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Innovation for Our Energy Future 

U.S. Department of Energy 

Workshop Report: 

Research Needs for Wind 

Resource Characterization 

Jointly Sponsored by: 

DOE Office of Science, 

Office of Biological and Environmental 

Research 

and 

DOE Office of Energy Efficiency and 

Renewable Energy, 

Wind & Hydropower Technologies 

Program 

Co-Chairs: 

S. Schreck, J. Lundquist, and W. Shaw 

Technical Report 

NREL/TP-500-43521 

June 2008 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
U.S. Department of Energy 

Workshop Report: 

Research Needs for Wind 

Resource Characterization 

Jointly Sponsored by: 

DOE Office of Science, 

Office of Biological and Environmental 

Research 

and 

DOE Office of Energy Efficiency and 

Renewable Energy, 

Wind & Hydropower Technologies 

Program 

Co-Chairs: 

S. Schreck, J. Lundquist, and W. Shaw 

Prepared under Task No. WER7.0201 

Technical Report 

NREL/TP-500-43521 

June 2008

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U.S. Department of Energy Workshop Report 

Research Needs for Wind Resource Characterization 

Jointly Sponsored by: 

DOE Office of Science, 

Office of Biological and Environmental Research 

and 

DOE Office of Energy Efficiency and Renewable Energy, 

Wind & Hydropower Technologies Program 

January 14 – 16, 2008 

Broomfield, Colorado 
U.S. Department of Energy Workshop Report

Research Needs for Wind Resource Characterization 

Co-Chairs 

Julie Lundquist, Lawrence Livermore National Laboratory 

Scott Schreck, National Renewable Energy Laboratory 

William Shaw, Pacific Northwest National Laboratory 

DOE Office of Biological and Environmental Research 

Rickey Petty, Program Manager 

Ashley Williamson, Program Manager 

DOE Office of Energy Efficiency and Renewable Energy 

Sam Baldwin, Chief Technology Officer 

Administration

Stacey Burge, National Renewable Energy Laboratory 

Bruce Green, National Renewable Energy Laboratory 

Report Publication and Website 

National Renewable Energy Laboratory 

 ii
Recommendation Panel Chairs and Authors 

Turbine Dynamics Panel 

Co-Chaired by Scott Schreck, NREL; and Larry Carr, US Army AMRDEC 

Isolated Turbine Inflow Characterization

Jonathon Naughton, University of Wyoming 

Ingestion of Wake from Upwind Turbines

Scott Schreck, NREL 

Extreme and Anomalous Inflow Events

Jason Jonkman, NREL 

Micrositing and Array Effects Panel 

Chaired by Pat Moriarty, NREL 

Improved Wake Models

Rebecca Barthelmie, Edinburgh University / Indiana University 

Planetary Boundary Layer Research and Development

Edward Patton, NCAR 

Acquiring and Exploiting Large-Scale Data Sets

Patrick Moriarty, NREL 

Technology Transfer and Tool Integration

Patrick Moriarty, NREL 

Mesoscale Processes Panel 

Chaired by Walter Bach, ARO 

Wind Energy Testbed Development 

Jim Wilczak, NOAA 

Al Germain, BP Alternative Energy 

Improve Industry and Atmospheric Modeling 

Bob Banta, NOAA, 

Jerome Fast, PNNL 

Create and Convene a Cooperative Working Group 

Walter Bach, Army Research Office 

Tom Acker, Northern Arizona University 

 iii
Climate Effects Panel 

Chaired by Ruby Leung, PNNL 

Improve Quantification and Identify Causes of Historical Change and 

Variability of Wind Resources 

Jeff Freedman, AWS True Wind 

Darko Koracin, Desert Research Institute 

Gene Takle, Iowa State University 

Sara Pryor, Indiana University 

Improved Quantification of Future Changes in the Mean and Variability of 

Wind Climates/Resources 

Sara Pryor, Indiana University 

Ruby Leung, PNNL 

Darko Koracin, Desert Research Institute 

Dora Yen Nakafuji, LLNL/California Energy Commission 

Interactions Between Wind Plants and Local, Regional, and Global Climates 

Gene Takle, Iowa State University 

Manda Adams, University of Calgary 

Steve Chin, LLNL 

 iv
 v

List of Acronyms 

ABL – Atmospheric Boundary Layer 

ACARS – Aircraft Communications Addressing and Reporting System

AIAA – American Institute of Aeronautics and Astronautics 

AOGCM – Atmosphere-Ocean General Circulation Models 

ARM – Atmospheric Radiation Measurement 

ASCOT – Atmospheric Studies in Complex Terrain 

ASME – American Society of Mechanical Engineers 

ASOS/AWOS – Automated Surface/Weather Observation System 

AWEA – American Wind Energy Association 

BEM – Blade Element Momentum 

CASES-99 – Cooperative Atmosphere-Surface Exchange Study 1999 

CFD – Computational Fluid Dynamics 

DES – Detached Eddy Simulation 

DNS – Direct Numerical Simulation 

GBRS – Ground-Based Remote Sensing 

IEC – International Electrotechnical Commission 

IPCC – Intergovernmental Panel on Climate Change 

LES – Large Eddy Simulation 

LIDAR – Light Detection and Ranging 

LTER – Long Term Ecological Research 

MEMS – Microelectromechanical Systems 

NWP – Numerical Weather Prediction NCAR – National Center for Atmospheric Research 

NCDC – National Climatic Data Center 

NARCCAP – North American Regional Climate Change Assessment Program

PBL – Planetary Boundary Layer 

PCMDI – Program for Climate Model Diagnosis and Intercomparison 

RANS – Reynolds Averaged Navier-Stokes 

SODAR – SOnic Detection And Ranging 

VAD – Velocity Azimuth Display 

WAsP – Wind Atlas Analysis and Application Program 

WRF – Weather Research and Forecasting model 
Executive Summary 

The U.S. Energy Information Agency estimates that our nation’s electricity demand will rise by 

39 percent in the years 2005 to 2030 and will reach 5.8 billion megawatt-hours by 2030. At 

present, wind energy provides approximately 1 percent of total U.S. electricity generation. To 

produce 20 percent of that electricity, U.S. wind power capacity will need to exceed 300 

gigawatts, corresponding to an increase of more than 285 GW over 2007 levels. A recent 

analysis (available at: http://www1.eere.energy.gov/windandhydro/pdfs/41869.pdf) by the U.S. 

Department of Energy (DOE), National Renewable Energy Laboratory (NREL), and American 

Wind Energy Association (AWEA) demonstrated the feasibility of expanding the U.S. wind 

industry to this level. While the 20 percent wind scenario is not a prediction of the future, it does 

represent a plausible scenario for wind energy in the U.S. electricity generation portfolio. 

To exploit these opportunities and address attendant problems, a workshop concentrating on 

Research Needs for Wind Resource Characterization was convened during January 14 – 16, 2008 

in Broomfield, Colorado. This event was organized collaboratively on behalf of two DOE 

organizations; the Office of Biological and Environmental Research (OBER) and the Office of 

Energy Efficiency and Renewable Energy (EERE). This workshop had two purposes. First, it 

brought together select researchers from the atmospheric science and wind energy engineering 

communities to identify leading problems in these two areas. Second, it solicited 

recommendations from these experts regarding productive future DOE research thrust areas. 

Ultimately, the workshop was attended by over 120 atmospheric science and wind energy 

researchers, from industry, academia, and federal laboratories in North America and Europe. 

To provide a framework for the workshop, four focus areas were delineated: 1) Turbine 

Dynamics, 2) Micrositing and Array Effects, 3) Mesoscale Processes, and 4) Climate Effects. 

These areas were introduced in four plenary presentations delivered by internationally 

recognized experts representing these fields. Participants then joined one of three “Problem 

Definition” breakout groups to address these focus areas. Each group included participants from 

the atmospheric science and wind energy engineering communities to promote knowledge 

exchange between the two. After the Problem Definition groups had addressed each of the four 

focus areas, participants then joined one of four “Recommendation” breakout groups aligned 

with their individual expertise to distill the problem definition discussions into a cohesive set of 

research thrust recommendations. The resulting research thrust recommendations are 

documented in detail in the following report, and summarized below. 

Workshop discussions made it clear that research in each of the four focus areas had developed 

in relative isolation from the others. The nonlinear fluid mechanic character underlying these 

areas compelled this compartmentalization to achieve tractability, and the physical separation in 

spatial and temporal scales generally facilitated success. However, one major theme that 

emerged from the workshop was that continued progress in wind energy technology would 

require interdisciplinary unification with the atmospheric sciences to exploit previously untapped 

synergies. A second major theme that emerged from the workshop was the need to apply 

experiments and observations in a coordinated fashion with computation and theory. In addition 

to these two comprehensive recommendations that were common to all four focus areas, specific 

recommendations were delineated for each focus area. 

 1
In the Turbine Dynamics focus area, detailed characterizations of inflows and turbine flow fields 

were deemed crucial to attaining the accuracy levels in aerodynamics loads that will be required 

for future machine designs. To effectively address the complexities inherent in this area, an 

incremental approach involving hierarchical computational modeling and detailed measurements 

was recommended, wherein the isolated turbine would be considered initially, and then the 

ingestion of a wake trailed from an upwind turbine would be addressed. In addition, a third 

thrust was recommended that would entail modeling extreme and anomalous atmospheric inflow 

events as well as the aerostructural responses of turbines immersed in these events. 

The Micrositing and Array Effects focus area considered improved wake models to be important 

for more accurately characterizing energy capture losses and higher turbulence downwind of 

large, multiple row wind plants. Planetary boundary layer R & D was deemed necessary to 

achieve accurate, reliable determination of inflow mean structure and turbulence statistics in the 

presence of various atmospheric stability effects and complex land-surface characteristics. 

Finally, a requirement was identified for acquiring and exploiting large-scale wind inflow data 

sets, which will need to cover heights to 200 m and satisfy high spatial and temporal resolution 

requirements unique to wind energy needs. 

The Mesoscale Processes focus area deemed improvements in fundamental understanding of 

mesoscale and local flows crucial to providing enhanced model outputs suited for wind energy 

production forecasts and wind plant siting. This improved understanding would be exploited by 

developing wind forecasting technologies well suited to wind plant siting and operations. 

Modeling approaches must be developed to resolve spatial scales in the 100-m to 1000-m range, 

a notable gap in current capabilities. Validation of these models will require development and 

deployment of new instruments and observational strategies, including additional analyses of 

existing measurements and additional longer-term measurements. 

In the focus area of Climate Effects, theoretical, numerical, and observational work was 

recommended to identify and understand historic trends in the variability of wind resources to 

increase confidence in resource estimation for future planning and validation. For the same 

reasons, similarly multifaceted research was suggested to improve quantification of future 

changes in the mean and variability of wind climate and resources. Workshop participants also 

considered it important to characterize interactions between wind plants and local/regional/global 

climates through modeling and observations aimed at the physical link between wind plants and 

atmospheric boundary layer dynamics. 

High-penetration wind energy deployment to meet long range U.S. energy goals represents a 

daunting though attainable and crucial national objective. Meeting these goals will require an 

unprecedented ability to characterize the operation of large wind turbines deployed in gigawatt 

wind plants and extracting elevated energy levels from the planetary boundary layer. Success 

will demand accurate, reliable computations and measurements across an expansive scale range 

that extends from microns to kilometers. Assets residing within the DOE national laboratory 

complex, allied with researchers from industry and academia, represent a formidable capability 

for realizing these objectives. Together, these resources will lay the foundations for a 

challenging collaborative research agenda of wind resource characterization in support of 

aggressive wind energy deployment to meet strategic U.S. energy needs. 

 2
Contents 

Executive Summary........................... .............. ................................................................1 

1. Introduction and Background....................................... ........... .........................................4 

2. Summary of Recommendations............................................. .......... ................................6 

Detailed Recommendations – Turbine Dynamics............... ................ .......................................17 

3. Isolated Turbine Inflow Characterization.................... ........... ...........................................18 

4. Ingestion of Wake from Upwind Turbines.................. ........... ............................................25 

5. Extreme and Anomalous Inflow Events..................... ............ ...........................................31 

Detailed Recommendations – Micrositing and Array Effects............ ........ ...................................37 

6. Improved Wake Models.............. ......... ........................................................................38 

7. Planetary Boundary Layer Research and Development...... ..... ...........................................45 

8. Acquiring and Exploiting Large-Scale Data Sets...... .......... ................................................54 

9. Technology Transfer and Tool Integration............... ......... ................................................60 

Detailed Recommendations – Mesoscale Processes......... ....... ............................................63 

10. Wind Energy Testbed Development........................ ...... ................................................64 

11. Improve Industry and Atmospheric Modeling.................... ...............................................69 

12. Create and Convene a Cooperative Working Group............ ... .........................................74 

Detailed Recommendations – Climate Effects.................. ........... .........................................77 

13. Improve Quantification and Identify Causes of Historical Change and Variability of Wind 

Resources....... .............................................................................................................................. 78 

14. Improved Quantification of Future Changes in the Mean and Variability of Wind 

Climates/Resources.............. .................................................................................................... ...86 

15. Interactions Between Wind Plants and Local, Regional, and Global Climates .......................93 

Appendix A – Workshop Logistics... ............. .....................................................................100 

Appendix B – Workshop Agenda...... ............ ....................................................................101 

Appendix C – Workshop Attendee List... ............ ...............................................................102 

 3
1. Introduction and Background 

1.1. Wind Energy Prospects 

The U.S. Energy Information Agency estimates that our nation’s electricity demand will rise by 

39 percent in the years 2005 to 2030, and will reach 5.8 billion megawatt-hours by 2030. To 

produce 20 percent of that electricity, U.S. wind power capacity will need to exceed 300 

gigawatts, corresponding to an increase of more than 285 GW over 2007 levels. A recent 

analysis by the DOE, NREL and the American Wind Energy Association (AWEA) demonstrated 

the technical feasibility of expanding the U.S. wind industry to this level 

(http://www1.eere.energy.gov/windandhydro/pdfs/41869.pdf). While the 20 percent wind 

scenario is not a prediction of the future, it does represent a plausible scenario for the role of 

wind energy in the U.S. electricity generation portfolio. 

In 2006, wind energy production costs ranged from 5 to 8.5 cents per kWh (before any reduction 

by the federal production tax credit, at 1.9 cents/kWh in 2006). Since 2004, turbine costs have 

increased due to strong demand for new wind equipment, commodity price increases for 

materials like steel and copper, and the weakening U.S. dollar. As wind energy continues to 

successfully confront these difficulties, it is clear that research and development has enabled 

wind to become and remain a viable power source in today’s energy market. Nonetheless, wind 

energy still provides less than 1 percent of total U.S. electricity generation. Accelerating 

deployment and further improving cost effectiveness will entail a combination of reducing 

initial, operations and maintenance, and replacement costs, while maintaining or increasing 

energy capture. 

Various risks threaten the U.S. wind industry’s ability to meet this challenge. Wind plant 

performance, in terms of revenues and operating costs, compared with the projections used in 

financing will drive the risk level of future installations. In addition, risk associated with 

introducing new technology at the same time that manufacturing is scaling up and production is 

accelerating to unprecedented levels will be substantial. Before turbine manufacturers can stake 

the next product on a new feature, performance of that innovation needs to be firmly established 

and durability needs to be characterized as well as possible. The consequences of these risks 

both directly affect the revenues of wind equipment manufacturers and wind plant operators, and 

indirectly affect the continued growth of investment in wind. Direct impacts include increasing 

operations and maintenance costs, poor availability driven by low reliability, and poor wind plant 

array efficiencies. Indirect impacts include increased cost of insurance and financing, slowing or 

stopping development, and loss of public support. 

1.2 Workshop Motivation

Ultimately, investment in wind resource characterization research aimed at improving wind 

turbine and wind plant performance while mitigating risk and uncertainty holds the potential to 

save billions of dollars in energy costs annually with a U.S. power generation portfolio that 

includes 20 percent wind. To exploit these opportunities and address attendant problems, a 

workshop focusing on research needs for wind resource characterization was convened during 

January 14 – 16, 2008, in Broomfield, Colorado. This event was organized collaboratively on 

 4
behalf of two DOE organizations, the Office of Biological and Environmental Research (OBER) 

and the Office of Energy Efficiency and Renewable Energy (EERE). 

This workshop had two purposes. The first was to bring together select researchers from the 

atmospheric science and wind energy engineering communities to survey the state of the art and 

identify leading problems in these two areas. The second was to solicit recommendations from 

the assembled scientific and technical community regarding potentially productive future DOE 

research thrust areas. The results of this workshop are documented in the following report. 

1.3 Report Structure 

Immediately following the current section, abbreviated summaries of research thrust 

recommendations are presented for the four workshop focus areas of turbine dynamics, 

micrositing and array effects, mesoscale processes, and climate effects. Preceding these 

summaries of individual research thrust recommendations are synopses of two research themes 

that emerged repeatedly throughout the workshop. In addition to the several research related 

summaries, three recommendations for organization and process within the wind engineering and 

atmospheric science communities are summarized. Finally, after some brief remarks to conclude 

the condensed portion of the report, four to six-page reports offering considerably greater detail 

for each research thrust recommendation are presented. Comprising the report appendices are a 

description of workshop logistics, the workshop agenda, and a list of attendees.

 5
2. Summary of Recommendations

2.1 Cross-Cutting Research Themes 

Two common themes emerged as recommended research thrusts from across the spatio-temporal 

scales discussed at the workshop: the need for observations for model improvement and 

validation, and the need for improved modeling capabilities, which span spatio-temporal 

scales from those associated with wind turbine dynamics to those pertaining to climate 

effects. This convergence of scientific and technical needs provides opportunities for leveraging 

investments and encouraging collaboration across spatio-temporal scales. For example, any 

investment in collecting observations required for advances in one area will likely contain the 

possibility that, for an additional incremental cost, the scientific needs of other communities can 

be addressed as well. Additionally, many of the modeling research thrust recommendations 

recognized that, for scientific progress to occur, integrated approaches, which bridge spatiotemporal scales, are required. Maximizing this opportunity will require integrated planning 

between the mesoscale/climate research communities and the turbine-scale/micrositing research 

communities. 

2.1.1 Acquire observations for model validation and improvement 

Each of the four focus areas – turbine dynamics, micrositing and array effects, mesoscale 

processes, and climate effects – identified an outstanding need for integrated datasets that offer 

information for developing and validating models of their respective domains. These integrated 

datasets include profiles of wind measurements, as well as characterization of atmospheric 

stability and the necessary information to characterize the site where the observations are 

collected, through diurnal and seasonal cycles. Most wind turbines reside in the lowest portion of 

the planetary boundary layer, from the surface to about 200 m above the surface. Although there 

are networks of wind speed observations at 10-m heights, these observations are not 

representative of the vertical profile complexity of wind and atmospheric stability which are 

needed for improved turbine structural design, for forecasting wind resources on short and long 

timescales, or for understanding the relationship between wind plants and climate change, both 

local and global. Additionally, because wind plants are and will be deployed in diverse regions, 

including complex terrain and offshore situations, observations representative of these multiple 

regions throughout the diurnal and seasonal cycles are required. The DOE Atmospheric 

Radiation Measurement program provides a good example for such long-term measurements. 

2.1.2 Improve modeling capabilities across scale range 

Critical areas for model improvement and validation have been identified within each of the four 

spatio-temporal focus areas (turbine dynamics, micrositing and array effects, mesoscale 

processes, and climate effect), as well as in the “terra incognita” between these scales. For 

example, to understand the effects of turbine wakes on downwind turbines and the downwind 

microclimate, simulation tools are needed which can quantify turbine wakes, the evolution of the 

background atmosphere, and the interaction of the atmosphere with the surface. Therefore, this 

problem requires scientific expertise and development in the turbine scale, the micrositing scale, 

and the mesoscale focus areas, and the application is relevant to the climate-scale focus area. It is 

 6
critical to acknowledge that many of the modeling research thrusts identified in this workshop 

span the spatio-temporal focus areas. Therefore, successful and productive scientific efforts to 

address these needs must integrate expertise across these spatio-temporal scales rather than 

promoting isolated development within each scale separately. 

To maximize positive impact on the wind industry, the following measures should be taken to 

facilitate transition of these scientific advances to engineering application. 

• Computational methodologies should be taken from the turbine dynamics, micrositing 

and array effects, mesoscale processes, and climate effects areas and integrated into 

appropriate tools. Considerations like resolution, reliability, and accuracy will need to be 

balanced against those like complexity, resource requirement, and turnaround time. 

• Uncertainties in the individual models as well as the resulting integrated model need to be 

carefully quantified, documented, and, to the extent possible, mitigated. 

2.2 Turbine Dynamics Summary

The Turbine Dynamics focus area identified and documented three research thrust 

recommendations: 1) Isolated turbine inflow characterization, 2) Ingestion of wake from an 

upwind turbine, and 3) Extreme and anomalous inflow events. These recommendations are 

summarized below. 

2.2.1 Develop accurate models for isolated turbine inflow characterization 

Currently, detailed inflow information is lacking, even for turbines operating in isolation from 

other turbines or wind plants. Crucial inflow information includes mean wind speed, horizontal 

and vertical shears, and turbulent velocity fields across the rotor swept area. Overall, these 

shortcomings limit accurate prediction of rotor loads, energy production, and machine lifetime 

during wind turbine design. Clear understanding and detailed characterization of the physical 

parameters that govern turbine inflows would enable construction of more accurate, reliable 

turbine inflow models. A hierarchy of computational models will be crucial for successfully 

characterizing these turbulent inflows, including large eddy simulation models. Requisite model 

validations will entail data with temporal and spatial resolution sufficient to capture turbulence 

scales pertinent to turbine inflows and aerodynamics. These data will need to be of sufficient 

duration and frequency to document variations in inflow conditions through cycles of varying 

periods (diurnal, seasonal, etc.). Achievement of this inflow modeling capability will enable 

more accurate prediction of loads, energy production, and lifetime during the design process. 

This, in turn, will reduce uncertainty in deployed performance, help decrease finance costs, and 

drive down wind energy costs. 

2.2.2 Develop models to characterize ingestion of wakes from upwind turbines 

Wind turbine vortical wakes persist over significant distances, and impinge on downwind 

turbines in wind plant arrays. While atmospheric inflows alone present significant physical 

complexities, addition of turbine wakes containing distributed vorticity and concentrated vortices 

will require that a more expansive range of spatial and temporal scales be characterized. 

Efficient resolution of the full range of pertinent scales will require a hierarchical modeling suite 

 7
combined with adaptive gridding. In a similar manner, measured validation data must 

adequately resolve distributed vorticity and cohesive vortices in addition to atmospheric 

structures. Combined with other types of instrumentation, three-dimensional scanning LIDARs 

(LIght Detection and Ranging) will provide the spatial coverage and resolution needed to acquire 

these data. In addition to field testing, it is likely that carefully targeted experiments in large 

wind tunnels will be useful for acquiring measurements under controlled conditions not possible 

in the field environment. This inflow modeling capability will furnish more accurate prediction 

of loads, energy capture, and lifetime for turbines deployed in wind plants. This, in turn, will 

reduce uncertainty in deployed performance, help decrease finance costs, and drive down wind 

energy costs. 

2.2.3 Characterize extreme and anomalous inflow events 

Concentrated wind gusts, rapid wind direction changes, or passage of energetic atmospheric 

structures impose critical loads on wind turbines. These extreme events expend turbine fatigue 

life, cause component failures, and can even threaten catastrophic machine failure. Advanced 

Reynolds averaged Navier-Stokes (RANS) and large eddy simulation (LES) atmospheric models 

likely can provide more spatially detailed, time-accurate representations of atmospheric events. 

These can then be coupled with turbine aeroelastic models to predict the impact of these extreme 

events on wind turbine structures and operation. Measured data, having required resolution in 

time and space, could be acquired via current 1-D or future 3-D scanning LIDARs. In addition, 

data from current atmospheric measurement systems could be examined using methodologies 

appropriate to detecting and characterizing events considered extreme or anomalous by the wind 

energy community. Ultimately, comprehension of these events could enable real-time sensing 

and control response, to mitigate impacts on operating turbines. These measures would improve 

deployed turbine reliability, directly decreasing operations and maintenance costs and increasing 

energy capture through reduced downtime. 

2.3 Micrositing and Array Effects Summary

Two issues identified as relevant to power plant performance were micrositing and array effects. 

These are of particular importance for wind plants that are large, sited in complex terrain, or 

subject to unique atmospheric phenomena. Micrositing refers to changing the particular location 

of a turbine with respect to the terrain or local inflow to increase energy capture, or to reduce 

inflow turbulence and the adverse loads it generates. Array effects consist of blockage and wake 

influences, both of which originate with individual turbines and are aggregated across entire 

wind plants. In either case, wake effects result in decreased inflow speeds and energy capture, 

and higher turbulence levels and fatigue loading. 

2.3.1 Construct improved wake models 

Power losses due to reduced flow behind individual turbines and higher turbulence (wakes) lead 

to problems optimizing array spacing. Wind plant models appear to make reasonable predictions 

of power losses due to wakes in small wind plants (less than 3 – 4 rows). However, large 

offshore wind plant experience shows a very large discrepancy exists between standard 

predictions of the average power loss due to wakes which are 10 to 15 percent of total average 

 8
power and those which have been observed. In large wind plants in complex terrain, it is 

postulated that similar discrepancies exist. In brief, the most urgent research needs are: 

• Energy capture losses in large wind plants are under-predicted, and improper wake 

modeling is a likely source. The scale and cause of this problem should be assessed. 

• The effect of wakes on fatigue loads is unclear over project lifetimes. The impact of 

wakes on turbine loads is significant but needs to be quantified. 

• The effect of wind plants on each other is unclear. As multiple clusters of wind plants are 

developed within 20 km of each other, particularly offshore, tools to evaluate the impact 

or shadowing effect of one wind plant on a nearby wind plant are needed. 

• Turbulence/load effects and power production estimates are often uncoupled and are not 

yet incorporated into current models. These two computations are the critical components 

of the next generation models to optimize wind turbine spacing which will maximize 

energy output and minimize loads for a given site or area giving optimal energy costs and 

project lifetimes. 

2.3.2 Advance planetary boundary layer research and development 

As determined by current and future rotor swept areas, improved understanding and prediction of 

the planetary boundary layer (PBL) between heights of 50 m and 200 m will hold central 

importance for the wind industry. Accurate, reliable determination of inflow mean and 

turbulence statistics will entail characterization of atmospheric stability effects, as well as 

impacts due to orography and land-surface characteristics. Improved predictions of wind shear, 

extreme winds, and turbulent fluctuations will enable improved design predictions of fatigue and 

extreme loads, and will provide direct guidance for individual turbine micrositing and overall 

wind plant array placement to preclude underperformance issues. The following areas will be 

crucial to achieving these objectives. 

• Instrumentation and techniques need to be developed for observing the 50 - 200-m 

portion of the lower atmosphere and acquiring data pertinent to micrositing and array 

issues. These data likely will encompass a timescale range and involve levels of 

accuracy and precision greater than those generally required for standard weather 

forecasting. 

• Atmospheric boundary layer parameterizations of surface flux phenomena need to be 

generalized, to relax constraints associated with assumptions of spatial homogeneity, and 

to provide more detailed estimates of fluctuating and mean inflow components under 

varying stability conditions. This likely will entail higher fidelity representations of 

multi-faceted surface layer physics in the presence of pronounced near-surface 

horizontal/vertical gradients. 

• More capable modeling methodologies are needed for characterizing the combined 

effects of complex terrain, ground cover, heat transfer, and wind turbine presence. 

Computational resource growth will enable use of direct numerical simulation (DNS) and 

LES algorithms, and could lead to embedding these within mesoscale models like 

NCAR’s Weather Research and Forecasting (WRF) model. At the same time, resource 

limitations will secure a niche for subgrid modeling techniques. 

 9
• Appropriately resolved observations will be needed for understanding the physics that 

govern planetary boundary layer, micrositing, and array effects. These observations will 

need to be carried out in locations/conditions representative of existing and planned wind 

plants. To achieve this, a balance must be found between detailed, short-term 

characterizations at numerous sites and extended campaigns at fewer locations. 

2.3.3 Acquire and exploit large-scale data sets 

Both industry designers and laboratory researchers need reference data sets of higher quality and 

for a broader range of conditions than those available at present, to better quantify the 

mechanisms that govern the conversion of wind inflow to mechanical energy. Pertinent data sets 

will be unique to wind energy since current utility-class wind turbines operate in a region of the 

atmosphere (50 – 200 m above ground) that has not been thoroughly studied and remains poorly 

understood. In addition, the requirements for spatial and temporal resolution needed to 

characterize and understand detailed turbine-atmosphere interactions are more stringent than 

current data sets can provide. 

• Existing manufacturer, developer, or operator data that pertain to wind plant or wind 

turbine operation need to be assembled in a common repository with a standard format, 

and made broadly accessible. In addition to technical issues like data quality, formatting, 

and archiving, legal issues associated with intellectual property or competitive position 

will need to be resolved. 

• A network of measuring sites needs to be created to acquire new reference data. These 

sites should be located near substantial wind resources, where wind plants currently exist 

or are likely to exist in the future. Certain sites with interesting atmospheric (e.g. lowlevel jet in the Great Plains) or turbine behavior might be better resolved in space or time, 

or measured for extended periods. 

• Computational methods will be useful adjuncts for acquiring new data, and essential 

elements for exploiting data. To reduce measurement campaign time and expense, 

computational tools can be used to design and optimize experiments. Once data have 

been acquired, data mining techniques will facilitate extracting meaningful information 

from the large-scale data sets. Finally, physics-based computational methodologies can 

be applied to compensate for shortcomings in spatial or temporal resolution. 

• As atmospheric data are acquired and exploited, deficiencies in understanding and gaps 

in data will become apparent. When atmospheric measurements are deemed infeasible 

for addressing these shortcomings, appropriately designed wind tunnel experiments may 

offer an alternative. 

2.4 Mesoscale Processes Summary 

The Mesoscale Processes group identified one administrative recommendation; to convene an 

industry-research working group, and two research thrust recommendations; to establish and 

support a Wind Energy Testbed to provide datasets for characterization of the atmospheric 

boundary layer for wind energy applications, and to improve industry and research modeling 

capabilities relevant to the atmospheric boundary layer. Participant input regarding these 

 10
recommendations are included in this report. The scientific research thrusts raised by the 

Mesoscale Processes working group span the following three general areas: 

1) Improvements in Fundamental Understanding of Mesoscale and Local Flows, 

2) Development of Wind Forecasting Technologies for Siting and for Adaptive 

Operation, and 

3) Development and Deployment of New Instruments and Observational Strategies. 

