The Oklahoma Wind Power Assessment Initiative
A Report on Progress and Products Developed During
Period of Work July 1 to November 10, 2000
The Oklahoma Department of Commerce
November 10, 2000
Investigators - The University of Oklahoma
Tim Hughes, Principal Investigator
Research Associate, The Environmental Verification and Analysis Center
Professor, Science and Public Policy Program
Staff Climatologist, The Oklahoma Climatological Survey
Associate Professor, Department of Geography
Investigators - Oklahoma State University
Spatial and Environmental Information Clearinghouse
Professor, Department of Geography
Table of Contents
I. Overview 2
II. Wind Power Products 5
III. Web pages 8
IV. Educational Outreach 9
V. Related Activities 11
VI. Ancillary data and products 13
A. Tables 14
B. Figures 18
C. Tutorials 22
D. Letters 33
Policy Report: Review of States' Policy Incentives for the
Development of Wind Energy Facilities
Oklahoma wind power brochure
Section I. Overview
The following are summaries of work accomplished by the OWPAI team to date.
Wind Power Products
For the first quarter, OWPAI's expected product was to be a statewide wind power assessment at 10
meters (a level suitable for small wind turbines). Work to gather input components has produced
1) Elevation data - digital topographic maps in DEM (digital elevation model) format at
1:250,000 scale were obtained from the U. S. Geological Survey (USGS) and were combined to
produce an appropriate elevation grid for input into the model for estimating wind power
2) Vegetation data - recent landuse/landcover data for Oklahoma has been obtained from the
National Gap Analysis Program (GAP). The GAP program is administered by the USGS
National Biological Survey for purposes of identifying and managing the diversity of wildlife.
This data has extremely fine resolution (30 meters) and is used to determine the vegetative
"roughness" values needed for input into the wind power model.
3) Oklahoma Mesonet data - wind speed and direction summary products ("wind rose" tables)
representing five years of data (early 1994 to late 1998) were distilled to provide the necessary
inputs into the model.
Tests are also being conducted to evaluate and optimize performance of the model software. The
output product described in this report (shown in Figure 1) is not for the entire state, but is for a
section in western Oklahoma expected to have very good wind resource. Work to produce a
product for the entire state is expected to proceed rapidly at this point and a product will be posted
on OWPAI's web pages as soon as it becomes available.
Discussions with wind resource evaluation experts at the National Renewable Energy Lab (NREL)
will continue in an endeavor to provide the highest quality assessment.
The web site for the project has been constructed at OSU and can be viewed at
http://www.seic.okstate.edu/owpai. Figure 5 shows the home page for the Web site
Work as part of OWPAI's educational outreach endeavors has included the following.
1) The first 3 tutorials (in a series expected to number about 10) have been developed to teach the
fundamentals of evaluating wind power resources. Hardcopy versions can be found in
Appendix C and can be viewed at: http://www.seic.okstate.edu/owpai/tutorials.htm
2) A brochure has been developed to serve as a general introduction to Oklahomans on wind power
and its potential benefits to our state (see the attached sample copy). These will be back from
the printers in early December.
3) Meetings have been held or contact made with the following individuals and groups to explain
the OWPAI program and to discuss ideas for future development of educational materials
related to wind.
• Dr. Victoria Duca-Snowden, Director, NASA Oklahoma Space Grant Consortium
• Dr. Bill James, Associate Director, NASA Oklahoma Space Grant Consortium and Director
of Science Education Programs at Cameron University, Lawton Oklahoma
• Directors of the seven Oklahoma Professional Development Centers (PDCs)
• Science Education Programs, Norman Public Schools
The following summarize activities by OWPAI team members that are related to current wind
power work or are in anticipation of future work.
1) Hughes, Meo and Stadler are planning committee members for the Oklahoma wind power
workshop, now slated for early May, 2001. The planning committee is also working on plans
for an informative breakfast to be held for legislators in mid-January, 2001.
2) Hughes attended wind power workshops in Kansas and South Dakota, and a meeting of the
National Wind Coordinating Committee (NWCC), to learn about policies, programs, and wind
assessments in other states.
3) Hughes has met with Greg Adams of Buffalo, Oklahoma to review a potential wind assessment
site and to discuss possible collaboration that could facilitate OWPAI's data collection activities.
