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

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Turbine Inflow Characterization 

at the National Wind Technology 

Center

Preprint

A. Clifton, S. Schreck, G. Scott, and N. Kelley 

National Renewable Energy Laboratory

J. Lundquist 

National Renewable Energy Laboratory and 

University of Colorado at Boulder

To be presented at the 50th AIAA Aerospace Sciences Meeting

Nashville, Tennessee

January 9-12, 2012

Conference Paper

NREL/CP-5000-53525

January 2012
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Turbine Inflow Characterization at the National Wind
 

Technology Center

Andrew Clifton∗ Scott Schreck † George Scott ‡

Neil Kelley §

National Renewable Energy Laboratory, Golden, CO 80401 U.S.A.

Julie K. Lundquist ¶

National Renewable Energy Laboratory, Golden, CO 80401 U.S.A.

University of Colorado at Boulder, Boulder, CO, 80309 U.S.A.

Utility-scale wind turbines operate in dynamic flows that can vary significantly over

timescales from less than a second to several years. To better understand the inflow to

utility-scale turbines, two inflow towers were installed and commissioned at the National

Renewable Energy Laboratory’s (NREL) National Wind Technology Center near Boulder,

Colorado, in 2011. These towers are 135 m tall and instrumented with a combination

of sonic anemometers, cup anemometers, wind vanes, and temperature measurements to

characterize the inflow wind speed and direction, turbulence, stability and thermal stratification to two utility-scale turbines. Herein, we present variations in mean and turbulent

wind parameters with height, atmospheric stability, and as a function of wind direction that

could be important for turbine operation as well as persistence of turbine wakes. Wind

speed, turbulence intensity, and dissipation are all factors that affect turbine performance.

Our results show that these all vary with height across the rotor disk, demonstrating the

importance of measuring atmospheric conditions that influence wind turbine performance

at multiple heights in the rotor disk, rather than relying on extrapolation from lower levels.

Nomenclature

Cp

e

f

g

I

L

P

q

Q0

R

Ri

RiS

T

Td

=

=

=

=

=

=

=

=

=

=

=

=

=

=

specific heat capacity at constant pressure, 1005 J Kg-1 K-1

vapor pressure

cyclical frequency

acceleration due to gravity, 9.81 m/s2

turbulence intensity

Monin-Obukhov length

barometric pressure

specific humidity

surface heat flux

gas constant of dry air, 287 J Kg-1 K-1

gradient Richardson number

speed Richardson number

absolute temperature

dew point temperature

∗Senior Engineer, National Wind Technology Center, Golden, CO.

†Principal Engineer, National Wind Technology Center, Golden, CO.

‡Scientist, National Wind Technology Center, Golden, CO.
 §Principal Scientist, National Wind Technology Center, Golden, CO.
 ¶Assistant Professor, Department of Atmospheric and Oceanic Sciences, University of Colorado at Boulder, Boulder, CO,
 

80309 U.S.A.

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Tv = virtual temperature

U = stream-wise velocity

u∗ = friction velocity

um = zonal (west-east) wind component

vm = meridional (south-north) wind component

w = vertical wind component

z = height above ground

Symbols

E = dissipation rate

κ = Von K ́arm ́an constant, 0.41

θ = potential temperature

θv = virtual potential temperature

ζ = ratio z/L

I. Introduction

Modern utility-scale wind turbines have hub heights of 80 m or more, and rotor diameters upwards of 100

m. Since the 1980s there has been a trend of continuous growth in turbine size and power rating.1 In 2011,

several manufacturers announced turbines with hubs at more than 100 m above ground and rotor diameters

of more than 120 m. Wind turbines operate in an atmospheric boundary layer characterized by turbulence.

This layer experiences significant changes in heat fluxes at the lower boundary, switching from convective

conditions during the day to stable conditions overnight. The change in stability is known to alter turbulence

(which varies by site as well2), impact turbine performance3 and also affects turbine loads.4 The rotation

of the earth, manifested in the Coriolis effect, also influences winds, leading to a change in wind direction

with height that can be further complicated by synoptic forcing. Stability, wind direction veer, and jets all

represent a departure from the predictable flow suggested by canonical power or logarithmic law flows.5 This

combination of continued growth in turbine size and dynamic boundary layer conditions requires careful,

coupled monitoring of turbine behavior and wind inflow conditions to understand and improve performance

and reliability.6

The importance of atmospheric stability and coherent turbulent structures in wind for turbine behavior

was shown by a series of measurements in a large, 41-row wind farm in the San Gorgonio Pass, California.

Observations there showed that upwind turbines, particularly under stable night-time conditions, enhanced

turbulence within the turbine array.7 Wavelet analysis methods revealed that organized or coherent turbulence was responsible for an increase in damage-equivalent loading. This effect is expected to increase for

large arrays of turbines.7

A later series of experiments at the National Wind Technology Center (NWTC) used an array of sonic

anemometers to measure inflow to the 42-m-diameter, 600-kW Advanced Research Turbine (ART). These

experiments were part of the Long-Term Inflow and Structural Testing (LIST) program to establish the

sensitivity of wind turbines to inflow conditions, quantify the impact of boundary layer stability, and develop

a boundary layer simulation tool. Results from those tests suggested that turbine loads were sensitive to

coherent structures found in stable nocturnal boundary layers.4, 8 The authors suggested that this would

become ever more important as turbines increased in size and were more heavily impacted by these flow

phenomena, particularly below low-level jets.

The Lamar Low-Level Jet Program (LLLJP) measurement campaign quantified the frequency and magnitude of the low-level jet near Lamar in southeast Colorado in 2003 using a combination of an instrumented

tower, SODAR, and NOAA’s three-dimensional scanning wind LIDAR, the High Resolution Doppler LIDAR

(HRDL). Results showed that the jet was responsible for the formation of Kelvin-Helmholtz instabilities

(KHI) at elevations typical of modern turbine rotor disks.9, 10 The Kelvin-Helmholtz instabilities ultimately

collapse to create coherent turbulent structures that then contribute to significantly enhanced turbine loads.

These flow phenomena occur throughout the U.S. Midwest in states that represent a significant proportion

of the installed and future potential wind energy capacity in the United States.

A comparison of atmospheric data from the San Gorgonio and LIST measurement campaigns showed

that loads peaked when the local turbine-layer Richardson number was in the range 0.01 > RiT L > 0.05.10

This range corresponds with the formation of Kelvin-Helmholtz Instabilities and peak values of the coherent

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turbulent kinetic energy.

