turbines. We found, for example, that the severity 

of structural loads generated by the turbine rotor passing through coherent turbulence can be 

scaled by knowledge of the vertical stability of the layer of air above the ground within which 

the turbine resides, a fluid dynamic property that represents the intensity (not turbulence 

intensity!) of the turbulence, and the standard deviation of the vertical wind speed measured 

within the rotor disk. 

We determine the turbine layer vertical stability by the dimensionless gradient Richardson 

number: 

2 ( / )( / ) /( / ) R gi gT T z U z = ΔΔ Δ Δ (1) 

where 

g = the gravity acceleration 

T = the absolute air temperature 

U = the wind speed 

Z = the height above the ground. 

Equation (1) represents the ratio of turbulence generation by vertical temperature (density) 

differences or buoyancy in the numerator to that by wind shear (the rate of change of wind speed 

with height) in the denominator. A negative value of Rig corresponds to unstable flow conditions 

within the layer from the surface to the top of the turbine rotor (what we refer to as the turbine 

layer), a positive value corresponds to stable conditions, and zero neutral. In unstable flows (Rig

< 0) vertical temperature differences with height (positive buoyancy or convection) add kinetic 

energy to the turbulence while in stable (Rig > 0) ones, energy is removed (negative buoyancy 

damping). Neutral conditions (Rig = 0) represent a flow in which turbulence is being generated 

only through the action of wind shear; buoyancy has no influence. Neutrally stable flows are of 

theoretical interest because of the simplifications they allow, but the condition is infrequent in 

the real atmosphere (or at least in the portion of the atmosphere occupied by wind turbines). For 

2
example, in our recent experiment in 

Lamar, Colorado, true neutral 

conditions in the layer 3−116 m 

above ground level (AGL) were 

observed on only 14 out of 4,676 

hours of record or less than 0.3% of 

the time. 

As an illustration, Figure 1 plots the 

damage equivalent loads (DELs) 

measured at the root of one of the 

blades of the Advanced Wind 

Turbine (ART) at the National Wind 

Technology Center (NWTC). The 

highest loads occur within a small 

range of just slightly positive or 

weakly stable values of Rig between 

0 and +0.1. Similar results have 

often been observed at other sites 

and with other turbine designs. 

 

The intensity of the coherent

turbulence as measured by a property of the flow that we have defined as the coherent turbulent 

kinetic energy or CTKE and is expressed as 

Figure 1. The observed variation in the damage equivalent 

blade root load seen on the NWTC ART as a function of 

atmospheric stability expressed by the turbine layer 

gradient Richardson number, Rig. 

 CTKE = 1 [m 22 2 1/ 2 / 2[( ' ') ( ' ') ( ' ') ] uw uv vw + + 2/s2] (2) 

where u’, v’, and w’ are the instantaneous fluctuating streamwise, lateral or crosswind, and 

vertical wind velocity components (with the mean values removed). Finally we represent the 

standard deviation of the vertical wind component as σw. All these variables play important roles 

in KHI. 

The Need for Site-Specific Information in Turbine Design

As the size of turbines has continued to increase the demands to reduce weight, and particularly 

the weight at the top of the tower, the need to use lighter and stronger materials has become 

acute. The combination of lighter materials and the shear size of the newest turbines have led to 

machines that are now much more dynamically active than their smaller and less flexible 

predecessors. For example, a linear frequency analysis of the NWTC 600 kW ART turbine 

found 12 static vibrational modes ranging from 0.03 to 10.8 Hz up to a maximum of 15 Hz. A 

similar analysis of a fictitious 1.5-MW design developed by Malcolm and Hansen [3] for the 

DOE Wind Partnership for Advanced Component Technologies (WindPACT) turbine rotor 

design study exhibited 31 modes ranging from 0.4 to 20.4 Hz up to a maximum of 20.4 Hz. 

Jonkman, et al. [4] recently developed a 5-MW virtual baseline turbine design that will be used 

primarily for offshore studies. This design encompasses 52 static modes in the frequency range 

of 0.32 to 19.84 Hz up to 20 Hz. The actual number of participating modal frequencies is even 

3
greater when nonlinear and rotational modes are considered. The message is that these larger 

designs may be more susceptible to turbulent inflow characteristics because their rotors are 

operating throughout a larger atmospheric depth and whose dynamic response includes the 

contributions of an increasing number of active vibrational modes, raising the possibility both of 

nonlinear interactions between them and of dynamic amplification. There is some evidence 

available in the public domain, albeit sketchy, that this is occurring. 