4) Hughes has begun discussions with Mr. Steve Palomo, U.S. DOE Denver Regional Office,
regarding the merits of establishing an Oklahoma "Anemometer Loan Program" modeled after
National Renewable Energy Lab's similar instrument loan program
5) Meetings have been held with Western Farmers Electric Coop and one Rural Electric Coop in
Lindsay, Oklahoma, to discuss future prospects for their involvement in wind power
development and marketing.
Ancillary data and products
Tower location data. OWPAI has purchased a dataset containing location and ownership data for
all existing towers 200 feet and taller. Where existing towers coincide with good potential resource,
OWPAI may avoid the cost of new tower installation - a significant cost.
Mesonet site review for wind data measurement conditions. OWPAI has compiled a subjective
appraisal of Mesonet sites' wind fetch conditions, using panorama pictures, a landuse/landcover
(LULC) information grid, and where appropriate, aerial pictures. The purposes of this appraisal are
to review site conditions with specific consideration toward features (obstructions, vegetation,
topography, etc.) that may impact wind resource assessment. The results of this work are listed in
Attachment: Policy Report
The policy report for the first quarter is attached as a separate document, in order to facilitate the
sharing of copies with interested state officials.
This report contains a summary of policy options and incentives used for the development of wind
energy in other states. It is not an exhaustive list, nor is it meant as recommendations regarding
what policies would work best for Oklahoma. Rather, this document is intended as background
materials for interested individuals, policy analysts, and decision-makers
In order to determine the appropriate mix of policies that would stimulate an emerging wind energy
market in Oklahoma, it is necessary to examine issues related to economic development and
changes in the energy market place. These will be subjects of future reports in the fourth quarter of
the project. At that time, a more complete picture will emerge of how these policy options and
experiences from other states can affect the development of wind energy in Oklahoma.
Section II. Wind Power Products
Report from Hughes and Yuan
Preliminary testing of Windmap software
Hughes has performed some preliminary tests on the Windmap software package. Early indications
are that the program's results underestimate actual wind power density by about 25%, though more
rigorous testing needs to be performed in the second quarter with the same inputs used by the OSU
Plans for supplemental means of wind power resource assessment
Hughes and Yuan are looking at a supplemental means to evaluate wind resource that will involve
mean wind speeds and directly calculated wind power density values from Mesonet sites, and the
relationship to elevation, terrain exposure (the relative height of a location with respect to
surrounding terrain) and vegetative roughness. It is thought that this will provide a technique more
like that used by the National Renewable Energy Lab, and will provide a basis for comparison with
Report from Salisbury and Stadler
Procedures and input data
Procedures and input data for generating GIS maps of estimated annual average wind power
available at 10-meter level have been created and tested and we are ready to generate maps for the
entire state. This switch to WindMap.software deviates from the original plan and did, in a sense,
put the OSU portion of the project behind. However, because WindMap will simultaneously
generate 10 m and 50 m level products, we should be able to gain a considerable amount of time in
the work during the second quarter. An example of the model output for the Hobart area is shown
in Figure 1. Because of the long time required for model execution, this subset of the state has been
used for extensive evaluation of the WindMap model and required input data. Model outputs are
now being produced with confidence.
· WindMap model requirements and operational procedures have been deciphered and tested
from the information provided in the software manual. Technical information was not always
clear, complete, or correct.
· Procedures for running the WindMap model in conjunction with ArcView GIS software
were also developed and tested.
· OU team members Hughes and Shafer provided long-term wind rose data from the Oklahoma
Mesonetwork. The data were in the input format required for WindMap. No pre-processing
· All statewide digital land surface data sets were acquired and converted to proper format for
model input. These data sets and pre-processing procedures are described below.
· Many model runs were performed while testing the input formats, sensitivity to data values, and
hardware configurations. We are confident that the model is running properly for the Hobart
subset of the state and are preparing for statewide model runs.
Software description. WindMap interpolates wind values (from wind roses) between Mesonet
stations, then modifies the wind fields with respect to land surface topography/elevation and land
surface roughness. Two data sets were acquired and pre-processed to create the required digital
GIS inputs to WindMap: topography/elevation (DEM) and land use/land cover (LU/LC) to which
roughness coefficients were assigned.