The stochastic wind field simulator TurbSim was created using lessons learned from San Gorgonio, LIST,

and LLLJP to produce a desktop simulation of a realistic atmospheric boundary layer.11 This tool generates

a wind field with similar statistical properties to those seen during these studies and is designed to be

interfaced to turbine aerostructural models to estimate structural loading. TurbSim can also be used to

generate boundary conditions for computational fluid dynamics calculations.12

The National Wind Technology Center is situated about 20 miles to the northwest of Denver, Colorado, at

the foot of the Front Range at an elevation of around 1850 m above sea level. Winds on site are dominated

by strong westerly winds, typically resulting from a drainage flow out of the nearby Eldorado Canyon,13

visible in the upper right quadrant of Figure 1. The NWTC is flat and undeveloped, and forms a “wind

reservation” with very uniform surface cover to help reduce the variation seen in the wind profiles at the east

end of the site by the time they reach the turbine test stands at the west end of the site. Although the mean

wind speed on site is low, winds can be extremely gusty and turbulent. For this reason, and because of the

NWTC’s accreditation as a turbine test location with the American Association for Laboratory Accreditation

(A2LA), the site is a preferred location for many manufacturers to test turbines and establish performance,

reliability, and survivability. The U.S. Department of Energy (DOE) installed the DOE/GE 1.5-MW turbine

with 80-m hub height and 78-m rotor diameter at the NWTC in 2009. Three other utility-scale turbines

have been installed on site since then, including a Siemens 2.3-MW turbine in 2009, and an Alstom 3-MW

Eco100 and Gamesa 2-MW G97 turbine in 2011.

Figure 1. The view to the northwest across the NWTC in May 2011. Three utility-scale turbines are in the foreground

of the picture to the left and right. The 38-m hub height Advanced Research Turbines are slightly set back. Photo by

Dennis Schroeder, NREL/PIX 19018.

A range of inflow data has been collected at the NWTC. An 80-m tower at the west end of the site

monitors inflow from the mountains, from where the strongest winds typically come. This tower, designated

‘M2’, has been in operation since 1996, and data are publicly available online. Inflow into the two- and threebladed Advanced Research Turbines (ART) is monitored at several heights by cup anemometers and vanes,

and turbulence is measured using a sonic anemometer at the hub height. When turbines are undergoing

performance testing, masts are installed upstream and instrumented with hub-height cup anemometers.

These cups are mounted on long booms and calibrated to relevant standards. Other towers are instrumented

specifically for cooperative research projects or for certain tasks, such as in the LIST experiments. Several

remote sensing devices have been used on site over the last 20 years, including commercial and research

LIDAR and SODAR systems.13–16 Drawing on the lessons learned from the San Gorgonio, LIST, and

LLLJP studies, 135-m inflow monitoring towers were installed upwind of two of the NWTC test turbines in

late 2010. A key goal of the tower measurements is to quantify turbulence and thermal stratification for the

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validation of the TurbSim model. Coupled measurements of inflow and turbine conditions will be analyzed

in detail when high-load events occur.

A summary of inflow monitoring systems that operated during 2011, and the meteorological variable

they measured, is given in Table 1. Systems that operated temporarily as part of measurement campaigns

are indicated. Other meteorological observation systems operate at the NWTC, including precipitation

measurements, barometric pressure, atmospheric electric field strength, relative humidity and incoming solar

radiation.

Site Purpose Device Variables N Heights (m) Operates

M2 Tower Cup anemometer, U, T, WD 6 2-80 All year

wind vane,

thermistors.

ART Inflow Tower Cup anemometer, U, T, WD 3 18 - 58 Campaigns

wind vane

Sonic anemometer u, v, w, T 1 38 Campaigns

M4 Inflow Tower Cup anemometer U 1 80

Cup anemometer, U, T, WD 5 3-134 All year

wind vane

Sonic anemometer u, v, w, T 6 15-131 All year

M5 Inflow Tower Cup anemometer U 6 30-130

Cup anemometer, U, T, WD 5 3-122 All year

wind vanes

Sonic anemometer u, v, w, T 6 15-119 All year

Various Turbine testing Cup anemometer, U, WD - hub Campaigns

wind vane

Research Scanning LIDAR u, v, w - to 1,000 m Campaigns

Research Profiling LIDAR U, WD, w - to 200 m Campaigns16

Research Profiling SODAR U, WD, w - to 200 m Campaigns

Research Radiometer T - to 2,000 m Campaigns16

Table 1. Sources of inflow data at the NWTC in 2011. Measurement heights are nominal values. Measured parameters

include the stream-wise velocity U; temperature T; wind direction WD and orthogonal wind vectors u, v, and w at N

heights. Research LIDAR and SODAR have adjustable measurement heights to the maximum range given in the table.

In this paper we show how the 135-m meteorological towers and measurement systems have been specially designed to capture relevant flow parameters. Focusing on a month of data obtained in October and

November 2011, we introduce some of the characteristics of the winds locally and discuss the implications of

our measurements for turbine performance.

II. Methods

Two new 135 m meteorological towers have been installed towards the eastern side of the NWTC site.

The towers are approximately 2 rotor diameters upwind of two, utility-scale wind turbines and are designed

to quantify the inflow into the turbines. The towers are designated ‘M4’, upwind of the Siemens 2.3-MW

turbine and ‘M5’, upwind of the DOE/GE 1.5-MW turbine.

Turbine inflow is quantified in terms of wind speed, wind direction, three-dimensional turbulence, and

temperature at several heights across the turbine rotor. A full schematic of the instrumentation installed

on each of the towers is shown in Figure 2. The tower instrumentation includes six three-dimensional sonic

anemometers on each tower; at least 5 paired cup anemometers; vanes logarithmically distributed over the

tower; and absolute, differential, and dew point temperature measurements. No humidity measurements are

collected, but relative humidity is calculated from absolute and dew point temperatures (see section II.D).

Instrumentation are at slightly different heights on the M4 and M5 towers to align precisely with the turbine

hubs, blade tips and blade mid-span. The wind measurement devices are mounted on booms that extend

horizontally from the tower structure into the prevailing winds at an angle of 285◦ (compare to Fig. 6).

The length of the booms for the sonic anemometers are 5.8 times the width of the tower face, while cup

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anemometers and sonics are on booms that are 3.5 times as long as the tower face width. We also measure

barometric pressure and precipitation intensity at the ground.

The towers are stabilized by guy lines connected to anchors spaced at 120◦ intervals around the tower

and connected to the tower structure at 6 heights (Fig. 2). The effect of these anchors is to increase the

tower stiffness, reduce tower torsion, and raise the resonant frequency of the tower above the measurement

frequency.

Figure 2. Schematic view of the NWTC 135-m M4 inflow monitoring tower. Boom heights are approximate. All booms

face 285◦.

II.A. Data acquisition and processing

Data from each tower are obtained at 20 Hz by a data acquisition system built around National Instruments

LabVIEW software and National Instruments PCI boards. Separate, identical systems are used at each

tower. Using duplicate systems based on standard commercially available architecture for both projects

helps in commissioning and gives flexibility in adding extra instrumentation at a later date.

Our commissioning process followed several steps after the instruments were installed on the tower. First,

an end-to-end signal check showed that the correct device was associated with each measurement. Other

measurements established the noise floor of the system, which influences the accuracy of our measurements.

Tests then confirmed the ability of the system to maintain a true 20-Hz data acquisition frequency. We also

investigated the response of the system to the (simulated) failure of an instrument to ensure that failure of

one device would not jeopardize data from the other instruments.