Design Criteria 

Current design practice depends heavily on inflow conditions specified by the International 

Electrotechnical Commission (IEC) in its third edition of the consensus-derived IEC 61400-1 

design standard [5]. Page 7 of this document specifies that it “outlines design requirements for 

wind turbines and is not intended for use of a complete design specification or instruction 

manual.” The standard is primarily aimed toward ensuring turbine operating safety and 

survivability with the required series of discrete loading conditions generally associated with 

severe operating events that have occurrences with one- and 50-year return cycles. “Normal” 

operating conditions are specified via a vertical shear expressed by a power law variation across 

the rotor disk with a shear exponent of 0.2 (the Normal Wind Profile or NWP) and the user’s 

choice of two recommended turbulence models for a neutrally stable atmosphere. The velocity 

spectrum is scaled by a turbulence level that is based on the 90th percentile of the observed 

standard deviation of the streamwise wind component (u) defined in terms of one of three 

severity classes that have expected values of turbulence intensities of 12%, 14%, and 16%. A 

model is provided for spatial correlation or coherence in only the streamwise (u) wind 

component. In the case of the IEC Kaimal [6] Normal Turbulence Model (NTM) the velocities 

at all points within the rotor disk are scaled uniformly. The only variation occurs in the 

streamwise component when the spatial coherence model is applied. The IEC version of the 

Mann [7,8] uniform shear turbulence model generates a turbulence spectrum that redistributes 

initially the equal amount of kinetic energy in each of the three wind components from the 

vertical (w) to the streamwise (u), and crosswind (v) components as the result of a uniform mean 

shear. In both models the velocity fields produced are assumed to be stationary with zero-mean 

Gaussian statistics. The idealized IEC inflow and turbulence models are meant as a reference for 

load calculation comparisons and do not reflect any particular specific operating environment. It 

is the responsibility of the designer to identify and include any site specific inflow conditions and 

the loads resulting from them that may exceed IEC criteria through the use of the “Special Class” 

category. 

Actual Turbulent Inflow and Its Consequences

Boundary layer flows in the real atmosphere have properties that often differ considerably from 

those outlined by the IEC criteria. We previously discussed the role of coherent, nonisotropic 

and nonstationary turbulent inflows on the dynamic response of wind turbine rotors and 

structural components [9]. In this paper we demonstrated that the increased fatigue damage seen 

on turbines during the nighttime hours is largely the result of operating in coherent turbulence 

that develops in the stable atmospheric boundary layer. Intense vertical wind shear and 

temperature gradients create resonant flow fields that can impart short-period loading and 

vibrational energy as wind turbine rotor blades pass through regions of organized or coherent 

4
turbulence. This energy is subsequently propagated throughout the remainder of the structure, 

where it is likely to be dissipated locally. Such conditions cannot be reproduced well, or perhaps 

not at all, with the inflow as defined by the IEC NTM, although some of the specified discrete 

loading events are as intense (or even more so) than those seen in the real atmosphere. The 

problem occurs when these high shear and coherent flow conditions occur with significant 

frequency. If a turbine design is not evaluated with simulations of such inflows and the loads 

created occasionally exceed IEC criteria, problems may result when the turbine is installed in a 

location with flows that exhibit such characteristics. 

Site-Specific Inflow Simulations 

We have used the results of three major field experiments to develop site-specific inflow 

simulations that replicate the characteristics of inflows found: 

• Upwind, within, and downwind of a large wind park that consists of small (by today’s 

standards) turbines with generating capacities of 44 to 105 kW 

• Downwind of complex terrain (the NWTC) 

• At a site in the Great Plains (Lamar, Colorado) with and without the presence of a nocturnal 

LLJ. 

The most extensive set of data is available for the latter two sites, so we focused our efforts 

based on them. Although we have found that coherent structures are consistent features of 

nocturnal flows at all three locations, we have more information about them from data collected 

at the NWTC and the high plains south of Lamar. 

Known Impacts of Nocturnal Flows in the Great Plains 

Unfortunately, we do not have access to a large body of information that summarizes turbine 

operational problems in various locations in the Great Plains, as it is proprietary. Smith et al. 

[10] analyzed an extensive set of data collected from the long-term monitoring of a wind farm in 

Texas, in which they found that the diurnal variation of number of hours with turbines in a 

faulted condition began to increase just before midnight, reached a peak just before sunrise, and 

then declined and remained relatively low until a slight rise began again about mid-afternoon. 

They also found that the mean wind shear closely followed the trend in fault frequencies but 

began rising late in the afternoon reaching a broad peak between midnight and sunrise before 

decreasing in a similar manner as the fault data. These results suggest a decent correlation 

between conditions seen in the nocturnal boundary layer and the operational experience of a 

large wind farm. 