2.4.1 Improve fundamental understanding of mesoscale and local flows 

The current generation of numerical weather prediction (NWP) models exhibits skill in 

predicting the synoptic weather for conventional purposes. However, the parameters critical for 

wind energy purposes, such as near-surface wind speeds, wind directions, vertical wind shears 

between the surface and turbine blade tip, and turbulence, have less impact on routine weather 

forecasts, and so have not received as much attention as severe weather (precipitation, 

thunderstorms, hurricanes). These errors in boundary-layer parameters, however, significantly 

impact the wind energy community, contributing to errors in wind plant siting and energy 

forecasts. Fundamental knowledge gaps need to be addressed in order to provide improved 

representation in models of winds, wind shears, and turbulence critical for wind energy activities. 

New or improved theories based on measurements and simulations are needed for a range of 

small-scale atmospheric processes. For example, how do boundary-layer phenomena like the 

nocturnal low-level jet and the resulting wind shear and turbulence affect wind power 

performance and machine loading? A better understanding of the origin and evolution of these 

phenomena would enable their prediction on the multiple timescales relevant for wind energy 

applications. 

Areas of research include: 

• Low-level jets 

• Stable boundary layers 

• Surface roughness properties such as canopies and complex terrain topography 

• Energy exchange between the surface and the atmosphere. 

2.4.2 Develop wind forecasting technologies for siting and adaptive operations 

NWP models have not been optimized to address the needs of the wind energy community. In 

fact, even the spatial scales addressed by these models are inappropriate for wind energy 

forecasting needs. To predict smaller scale variations in terrain and other surface roughness 

elements, including the interactions among turbines in wind plants, which affect local wind 

fields, resolution on spatial scales less than 1 km but greater than 100 m is desired. 

Unfortunately, mesoscale models rely on parameterizations valid for grid spacings as small as 

approximately 1 km, and should not be run at resolutions finer than 1 km because these models’ 

parameterizations are not valid at those scales. Large-eddy simulation (LES) models which 

employ different turbulence closure methods have been designed for spatial scales less than 100 

m. The simulation gap between mesoscale and eddy-resolving models should be bridged to 

provide local wind variation forecasts for both siting and adaptive operations. In complex terrain, 

 11
where slopes exceed 30 degrees and many wind turbines are sited, different approaches may be 

required. 

Development, testing, and validation of existing approaches tailored specifically to wind energy 

applications, such as ensemble modeling and/or data assimilation approaches would be fruitful. 

These specific developments for forecasting capability would benefit from rapid update cycles 

and assimilation of turbulence measurements, among others. Finally, efforts should be made to 

incorporate into forecasting models the ongoing research supported by other agencies’ activities, 

such as advances in cloud processes for improved parameterization of downbursts. 

Areas where research is needed include: 

• Data assimilation, including assimilation of turbulence observations, with rapid updating

• Parameterization of fundamental processes on scales needed to inform turbine operation

• Development of methods to quantify uncertainties in forecasts for operations

• Improvement in techniques to link computational fluid dynamics (CFD) and LES 

modeling at the turbine scale with mesoscale and synoptic scale models

• Incorporation of the wake effects of turbines into these forecasting models to inform 

adaptive operations.

2.4.3 Develop and deploy new instruments and observational strategies 

The forecasting technologies and uncertainty quantification described in the previous section will 

require the development of new observational capabilities and strategies, including additional 

analyses of existing measurements and additional longer-term measurements. Some of the data 

required, such as profiles of turbulence between the surface and turbine blade tip, are not 

currently available with existing technology, although multiple-LIDAR developments show 

promise in this area. Even when the observations needed are available, the appropriate methods 

for comparison of point observations with volumetric model output are poorly understood. 

Previous model evaluations of near-surface vertical gradients in wind speed, direction, and 

turbulent properties have relied on field campaign measurements made over a short period of 

time (often less than a month) and at specific sites. However, industry experience indicates that 

this strategy is inadequate for more generic model applications for all locations and time periods. 

Therefore, measurements at a variety of wind turbine sites over several years are needed to better 

assess the performance of current mesoscale model parameterizations and develop improved 

parameterizations based on new theoretical relationships. 

Areas of research need include: 

• Development of new measurement technologies and strategies including integrated 

remote sensing systems 

• Improvement in methodologies for comparing model grid values with in situ and point 

measurements 

• Characterization of atmospheric flow over multiple seasons and in multiple 

locations to test theoretical and numerical descriptions of the atmosphere in regions and 

on time scales critical for wind energy purposes. 

 12
2.5 Climate Effects Summary 

Climate change has the potential for significant financial consequences within the lifetime of a 

wind plant. The following research thrust areas address issues of climate effects on wind power 

production. 

2.5.1 Quantify and understand historic trends and variability of wind resources 

The magnitude and causes of historic changes in the wind resource are not well defined or 

understood. This stems from inadequate long-term data and metadata, an inadequate inventory of 

existing data, and an incomplete understanding of discrepancies in historical trends derived from 

different data sets. In addition, there is a need to quantify how well currently recognized regional 

and global climate indices explain wind climates across a range of scales. 

Theoretical and numerical efforts that will be required include: 

• Assessment of the strengths and weaknesses of current-generation models 

(global/regional/local) for reproducing trends and variability in the wind resource. 

• Development of metrics for model evaluations 

• Development of a coordinated set of model experiments to understand wind resources, 

trends, and variability 

• Quantification of the scales of coherent variability of wind speeds (both temporal and 

spatial) and links to global and regional forcings. 

Observational requirements include: 

• Creation of an inventory of quality-controlled data sets suitable for wind energy 

applications 

• Development of techniques to analyze data sets for wind resources, especially above the 

surface 

• Comprehensive evaluations of applications of statistical methods for wind extremes. 

The expected impacts of this research thrust include increased confidence in resource estimation 

for future planning and validation, improved understanding of physical mechanisms responsible 

for trends and variability in wind resources, facilitation of future model development, and 

reduced risk and uncertainty for investments in wind energy. 

2.5.2 Improve predictions of wind resource mean and variability 

There is currently no cohesive assessment of future wind climates over the United States. This is 

a critical need for planning major investments in wind power. 

Numerical and theoretical steps required to address this need include: 

• Development of new techniques for scale reconciliation (downscaling) 

 13
• Performance of multiple global and regional numerical simulations for evaluation of 

existing downscaling techniques and for facilitating the intercomparison of techniques 

• Development of uncertainty estimates and the assessment of major sources of uncertainty 

• Archival of appropriate variables and time scales from AOGCM 

• Examination of the probability of changes in extreme events relevant to wind energy. 

Observational needs include: 

• Development of a long-term measurement program to identify future trends; these would 

especially include measurements of wind shear and boundary layer observations above 

10 m. 

• Continued and continuous satellite- and surface-based remote sensing data for continued 

development of observation and analysis techniques. 

Efforts in this area will lead to increased confidence in resource estimation for future planning 

and validation, improved understanding of physical mechanisms responsible for future changes, 

continuing improvement of modeling capabilities and statistical analysis tools, reduced risk for 

investments in the wind resource, improved assessment of other climate change impacts, and 

better recommendations for wind plant design and siting within the context of climate change. 

2.5.3 Characterize interactions between wind plants and local/regional/global 

climates 

There is currently considerable uncertainty regarding physical feedback between wind plants and 

boundary layer dynamics and uncertainty regarding how those feedbacks might interact with or 

affect climate change. At the same time there are very few observations that could be used to 

measure the impact of existing wind plants on boundary layer dynamics. 

To respond to this issue, the following research activities are needed: 

• Development and evaluation of coupled (multi-scale) models that explicitly treat wind 

plants and incorporate them in mesoscale simulations 

• Development and evaluation of appropriate parameterizations of wind plants and other 

structures that are suitable for inclusion in models 

• Development of observational data sets that can be used to quantify the interactions 

between wind plants and boundary layer properties 

• Deployment of existing remote sensing technologies at currently operating wind plants. 

The effect of this research thrust will be to better predict the effect of wind plants on the local 

environment, to better identify optimal spacing of wind plants, and to facilitate comparative 

analyses of the impact of wind plants relative to other kinds of power plants. 

 14
2.6 Organizational and Process Summary 

In addition to the scientific research thrusts defined by the working groups, participants 

articulated three administrative recommendations. All four of the working groups, spanning the 

spatio-temporal scales from turbine dynamics to climate effects, commented on the perceived 

utility of the extensive but private meteorological datasets collected by industry participants. The 

Mesoscale Processes working group called for the continuation of the discussions initiated at this 

workshop via the establishment of an ongoing industry-research working group. 

 

2.6.1 Enable Data Access and Data Sharing 

Principals in the wind energy industry routinely collect large amounts of data – meteorological 

observations at the turbine and upwind, and amounts of power collected at each turbine in an 

array, for example – which could be used to understand complex meteorological phenomena and 

their effects on wind energy, to develop new parameterization approaches, and to test or validate 

new models. These data are not routinely shared because of intellectual property concerns and 

concerns over competitive advantages or disadvantages. By defining a quality control standard 

for such data and creating a legal and/or technical mechanism for data exchange and aggregation, 

a potentially rich source of data could become available for addressing outstanding scientific and 

technical questions.

 

2.6.2 Convene a Cooperative Industry-Research Working Group 

This workshop provided a one-time forum for members of the wind industry and meteorological 

research communities to interact, identify common priorities, and understand the other 

community’s operational capabilities and needs. The numerous common interests and activities 

of the research and industry communities could be identified and addressed in a coordinated 

manner via a long-standing cooperative working group. This group would provide guidance for 

ongoing research efforts and provide a forum to ensure the transfer of technology and 

understanding from research to industry. Specific short-term tasks could include: 

• Generate recommendations on how to best configure current state-of-the-science models 

(horizontal and vertical resolution, parameterizations) for wind energy applications. 

• Address and resolve the intellectual property concerns regarding the “data sharing” 

recommendation, above. 

2.6.3 Facilitate Technology Transfer 

Unquantified uncertainties culminate in higher risk for investors, economic penalties to 

developers for under-producing plants, and a higher cost of energy. More rapid transfer of wind 

prediction technology from research to industry would enable more accurate predictions for 

siting and operating wind plants. Efforts should include increased communications between 

research and industry via special sessions at both industry (e.g., AWEA Windpower or 

AIAA/ASME Wind Energy Symposium) and academic (e.g., American Meteorological Society's 

Boundary Layers and Turbulence Meeting) conferences. 

 15
2.7 Conclusion 

Accomplishing high penetration wind energy deployment goals to meet long-range U.S. energy 

requirements represents a daunting, though attainable and crucial, national objective. Meeting 

these deployment goals will require an unprecedented ability to characterize the operation of 

multi-megawatt wind turbines, deployed in gigawatt wind plants and extracting unprecedented 

levels of energy from the earth’s planetary boundary layer. 

From a science and engineering perspective, success will demand accurate, reliable computations 

and measurements across an expansive scale range that extends from microns to kilometers. 

OBER and EERE assets residing within the DOE national laboratory complex, allied with 

researchers from industry and academia, represent a formidable resource for realizing these 

objectives. 

The recommendations documented in this report represent the wind energy and atmospheric 

physics research communities’ incipient thoughts, and advancement beyond this initial stage will 

require more detailed documentation and long range planning. Together, these will lay the 

foundations for a challenging collaborative research agenda of wind resource characterization in 

support of aggressive wind energy deployment to meet strategic U.S. energy needs.

 16
Detailed Recommendations – Turbine Dynamics

3. Isolated Turbine Inflow Characterization.................... ................. ...........................................18 

4. Ingestion of Wake from Upwind Turbines.................. ................. ............................................25 

5. Extreme and Anomalous Inflow Events..................... ................. ...........................................31 

 17
3. Isolated Turbine Inflow Characterization 

3.1 Abstract 

An accurate representation of the inflow to a wind turbine is critical to several aspects of wind 

turbine design, deployment, and operation. Chief among the needs is a realistic inflow for 

designing the wind turbine. Unfortunately, the current ability to predict wind flows for specific 

sites (e.g. the Great Plains) that are relevant to today’s wind turbines are good approximations at 

best and totally inappropriate at worst. Related to this lack of modeling capability is the lack of 

information concerning the planetary boundary layer between 100 m and 1 km, particularly those 

properties of the atmosphere that are most critical to wind turbines. The solution to this lack of 

understanding and modeling capability is an integrated experimental/observational and 

computational/theoretical effort. The wind energy industry would benefit from such work in 

many ways, particularly if it is carried out over the next 5 – 8 years so that the results can have a 

large impact as the industry rapidly grows. 

3.2 Problem or Opportunity 

3.2.1 The Need 

Current wind turbines suffer premature failures and reduced lifetimes from those predicted 

during design. Such shortfalls represent uncertainty to prospective investors. Turbine downtime and complete turbine failure lead to a lower return on the investment than predicted. This 

lowers the confidence of investors and thus increases the cost of raising capital necessary to 

develop a wind plant. 

A key reason for these early failures is our lack of knowledge of the unsteady wind inflow that 

approaches and then interacts with the turbine. It is known that unsteady blade aerodynamics are 

the cause of unsteady loads on blades that leads to failure, but these unsteady loads can not be 

predicted if unsteady inflow is not well characterized. This example shows how wind inflow is 

inexorably linked to blade loads and thus to every system in the turbine. For example, control of 

the turbine might be used to mitigate harmful loads, but without the ability to simulate these 

loads accurately, how can effective control strategies be developed? From this example, it is 

clear that an accurate wind inflow is absolutely necessary to improvement of wind turbines as it 

is the root of all other issues considered. Improvement in our understanding and modeling of 

turbine wind inflow would have an immediate beneficial impact in several areas of turbine 

design. 

Aside from the turbine itself, there are other issues why accurate wind inflow simulations are 

needed. When siting wind plants, the developer needs a tool that can accurately estimate the 

local wind resource to optimize turbine placement. When operating a wind plant, accurate 

forecasts of the wind resource facilitate the integration of wind energy into the electrical grid and 

thereby making the power more valuable. 

 18
3.2.2 The Opportunities 

Although a clear need for better wind inflow models has been evident for a long time, this is not 

an easy need to address. The complexity of the near-surface winds (those at altitudes less than 1 

km above the ground) and wide range of scales associated with the wind flow, from mm to km, 

makes simulation of these flows challenging. Traditional mesoscale models developed for 

weather forecasting were not designed to predict wind with the level of detail required for wind 

turbine application. A large gap in our knowledge of winds between 100 m and 1 km exists. 

Current large utility-scale turbines reach above 100 m, and wind structure that interacts with the 

turbine often descends from well above the 100 m level. Large eddy simulations (LES) 

embedded within a mesoscale model may aid in understanding this region of the planetary 

boundary layer. Such approaches are being used for other applications (e.g. biological and 

chemical dispersion), but challenges still exist. For instance, how are the boundaries where the 

embedded LES starts treated? What data can be used to validate the simulations? 

Our lack of knowledge of the wind inflow resource is directly related to the lack of field 

experiments relevant to wind turbines. Although many field studies have been carried out and 

long-term wind data from airports exist, they are at the wrong heights and do not acquire and 

archive the type of data needed for characterizing the inflow. Field campaigns with a focus on 

wind field properties important to wind turbines are needed. 

3.3 Research Approach 

To develop relevant unsteady wind inflow modeling capability, an integrated approach using 

both simulation and theory, as well as experiment and field observation, is logical. Initial 

simulations would provide guidance on how to perform effective field experiments. Field 

experiments could then be used to validate and improve existing simulation capabilities as well 

as to assist in developing new simulation strategies. In addition to performing field studies, 

efforts toward developing instrumentation that can provide required atmospheric data should also 

be addressed. In the following sections, suggested experiments and computational/theoretical 

needed to move the field ahead are proposed. 

3.4 Needs from Computation/Theory 

In this integrated experimental/computational effort, the first modeling efforts should be 

oriented toward identifying locations for field campaigns and investigating how to distribute 

various instrumentation systems to optimize the data obtained. Existing simulation methods 

can be used for this effort. 

Although many simulation methodologies exist, they are not always representative of the true 

inflow. Data from field campaigns should be used to evaluate existing models and to identify 

their shortcomings. Out of this effort, a new hierarchy of simulation methodologies should be 

developed. Highly complex models that capture many of the essential flow physics should be 

developed in parallel with lower-order models that capture key details of flow at significantly 

lower computational cost. 

 19
Development of complex physical models should be undertaken to provide a better 

understanding of near-surface winds. Results from such models would aid in our 

understanding of the processes occurring in the planetary boundary layer as well as guide 

development of lower-order models. These models could also be valuable in identifying those 

types of disturbances to which turbines are most sensitive. For instance, wind inflow from such 

a model could be used with a turbine design code to identify those structures that are driving 

fatigue. One approach commonly used for developing such wind inflows is LES of the planetary 

boundary layer. Some of these models are run stand-alone with periodic boundary conditions. 

Pressure gradients typical of the area (which can be determined from mesoscale models) are used 

to drive the flow. Such a simulation is shown in Figure 1 where a Kelvin-Helmholtz billow is 

simulated in a flow typical of a low-level jet. Another approach is to embed LES simulations 

within a larger mesoscale model. For both approaches, the LES resolution required for wind 

turbine inflow must be considered (is 1 m enough?), as well as identifying those sub-grid scale 

models that are computationally efficient yet provide realistic results. For the embedded LES 

approach, how are the boundaries of the LES simulations initialized using the mesoscale model 

results? 

t = 19 t = 31 t = 47 

t = 58 t = 75 t = 125 

Figure 1 - Time evolution of 2-D slices of temperature from a 3-D large-eddy simulation (LES) of 

a Kelvin-Helmoltz billow (purple: hot, red: cold) generated by the National Center for 

Atmospheric Research’s LES code (see Kelley et al. (2004) for details). Times are made nondimensional by a characteristic time scale defined by the height of the billow and the freestream velocity defining the magnitude of the velocity shear. 

In addition to using wind inflow simulations to better understand the environment in which these 

wind turbines operate, it is also often necessary to run a set of parametric inflows for design 

purposes or to develop models that can run over very long times. As a result, in tandem with the 

more complex models, reduced order models that are computationally efficient yet capture the 

 20
important essential physics should be developed. Development of methods that complement the 

existing stochastic spectral approaches should be undertaken. In the development of these 

methods, a balance between the physics captured and the computational efficiency must be 

struck. Field test data and the results from the more complex simulation approaches can be used 

to aid in selecting those physics that are essential to keep. Tying these approaches to mesoscale 

models capable of capturing larger scale effects of weather and terrain would be beneficial. Such 

models would obviously be valuable for wind plant siting and wind forecasting. 

3.5 Needs from Observations/Experiments 

Wind resource field observations provide two valuable roles: they provide validation cases for 

models of wind inflow, and they provide insight about winds in the 100 m to 1 km gap where our 

knowledge of wind field dynamics is not complete. To develop an understanding of the wind 

resource in this vertical region, a set of field campaigns at several “typical” sites is 

recommended. These sites could include, for example, one in the Great Plains, one in a 

Mountain Valley, one in an Eastern Forest, and one on the Great Lakes. These field 

campaigns would build upon previous successful wind resource characterization campaigns with 

turbines (e.g. the Long Term Inflow and Structural Testing (LIST) experiment shown in 

Figure 2) and without turbines (e.g. the low-level jet field test shown in Figure 3). Future 

experiments will ensure that data with time and space resolution sufficient to characterize 

relevant unsteadiness/turbulence in the wind turbine inflow environment are obtained. Single 

towers, even so-called tall towers, are not sufficient for these types of tests. Instead, tests should 

be driven by perceived needs. For example, horizontal and vertical correlations, spatial 

information to 30 m, and identification of the inflow unsteadiness/turbulence that significantly 

impacts the turbine are all important to wind resource characterization. Although sufficient 

spatial and temporal resolution is needed, a sufficiently long test duration is also necessary to 

capture diurnal and seasonal variations as well to characterize the nature of extreme events 

present at that location. At all sites, the vertical and directional shear as well as the effects of 

atmospheric stability should be characterized. In addition, features particular to the given 

location, such as low-level jets in the Great Plains (Figure 4) and mountain waves in the lee of 

the Rockies, should be investigated. Although intensive measurements will be of limited 

duration, less involved long-term measurements should be continued. In this way, the long-term 

characteristics of the site would be developed and follow on investigations with new 

instrumentation (as it is developed) could be carried out. 

Although the pressing need for such measurements will necessitate the use of available 

instrumentation such as arrays of towers, SOnic Detection And Ranging (SODAR), and LIght 

Detection And Ranging (LIDAR), development of instrumentation for measurements needed for 

wind resource characterization should be pursued. Although time-resolved measurements over 

an entire wind inflow plane would be the ultimate goal, intermediate steps such as multiple point 

scanning systems capable of reaching to 300 m would be beneficial. To go along with such 

measurements, techniques to estimate the wind-inflow dynamics from limited measurements 

should be developed. As instrumentation and analysis methods are developed, field tests at the 

same sites listed above (Great Plains and Rockies) should be carried out to further improve our 

understanding of the near-surface wind field. A related instrumentation effort is development of 

an inexpensive system for monitoring wind from the wind turbine for anticipating the wind 

 21
inflow. Such capability would permit identification of the inflow needed to implement control to 

reduce turbine loading and increase efficiency. 

Figure 2 - NREL Long-Term Inflow and Structural Testing (LIST) Program (see Kelley et al. 

(2002) for details). Inflow turbulence measurement array looking downwind towards the NWTC 

600-kW CART turbine. Photo by Warren Gretz (NREL). 

 (a) (b) 

Figure 3 - Low-level jet field test near Lamar, CO (See Kelley et al. (2004) for details) : (a) GE 

Wind 120-meter meteorology tower, and (b) acoustic wind profiler (SODAR) flat-plate, 64-

element phased array antenna base. Photos by Warren Gretz (NREL). 

3.6 Potential Wind Energy Impact 

The impact of the work proposed here will be felt quickly as our understanding and ability to 

model the unsteady turbine inflow improve. Understanding what fatigues wind turbines will 

enable more reliable turbine designs. Likewise, better inflow models will allow for the 

development of turbine control strategies that can minimize unfavorable loading and can increase 

efficiency. As the ability to simulate winds at specific sites improves, improved siting of wind 

 22
plants and enhanced power production prediction will be possible. Involvement of industry in 

work suggested here is critical so they can benefit from some results immediately and plan for 

using the new tools as they become available. Height above ground level (m)

50

100

150

200

250

300

350

400

450

500

50

100

150

200

250

300

350

400

450

500

8 

10 

12 

14 

16 

18 

Local standard time 

 01:00:00 02:00:00 03:00:00 04:00:00

Mean

Wind

Speed

(m/s)

Figure 4 - The evolution of well-formed low-level jet structure observed during the early morning 

hours of June 17, 2002, on the high plains south of Lamar, Colorado. The contours of 10-

minute mean wind speeds with height show an initial jet forming about 00:30 h at a height of 

about 250 m, but then replaced by a higher jet forming between 450 and 500 m at 01:30 h. The 

latter jet then increases in speed and descends back to 250 m as the night goes on. This jet 

does not break down into turbulence, but induces very high values of vertical wind shear in the 

area outlined in cyan representing the height range occupied by a GE 1.5-MW wind turbine 

rotor. See Kelly et al. (2004) for details. 

3.7 Key Events and Approximate Timing 

The earlier this effort starts and the more intensive the effort, the sooner it can have an impact on 

the wind energy penetration into the market. Better turbines, improved siting, and more accurate 

forecasting that will be possible with the models developed here will all improve the deployed 

performance of wind turbines. With reduced uncertainty about the turbine’s operation, investor 

confidence will rise, reducing the financing cost, and thus, the overall cost of wind energy. The 

federal government has indicated that up to 20% of our electricity can be supplied by wind 

(Advanced Energy Initiative, February 2006). For this to occur in a timely fashion, it is 

important that work suggested here is undertaken and completed within the next 5 – 8 years so 

 23
that it can have an impact on the large number of wind turbines to be installed in the 2015 to 

2030 time frame. 

References 

Advanced Energy Initiative, The White House National Economic Council, pg. 13, 

http://www.whitehouse.gov/stateoftheunion/2006/energy/. 

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

“Lamar Low-Level Jet Program Interim Report,” NREL Report No. TP-500-34593, 2004, 

http://www.nrel.gov/docs/fy04osti/34593.pdf. 

Kelley, N., Hand, M., Larwood, S., and McKenna, E., “NREL Large-Scale Turbine Inflow and 

Response Experiment--Preliminary Results,” NREL Report No. CP-500-30917, 2002 (see also 

2002 ASME Wind Energy Symposium, January 2002, pp. 412—426), 

http://www.nrel.gov/docs/fy02osti/30917.pdf. 

 24
4. Ingestion of Wake from Upwind Turbines 

4.1 Abstract 

Wind turbine blade and wake flow fields are energetic, complex, and challenging to characterize, 

even in isolation from each other. Moreover, when a wind turbine rotor ingests the wake 

produced by an upwind turbine, complexity proliferates as powerful physical mechanisms 

restrict energy capture and amplify destructive fatigue loads. Though these adverse effects 

strongly impede turbine operation and substantially increase cost of energy, present capabilities 

for measuring or predicting them remain limited. Development of accurate, reliable methods for 

characterizing turbine operation in the presence of wake ingestion would help formulate effective 

solutions to problems that currently hinder wind turbine operation and wind plant productivity. 

4.2 Problem or Opportunity 

During routine operation, a wind turbine rotor produces a wake consisting of vorticity shed from 

the rotating blades. The principal structures present in the wake are cohesive helical vortices 

trailed from the blade root and tip, and helicoidal vorticity sheets simultaneously emitted from 

the blade’s central portion. Because of rotor energy extraction, this wake incurs a momentum 

deficit with respect to the undisturbed flow outside the wake. Wake structure becomes even 

more complicated when vertical or horizontal shears are added to the inflow velocity profile, or 

the rotor is yawed with respect to the wind inflow direction. Under these conditions, the blades 

undergo dynamic stall, which disrupts the circumferential uniformity of the tip vortex, and 

injects large, energetic vortices into the previously undisturbed helicoidal vorticity sheets. 

Though diffusion, vortex instabilities, and similar phenomena tend to dissipate the wake, it 

persists downwind of the rotor for several disc diameters. 

In wind plant arrays, turbine wakes can be ingested by downstream turbine rotors, depending on 

wind inflow conditions. When this occurs, turbine operation and cost of energy are adversely 

impacted in two general respects. First, because the downwind rotor is at least partially 

immersed in the low momentum wake trailed by the upwind rotor, less energy is available for 

extraction, thereby reducing energy capture by the downwind turbine. Second, as the downwind 

rotor blades intersect the spatially complex vorticity field generated by the upwind rotor, 

downwind rotor aerodynamic loads suffer large, rapid oscillations, which prematurely exhaust 

turbine structural life. It is likely that these two phenomena, as well as other unidentified wake 

ingestion interactions play prominent roles in wind plant underperformance and premature 

failure of blades and gearboxes. However, current understanding of turbine wakes and ingestion 

events remains incomplete. This precludes accurate modeling of these effects and reliable 

prediction of adverse impacts on wind plant operation and turbine reliability. While initial 

concentration will focus on the two-turbine circumstance, longer term approaches ultimately will 

need to address multiple turbine rows in modern wind plants. 

4.3 Research Approach 

Fundamentally, the turbine wake ingestion problem consists of interaction between a wake 

generated by an upwind turbine and the rotor blades of a downwind turbine. Thus, accurate, 

 25
reliable prediction of wake ingestion and resultant effects will require detailed characterization 

and understanding of both blade and wake aerodynamics. However, spatial scales that govern 

these phenomena range from microns up to hundreds of meters, and involve a correspondingly 

broad range of temporal scales. This scale range requires developing and maturing approaches 

capable of accurately, reliably, and efficiently characterizing these expansive scale ranges. 

Specifically, there exists a need for complementary prediction and measurement capabilities, 

which can be used in mutually supportive fashion to furnish information and formulate solutions 

to problems precipitated by turbine wake ingestion. 

 

4.3.1 Computational Prediction 

Dynamic mesh adaptation can help increase predictive accuracy and bound computational 

workload by increasing mesh density only where higher spatial resolution is deemed necessary, 

as determined with reference to the evolving computational solution (Duque et al.). Anisotropic 

adaptation to match grid element shape to the local physical length scales offers substantial 

reduction in computational effort (Sahni et al.). Although effective in limiting the total number of 

mesh points, grid adaptation can lead to load balancing difficulties on parallel architectures, and 

can carry significant implications with respect to solver algorithms. Also requiring consideration 

are grid condition in the domain interior, interconnection of adjacent grid blocks, and imposition 

of grid boundaries at physical surfaces, all of which can impair solution accuracy. 

Adaptation of the solver algorithm also holds potential for improving accuracy and managing 

computational workload. Across the hierarchy of solver algorithms, direct numerical simulation 

(DNS), large eddy simulation (LES), detached eddy simulation (DES), and Reynolds averaged 

Navier-Stokes (RANS) employ differing degrees of phenomenological approximation and offer 

varying levels of accuracy. DNS and LES place greater reliance on first principles computation 

to gain improved accuracy, but carry significantly higher computational costs. Alternatively, 

DES and RANS reduce computational costs, but do so by placing greater reliance on 

nonphysical models and allowing associated reductions in fidelity. 

RANS LES 

Figure 5: Hybrid models apply high fidelity 

models only where required, simple models 

elsewhere, and manage “switch over” in between 

(S3 interior data augments S1 model boundary 

data). 

Wind turbine blade and wake size coupled 

with DNS and LES computational costs 

preclude uniform application throughout the 

computational domain, even using petascale 

architectures (Spalart 1997). However, 

these models could be selectively applied as 

the flow field solution evolves, in separation 

regions, shear layers, vortices, and other 

regions of strong nonlinearity, to resolve 

these structures and mitigate numerical 

diffusion. In the remainder of the flow field, 

less computationally expensive methods 

could be applied. Achieving this goal also 

will require that the “switch over” between 

models and corresponding flow field subregions (Figure 5) be rigorously understood 

and correctly addressed at the algorithmic 

 26
level (Araya 2006). This will be particularly important for wind turbines, since inflow 

conditions change rapidly, dictating similarly rapid alterations in flow field structure. 

4.3.2 Field Measurements 

Modern utility-class wind turbine rotor diameters currently exceed 100 meters, with blade tips 

that extend as high as 150 meters above ground level. Future conceptual designs have rotors of 

substantially greater diameter, and blades will reach 200 to 300 meters into the atmospheric 

boundary layer. Turbine rotor wakes of interest in the future will be 100 to 200 meters in 

diameter, several hundreds of meters long, and 100 to 200 meters above the ground. The domain 

of interest is expansive, and the spatial scales of interest extend from wake dimensions (100’s of 

meters) down to blade boundary layer scales (microns). The spectrum of temporal scales are 

similarly broad. 

100

90

80

70

60

50

40

30

20

10

0

%

Radius (5.023m

Radius)

0 10 20 30 40 50 60 70 80 90 100

% Chord (0.457m Chord )

S809 Airf oil

Pressure Tap

Full Pressure

Tap Dist rib ut ion

Pressure Tap s

at 4% and 36%

Chord Only

Opt ional if

t railing edge

t ap is b locked

Figure 6: Schematic of NREL Phase V 

Unsteady Aerodynamics Experiment turbine 

blade surface pressure measurement 

locations. 