The variables listed in Table 1 are measured at 20 Hz on both of the 135-m towers, and then stored

in data files of 10 minute duration. Tower data are synchronized with turbine measurements using Global

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- -

- - -

Positioning System (GPS) time signals, which allow us to link inflow winds to turbine response.

II.B. Quantifying the mean flow

The characteristics of the mean flow are calculated for each of the 10-minute data files written by the data

acquisition system. A 10-minute averaging period was chosen per IEC standard 61400 for wind turbines.

We calculate the mean wind speed and direction at each height by first converting the 20-Hz wind speed

U measured by the cup anemometers and direction W D measured by the vanes into 20-Hz orthogonal wind

components in the meteorological zonal (west-east, um) and meteorological meridional (south-north, vm)

directions:

 π · W D

um = −U · sin (1) 180

 π · W D

vm = −U · cos (2) 180

where positive um indicates a wind blowing to the east, and positive vm is a wind blowing to the north. The

mean wind speed is then calculated for each 10-minute interval as the vector mean of the orthogonal wind

components:

 (1/2) 2 2 U = um + vm (3)

The mean wind direction is the direction that the wind comes from, in degrees:

180 W D = atan2(−um/−vm) × (4) π

where the function atan2 (x) is the arc tangent of x in the range ±π radians.

II.C. Quantifying turbulence

Because the spectrum of turbulent fluctuations includes both large and small scales, measurement devices

must be capable of resolving a wide range of wind speeds, and capturing rapid changes. We use sonic

anemometers to make wind turbulence measurements as they have no inertia, a small measurement volume

and can make high-frequency measurements. In comparison, the inertia of cup anemometers makes them

unreliable for high-frequency turbulence measurements. Sonic anemometers measure winds in 3 orthogonal components, rather than a wind speed and direction as with cups and vanes. To calculate the mean

wind vector, 10-minute blocks of measurements are rotated into the prevailing wind direction during post

processing.17 The rotation fits the measured data to find a 3-dimensional wind vector with a stream-wise

component u, transverse component v and vertical component w for the 10-minute interval. The mean

stream-wise velocity is maximized, while the mean transverse and vertical components are vertical over the

interval.

The turbulent velocity components u , v and w- are defined as the difference from the mean velocity

component, so that u = u(t) − (u), v = v(t) − (v) and w = w(t) − (w), where (v) = (w) = 0. Turbulence

intensity, I, is the ratio of the standard deviation of the turbulent components to the stream-wise mean

speed, expressed as a percentage.18 For flow in the stream-wise direction (u), this percentage is

σ (u-

) I(u) = × 100. (5) U

Turbulence intensity is calculated only for the horizontal velocity measured by the cups, as seen in Figures

7(b) and 9(b).

The local friction velocity u∗ is calculated from the turbulent velocity fluctuations measured by the sonic

anemometers18 at each height as

 

1/2 - u∗ = u w- , (6)

where the overbar indicates the average for a ten minute interval.

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- - -

- -

Turbulence kinetic energy (TKE) is a measure of the energy in the turbulent velocity fluctuations that

includes all three velocity components, rather than the turbulence intensity which only includes the streamwise component.19 The mean TKE over a 10 minute interval is defined as

1 TKE = [u-2 + v-2 + w-2] (7) 2

As was noted in the introduction, coherent turbulent kinetic energy (CTKE) is a significant contributor

to turbine loads. CTKE is defined as

1 (1/2) -

]

2 CTKE = [u w-

]

2 + [u v-

]

2 + [v w . (8) 2

Turbulence is generated at low frequencies by flow interacting with terrain (mechanical production),

through buoyancy, and by the motion of the atmosphere. TKE is dissipated at high frequencies into heat

through viscous dissipation. Understanding the power spectra of turbulence is important for turbine design,

as this influences the energy that is transferred into the turbine structure. One useful measure of turbulence

is the integral length scale, which describes the mean length scale of turbulent eddies in the flow. The

turbulence integral length scale (Λ) for a velocity component (u , v or w-

) is calculated from the time

series of the turbulent velocity component. First, the characteristic time (τe) for the autocorrelation of the

turbulent component to drop to 1/e is calculated. τe is multiplied by the mean wind speed to give the

turbulence integral length scale, Λ(u) = U × τe (u). Because the integral length scale is of the order of the

measurement height,19 the characteristic time of flows at a turbine hub (around 80 m) at rated speed (10 to

12 m s-1) is approximately 10 seconds, which can be resolved by our 20-Hz data acquisition system.

The dissipation rate E is the rate at which turbulent kinetic energy is dissipated into heat at the smallest

eddy scale in turbulent flow. Because this occurs at higher frequencies than can be resolved directly by the

sonic anemometers, it has to be inferred from the turbulent power spectra. Here we calculate E using the- structure function method.18 The structure function for a time δt is the mean squared difference between u

at times t and δt: 2 DAA (δt) = [u- (t + δt) − u- (t)] (9)

Next, we calculate the ratio of the structure function to the cube root of the lag:

⎡ ⎤ DAA (δt) C ⎣ ⎦ v2 (δt) = . (10)

 2

3 Uδt

The dissipation rate is then given by:

 C

 v2 E =

3

2

(11) 2

The dissipation rate is limited to the inertial subrange by using 0.05 ≤ δt ≤ 2, which corresponds to

frequencies between 0.5 and 20 Hz. From power spectra of the turbulent velocity components (e.g. Fig.

5), this is within the inertial subrange where energy cascades from the larger scales to smaller scales at a

constant rate. We will investigate other methods to quantify the dissipation rate of turbulent kinetic energy

from the tower data in the future.20

II.D. Quantifying thermodynamic properties

To calculate the stability profile in the boundary layer, we need to quantify the air temperature and humidity

profile. This requires a series of calculations, set out below. Absolute and dew point temperature and

barometric pressure are measured at 3 m above ground. These values are denoted T0, Td0 and P0, respectively.

The absolute temperature profile T (z) on the M4 tower is measured as the sum of T0 and temperature

differences between 3 and 26 m, 26 and 88 m, and 88 and 134 m above ground. Using differential temperature

measurements with an accuracy of 0.1◦ gives improved accuracy compared to using two local absolute

temperature measurement with a typical accuracy of 0.5◦.

The local saturation vapor pressure es is calculated at each height from the air temperature T(z) in

degrees Celsius:

es(z) = 6.11 × 10[(T(z)·A)/(T(z)+B)) (12)

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where A =7.5 and B = 237.3 if T(z) ≥ 0◦C. Otherwise, A = 9.5 and B = 265.5. The actual local vapor

pressure e(z) is calculated from Eq. (12) using the dew point temperature Td measured at the different

heights on the tower, also in degrees celsius, instead of the absolute temperature.

The specific humidity q is the ratio of mass of water vapor to the total mass of the air.19 It is calculated

from the saturation and local vapor pressures:

e

q = 0.622 (13) P

where 0.622 is the ratio of the gas constant for dry air (287 J Kg-1 K-1) to the gas constant for water vapor

(461.5 J Kg-1 K-1).