We were fortunate to have access to a small number of operations data from a wind farm in the 

northern Great Plains taken during the month of August. A number of fault categories were 

analyzed diurnally for the month. Figure 2a plots the mean hub-height wind speeds and shear 

exponent measured from the bottom of the rotor to the height of the nacelle for each hour starting 

at 18:00 local time. The shear exponent exceeds the IEC value of 0.2 until 10:00 and remains 

below that value until 17:00. The mean wind speeds are slightly higher during the nighttime 

5
Mean Wind Speed & Vertical Shear - August

Local time (h)

lower

disk

shear 

exponent

0.05

0.10

0.15

0.20

0.25

0.30

0.35

4

5

6

7

8

9

hours. The number of excessive brake wear faults that occurred for the month is plotted by the 

hour in Figure 2b, and again the correlation with the periods of high shear is quite good, 

suggesting that there may be a cause and effect. The number of hours in which there was either 

an excessive vibration or cable-over-twist fault are plotted in Figure 2c. It shows that excessive 

vibration faults are common starting about mid-afternoon and reach a peak about 19:00. The 

cable-over-twist plot shows fewer faults but a similar occurrence pattern. Between 16:00 and 

23:00 the boundary layer transitions from a daytime convective regime to a nocturnal stable 

characteristic. Several turbulent processes take place during this period and may influence the 

generation of these faults. We will discuss these processes more fully later. 

Coherent Turbulence Conditions in the Great Plains 

The Great Plains provide an excellent wind resource but are also home to atmospheric 

phenomena that can produce occasional intense coherent turbulent activity. Severe local storms 

can generate gust fronts, microbursts, and tornadic winds. These, fortunately, are seasonal for a 

shear exponent

IEC NWP exponent

mean wind speed

Hub

mean

wind

speed

(m/s)

18 20 22 00 02 04 06 08 10 12 14 16

Local time (h)

Number of occurences

0

100

200

300

400

500 50

Excess vibration Excess break wear Cable over-twist

0

10

20

30

40

Number of occurences

18 20 22 00 02 04 06 08 10 12 14 16 18 20 22 00 02 04 06 08 10 12 14 16

(a) 

(b) (c) 

Local time (h)

Figure 2. Example of correlation between diurnal wind conditions and turbine fault history: 

(a) hourly average wind speed and shear, (b) excessive break wear fault frequency, (c) 

excessive vibration and cable over-twist fault frequencies. 

6
specific location and generally 

occur infrequently; however, 

some regions have a higher 

probability than others. The 

U.S. National Severe Storms 

Laboratory calculates that the 

probability of experiencing a 

tornado on a given day in the 

vicinity of Lamar, Colorado, is 

1.2% during May and June but 

essentially zero in December. 

The probability of experiencing 

a severe thunderstorm gust 

faster than 26 m/s (58 mph) is 

slightly higher at 1.4% in MayJune but less than 0.05% in 

December. We analyzed the 

year-long record of nocturnal 

turbulence measurements 

collected between 16:00 and 

08:00 LST from the GE Wind 

120-m tower south of Lamar to 

establish the probabilities of 

encountering two intensity 

levels of coherent turbulent 

structures. Experience has 

shown that structures that 

contain peak CTKE intensities 

of at least 2 m2

s

-2 are sufficient 

to induce a noticeable structural 

response in an operating wind 

turbine. Structures that contain 

peak CTKE values 10 m2

s

-2

 or 

higher may be considered 

intense and are capable of 

inducing a significant turbine 

structural response. Our analysis of the available period between March 2002 and March 2003 

found the probability of a turbine rotor encountering a coherent structure between the hours of 

16:00 and 08:00 LST with CTKE intensities 2 m2s

-2 or greater during May and June is 52% in 

May and June and 6% in December. The probability of ingesting an intense structure is 6% and 

0.2% during those respective periods. Thus, at the Lamar site turbines are exposed to turbulent 

coherent structures related to instabilities in the nocturnal boundary layer far more often than 

those accompanying a severe thunderstorm or even a tornado. 

52-113m (rotor) layer mean wind speed (m/s)

4 8 12 16 20 24 28

Probabiliy (%)

0.01

0.05

0.1

0.2

0.5

1

2

5 Warm Season

Cold Season

nominal rated

wind speed

52-113m (rotor) layer mean wind speed (m/s)

4 8 12 16 20 24 28

Probabiliy (%)

0.01

0.05

0.1

0.2

0.5

1

2

5 Warm Season

Cold Season

nominal rated

wind speed

(a) 

(b) 

Figure 3. Annual probability distributions of the 52−113m 

layer mean wind speed for the Lamar Site: (a) 08:00 to 16:00 

LST, (b) 16:00 to 08:00 LST. 

7
JAN FEB MAR APR MAY JUN* JUL AUG SEP OCT NOV DEC

Probability of occure ) nce (%

Mitchell et al. [11] and Whiteman et 

al. [12] found two distinct seasons for 

LLJ formation. Jets during the “Warm 

Season” (April-September) are more 

frequently associated with southerly 

winds and are more persistent and 

intense with the height of their wind 

speed maximums nearer to the ground. 