Flow field characterization on blade surfaces of 

turbines operating in the atmosphere currently 

can be carried out with high accuracy and 

reliability, although sensor physical size and 

cost constraints tend to limit spatial resolution. 

Surface pressure measurement using 

commercially available transducer technology 

has matured significantly, and associated 

uncertainties have been driven well below 

desired levels. Figure 6 shows a schematic 

drawing of the blade surface pressure 

measurement locations for the Phase V 

Unsteady Aerodynamics Experiment turbine 

(Hand et al., 2001). Characterization of the 

blade boundarylayer turbulence state during 

field operation using commercially available 

microphone technology has been proposed, but 

has not yet been implemented. Next generation 

MEMS based pressure transducers and 

microphones could reduce physical size and 

cost, thus relieving current limitations on 

spatial resolution. Flow field characterization 

above the blade surface, whether quantitative 

point measurements or qualitative flow 

visualization, would be time and resource 

intensive, and used in a precisely targeted 

fashion. 

Characterization of turbine wakes using tall (100 to 200 meter) towers enables a variety of high 

performance instruments to be precisely located on a long term basis. Although extremely 

costly, this significantly reduces spatial uncertainty in the measurement, and provides some 

flexibility concerning instrumentation choice. However, since the tower is fixed, virtually no 

flexibility exists for selectively locating instrumentation in the wake. Moreover, under some 

 27
inflow conditions, the wake will miss the tower and thus render wake measurement impossible. 

Alternatively, remote sensing approaches, like SODAR and LIDAR (Harris, et al.), would enable 

flexible movement of the measurement volume through the turbine inflow or wake, as shown in 

Figure 7. At present, both types of instruments readily measure along line of sight. Threedimensional measurements in a compact measurement volume, with the ability to rapidly scan 

the measurement volume through the wake/inflow domain, would entail significant technical 

challenges, but would provide a crucial capability for detailed flow field characterization. 

Figure 7: Wind turbine inflow measurements using hub-mounted LIDAR . 

4.3.3 Data exploitation 

Because of the expansive domain of interest, broad spatial and temporal scale range, diverse data 

(computational and experimental), and the range of applicable turbine operating conditions, data 

sets will be overwhelmingly large. For example, a computational simulation using a 109

 point 

mesh and run for 106

 time steps will yield a petascale data set. Field experiments will use 

substantially fewer measurement points, but will be run for much longer times to capture inflow 

conditions representative of the statistical range, and also will yield staggeringly large data sets. 

Because of the nonlinear, time dependent nature of wind turbine flow fields, crucial flow 

structures and events may be spatially and/or temporally compressed, and thus not readily 

detected. To efficiently interrogate these extremely large computational and experimental 

datasets in a consistent and mutually supportive manner, reliably identify key flow structures and 

events, and arrive at thorough comprehension of key machine responses, automated data mining 

methodologies will be required. 

4.4 Needs from Computation/Theory 

 Dynamic mesh adaptation to help resolve/preserve key flow structures/processes and bound 

computational workload 

 Adaptative solver algorithms that span the DNS/LES/DES/RANS hierarchy to improve 

physical fidelity and manage computational workload 

 Algorithms to interrogate extremely large computational and experimental data sets, in a way 

that exploits synergies between predicted and measured data. 

 28
4.5 Needs from Observations/Experiments 

 Application of state-of-the-art blade flow field measurement instrumentation and targeted 

transition to next generation MEMS-based technologies 

 Development and application of remote sensing technologies for wake and inflow 

characterization, particularly 3-D scanning LIDAR 

 Selective use of instrumented tall towers to acquire specific data in advantageous conditions 

or environments. 

4.6 Potential Wind Energy Impact 

Detailed physical understanding and accurate, reliable prediction of wake ingestion by wind 

turbines will confer several benefits on wind energy machine technology and wind plant 

operations. Initially, understanding and prediction will focus on the fundamental two-turbine 

interaction, but ultimately will advance to encompass interactions between multiple turbine rows 

like those in modern wind plants. Specific impacts will favorably impact wind cost of energy 

through turbine capital cost and lifetime, operations and maintenance costs, replacement costs, 

finance costs, and energy capture. Specific impacts include those listed below. 

 More reliable predictions of wind plant energy capture performance 

 More credible forecasts of turbine lifetime and component failure 

 Operating practices that reduce upwind wake shedding and mitigate downwind wake effects 

 Turbines designed and built to better tolerate wake ingestion 

 Wind plant optimization that intelligently balances land area usage and turbine effectiveness. 

4.7 Key Events and Approximate Timing 

All times below are referenced to project funding initiation at Year 0. 

 Years 1 – 3: Develop blade and wake flow initial measurement capability 

 Years 1 – 3: Develop initial predictive methodologies for two-turbine interaction 

 Year 4: Acquire initial blade and wake flow measurements 

 Year 4: Validate initial predictive methodologies for two-turbine interaction 

 Year 5: Acquire additional measurements in response to predictions 

 Year 5: Revise predictive methodologies in response to validations. 

References 

Araya, G., Bohr, E., Jansen, K.E. and Castillo, L., 2006. “Generation of turbulent inlet conditions 

for velocity/thermal boundary layer simulations,” In Progress in Turbulence 2, M. 

Oberlack, S. Guenther, T. Weller, G. Khujadze, A. Osman, M. Frewer, J. Peinke (Eds.), 

Springer Proceedings in Physics, vol. 109. 

Duque, E.P.N., Sankar, L., Menon, S., Bauchau, O., Ruffin, S., Smith, M., Ahuja, K., Brentner, 

K., Long, L., Morris, P., Gandhi, F., “Revolutionary Physics-Based Design Tools for Quiet 

Helicopters,” 44th AIAA Aerospace Sciences Meeting, January 2006. 

 29
Hand, M. M.; Simms, D. A.; Fingersh, L. J.; Jager, D. W.; Cotrell, J. R., “Unsteady 

Aerodynamics Experiment Phase V: Test Configuration and Available Data Campaigns,” NREL 

Report No. TP-500-29491, August 2001, 

http://www.osti.gov/bridge/product.biblio.jsp?osti_id=787980. 

Harris, M.; Hand, M.; Wright, A., “LIDAR for Turbine Control,” NREL Report No. TP-500-

39154, January 2006, http://www.nrel.gov/wind/pdfs/39154.pdf. 

Sahni, O.. Jansen, K., Shephard, M., Taylor, C., and Beall, M., “Adaptive Boundary Layer 

Meshing for Viscous Flow Simulations”, Engineering with Computers, (accepted 2007). 

Spalart, P.R., Jou, W.H., Strelets M., and Allmaras, S.R., 1997, “Comments on the feasibility of 

LES for wings and on a hybrid RANS/LES approach,” Advances in DNS/LES, Greyden Press, 

Columbus, 137–147. 

 30
5. Extreme and Anomalous Inflow Events 

5.1 Abstract 

Wind turbines are subject to extreme and anomalous wind-inflow events that affect their 

operation, loading, and durability. Improved foreknowledge of such events in the design stage 

will help wind turbine designers better develop configurations and control methodologies that 

mitigate the impact from the events, reducing ultimate loads and fatigue damage. The research 

approach calls for observations and computations to better characterize important inflow 

conditions, incorporation of the improved inflow conditions into the design process through 

modifications to the wind turbine design standard, and sensor and controls development for realtime detection and mitigation of inflow events. This research will decrease a wind turbine’s 

design overhead and its associated costs, make turbine design refinement easier, and reduce the 

overall cost of wind energy. 

5.2 Problem or Opportunity 

Wind turbines are subject to wind-inflow conditions that affect their operation, loading, and 

durability. Of particular concern are extreme wind conditions that exceed the inflow 

prescriptions used as a basis for the wind turbine design and/or anomalous inflow events that 

were not accounted for in the design. Extreme and anomalous wind-inflow events are those that 

have spatial-temporal characteristics which make them potentially damaging to a wind turbine. 

Examples include wind gusts, rapid changes in the wind direction, shears of wind speed and 

wind direction across the rotor, spatial coherence in wind components, and combinations of these 

conditions. 

The characterization of extreme and anomalous inflow events is complicated by the reality that it 

is not the magnitude of the inflow condition that is important, but rather its impact on the wind 

turbine. The impact of an inflow event on a wind turbine depends on the turbine’s configuration 

and operational state (i.e., power production, start-up events, shut-down events, parked and/or 

idling, and fault conditions). Extreme and anomalous inflow events are likely to generate the 

highest (i.e., ultimate) loads experienced by a turbine, but may also contribute to its fatigue 

damage depending on the frequency of the events at any particular site and the extent to which a 

turbine component’s fatigue resistance is influenced by the largest amplitude cycles. 

The International Electrotechnical Commission (IEC) 61400–1 design standard [1] specifies the 

design requirements for utility-scale land-based wind turbines. This design standard requires 

that an integrated loads analysis be carried out. Loads analysis involves verifying the structural 

integrity of a wind turbine by running a series of design load cases (DLCs) using comprehensive 

aero-servo-elastic design tools to determine the ultimate and fatigue loads expected over the 

lifetime of the machine. The loads (i.e., forces and moments) are examined within the primary 

members of the wind turbine, including the blades, drivetrain, nacelle, and tower. The required 

DLCs cover essential design-driving situations by pairing up the wind turbine’s operational 

states with appropriate normal and extreme external conditions. 

 31
Table 1: Description of the IEC Idealized Wind Models.

Abbr. Definition Description 

ECD Extreme Coherent 

Gust with Direction 

Change 

This deterministic wind model consists of an unsheared gust superimposed on 

a NWP. The gust rises to 15 m/s over a 10-s period. Occurring concurrently, 

the wind direction changes inversely proportional to the given hub-height 

wind speed. Both positive and negative direction changes are considered. 

EDC Extreme Direction 

Change 

This deterministic wind model consists of a transient direction change to the 

NWP. Over a 6-s transient, the wind direction changes depending on the wind 

class and inversely proportional to the given hub-height wind speed. Both 

positive and negative direction changes are considered. 

EOG Extreme Operating 

Gust 

This deterministic wind model consists of an unsheared gust superimposed on 

a NWP. Over a 10.5-s transient, the gust first dips, rises to a maximum, and 

then dips again before disappearing. Its magnitude depends on the wind class 

and increases with the given hub-height wind speed. 

ETM Extreme Turbulence 

Model 

This model is similar to the NTM but consists of full-field 3-component 

stochastic winds with a higher turbulence standard deviation depending on the 

wind class and increasing with the given hub-height wind speed. Like the 

NTM, the full-field turbulence is superimposed on a NWP. 

EWM Steady and Turbulent 

Extreme Wind Models 

Models consist both of high, steady, and turbulent wind speeds dependent on 

the wind class. The turbulent model consists of full-field 3-component 

stochastic winds. The steady winds and full-field turbulence are superimposed 

on a wind profile with a vertical power-law shear exponent of 0.11. 

EWS Extreme Wind Shear This deterministic wind model consists of a linear speed shear superimposed 

on a NWP. Over a 12-s transient, the shear rises to a maximum, and then 

decreases again before disappearing. Its magnitude depends on the turbulence 

category and increases with the given hub-height wind speed. Positive and 

negative, vertical and horizontal, shears are considered independently. 

NTM Normal Turbulence 

Model 

This model consists of full-field 3-component stochastic winds with a 

turbulence standard deviation given by the 90% quantile depending on the 

wind turbine turbulence category and increasing with the given hub-height 

wind speed. The full-field turbulence is superimposed on a NWP. 

NWP Normal Wind Profile Wind speed profile with a vertical power-law shear exponent of 0.2. 

 32
The design standard uses an assortment of idealized wind models (including normal and extreme, 

and turbulent and deterministic models) as the basis for the turbine design. The model 

parameters are quantified through the specification of a wind speed class and turbulence 

category. The wind models are described in Table 1 for readers who are unfamiliar with the IEC 

terminology. The extreme wind models were formulated for the design standard based on a 

limited set of observations that were made during the early years of modern wind turbine 

development and known to be damaging to turbines of that era. 

The developers of the design standard understood that the design basis and loads analysis 

calculations are imperfect, so the design standard requires that partial safety factors be applied to 

the loads (among other factors). This introduces a design overhead that adds cost to the wind 

turbine and makes design refinement difficult. 

Wind models formulated in the design standard—and the overall design process in general—

could be improved if more spatially-detailed and time-accurate representations of extreme and 

anomalous inflow events were available. Important inflow events could be characterized using a 

combination of observations and computations. Improved foreknowledge of extreme and 

anomalous wind-inflow events in the design stage will help wind turbine designers develop 

better configurations and components that mitigate the impact from the events, reducing ultimate 

loads and fatigue damage. Moreover, if impinging conditions could be detected in real time, 

information about the conditions could be passed to the wind turbine’s safety and protection 

system so that the turbine’s control system could take action before damage occurs. Dramatic 

loads reductions could be obtained through such feed-forward control strategies. 

5.3 Research Approach 

In light of these problems and opportunities, a multifaceted research project will be required to 

characterize extreme and anomalous inflow events and to modify the design process and design 

features of wind turbines to mitigate the impact of the events. 

The first research task must be to obtain more spatially-detailed and time-accurate 

representations of extreme and anomalous inflow events. This will require a combination of 

observations and computations. 

Experimental observations are needed to establish a reference of physically measured inflow 

conditions. Although individual anemometers are useful in measuring the annual distributions of 

wind speed and the magnitude of extreme wind speed gusts, individual anemometers are not 

useful for measuring the spatial-variability of the inflow. As the spatial-temporal characteristics 

are also attributes that make them potentially damaging to a wind turbine, more detailed 

observations are required. Capturing the needed spatial-temporal characteristics of the inflow 

will require the use of arrays of sonic anemometers, SODAR instruments, and/or scanning 

LIDAR instruments. 

Observations should be taken from a variety of locations to reflect possible site-to-site variability 

(e.g., complex terrain, Great Plains, near shore, or offshore sites) in the inflow characteristics. 

Extreme and anomalous inflow events by their nature may not occur frequently at all sites, so it 

is also important to make observations from a variety of locations simultaneously. Furthermore, 

 33
it will be beneficial to make some of the measurements near—and correlated with—the response 

measurements of one or more turbines in wind plants. This correlation will help identify the 

inflow characteristics that most adversely affect the turbines. Not all inflow measurement 

locations must be collocated with wind plants, however, as inflow characteristics that are benign 

to one type of wind turbine may be important to another type, and vice versa. 

Such a large-scale observation campaign can be established most easily by spreading the work 

across multiple research organizations around the United States. A publicly available and 

editable database should be established to share information about phenomena that have been 

measured. This database should first be populated with historical records on measured extreme 

and anomalous inflow events, such as the data used to formulate the extreme wind models in the 

IEC wind turbine design standard. 

The amount of information collected about the spatial-temporal characteristics of extreme and 

anomalous inflow events in observations is nevertheless limited. Consequently, the observations 

must be supplemented by mesoscale and microscale atmospheric computations. Mesoscale 

atmospheric models should be developed and run to help quantify variations in the inflow event 

characteristics for scales in the range of 1 to 1,000 km—that is, between the synoptic and 

microscopic scales. These scales are important for quantifying flows involving storm 

convection, complex terrains, sea breezes, and the progression of storms across multiple wind 

plants in any particular region. Microscale atmospheric models, such as LES models, should be 

developed and run to help quantify the detailed flow physics for scales less than 1 km—that is, 

inflow local to a single wind plant or wind turbine. Where even more detail is required in the 

LES’s sub-grid scale, DNS models must be applied. 

The outputs from the microscale atmospheric models should then be used as inputs to 

computational aero-servo-elastic models of wind turbines to help isolate the inflow phenomena 

that are important to the turbine response. Neil Kelley and Bonnie Jonkman of NREL have 

initiated research in this area [2]. Currently, Blade-Element / Momentum (BEM) and 

Generalized Dynamic-Wake (GDW) are the rotor aerodynamic theories used in routine design, 

but it may be necessary to apply higher fidelity theories such as vortex (free) wake methods or 

Computational Fluid Dynamics (CFD) because extreme and anomalous events are likely to 

involve high nacelle-yaw errors and transient conditions that are not modeled well by the simpler 

theories. 

Once the extreme and anomalous wind-inflow events are better characterized, the next research 

task should be to update the IEC design standard with modified parameters in the extreme wind 

models and/or with new models and load cases. Updating the design standard is important 

because it dictates the design of wind turbines. The form, magnitude, and duration of the wind 

gusts, direction changes, and shears prescribed in the deterministic ECD, EDC, EOG, and EWS 

wind models (see Table 1) should be updated to better represent the observations and 

computations obtained in the first research task. New inflow models should be developed that 

better capture the boundary layer flow physics of other inflow events, such as wind direction 

shears and coherent turbulent structures brought about by the presence of low-level jets. When 

such conditions interact adversely with the various operational states of a wind turbine, new load 

cases should be added to the design standard. The design standard should also give guidance on 

 34
how the model parameters vary from site to site. Because improvements to the design basis 

translate directly into reduced uncertainty in loads analysis, improvements to the design basis 

should also be followed by a reduction in the partial safety factor for loads. 

Once the extreme and anomalous wind-inflow events are better characterized, it may also be 

possible, with further research, to develop methods that can identify inflow conditions in real 

time. The final research task, then, is to develop methods for detecting specific inflow 

conditions in real time and to devise feed-forward control strategies that use the real time 

information to take action in a wind turbine before damage occurs. Perhaps the simplest method 

of detection would be to establish routine interaction between wind plant operators and 

meteorologists; meteorologists routinely track storms and could provide wind plant operators 

with important information regarding the possibility that specific atmospheric phenomena may 

occur. More useful, though, would be the development of advanced—but inexpensive—sensors 

such as LIDAR instruments, which could be placed within wind plants or on individual wind 

turbines to identify specific inflow conditions moments before they interact with a turbine. With 

the right information passed to the wind turbine’s safety and protection system, feed-forward 

control strategies could be developed to take preemptive measures. For example, the blade-pitch 

angles could be preemptively feathered collectively or individually to mitigate the potential 

loading brought about by drastic wind gusts or speed shears, respectively. Other more advanced 

control strategies that use a combination of blade-pitch, nacelle-yaw, generator-torque, and other 

control actuations could also be devised to minimize the potential damage caused by extreme and 

anomalous events. 

5.4 Needs from Computation/Theory 

To summarize the research approach with respect to computational needs, computations are 

needed to fill in the data gaps regarding the spatial-temporal characteristics of extreme and 

anomalous inflow events that will be missing from the observations. Mesoscale and microscale 

(i.e., LES and DNS) atmospheric models need to be developed to characterize the detailed flow 

physics for conditions in the regional (i.e., 1 to 1,000 km) and local (i.e., 1 km and less) scales, 

respectively. The outputs to these models then need to be used to drive aero-servo-elastic 

models of wind turbines to determine their affect on turbine response and to develop lower-order 

models of specific inflow conditions that are suitable for use within the IEC wind turbine design 

standard. Vortex wake methods or CFD may be needed to model the potentially complex 

interaction between the inflow structures and the turbine response. 

5.5 Needs from Observations/Experiments 

To summarize the research approach with respect to observational needs, experimental 

observations are needed to establish a reference of physically measured inflow conditions. 

Observations need to capture the spatial-temporal characteristics of the inflow and so should 

involve measurements using arrays of sonic anemometers, SODAR and/or LIDAR. The 

observations also need to be taken from a variety of locations simultaneously, with some of the 

measurement locations correlated with response measurements taken from operational wind 

turbines. The variety of measurement locations is necessary to capture conditions that do not 

occur frequently, to reflect the possible site-to-site variable of the inflow conditions, and to 

identify conditions that most adversely impact modern wind turbines. Information gathered from 

 35
the observations, as well as historical wind measurements, should be shared in a public database. 

Advanced observational sensors also need to be developed to identify specific inflow conditions 

real time for use in wind turbine control. 

5.6 Potential Wind Energy Impact 

Proper modeling of wind-inflow conditions in the design process will aid wind turbine designers 

to develop better configurations and components that can effectively withstand the induced loads 

and to develop control methodologies that can effectively mitigate their impact. Improved 

knowledge of the site variability of inflow conditions will also allow wind power developers to 

better evaluate turbine placement and aid in site suitability analysis. This will result in improved 

operational performance and reliability, lessened uncertainty in the planning of Operations and 

Maintenance (O&M), reduced ultimate loads, and diminished fatigue damage of wind turbines. 

The wind turbine loads, in particular, could be drastically reduced by applying feed-forward 

control strategies. All of these benefits will decrease design overhead and its associated costs, 

make turbine design refinement easier, and reduce the overall cost of wind energy. 

5.7 Key Events and Approximate Timing 

Better characterization of important inflow conditions using observations and computations is 

the first (and ongoing) key research event. This research, by itself, will not likely impact the 

development of wind energy until the new information is incorporated into the design process 

through improved inflow models. Incorporating improved inflow models in a modified wind 

turbine design standard is the second key research event and will directly improve design of the 

next generation of wind turbines created using the modified design standard. The final key 

research event is the development of sensors and controls for real-time detection and mitigation 

of inflow events. This research will have an immediate impact on the reliability and cost of wind 

turbines. 

References 

[1] IEC 61400–1 Ed. 3, Wind Turbines – Part 1: Design Requirements, International 

Electrotechnical Commission (IEC), 2005, http://www.awea.org/standards/iec_stds.html. 

[2] Kelley, N. and Jonkman, B., “TurbSim: A Stochastic, Full-Field, Turbulent-Wind Simulator 

for use with the AeroDyn-Based Design Codes (YawDyn, FAST, and MSC.ADAMS®),” NWTC 

Design Codes [online database], URL: http://wind.nrel.gov/designcodes/preprocessors/turbsim/

[cited 26 February 2008]. 

 36
Detailed Recommendations – Micrositing and Array Effects 

6. Improved Wake Models.............. ............... ........................................................................38 

7. Planetary Boundary Layer Research and Development...... .......... ...........................................45 

8. Acquiring and Exploiting Large-Scale Data Sets...... ............... ................................................54 

9. Technology Transfer and Tool Integration............... .............. ................................................60 

 37
6. Improved Wake Models 

6.1 Abstract 

The majority of wind plants now comprise large arrays of wind turbines arranged in multiple 

rows. In addition, many wind arrays (discrete clusters of turbines or wind plants) are located 

within close proximity (a few miles) potentially leading to array interactions where the 

downstream clusters experience reduced wind speeds and hence power output. This, together 

with an increase in the size of wind turbines extending into traditionally under-sampled regions 

of the atmosphere (between 10 and 200 m) mean that current wind plant design and micro-siting 

models are being used beyond their intended purpose. Models routinely under-predict power 

performance likely due to; a misrepresentation of the vertical wind speed and turbulence profile, 

a lack of understanding of the impact of complex terrain on flow, fundamental errors in modeling 

of array effects/wind turbine wakes and/or a combination of these effects. A major research 

effort is needed to produce the next generation of wind plant models which are capable of 

accurately predicting power output for large wind plants in both complex terrain and offshore. 

6.2 Problem or Opportunity 

Like most renewable energy technology, wind plants require most investment before and during 

construction, with maintenance costs being relatively small and fuel costs being zero. Because 

this initial investment is often obtained from third parties like banks and investment companies, 

the financing package details can be crucial to successful development. This means that initial 

modeling of power output over a long-time frame must be highly accurate, since underproduction of power will cause financial penalties. However, prediction of power output over a 

20- or 30-year time horizon is inherently uncertain. 

Once a site has been identified as a highly likely contender for development, a highly specialized 

monitoring program will be undertaken to quantify the mean wind and turbulence characteristics 

and the results related to the long-term mean conditions. The process of optimizing power output 

and minimizing fatigue loads, by optimal design of wind plant layouts, can then be undertaken. 

Models describing wind turbine wakes were developed mainly in the 1980’s (e.g., Ainslie 1988, 

Troen & Petersen 1989) when wind turbines were relatively modest in size and wind plants 

usually contained a modest number of turbines. By necessity, these wake models had to be fairly 

straightforward, building on relatively few wake measurements and constrained to avoid 

excessive computational demands. However, for single wakes or small wind plants in fairly 

straightforward (i.e., homogeneous) environments, these tend to exhibit relative good agreement 

with the available data in terms of power losses ( e.g., Crespo et. al. 1999, Barthelmie et. al. 

2006). Note, modeling of turbulence in wakes (Crespo & Hernandez 1996) for load calculations 

tends to focus on for specific cases (Thomsen & Sørensen 1999) (Frandsen and Thøgersen, 

1999) while power loss modeling has to encompass the full range of wind speeds and directions. 

It was pointed out in 1991 that the model performance for large arrays was unknown (Elliott & 

Barnard 1990) but few surveys have been published to establish model performance even for 

onshore arrays (Smith et al,2006). It has recently become clear that when these wake models are 

applied to large offshore wind plant arrays they exhibit considerably lower skill relative to 

 38
observations (Mechali et al,2006, Barthelmie et al, 2008). In complex terrain, even mean flow is 

difficult to model (Ayotte et al, 2001) to the accuracy required to perform effective micrositing 

and the performance of the wind plant/wake models in complex terrain is limited 

(Prospathopoulos et al, 2008). 

6.3 Research Approach 

Recognizing that developing wind plant 

modeling requires significant investment, 

the European Union funded a number of 

projects over the last few years including 

ENDOW (Barthelmie et al. 2002), 

POW’WOW and Upwind; all of which 

have a component on wake model 

development and evaluation (Barthelmie et 

al, 2007). The Danish government 

(Denmark is a major supplier of wind 

turbines and wind energy expertise) also 

invested in R&D to allow wind plant 

developers and researchers to work together 

to evaluate the disparity between wind plant 

predicted energy output and production 

(Frandsen 2005, Frandsen et al, 2007). 

These projects indicate that wind plant 

models appear to make reasonable 

predictions of power losses due to wakes in 

small wind arrays (less than 3 – 4 rows). 

However, in large offshore wind plants, a 

very large discrepancy exists between 

standard predictions of average power loss 

due to wakes and those which have been 

observed, which are at minimum 15% of 

total average power. In large wind arrays on 

land and/or in complex terrain, it is 

postulated that similar discrepancies exist. 

However, because of the commercial nature 

of wind energy development, this 

information is rarely made public for 

individual wind arrays, and owners are 

reluctant to share wind array data. 

 39

It is generally acknowledged that wind 

arrays under-produce power compared to their 

initial predictions. This is a serious concern to 

developers and puts at risk developments of 

wind energy in areas where resources are 

marginal. 

12345678

Turbine number

0.2

0.4

0.6

0.8

1

Normalised power

12345678

0.2

0.4

0.6

0.8

1

Normalised power

Observed

Model A 

Model B

Model C

Model D

12345678

0.2

0.4

0.6

0.8

1

Normalised power

±1o

12345678

0.2

0.4

0.6

0.8

1

Normalised power

±5o

±10o

±15o

Figure 8. Adapted from (Barthelmie et al. 2008). 

The observations are from the Horns Rev wind 

plant taking data from direction 274o (exactly 

down a row of wind turbines) with different sector 

widths. The model results shown are from the 

UPWIND project incorporating both wind plant 

(parameterized) and CFD models. 
6.4 Research Priorities 

The scale of wind plant developments is difficult for the public to comprehend. While wind and 

turbulence could be adequately assessed to about 50 m in the atmosphere, the new turbines with 

hub-heights of 80 m and rotor diameters of up to 126 m are extending more than 140 m into the 

atmosphere where there are very few measurements, and it has already been shown that simple 

logarithmic type profiles used in wind plant models are inadequate (Gryning et al, 2007). Lack of 

understanding of the basic input parameters to wake/wind plant models over at least 150-m 

height can therefore be seen as a primary research objective. 

The major research priority here is to address the systematic under-prediction of wake losses 

from large wind plants and hence, the discrepancy between predicted and actual power output 

that means power production is routinely over-estimated. The most basic approach is to assemble 

a number of data sets from wind plant operators that can be used first to quantify the extent of 

the under-prediction, to evaluate the major variables such as wind turbine type, wind speed, 

turbulence and wind turbine spacing, and to establish whether there are major differences 

between wind plant behavior in moderate and complex terrain compared with offshore. This 

research priority can be addressed using data analysis combined with statistical and physical 

modeling. Although this research sounds straightforward, the complexity of wakes combined 

with the quantity and quality of datasets available mean that it should not be underestimated. 

An additional and parallel approach would be to develop (preferably with a wind energy 

developer) a long-term wind super-site. This site would have enhanced data collection on the 

wind turbines, and additional instrumentation upwind and downwind. Additional upwind and 

downwind instrumentation would include high-resolution (space and time) measurements of the 

wind and turbulence characteristics upstream, as well as wind and wind plant characteristics 

downstream. 

Once this combined model and data program is underway, priority needs to be given to 

addressing deficiencies in wake and wind plant modeling in order that uncertainties in 

micrositing and array effects are reduced. The new generation of models needs to be able to 

optimize wind plant layouts for large arrays in complex terrain and offshore using newly 

developed theoretical approaches combined with state-of-the-art modeling techniques, while 

retaining the current simplicity of input data and use that makes these tools practical for wind 

energy developers. 

In brief, the research plan would: 

- Identify wind plant data partners 

- Develop methods of protecting proprietary data and enabling industry buy-in 

- Collect and analyze wind plant data – ongoing; likely a 1 – 2-year period 

- Compare existing models and identify model deficiencies 

- Improve model parameterizations and demonstrate improved performance. 

6.5 Needs from Computation/Theory 

Increased computational resources and improved observational capabilities mean (1) wake 

modeling is no longer confined to engineering approximations and that CFD modeling of the 

40 
whole wind plant can be undertaken, and (2) the quality of observations available for both 

process-level studies and model evaluation is now sufficient to facilitate more meaningful 

diagnostic analyses and more rigorous model evaluations. This brings a new dimension to wake 

models in terms of the detailed temporal and spatial variation that can be modeled. There is a gap 

between analytic solutions that are used in commercially available wind plant models (e.g., 

WAsP from Risø National Laboratory and WindFarmer from Garrad Hassan) due to ease of use 

and speed and CFD models. A bridge is needed between these to provide more detailed 

information for modeling power losses, for better wind plant and turbine design, and (later) for 

more sophisticated control strategies and load calculations. Challenges remain in terms of model 

evaluation in operational wind plants because measurements are still not available on a finely 

spaced mesh over the wind plant, nor (typically) at high time resolution. 

Figure 9. Example of CFD modeling at Vindeby from (Rados et al,, 2002) 

y/d 

x=3D 

x=5D

x=7D

x=8.6D

x=10D

x=12D

z/d 

Wind Direction: 23o

Turbine Meteorological Mast 

A parallel approach is needed to develop theoretical methods to account for behavior of wakes in 

large wind arrays (expansion, lateral and downwind merging) and interaction between wakes and 

the overlying boundary-layer (Frandsen et al, 2006). Although these may ultimately prove too 

complex for routine use (in terms of input parameters and /or computing time), this research is 

essential if sufficient understanding of these physical process is to be generated to find 

engineering solutions which can be applied in the wind plant/array models that are currently used 

by wind energy developers (Schlez et al, 2006). 