The virtual temperature Tv at the lowest level on the tower is given by:19

Tv = T (1 + 0.61q). (14)

The virtual temperature and pressure at the lowest measurement height are used to calculate the pressure

gradient, dP/dz from the equation of state:19

dP gP0 = − (15) dz RTv

where g is the acceleration due to gravity (9.81 m s-2) and R is the gas constant of dry air.

The pressure at other observation heights (Δz above the ground) is then calculated from the measured

ground pressure and the pressure gradient as P(z) = P0 + Δz · dP/dz. Once the pressure profile has been

estimated, the potential temperature profile is calculated. The potential temperature Θ is the temperature

that air at the ground would have if moved to a reference pressure level, Pref , in this case 1000 hPa.19 The

potential temperature is:

R/Cp Pref Θ (z) = T (z) (16) P (z)

where Cp is the specific heat capacity at constant pressure (1005 J Kg-1 K-1). The ratio R/Cp = 0.286.

The virtual potential temperature Θv is the potential temperature that dry air air would require to have

the same density as moist air.19 The virtual potential temperature is:

Θv (z) = Θ (z) (1 + 0.61q (z)) (17)

where q is the specific humidity at each height (Eq. 13). Profiles of T, Θ and Θv can then be used to

visualize and quantify stability using a variety of metrics, described in II.E.

II.E. Quantifying stability

Several methods exist to quantify stratification. One method is to use the ratio of shear-driven turbulence

to buoyancy-generated turbulence using the Monin-Obukhov length, L:

3 u∗Θv L = − . (18)

κgwΘv

The buoyancy term wΘv in Eq. (18) is calculated from turbulent components of vertical velocity and

temperature measured by the sonic anemometer, as the turbulent fluctuations of temperature measured by

a sonic anemometer approximate the turbulent component of the virtual potential temperature, Θ- . The

mean value of virtual potential temperature in Eq. (18), Θv, is calculated from the tower temperature

and dew-point temperature profiles. The Monin-Obukhov length is usually normalized by the measurement

height z (in this case the sonic anemometer height) to give the ratio ζ = z/L. Locally convective conditions

give z/L < 0, and stable conditions give z/L > 0, while in neutral conditions, L → ∞ (Table 2).

We also quantify stability with the gradient Richardson number, which is calculated from 10-minute

average temperatures and gradients of wind components and virtual potential temperature from one height

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v
 

(z1) to another (z2).19 In this respect, the Richardson number can be considered representative of the entire

layer between z1 and z2. The gradient Richardson number is:

g dΘv/dz Ri = 2 , (19) 2 Θv (dum/dz) + (dvm/dz)

1 where the mean virtual potential temperature between z1 and z2 is < Θv >= [ΘV (z1) + Θv (z2)]. 2

A simplified Richardson number has also been used in some applications.21–23 This considers just the

gradient of the mean wind speed U, rather than including directional shear as in Eq. (19). To distinguish this

from the gradient Richardson number, this is described in this context as the ‘Speed Richardson Number’,

RiS:

g dΘv/dz RiS = 2 . (20) Θv dU/dz

Calculations of Ri and RiS from the ground to the turbine hub or tip use the mean of all of the temperature, wind speed and wind component gradients.4

Businger21 found that for unstable conditions, z/L ≈ RiS, while RiS tends towards a constant value as

z/L → ∞. If ζ = z/L then:

RiS =

⎧

⎨

⎩

(1+15ζ)1/2

0.74ζ if RiS < 0; (1−9ζ)1/2

ζ(0.74+4.7ζ) if RiS > 0. (1+4.7ζ)2

(21)

We define several stratification classes from the Richardson number and normalized Monin-Obukhov

length. These are listed in Table 2. The Richardson number bands are based on previous work on the

interaction of turbines and stability,4 and explicitly identify the slightly stable region that was found to be

linked to production of CTKE. We also identify the strongly stable regime of Ri > 0.25 where turbulence is

rapidly damped by stability.19 A range of L has been used by different authors to define neutral conditions.

When referenced back to the hub height, zhub, these correspond to |zhub/L| ; 0.1.3, 24 From Eq. (21) it

can be seen that for near-neutral conditions, Ri ≈ z/L, but as stability increases, z/L → ∞. Because a

definition of neutral conditions as |z/L| < 0.1 potentially includes the slightly stable regime, we use the

narrower range of |z/L| < 0.01 that allows us to better distinguish changes in the atmosphere in this region.

Stratification Label Criteria

Ri z/L

Unstable U <-0.01 <-0.01

Neutral N |Ri| ≤ 0.01

0.01 < Ri ≤ 0.05 ⎫

⎬

|z/L| ≤ 0.01

S: >0.01

⎭

Slightly stable S1

Stable S2 0.05 < Ri ≤ 0.25
 

Strongly stable S3 >0.25
 

Table 2. Stratification classes using Ri or z/L.

II.F. Quality control

Calculating the Richardson number, Monin-Obukhov length and turbulence requires low instrument noise,

regular sampling intervals and continuous sampling over long periods of time. We use high-quality, calibrated

instruments and check the frequency of the data acquisition system as part of our post processing routines.

Data from each instrument are checked against simple quality control measures. These quality control

measures include testing for data acquisition rates of 20 Hz, and detecting flat-line data (which indicates a

malfunctioning device) by checking for standard deviations that are less than 0.01 per cent. A check is also

made on the number of valid data points per 10-minute interval, per channel. We chose to flag data if the

number is less than 95% of the 12,000 data points that could be collected during a 10-minute interval. Flags

propagate through calculations, so that if data from two channels a and b are used to calculate another value

y = f(a, b), output y will inherit the flags of variables a and b.

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II.G. Uncertainty Estimates

An important part of any measurement is to understand the behavior of the instrumentation system and how

it interacts with the property being measured. The result of this is an uncertainty estimate, which includes

the uncertainty of the data acquisition process, the impact of the tower on the free stream and the inherent

uncertainty of the measurement devices. The commissioning steps described in II.A allow us to quantify the

uncertainty of our recorded data. We also plan to carry out studies of the extent of the distortion introduced

by the tower structure into the free stream, which will allow us to quantify another part of the uncertainty.

Finally, devices installed on the towers have been calibrated under controlled conditions, which quantifies

the device uncertainty. These steps are relatively easy to describe but the effort required to quantify and

reduce uncertainty to target levels can be considerable. This effort should be expected when installing such

a major inflow-monitoring tower. The extensive effort required to quantify the uncertainty is outside the

scope of the current paper but will be addressed in future.

II.H. Tower shadowing and flow impact

A tower’s own structure will have an unavoidable impact on flow measurements made around the tower.

The tower is not completely porous, and so flow is deflected around the tower structure, causing deceleration

immediately upstream, and acceleration around the tower. The same effect is seen whenever an object is

placed in an otherwise uniform flow.25 A wake forms downwind of the structure, characterized by reduced

wind speeds and high turbulence. These effects can lead to measurable differences between free stream

measurements and measurements on the tower. To avoid the wake contaminating data, measurements made

when the tower wake region crosses an instrument are usually removed.