From October through March (the 

“Cold Season”), LLJs are more 

transient, less intense, and often 

associated with the northerly winds 

behind passing cold fronts. We found 

for the Lamar site that the wind 

resource is better during the nocturnal 

hours in both the warm and cold jet 

seasons. The probability plots of the 

mean wind speed for the 52- to 113-m 

layer that are shown in Figure 3 

demonstrate this. Monthly 

distributions of the probability of 

coherent structure intensities are 

presented in Figure 4. Clearly the 

warm season months have a greater 

probability of coherent structures, 

including the more intense ones. The 

probabilities shown during the months 

of July and August 2002 may be lower 

than would normally be expected 

because of the severe drought and the 

very stable and hot atmosphere present 

during that period of time. 

Probability of occurence(%)

0

1

2

3

4

5

6

7

54m 

67m

85m

116m

LLJ warm season

Progress in Simulating Great Plains 

Turbulent Inflows

We have recently completed and released 

for public use our first version of a 

turbulence spectral model that simulates 

turbine inflows at the Lamar Great Plains Site and incorporated it into the National Renewable 

Energy Laboratory (NREL) TurbSim turbulence simulation code [13]. The new model (referred 

to as GP_LLJ spectral model for Great Plains Low-Level Jet) simulates flow conditions 

associated with the large vertical shears and coherent turbulence encountered within and beneath 

Great Plains nocturnal LLJs similar to those shown in Figure 5. The model was constructed by 

using local turbulence scaling derived from measurements collected on the GE Wind 120-m 

tower and LLJ vertical profiles of wind speed and direction acquired with a medium range 

0

10

20

30

40

50

60

(a) 54m 

67m

85m

116m

JAN FEB MAR APR MAY JUN* JUL AUG SEP OCT NOV DEC

LLJ warm season

(b) 

Figure 4. Probability distributions of: (a) 

coherent turbulent events with intensities of 

2m2

/s2 or greater, (b) intensities 10 m2/s2 or 

greater. (*June was limited to range of 19:00 to 

05:00 LST because of an undetected 

instrumentation problem)

8
50

100

150

200

250

300

350

400

450

500

10-minute mean wind speed (m/s)

Local Std Time

02:40

02:50

Height AGL (m)

03:00

03:10

03:20

03:30

03:40

03:50

04:00

6 8 10 12 14 16 18 20 22

Doppler acoustic wind 

profiler (SODAR) [2]. 

The GP_LLJ model 

allows the user to specify 

the jet height and peak 

wind speed (intensity) or 

to let the code randomly 

choose a jet height or 

wind speed based on 

other specified boundary 

conditions. It provides 

12 independent sources 

of random variability or 

stochastic degrees of 

freedom compared to 

only one for the IEC 

NTM. The GP_LLJ 

model produces vertical 

profiles of wind speed and direction profiles that can be extended down to a height of 3 m AGL 

to simulate tower loading when the “JET” profile option is chosen. In the analysis of the Lamar 

data in [2] we showed that LLJs do not always break down into bursts of coherent turbulence but 

remain well-defined for many hours. Without this breakdown, the wind speed shear exponent 

often remains significantly above the IEC design criteria of 0.2. The code allows the user to 

simulate such conditions by requesting coherent turbulence not be added to the turbulent inflow. 

We have extrapolated the vertical scaling measured on the Lamar 120-m tower and believe the 

GP_LLJ model is applicable up to a height of 230 m AGL based on limited SODAR 

observations. We plan to further evaluate the model simulation performance in the 120- to 230-

m height range (5- to 10-MW turbine rotors) with data from a Doppler laser wind profiler 

(LIDAR). 

Figure 5. Example of the observed evolution of the vertical profiles 

of wind speed associated with an LLJ using a Doppler SODAR. 

Example of Comparing the TurbSim IEC NTM and GP_LLJ Spectral Model 

Simulations

We are in the process of comparing the collective response of the 52 static modes of the NREL 

virtual 5-MW Reference Turbine with inflow turbulence generated by the TurbSim Code. The 

physical characteristics of the NREL 5-MW Reference Turbine are summarized in Table 1. As 

an example, we created inflow simulations using TurbSim for two jets with their peak wind 

speeds at commonly observed heights of 80 and 460 m. We specified the IEC Kaimal NTM 

9
Table 1. NREL 5MW Reference Turbine Specifications [4] 

Property Specification 

Rotor Diameter 126 m 

Rotor Orientation Upwind 

Control Variable Speed, Collective Pitch 

Hub Height 90 m 

Maximum Tip Speed 80 m/s 

Rated Wind Speed 11.4 m/s 

Maximum Rotor Speed 12.1 rpm 

Table 2. Simulated Vertical Wind Profile Characteristics

80-m Jet 460-m Jet 

TurbSim 

Spectral 

Model 

Jet 

Maximum 

Wind 

Speed (m/s) 

Hub (90 m) 