6.6 Needs from Observations/Experiments 

Most wind arrays now have Supervisory Control and Data Acquisition systems that, at a 

minimum, record power output and a status signal. Unfortunately, this is routinely of poor 

quality or not stored at high resolution because its long-term utility to the wind array owner does 

not require this investment. The first task must be to establish good practices that enable the 

routine data output of many wind arrays to be utilized for fundamental data analysis. Ideally, 

more signals would be recorded including, for example, yaw angle and wind speed of the nacelle 

anemometer. For power output analysis, it is likely that 10-minute averages and standard 

deviations are sufficient, but it might be prudent at this point to also consider what would be 

required for fatigue and extreme load modeling and institute one set of guidelines to be 

41 
established. In addition to these data, data from supporting metrology masts in and around the 

site are useful. 

Beyond this, it is possible to elaborate on the design of a wind ‘super-site’ where many more 

purpose made measurements could be conducted to support detailed studies of wind and 

turbulence characteristics in wind turbine wakes (see above, in Research Priorities). This would 

require a long-term commitment of funding, but is likely the most direct route to providing 

sufficiently detailed temporal and spatial data for model development and evaluation. 

6.7 Potential Wind Energy Impact 

The current uncertainty in wake model predictions is one of the major uncertainties in prediction 

of power output from large wind arrays. More accurate wake/wind plant models will lead to: 

- Significantly improved accuracy of wake loss estimates that are used in wind array 

economic planning and may ultimately be used in short-term forecasting. 

- More certain overall array wake loss estimates. Quantifying uncertainties is important for 

both wind array operation and economics. 

- Better load/suitability fatigue estimates. These are needed to ensure that individual wind 

turbines are not subject to excessive loading which will impact component lifetimes. 

- Optimized wind power plant electricity production. Ensuring the maximum energy output 

from each site at the lowest possible cost is crucial to the success of individual projects 

and to the overall energy demand goals. 

It should be clear that improving predictions of energy output by adjusting locations or turbine 

spacing may increase the energy output from wind turbines or a wind plants by a few percent or 

less. However, in real terms this has a very large impact on an investment. For example, in a 

modern 200-wind turbine array, decreasing wake losses from 15% to 14% could mean an 

increase in annual output of about 10,000 MWh, and if each kWh is worth $0.10 to the producer, 

the net difference is over $1 million a year. Similarly, if the mean wind speed experienced by 

each turbine in the same wind array were increased by 0.1 m/s the net present value of the 

project will increase by approximately $20 million. The key result required is better match 

between model predictions and observed power and reduced uncertainty in power prediction. 

6.8 Key Events and Approximate Timing 

“The United States reported a record 5,244 MW installed in 2007, more than double the 2006 

figure, accounting for about 30% of the country’s new power-producing capacity in 2007. 

Overall U.S. wind power generating capacity grew 45% in 2007, with total installed capacity 

now standing at 16.8 GW” (www.ewea.org). Given this significant growth, there is an urgent 

need to solve some basic research issues which could assist the successful development of wind 

arrays in areas now considered marginal. By reducing uncertainties, wind array economics can 

be improved, driving down costs and ensuring a continued high level of development. The wind 

energy capacity installed in the United States in 2006 was worth $4 billion. Continuing success 

of this industry should be seen as top priority for energy research. 

42 
Specific milestones should include: 

– Identify wind plant data partners by early 2009 

– Develop methods of protecting proprietary data and enabling industry buy-in by 

early 2009 

– Wind plant data collected within 3 years and model validation within 5 years 

– Wind plants designed with new tools demonstrate improved performance – 

immediately following design with new tools. 

References 

Ainslie JF (1988) Calculating the flow field in the wake of wind turbines. Journal of Wind 

Engineering and Industrial Aerodynamics 27:213-224. 

Ayotte K, Daby R, Coppin R (2001) A simple temporal and spatial analysis of flow in complex 

terrain in the context of wind energy modelling. Boundary-Layer Meteorology 98:277. 

Barthelmie RJ, Folkerts L, Rados K, Larsen GC, Pryor SC, Frandsen S, Lange B, Schepers G 

(2006) Comparison of wake model simulations with offshore wind turbine wake profiles 

measured by sodar. Journal of Atmospheric and Oceanic Technology 23(7):888-901. 

Barthelmie RJ, Frandsen ST, Rathmann O, Hansen K, Politis E, Prospathopoulos J, Cabezón D, 

Rados K, van der Pijl S, Schepers G, Schlez W, Phillips J, Neubert A (2008) Flow and 

wakes in large wind farms in complex terrain and offshore European Wind Energy 

Conference, Brussels, p 10. 

Barthelmie RJ, Larsen G, Bergstrom H, Magnusson, Schlez W, Rados K, Lange B, Vølund P, 

Neckelmann S, Christensen L, Schepers G, Hegberg T, Folkerts L (2002) ENDOW: 

Efficient Development of Offshore Windfarms. Wind Engineering 25:263-270. 

Barthelmie RJ, Schepers G, van der Pijl S, Rathmann O, Frandsen ST, Cabezón D, Politis E, 

Prospathopoulos J, Rados K, Hansen K, Schlez W, Phillips J, Neubert A (2007) Flow and 

wakes in complex terrain and offshore: Model development and verification in UpWind 

European Wind Energy Conference, Milan, p 10. 

Crespo A, Hernandez J (1996) Turbulence characteristic in wind turbine wakes. Journal of Wind 

Engineering and Industrial Aerodynamics 61:71-85. 

Crespo A, Hernandez J, Frandsen S (1999) Survey and modeling methods for wind turbine 

wakes and wind farms. Wind Energy 2:1-24. 

Elliott D, Barnard J (1990) Observations of wind turbine wakes and surface roughness. Solar 

Energy 45:265-283. 

Frandsen S (2005) Turbulence and turbulence-generated fatigue loading in wind turbine clusters. 

Risoe National Laboratory, Roskilde, p 128. 

Frandsen S, Barthelmie R, Pryor S, Rathmann O, Larsen S, Højstrup J, Thøgersen M (2006) 

Analytical modeling of wind speed deficit in large offshore wind farms. Wind Energy 

9:39-53. 

Frandsen ST, Barthelmie RJ, Rathmann O, Jørgensen HE, Badger J, Hansen K, Ott S, Rethore 

PE, Larsen SE, Jensen LE (2007) Summary report: The shadow effect of large wind 

farms: measurements, data analysis and modeling. Report No. Risø-R-1615(EN), Risø 

National Laboratory/DTU, Roskilde. 

Gryning SE, Batchvarova E, Brümmer B, Jørgensen HE, Larsen SE (2007) On the extension of 

the wind profile over homogeneous terrain beyond the surface boundary layer. BoundaryLayer Meteorology, 124, 251-259. 

43 
Mechali M, Jensen L, Barthelmie R, Frandsen S, Rethore PE (2006) Wake effects at Horns Rev 

and their influence on energy production European Wind Energy Conference and 

Exhibition, Athens, Greece, p 10. 

Prospathopoulos JM, Politis ES, Chaviaropoulos PK (2008) Modelling wind turbine wakes in 

complex terrain European Wind Energy Association Conference (Scientific track). 

EWEA, Brussels March 2008. 

Rados K, Larsen G, Barthelmie R, Schelz W, Lange B, Schepers G, Magnusson M (2002) 

Comparison of models with data for offshore windfarms. Wind Engineering 25:271-280 

Schlez W, Neubert A, Smith G (2006) New developments in precision wind plant modelling 

Deutsche Windenergie Konferenz, Bremen, p 5 pp. 

Smith G, Schlez W, Liddell A, Neubert A, Pena A (2006) Advanced wake model for very 

closely spaced turbines European Wind Energy Conference and Exhibition, Athens, 

Greece, p 9. 

Thomsen K, Sørensen P (1999) Fatigue loads for wind turbines operating in wakes. Journal of 

Wind Engineering and Industrial Aerodynamics 80:121-136. 

Troen I, Petersen EL (1989) European Wind Atlas, Vol. Risø National Laboratory, Roskilde, 

Denmark p 656. 

44 
7. Planetary Boundary Layer Research and Development

7.1 Abstract 

To reduce the cost of wind energy from the micositing and wind plant deployment perspective, 

the community needs to improve their ability to predict the wind field spanning typical utilitysized turbine swept areas (50 – 200 m above ground level). Key complicating factors include: 

impacts of atmospheric stability, orography, and land-surface characteristics on turbulence 

statistics and structure. Improved prediction of these key factors and their short- and long-term 

influence on atmospheric turbulence will provide direct guidance towards individual turbine 

placement and deployment of a farm as a whole. Improved predictions of wind shear, extremes 

and turbulence will also allow for better fatigue and extreme load calculations. With this 

information, optimal financing packages and turbines specifically designed to withstand site 

conditions can be obtained, along with proper placement of turbines leading to lower costs of 

energy production. 

7.2 Problem 

To improve upon the current strategies for turbine micrositing and within-array placement, there 

are significant outstanding challenges within PBL research that require solutions in order to 

reduce the uncertainty associated with wind plant development. One key issue is that today’s 

turbines are operating in the 50 – 200-m portion of the lower atmosphere; a region that is 

difficult to observe with traditional instrumentation and therefore has not been well sampled. It is 

also important to note that proper characterization of this region of the atmosphere requires wind 

measurements on long and short timescales to a very high degree of accuracy and precision, 

which is not generally required for standard weather forecasting. 

Parameterizations. Parameterizations of PBL turbulence assume spatial homogeneity in the 

horizontal direction and only provide estimates of the average state of the mean and turbulent 

flow fields. Turbulence in the PBL is a highly complex three-dimensional and time-dependent 

entity, and turbulence structure evolves dramatically based upon the mechanisms currently 

responsible for its generation and maintenance (e.g., atmospheric stability, interactions with 

water waves, orography, or tall vegetation). Not only does the generation mechanism change the 

turbulence statistics, but the structure of the turbulence also changes dramatically from when 

generated largely by buoyancy to that generated mostly by shear (e.g., Moeng and Sullivan, 

1994). The character changes yet again at night when buoyancy inhibits turbulence, the 

dominant length scales become independent from the distance from the ground, and mechanisms 

responsible for generating the turbulence can be far away (e.g., the breaking of waves on the 

underside of a low-level jet, or an upstream disturbance like a building or a hill). 

Complex orography. Wind turbines are frequently deployed in regions of undulating 

topography to take advantage of the expected speed-up of wind as the atmosphere is forced up 

over the hill. While this reasoning is quite simple for an idealized isolated hill in a non-stratified 

environment, the orographic and thermodynamic impacts on the PBL are extremely complex. 

Turbines placed on these hills complicate the fluid dynamics yet again due to the ability for 

turbine-induced pressure drag to modify the appearance of topography to flow. Placement of any 

45 
individual turbine cannot be performed without consideration of the turbines making up the 

entire wind plant. Choosing the best location for an individual turbine in such complex terrain 

requires a detailed understanding of the local topography, the wakes of the other turbines in the 

farm, and the impact of diurnally evolving atmospheric stability on the turbulent structure as a 

function of wind direction, and impact of tall vegetation located over the hill on turbulence and 

its structure. 

Vegetation. Thirty percent or more of the Earth’s land surface is covered by vegetation. It is 

well known that tall vegetation can act to reduce wind speeds, increase turbulence intensities, 

and modify the turbulence structure and atmospheric stability (e.g., Finnigan, 2000). Wind plant 

developers have traditionally avoided vegetated regimes for these reasons. However, many 

communities are pushing the industry to shift into these regions because vegetation blocks the 

turbines from view. There is a tremendous potential for further wind energy development in 

these vegetated regions, but accurate prediction of the expected wind field is required. Current 

numerical modeling strategies used in site characterization for wind energy resource assessment 

and wind plant siting do not accurately capture the influence of tall vegetation on the predicted 

mean and turbulence fields. 

Wind plant under- performance. Another key outstanding issue in array micrositing is the fact 

that current wind plants can often under-perform predicted performance by more than ten percent 

when designed with the current generation of siting tools. Individual turbine placement within a 

wind plant is typically determined by infrastructure requirements and potential mean energy 

capture predictions using numerical models that inaccurately predict impact of momentum 

absorption, energy extraction, and wake generation from upstream turbines. Isolated wind plants 

appear to the flow much like a windbreak or a forest edge, as a sharp change in roughness 

distributed both vertically and horizontally. For windbreaks or forest edges, the speed and 

distance downstream required for the mean flow and the turbulence to equilibrate depends on the 

drag element density distribution (see for example Patton et. al, 1998 or Dupont and Brunet, 

2008) and likely also depends on atmospheric stability. Therefore, consideration for individual 

turbine placement or operation within a wind plant or other strategies could serve to increase the 

efficiency of the plants as a whole. 

 

7.3 Needs from Computation/Theory 

7.3.1 Theories and low-dimensional models

Flat terrain. Wind plant and wind turbine wake models are in general about 20 years old. While 

improvements have been made, the timing is optimal for utilization of more intensive models 

such as computational fluid dynamics (CFD) codes and mesoscale models like those used in 

weather forecasting being brought to bear on issues relating to resource and load forecasting. 

Standard Monin-Obukhov similarity theory (MOST) describes wind speed profiles up to 50 m, 

but does not perform adequately at heights between 100 – 200 m. MOST also makes some 

important and limiting assumptions, the key of these assumptions being that terrain and forcing 

fields driving flow are horizontally homogeneous. Assumptions of this sort dramatically limit 

the applicable range of these low-dimensional models. Cuxart et al (2006) recently demonstrated 

that the currently available simple formulations perform quite miserably for nighttime stably 

stratified conditions (Figure 10). Studies testing similar single column models and their ability 

46 
Figure 10: Predicted mean wind profiles from a range of one-dimensional column models 

attempting to reproduce a stably stratified PBL. The profiles on the left are first-order 

parameterizations, and those on the right are higher-order parameterizations. The shaded 

profiles show the range of results from 15 different turbulence-resolving large-eddy 

simulations responding to the same forcing. From Cuxart et al, 2006.

to predict a single diurnal cycle perform equally poorly (Svensson et al, 2008). Model 

improvements to accurately predict vertical profiles of mean and turbulent quantities under a 

wide variety of atmospheric stabilities are essential. Taking advantage of recent improvements 

to MOST that account for the enhanced mixing associated with vegetation (Harman and 

Finnigan, 2007) might be useful for capturing effects of a wind plant itself. 

Orography. Several theoretical formulations exist for capturing the essence of flow over hills 

(e.g., Hunt et al., 1988, HLR hereafter). While theories like HLR are extremely useful, they are 

typically linear analytic solutions that are only applicable to flow over low-hills with limited 

thermal stratification. The Wind Atlas Analysis and Application Program (WAsP) is one of the 

most utilized tools to extrapolate tower-based wind measurements to a potential wind plant site 

and is based on similar assumptions, and is therefore equally limited (Bowen and Mortensen, 

2004). The utility in models like WAsP that are based upon linear formulations lies largely in 

their computational speed; they include a significant portion of the physics, but can also produce 

results with limited computational cost. However, their utility falls off rapidly when applied in 

relatively steep terrain or if the weather fluctuates much. Several non-linear formulations have 

been developed (e.g., Xu et al, 1994) that allow for turbulence prediction in steeper terrain. 

These formulations are more costly than their linear counterparts, but provide more accurate 

results. Few, if any, formulations being utilized for micrositing or wind plant deployment in 

hilly terrain properly account for the influence of thermal stratification, tall vegetation, or the 

turbines themselves. 

47 
7.3.2 High-fidelity simulation

A key limitation with the above mentioned theoretical and modeling frameworks is that they 

only predict mean and turbulent statistics. Decidedly different turbulent flow fields can generate 

similar statistics. Wind turbines rarely operate in an average flow field. Rather, they regularly 

operate in the instantaneous flow field riddled with organized structures that are evolving on 

time-scales from seconds, to hours, up to days and with length scales ranging from millimeters 

up to kilometers. 

Computational capabilities are now allowing for the use of turbulence-resolving calculations 

using tools such as DNS or LES. DNS directly solves the Navier-Stokes equations and resolves 

all scales of motion from the largest allowable in the domain (i.e., the domain size) down to the 

scales at which the turbulent energy is dissipated to heat (i.e., the viscous scales). However, 

because DNS relies on resolving the viscous scales of motion (~ 1mm), the largest scales of 

motion are limited by the available computing power; therefore, the largest length scale captured 

by these simulations is limited to less than about a meter. The atmospheric PBL contains much 

larger scales of motion, ranging from one millimeter to a kilometer or two. Because we know 

that in turbulent flows, the small scales are largely generated through vortex stretching by the 

larger scales (Tennekes and Lumley, 1972), in atmospheric LES, the Navier-Stokes equations are 

spatially filtered at small scales (~ 20 – 50 m) and uses a subfilter-scale model to predict the 

impact of the small scales of motion that are filtered out. Recently, the available computational 

power has allowed for computations of the entire PBL where the filter-scale sits down at about a 

meter (see Figure 11). Given enough resolution and a terrain following coordinate 

transformation, eddy-resolving simulation tools are capable of simulating flows over orography. 

So, high-fidelity CFD simulations are essential tools to investigate turbine-turbine interactions, 

stability effects, terrain effects, wind plants as a whole. 

However, a limitation of these high-fidelity CFD simulations is that they are also extremely 

computationally intensive. So even though the computational power exists to perform these 

types of simulations, the wind energy industry needs answers more rapidly than is currently 

possible. A key vision to keep in mind is that these higher fidelity simulations need to be 

focused on improving accuracy of lower-fidelity simulations, and placing bounds on the 

resulting error bars, thereby reducing the uncertainty in the predictions. 

48 
Figure 11: Horizontal slices of instantaneous vertical velocity at two heights (40 m and 120 

m) from a 10243

 gridpoint large eddy simulation of a PBL with dimension 5120 m x 5120 m x 

2048 m. The simulation is forced by a horizontally homogeneous prescribed surface 

heating (~ 240 Wm-2) and geostrophic wind (1 ms-1), so the turbulence is mostly buoyantly 

driven and is similar to a weak wind daytime condition. To note is that even within a single 

stability regime the atmospheric structure varies dramatically with height. See Sullivan and 

Patton (2008) for further details. 

7.3.3 Boundary conditions

These high-resolution three-dimensional and time-dependent calculations are extremely useful 

tools for simulating and investigating turbulence structure and dynamics in the 100 – 200-m 

regime in the PBL. However, there are limitations. One key limitation is the availability of 

realistic turbulence inflow conditions. The easiest is to use periodic boundary conditions, but 

this situation complicates the use of these tools when interested in flow over horizontally 

heterogeneous surfaces. Other methods to obtain these turbulent inflow conditions include 

running a separate simulation with periodic boundary conditions and taking slices of this 

simulation as inflow conditions for the simulation of interest. Or similarly, one could imagine 

simulating an extremely large domain, and recycling the fluctuations from an interior slice in the 

domain prior to any heterogeneity and laying those fluctuations over the top of a prescribed 

mean wind profile at the upwind slice (e.g., Mayor et al, 2002). While these options are quite 

useful for somewhat idealized situations, there is a sincere push towards nesting eddy-resolving 

LES within mesoscale models such as the Weather Research and Forecasting model (WRF) from 

NCAR to be able to investigate the influence of regional weather on local sites. Although 

mesoscale models like WRF are extremely useful for predicting the average state and impact of 

turbulence as it responds to evolving weather patterns and to the land surface, they lack the 

ability to capture the turbulence structure like that shown in Figure 11. Therefore, these models 

are unable to provide inflow conditions to an embedded LES model, which severely limits their 

utility within the wind energy context. Therefore, improved boundary conditions for simulations 

of turbulence would be extremely useful. 

7.3.4 Subfilter-scale models

All of the simulation techniques mentioned above perform some sort of averaging or filtering of 

the Navier-Stokes equations and therefore require a model or parameterization describing the 

influence of the atmospheric motions that are lost in that process; they are typically called 

49 
subfilter-scale (SFS) models. The problem is that SFS models need to evolve with the scales at 

which they are being applied (e.g., Wyngaard, 2004), with atmospheric stability (e.g., Sullivan et 

al, 2004), and with the influence of any obstacles (i.e., wind turbines) or land-surface 

characteristic changes (i.e., orography or vegetation) that might modify the flow (e.g., Patton et 

al, 2008). Improvements in SFS models for numerical simulations are essential for accurate 

wind plant siting and wind plant design. 

Due to their minimal cost, compared to observation and/or experiment, computations are an 

extremely valuable tool for improved micro siting and wind plant design, but they’re not nearly 

as useful if observations are not available to validate their predictions against.

7.4 Needs from Observations/Experiments 

Significant advances could be made immediately if sufficient research effort was devoted to 

particular issues and combined academic understanding with extensive industry experience. In 

addition, research effort must be devoted to developing new measurement techniques and to 

providing infrastructure for the kind of long-term observations which are routinely available for 

other purposes, but have not yet been invested in this energy field. The purpose of developing 

such instruments and facilities is not only to advance understanding, which translates directly 

into improved energy costs, but also to participate in the world market for wind energy. 

7.4.1 Measurements in variety of terrains/locations

Observations need to be taken in regions likely for wind plant development. Target locations for 

wind plant development in the United States include regions off-shore, over the Great Lakes, and 

regions of the Great Plains just east of the Rocky Mountains. It is essential that some of these 

observations be taken near (upstream, within, or downstream) of already operational wind plants, 

so that we can assess the impact and response of the turbines and the wind plant as a whole. 

 

Horizontally-homogeneous flat terrain. In the Great Plains, which can be considered to be 

close to horizontally homogeneous terrain, one key element for wind energy is the frequent 

development of a low-level jet near the top of the PBL. The height of this low-level jet varies 

between about 50 m and 400 m, but typically occurs at about 200 m (Banta et al, 2002). There 

are many questions as to how the jet impacts turbine operations. In particular, if the jet forms at 

about hub-height, then during a single rotation of the turbine, the blades can be both within and 

above the boundary layer. Under these conditions, the turbines could be under extreme loads. 

The velocity shear on the underside of the jet generates turbulence not affiliated with interactions 

with the ground (Banta et al, 2003), and the momentum extraction by upstream turbines could 

potentially enhance that velocity shear to a critical state and generate breaking Kelvin-Helmoltz 

waves that could damage downwind turbines. To properly sample the rotor swept area, highresolution (spatially and temporally) measurements of wind speed and temperature spanning the 

50 – 200-m region for extended duration are essential. These measurements could be 

accomplished with a combination of tall-tower based in-situ measurements, or remotely sensed 

using something like Doppler LIDAR (see Figure 12 for example). If these measurements were 

to take place near or within an operational wind plant, then a number of the turbines could also 

be instrumented to establish direct connections between the measured winds/temperatures and 

the turbine response. 

50 
Figure 12: Sample in-situ tower measuring vertical profiles of turbulent wind 

components and a scanning Dopper LIDAR from the Lamar experiment (from 

Kelley et al, 2007). 

Complex orography. Orography presents a terrifically difficult problem, generally stemming 

from the fact that no two hills are necessarily alike, nor are they typically similar to any idealized 

sinusoidal form. There are extremely limited measurements available that characterize the 

detailed impact of hills on turbulent flows. The wind energy industry has been placing single 

towers on hilltops for many years, but much of this data is proprietary and limited solely to the 

hilltop. A good portion of complex fluid dynamics occurs on the upwind and downwind sides of 

the hill. Towers on the hill top capture the net result, but not the mechanisms responsible. Mean 

wind direction also complicates the situation because hills are not generally symmetric. Tall 

vegetation on hills complicates the fluid dynamics even more because the traditional hillturbulence interactions are modified by the vegetation-induced pressure drag. Turbines should 

act similarly on flow. Some think that valley channeling could be a potential for future wind 

plant siting, but observations are lacking. To improve our ability to site and place turbines 

individually and within a wind plant in complex orography, it is essential that detailed 

observations of turbulence characteristics be taken at numerous sites both with and without 

turbines. 

7.4.2 Short-term versus long-term measurements 

Resulting largely from financial constraints, much of the atmospheric community tends to 

perform observational campaigns as short-term experiments. While this style of observing is 

extremely useful for establishing a process level understanding, the wind energy industry needs 

to design wind turbines and wind plants that operate for durations more like 5 – 20 years. A 

campaign lasting one month or even a year at a particular site cannot pretend to capture the 

variability that a wind plant might encounter over its twenty-year lifetime. There is significant 

utility in both observational methods. It is recommended that DOE expand their capabilities to 

characterize numerous locations in a detailed but shorter term fashion, while at the same time 

51 
establishing some longer term capabilities at a somewhat lower fidelity measurement capability. 

To have its greatest impact on current wind energy stakeholders, any field study would need to 

be carefully planned, but should be developed extremely quickly with direct input from a healthy 

balance of boundary layer meteorologists (some being complex terrain wind-phenomena focused 

and others with specific turbulence experience), canopy/soil/near-surface radiation experts, 

micrometeorologists/surface layer meteorologists (with shear instability experience), wind 

energy research focused scientists, high-resolution numerical modelers, and industry 

practitioners. 

7.4.3 Improvements to DOE field laboratories

DOE field laboratories (like the DOE Atmospheric Radiation Measurment (ARM) Program 

sites) are uniquely poised to aid in the above mentioned measurement issue. These facilities 

have been making observations since about 1992 and have been measuring key wind variables 

for much of that duration. Although they’re extremely useful, the focus of these measurements 

has not been on wind energy. Typical wind profiles are hourly-averaged and are not directed at 

characterizing the atmospheric turbulence that is essential for wind plant siting. Newly 

developing technologies such as scanning high-resolution Doppler LIDAR could be incorporated 

into DOE measurement suites, which would dramatically increase the utility of the ARM sites 

for the wind energy industry. 

7.5 Impact 

The major driver is to lower the cost of energy from wind plants. The research needs proposed 

here will reduce energy costs from wind plants through: 1) an industry-wide reduction in the 

uncertainty of wind speed/energy production estimates, 2) improved operations and maintenance 

costs, 3) improved turbine reliability, and 4) technology for improved weather forecasting that 

will transfer to many atmospheric applications. 

7.6 Key Events and Approximate Timing 

Wind energy is a fast moving business with urgent research needs. We anticipate the following: 

1) immediate definition of detailed stakeholder measurements and modeling needs/locations, 2) 

first observatory with observations to 200-m height by 2009, and 3) AWEA/EWEA/IEA 

workshops comparing models with observations and describing PBL and 0 – 200-m atmospheric 

characteristics at a specific site by beginning 2009. 

References 

Banta, R. M., R. K. Newsom, J. K. Lundquist, Y. L. Pichugina, R. L. Coulter, and L. Mahrt, 

2002: Nocturnal low-level jet characteristics over Kansas during CASES-99, Bound.-Layer 

Meteor., 105, 221-252. 

Banta, R.M., Y.L. Pichugina, and R.K. Newsom, 2003: Relationship between low-level 

properties and turbulence kinetic energy in the nocturnal stable boundary layer, J. Atmos. 

Sci., 60, 2549-2555. 

Bowen, A. J. and N. G. Mortensen, 2004: WAsP prediction errors due to site orography, Risø 

National Laboratory, Roskilde, Denmark, Risø-R-995(EN), 66 pp, ISBN 87-550-2320-7. 

52 
Cuxart, J. and co-authors, 2006: Single-column model intercomparison for a stably stratified 

atmospheric boundary layer, Bound.-Layer Meteorol., 118, 273-303. 

Deardorff, J. W., 1970: A numerical study of three-dimensional turbulent channel flow at large 

Reynolds numbers, J. Fluid Mech., 41, 453-480. 

Dupont, S. and Y. Brunet, 2008: Edge flow and canopy structure: A large-eddy simulation study, 

Bound.-Layer Meteorol., 126, 51-71. 

Harman, I. N. and J. J. Finnigan, 2007: A simple unified theory for flow in the canopy and 

roughness sublayer, Bound.-Layer Meteorol., 123, 339-363. 

Hunt, J. C. R., S. Leibovich, and K. J. Richards, 1988: Turbulent shear flow over low hills, Q. J. 

R. Meteorol. Soc., 114, 1435-1470. 

Finnigan, J. J., 2000: Turbulence in plant canopies, Annu. Rev. Fluid Mech., 32, 519-571. 

Finnigan, J. J., R. H. Shaw and E. G. Patton, 2008: Turbulence structure above and within a 

forest canopy, J. Fluid Mech., submitted. 

Kelley, N. D., B. J. Jonkman, G. N. Scott, and Y. L. Pichugina, 2007: Comparing pulsed Doppler 

LIDAR with SODAR and direct measurements for wind assessment, National Renewable 

Energy Internal Report, NREL/CP-500-41792, 21pp. 

Mayor, S. D., P. R. Spalart, and G. J. Tripoli, 2002: Application of a perturbation recycling 

method in the large-eddy simulation of a mesoscale convective internal boundary layer, J. 

Atmos. Sci., 59, 2385-2395. 

Moeng, C.-H. and P. P. Sullivan, 1994: A comparison of shear- and buoyancy-driven planetary 

boundary layer flows, J. Atmos. Sci., 51, 999-1022 

Patton, E. G., R. H. Shaw, M. J. Judd, and M. R. Raupach, 1998: Large-eddy simulation of 

windbreak flow, Bound.-Layer Meteorol., 87, 275-306. 

Patton, E. G. and many others, 2008: The canopy horizontal array turbulence study (CHATS), 

Bull. Amer. Meteorol. Soc., submitted. 

Sullivan, P. P., T. W. Horst, D. H. Lenschow, C.-H. Moeng, and J. C. Weil, 2003: Structure of 

subfilter-scale fluxes in the atmospheric surface layer with application to LES modeling, J. 

Fluid Mech., 482, 101-139. 

Sullivan, P. P. and E. G. Patton, 2008: A highly parallel algorithm for turbulence simulations in 

planetary boundary layers: Results with meshes up to 20483

, 18th American Meteorological 

Society Symposium on Boundary Layers and Turbulence, June 9-13, Stockholm, Sweden, 

Abstract: 11B.5. 

Svensson, G. and co-authors, 2008: The diurnal cycles in single-column models – the GABLS 

second experiment. In preparation.

Tennekes, H., and J. L. Lumley, 1972: A first course in turbulence, MIT Press, Cambridge, 

Massachusetts, 300 pp, ISBN 0 262 20019 8. 

Wyngaard, J. W., 2004: Toward numerical modeling in the “Terra Incognita”, J. Atmos. Sci., 61, 

1816-1826. 

Xu, D., K. W. Ayotte, and P. A. Taylor, 1994: Development of a non-linear mixed spectral finite 

difference model for turbulent boundary-layer flow over topography, Bound.-Layer 

Meteorol., 70, 341-367. 