To understand the tower’s impact on the wind speeds measured with the sonic and cup anemometers, we

measured the free stream windspeed approximately 200 m to the west of the M4 tower using a commercial

doppler wind LIDAR system. Such LIDAR systems have been shown to give measurements within 2% of

sonic anemometer measurements,26 but in contrast to the tower, LIDAR wind speed measurements are not

impacted by any kind of support structure. The LIDAR measured the wind speed in 20-m bins centered

at 120 and 140 m above ground. Data from these two bins were then interpolated to 131 m above ground.

These interpolated data were then compared to data from a sonic anemometer at that height on the M4

tower (Figure 3(a)). This was carried out during the tower commissioning in May and June of 2011. A

comparison was then made between the 131-m sonic anemometer and a cup anemometer at 134 m (Figure

3(b)) using data from the October to November measurement period.

0 100 200 300

0.9

0.95

1

1.05

1.1

1.15

140 m Freestream WD [°]

131 m Freestream WS / 131 m Sonic WS [−]

All data

3 − 4

4 − 5

5 − 6

6 − 7

7 − 8

8 − 9

9 − 10

10 − 11

11 − 12

12 − 13

13 − 14

14 − 15

15 − 16

16 − 17

17 − 18

18 − 19

19 − 20

20 − 21

21 − 22

(a) Ratio of freestream LIDAR and sonic anemometer wind

speeds at 131 m above ground on the M4 tower. Data are

plotted against LIDAR wind direction and grouped by LIDAR wind speed.

0 100 200 300

0.9

0.95

1

1.05

1.1

1.15

131 m Sonic WD [°]

131 m Sonic WS / 134 m Cup WS [−]

3 − 4

4 − 5

5 − 6

6 − 7

7 − 8

8 − 9

9 − 10

10 − 11

11 − 12

12 − 13

13 − 14

14 − 15

15 − 16

16 − 17

17 − 18

18 − 19

(b) Ratio of sonic anemometer wind speeds at 131 m above

ground on the M4 tower to cup anemometers at 134 m.

Data are plotted against sonic anemometer wind direction

and grouped by sonic anemometer wind speed.

Figure 3. Comparison of wind speeds measured by LIDAR, sonic anemometers and cup anemometers on the M4 tower.

Wind speeds measured by the sonic anemometers agree well with the free stream wind speeds measured

by the LIDAR (Figure 3(a)). When wind flows through the tower on to the sonics, there is a clear reduction

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in wind speed measured by the sonics compared to the free stream. This effect can be seen for winds between

approximately 100 and 135◦. Assuming that the wake effect is largest when flow approaches the tower from

105◦ (the opposite direction to the booms) and so the instruments are in the middle of the wake, this implies

a 60◦-wide wake region. When flows are aligned with the booms (flows from 285◦, Figure 3(a)), there may

be an increase in the ratio of the wind speed measured in the free-stream to that seen by boom-mounted

sonic anemometers, suggesting a slowdown of the flow around the tower at this distance, although the data

set does not include many points in this region.

A different trend is seen in Figure 3(b), where measurements from the boom-mounted sonic are compared

with cup data. There is a strong decrease in the ratio of the sonic wind speed to the cup wind speed when

the flow is aligned with the booms, compared to perpendicular flows. The cup anemometers are mounted

on booms with lengths that are 3.5 times the tower face width, while the sonic anemometers are mounted

on booms that are 5.8 times as long as the tower face width. Together, this data suggest that the tower

modifies the free stream flow, causing flow to slow down some distance from the tower, before accelerating

around the tower body. This effect appears to be independent of free stream wind speed. In the one-month

data set shown in Figure 3, the effect is well defined using wind speed and flow direction and so could be

corrected during post-processing. However the spread in the ratio of winds speeds for a given wind direction

would still be approximately 2-3% around a ratio of 1.0. This spread can be considered the noise due to

sensor uncertainty and atmospheric effects.

III. Results

On the 11th of October 2011, low surface pressure areas over the Gulf of Alaska, the Canadian Prairies

and Newfoundland and high pressure over Colorado led to sustained NW winds across the western United

States and the gradual passage of a frontal system from the NW coast into the Midwest. Winds at the

NWTC during this period were predominantly from the NW, and wind speeds stayed above 3 m s-1 for more

than 15 hours (Figure 4). Mean wind directions and RiS for each of the 10-minute intervals over which the

sonic data were rotated are shown, indicating that the flow was generally from the NW sector during this

period, but switched from stable conditions during the night (2:00 UTC to 14:00 UTC, approximately) to

convective conditions during the day.

15:00 18:00 21:00 00:00 03:00 06:00 09:00

0

90

180

270

360

WD [

°

]

15:00 18:00 21:00 00:00 03:00 06:00 09:00

−10

0

10

Ri

S

10/11 15:00 10/11 18:00 10/11 21:00 10/12 00:00 10/12 03:00 10/12 06:00 10/12 09:00

−10

0

10

20

30

Speed [m s−1

]

UTC Time [mm/dd HH:MM]

Streamwise (u)

Transvers (v)

Vertical (w)

Figure 4. Wind components, RiS from 3 to 134 m, and wind direction at 76 m above ground during a 15-hour period

starting at 16:40 UTC on October 11, 2011. The velocity component time series is concatenated 10-minute records of

20-Hz data that have been rotated into the mean flow during that interval. WD and RiS are the mean values for each

10-minute interval.

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A power spectra for the 20-Hz orthogonal velocity components at 76 m above ground during the 15-hour

period is shown in Figure 5. A 15-hour time series of orthogonal hub-height wind speeds for this period with

20 Hz resolution was built by concatenating rotated velocity data from the sonic anemometer at 76 m into

a single time series (Figure 4). The raw spectra have been smoothed by calculating the log-mean power in

each of 5 logarithmically spaced bins per frequency decade, and are plotted on logarithmic scales to show

the large variation in both frequency and power. At frequencies below 0.1 Hz, the contribution from the

stream-wise component rises more than the transverse, while the power of the vertical component decreases

rapidly. The power spectra are identical at frequencies above 0.1 Hz in the inertial subrange, where energy

cascades isotropically from larger to smaller scales.

10

−4

10

−2

10

0

10

−8

10

−6

10

−4

10

−2

10

0

Frequency [Hz]

P(f) [m

2

s−1

]

1 hr 10 min 1 min 1 s

streamwise (u)

lateral (v)

vertical (w)

Figure 5. Turbulent velocity component power spectra for the entire period shown in Figure 4.

A spectral gap in the stream-wise flow has been suggested by earlier research.27, 28 The spectral gap

would appear as a reduction in power at frequencies below 10 minutes. The presence of the spectral gap

in data is sometimes used as justification for the 10-minute averaging period, with the argument being that

this averaging period includes the contribution of turbulence only, and not the passage of mesoscale weather

systems. The spectral gap is expected to change in size and magnitude depending on terrain (which can

contribute to turbulence) and atmospheric forcing.27, 28 However, no spectral gap is seen in these tower

measurements even at time periods up to 15 hours. It is likely that the variation in terrain around the tower

and strong channeling by the Front Range acts to reduce variation in the stream-wise flow, and thus reduces

the effect of synoptic systems that would contribute to the formation of the spectral gap.