Mean Wind 

Speed 

(m/s) 

Calculated 

Rotor Disk 

Shear 

Exponent 

Jet 

Maximum 

Wind Speed 

(m/s) 

Hub (90 m) 

Mean Wind 

Speed 

(m/s) 

Calculated 

Rotor Disk 

Shear 

Exponent 

IEC NTM 

“C” n.a. 11.59 0.200 n.a. 13.38 0.200 

GP_LLJ 11.64 11.59 0.020 27.68 13.38 0.240 

spectral model with IEC-specified “C-level” turbulence intensity (12% at a hub-height wind 

speed of 15 m/s). This choice is based on an analysis of the turbulence levels observed at the 

Lamar site. We employed the GP_LLJ spectral model with and without coherent turbulent 

structures. The characteristics of the resulting wind profiles across the rotor disk are listed in 

Table 2 and plotted in Figure 6. In the first case, the jet height of 80 m places its peak winds 

within the turbine rotor disk; in the second, the more intense 460-m jet maximum occurs well 

above the highest elevation of the rotor. For both jet heights, the mean hub-height wind speeds 

are identical for both spectral models, but the rotor disk speed shear profiles and corresponding 

exponents differ considerably. The rates of change of wind direction with height (direction 

shear) for the two jet heights also are quite different. 

We used a 25 x 25 grid to generate 31, 10-minute full-field inflow realizations for each set of 

boundary conditions. The simulations used the GP_LLJ spectral model and were run with and 

without coherent structures requested. Each inflow realization was used to drive an 

MSC.ADAMS® [14] numerical model of the 5-MW reference turbine. The resulting time series 

of linear and angular displacements and force and moment loads from each realization were 

rainflow cycle-counted and equivalent displacements and fatigue damage equivalent load values 

calculated. Ensemble statistics were then derived for each case. 

Our analysis of the results shows that, at least for the two cases studied and the two spectral 

models used, the IEC NTM spectral model was generally more severe but the results exhibited 

limited variability compared to the GP_LLJ model simulations. The exceptions are higher loads 

(and displacements) seen on the low-speed shaft (drivetrain), tower-top yaw bearings, and 

various tower locations. As expected from the large difference in the number of sources of 

random variability in each of the models, the GP_LLJ results often displayed wide distributions 

10
Wind speed (m/s)

8 9 10 11 12 13 14

Height, AGL (m)

0

20

40

60

80

100

120

140

160

180

-10 -8 -6 -4 -2 0 2

9 10 11 12 13 14 15 16 17

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Wind direction relative to hub height (deg) 

Wind speed (m/s) 

IEC NTM 

LLJ speed 

LLJ direction 

80m jet 460m jet 

Figure 6. Vertical wind speed and direction profiles over 5-MW turbine rotor disk for simulated 80- 

and 460-m LLJs. 

with positive skewness compared to the IEC NTM indicating that some of the realizations within 

the ensembles contained very large displacements or load excursions relative to the median or 

mean. Generally the means and distributions of displacements and loads resulting from the jets 

that included coherent turbulent structures were greater than those that did not contain coherent 

turbulent structures. 

In Figure 7 we present a subset of equivalent displacement and load comparisons for the two jet 

heights and spectral models. We use the box plot graphic notation developed by Tukey [15] to 

present the ensemble results relative to each spectral model. In this presentation format the 

height of the shaded box represents the interquartile range (IQR) P25 to P75 of the distribution. 

We use the PXX nomenclature to indicate the XX-percentile of the ensemble distribution. The 

thin solid and heavier dashed lines in the box mark the distribution median (P50) and the mean 

(expected) value, respectively. The height of the line terminated with the “whiskers” represents 

the P10 to P90 range of the distribution; the distance between the black dots signifies the range 

between P05 to P95. The abscissa notation in Figure 7 “Jet w/o CS” refers to the box plot in 

which a jet had no coherent structures associated with it and “Jet w/CS” is a jet that did. 

Except for the blade root out-of-plane moment loads in Figures 7-a and 7-b, medians and means 

and distribution widths are greater for the two jet-related simulations with the 80-m jet cases 

11
typically having the largest relative difference to the IEC NTM. The mean values for the 460-m 

jet are larger than for the 80-m jet with all three models because of the higher hub-height mean 

wind speed (13.38. versus 11.59 m/s). The P95 tail values are also relatively larger for the 

GP_LLJ spectral model compared with the IEC NTM signaling the presence of large excursions 

within some of the individual realizations. Our analyses of observed early morning jets 

occurring above 200 m at the Lamar Site has shown that they are often responsible for injecting 

downward bursts of intense CTKE into the layer occupied by the turbine rotors. This subject 

will be discussed more thoroughly later. Finally, these results demonstrate that a strong jet 

maximum well above a turbine rotor can significantly influence the turbine response. 