53 
8. Acquiring and Exploiting Large-Scale Data Sets 

8.1 Abstract 

To ensure future growth and cost effectiveness of wind energy, reference data sets of wind 

turbine behavior and the atmosphere surrounding them are needed to better understand the 

physics of wind energy production. Better understanding of the physics will lead to better 

prediction codes and less uncertainty for manufacturers and developers of wind turbines, 

resulting in lower overall costs of energy. Many existing data sets in the atmospheric community 

may not be directly applicable to the wind energy problem. But, the infrastructure used to gather 

them may be augmented to obtain more measurements that focus on the areas of interest for wind 

turbines. More measurement sites should also be developed that can measure a representative 

range of atmospheric conditions and topography consistent with wind plants installed in the 

United States. Development of new measurement sites must also be accompanied with 

development of new measurement techniques, as well as computational techniques for analyzing 

large data sets and prediction of extreme events. Finally, existing data sets in the wind turbine 

community, primarily from developers and operators, must be made available to researchers in 

order to improve models, while also protecting the intellectual property of the industry. 

8.2 Problem or Opportunity 

Researchers and industry need better reference data sets of atmospheric behavior and 

corresponding wind turbine operation to quantify the complex interaction between the wind and 

the mechanisms used to transform it into electrical energy. These data sets will be unique to the 

area of wind energy largely because current utility-sized wind turbines operate in a regime of the 

atmosphere (50 – 200 m above ground) that is poorly studied. Also, the requirements for spatial 

and temporal resolution needed to analyze detailed turbine-atmosphere interactions are more 

stringent than currently existing datasets can provide. 

Research measurement sites and re-analysis data from the atmospheric community, such as 

NCEP-NCAR (Kalney, 1996) or the NOAA-NWS Wind Profiler Network (Schlatter and Zbar, 

1994), have been gathering data for long periods of time, but only for relatively sparse grids 

around the globe or use relatively large averaging times or measure at heights well above 200 m. 

Augmenting or modifying such systems to gather data relevant to wind energy should be a 

starting step for increasing the amount of wind turbine-specific data available. Given that it is 

often advantageous to place turbines in areas of complex terrain because of wind accelerations 

due to topology, research data sets that focus on the effects of orography (Taylor, 1987) are also 

important. Data sets of operating wind plants in such terrain are currently not available in the 

public domain. 

Methods for sharing wind plant operating data with the research community while also 

potentially protecting commercial sensitivity should be developed. Existing data sets from 

industry, with the exception of a few offshore wind plant operators (Frandsen, 2007), are often 

treated as commercially sensitive and not made available for research purposes. Also, even 

though the industry has access to such large data sets within their own organizations, they often 

lack the time to evaluate any discrepancies between predicted behavior of wind plants and actual 

54 
power output. Therefore, even though operational data sets exist, they are currently not being 

used to advance modeling capabilities. 

Finally, the accuracy of standard-design extreme events for wind turbines, such as those in the 

IEC design standard (IEC 2005) is unknown. These design extreme events are often based on 

previous experience at a few select locations and may not reflect the true extreme behavior at 

many current wind plant locations. 

8.3 Research Approach 

To overcome a current lack of reference data for typical wind turbine operating sites and 

atmospheric conditions, many new data gathering measurement stations should be built and 

currently existing stations modified. The data sets gathered from these stations should be well 

resolved both spatially and temporally specifically for application to wind energy problems and 

the stations themselves built for long periods of data collection. These data sets should be 

gathered through a network of measuring sites at different locations representing a range of 

terrains and atmospheric behavior. Methods for allowing developers and wind plant operators to 

share data should also be developed so that this potentially large source of data can be made 

available without compromising the commercial interests of the industry. Finally, special 

attention should be paid to extreme event analysis and measurements that can capture these rare 

events that often drive wind turbine design. 

8.4 Needs from Computation/Theory 

While this summary is mainly concerned with observations and experiments of the atmosphere 

and operating wind turbines, there are some areas where computational methods will be useful. 

The first is using computational tools to design the experiments themselves. By their very nature, 

extensive field campaigns can be very costly and any predictions and optimizations of the 

measurement set-up that can be estimated computationally first will reduce the costs of such a 

campaign. Experimental design questions that might be answered computationally include: what 

are the optimal places to place meteorological towers to measure certain atmospheric behaviors 

and what kind of wind turbine responses should be measured to correlate with that atmospheric 

behavior? 

A large network of measurement sites would also produce extremely large data sets, which could 

be analyzed using newly developed data mining methods. These methods would search for 

patterns over a range of variables that may give insight into the physics of the turbine/wind plant 

interaction with the atmosphere (e.g., Tebaldi et al, 2007). 

Due to cost constraints, the measurement network will certainly have gaps in both space and time 

resolution. To fill these gaps, researchers will employ advanced computational methods for 

interpolation and/or extrapolation which will provide more useful data to modelers for code 

validation (e.g., Larsén and Mann, 2006).

55 
8.5 Needs from Observations/Experiments 

The first step in creating a set of reference data sets that will be useful for the wind energy 

community is to assemble currently available data in a common repository with a single standard 

format. These data sets may provide some additional insight into atmospheric behavior not yet 

considered by the wind energy community, and may also provide clues as to what additional data 

is needed from future measurements of the atmosphere. Examples of applicable data sets from 

the atmospheric community include the CASES-99 (Poulos et al, 2002) and the DOE ASCOT 

(Dickerson and Gudiksen, 1983) studies. 

A major effort should be initiated for developing a new network of measuring sites that will 

create reference data sets. This type of network has already been created for other large 

measurement campaigns, such as for cloud formation (Klein and Del Genio, 2006), solar 

resources (Gilgen and Ohmura, 1999), and carbon dioxide fluxes in forests (Baldocchi, 2001). 

Therefore, the procedures and typical costs to create such networks are readily available. Most 

importantly, these sites should be located in and around locations of large wind resources, where 

wind plants already exist or will potentially in the future. This should include a representative 

sample of different locations in the United States where wind plants may be located. 

Figure 13: National Weather Service/NOAA Wind Profiler near Glennallen, AK. 

Such a network of measurement sites can be created by starting from already existing 

measurement sites around the United States. Augmenting existing measurement systems such as 

the NOAA-NWS Profiler network (see Figure 13) to measure at lower heights or at faster sample 

rates will be mutually beneficial to both the wind energy sector and weather/climate 

measurement communities by providing more detailed measurements closer to the ground. 

Augmentation may include adding systems like scanning or Doppler LIDAR (Smith et al, 2006) 

and meteorological towers to currently existing sites. Certain sites with interesting atmospheric 

(e.g., low-level jet in the Great Plains) or turbine-specific behavior may be measured in much 

more detail depending on the type of behavior to be observed. These "super sites" may have 

much more instrumentation and sample at much higher resolutions over a fixed period of time.

56 
In addition to fixed measurement platforms dispersed around the country, it will also be useful to 

have at least one mobile DOE measurement laboratory that could temporarily measure at sites of 

interest. Such a mobile laboratory could include LIDARs (see Figure 14) and SODARs and 

would make it easier to measure in and around existing wind plants or met towers. Mobile 

facilities were used in the Lamar Low Level Jet Project (Kelley et al.) and are currently being 

developed at Risø in Denmark (Mikkelsen, 2007). Such facilities have also shown use in 

measuring of wakes downstream of turbines (Barthelmie, 2003), for which very few reference 

data sets are currently available.

Figure 14: Mobile LIDAR systems used for wind energy measurements. 

Because much of the research on wind climates related to wind energy is based on measurements 

of the flat terrain in northern Europe, measurement campaigns must also focus on a variety of 

terrains and atmospheric conditions. Areas that include complex terrain, forested topography, 

and offshore locations need further study. It would also be beneficial to include a greater variety 

of climates in the United States, such as the mesas of Texas or the high wind speed climates of 

the Great Plains. 

Many developers and operators of wind plants have data that would be extremely useful to the 

research community for the purpose of model development and validation. Methods for 

disseminating this data among researchers should be developed to allow for exchange of this 

useful commodity while also protecting the intellectual property of the commercial entities. Such 

methods may include normalization of quantities like power production or even individual 

intellectual property agreements with a given set of researchers. 

In creating these measurement sites, researchers will also need to continue development of new 

instrumentation to measure wind turbine behavior, wind, and other atmospheric quantities of 

interest. New measurement technologies are always being created and existing technologies 

improved. It is obviously advantageous to have the most accurate, yet cost-effective tools 

available for this task. 

57 
Until much longer-term measurements (over 10 years) of the atmosphere around critical wind 

sites are complete, it will be difficult to assess the accuracy of the extreme event prediction. 

Therefore, it will be important to plan for long-term and continuous measurements at some of 

these sites in order to capture these rare events. These measurements may be of lower fidelity 

due to cost and data collection constraints, but detailed enough to capture critical atmospheric 

and wind turbine behavior. 

Finally, it is possible that wind tunnel measurements can offer a cheaper alternative to extensive 

field campaigns for some atmospheric behaviors. Wind tunnels have been shown to be useful 

particularly in the study of complex terrain (Ayotte and Hughes, 2004) where complicated field 

experiments may be prohibitively expensive. 

8.6 Potential Wind Energy Impact 

The creation of world-class reference data sets for specific application to wind energy will have 

immediate impacts. Currently, there is very little data available for validating and improving the 

primary models used for micrositing of wind plants. The data that does exist is predominantly 

from land based sites in northern Europe and European offshore sites. Having new data available 

that reflects the atmospheric and topography effects commonly seen in the United States will 

lead to more accurate models for these applications and lower the uncertainty and risk for wind 

plant developers in the future. Given that many wind plants in the United States are currently 

under-producing by as much as 10%, the faster these data sets can be made available, the better. 

In addition, assuming that useful data can be extracted from existing data sets, new value will be 

added to these data sets by applying it to the wind energy arena. Similarly, new data sets created 

specifically for wind energy can be applied to other important areas of atmospheric research 

outside of wind. 

8.7 Key Events and Approximate Timing 

Developing a large long-term network of measurement sites will take many years to plan and 

execute. Some immediate goals for the development of reference data sets include: 

– Identify collaborative partners in building a large-scale measurement network to leverage 

funds and ease construction costs immediately (e.g. ARM, AWEA, NASA, NOAA NWS, 

Risø) 

– Locate and aggregate appropriate existing datasets by 2009 

– Commit funds immediately and begin development of DOE observational laboratories by 

2009 

– Augment NOAA NWS Wind Profiler Network by 2010. 

References 

Ayotte K.W. and Hughes D.E., (2004). Observations of Boundary-Layer Wind-Tunnel Flow 

over Isolated Ridges of Varying Steepness and Roughness, Boundary-Layer Meteorol., 112,

525-556. 

Barthelmie, R.J., L. Folkerts, F. Ormel, P. Sanderhoff, P. Eecen, O. Stobbe, and N.M. Nielsen (2003). 

Offshore wind turbine wakes measured by SODAR. Journal of Atmospheric and Oceanic 

Technology, 30, 466-477.

58 
Baldocchi, D. et al. (2001)."FLUXNET: A new tool to study the temporal and spatial variability 

of ecosystem-scale carbon dioxide, water vapor, and energy flux densities", Bulletin of the 

American Meteorological Society, 82, 11:2415-2434. 

Dickerson, M. H. and Gudiksen, P. H. (1983) Atmospheric studies in complex terrain, 

Technical Progress Report, 1979 – 1983, Lawrence Livermore National Lab., CA, ASCOT84-2. 

Frandsen ST, Barthelmie RJ, Rathmann O, Jørgensen HE, Badger J, Hansen K, Ott S, Rethore 

PE, Larsen SE, Jensen LE (2007) Summary report: The shadow effect of large wind farms: 

measurements, data analysis and modeling. Report No. Risø-R-1615(EN), Risø National 

Laboratory/DTU, Roskilde. 

Gilgen, H. and Ohmura, A. (1999) The Global Energy Balance Archive, Bull. Am. Met. Soc. 

(80) ; 831-850. 

International Electrotechnical Commission, (2005) IEC/TC88, 61400-1 ed. 3, Wind Turbines - 

Part 1: Design Requirements, International Electrotechnical Commission (IEC). 

Kalnay et al., (1996). The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 

77, 437-470. 

Kelley, N.; Shirazi, M.; Jager, D.; Wilde, S.; Adams, J.; Buhl, M.; Sullivan, P.; Patton, E. (2004). 

Lamar Low-Level Jet Program Interim Report. 216 pp.; NREL Report No. TP-500-34593. 

Klein, S. and Del Genio, A. (2006) ARM's Support for GCM Improvement: A White Paper, 

UCRL-MI-224010,ARM-06-012, Lawrence Livermore National Lab., CA.

Larsén X.G. and Mann J. (2006) The effects of disjunct sampling and averaging time on 

maximum mean wind speeds Journal of Wind Engineering and Industrial 

Aerodynamics, Volume 94, Issue 8, Pages 581-60. 

Mikkelsen T., Wind Scanner Project at Risø DTU, 2007-present. 

Poulos, G. S., et al., (2002) CASES-99: A Comprehensive Investigation of the Stable Nocturnal 

Boundary Layer, Bull. Amer. Meteor. Soc., 83, 555-581. 

Schlatter T.W. and F.S. Zbar, editors, (1994). Wind profiler assessment report and 

recommendations for future use (1987-1994). U.S. Department of Commerce, NOAA, Silver 

Spring, Maryland, 141 pp. 

Smith, D.A.; Harris, M.; Coffey, A.S.; Mikkelsen, T.; Jørgensen, H.E.; Mann, J.; Danielian, G., 

(2006) Wind lidar evaluation at the Danish wind test site in Høvsøre. Wind Energy, 9 , 87-93 

Taylor, P. A., H. W. Teunissen, (1987) The Askervein Hill Project: Overview and Background 

Data, Bound.-Layer Meteorol., 39, 15-39. 

Tebaldi, C., J. Berner, G. Brantstator, and D. Nychka, (2001) Looking for nonlinearities in the 

large scale dynamics of the atmosphere. In Proc. First SIAM International Conference on 

Data Mining, Chicago, IL. 

59 
9. Technology Transfer and Tool Integration 

9.1 Abstract 

To enable continued rapid growth of the wind industry, researchers and industrial partners need 

to accelerate the pace of technology transfer and tool integration between their communities. In 

the commercial sector, the current generation of design tools is grossly inaccurate for micrositing 

needs. Tools designed in the research sector can be applied to wind engineering specific 

applications, which will increase the accuracy and reduce the risk for developers. Validation data 

unique to wind energy must be acquired in order to ensure improved accuracy. Often, this data 

comes from commercial wind plant operators, and due to competitive sensitivities, methods for 

protecting proprietary data should be developed. Improved technology transfer and tool 

integration will increase the accuracy of industrial design tools and lowering the cost of energy. 

9.2 Problem or Opportunity 

Wind energy continues to be the fastest growing energy technology in the world, with a 27% 

growth in installed global capacity during 2007 alone (GWEC, 2008). And with large projected 

growth in the coming decades, this rate will likely continue for the foreseeable future: “Areas 

with good wind resources have the potential to supply up to 20% of the electricity consumption 

of the United States.” (Bush, 2006). The transfer of modeling capabilities from research to 

industry in the micrositing area needs to be accelerated to keep up with both industry growth 

rates and DOE research goals. 

Because of the rapid increase in installations, the wind industry has little time to incorporate 

research advances made in academic and laboratory settings, which may lead to drastic 

improvement in designs and overall cost of energy for power production. This is particularly true 

in the area of micrositing and array effects where under-production of wind plants can be directly 

attributed to the immature models used by industry to design them. The large uncertainty in 

commonly used micrositing models such as WAsP (Mortensen, 1993) can lead to higher risk for 

investors and economic penalties to developers for under-producing wind plants. Also, 

developers using existing tools are unaware of uncertainties in the current generation of models, 

which may lead them to apply models outside of their intended usage. Given large research 

investments in the atmospheric sciences in recent years, incredible opportunities exist to transfer 

technology from research endeavors to industry that will lower the cost of energy from wind. 

Often, even if these atmospheric tools exist, they are not optimized for wind resource analysis 

and also not integrated with wind turbine design tools. Future efforts should concentrate on 

integrating models from the atmospheric science community with commonly used tools in the 

wind turbine design community. 

9.3 Research Approach 

The approach for accelerating transfer of technologies from the research community to the wind 

turbine industry is part logistical and part research. Research is needed on how to apply 

atmospheric science tools (e.g., Moeng and Sullivan, 2002) and integrate them with wind turbine 

60 
design tools (e.g., Jonkman and Buhl, 2005) to improve the accuracy of micrositing methods. 

The quantification of the inherent uncertainties in both existing and future design tools will also 

be an important part of future research. Currently, the magnitude of these uncertainties is 

unknown, but can have a dramatic influence on decisions related to micrositing and power 

production. 

Logistically, it will be important to increase communications between the atmospheric and wind 

turbine communities. One way to accomplish this is through special sessions at respective 

conferences. In the wind turbine community, this would include a special session at the AWEA 

Windpower or AIAA/ASME Wind Energy Symposium focused on PBL modeling. In the 

atmospheric community, pertinent conferences that could introduce a wind turbine session are 

the American Meteorological Society's Boundary Layers and Turbulence Conference and the 

American Geophysical Union's annual conference. 

Finally, developing methods for protecting proprietary data while also ensuring its availability 

for publication will be important for benchmarking and validation of all models used in the 

community. Such methods could include normalizing the data so that absolute values are not 

revealed, or creating agreements with individual researchers that will use some parts of the data 

to validate their models, but not publish the commercially sensitive aspects. 

9.4 Needs from Computation/Theory 

Research efforts should be undertaken to improve lower fidelity models used by the industry. 

These models will have run times consistent with a typical industrial design cycle (several days), 

but with improved accuracy over the current generation of tools. Currently existing codes in the 

atmospheric science and wind turbine design community must be integrated into single packages 

to provide closely coupled modeling capabilities. How to best interface these codes is a research 

topic in itself, but seamless interfaces will lead to more consistent interoperability between these 

currently separated regimes. Better coupling will also reduce the amount of operator error that 

can be a significant contributor to the overall uncertainty of wind plant predictions. 

In addition to improving precision and accuracy of wind production and load estimates, major 

effort is needed to include the known uncertainty in all stages of design and operation. This can 

be done by performing very detailed sensitivity studies, where the input parameters are varied 

probabilistically and the influence on output parameters is quantified and correlated back to the 

inputs. Quantifying uncertainties will give future users a better understanding of how uncertainty 

of inputs influences the associated outputs, and also the relative importance of certain 

parameters. 

9.5 Needs from Observations/Experiments 

As modeling capabilities are transferred from the research community to industry, more 

validation data will be needed to ensure models are more accurate than the current generation. 

This validation data will involve unique measurements of the PBL and in and around wind plants 

and turbines. Existing data should be combed for potential applications to wind resource 

characterization. Once current data sets have been exhausted, measurements of the PBL need to 

be performed in areas that have been overlooked by the atmospheric science community; 

61 
between 50 – 200-m height above ground, which are typical hub heights for utility sized 

turbines. Also, the effects of turbines on the atmosphere and other wind turbines within a wind 

plant need to be quantified. This entails simultaneous measurements of wind fields upstream and 

downstream of individual wind turbines and wind plants. Operational data of turbines within 

these plants is also necessary to correlate with wind field measurements and improve validation 

efforts. Of course, developers may be reluctant to release such data to researchers wishing to 

publish, and in response, methods for protecting or sanitizing such data must be developed. 

9.6 Potential Wind Energy Impact 

Improving links between the research community and industry is an essential part of building a 

successful industry. Improved technology transferred between the communities will result in 

more accurate design tools for the commercial sector. These design tools will also be more 

closely integrated and reduce the uncertainty of wind plant production estimates. It may also 

increase the possibility of design tools used for site-specific design, where turbines and plants 

can be optimized for a given location and the cost of energy lowered relative to more generic 

designs. Finally, technology transfer and tool integration will result in greater likelihood of DOE 

research goals. 

9.7 Key Events and Approximate Timing 

To encourage the science behind wind energy to improve and to ensure technology transfer some 

early goals should be: 

– Micrositing and array effects code workshops at industry events (e.g., AWEA, EWEC etc.) 

starting in 2009 

– Develop methods of protecting proprietary data, enabling industry buy-in by early 2009 

– Wind plant data collected within 3 years and model validation within 5 years 

– Wind plants designed with new tools demonstrate improved performance – immediately 

following design with new tools. 

References 

Bush, G.W. (2006) The State of the Union Address - Advanced Energy Initiative, February 20, 

2006. 

Global Wind Energy Council “Continuing Boom in Wind Energy 20 GW of new capacity in 

2007,” January 18, 2008. 

Jonkman, J. M. and Buhl, M. L. Jr, (2005), FAST User’s Guide, National Renewable Energy 

Laboratory, NREL/EL- 500-38230, Golden, CO, 

http://wind.nrel.gov/designcodes/simulators/fast/FAST.pdf. 

Mortensen, N.G., Landberg, L., Troen, I. and Petersen, E.L. (1993). Wind Atlas Analysis and 

Application Program (WAsP), Vol. 1: Getting Started. Vol. 2: User’s Guide. Risø National 

Laboratory, Roskilde, Denmark, January 1993. 

Moeng, C-H. and P.P. Sullivan, 2002: Large Eddy Simulation. In: Encyclopedia of Atmospheric 

Sciences, 1140-1150. 

62 
Detailed Recommendations – Mesoscale Processes 

10. Wind Energy Testbed Development........................ ........... ................................................64 

11. Improve Industry and Atmospheric Modeling.......................... ...............................................69 

12. Create and Convene a Cooperative Working Group............ ......... .........................................74 

63 
10. Wind Energy Testbed Development

10.1 Problem or Opportunity 

At present, data sets that adequately address characterization of the atmospheric boundary layer 

structure for wind energy development do not exist. Better data sets would allow for the 

resolution of several key wind energy development barriers that include: 

• Lack of understanding of the range of inflow turbulent field conditions (including 

coherent structures) and their effects on power performance and machine loading 

• Lack of knowledge of the characteristics and frequency of occurrence of extreme 

meteorological events that contribute to the early failure of wind turbine components 

• Significant uncertainty in the accuracy of mesoscale and microscale meteorological 

models that could be used for predicting wind resources 

• Uncertainty in the accuracy of using mesoscale meteorological models for predicting, in 

real-time, changes in wind energy production associated with transient weather systems 

• Uncertainty in the ability of climate models to predict wind energy resource variation 

under various climate change scenarios 

• Where concerted measurement campaigns have occurred, the duration is typically too 

short to obtain good climatological averages. 

The reasons for the lack of adequate data are varied. They include the fact that most long-term, 

high temporal resolution measurements come from surface meteorological stations that are not 

representative of winds in the layer from 25 – 200 m, where most wind turbines reside. For 

winds aloft, the NOAA/NWS national radiosonde network of ~100 sites provides vertical profile 

wind information, but only twice daily, with a vertical resolution of ~ 25 m. This antiquated 

system is in the process of being replaced with the Radiosonde Replacement System (RRS) that 

uses new technology allowing for wind measurements with a higher vertical resolution. 

However, because balloon systems make point measurements in space and time, the 

representativeness of winds from these systems is poor, and they do not provide turbulence data. 

In addition, NOAA maintains a sparse network of 404 MHz wind profilers in the central United 

States, but the lowest level winds provided by these profilers is ~500 m, and therefore of limited 

utility for wind energy. 

Various government agencies also operate on the order of ten to twenty 915 MHz wind profilers 

in the United States. These profilers typically have a lowest range gate of approximately 100 m, 

with additional range gates at 60 to 100-m intervals above that, providing at most two or three 

measurements at turbine levels. Although of more potential use for wind energy, they still are 

not capable of addressing all of the meteorological issues hampering wind energy development. 

Wind profiles also can be derived from velocity azimuth display (VAD) scans from NOAA’s 

national network of WSR-88D weather radars. However, the lowest VAD range gate is 

approximately 300 m, with a 300- m vertical resolution, which again leaves them unsuitable for 

most wind energy applications. Finally, there are a very limited number of tall towers in the 

United States that have been instrumented with meteorological sensors. Most of these towers are 

less than 60-m tall, while several (perhaps no more than 2 or 3) reach 200 m. It is unknown at 

64 
present how many of these towers record continuous measurements, if there are large temporal 

gaps in the historical data, if the data are readily available, what types of measurements are 

taken, and how accurate the data are. 

10.2 Research Approach 

One solution to the need for better meteorological observations is to develop Wind Energy 

Testbed sites.

• The goal of the Wind Energy Testbeds is to accelerate the development of wind energy 

through use of new technologies from the atmospheric and wind energy research 

communities. This includes deployment of advanced tools (new observational systems, 

as well as weather, turbulence, and turbine prediction models) in a testbed setting where 

they are continuously refined, demonstrated, and evaluated. 

• The focus of the observations collected at each testbed would be a domain as large as 20 

x 20 km, and the layer from 20 – 200 m, which is poorly observed by current operational 

measurements, but essential for wind energy.

The testbed sites would center on long-term deployments of state-of-the-art instruments that 

provide the measurements necessary to 1) measure the turbulence intensity and wind shear 

profiles that affect the reliability and longevity of wind turbines, 2) evaluate and improve lowlevel wind simulations in microscale, mesoscale, and climate models, and 3) evaluate 

instruments that might commonly be deployed at potential wind energy sites. 

A requisite component of the testbed development is coordination with the atmospheric 

modeling (microscale, mesoscale and global climate scale) community. In particular, it is 

essential that observations be taken that are necessary for the initialization and evaluation of the 

models, as well as for parameterization development. In addition, model evaluation should be a 

continuous process, allowing for iterative improvements in both the observations and models. 

Also, it needs to be assured that resources are available for analysis and application of the testbed 

data, not just its collection. 

Ideally, there would be testbed sites at multiple geographic locations. This would allow for the 

achievement of regionally varying objectives. Requirements for the testbed program include that 

they measure 1) the central plains low-level nocturnal jet, 2) winds in complex terrain such as 

the Appalachians and Western U.S. mountains, 3) winds near operating wind energy projects, 

and 4) coastal wind processes, preferably at an offshore site. It is possible that the 

instrumentation for a testbed site could be moved to a new geographical location after some 

period of time. However, as measurements of at least several years are probably required at each 

site, this would delay the acquisition of data at some geographic locations where it might be of 

immediate use. We also note that the DOE ARM Southern Great Plains facility in Oklahoma 

would be an obvious candidate location for a site that focuses on the impact of the low-level jet. 

65 
10.3 Needs from Observations/Experiments 

The emphasis of the testbed is on the lower PBL, from the surface to approximately 200 m. We 

need to make accurate measurements in conditions up to ~30 m/s, primarily of wind velocity and 

turbulence profiles, but also stability (temperature profiles). All instrumentation systems have 

their strengths and weaknesses, and no single system will measure all of the parameters 

necessary for wind energy development. The depth of the PBL will be important for scaling 

laws, parameterization development and model evaluation. Except in exceptionally 

homogeneous terrain, it will be essential to have measurements at multiple nearby locations, as 

any single site will, in general, not be representative of the larger area. For example, what is 

measured at the crest of a hill/ridge will be very different from other nearby locations. 

10.3.1 In-situ 

Tall towers instrumented with multiple levels of sonic anemometers, cup anemometers or 

propeller vanes, and temperature sensors are ideal in many aspects for wind energy studies, as 

they provide high accuracy measurements of wind and turbulence at high vertical and temporal 

resolution. However, towers are expensive to build, with the cost increasing exponentially as the 

height of the tower increases. In complex terrain, towers only provide a measurement at one 

horizontal location. 

10.3.2 Remote sensing 

LIDAR. LIDAR’s have been successfully used in recent wind energy studies. The advantages 

of LIDARS are that they provide accurate, high resolution wind measurements. The only 

inherent spatial resolution limitation of the LIDAR is associated with the path averaging along its 

pencil beam, which is approximately 30 m. Each radial Doppler beam measurement takes on the 

order of 1 s, and in approximately 30s a vertical scan can be made providing a very vertical high 

resolution profile of radial winds. In addition, by staring at one elevation angle, time series of 

the turbulent winds can be made at the different range gates along the beam. Also, LIDARS are 

not limited by ground clutter. 

Wind profiling radars. The biggest problem with 915 MHz boundary layer wind profilers is 

their coarse vertical resolution (~60 m) and their lowest range gate (~80 – 100 m). A new highresolution Range Imaging (RIM) multi-frequency technique is being developed, which could 

potentially provide vertical resolution on the order of 10 m. However, the lowest range gate of a 

RIM 915 MHz wind profiler would still be about 80 m. Spaced array antennae profilers have 

the advantages that they measure a single column directly above the radar, and provide rapid 

updates of the wind profiles approximately every 30 – 40 s. However, the vertical resolution and 

lowest range of these systems is about the same as a standard 915 MHz wind profiler. Tests 

have been run on a RIM spaced-array antennae profiler, producing higher vertical resolution 

data, but with the first range gate still on the order of 80 m. Additional testing and validation of 

RIM and spaced antennae winds is necessary. Wind profiling radars also provide excellent 

depiction of the depth of the convective atmospheric boundary layer, and wind profilers with 

Radio Acoustic Sounding System (RASS) also measure the vertical temperature profile, although 

with the same lowest range gate and height resolution limitations as for winds. Finally, one 

potentially major shortcoming of wind profiling radars is that ground clutter generated by the 

66 
moving wind turbine blades would require that the profilers be located over the visual horizon 

from the turbines.

SODAR. These instruments can provide high-resolution vertical wind profiles, from the surface 

to 300 m. Their greatest limitation is that for wind speeds greater than about 15 m/s, the quality 

of the data rapidly degrades as the sound pulse is advected too far downstream of the receiver, 

and ambient noise from the wind blowing over the SODAR hardware and nearby obstacles 

overwhelms the desired meteorological signal.

Satellite. These provide surface wind measurements, but only over water. 

10.3.3 Supporting instrumentation 

In addition to mean wind, turbulence, and temperature profiles, supplementary measurements are 

needed to support the development and evaluation of atmospheric models. Parameters to be 

measured would include solar and net radiation, soil moisture and temperature profiles, PBL 

depth, surface heat and moisture fluxes, and the large-scale pressure fields. 

10.4 Potential Wind Energy Impact 

A successful Wind Energy Testbed program would: 

• Remedy the serious lack of long-term consistent data at the scales of interest. 

• Generate research interest at a wide range of public and private institutions. The data sets 

created by this observational effort would be unique in their scope and duration. Graduate 

students and other researchers would be able to conduct experiments using the “readymade” data rather than having to collect their own. 

• Provide data to develop more sophisticated power curve specifications, improve turbine 

wake modeling, identify mechanisms of structural loading, and research optimization of 

machine control algorithms for maximizing wind resource capture and minimizing structural 

loading. 

• Provide for the evaluation of models under the full range of atmospheric conditions. 