Topography DEM GIS. Digital topographic maps in DEM (digital elevation model) format at
1:250,000 scale were obtained from the U. S. Geological Survey (USGS). The basic resolution unit
is 3 arc seconds (approximately 90 m). This was deemed adequate for the beginning work at the
statewide level. Because the DEMs were in latitude and longitude, they were projected into the
NAD83 datum using an Albers Equal Area projection with standard parallels of 29°N and 45°N
(This projection will be used for all maps during the project). The data were resampled cell-by-cell
to a resolution of 30x30 meters. This was necessary to ensure exact alignment with the 30x30-
meter cells of the LU/LC data. Twenty nine DEM maps lie wholly or partially in Oklahoma. These
were joined to created a single DEM for the entire state (Figure 2).
Land use/land cover (LU/LC) GIS. Initially we planned to use the USDA National Resource
Conservation Service MIADS LU/LC data. The MIADS data has been widely used in Oklahoma,
has a grid cell resolution of 200 meters, and cover categories reasonable for statewide analysis.
However, the data are from 1984. Fortunately, in October, 2000, we were provided recent LU/LC
data for Oklahoma from the National Gap Analysis Program (GAP). Nationally, the GAP program
is administered by the USGS National Biological Survey for purposes of identifying and managing
the diversity of wildlife. In Oklahoma, the project has been conducted by the Oklahoma
Cooperative Fish and Wildlife Unit of the USGS, located on the OSU campus. The GAP data for
Oklahoma has not yet been officially released and, therefore, are not on the OWPAI Web site.
The GAP LU/LC data is a 30-meter-resolution GIS map of natural cover types (Figure 3). The GIS
was derived from Landsat Thematic Mapper multispectral imagery and subsequent field checking.
The data were projected to an Albers conic project with standard parallels at 29°N and 45°N, same
as the DEM GIS. Corresponding 30-meter cells were identified on the DEM and the GAP files so
they could be matched and overlaid perfectly. Surface Roughness GIS. LU/LC GAP data were
used to assign appropriate values of surface roughness coefficients for different cover types. These
values then form the roughness coefficient grid for input to the WindMap model. Although the
GAP categories were developed for wildlife purposes, it is obvious that they are also related to the
surface roughness needed by the WindMap model to estimate wind power density. In that we
were the first to make use of the GAP data for non-wildlife purposes, we spent some time
considering the meaning of the categories, and appropriate roughness values. Although the names
of GAP data categories contain strong hints as to their relative roughness, the GAP data do not
include measures of surface roughness. It was up to our group to build a table of reasonable values
for Oklahoma. The meteorological literature contains some guidance, but only for very generalized
categories of LU/LC. Many of the forty-seven GAP categories developed by the Oklahoma GAP
project are specific to the state; for instance, “Shinnery Oak Forest” does not have specific
roughness descriptions in the literature. Using knowledge of the Oklahoma landscape, the literature
was consulted so that we could develop “ballpark” roughness estimates. Four different sources
were consulted and it became a matter of interpolating roughness values for the Oklahoma
categories from existing estimates. It is clear that even within individual GAP categories there are
various roughness possible. Given the vagaries created by scale, time-averaging of Mesonet wind
data, and the sketchy nature of the corpus of roughness literature, we feel our roughness estimates to
be on the correct orders of magnitude and individually defensible. However, we intend to conduct
some simple sensitivity analysis to determine the implications of over- and under- estimation of
wind power density caused by changes in estimated roughness. Roughness estimates for GAP
categories were entered into an attribute table and a statewide roughness GIS layer was produced
(Figure 4). It is obvious that Oklahoma has vastly different roughness coefficients by region and
that this will heavily impact our coming statewide assessments.
Wind Roses and Station Locations.
Running WindMap. The WindMap model will operate only on gridded data sets of no more
than 300 rows by 300 columns. We selected the region centered on the Hobart Mesonet station as
the subset of the state to test model execution with the available data. A mean filter consisting of a
12 x 12 grid cell kernel was moved over the roughness GIS layer in order to produce an average
roughness for cells of 360 m by 360 m. Using nearest neighbor analysis, these larger cells were
resampled to cell sizes of 372 m. The same filter and nearest neighbor analysis were applied to the
DEM to produce 372 m by 372 m grid cells with exact spatial correspondence to the GIS layer of
roughness coefficients. The grid cells formed a 300 row by 300 column array centered on the
Hobart Mesonet site. This made for a test study area of 112 km by 112 km (approximately 1° of
latitude). The grid cells where Mesonet stations are located were identified. This study area size
seems to provide a sufficiently rigorous scale of examination in order to detect important landscape
differences in topography and surface roughness while maintaining a reasonable computation time.
A wind density map for the Hobart area is given as Figure 1.