Comparison of Turbulence Characteristics at the NWTC and Lamar Sites 

The NWTC and GP_LLJ spectral models available in the TurbSim Code generate two diverse 

site-specific inflows that can be used to simulate multi-megawatt size wind turbines. We have 

shown previously in [2] and [9] and in Figure 1 that turbine loads reach peak values within a 

narrow range of the gradient Richardson number (Rig) stability parameter calculated over the 

depth of the turbine layer. This critical range is bounded by the weakly stable Rig region of 

+0.000 to +0.010. We have also demonstrated the strong correlation with peak values of CTKE 

greater than 2 m2/s2 and turbine load excursions in [2]. In Figure 8 we compare observed hubheight peak CTKE values with Rig for the 37-m hub height of the NWTC ART with 85-m hub of 

an equivalent GE 1.5 MW turbine installed at the Lamar site. In this figure we have highlighted 

the critical Rig region. Many of the differences seen in the variation of peak CTKE with Rig have 

to do with how this value is determined. For the ART it was calculated over a layer between 3 m 

and 58 m, whereas for Lamar this resides between 3 m and 116 m. We have shown in [2] that 

the strong peaking in maximum values of CTKE in the critical Rig region at the NWTC is related 

to intense downward transport of CTKE from above the rotor disk. This process is most likely 

the result of KHI and breaking KH billows taking place in the lee of the Front Range of the 

Colorado Rocky Mountains. Some peaking takes place within the critical range at the Lamar 

site, but it is much less pronounced with peak values of CTKE occurring over a wider Rig range 

that occurs with unstable (Rig < 0) and very stable conditions (Rig > +0.25) with the latter being 

more frequent. Most of the largest values of peak CTKE occur with Rig values higher that the 

upper limit of the critical range. We believe this is the result of the downward injection of 

CTKE from jet structures located above the turbine.

12
g

Damage equivalent load (kNm) 

15

20

25

30

35

40Damage equivalent load (kNm)

50

60

70

Damage equivalent load (kNm)

50

60

70

p g

Damage equivalent load (kN)

0.5

0.6

0.7

0.8

80-m Jet Blade root out-of-plane moment 460-m Jet

Tower base fore-aft bending moment 

IEC NTM Jet w/o CS Jet w/CS IEC NTM Jet w/o CS Jet w/CS 

g

Damage equivalent load (kNm)

15

20

25

30

35

40

p p

Equivalent displacement cycles (mm)

0.30

0.35

0.40

0.45

p p

Equivalent displacement cycles (mm)

0.30

0.35

0.40

0.45

Tower-top fore-aft displacement 

p g

Damage equivalent load (kN)

0.5

0.6

0.7

0.8

Tower-top yaw bearing fore-aft shear force 

(a) (b) 

(d) (c) 

(e) (f) 

(g) (h) 

Figure 7. Box plots of ensemble distributions of selected response variables for simulations of 

5-MW reference turbine. 

13
The downward injection of CTKE 

into the turbine layer occurs as the 

result of a net downward transport 

or flux of horizontal momentum. 

We examine in Figure 9 the 

observed mean vertical transport 

or flux of horizontal momentum, 

wu ′′ D , across the rotor disk of the 

ART and an equivalent GE 1.5-

MW turbine as a function of Rig at 

the NWTC and Lamar sites, 

respectively. The strongest net 

downward fluxes at the NWTC 

occur in a narrow range of Rig

(+0.000 to +0.005) which also 

coincides with highest root 

flapwise damage equivalent loads 

measured on the ART in Figure 1. 

The downward flux at Lamar 

occurs over a broader range of Rig, 

peaking in the vicinity of Rig = 

+0.010. We have plotted the hubheight peak CTKE as a function of 

wu ′′ D for the TC and Lamar 

sites in Figure 10 to see if the 

suspected relationship is true. 

Clearly the bulk of the large 

CTKE values at the NWTC are 

related to net downward 

momentum fluxes across the ART 

rotor disk and, though smaller, the 

same generally holds true for Lamar. Figure 11 shows that the largest values of the hub-height 

wind speed standard deviation or σ

 NW

U are associated with negative or downward momentum 

fluxes across the rotors at both sites. The observed relationship between hub-height turbulence 

intensity (TI) and peak CTKE is presented in Figure 12 for both sites. There is little correlation 

particularly for the NWTC. The observed variations of hub-height peak CTKE and σU with 

mean wind speed for the two sites are presented in Figures 13 and 14, respectively. Finally, the 

variations of vertical speed shear (expressed in terms of the shear exponent α) across the ART 

disk and an equivalent Lamar GE 1.5 MW turbine rotor with Rig and hub-height mean wind 

speed are presented in Figure 15. 

Figure 8. Comparison of hub-height peak CTKE values 

versus turbine layer Rig for the NWTC ART (37 m) and the 

Lamar Site (85 m). 