Sustained measurements at a few geographically diverse sites, each of which experiences a 

commercially viable wind resource, will result in a rich set of data spanning a full range of 

operating conditions. These data can then be used to assess performance and uncertainty in 

models at relevant time-length scales. 

• Lead to development of improved models that would generate better wind resource 

information on annual, seasonal, diurnal scales for improved site design, reduced uncertainty 

in energy estimation, and increased performance of operational weather forecasting. 

Improved models, once developed, have the potential benefit of reducing the number of 

measurement sites and/or the amount of time required to characterize the wind resource at 

candidate project sites, both of which readily translate into economic advantage. 

67 
• Promote the development of coupled models operating across typical application scales (e.g. 

coupled mesoscale-LES models) and enable iterative experimental design combined with 

development and validation of models. Closing the gap between the time-length scales of 

the turbine and the mesoscale atmosphere is an important objective for the wind energy 

industry. 

• Allow for the development of instrumentation specifically suited for addressing critical 

observational gaps for wind energy development (e.g., high resolution winds, turbulence 

intensity and thermal stratification). 

10.5 Key Events and Approximate Timing 

• Develop programmatic collaborations with other agencies and institutions that have 

overlapping missions relevant to wind energy development and that could contribute 

resources to the Wind Energy Testbed. (Timing: Immediate) 

• Form a team of meteorological observation and model experts, turbine experts, and wind 

energy developers that will determine the highest priority presently existing instrumentation 

and prioritize the Testbed region(s) so we can maximize the effectiveness of the Testbeds 

with limited funds; and then establish funding. (Timing: Immediate) 

• Identify new instrumentation specifically suited for wind energy studies that potentially 

could be developed for this program. (Timing: Within 3 months after funding for the 

program is established) 

• Select the Testbed sites and obtain land leases. Ensure that basic infrastructure at the sites is 

available (power, communications, etc.) (Timing: Within 6 months after funding for the 

program is established) 

• Begin acquisition and testing of instrumentation for the Testbeds. (Timing: Within 6 months 

after funding for the program is established) 

• Deploy instrumentation at the Testbed sites. (Timing: Within 12 months after funding for 

the program is established) 

• Initiate model evaluation studies. (Timing: Within 12 months after funding for the program 

is established) 

• Revise experimental plans based on measurement findings and results from model 

evaluations. (Timing: Ongoing) 

An auxillary task is to form a team to categorize and make readily accessible historical data sets 

that may be of use for wind energy development. This would include an assessment of the 

quality and accuracy of the data sets, and may involve attempts to surmount legal and intellectual 

property barriers to data sharing. (Timing: Complete within 12 months after funding for the 

program is established) 

68 
11. Improve Industry and Atmospheric Modeling 

11.1 Problem or Opportunity 

Wind energy applications require meteorological information on a variety of time and space 

scales, most critically the wind speed and turbulence at the height of the turbine rotors. Because 

wind power is proportional to the cube of the speed, accurate predictions are essential. Important 

forecast periods include: 

• Near real time, from a few minutes up to one hour ahead, to provide alerts on severe 

weather events which could significantly change the output of the regional wind capacity 

and affect system reliability. 

• One to several hours ahead, to anticipate rapid changes in output from a wind plant to the 

regional electrical power grid and the need for scheduling adequate reserve capacity to 

accommodate up and down movements in the wind plant output. 

• One to several days ahead, to anticipate the need to start or stop large fossil-fueled units, 

which have long start-up times and lengthy down times between start-ups, in order to 

accommodate the diurnal weather patterns and the passage of weather systems. 

• Climatological spatial variability of wind properties at relatively fine spatial scales, to 

assess topographical enhancement or degradation of the wind resource for planning and 

siting. 

Forecasters generally rely on numerical weather prediction (NWP) models, which are available 

on a wide range of scales. Daily weather forecasts and climate predictions use mesoscale and 

global models, whereas at the other extreme, research models that may only cover a few hundred 

meters or a kilometer are able to resolve atmospheric motions down to a few meters; such 

models are used to study turbulence properties. Mesoscale models employ grid spacings 

typically between 1 and 30 km and have domains as small as urban areas or mountain valleys 

and as large as a continent. 

Mesoscale meteorological models are now being used routinely to produce weather forecasts. 

The output of these models is also used to drive dispersion, chemistry, and hydrology models. 

More recently, mesoscale models have been used for the wind energy community to map 

regional and national wind resources, determine the most suitable sites for wind turbines, and 

provide short-term and long-range forecasts that support wind turbine operations and aid the 

utility industry to efficiently integrate wind resources into the power grid, as described in the first 

paragraph. Mesoscale modeling is attractive because it is a relatively inexpensive means of 

estimating the wind characteristics over large regions, compared to the expense of deploying 

meteorological instrumentation at a large number of locations. Because of the particular 

requirements of the wind energy industry for quantitative output, it is important to consider the 

accuracy of current-generation NWP models. 

11.2 Need for Theory and Engineering

NWP models of today are complex and remarkable computer codes, capable of considerable skill 

in predicting the future state of the atmosphere. Larger-scale models can be used with skill to 

69 
predict weather, and mesoscale models can often produce wind fields in complex topography and 

coastal zones that resemble observed winds. Numerous studies over the past three decades have 

described the performance of mesoscale models and have noted that their ability to predict 

overall weather conditions has continually improved. The skill in predicting evolving synoptic 

weather is acceptable in many situations, but the details of predicting mesoscale features 

continue to be a challenge for such models. 

The critical question becomes, what is being asked of NWP models for wind energy 

applications? Accurate predictions of wind speed for several hours and for one to a few days, 

accurate predictions of when severe weather will strike a plant, accurate representation of the 

spatial variability of the wind resource over many different distance scales for siting and 

planning, and accurate characterization of atmospheric turbulent fluctuations are some 

requirements mentioned previously. The current generation of NWP models has not been 

optimized to address these needs. To be sure, model output would represent a considerable 

improvement over many engineering practices, for example, use of the power-law wind profile, 

but the wind industry requires much more detailed and accurate information and predictions of 

wind to use this resource most effectively. Obtaining these kinds of high-quality information 

from NWP models is possible, but will require improvements over current capabilities in many 

areas. 

Atmospheric phenomena that affect near-surface winds of interest to the wind energy community 

include low-level jets, complex terrain / coastal circulations, boundary-layer processes, and 

severe convection. Mesoscale models have been shown to qualitatively capture many of the 

characteristics of these atmospheric phenomena. However, relatively large errors still exist with 

the timing and magnitude of the mean winds associated with these phenomena, the interaction of 

the synoptic and smaller-scale flows, and the timing and location of severe convection. For 

example, the speed, height, and vertical profile characteristics of the nocturnal low-level jet, an 

important wind resource in the Great Plains of the United States, are not well represented in 

mesoscale models. Accurately representing boundary layer properties, such as turbulence, is 

also difficult for mesoscale models, especially during nighttime stable conditions. Relatively 

large errors in the predicted near-surface wind speeds, wind directions, vertical wind shears, and 

turbulence are normally not very important for routine weather forecasts, and thus have not 

received as much attention as severe weather (e.g. precipitation, thunderstorms, hurricanes). 

These errors, however, significantly impact the wind energy community. 

Increasing computing power using Linux clusters of tens to thousands of processors has enabled 

meteorological models to be run with smaller grid spacing, so that many small mesoscale 

phenomena are explicitly resolved. Although additional computational resources have proven 

very useful, they do not solve all of the problems associated with wind predictions. Most of the 

forecast errors can be attributed to inadequate representation of modeled processes, including the 

parameterized treatments of atmospheric processes occurring at length scales less than those 

resolved by the model. Fundamental knowledge gaps need to be addressed in order to provide 

improvements to these parameterizations, thereby improving wind field forecasts. 

70 
New and improved theories based on measurements are needed for a range of small-scale 

atmospheric processes. Additional research, coordinated between observational researchers and 

modelers, is needed in the following areas: 

 

• Representation of stable mixing and stable boundary-layer processes 

• Atmospheric radiative flux divergence in the boundary layer 

• Improved theory describing the relationships that couple the heat, moisture, and momentum 

fluxes in the atmospheric surface layer with the ground 

• Treatments of surface roughness elements, such as variations in vegetation, urban canopies, 

and turbines in a wind plant 

• Characterization of the vertical exchange of heat, moisture, and momentum within the PBL 

• Cloud parameterizations that can more accurately predict downbursts associated with severe 

weather. 

Cloud parameterization development is already an active area of research in NSF and NOAA; 

therefore, new information from these agencies needs to be transferred to wind energy modeling. 

In contrast, relatively little organized research is being pursued in the other areas that warrant a 

larger and more coordinated effort. 

Another fundamental problem for meteorological modeling relevant to wind energy applications 

is the appropriate spatial scales that models have been designed for. Mesoscale models contain 

parameterizations that are valid using grid spacings as small as approximately 1 km. LES 

models, that employ different turbulence closure methods, have been designed for spatial scales 

less than 100 m. It is desirable to predict variations in winds on spatial scales less than 1 km but 

greater than 100 m to resolve smaller scale variations in terrain and other surface roughness 

elements, including the interactions among turbines in wind plants, which affect local wind 

fields. Although mesoscale models can be run with grid spacings smaller than 1 km, they should 

not be used in this way because the current parameterizations are not valid at those scales. 

Therefore, turbulence closure and other parameterizations that enable models to employ grid 

spacings between 100 and 1000 m with valid parameterizations are needed to better estimate 

local wind variations for the wind energy industry. 

In addition to the need for improved theory, methodologies on how to best run and evaluate 

models need to be improved and developed including: 

• Data assimilation geared towards wind energy applications that include rapid update cycles 

and assimilation of turbulence measurements 

• Better characterization of “extreme events” from mesoscale model forecasts 

• Determining how to specify and interpret uncertainty for applications, 

• How to best employ ensemble modeling given that models have biases that cannot be 

corrected by averaging ensemble members 

• Developing appropriate evaluation strategies when comparing grid-cell values with point 

measurements and strategies for evaluating models using remote sensing scanning 

measurements (e.g.LIDARS, radars, SODARS, radar wind profilers). 

71 
11.3 Need for Numerical Experiments 

In parallel to new theory developed for mesoscale modeling, additional numerical experiments 

will be needed to transfer current research to wind energy applications and to evaluate new 

theoretical relationships. 

First, it is likely that many areas of new research already performed have not yet been 

incorporated into wind energy applications. The wind industry, for example, relies on surface 

wind fields predicted by current operational models, but vertical gradients in the wind fields 

could also be used to a greater extent. It has been noted that winds at turbine height are often 

estimated using observed or simulated winds that are then extrapolated using a power-law 

relationship. The power-law relationship is used despite the fact that it is known to be invalid 

under many atmospheric stability conditions. If mesoscale models produced accurate vertical 

wind shears, then this would be an opportunity to improve how models are used for wind energy 

applications. In this example, mesoscale models may be required to increase their near-surface 

vertical resolution to resolve wind shears for the wind energy industry. Cooperation among 

researchers, modelers, and wind energy personnel could generate recommendations on how to 

best run models, including such issues as model configuration (horizontal and vertical 

resolution), and which parameterizations are known to work best by the research community. 

Short-term benefits within a 1 – 2-year timeframe could be realized by establishing improved 

communication between the application and research communities. 

Additonal activities are needed to benefit the wind industry over time. Developing new 

theoretical relationships described in the previous section will require additional analysis of 

existing measurements and additional longer-term measurements. These measurements are also 

needed by mesoscale models for evaluation purposes to quantify the uncertainty in the predicted 

near-surface vertical gradients in wind speed, direction, and turbulent properties associated with 

new parameterizations. Previous model evaluations have relied a great deal on field campaign 

measurements made over a short period of time (often less than a month) and at specific sites. 

However, evaluations of model parameterizations have shown that this strategy is not adequate 

for more generic model applications for all locations and time periods. Therefore, measurements 

at a variety of wind turbine sites over several years are needed to better assess the performance 

of current mesoscale model parameterizations and develop improved parameterizations based on 

new theoretical relationships. 

It is important that these longer term measurements be part of a systematic strategy for 

evaluating mesoscale model forecasts for wind energy applications, which would be best 

accomplished by implementing a Developmental Testbed Center (DTC). Similar DTCs have 

been set up for specific applications such as hurricanes and precipitation forecasts 

[http:///www.dtcenter.org] to provide an objective means of assessing improved treatments for 

models. This DTC needs to be an on-going activity, similar to a long-term measurement 

program. The DTC methodology will also speed up transfer of knowledge from the research 

community to the application community, which is currently slow.

72 
11.4 Potential Wind Energy Impact 

Improved mesoscale models will lead to more accurate predictions of the 3-D variations in wind 

speed, direction, and turbulence. Better forecasts will subsequently improve turbine and plant 

design appropriate to realistic stresses, wind prospecting, and estimates of wind power to 

optimize wind resources for the nation’s power grid. 

The private sector currently does not have the resources or the capabilities to conduct a research 

program designed to improve mesoscale models. This role is more suited to government 

laboratory and university scientists, but such a research program will only succeed with close 

collaboration of the research and private sector. 

The proposed research will also impact other disciplines that employ mesoscale modeling, such 

as atmospheric dispersion, air quality, climate change.

73 
12. Create and Convene a Cooperative Working Group

12.1 Abstract 

Create and convene a Cooperative Working Group of wind energy industry professionals and 

researchers, including the utility industry and atmospheric scientists, to define wind industry 

needs related to understanding wind energy resources, modeling capabilities, and ability to 

support those needs. A parallel task of this work group is to convey to the wind industry the 

current state of knowledge concerning mesoscale processes, atmospheric measurement 

capabilities, and mesoscale models and their applicability to wind energy. The objectives of this 

working group are two-fold: 

1) Define in detail the data, modeling, and wind energy applications to be addressed by 

the other two research thrusts identified by the mesoscale working group: i) to 

establish and maintain a wind energy testbed, and, ii) to improve the atmospheric 

modeling capabilities of the wind industry. 

2) Provide guidance to on-going research efforts related to wind resource 

characterization and provide a forum for exchange of ideas and sharing of results and 

data. 

12.2 Problem or Opportunity 

A significant gathering of wind energy professionals, researchers, and representatives from the 

meteorological community and industry met at the DOE SC-EERE Wind Resource 

Characterization workshop (January 14 – 16, 2008). The purpose was to understand the current 

state of understanding in wind resource characterization across a large range of applications in 

wind energy, from turbine-scale inflow characteristics and its impact on turbine dynamics to 

mesoscale modeling and climate implications, and to assess the research needs. 

These two broad communities showed different cultural and operational backgrounds, 

vocabularies, and concepts of the essential ingredients for development and use of wind energy 

as a sustainable renewable national resource. The perspectives expressed by these two 

communities demonstrated diverse viewpoints, issues, concerns, and questions about science, 

economics, operations, and performance measures. It was evident from the discussions that there 

were inconsistencies of language, meaning, and priorities among the interested groups. For a 

successful national wind energy program, coordination of ideas, programs, resources, and 

development of a mutual understanding among the interested parties is essential so that common 

interests can be identified and addressed in a coordinated manner. 

Of the four focus groups that were convened, one was devoted to mesoscale processes, with the 

specific intention of defining the most significant research needs pertaining to mesoscale 

processes and wind energy. It was clear over the three days of the Wind Resource 

Characterization workshop that there would be much overlap between the research needs 

identified by the four groups. It was also obvious that there is not yet a good understanding on 

the part of the wind industry as to the state of understanding of wind flow characteristics (i.e., the 

atmospheric boundary layer, in-flow turbulence, flow prediction in complex terrain or even 

74 
relatively benign terrain) and the ability to predict wind flows reliably with mesoscale computer 

models. Likewise, there was not a good understanding on the part of the meteorological 

community of the information and understanding needed by the wind industry and researchers 

with regards to wind assessment, predicting wind power plant output, spatial and temporal 

variability of that output, and wind power forecasting. There was a sense, however, that given a 

coherent set of objectives, that the wind and meteorological communities could work towards 

transforming the current state of the art in mesoscale modeling into a tool better suited for wind 

energy. Thus, in order to bridge this gap in understanding, the first research thrust proposed by 

the mesoscale group was to create and convene a cooperative working group. 

Reviewing the collected “problems or objectives” brought forward by the “Problem Definition 

Groups” that met during the first two days of the workshop, virtually all items identified fit into 

the following three broad categories: 

1) Data needs – Data is needed to enable a better understanding of wind processes 

within the atmospheric boundary layer, in both benign and complex terrain, as well as 

under a variety of meteorological conditions (stable, unstable, prevailing winds, storm 

fronts, etc.). This data is also needed for initialization and validation of mesoscale 

models. 

2) Model Development – Improved models, both physical and numerical, are needed for 

accurate wind prediction, especially as applied to wind energy. This includes 

predicting regional winds, understanding the PBL, and for wind power forecasting. 

3) Application of atmospheric models for users – Forecasting, micro-siting, input to 

utility operators both operational and in understanding wind characteristics, for wind 

power developers, etc. 

The Cooperative Working Group will benefit greatly from the recommendations of the several 

focus groups in forging a comprehensive approach to bridging the gaps between the 

constituencies. They may also benefit from a recent report [1] sponsored by the Office of the 

Federal Coordinator for Meteorology to address the atmospheric sciences capabilities to meet a 

variety of user needs related to atmospheric transport and diffusion of materials. The report 

provides recommendations for research and development. The microscale and macroscale 

atmospheric processes applicable to wind energy are at the same scales as the transport and 

diffusion processes and share deficiencies in modeling, data resources and scaling of results.

12.3 Potential Wind Energy Impact 

The Cooperative Working Group will help bring some order to the broad range of issues 

confronting the wind power industry and foster cooperation with academia and industry to 

achieve a sustainable wind energy resource. It should bring understanding of the capabilities and 

limitations of the participants. More importantly, it begins the foundation of integrating the 

cultures, the research, the data, the measurement techniques, the environmental effects (wind on 

turbine(s) and turbines on local winds), the operational capabilities and needs of these two 

diverse communities. The working group can also serve an important role in facilitating 

communication of on-going research to all interested parties.

75 
12.4 Key Events and Approximate Timing 

The guiding principles of the Cooperative Working Group would be to begin prioritization of the 

workshop recommendations across all identified subject areas. Even though the full charter of 

the working group is not fully defined (that would be the job of the convening authority, likely 

DOE), implementation of the working group should proceed as soon as practical, with the 

purpose of addressing the two objectives: 

1) Define in detail the data, modeling, and wind energy applications to be addressed by 

the other two research thrusts identified by the mesoscale working group: i) to 

establish and maintain a wind energy testbed, and, ii) to improve the atmospheric 

modeling capabilities of the wind industry. 

2) Provide guidance to on-going research efforts related to wind resource 

characterization and provide a forum for exchange of ideas and sharing of results and 

data. 

The fundamental goal here is to bring leadership – technical, administrative and operational - of 

the groups together to start! This may result in a large group, for many voices are to be heard. 

Convening this group in the near term may help DOE in defining its funding priorities in wind 

resource characterization, and in identifying other federal agencies that may also benefit from 

this work and may be interested in collaborating. 

The working group should consider: 

• Development of a charter and its working structure. 

• Development of working reports of joint recommendations. 

• Development of working papers on technical subjects to be shared with the 

represented communities through publications and seminars. (That is, seek 

feedback through presentations of technical material or concepts to the 

appropriate working level.) 

• Development of workshops for specific issues. 

• Report progress and obstacles to the convening authority quarterly, or more 

frequent if needed. 

References 

[1] OFCM, 2004. Federal Research and Development Needs and priorities for Atmospheric 

Transport and Diffusion Modeling. Report FCM-R23-2004, Washington, D.C.: Office of the 

Federal Coordinator for Meteorological Services and Supporting Research 

76 
Detailed Recommendations – Climate Effects 

13. Improve Quantification and Identify Causes of Historical Change and Variability of Wind 

Resources....... .................................................................................................................................... 78 

14. Improved Quantification of Future Changes in the Mean and Variability of Wind 

Climates/Resources.............. .......................................................................................................... ...86 

15. Interactions Between Wind Plants and Local, Regional, and Global Climates ...... .......................93 

77 
13. Improve Quantification and Identify Causes of Historical Change 

and Variability of Wind Resources 

13.1 Problem or Opportunity 

Climate variability and energy consumption are inextricably intertwined. For wind power 

applications, variations in weather and climate play a critical role in determining both short-term 

power production from a wind plant and accurate site assessments for long-term electricity 

generation. Thus, the robustness and representativeness of existing climate data sets for 

identifying past, current, and future trends, and variability in the potential wind resource are 

crucial to the ability of the nation to achieve the goal of producing 20% of electrical demand 

from wind power by the year 2030. 

13.1.1 Inadequate data set inventory 

A primary task under this research thrust is to supplement existing wind-power potential 

assessments and develop an enhanced (spatially explicit) inventory of the wind resource across 

North America via: 

(i) Assessment and validation of existing observational data sets. 

(ii) Assessment and validation of numerical model simulations. 

Conducted with the following specific objectives: 

(i) Quantification of current wind speed and energy climates. 

(ii) Quantification of past variability and trends in wind speed and energy climates. 

(iii)Attribution of causality to historical trends and changes in; the mean, variability of 

wind speed climates (including extreme winds). 

Most current regional wind climatologies for the United States are based on interpolation of 

network surface station data presented in terms of wind atlases (e.g., Elliot et al, 1986), or 

developed from prognostic and diagnostic models using climatological compositing (Barthelmie 

et al, 2007). For site or local wind climates a range of techniques have been developed to 

extrapolate from short-term wind speed records to develop climatologically representative wind 

resource estimates. The most commonly applied include application of the WAsP model and 

measure-correlate predict both of which require long-term observations as input, and are 

critically dependent on the quality of those data. 

Since the comprehensive wind resource (Elliot et al, 1986) study, more networks of observing 

stations making wind measurements have been deployed or enhanced. The resulting 

measurements include: 

(i) Hourly wind speed and direction at 10 m from the ASOS/AWOS (Automated 

Surface/Weather Observation System, the majority of which were deployed in the 

1990’s) at nearly 1300 stations in the coterminous United States. 

(ii) Twice daily wind profiles throughout the troposphere at over 70 rawinsonde stations. 

(iii) Wind profiles in the near-surface layer (and above) from dozens of wind profilers. 

78 
(iv) Hourly wind speed and direction observations over surrounding waters (and the Great 

Lakes, though those platforms are removed from operation during the winter) at heights 

of 2 – 10 m above sea level. 

(v) Ancillary data from NOAA’s Real-time Observation Monitor and Analysis Network 

(ROMAN) and the more than 2200 interagency surface Remote Automated Weather 

Stations (RAWS) located throughout the United States. 

(vi) Tower and other measurements from Ameriflux and Long Term Ecological Research 

LTER networks 

In addition, there are a plethora of private observing networks such as is available from 

commercial entities (e.g., Weatherbug). Importantly, some of these networks also report 

parameters critical to determining stability and turbulence characteristics. Other potential 

archives of wind resource assessment data include those from power plant permitting processes, 

nuclear power plant monitoring, and state and federal air quality monitoring networks (e.g., the 

Environmental Protection Agency’s “AirData” site at http://www.epa.gov/air/data/). 

One problem inherent in long-term surface wind speed and direction data sets is the influence of 

changing characteristics of instrumentation, station location, and surrounding land use on the 

resulting observations. Hence, once possible data sources have been identified, there is a need to 

collate the data along with information regarding station histories, instrumentation, and qualitycontrol methodologies, and to use statistical tools in conjunction with numerical modeling 

approaches to define the temporal and spatial representativeness. Some initial approaches to this 

issue are documented in Elliot et al (1986) and Pryor et al, (2007). 

13.1.2 Inadequate long-term data and metadata 

The quality of station histories varies greatly among existing data sets (for example, those 

mentioned in “Inadequate data set inventory” above), and is not necessarily complete. Although 

atmospheric measurements and modeling techniques have significantly advanced in recent years 

to provide a richer basis of information for estimating wind resources, data quality assurance 

remains paramount. In particular, two components of specifying wind resources are crucial: 

spatial and temporal coverage. 

Spatial and temporal coverage. The ASOS/AWOS network is probably the most relevant 

source of approximately decade-long wind speed data in the United States, but its spatial 

coverage and terrain representativeness is dictated by aviation requirements; hence, most stations 

are located in valleys or relatively low terrain. Additionally, the data record from the ASOS 

stations is extremely limited in a climatological context (i.e., only approximately one decade of 

data are available for most stations). Longer data records are available from the National 

Climatic Data Center (NCDC) are subject to changes in recording protocols and instrumentation. 

Assessment of offshore winds is severely limited by the scarcity of observations over the ocean. 

Although remote sensing platforms on satellites offer invaluable information on the surface 

winds over the ocean, they have limitations in the coastal zone due to contamination of the ocean 

pixels with land, obscuration by clouds, and coarse spatial (~ 12 – 25 km) and temporal (once or 

twice per day) resolution. New remote sensing technologies (for example, Synthetic Aperture 

Radar) provide highly spatially resolved wind speeds (and are cloud penetrating) (Hasager et al, 

79 
2005) and, although certain constraints limit verbatim use of the data (Pryor et al, 2004), they 

should be included in the aforementioned data archiving efforts. 

In both cases – over the land and the ocean – there is need to integrate measurements and models 

to provide gridded coverage of meteorological parameters including the winds. Key among 

readily available model output are: 

(i) Reanalysis fields of 10-m wind speeds created by the National Centers for Environmental 

Prediction (NCEP)/National Center for Atmospheric Research (NCAR) on global scale 

are available from 1948 to the present, but with a coarse resolution of 2.5° x 2.5° (Kalnay 

et al, 2001). 

(ii) Reanalysis fields of 10-m wind speeds created by European Centre for Medium-Range 

Weather Forecasts (ECMWF) on global scale are available for a shorter-period (ending in 

2002) then NCEP/NCAR and again at low resolution (Uppala et al, 2005). 

(iii) The North American Regional Reanalysis of 10-m wind speeds created by the National 

Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research 

(NCAR) for north America (at a resolution of 0.5° × 0.5°) 

Such data sets will be critical in providing a climatological context for short-term wind speed 

observational records. 

Future modeling efforts that may contribute to such endeavors include the following projects. In 

FY07, NOAA began a project that provides new resources to support NOAA’s climate reanalysis 

efforts. The project will support a variety of reanalysis activities including data set development, 

historical reanalysis efforts for a number of time periods, and data archive and delivery to the 

scientific community. The first task will be for 1979 and will present global reanalysis and reforecast by the National Centers for Environmental Prediction, Climate Forecast System. NOAA 

recently held a Town Hall Meeting at the AMS Annual Meeting in New Orleans to discuss 

NOAA Reanalysis user needs. Some of the new efforts to provide high spatial resolution are 

illustrated by the NOAA’s Rapid Update Cycle (RUC) system used mainly for weather 

forecasting. The RUC system assimilates all available data including sub-synoptic measurements 

of opportunity such as remote sensing, special stations, towers, etc. This system provides gridded 

meteorology on resolutions of 10 – 20 km over the continental United States. 

Extreme Events. To quantify extreme wind speeds that are relevant for wind energy 

applications data (e.g., 50-year return period wind speeds), model formulation and quality, and 

climatological representativeness become ever more important (and demanding) issues. 

Additional related issues pertain to uncertainties in characterizing inherently rare events and the 

appropriate statistics to apply to wind climate metrics (Brabson & Palutikof, 2000). 

Finally, to ensure full and accurate use of data derived from field programs, an infrastructure 

must be established for processing, quality control, archiving, and dissemination of data. Special 

attention should be paid to establishing standards for metadata, and standard formatting 

conventions (e.g., NetCDF) should be used. An archive similar to that developed by the Program 

for Climate Model Diagnosis and Intercomparison (PCMDI) for the Fourth Assessment Report 

of the IPCC might provide a useful model. 

80 
13.1.3 Incomplete understanding of discrepancies in historical trends derived 

from different data sets 

Traditionally, wind resource site assessment has relied upon historical data. In a climatological 

context with a stationary climate, it is often assumed that a 30-year observational time series is 

adequate to characterize the mean wind speed and some characteristics of the probability 

distribution (e.g., the Webiull factors), plus intra- and inter-annual variability with a relatively 

high degree of confidence. Hence, it has to-date been deemed best practice to use resource 

characterization based on historical data to map likely current and future resources. There is 

some evidence that wind climates are evolving as a consequence of global climate change, but 

further analyses and integration of model simulations is warranted. 

Recent work (Pryor et al, 2007; Freedman et al, 2008) has shown that the ability to detect wind 

speed trends is even more of a challenge than other climate variables, such as temperature or 

precipitation. Further, disparate trends have been determined using differing reanalysis data sets 

and/or reanalysis data relative to in situ observations (Pryor et al, 2006a) (Pryor et al, 2008). 

Reconciling these differences and determining whether trends in wind speed are the result of 

natural fluctuations (both periodic and aperiodic), a consequence of long-term trends related to 

global climate change, or a combination of both remains uncertain. 

Downscaling studies of past and possible future climates are relatively sparse (and principally 

focused on Europe ( e.g., Pryor et al, 2005a, Pryor et al, 2005b, 2006b). In addition, further 

application of tools designed to generate regional/local climate realizations from downscaling of 

global climate models (e.g., Regional Climate Models and empirical downscaling models) to 

both historical and prognostic periods will help in quantifying and assigning causality to changes 

in wind climates. 

It is crucial that the quality, spatial, and temporal characteristics of all existing wind resource 

data sets be critically inventoried and inadequacies characterized for improving research into past 

climate, use in evaluation and validation of climate models, and identification of observational 

weaknesses. 

13.1.4 Need to quantify explanatory power of regional/global forcings of wind 

climates across a range of scales 

Accurate estimates of the local wind resource and effects upon turbine performance are 

dependent upon atmospheric and land-surface forcings across a spectrum of scales – from the 

synoptic (thousands of km2

), through the mesoscale (tens to hundreds of km2), down to local 

(km2

 or less), and even micro scales (mm2). A thorough understanding of how this energy 

cascade affects resource assessment and estimated wind power production is needed. Currently, 

these assessments are done on a site-by-site basis, using a potpourri of existing station data and 

limited on-site monitoring data, together with tools described in brief above, to estimate annual 

and seasonal scaled up wind speeds, speed shears, icing losses, and return periods for extreme 

events — all necessary to estimate long-term energy production at sites ranging from simple to 

complex terrain. Given that a 1% error in wind speed estimates for a 100-MW wind generation 

facility can lead to losses approaching $12,000,000 over the lifetime of that plant, a better 

81 
understanding of the physical and dynamic processes across the range of scales that create a 

particular wind climate is needed. 