The period of strong NW winds shown in Fig. 4 is typical of the wind climate of the NWTC. The

strongest winds on site come from the WNW at a direction of approximately 280◦ to 290◦. Winds from

the 45◦ sector from W to NW represent 18% of all winds above 1 m/s at this height. There are secondary

peaks in wind frequency to the SSE, and slightly west of north (Figure 6(a)). More detailed analysis using

k-means clustering on the 14 years of data available from the M2 tower reveals a strong annual wind cycle,

with winds from the NW sector peaking during the winter months and weaker southerly or northerly winds

occurring during the summer months.29

This paper presents data collected on the 135 m tower during a 4-week period from October 7, 2011 to

November 7, 2011. The wind rose of valid data from the M4 tower for this period (Figure 6(b)) is similar to

the long-term average, with the most frequent winds from the WNW, which are also the direction of peak

wind speeds on site. Data is limited to observations passing the quality control tests described in Section

II.F, and so Figure 6(b) is only a representation of valid data, and not a climatology. A secondary peak in

wind activity during this time is seen in flows from the SSE, at approximately 175◦. Conditions included

stable, neutral and unstable stratification, and over the 1-month period show here, less than 3 hours of

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N

S

W E

Calm:

7.0 %

< 1m/s

0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

32

34

36

Calm:

 0 %

< 1m/s

0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

32

34

36

N

S

W E

(a) 1996-2011 M2 wind rose (80 m above ground)	 (b) October - November M4 wind rose (76 m above

ground)

Figure 6. The frequency and speed of valid measurements of NWTC winds at around 80 m above ground. a) From

January 1996 through the end of December 2010 on the M2 tower and b) from 10/7/2011 to 11/7/2011 from the M4

tower. Data are grouped by direction in 7.2◦ bins and by wind speed in 2 m/s bins. Color bars show wind speeds in 2

m -1 s bins.

neutral conditions coincided with wind speeds above 3 ms-1.

III.A. Inflow characteristics

Because of the location of the NWTC at the western edge of the Front Range, winds from different sectors

travel across markedly different terrain depending on flow direction. The following sections discuss how the

flow structure changes between the WNW and SSE flows at hub height wind speeds between 11 and 13 m

s-1, which are typical design wind speeds for large turbines.

III.A.1. Prevailing winds

Flow from the WNW sector (a direction of 285◦±15◦) at speeds between 11 and 13 m s-1 was detected by the

sonic anemometer in 71 10-minute intervals during the period from October 7, 2011 to November 7. Using

the Ri stability classes in Table 2, we found only one 10-minute interval of neutral conditions, or less than

2% of the total. Overall, conditions were unstable in 24% of intervals, slightly stable (‘S1’) in 4%, stable

(‘S2’) in 46%, and strongly stable (‘S3’) in the remaining 24%.

A logarithmic increase of mean wind speed with height occurs in all WNW flows at hub-height wind

speeds between 11 and 13 m s-1 (Figure 7) up to approximately 100 m above ground. This is expected for

neutral flows,18 while stable and unstable flows show a clear departure from the logarithm wind speed profile.

Above 100 m, the rate of wind speed increase with height decreases in all stability cases, corresponding to

reduced wind shear and a departure from the logarithmic wind profile.18 Turbulence intensity decreases with

height above ground and also in more stable conditions, compared to convective conditions, as has been seen

in other studies of the inflow conditions of wind turbines3 and of the atmospheric boundary layer.30 Both

TKE and peak CTKE are highest in stable conditions (Figure 8) and drop rapidly as stability increases or

conditions become unstable.

In the case of flow from the WNW, the integral length scale in the horizontal direction peaks at around

76 m in stable conditions, but continues to increase with height in less stable conditions (Figure 8). This

increase in the vertical length scale with height in unstable conditions, compared to stable conditions, reflects

the vertical growth of the boundary in unstable (convective) conditions compared to stable conditions. The

vertical length scale continues to grow in all conditions, and is of a similar size to the height above ground,

which would be expected as the vertical size of eddies is constrained by the ground and the upper edge of

the boundary layer. The vertical length scale does increase slightly in convective conditions, compared to

stable conditions. Dissipation rate E gradually decreases with height, reaching a minimum at around 100 m

in stable conditions, but continues to fall with increasing height in unstable conditions. The minimum level

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at about the hub height in stable conditions suggests that wakes will persist longest at the hub, but dissipate

more closer to the ground and at the turbine tip. In unstable conditions, wakes will dissipate more rapidly

than stable conditions.

III.A.2. Southerly Flows

Southerly flows, from the sector 175◦±15◦, were less frequent during this period than the WNW flows, with

only 17, 10-minute periods where the 76-m wind speed was between 11 and 13 m s-1. Of these flows, 18%

were stable (‘S2’), while the rest were strongly stable (‘S3’).

The speed profile of winds from the south shows a similar trend to that from the WNW. During stable

conditions, winds have low shear above the 76-m anemometer. Below that height, wind speeds increase

relative to winds during slightly stable conditions (Figure 9). The turbulence intensity of the SSE flow is

lower than the WNW flows, but shows the same decrease with height as the WNW flows. This reduced

turbulence compared to the WNW flow suggests that the increased turbulence seen in the WNW flows is

generated by the interaction of the wind and terrain of the Front Range, rather than being generated by

buoyancy. The turbulent kinetic energy and coherent TKE are both reduced in comparison to the NW flow,

but both TKE and peak CTKE show similar reductions near the ground as stability rises (Figure 10).

The turbulence length scale Λu profile of the SSE flow (Figure 10) behaves differently in changing stability

conditions than the NNW flows (Figure 8). The length scale of the horizontal flows are markedly increased

in strongly stable conditions compared to the slightly stable conditions, which is opposite to the trend seen

in the WNW flows, although a maximum is seen in flows from both directions at around 76 m. Vertical

length scales appear to peak at around 100 m, in comparison to the WNW flows where vertical length scales

continued to increase with increasing height above ground. Dissipation rates for the SSE flows are about half

that of the WNW flows, although strongly stable flows are the least dissipative in both cases. Both WNW

and SSE flows appear to have a minimum in the dissipation rate profile near 100 m for strongly stable (‘S3’)

conditions.

Observations for this period show that for WNW flows above 3 m s-1, peak CTKE was highest in slightly

stable or stable conditions (Fig. 11). The maximum values of TKE and CTKE occurred in the range

0.0 ; RiS ; 0.1. This range is consistent with previous observations at locations in the Great Plains of

the United States and in an operating wind farm in California.31 The large variation in peak CTKE with

stability depending on wind direction (compare Figures 8(b) and 10(b)) suggests that at this location during

this period, CTKE may be associated with both wind direction and stability, rather than just stability as in

simpler sites.