Computational Requirements. In our WindMap test runs we used OSU computers at SEIC and
the Geography Department. Although the speeds of the machines vary, our experience is that the
real time to compute the 10 m wind density is on the order of 12 hours per run if six Mesonet sites
are used and 14 hours if eight sites are used. Fortunately, we have access to several PCs on which
we have been making runs in parallel. In addition, a new CPU with higher speed and larger RAM
cache storage has been purchased and dedicated to this project. It will be installed as a parallel co-
processor with existing hardware to greatly decrease computational times.
Statewide GIS maps. The procedures for producing the statewide 10-m wind power density map
are now pro forma. After more experimentation with the Hobart area, we will be proceeding to the
statewide 10 m map work. The state has been divided into 30 overlapping areas of the size and
spatial resolution of the Hobart test area. The overlap is necessary to lessen interpolation
differences between areas and boundary effects. Although there will be considerable set-up time for
each run, the WindMap model will be run for each sub-region. The digital output maps will then
be joined into a statewide map. This will be accomplished within ArcView and facilitate future
GIS phases of this work.
Section III. Web pages
Report from Salisbury
The web site for the project (http://www.seic.okstate.edu/owpai) has been constructed at OSU.
Figure 5 shows the home page for the Web site. The site is written in ISO/IEC 15445 HTML 4.0
because of universality and robustness of the language. Ten sections have been developed so far.
The Site Map lists the content of each section. Only two sections have no information as of yet:
News & Updates and Wind Mapping & GIS. In the latter will be the input wind and GIS data and
the model output maps. The other Web site sections will also continue to expand as new maps,
documents, tutorials, links, and news items are provided.
Section IV. Educational Outreach
In keeping with OWPAI's goals of aiding development of educational programs related to wind
power, the following efforts and accomplishments have been made in the first quarter.
Development of tutorials
Hughes has written three tutorials for posting on OWPAI's web-pages, covering some fundamentals
of wind power assessment. These tutorials have been, and will continue to be, shared with
professional educators to illustrate the kinds of scientific learning materials that may be developed,
using wind power (and later energy issues as a whole) as a theme. These tutorials will also find
use among landowners and others concerned with economic development, as a tool to help
determine the value of local wind resources. Work on additional tutorials will continue.
Hardcopies of the tutorials are included in Appendix C and electronic versions may be viewed or
downloaded from http://www.seic.okstate.edu/owpai/tutorials.htm.
Development of brochure
Hughes has developed a 6-leaf brochure (8.5 x 11 tri-fold) which is meant as a general introduction
to Oklahomans on wind power and its potential benefits for our state. Included is a reference, with
contact information, to the wind power assessment being carried out by the OWPAI team. This
brochure will be for the public at large, but is also intended for sharing with students and teachers.
Printed copies are expected by early December. A preview copy is attached to this report.
Meetings with teaching professionals
Hughes has met with the following individuals and groups to explain the OWPAI program and to
discuss ideas for future development of educational materials related to wind power (and later other
Dr. Victoria Duca-Snowden, Director, NASA Oklahoma Space Grant Consortium
In 1991, NASA awarded the state of Oklahoma a grant to establish the NASA Oklahoma Space
Grant Consortium (OSGC). OSGC's activities focus the power of the state's higher education
system in advancing Oklahoma's participation in the aerospace industry. The OSGC promotes
activities in science, mathematics, technology, and other related studies throughout the state's
educational system. Dr. Duca-Snowden would like to combine efforts utilizing OSGC's
infrastructure, to assist OWPAI in its educational outreach endeavors. (Dr. Duca-Snowden's letter
of support, including a brief history of OSGC's outreach successes, is included in Appendix D.).
Some formalization of cooperative activities is expected by OWPAI's third quarter.
Dr. Bill James, Associate Director, NASA Oklahoma Space Grant Consortium and Director of
Science Education Programs at Cameron University, Lawton Oklahoma
Due to Cameron's proximity to a good area for wind power development, their significant
enrollment of Native American students, and Dr. James' position as head of a university science
education department, this looks like a promising relationship for education outreach activities.
Directors of the seven Oklahoma Professional Development Centers (PDCs)
The Oklahoma Department of Education maintains PDCs in seven communities across Oklahoma,
to provide access for all educators to information and resources that promote quality instruction.