13.2 Needs from Computation/Theory 

General circulation models (GCM) used for climate simulations are not useful for direct 

simulation of near-surface wind speeds (Pryor et al, 2006b), however, they are critical tools for 

understanding the climate system under an evolving atmospheric composition and they are 

increasingly skillful in simulating many of the features of the global climate (IPCC, 2007). Both 

GCM and regional climate models (RCMs) exhibit some weaknesses in determining the 

accuracy of atmosphere-surface exchange and energy partitioning at the surface that critically 

determine the simulation of the atmospheric boundary layer (ABL) (Emmanuel, 1994; Garratt 

1992). However, such models are a critical component of efforts to consistently simulate wind 

climates and energy resources across large spatial scales and to develop physically consistent 

linkages to aspects of the global climate system. Research undertaken to date implies regional 

climate simulations of wind climates are strongly dependent on the GCM used to provide lateral 

boundary conditions for RCM (Pryor et al, 2005a), but few such comprehensive studies have 

been conducted over the United States, although preliminary efforts are being conducted in 

conjunction with the North American Regional Climate Change Assessment Program 

(NARCCAP). Such efforts would be enhanced by implementation of innovative statistics (e.g., 

Baysian ensembling) and development of enhanced metrics of model skill. 

With large-scale penetration of wind energy into the electricity generation network, there is also 

a need to better understand the spatial and temporal scales on which wind exhibits coherent 

behavior. Such research can be conducted using RCM and reanalysis products in addition to 

observational records to determine an optimal geographic dispersion of wind plant developments 

(Pryor et al, 2006a) to ensure fluctuations in power production are minimized and can 

accommodated in the system without the need for excessive spinning reserve. 

As described above for local assessment of wind resources, RCM and reanalysis products can be 

used to provide lateral boundary conditions for mesoscale models (e.g., MM5) and at higher 

resolution, LES can be conducted for case-study simulations. Present ABL parameterizations in 

GCM/RCM and mesoscale models use a variety of closure schemes, incorporating Monin–

Obukov similarity relationships. First order (ensemble or K-theory) works fairly well for the 

surface (“constant flux”) layer, but fails in turbulent flows dominated by large eddies (e.g., in the 

convective ABL). Higher (one-and-a-half, second order) closure schemes are also used with 

varying success. In LES, the large-scale motions of the flow are calculated, while the effect of 

the smaller universal scales—the so called sub-grid scales—are modeled using sub-grid scale 

parameterizations. As discussed by the mesoscale scale modeling group, there is an important 

discontinuity at 100 m to 1 km between modeling at the mesoscale and using LES. This is an 

important scale that needs to be explicitly resolved (or at least sufficiently parameterized) in 

order to facilitate modeling for wind resource assessment purposes. 

13.3 Needs from Observations/Experiments 

As articulated above, key aspects of the needs within this recommendation are: 

82 
(i) Creation of an inventory of quality controlled data sets for wind energy applications. These 

observations exist and hence the major need is to evaluate the data, generate high-quality 

data products and facilitate access to those data. 

(ii) Since much of these data are available only very close to the Earth’s surface (i.e., at 10 m), 

they are not directly pertinent to determining the wind energy resource at typical turbine hub 

heights (currently 70 m, but increasing). Such efforts should include development of 

information on actual vertical profiles of winds and turbulence. A key component of future 

field projects should be the development of partnerships between those involved in research 

in the private sector and in government and academia. Key resources in such measurement 

campaigns include: meteorological towers, tethered balloons, aircraft studies, and remote 

sensing platforms including acoustic sounders, wind profilers, and LIDARS. 

(iii)Both (i) and (ii) also need to address issues related to extreme events (wind speed, vertical 

speed and direction shear, temperature, and icing) that affect turbine operations. Maximum 

winds and gustiness is frequently missing or limited because of coarse time intervals. Efforts 

should be directed to investigate optimum sampling intervals that are relevant to turbine 

operations and define their statistical and dynamical distributions. Further comprehensive 

evaluations of applications of statistical methods for wind extremes should be undertaken. 

(iv)The majority of the tools developed and applied to climate analysis to establish model skill 

have not focused on wind climates, hence additional technique development may be 

necessary. 

13.4 Potential Wind Energy Impact 

The proposed research articulated herein will provide: 

• Increased confidence in resource estimation for future planning and validation. 

• Improved understanding of physical mechanisms responsible for trends and variability 

for wind resource assessment. 

• Facilitate future model development for enhanced estimation of the wind energy 

resource and variability of that resource. 

• Reduced risk/uncertainty for financial investment in wind plants and improved warranty 

terms. 

13.5 Key Events and Approximate Timing 

For the modeling aspect of this research thrust, the following needs (and proposed timelines) 

have been identified: 

1. Assess the strengths and weaknesses of current generation models (global/regional/local) to 

reproduce the trends and variability (timeframe = 3 yrs). 

2. Develop metrics for model evaluations (timeframe = 1 yr). 

3. Develop a coordinated set of model experiments to understand wind resources, trends and 

variability (timeframe = 3 – 5 yrs). 

4. Quantify the scales of coherent variability of wind speeds (both temporal and spatial) and 

links to global and regional forcings (timeframe = 1– 3 yrs). 

 

83 
For the observational aspect of this research thrust, overarching goals and timelines include: 

1. Create an inventory of quality controlled data sets for wind energy applications (timeframe = 

1 – 2 yrs). 

2. Develop techniques to analyze data sets for wind resources (timeframe = 1 yr). 

3. Produce comprehensive evaluations of applications of statistical methods for wind extremes 

(timeframe = 2 – 3 yrs). 

References 

Barthelmie, R.J., Badger, J., Pryor, S.C., Hasager, C.B., Christiansen, M.B. and Jørgensen, B.H.: 

2007, 'Wind speed gradients in the coastal offshore environment: Issues pertaining to 

design and development of large offshore wind farms', Wind Engineering 31 369-382. 

Brabson B, Palutikof JP (2000) Tests of the generalized Pareto distribution for predicting 

extreme wind speeds. Journal of Applied Meteorology 39:1627-1640 

Clifford et al – Ground-Based Remote Profiling in Atmospheric Studies, Proceedings of the 

IEEE, Vol. 82, pp313-347, 1994 

de Roy WC and Kok K - A Combined Physical–Statistical Approach for the Downscaling of 

Model Wind Speed, Weather and Forecasting, Vol. 19, pp. 485-495, 2004 

Elliot, D. L., C. G. Holladay, W. R. Barchet, H. P. Foote, and W. F. Sandusky, 1986: Wind 

Energy Resource Atlas of the United States. DOE/CH Publication, DE86004442. 

Emanuel KA – Atmospheric Convection, Oxford University Press, 1994 

Freedman, J. M., J. W. Zack, and B. H. Bailey 2008: Climate and wind energy: recent trends in 

wind speed and potential effects of climate change. 20th Conference on Climate 

Variability and Change, American Meteorological Society, New Orleans. 

Hasager CB, Nielsen M, Astrup P, Barthelmie R, Dellwik E, Jensen NO, Jørgensen B, Pryor S, 

Rathmann O, Furevik B (2005) Offshore wind resource assessed from satellite SAR wind 

field maps. Wind Energy 8:403-419 

Luettich RA – Consideration in the calculation of vertical velocity in 3-D models J Atmos Ocean 

Tech, Vol. 19, pp. 206-2076, 2002 

Kistler, R., E. Kalnay, W. Collins, S. Saha, g. G. White, J. Woollen, M. Chelliah, W. Ebisuzaki, 

M. Kanamitsu, V. Kousky, H. van den Dool, R. Jenne, and M. Fiorino, 2001: The 

NCEP–NCAR 50–Year Reanalysis: Monthly Means CD–ROM and Documentation. Bull. 

Amer. Meteor. Soc., 82, 247-267. 

Peters G – Ground Based Profiling of the Atmosphere, Phys. Chem. Earth, Vol. 26, 2001, pp. 

175-180. 

Pryor SC, Barthelmie RJ,Riley ES (2007): Historical evolution of wind climates in the USA. 

Journal of Physics Conference Series 75: 012065. 

Pryor SC, Barthelmie RJ, Kjellström E (2005a) Analyses of the potential climate change impact 

on wind energy resources in northern Europe using output from a Regional Climate 

Model. Climate Dynamics 25:815-835 

Pryor SC, Barthelmie RJ, Schoof JT (2006a) Inter-annual variability of wind indices across 

Europe. Wind Energy 9:27-38 

Pryor SC, Nielsen M, Barthelmie RJ, Mann J (2004) Can satellite sampling of offshore wind 

speeds realistically represent wind speed distributions? Part II: Quantifying uncertainties 

associated with sampling strategy and distribution fitting methods. Journal of Applied 

Meteorology 43:739-750 

84 
Pryor SC, Schoof JT, Barthelmie RJ (2005b) Empirical downscaling of wind speed probability 

distributions. Journal of Geophysical Research 110:doi: 10.1029/2005JD005899. 

Pryor SC, Schoof JT, Barthelmie RJ (2006b) Winds of Change? Projections of near-surface 

winds under climate change scenarios. Geophysical Research Letters 

33:doi:10.1029/2006GL026000 

Uppala SM, Kållberg PW, Simmons AJ, Andrae U, da Costa Bechtold V, Fiorino M, Gibson JK, 

Haseler J, Hernandez A, Kelly GA, Li X, Onogi K, Saarinen S, Sokka N, Allan RP, 

Andersson E, Arpe K, Balmaseda MA, Beljaars ACM, van de Berg L, Bidlot J, Bormann 

N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, 

Hólm E, Hoskins BJ, Isaksen L, Janssen PAEM, Jenne R, McNally AP, Mahfouf J-F, 

Morcrette J-J, Rayner NA, Saunders RW, Simon P, Sterl A, Trenberth KE, Untch A, 

Vasiljevic D, Viterbo P, Woollen J (2005) The ERA-40 re-analysis. Quarterly Journal of 

the Royal Meteorological Society 131:2961-3012 

85 
14. Improved Quantification of Future Changes in the Mean and 

Variability of Wind Climates/Resources

14.1 Problem or Opportunity 

Wind resources are strongly dependent on climate, which varies on a wide range of temporal and 

spatial scales. In the free troposphere, winds are determined by large-scale pressure gradients 

that are forced by the thermal contrasts between continents and oceans, and mountains and 

valleys. Within the PBL, small-scale turbulent motions are induced by flow over terrain features. 

Hence, large-scale circulation and local or regional scale terrain features are two important 

factors that determine wind climates. 

A robust large-scale feature simulated by global climate models for the future is the northward 

shift of storm tracks and expansion of the subtropical high in the mid-latitudes (IPCC 2007), 

which are related to changes in the north-south thermal gradient and stability of the jet stream as 

the polar region warms up more than the lower latitudes under greenhouse warming. Such 

changes in the large-scale circulation and their interactions with the terrain could have significant 

impacts on wind patterns in the United States. At the regional scale, wind resources could also be 

influenced by changes in land use and other anthropogenic factors that alter the spatial 

distribution of heating and atmospheric stability. 

There is an urgent need from industrial and developmental perspective for future wind resource 

projections to assess the viability of existing wind plants and planned future wind plants. 

However, no cohesive assessment of possible future wind climates is currently available for the 

United States to provide the information needed for future planning of wind resources. 

 

14.1.1 Assessment of future wind climates 

Coupled Atmosphere-Ocean General Circulation Models (AOGCM) are critical (and 

increasingly skillful) tools for assessing future climate states (IPCC, 2007), but operating on 

spatial scales on the orders of hundreds of kilometers in the horizontal and very low resolution in 

the vertical, they are incompatible with the needs for assessing regional and local wind climates, 

and exhibit low skill in reproducing historical wind speeds (Pryor et al, 2006). Hence, it is 

necessary to employ downscaling techniques for generation of higher spatial resolution climate 

parameters. These studies may employ: 

• Physical/dynamical methods, where a Regional Climate Model (RCM) or high resolution 

or variable resolution atmospheric GCMs are used to produce finer resolution fields (e.g., 

10 – 50 km) of the parameter of interest in the study region from the large scale 

description of climate produced by AOGCM (Giorgi and Mearns, 1999; Leung et al, 

2003 and 2006). 

• Statistical/empirical methods, where a transfer function (or functions) is developed that 

statistically relates the large scale climate parameters generated by the AOGCM to the 

near-surface parameter of interest (Pfizenmayer and von Storch, 2001; Katz et al, 2002). 

• Or a hybrid combination of the above two methods. 

86 
Additional operational tools are emerging for seasonal type climate forecasts (9 months) that 

should be examined and tested for prediction of short climate trends (Saha et al, 2006). This type 

of forecast predicts winds at 10 m and 850 mb that can be used for the analysis of seasonal 

variability. 

Although many studies have evaluated downscaling and seasonal forecast techniques for 

hydrologic and thermal parameters (e.g., Anderson et al, 2003; Leung et al, 2004), very few have 

analyzed them in terms of their skill relative to wind climates. The latter includes studies over 

northern Europe (Pryor et al, 2005a; Pryor et al, 2005b, c, 2006) and in the United States (Conil 

and Hall, 2006; Elguindi and Giorgi, 2005; Gustafson and Leung, 2007) to evaluate the 

downscaled winds when the large-scale circulation is provided by AOGCMs or global 

reanalysis. These studies have identified strengths and weaknesses of current downscaling 

techniques in capturing the large spectrum of wind variability. 

Besides reconciling the spatial scales between global models and wind resources, there is also a 

need to reconcile the temporal windows to better coincide with the needs of the wind energy 

industry – i.e. climate impact studies are typically performed to assess changes 30 – 50 years into 

the future in order to differentiate the climate change signal from inter-annual variability (or 

noise), while the industry need is focused on the planning horizon of 10 – 30 years. The climate 

community is beginning to tackle decadal climate prediction (Smith et al, 2007), so much 

research is needed to combine and exploit both knowledge of the initial land and ocean states and 

sensitivity to external forcings. 

Facing the challenge of scale reconciliation, research must be performed to refine downscaling 

techniques to quantify and reduce the sources of model errors. A comprehensive study to 

evaluate and inter-compare climate model projections of winds and wind extremes would have 

significant value for wind resources planning. The study should be complemented by the hindcast evaluation of the climate models using available climate data including satellites to 

determine the model skill in capturing historical variability in wind climates. This intercomparison would provide guidance on confidence levels in resource estimation for future 

planning and validation. 

14.1.2 Comprehensive measurements of wind climates and resources 

To assess trends in wind climates, it is crucial to develop a plan for long-term observations of the 

boundary layer parameters including winds and turbulence. The observations should be spatially 

and temporarily representative with an emphasis on measurements above the usual 10-m heights. 

Although some studies have attempted to analyze data from heights relevant to wind turbines 

that have hub-heights of 50 –100 m, these are usually limited in terms of the duration of record 

(Archer and Jacobson, 2005) and/or geographic coverage (Klink, 2007). 

Another area that is extremely important to pursue is turbulence measurements aloft for longterm monitoring as well as comparison of standard and sonic anemometers for wind statistics 

(Koracin et al, 2007). A consensus on defining important turbulence descriptors and their 

sampling and averaging time needs to be reached by research and application entities as well as 

utilities and wind and turbine developers. 

87 
The use of satellite and other ground-based remote sensing for wind resource assessment should 

be further evaluated and standard methods should be designed to provide high horizontal and 

vertical resolution of the wind fields (Barthelmie et al, 2003; Barthelmie et al, 2006; Hasager et 

al, 2006; Hasager et al, 2005). Satellite borne sensors such as QuikSCAT (Chelton and Freilich, 

2005; Sampe and Xie, 2007) and SAR are key components of this capability. However, the 

former satellite has been operational for the last seven years, and unfortunately, there are no firm 

commitments for extending its operational mission for the future. 

Thus, in brief, a number of challenges arise when we begin to connect future climate 

considerations with what is necessary for planning today. These challenges include: 

• Incomplete development and evaluation of downscaling tools used to simulate wind 

climates, including the extremes. 

• No coordinated effort to develop and inter-compare future wind climate projections, 

quantify sources of error, and assess uncertainties. 

• A disconnect between industry transmission planning which looks ahead at the 10 – 30 

year planning horizons and challenges in decadal climate prediction. 

• Limited long-term data sets and monitoring of wind resources in both a historical and 

future climate context. 

• Lack of efforts to combine ground-based and remotely sensed information. 

14.2 Needs from Computation/Theory 

Consistent with the other recommendations for this topical area, improved and new techniques 

for scale reconciliation in both time and geographic (i.e., downscaling) coverage is essential to 

connect climate considerations to management of variability of wind resources. Utilizing existing 

models and archived global climate simulations, we can begin to examine projected changes in 

winds, determine any robust signals across the multiple scenarios, and relate the changes to 

thermal and dynamical changes in circulation regimes. A coordinated effort should be organized 

to develop and compare multiple downscaled future projections of winds for the United States to 

enable more detailed analysis of wind changes at the regional scale in response to large-scale 

circulation changes as well as changes that result from interactions between large-scale changes 

and terrain features and changes in regional forcings such as land use. Comparison between 

global and regional simulations, as well as across different downscaling techniques should be 

performed. These efforts will identify the largest sources of uncertainty in projecting future wind 

climates to enable more focused research to better characterize and reduce uncertainty. 

In addition, by combining existing models and historical data sets, we can immediately begin to 

examine the relationships between extreme winds and the large-scale and mesoscale circulation 

(e.g., interdependencies between winds, temperature, and moisture) to provide a useful 

framework to understand extreme winds in the historical records. This framework can be applied 

to existing and new AOGCM and downscaled simulations to assess the nature and likelihood of 

extreme wind changes in the future. 

 

88 
14.3 Needs from Observations/Experiments 

High-fidelity observational data are necessary to quantify and validate wind resources and to 

support long-term, trend analyses and modeling for the purposes of quantifying future climate 

change impacts. Leveraging state and DOE efforts (e.g., DOE Tall Tower facilities), there is a 

need to continue robust measurement programs to quantify future trends, especially focused at 

the wind turbine operational levels (ground to 300 m) accounting for wind shear and boundarylayer interactions. Providing necessary data to fill the interface between global, regional, and 

local mesoscale models is critical as these interfaces often provide the highest degree of 

modeling challenge. Remotely sensed data from satellites and ground-based platforms, as well as 

continued development of techniques/instrumentation need to be supported. Highly instrumented 

locations near appropriate wind resource-quality sites need to be linked with the climate 

modeling efforts to enable the link between climate impacts to energy planning needs. Promising 

research areas and topics are as follows: 

 

Winds from the surface to above the rotor plane--the Atmospheric Boundary Layer 

(ABL). The rapid development of turbine technology and accompanying rise in with hub heights 

imposes the need for estimating winds in the range up to 100 - 200 m and above. This 

necessitates the incorporation of existing knowledge of the ABL and development of new 

techniques for observations and modeling of turbine interaction with the boundary layer. 

Additionally, the atmosphere beyond these heights needs further study to understand the possible 

influence of phenomena such as low-level jets, vortices, and wind rotors. The main information 

sources are meteorological towers, tethered balloons, aircraft studies, and remote sensing 

platforms including acoustic sounders, wind profilers, and LIDARS. 

Ground-based remote sensing (GBRS): including but not limited to SODAR, LIDAR, 

RASS, MAPR, and NEXRAD. These measurements have traditionally focused on the study 

and observations of small- and mesoscale flow patterns. Turbulence parameters, for purposes of 

wind resource assessment, require long-period estimates both near the surface and aloft as they 

make a significant impact on turbine operations and the life-time of turbine components. 

However, there is little knowledge of what turbulence parameters (that is, the components of the 

Reynolds’ stresses) are of most interest, besides turbulence intensity. A consensus on sampling 

and averaging intervals should be reached since raw turbulence data are not necessarily archived. 

Detection of turbulence by remote sensing should be calibrated, evaluated, and compared to 

tower-based turbulence measurements. Standardized sampling, averaging, and evaluations 

should lead to a protocol for turbulence measurements of interests for wind resource assessment. 

Extreme events. There is no firm guidance how extreme events (wind speed, vertical speed and 

direction shear, temperature, icing) affect turbine operations. Maximum winds and gustiness is 

frequently missing or limited because of coarse time intervals. Efforts should be directed to 

investigate optimum sampling intervals that are relevant to turbine operations and define their 

statistical and dynamical distributions. Climate periods of at least 20 – 50 years should be 

considered. 

Measurements over the ocean. There are only sparse observations and short-term (~ 20 years 

or less) data sets currently available. Satellite remote sensing is, by practicality, the only solution 

for comprehensive measurements over the ocean. However, these measurements are spatially 

89 
and temporally constrained. Thus, there is a need for increased resolution of the satellite-borne 

instruments, new retrieval algorithms, and data validation through surface observations. 

Additionally, it may be desirable to develop a permanent test bed and field observation facility 

focused on wind resource assessment, similar to the Atmospheric Radiation Measurement 

(ARM) Program sponsored by DOE. Further, shorter-term field projects should be deployed at 

existing wind plants in a variety of terrain (simple, complex, marine) to capture the spectrum of 

ABL characteristics, including feedbacks between the physical structure of the wind plant and its 

surrounding environment. With added instrumentations related to wind, the ARM Mobile 

Facility (AMF) can be a significant resource for this purpose. 

14.4 Potential Wind Energy Impact 

The proposed research articulated herein will provide: 

• Increased confidence in resource estimation for future planning and validation. 

• Improved understanding of physical mechanisms responsible for trends and variability 

for wind resource assessment. 

• Continued improvement of modeling capabilities and statistical analysis tools. 

• Facilitate future model development for enhanced estimation of the wind energy 

resource and variability of that resource. 

• Reduced risk and uncertainty for financial investment in wind plants and improved 

warranty terms. 

• Payoffs in assessment of other climate change impacts (e.g., improvement in estimates 

of evapo-transpiration or air pollution forecasts). 

• Recommendations with regard to wind plant design and planning within the context of 

climate change. 

14.5 Key Events and Approximate Timing 

The following needs from computation and theory (and proposed timeframes) have been 

identified: 

• Refine or develop new techniques for scale reconciliation (downscaling) (timeframe = 3 –

5 yrs). 

• Perform multiple global and regional model simulations for evaluation of existing 

downscaling techniques and to facilitate technique inter-comparisons (timeframe = 3 – 5 

yrs, continuous). 

• Develop certainty/uncertainty quantification and assess major sources of uncertainty (in 

this case e.g. model divergence, but also strongly linked to recommendation 1) 

(timeframe = 3 – 5 yrs). 

• Archive appropriate parameters/time scales from AOGCM (it may be necessary to 

request additional model simulations) (timeframe = 3 – 5 yrs, linked to IPCC cycle). 

• Examine probability of changes in extreme events relevant to wind energy (timeframe = 

start immediately but 5+ yrs for reaching high level of skill). 

90 
Overarching goals and timelines of the experimental component of this thrust are: 

• Develop a long-term, robust measurement (e.g. wind shear) program to quantify future 

trends (especially boundary-layer observations - i.e. above 10 m - similar to LTER). 

Precise variables to be based on data analysis conducted in recommendation 1. (ongoing, 

annual review). 

• Strong need for continued and continuous remote sensing data from satellites and groundbased platforms and for continued technique/instrument development (ongoing, annual 

review). 

References 

Anderson, C. .J., R. W. Arritt, E. S. Takle, Z. Pan, W. J. Gutowski, R. da Silva, and PIRCS 

modelers, 2003: Hydrologic processes in regional climate model simulations of the 

central United States flood of June-July 1993. J. Hydrometeor., 4, 584-598. 

Archer, C. and Jacobsen, M.: 2005, 'Evaluation of global wind power', Journal of Geophysical 

Research 110, doi:10.1029/2004JD005462. 

Barthelmie, R.J., Folkerts, L., Ormel, F., Sanderhoff, P., Eecen, P., Stobbe, O. and Nielsen, 

N.M.: 2003, 'Offshore wind turbine wakes measured by SODAR', Journal of 

Atmospheric and Oceanic Technology 30, 466-477. 

Barthelmie, R.J., Folkerts, L., Rados, K., Larsen, G.C., Pryor, S.C., Frandsen, S., Lange, B. and 

Schepers, G.: 2006, 'Comparison of wake model simulations with offshore wind turbine 

wake profiles measured by sodar', Journal of Atmospheric and Oceanic Technology 

23(7), 888-901. 

Chelton, D.B., M.H. Freilich, J.M. Sienkiewicz, and J.M. Von Ahn, 2006: On the use of 

QuikSCAT Scatterometer measurements of surface winds for marine weather prediction. 

Mon. Wea. Rev., 134, 2055–2071. 

Conil, S. and A. Hall, 2006: Local regimes of atmospheric variability: A case study of southern 

California. J. Clim., 19, 4308-4325. 

Giorgi, F., and L.O. Mearns, 1999: Introduction to special section: Regional climate modeling 

revisited. J. Geophys. Res., 104(D6), 6335-6352. 

Gustafson, W., Jr. and L. R. Leung, 2007: Regional downscaling for air quality assessment: a 

reasonable proposition? Bulletin of the American Meteorological Society, 88, 1215-1227. 

Hasager, C.B., Barthelmie, R., Christiansen, M., Nielsen, M. and Pryor, S.: 2006, 'Quantifying 

offshore wind resources from satellite maps: study area the North Sea', Wind Energy 9, 

63-74. 

Hasager, C.B., Nielsen, M., Astrup, P., Barthelmie, R., Dellwik, E., Jensen, N.O., Jørgensen, B., 

Pryor, S., Rathmann, O. and Furevik, B.: 2005, 'Offshore wind resource assessed from 

satellite SAR wind field maps', Wind Energy 8, 403-419. 

IPCC: 2007, Climate Change 2007: The Physical Science Basis. Contribution of Working Group 

I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 

Cambridge University Press, Cambridge, United Kingdom p. 996. 

Katz, R.W., M. B. Parlange, and P. Naveau, 2002: Statistics of extremes in hydrology. Advances 

in Water Resources, 25, 1287-1304. 

Klink, K.: 2007, 'Atmospheric circulation effects on wind speed variability at turbine height', 

Journal of Applied Meteorology and Climatology 46, 445-456. 

91 
Koracin, D., R. Reinhardt, G. McCurdy, M. Liddle, T. McCord, R. Vellore, T. Minor, B. Lyles, 

D. Miller, and L. Ronchetti, 2007: Wind Energy Assessment Study for Nevada – Tall 

Tower Deployment. Final Report. Submitted to DOE NREL, 162 pp. 

Leung, L.R., Mearns, L.O., F. Giorgi, and R. Wilby, 2003: Workshop on regional climate 

research: Needs and opportunities. Bull. Amer. Meteorol. Soc., 84(1), 89-95. 

Leung, L.R., Y. Qian, X. Bian, W.M. Washington, J. Han, and J.O. Roads, 2004: Mid-century 

ensemble regional climate change scenarios for the western United States. Climatic 

Change, 62(1-3), 75-113. 

Leung, L. R., Y.-H. Kuo, and J. Tribbia, 2006: Research needs and directions on regional climate 

modeling using WRF and CCSM. Bull. Amer. Met. Soc., 87, 1747-1751. 

Pfizenmayer, A., and von Storch, 2001: Anthropogenic climate change shown by local wave 

conditions in the North Sea. Climate Research, 19, 15-23. 

Pryor, S.C., Barthelmie, R.J. and Kjellström, E.: 2005a, 'Analyses of the potential climate change 

impact on wind energy resources in northern Europe using output from a Regional 

Climate Model', Climate Dynamics 25, 815-835. 

Pryor, S.C., Schoof, J.T. and Barthelmie, R.J.: 2005b, 'Climate change impacts on wind speeds 

and wind energy density in northern Europe: Results from empirical downscaling of 

multiple AOGCMs', Climate Research 29, 183-198. 

Pryor, S.C., Schoof, J.T. and Barthelmie, R.J.: 2005c, 'Empirical downscaling of wind speed 

probability distributions', Journal of Geophysical Research 110, doi: 

10.1029/2005JD005899. 

Pryor, S.C., Schoof, J.T. and Barthelmie, R.J.: 2006, 'Winds of Change? Projections of nearsurface winds under climate change scenarios', Geophysical Research Letters 33, 

doi:10.1029/2006GL026000. 

Saha, S., S. Nadiga, C. Thiaw, J. Wang, W. Wang, Q. Zhang, H. M. van den Dool, H.-L. Pan, S. 

Moorthi, D. Behringer, D. Stokes, M. Peña, S. Lord, G. White, W. Ebisuzaki, P. Peng, P. 

Xie, 2006: The NCEP Climate Forecast System. J. of Climate, 19, 3483-3517. 

Sampe, T., and S.P. Xie, 2007: Mapping High Sea Winds from Space. Bull. Amer. Meteor. Soc., 

88, 1965–1978. 

Smith, D.M., S. Cusack, A.W. Colman, C.K. Folland, G.R. Harris, and J.M. Murphy, 2007: 

Improved surface temperature prediction for the coming decade from a global climate 

model. Science, 317, 796-799. 

 

92 
15. Interactions Between Wind Plants and Local, Regional, and Global 

Climates 

15.1 Problem or Opportunity 

Wind plants extract momentum from the free atmosphere from their turbine positions in the 

lower part of the ABL. The ABL depth changes with time of day and time of year. In the warm 

season the daytime boundary layer grows rapidly in early morning to a daytime maximum depth 

generally in mid-afternoon that may be of order 10 – 20 times the hub height of currently used 

turbines. With diminished surface heating in the late afternoon, large-scale turbulent eddies in 

the ABL gradually diminish in strength, thereby decoupling the surface flow from the free 

atmosphere. This allows for slow growth of the surface-based nocturnal boundary layer, which 

is much less deep and by sunrise, may have maximum depth of the order of the hub height of 

turbines. The cold season ABL diurnal cycle follows a similar development, although its 

maximum daytime depth is usually much lower due to reduced surface-sensible heat flux 

compared to the warm season. Of course, cloudiness, large-scale baroclinicity, and passing 

synoptic-scale weather systems can create large departures of ABL behavior from this highly 

idealized picture. 

Impacts of wind plants on local and regional meteorological conditions span influences at the 

microscale, as represented by surface-sensible and latent heat flux, to the regional scale where 

these microscale changes aggregate to influence convergence/divergence patterns, low-level 

moistening and heating, and ultimately precipitation processes. The potential impacts of 

continental-scale wind power are global in extent (Kirk-Davidoff and Keith, 2008). Between 

these spatial extremes are influences on turbulent dispersion and interactions with other local 

scale factors such as urban influences, non-classical mesoscale circulations, sea-breezes, and 

mountain-valley circulations, etc. Land use in the environmental “influence-footprint” of wind 

plants may include natural ecosystems in complex terrain and coastal areas, low-density 

residential and commercial, grazing, and intensive-cropping agriculture. Changes in surface 

sensible and latent heat exchanges can have significant economic impacts, particularly for 

example with regions of intensive agriculture where changes in surface wind, dew-deposition 

periods, daily max and min temperatures, spray and pollen dispersing potential, insect 

populations (both pests and pollinators) due to wind plants may affect landowner income derived 

from non-wind power sources. Not all impacts of wind plants will be bad. For example, they 

may provide some protection from frost occurrences in some regions where frost-sensitive crops 

are grown. 