Clearly there are marked differences between the turbine operating environments at the NWTC 

and those at the Lamar site. The NWTC is located in the interface between the mountains and 

Great Plains. This places it in the lee flows of the Front Range of the Colorado Rocky 

Mountains where it is subject to atmospheric processes that produce winds containing intense 

organized or coherent turbulent elements, high turbulence levels, and low shears over the rotor 

14
disk of the NWTC ART. In contrast, a larger turbine installed at Lamar needs to frequently 

endure coherent turbulent structures that are less intense but of sufficient intensity to induce 

turbine responses that may occasionally become significant. At Lamar these structures occur in 

conjunction with much higher mean shears across the rotor disk than are typical of the NWTC. 

The coherent turbulence generation process is quite different for the two sites. At the NWTC 

instabilities in the lee flows from the complex terrain of the mountains immediately to the west 

of the site produce strong winds near the surface and enhance the downward flux of turbulent 

momentum much of which contains intense coherent elements. The Lamar site is located on the 

high plains of Southeast Colorado, about 200 km east (downstream) of the Rocky Mountains. 

Here the primary mechanism for coherent turbulence generation is the dynamics associated with 

the development and life cycle of the LLJ. Intense bursts of CTKE are associated with the 

downward momentum fluxes accompanying instabilities that develop in the highly sheared flows 

beneath the LLJs. Both these contrasting environments can now be numerically simulated by the 

NREL TurbSim Code to assist turbine designers, wind farm planners, and operators. 

Conclusions 

The Great Plains turbulence operating environment, as exemplified by the Lamar measurements, 

initially appears to be much less rigorous than conditions seen in and near complex terrain (such 

as that near the NWTC). However, a closer examination of many of the data presented in this 

paper underscores the challenges for designers to build large, reliable, multi-megawatt turbines, 

principally because of the vertical inhomogenities and instabilities associated with nocturnal 

boundary layer flows that are also coupled with the best wind resources. 

In this paper we have demonstrated that turbulence conditions in the Great Plains can, under the 

right circumstances, be potentially more harmful to turbine operations than are currently being 

designed to. How these differences ultimately impact the productivity, reliability, and lifetime of 

a given turbine or turbine design will depend on the number of hours it operates in such 

conditions. Other than for the Lamar site, little information is available. Thus, we recommend 

that turbine operators undertake a more careful analysis of their operating data derived from 

onsite system control and data acquisition (SCADA) systems by summarizing hourly, daily, and 

monthly operating events and wind resources to identify trends similar to the ones we showed 

that may be correlated with LLJ activity and the accompanying turbulence-induced events 

discussed in this paper. 

The NREL TurbSim Code now enables the turbine designer to replicate the Great Plains 

turbulence environment and conditions associated with boundary layer flows in and around 

complex terrain locations. It also now enables the user to test a turbine design with an extreme 

coherent atmospheric turbulent structure that can infrequently occur in the free atmosphere. 

Because of the number of sources of random variability built into these simulations, we strongly 

urge users to perform ensemble analysis to properly interpret the results. What the code does not 

do currently is to provide a more realistic turbulence environment within a multi-row wind park 

in the Great Plains. Hopefully that shortcoming can be addressed in the future. 

15
Figure 9. Comparison of the observed mean vertical flux of horizontal 

momentum versus Rig across the rotor disks of the NWTC ART and an 

equivalent GE 1.5-MW turbine installed at the Lamar Site. 

Figure 10. Comparison of the observed hub-height peak CTKE values versus 

the mean vertical flux of horizontal momentum across the rotor disks of the 

NWTC ART and an equivalent GE 1.5-MW turbine installed at the Lamar Site. 

16
Figure 11. Comparison of the observed hub-height σU versus the mean 

vertical flux of horizontal momentum across the rotor disks of the NWTC 

ART and an equivalent GE 1.5-MW turbine installed at the Lamar Site. 

Figure 12. Comparison of the observed variation of hub-height peak CTKE 

with turbulence intensity (TI) for the NWTC ART and an equivalent hubheight of a GE 1.5-MW turbine installed at the Lamar Site. 

17
Figure 13. Comparison of the observed variation of hub-height peak CTKE 

with mean wind speed for the NWTC ART and an equivalent hub-height of a 

GE 1.5-MW turbine installed at the Lamar Site. 

Figure 14. Comparison of the observed variation of hub-height σU with 

mean wind speed for the NWTC ART and at an equivalent hub-height of a 

GE 1.5-MW turbine installed at the Lamar Site. 

18
(b) 

(a) 

Figure 15. Comparison of the observed rotor disk shear exponent, α, with: (a) 

turbine layer Rig, and (b) hub-height mean wind speed for the NWTC ART and over an 

equivalent rotor disk and hub-height of a GE 1.5 MW turbine installed at the Lamar 

Site. The IEC Normal Wind Profile (NWP) value of 0.2 is shown as 

19
Acknowledgments

This work is supported by the U. S. Department of Energy under contract No. DE-AC36-

99GO10337. We wish to thank the Office of Wind and Hydropower Technologies for 

supporting this effort. 