After receiving a letter of invitation (see Appendix D), Hughes met with the directors on November
1st to explain the OWPAI educational outreach objectives and show the tutorial development work
performed to date. The directors invited Hughes to give a presentation on OWPAI at "Tech-Day", a
conference for Oklahoma teachers to be held February 9th, 2001, to showcase technological teaching
tools. Additionally, the directors for the Lawton and Woodward PDCs have offered their help in
making connections with appropriate personnel at technology training institutes in their
communities. Meetings with these personnel are expected in the second quarter of OWPAI
Science Education Programs, Norman Public Schools
At the urging of the PDC directors, contact has been made with Ms. Peggy Ricketts of the Norman
Public School systems. A personal meeting will be held soon.
Miscellaneous related activities for educational outreach
• Hughes has leads on contacts to help technical training centers find surplus wind turbine
equipment, for hands-on training.
• Hughes is coordinating his first wind power related town meeting, likely held in or near Buffalo,
Oklahoma. Teachers will be invited and educational outreach will be addressed at this meeting.
Section V. Related Activities
Wind power workshop
Hughes, Meo and Stadler are members of the committee planning for the Oklahoma wind power
workshop, now slated for early May, 2001. This work has entailed compilation of lists of potential
cosponsors, audiences, and speakers. Also, a mission statement and letter of invitation has been
drafted. While this work is not directly funded or called for in the OWPAI program, it is expected
that a wind power workshop will assist greatly in achieving a principal goal - to educate the public
and policy makers about the potential for economic development that wind power offers.
The planning committee is also working on plans for a breakfast to be held for legislators in mid-
January, 2001. The purpose of the breakfast will be to inform these policy makers (especially key
committee members meeting at that time to draft new legislation) about the potential benefits that
wind power holds for our state.
Travel to Kansas and South Dakota wind power workshops
Hughes attended the Kansas wind power workshop, held in Manhattan, Kansas on July 24, a
meeting of the National Wind Coordinating Committee (NWCC), also held in Manhattan, on July
25, and a workshop held in Brookings, S. Dakota on October 18th - 19th. The purposes for attending
these meetings were to:
• study policies and programs in development in Kansas, S. Dakota, and other states;
• meet professionals working on wind power related issues;
• obtain information from NWCC regarding policies in other states;
• learn what wind resource assessment work has been done in Kansas;
• learn wind resource assessment techniques employed in Kansas (and other states); and
• gain familiarity with issues addressed at such workshops, to aid in designing such a workshop
Prospects for collaboration in Oklahoma wind resource assessment
Mr. Greg Adams and Chermac Energy Corporation
Hughes met with Greg Adams of Buffalo, Oklahoma to discuss possible collaboration on
instrumenting a tower on his land, for wind resource development. Details remain to be worked out
and approval needs to be sought from ODOC to collaborate with Mr. Adams and the Chermac
Energy Corporation of Edmond, Oklahoma. However, this looks like a promising partnership for
the following reasons.
• Chermac is an independent oil and gas company, part of a target Oklahoma audience for
stimulating investment in wind power by in-state businesses.
• Chermac would provide a permanent tower structure and Mr. Adams would provide free use of
his land, saving OWPAI considerable funds (with this savings, it may be possible to instrument
two sites in Oklahoma in the first year, rather than just the one budgeted). In return, OWPAI
would provide the instrumentation and data collection hardware and software, and oversee data
collection and review.
• There is a quarter section of Oklahoma School Land adjacent to Mr. Adams property. An
assessment of his area will serve to prove the profitability of additional wind turbines located on
this school land (and proceeds from these machines could be returned to the state's education
budget thereby benefiting educational programs).
• Data from this site can be shared freely (Chermac has no demands for proprietary use). Aside
from potentially benefiting Oklahoma schools, proving the profitability of this area would
stimulate interest by other in-state oil and gas companies in wind power investment.
Steve Palomo, U.S. DOE, Denver Regional Office
Hughes is discussing with Mr. Palomo the merits of establishing an Oklahoma "Anemometer Loan
Program" modeled after National Renewable Energy Lab's similar instrument loan program. For
such a program, it is envisioned that support will come from multiple public and private entities to
provide free or low-cost loans of wind resource monitoring equipment to areas with good resource
and with particular need for economic development.
Rural Electric Cooperative personnel, Lindsay, OK
Hughes and Meo met with Ed Bevers of the REC located in Lindsay, Oklahoma. Mr. Bevers and
others at REC have an interest in emerging technologies, including fuel cells and wind power.
Because of the encouragement from Wind Powering America on involving rural electric coops in
wind power activities, this looks like a promising collaborative relationship.