III.B. Choice of stratification measures

As was noted in Section II.E, stratification can be quantified using the normalized Monin-Obukhov Length

z/L and the Richardson number, Ri. Both calculations require data from several different instruments.

The Monin-Obukhov length compares the ratio of turbulent kinetic energy produced by shear to that

produced by buoyancy. It is calculated from turbulent component and temperature data from sonic anemometers, and from virtual potential temperature from absolute and dew point temperature sensors on the tower

(Eq. 18). Over large, flat areas, buoyancy in the boundary layer is driven by the surface heat flux. The

surface heat flux Q0 = ρCpwθ- (Figure 12(a)) at the NWTC closely follows the local solar diurnal cycle, v

peaking during the day and dropping to negative values overnight. The positive heat flux indicates net transfer of heat into the ground. As this diurnal heat flux cycle follows textbook examples18, 32 it also provides

a useful check on the data processing routines. The diurnal cycle of z/L follows the heat flux, switching

from stable conditions at night to unstable conditions during the day. The number of 10-minute intervals

per hour is not constant as data are filtered to remove wind speeds below 3 m s-1.

Although heat fluxes are usually positive during the day and negative at night, this can change depending

on surface cover. The daytime stable periods in Figure 12(b) coincided with 24 hours of snowfall followed

by snow on the ground for several days, which potentially caused a heat flux into the ground during the day.

Similarly, occasional positive heat fluxes during the evening cause infrequent night-time unstable conditions

at the hub-height (Figure 12(b)). At the NWTC these night-time unstable conditions could be caused by

warm air being convected from upwind of the monitoring towers.

In comparison, layer stability is quantified using the Richardson number. This is based on measurements

of wind speed and direction using cups and vanes, and temperature profiles from absolute, differential and

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0.6 0.7 0.8 0.9 1 1.1

 10

 20

 40

 60

 80

100

120

140

U/U(76) [−]

z [m]

U

N

S1

S2

S3

0 5 10 15 20 25

 10

 20

 40

 60

 80

100

120

140

Ti(u) [%]

z [m]

U

N

S1

S2

S3

(a) Velocity profile measured by sonic anemometers, normalized (b) Turbulence intensity measured by cup anemometers

by 76-m velocity

Figure 7. Profiles of velocity and turbulence intensity for WNW flows (285◦±15◦) at 76-m wind speeds between 11 -1 and 13 m s during the period from 10/7/2011 to 11/7/2011. Data are grouped by stability according to the limits

in Table 2. Markers are plotted at the mean values at each height. Bars extend from the 25th to 75th percentiles.

Markers and bars are displaced by small amounts vertically (less than 1 m) to allow bars to be seen.

0 5 10 15

 10

 20

 40

 60

 80

100

120

140

TKE [m2s

−2]

z [m]

U

N

S1

S2

S3

0 5 10 15 20 25 30

 10

 20

 40

 60

 80

100

120

140

Peak CTKE [m2s

−2]

z [m]

U

N

S1

S2

S3

(a) TKE (b) Peak CTKE

0 200 400 600 800

 10

 20

 40

 60

 80

100

120

140

Λ(u) [m]

z [m]

U

N

S1

S2

S3

(c) Horizontal turbulence integral

length scale

0 50 100 150 200

 10

 20

 40

 60

 80

100

120

140

Λ(w) [m]

z [m]

U

N

S1

S2

S3

(d) Vertical turbulence integral length

scale

0 0.05 0.1

 10

 20

 40

 60

 80

100

120

140

ε [m2s

−3]

z [m]

U

N

S1

S2

S3

(e) Dissipation rate

Figure 8. Profiles of turbulence and dissipation parameters for WNW flows (285◦±15◦) at 76-m wind speeds between

11 and 13 m s-1. Plots use the same data and conventions as Figure 7.

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0.6 0.7 0.8 0.9 1 1.1

 10

 20

 40

 60

 80

100

120

140

U/U(76) [−]

z [m]

S2

S3

0 5 10 15 20 25

 10

 20

 40

 60

 80

100

120

140

Ti(u) [%]

z [m]

S2

S3

(a) Velocity profile measured by sonic anemometers, normalized (b) Turbulence intensity measured by cup anemometers

by 76-m velocity

Figure 9. Profiles of velocity and turbulence intensity for SSE flows (175◦±15◦) at 76-m wind speeds between 11 and

13 m s-1 during the period from 10/7/2011 to 11/7/2011. Data are grouped by stability according to the limits in Table

2. Markers are plotted at the mean values at each height. Bars extend from the 25th to 75th percentiles. Markers and

bars are displaced by small amounts vertically (less than 1 m) to allow bars to be seen.

0 5 10 15

 10

 20

 40

 60

 80

100

120

140

TKE [m2s

−2]

z [m]

S2

S3

0 5 10 15 20 25 30

 10

 20

 40

 60

 80

100

120

140

Peak CTKE [m2s

−2]

z [m]

S2

S3

(a) TKE (b) Peak CTKE

0 200 400 600 800

 10

 20

 40

 60

 80

100

120

140

Λ(u) [m]

z [m]

S2

S3

(c) Horizontal turbulence integral

length scale

0 50 100 150 200

 10

 20

 40

 60

 80

100

120

140

Λ(w) [m]

z [m]

S2

S3

(d) Vertical turbulence integral length

scale

0 0.05 0.1

 10

 20

 40

 60

 80

100

120

140

ε [m2s

−3]

z [m]

S2

S3

(e) Dissipation rate

Figure 10. Profiles of turbulence and dissipation parameters for SSE flows (175◦±15◦) at 76-m wind speeds between

11 and 13 m s-1. Plots use the same data and conventions as Figure 9.

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0

5

10

15

U N S1 S2 S3

Stratification

TKE (m

2

s−2

)

−2 −1.5 −1 −0.5 0 0.5 1 1.5 2

0

5

10

15

20

RiS (3:134m)

TKE (m

2

s−2

)

0

20

40

60

U N S1 S2 S3

Stratification

Peak CTKE (m

2

s−2

)

−2 −1.5 −1 −0.5 0 0.5 1 1.5 2

0

20

40

60

80

RiS (3:134m)

Peak CTKE (m

2

s−2

)

(a) Mean turbulent kinetic energy at 76 m (b) Peak CTKE at 76 m

Figure 11. Variation of a) mean hub-height turbulent kinetic energy and b) peak hub-height coherent turbulent kinetic -1 energy with −2 < RiS < 2. Data are limited to 76 m mean wind speeds above 3 m s and flows in the WNW sector

(285±15◦). Data are 10-minute average values obtained during the period from 10/7/2011 to 11/7/2011.

dew point temperature sensors (Eq. 19 and 20), and has been used in previous investigations of wind turbine

performance.3, 4 At the NWTC from early October 2011 to early November 2011 the Richardson number

indicated stable conditions overnight, switching to unstable conditions during the day (Fig. 12(b)). The

pattern of stable nights and unstable days agrees with the cycle from the Monin-Obukhov length.