References 

1. Kelley, N.D. (March 2004). “An Initial Overview of Turbulence Conditions Seen at 

Higher Elevations over the Western Great Plains,” Global Windpower 2004 Conference 

Proceedings (CD-ROM), 28-31 March 2004, Chicago, Illinois. Washington, DC: 

American Wind Energy Association; Omni Press; 12 pp.; NREL Report No. CP-500-

35970. 

2. Kelley, N.; Shirazi, M.; Jager, D.; Wilde, S.; Adams, J.; Buhl, M.; Sullivan, P.; Patton, E. 

(2004) Lamar Low-Level Jet Program − Interim Report. National Renewable Energy 

Laboratory. Golden, CO. NREL Report TP-500-34593. 216 pp. 

3. Malcolm, D.J.; Hansen, A.C. (August 2002). WindPACT Turbine Rotor Design Study. 

Global Energy Concepts LLC and Windward Engineering for the National Renewable 

Energy Laboratory. Golden, CO. NREL Report TP-500-34593, 71 pp. 

4. Jonkman, J.; Butterfield, S.; Musial, W.; and Scott, G. (January 2006). “Definition of a 

5 MW Reference Wind Turbine for Offshore System Development,” Golden, CO: 

National Renewable Energy Laboratory. NREL Report TP-500-38060 (to be published). 

5. International Electrotechnical Commission. (2005). Wind Turbines-Part 1: Design 

Standards. 61400-1, 3ed. http://www.iec.ch. 

6. Kaimal, J.C.; Wyngaard, J.C.; Izumi, Y.; Cote, O.R. (1972). “Spectral Characteristics of 

Surface-Layer Turbulence.” Quarterly Journal of the Royal Meteorological Society (21), 

pp. 563−598. 

7. Mann, J. (1994). “The Spatial Structure of Neutral Atmospheric Surface-Layer 

Turbulence.” Journal of Fluid Mechanics (273), pp. 41−168. 

8. Mann, J. (1998). “Wind Field Simulation.” Journal of Probabilistic Engineering 

Mechanics (13), pp. 269−282. 

9. Kelley, N.D.; Jonkman, B.J.; Scott, G.N.; Bialasiewicz, J.T.; Redmond, L.S. (May 

2005). “The Impact of Coherent Turbulence on Wind Turbine Aeroelastic Response and 

Its Simulation,” Windpower 2005 Conference Proceedings (CD-ROM), 15-18 May 2005, 

Denver, CO. Washington, DC: American Wind Energy Association; Omni Press; 19 

pp.; NREL Report CP-500-38074. 

20
10. Smith, K.; Randall, G.; Malcolm, D.; Kelley, N.; Smith, B. (June 2002). “Evaluation of 

Wind Shear Patterns at Midwest Wind Energy Facilities.” Windpower 2002 Conference 

Proceedings, 2−5 June 2002, Portland, OR. Washington, DC: American Wind Energy 

Association; 16 pp. 

11. Mitchell, M.K.; Arritt, R.W.; Labas, K. (September 1995). “An Hourly Climatology of 

the Summertime Great Plains Low-Level Jet Using Wind Profiler Observations.” 

Weather Forecasting (10), pp. 576−591. 

12. Whiteman, C.D.; Bian, X.; Zhong, S. (October 1997). “Low-Level Jet Climatology from 

Enhanced Rawinsonde Observations at a Site in the Southern Great Plains.” Journal of 

Applied Meteorology (36), pp. 1363−1376. 

13. Kelley, N.D.; Jonkman, B.J. (April 2006). “Overview of the TurbSim Stochastic Inflow 

Turbulence Simulator – Version 1.10.” Golden, CO: National Renewable Energy 

Laboratory. NREL Report TP-500-39796. It is available at 

http://wind.nrel.gov/designcodes/preprocessors/turbsim 

14. MSC. Software Corporation. ADAMS/Solver Task Documentation Kit. Version 12.0 

Santa Ana, CA: MSC. Software Corporation. 

15. Tukey, J.W. (1977). Exploratory Data Analysis. Reading, MA: Addison-Wesley. 

21
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The Great Plains Turbulence Environment: Its Origins, Impact, and 

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This paper summarizes the known impacts of nocturnal turbulence on wind turbine performance and operations and 

discusses NREL's progress in numerically simulated coherent inflow turbulent conditions generated by atmospheric 

instabilities that are frequently assiciated with a Great Plains nocturnal low-level jet stream. 

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wind energy; wind plant development; Great Plains turbulence; wind resources; wind turbine siting 

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