Lacking long-term observations of wind speeds in and around wind plants, we can only speculate 

on how collections of wind turbines interact with this diurnally and seasonally varying flow. The 

layer of momentum extraction defined by the maximum and minimum turbine blade elevations 

will be coupled to overlying and underlying layers through turbulent processes that depend on 

diurnally varying and height-varying atmospheric stability. The scales of turbulence that 

vertically transmit the influence of momentum extraction in the turbine layer may exceed the 

blade diameter under strong convection, but surely will be much smaller than blade diameter 

under stably stratified conditions. Mechanical mixing by the turbulence kinetic energy (TKE) 

generated by the blades will alter the undisturbed stratification, particularly during the period 

93 
from early evening to early morning when turbine-generated TKE will have space and time 

scales greater than those of the undisturbed ABL. Wind-plant induced changes in surface-layer 

sensible and latent heat exchange processes could be quite significant during this period, 

particularly in relatively flat rural areas where terrain or other surface inhomogeneities are not 

present to introduce larger scales of turbulence. 

To understand long-term effects of wind plants will require better quantification of interactions 

between wind plants and the local/regional climate, and possibly some larger scale effects, rather 

than a case by case or general description. Yet, the lack of observations precludes substantial 

refinements to even a general description. Vertical profiles of mean and turbulent wind 

characteristics are urgently needed for better understanding both of conditions within the 

momentum extraction layer that influence power generated and blade/gearbox stresses, and of 

conditions above and below this layer to characterize the transmission of turbine influences both 

to the surface layer below and the free atmosphere above. Increases in greenhouse gas 

concentrations likely will decrease surface-layer stratification under undisturbed conditions, but 

the presence of a momentum-deficient layer aloft created by wind turbines surely will modify 

this situation. And global climate change will alter synoptic and mesoscale circulation patterns 

with yet-to-be-determined consequences on surface wind speeds. 

15.2 Needs from Computation/Theory 

The simplistic conceptual model presented in the previous section calls for more rigorous 

mathematical description and testing. Modeling studies of the large-scale impact of wind farms 

(Keith et al, 2004; Baidya et al, 2004; Rooijmans, 2004; Adams and Keith, 2007; Kirk-Davidoff 

and Keith, 2008; Barrie and Kirk-Davidoff, 2007) have used global and regional scale models 

such as CAM, WRF, RAMS, or MM5 with grid spacing as large as 250 km and as low as 1 km. 

These studies indicate that the existence of substantial remote impacts on climate depends 

strongly on the horizontal scale of the region over which momentum is extracted. Fast (2008) 

has identified the horizontal scale interval of 100 m to 1 km as the “no-mans land” between LES 

models and mesoscale models where much better understanding is needed of the momentum 

extraction process. This will require more detailed momentum extraction models (MEMs), such 

as described by Wang et al (2001) for flow through vegetation, that recognize the spatial 

placement of turbines and the spatial extent and details of how mean kinetic energy (MKE) is 

converted to TKE. 

Moreover, rotational motions of blades will create a special class of motions that excite specific 

physical processes and scales of turbulence that must be simulated. Such models must then be 

inserted into conventional ABL models or LES models that link the free atmosphere to the 

surface [3 years for development, 5 years for evaluation]. Such ABL/LES/MEMs must further 

be coupled to conventional mesoscale weather models or regional climate models for assessing 

larger-scale impacts [5+ years]. Improved simulation and model verification at the regional scale 

will provide a basis for developing better mesoscale and LES parameterizations needed to better 

assess the global impact of wind plants through GCMs. It is expected that individually these 

component models will be highly computationally intensive. Simulations using fully coupled 

modeling systems across scale should take advantage of high performance computers to fully 

explore scale interactions and model coupling. 

94 
However, operational use of fully coupled modeling systems across scales is unrealistic for many 

practical purposes. This calls for continued development of “wind-plant parameterizations”, 

such as Adams and Keith (2007), which go beyond simple increases in mesoscale grid point 

surface roughness that can be used to represent the overall influences of such parks on surface 

and free-atmospheric conditions without excessive computational burden. 

15.3 Needs from Observations/Experiments 

Existing wind climatologies lack information on conditions at typical hub heights of modern 

turbines. The region of interest falls in a gap between surface and short tower measurements at 

the lower end and lowest level of radiosonde measurements at the upper end. Diurnal, seasonal 

and interannual patterns of mean and turbulent wind fields are needed as a baseline for 

establishing “undisturbed” conditions in the vicinity of wind plants. A systematic analysis of 

data from all available existing tall (>100 m) towers (even in regions not having or considered as 

highly desirable for wind-power development) is needed to improve understanding the 

climatology of conditions above the atmospheric surface layer. Special attention should be given 

to surface and radiosonde data concurrent with these hub-height data in order to develop 

parameterizations of conditions from the power-extraction region from more readily available 

data. Opportunities for using Aircraft Communications Addressing and Reporting System 

(ACARS) data also should be explored. 

Observational facilities specifically devoted to assessing impact of wind plants on undisturbed 

boundary-layer flow are needed—ideally, one such facility in complex terrain and one in 

relatively flat terrain [start immediately, continue 5+ years]. Such facilities should consist of 

networks of meteorological towers of a height 1.5 times the hub height located upwind, 

downwind, and within the footprint of each wind plant. Measurements should include mean 

wind speed and direction, turbulence, temperature, and dew-point temperature at multiple levels 

within, above, and below the momentum extraction layer. Surface sensible and latent heat 

budgets also should be measured (i.e., soil moisture, soil temperature, and precipitation) as well 

as conditions aloft above the tower layer. Measurements of high frequency (~1 Hz) soil-surface 

pressure fluctuations induced by turbines would provide opportunities for documenting potential 

influences on mammal auditory function and “pressure-pumping” of water-vapor and other trace 

gases from sub-surface ecosystems (Takle et al, 2004). Multi-year measurement facilities and 

programs should be established to ensure full assessment of interannual variability. 

Currently available remote-sensing technologies should be deployed at operational wind parks 

that also have in-situ observations for more full three-dimensional wind field [start immediately, 

continue 5+ years]. Wind profilers operating at 915 MHz have demonstrated success in 

observing ABL depth, wind speed, direction, and virtual temperature profiles (e.g., White, et al, 

2007; Wilczak et al, 2007; MacDonald et al, 2007; Shaw et al, 2007). Satellites using synthetic 

aperture radar (SAR) have proven to be capable of observing the wakes of off-shore wind plants 

(Christiansen & Hasager, 2005). The ARM Mobile Facility (AMF) can play a central role in 

advancing the measurements in the vicinity of wind plants.

95 
15.4 Potential Wind Energy Impact 

The payoff of better understanding of interactions of turbine parks with the atmospheric 

boundary-layer includes better knowledge of influences on natural or managed land use of the 

landscape within and in the near vicinity of the wind park, better knowledge of possible regional 

influences on mesoscale meteorological systems beyond the immediate vicinity of the wind park, 

and better knowledge of how one wind plant might influence another nearby wind plant 

(particularly pertinent to off-shore wind plants). Better understanding of these environmental 

impacts will enable comparative analyses of the sustainability of alternative electrical generation 

sources. The potential of measurable environmental impact of wind plants brings potential legal 

exposure for negative impacts on adjacent land use. For instance, changes in daily maximum or 

minimum temperatures or evaporation and dew deposition in adjacent agricultural lands might 

influence pest or pathogen infestations or crop pollination success. Quantitative measurements 

and modeling would help establish limits on risk exposure and suggest possible advantages for 

alternative land use. 

15.5 Key Problems and Opportunities 

Arrangements should be made with wind plant operators to acquire data for use in these studies. 

Negotiations on data availability should be managed by DOE in conjunction with a third-party 

analysis team, such as from a university, to ensure that confidentiality on sensitive data is 

maintained. This will provide data on both basic meteorological conditions (see above list of 

variables and boundary-layer characteristics to be analyzed) and wind power production. 

Modeling studies also should be conducted, preferably with multiple models and coupled 

modeling systems, to assess the ability of models to capture mean and turbulence characteristics 

and extreme events. This would enable assessment of model ability to provide useful predictive 

capabilities for power generation planning and pre-emptive actions by wind plant operators, a 

prospect that should offer incentive for industry participation. 

Understanding of boundary-layer conditions associated with power production (including 

conditions potentially hazardous to turbines) and environmental impacts of wind plants will 

enable studies of long-term wind power production by use of regional climate models. The 

forthcoming North American Regional Climate Change Assessment Program (NARCCAP, 

2008) simulations will provide future scenario climates at regional scales for planning future 

wind plant development and energy production. The recent results of Pryor et al (2007) that 

noted substantial apparent declines in wind speeds over the United States, particularly the eastern 

half, over the period 1979 – 2004 need to be replicated with models as a basis for assessing 

potential changes in wind power potential with future climate change. 

Spatial (both vertical and horizontal) and temporal scales of importance for measurement and 

modeling must be recognized, both in conjunction with measurements at wind plants and in with 

models’ (both reanalysis models and regional climate models) abilities to assess boundary-layer 

conditions in the lowest 300 m. Statistical downscaling should be used in addition to dynamical 

downscaling for comparison with site-specific measurements. Existing ARM sites and 

Ameriflux towers offer opportunities for such comparisons in a variety of locations throughout 

the United States. 

96 
Finally, to ensure full and accurate use of data derived from field programs, an infrastructure 

must be established for processing, quality control, archive, and dissemination of data. Special 

attention should be paid to establishing standards for metadata, and standard formatting 

conventions (e.g., NetCDF) should be used. 

References 

Adams, A. S., and D. W. Keith, 2007: Wind Energy and Climate: Modeling the Atmospheric 

Impacts of Wind Energy Turbines. Eos Trans. AGU, 88 (52) Fall Meet. Suppl., Abstract A44B08. 

Baidya Roy, S., R. Walko and S. W. Pacala, 2004: Can large windfarms affect local 

meteorology? J. Geophys. Res., 108 (D24), 4793, DOI:10.1029/2003JD003565. 

Barrie, D., and D. Kirk-Davidoff, 2007: Wind farms and weather: the predictability of wind 

plant-induced changes in the downstream atmosphere. Eos Trans. AGU, 88 (52) Fall Meet. 

Suppl., Abstract A41B-0452. 

Christiansen, M.B.; Hasager, C.B., Wake effects of large offshore wind farms identified from 

satellite SAR. Remote Sens. Environ. (2005) 98, 251-268 

Fast, J. D., 2008: Modeling mesoscale processes. DOE Workshop on Research Needs for Wind 

Resource Characterization. Broomfield CO, Jan 14-16. 

IPCC (Intergovernmental Panel on Climate Change), 2007. Climate Change 2007: The Physical 

Science Basis. Contributions of Working Group I to the Fourth Assessment Report of the 

Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M.. Manning, Z. Chen, M. 

Marquis, K. B. Averyt, M. Tignor, and H. L. Miller (eds.)]. Cambridge University Press, 

Cambridge, United Kingdom and New York, USA, 996 pp. 

Keith, D. W., J. F. DeCarolis, D. C. Denkenberger, D. H. Lenschow, S. L. Malyshev, S. Pacala 

and P. J. Rasch, 2004. The influence of large-scale wind-power on global climate. Proc. Nat. 

Acad. Sci., 101, 16115-16120. 

Kirk-Davidoff, D.B, and Keith, D.W., 2008: On the climate impact of surface roughness 

anomalies. In press at J. Atmos. Sci. 

MacDonald, C., C. A. Knoderer, and P. Zahn, 2007: The relationship among air quality, mixing 

heights, and winds observed during the entire TexAQS-II field study. Eos Trans. AGU, 88 (52) 

Fall Meet. Suppl., Abstract A51B-0355. 

NARCCAP (North American Regional Climate Change Assessment Program), 2008: 

http://www.narccap.ucar.edu/ 

Pryor, S. C., R. J. Barthelmie, and E. S. Riley, Historical evolution of wind climates in the 

USA. J. Phys. Conf. Ser., 75, 8 pp. 

97 
Rooijmans, Pim (2004). Impact of a large-scale offshore wind farm on meteorology: Numerical 

simulations with a mesoscale circulation model. Masters thesis, Utrecht University , Utrecht , 

The Netherlands. 

Shaw, W. J., M. S. Pekour, R. L. Coulter, T. J. Martin, and J. Walters, 2007: The daytime mixed 

layer observed by radiosonde, profiler, and lidar during MILAGRO. Eos Trans. AGU, 88 (52) 

Fall Meet. Suppl., Abstract A51G-06. 

Takle, E. S., W. J. Massman, J. R. Brandle, R. A. Schmidt, R. Garcia, I. V. Litvina, X. Zhou, G. 

Doyle, and C. W. Rice, 2004: Influence of high-frequency ambient pressure pumping on carbon 

dioxide efflux from soil. Agric. and Forest Meteor.. 124, 193-206. 

Wang, H., E. S. Takle, and J. Shen, 2001: Shelterbelts and Windbreaks: Mathematical Modeling 

and Computer Simulation of Turbulent Flows. Ann. Rev. Fluid Mech., 33, 549-586. (Invited 

review paper) 

White, A. B., J. Bao, and I. V. Djalalova, 2007: A comparison of wind profiler and model-based 

trajectory calculations. Eos Trans. AGU, 88 (52) Fall Meet. Suppl., Abstract A51B-0352. 

Wilczak, J., I. Djalalova, and S. McKeen, 2007: Boundary layer structure observed by a network 

of wind profiling radars and simulate in air quality forecast models during the TEXAQS 2006 

Field Program. Eos Trans. AGU, 88 (52) Fall Meet. Suppl., Abstract A51B-0340. 

 

98 
Appendices 

Appendix A – Workshop Logistics.....................................................................100 

Appendix B – Workshop Agenda.......................................................................101 

Appendix C – Workshop Attendee List................................................................102 

99 
Appendix A – Workshop Logistics 

Workshop registrants totaled 125, with actual attendance exceeding 95 percent. To ensure 

productive discussions and cogent recommendations, invitees were carefully chosen from the 

atmospheric science and wind energy engineering communities, and then personally invited to 

attend the workshop. The workshop enjoyed vigorous participation from both industry and 

research laboratories. Approximately 30% of the participants represented turbine manufacturers, 

wind plant owners/operators, wind resource estimation industry, or utility participants. 

Researchers from universities and federal laboratories constituted 66% of the registrants. Of the 

federal laboratory researchers, DOE laboratories were well represented (31 of 52), with strong 

participation from abroad (7 of 52, coming from Denmark, Canada, and Spain), from NCAR (5 

of 52), from NOAA (8 of 52), as well as one DOD participant. The remaining 4% of the 

attendees represented federal agencies at managerial levels. 

Wind Resource Characterization Workshop Participants

Federal Agency Representatives

Industry Participants

University Researchers

Federal Laboratory Researchers

Figure 1: Breakdown of workshop registrants 

Federal Laboratory Registrants, Wind Resource Characterization 

2008 Workshop

ANL

DOD

international

LANL

LLNL

NOAA

NCAR

NREL

PNNL

SNL

NREL - 17

PNNL - 6

LLNL - 5

NOAA & CIRES - 8

NCAR - 5

international - 7

Figure 2: Breakdown of federal laboratory 

participants

Organization of the workshop promoted discussions of future research needs and methodologies 

across time and spatial scales, and between industry participants and members of the research 

community. The four focus areas of Turbine Dynamics, Micrositing and Array Effects, 

Mesoscale Processes, and Climate Effects anchored the discussions. These areas were 

introduced in four plenary presentations delivered by internationally recognized experts currently 

working in these fields. Participants then joined one of three “Problem Definition” breakout 

groups to address these focus areas. Each group consisted of about 40 people, and included 

participants from each community to promote knowledge exchange between the industry and the 

research communities. After the Problem Definition groups had addressed each of the four focus 

areas, participants then joined one of four “Recommendation” breakout groups aligned with their 

individual expertise to distill the problem definition discussions into a set of research thrust 

recommendations. Research thrust recommendations from each of the four groups were 

summarized in a plenary session at the conclusion of the workshop. Some of the thrusts were 

common to multiple recommendation groups. 

100 
101 

Appendix B – Workshop Agenda

9:00 - 11:00

11:30 -1:00

1:00 - 1:15

1:15 - 1:30

1:30 - 2:15

2:15 - 3:00

3:00 - 3:30

7:00 - 8:00

8:00 - 8:45

8:45 - 9:30

9:30 - 10:00

12:00 - 1:00

Workshop on Research Needs for Wind Resource Characterization

14 - 16 January 2008

Omni Interlocken Hotel, Broomfield, Colorado

Monday

Problem Definition Group C

(Chairs: T. Mikkelsen, D. Lenschow)

− Turbine Dynamics

− Micrositing & Array

Problem Definition Group C

(Continued)

− Mesoscale Processes

− Climate Effects

Register and Prepare for Workshop

Lunch and Introduction

3:30 - 5:30

10:00 - 12:00

Problem Definition Group A

(Chairs: S. Butterfield, R. Banta)

− Turbine Dynamics

− Micrositing & Array

Problem Definition Group B

(Chairs: P. Veers, S. Zhong)

− Turbine Dynamics

− Micrositing & Array

OBER Workshop Goals and Welcome − Ashley Williamson

EERE Workshop Goals and Welcome − Sam Baldwin

Turbine Dynamics Plenary − Sandy Butterfield

Micrositing and Array Effects Plenary − Jakob Mann

Tuesday Lunch

Break

Breakfast

Mesoscale Processes Plenary − Jerome Fast

Climate Effects Plenary − Katherine Klink

Problem Definition Group A

(Continued)

− Mesoscale Processes

− Climate Effects

Problem Definition Group B

(Continued)

− Mesoscale Processes

− Climate Effects

Break

3:00 - 3:30

7:30 - 8:30

8:30 - 8:45

8:45 - 9:00

9:00 - 9:30

9:30 - 10:00

10:00 - 10:30

10:30 - 11:00

11:00 - 11:30

12:00

Wednesday

1:00 - 3:00

3:30 - 6:00

EERE Program Manager Remarks − Mike Robinson

OBER Program Manager Remarks − Rick Petty

Break

Climate Effects Recommendations Plenary

Recommendation Group

Climate Effects

(Continued)

Recommendation Group

Mesoscale Processes

(Continued)

Recommendation Group

Micrositing & Array Effects

(Continued)

Recommendation Group

Turbine Dynamics

(Continued)

Turbine Dynamics Recommendations Plenary

Discussion and Closing Remarks

All Depart

Recommendation Group

Climate Effects

(R. Leung)

Recommendation Group

Mesoscale Processes

(W. Bach)

Recommendation Group

Micrositing & Array Effects

(P. Moriarty)

Recommendation Group

Turbine Dynamics

(L. Carr, S.Schreck)

Mesoscale Processes Recommendations Plenary

Micrositing & Array Effects Recommendations Plenary

Breakfast
Appendix C – Workshop Attendee List

Tom Acker 

Northern Arizona University 

Email: tom.acker@nau.edu 

Manda Adams 

Institute for Sustainable Energy 

Environment and Economy 

University of Calgary 

Email: manda.adams@ucalgary.ca 

Chris Anderson 

NOAA/ESRL/GSD 

Email: sky.doc@gmail.com 

Wayne Angevine 

CIRES / NOAA ESRL 

Email: wayne.m.angevine@noaa.gov 

Walter Bach 

Army Research Office 

Email: bach@aro-emh1.army.mil 

Mark Balas 

ECE Department 

Email: mbalas@uwyo.edu 

Sam Baldwin 

EERE/USDOE 

Email: sam.baldwin@ee.doe.gov 

Robert Banata 

NOAA/ESRL - CSD3 

Email: robert.banta@noaa.gov 

Rebecca Barthelmie Barthelmie 

Indiana University/Edinburgh University 

Email: r.barthelmie@ed.ac.uk 

Suresh Baskaran 

Pacific Northwest National Laboratory 

Email: suresh.baskaran@pnl.gov 

Sukanta Basu 

Texas Tech University 

Email: sukanta.basu@ttu.edu 

Larry Berg 

Pacific Northwest National Lab 

Email: larry.berg@pnl.gov 

Kenny Braun 

Vestas 

Email: kebra@vestas.com 

Kenneth Brentner 

Penn State University 

Email: ksbrentner@psu.edu 

Ari Bronstein 

DONG Energy 

Email: ari.n.bronstein@gmail.com 

Kevin Brundage 

NOAA/GSD 

Email: Kevin.Brundage@noaa.gov 

Sandy Butterfield 

NREL 

Email: sandy_butterfield@nrel.gov 

Daniel Cabezon 

CENER 

Email: dcabezon@cener.com 

Lawrence Carr 

Emeritus Scientist, US Army 

Aeroflightdynamics Dir. 

Email: lcarr@mail.arc.nasa.gov 

Hung-Neng Chin 

Lawrence Livermore National Laboratory 

Email: chin2@llnl.gov 

102 
Robert Conzemius 

WindLogics, Inc. 

Email: bobc@windlogics.com 

Dave Corbus 

NREL 

Email: david_corbus@nrel.gov 

Richard Coulter 

Argonne National laboratory 

Email: rl_coulter@anl.gov 

Jerry Crescenti 

PPM Energy 

Email: jerry.crescenti@ppmenergy.com 

Ed DeMeo 

Renewable Energy Consulting Services, Inc. 

Email: edemeo@earthlink.net 

Lori Diachin 

LLNL 

Email: diachin2@llnl.gov 

Dennis Elliott 

NREL/NWTC 

Email: dennis_elliott@nrel.gov 

Jerry Elwood 

Department of Energy 

Email: jerry.elwood@science.doe.gov 

Peder Enevoldsen 

Siemens Wind Power 

Email: pe@siemens.com 

Dariush Faghani 

Helimax Energie inc. - GL Group 

Email: faghanid@helimax.com 

Jerome Fast 

Pacific Northwest National Laboratory 

Email: jerome.fast@pnl.gov 

Evgeni Fedorovich 

School of Meteorology, OU 

Email: fedorovich@ou.edu 

Lee Jay Fingersh 

NREL 

Email: lee_fingersh@nrel.gov 

Frank Flottemesch 

General Electric Wind Energy 

Email: frank.flottemesch@ge.com 

Sten Tronaes Frandsen 

Risoe National Laboratory DTU 

Email: sten.frandsen@risoe.dk 

Jeff Freedman 

AWS Truewind LLC 

Email: jfreedman@awstruewind.com 

Rod Frehlich 

University of Colorado 

Email: rgf@cires.colorado.edu 

Bobi Garrett 

NREL 

Email: bobi_garrett@nrel.gov 

Charlette Geffen 

PNNL 

Email: ca.geffen@pnl.gov 

Al Germain 

BP Alternative Energy - Wind 

Email: al.germain@bp.com 

Philippe Giguere 

General Electric 

Email: philippe.giguere@ge.com 

103 
Lucas Gomez 

Iberdrola Renewable Eenergies 

Email: lgomez@iberdrolausa.com 

Bruce Green 

NREL 

Email: bruce_green@nrel.gov 

Martin Greiner 

Siemens AG 

Email: martin.greiner@siemens.com 

Scott Grindle 

RES AMERICAS 

Email: scott.grindle@res-americas.com 

Maureen Hand 

NREL 

Email: maureen_hand@nrel.gov 

Horia Hangan 

Boundary Layer Wind Tunnel Laboratory 

Email: hmh@blwtl.uwo.ca 

Stephen Hannon 

Lockheed Martin Coherent Technologies 

Email: steve.hannon@lmco.com 

Scott Haynes 

PPM Energy 

Email: scott.haynes@ppmenergy.com 

Cheng-Hu Hu 

Vestas Asia Pacific A/S 

Email: chhhu@vestas.com 

Kathryn Johnson 

Colorado School of Mines 

Email: kjohnson@mines.edu 

Wesley Jones 

NREL 

Email: wesley_jones@nrel.gov 

Bonnie Jonkman 

National Renewable Energy Lab / National 

Wind Technology Center 

Email: bonnie_jonkman@nrel.gov 

Jason Jonkman 

National Renewable Energy Laboratory 

Email: jason_jonkman@nrel.gov 

Gary Kanaby 

Knight & Carver Wind Group 

Email: gkanaby@knightandcarver.com 

Neil Kelley 

NREL/NWTC 

Email: neil_kelley@nrel.gov 

Katherine Klink 

University of Minnesota 

Email: klink@umn.edu 

Eunmo Koo 

Los Alamos National Laboratory 

Email: koo_e@lanl.gov 

Darko Koracin 

Desert Research Institute 

Email: darko.koracin@dri.edu 

Leif Kristensen 

Risoe National Laboratory/Technical 

University of Denmark 

Email: leif.kristensen@tele2.dk 

Don Lenschow 

NCAR 

Email: lenschow@ucar.edu 

L. Ruby Leung 

Pacific Northwest National Laboratory 

Email: ruby.leung@pnl.gov 

William Lindberg Lindberg 

University of Wyoming 

Email: lindberg@uwyo.edu 

104 
Steve Lindenberg 

U.S. DOE Wind Program 

Email: steve.lindenberg@ee.doe.gov 

Julie Lundquist 

Lawrence Livermore National Laboratory 

Email: lundquist1@llnl.gov 

David Malcolm 

Global Energy Concepts, LLC 

Email: 

dmalcolm@globalenergyconcepts.com 

Jakob Mann 

Risø National Laboratory - DTU 

Email: jakob.mann@risoe.dk 

Lance Manuel 

University of Texas at Austin 

Email: lmanuel@mail.utexas.edu 

Melinda Marquis 

NOAA Earth System Research Laboratory 

Email: melinda.marquis@noaa.gov 

Jim McCaa 

3TIER 

Email: jmccaa@3tiergroup.com 

Jon McGowan 

University of Massachusetts 

Email: jgmcgowa@ecs.umass.edu 

Tony Meys 

WindLogics 

Email: tmeys@windlogics.com 

Torben Mikkelsen 

Risø National Laboratory, Denmark RISØDTU 

Email: torben.mikkelsen@risoe.dk 

Thomas Mills 

Vestas Americas 

Email: thmi@vestas.com 

Jeffrey Mirocha 

Lawrence Livermore National Laboratory 

Email: mirocha2@llnl.gov 

Soeren Mogensen 

Vestas Asia Pacific, Wind & Site 

Competence Center 

Email: shm@vestas.com 

Patrick Moriarty 

NREL 

Email: patrick_moriarty@nrel.gov 

Philip Morris 

Penn State University 

Email: pjmaer@engr.psu.edu 

Walt Musial 

NREL 

Email: walter_musial@nrel.gov 

Dora Nakafuji 

Lawrence Livermore National Laboratory 

Email: nakafuji2@llnl.gov 

Jonathan Naughton 

University of Wyoming 

Email: naughton@uwyo.edu 

Andrew Oliver 

RES Americas Inc 

Email: andrew.oliver@res-americas.com 

Joe Olson 

NOAA-ESRL 

Email: joe.ols@gmail.com 

Lucille Olszewski 

CH2M HILL 

Email: lucille.olszewski@ch2m.com 

Andy Paliszewski 

Siemens Power Genration 

Email: andy.paliszewski@siemens.com 

105 
Thomas Parish 

University of Wyoming 

Email: parish@uwyo.edu 

David Parsons 

NCAR 

Email: parsons@ucar.edu 

Edward (Ned) Patton 

National Center for Atmospheric Research 

Email: patton@ucar.edu 

Gary Pawlas 

University of Colorado at Boulder 

Email: gary.pawlas@colorado.edu 

Stanton Peterson 

BP America 

Email: stanton.peterson@bp.com 

Franco Petrucci 

Environment Canada 

Email: franco.petrucci@ec.gc.ca 

Rickey Petty 

U.S Department of Energy - SC 23.3 

Email: rick.petty@science.doe.gov 

Yelena Pichugina 

CIRES/CU Boulder/NOAA 

Email: yelena.pichugina@noaa.gov 

Robert Poore 

Global Energy Concepts, LLC 

Email: rpoore@globalenergyconcepts.com 

Greg Poulos 

Clipper Windpower 

Email: gpoulos@clipperwind.com 

Sara Pryor 

Indiana University 

Email: spryor@indiana.edu 

Radian Belu 

Desert Research Institute 

Email: rbelu@dri.edu 

Daran Rife 

National Center for Atmospheric Research 

Email: drife@ucar.edu 

Mike Robinson 

NREL 

Email: mike_robinson@nrel.gov 

Sven Schmitz 

University of California Davis 

Email: shschmitz@ucdavis.edu 

Scott Schreck 

NREL's National Wind Technology Center 

Email: scott_schreck@nrel.gov 

Marc Schwartz 

National Renewable Energy Laboratory 

Email: marc_schwartz@nrel.gov 

George Scott 

National Renewable Energy Laboratory 

Email: george_scott@nrel.gov 

Justin Sharp 

PPM Energy, Inc. 

Email: justin.sharp@ppmenergy.com 

Will Shaw 

Pacific Northwest National Laboratory 

Email: will.shaw@pnl.gov 

J. Charles Smith 

UWIG 

Email: jcharlessmith@comcast.net 

James Sonnenmeier 

Penn State Erie, The Behrend College 

Email: jrs36@psu.edu 

106 
107 

Brandon Storm 

Texas Tech Univ. 

Email: brandon.storm@ttu.edu 

Peter Sullivan 

National Center for Atmospheric Research 

Email: pps@ucar.edu 

Michael Susedik 

Vaisala, Inc. 

Email: mike.susedik@vaisala.com 

Mikko Suuntala 

Vaisala 

Email: mikko.suuntala@vaisala.com 

Eugene Takle 

Iowa State University 

Email: gstakle@iastate.edu 

Peter Taylor 

ESS, York University 

Email: pat@yorku.ca 

Leo Thesbjerg 

Vestas Wind Systems A/S 

Email: lt@vestas.com 

Robert Thresher 

NREL - National Wind Technology Center 

Email: robert_thresher@nrel.gov 

Kenneth Underwood 

Atmospheric Systems Corporation 

Email: ken@minisodar.com 

Paul Veers 

Sandia National Laboratories 

Email: psveers@sandia.gov 

Kenneth Waight 

AWS Truewind 

Email: ken@meso.com 

Kevin Walter 

TradeWind Energy, LLC 

Email: kwalter@tradewindenergy.com 

Thomas Warner 

National Center for Atmospheric Research 

Email: warner@ucar.edu 

C. David Whiteman 

University of Utah 

Email: dave.whiteman@utah.edu 

James Wilczak 

NOAA/ESRL 

Email: james.m.wilczak@noaa.gov 

Ashley Williamson 

DOE /SC 

Email: ashley.williamson@science.doe.gov 

Wei Yu 

Environment Canada 

Email: wei.yu@ec.gc.ca 

Sharon Zhong 

Michigan State University 

Email: zhongs@msu.edu 
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U.S. Department of Energy Workshop Report: Research Needs for 

Wind Resource Characterization 

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This workshop brought different atmospheric and wind technology specialists together to evaluate research needs for 

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wind; characterization; meteorology; climatology; wind resource; atmospheric science; modeling; forecasting. 

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