The occasional difference between layer stability quantified using RiS and local stability quantified using

z/L reflects the complex stratification that may exist at this site. It is possible that a stable layer might

overlay an unstable layer (or vice versa) for a short period of time, which can cause apparent differences in

layer versus local stratification.

The two Richardson numbers that were defined in Section II.E always agree in sign (Figure 13(a)), but

not in magnitude. This difference in magnitude but not sign is explained by a comparison of Eq. (19) and

2 2 2 (20), as (dum/dz) + (dvm/dz) is always greater than (dU/dz) , and it is likely that there will be a small

amount of directional veer between the ground and 134 m. Comparing Ri with z/L (Figure 13(b)) shows

that the two measures do not always give the same stability, which is also seen in Figure 12(b). There is

also wide scatter around the Businger-Dyer relationships (Eq. 21), which were generated from analysis of

measurements over flat and uniform surfaces33 and have been confirmed by other measurements.20 Because

the terrain upwind of the NWTC is not flat or uniform, more turbulence is generated mechanically than

over flat, uniform terrain, particularly in the vertical and transverse directions. This modification of the

turbulence leads to large scatter compared to flat-field reference cases. This difference may not be as large

on sites with longer upwind fetch or more uniform terrain.

III.C. Implications for other sites

Modern utility-scale turbines frequently have hubs at 80 m above ground or higher, and rotor disks of 80-m

diameter or larger. Figures 7, 8, 9, and 10 suggest that in this particular location, for flows from the WNW

and SSE and at this speed, turbines extend out of the surface layer where flows are strongly influenced by

the surface and into a different region of the atmosphere. Because of the change in gradients at around 80

m, extrapolation from measurements at lower elevations to the turbine hub height will be prone to error,

particularly velocity profiles in stable conditions. Although the change in velocity profile and turbulence that

we see at the NWTC might not occur at all sites at this height, this behavior cannot be known a priori. This

uncertainty is a strong argument for a careful survey of the atmosphere using direct measurement rather

than extrapolation as part of the wind resource assessment process.

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 0 5 10 15 20

−300

−200

−100

0

100

200

300

400

Midnight

MST

Midday

MST

Hour of Day [UTC]

Q0 (15 m) [w m−2

]

0 5 10 15 20

0

50

100

150

Hour of Day [UTC]

Frequency [x10 min]

Frequency of z/L (76 m)

U

N

S

0 5 10 15 20

0

50

100

150

Frequency [x 10 min]

Frequency of Speed Ri (3:134 m)

U

N

S1

S2

S3

(a) Diurnal heat flux cycle (b) Diurnal stability cycle

Figure 12. Diurnal cycles of a) Heat flux Q0 at 15 m and b) RiS from 3 to 134 m and z/L at 76 m. Stability classes

shown in (b) are listed in Table 2. Data are 10-minute average values from 10/7/2011 to 11/7/2011 for 76 m wind

speeds above 3 m s-1. Data include all wind directions except the tower wake (105◦ ± 30◦).

−10 −5 0 5 10

−10

−8

−6

−4

−2

0

2

4

6

8

10

RiS (3:134m)

Gradient Ri (3:134m)

(a) Comparison of Richardson numbers calculated with (Gradient Ri) and without directional shear (RiS, also Speed Ri).

−2 −1.5 −1 −0.5 0 0.5 1 1.5 2

−2

−1.5

−1

−0.5

0

0.5

1

1.5

2

z/L (76 m)

Ri

S (3:134m)

Tower data

Businger−Dyer

(b) Comparison of Speed Richardson number RiS and normalized hub-height Monin-Obukhov Length z/L.

Figure 13. Comparison of the layer stability measures (a) Ri and RiS and (b) layer stability measure RiS with the

local stability measure z/L on the tower. The Businger-Dyer relationship between RiS and z/L (Eq. 21) is shown for

reference. Data are limited to 76 m mean wind speeds over 3 m s-1. Data include all wind directions except the tower

wake (105◦ ± 30◦).

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Vertical profiles of the wind speed, turbulence and dissipation rate also show that there are significant

differences between conditions that a turbine experiences, as stability changes but wind speed and direction

stay constant. Figures 7, 8, 9 and 10 show that TKE can vary by a factor two between strongly stable and

unstable conditions, while TKE and peak CTKE are both highest in low RiS conditions that are also linked

to high turbine loads. Stability, TKE, peak CTKE, and dissipation all influence wake propagation and so

including their real values or probability distributions in a site optimization process may lead to improved

turbine siting.

The relative insensitivity of the Richardson number to local effects, compared to Monin-Obukhov length

(Figures 12(b) and 13(b)), is a good argument for the use of the Richardson number to quantify flows when

considering the interaction of a turbine with the wind. Another reason to use the Richardson number is that

the Richardson number integrates conditions over the entire height of the turbine, and the turbine uses all

of the flow through the turbine rotor to produce power. In comparison, the Monin-Obukhov length L uses

data measured at a few discrete heights. The Richardson number can be calculated from data taken at 1 Hz

by cup anemometers, wind vanes, differential temperature sensors, and humidity sensors, and so meaningful

stability data can be obtained as part of a typical wind resource assessment campaign.

IV. Conclusion

Designing and instrumenting a measurement mast for inflow characterization requires careful consideration of the measurement goals and flow characteristics. At the National Wind Technology Center, two

new 135-m meteorological towers have been instrumented with sonic anemometers, temperature sensors, cup

anemometers, and wind vanes. Importantly, data from the inflow towers and turbines at the NWTC are all

timestamped with time signals from GPS satellites, allowing measurements to be synchronized.

The NWTC measurement suite allows inflow mean conditions and turbulence to be quantified as well

as local and layer stability. This high-resolution data set will be used to investigate atmospheric conditions

when high turbine loads occur; to investigate links between turbulence and stability; to further validate the

stochastic flow model TurbSim;11 for comparison with remote sensing instrumentation,16 and as test data

for nacelle-mounted LIDAR turbine control techniques.34

First data from the new 135-m tower in October and November 2011 show that wind conditions vary

considerably depending on wind direction and atmospheric stability. For the same wind speeds, as conditions

become strongly stable, wind shear increases but turbulence intensity and dissipation rate decreases compared

to unstable conditions. The change in dissipation rate will be important for the duration of wakes downstream

of operational turbines, resulting in more persistent wakes in stable, nighttime conditions. Wind speed,

turbulence, dissipation and length scales all show different vertical profiles depending on wind direction.

TKE, peak CTKE and the dissipation rate E in flows from the same direction all peak under slightly stable

conditions, supporting previous studies. Results also show changes in wind speed gradients at or near

turbine hub heights (80 to 100 m above ground). Therefore, incorrect estimates of turbine hub or rotor disk

conditions would be made if data are extrapolated from lower-level data, such as 60- or 80-m-tall towers.

The data resulting from the long-term operation of these towers and turbines will be crucial for validating

existing aerostructural design models for multi-megawatt turbines, and for developing improved models for

designing larger, more efficient next-generation turbines.

Acknowledgments

This work was carried out with the support of the DOE/EERE Wind and Water Program.

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