Western Farmers Electric Cooperative (WFEC), Anadarko, OK
Hughes and Meo met with personnel at WFEC to discuss the OWPAI program and the Oklahoma
wind power workshop. WFEC expressed much interest in cosponsoring the workshop and in aiding
our wind resource assessment activities.
Native American Anemometer Loan Program
Hughes is investigating the possibility of collaborating with Native American groups in Oklahoma
to apply to Wind Powering America's anemometer loan program (through NREL). With such
collaboration, the NA groups would obtain assistance with installation and collection of data, and
OWPAI could increase the number of sites from which it collects wind data by end of the first year.
Section VI. Ancillary data and products
Tower location data
Hughes has purchased a dataset containing location and ownership data for all existing towers 200
feet and taller. The towers located in Oklahoma have been culled and plotted, and these locations
will be considered for future installation of wind instruments at the higher levels needed for large
scale wind power assessment. Where existing towers coincide with good potential resource,
OWPAI may avoid the cost of new tower installation - a significant cost.
Mesonet site review for wind data measurement conditions
Hughes and his undergraduate assistant compiled a subjective appraisal of Mesonet sites' wind fetch
conditions, using panorama pictures, a landuse/landcover (LULC) information grid, and where
appropriate, aerial pictures. The purposes of this appraisal are the following.
• To discover sites where the LULC data set indicated an inappropriate usage (thereby leading to
an inaccurate estimate for roughness). For example, the LULC grid shows some sites on
airport property to have a land usage class of "urban" (perhaps due to presence of concrete and a
very few small buildings) while in fact cultivated fields surround the sites. Such a
misclassification would lead to overestimates in roughness values, thereby impacting the wind
resource assessment based on those sites' wind data.
• To draw attention to sites where vegetative conditions in immediate proximity to the site may
not reflect those in a broader view (a circle of 10-km. radius).
• To draw attention to sites where wind data may be compromised (for the purposes of wind
power assessment), therefore calling for possible omission of those sites' data, or perhaps to just
generate a warning that the wind resource assessment may be affected in those areas.
• To provide guidance for future development of a more rigorous procedure for evaluating quality
of wind data from Mesonet sites.
The results of this work are listed in Table 1. It should be noted that this appraisal is only intended
for guidance for this particular study and should not be taken to reflect on quality of Mesonet data
Appendix A. Tables
Table 1.: Mesonet Site Conditions Review
STID LULC class Dom. Class used used used fetch conditions comment
in nearby in area w/ r~ LULC? DOQ? pan. (overall - not by [note 1: bold in this column means class changed from LULC predicted]
area (note 1) 10km. (note 2) pics? direction) [note 2: bold in this column means LULC for wider area differs from site's]
ACME Pasture-H Pasture-H y good
ADAX Urban-L Range-H ? y y y good Urban-L dominates from SSW to SE
ALTU Crop-L Crop-L y y good
ALVA Crop-L Crop-L y good distant bldgs and trees
ANTL Pasture-H Pasture-H y y poor to fair somewhat representative of area since lots of forest to N & SW
APAC Range-M Crop-L y good
ARDM Range-M Forest-L y y y good a real mix, but forest-L densest to N and S.
ARNE Range-L Range-L y y good
BBOW Forest-L Forest-H y n y poor poor, but perhaps still representative of larger area
BEAV Crop-L Crop-L y y good Crop-L/Range-M share dominance to S.
BESS Range-M Range-M y y excellent Crop-L also dominant to S.
BIXB Crop-L Crop-L y y fair widely varying veg.;bldgs to NNE; crop-L dom. to S/SE
BLAC Crop-L Crop-L y y fair
BOIS Crop-L Crop-L y y excellent
BOWL Forest-L Pasture-H y y fair Forest-L dominates from S to SE
BREC Crop-L Crop-L y y good
BRIS Range-M Range-M y y fair poor to S and NNE (trees, bldgs); Forest-L also dominant
BUFF Crop-L Range-M y y y fair (poor to S.) bldgs and trees to SE to SW - serious obstruct. in prevail. dir.
BURB Crop-L Range-M y y fair trees to SW
BURN Crop-L ???? y y good trees S to SE & N to NE;not sure on larger area (no info to so. of Red River) - probably CROP
BUTL Range-M Range-M y y good water dominates to SE
BYAR Range-M Range-M y y good Crop-L also dominates S to SE
CALV Pasture-H Range-M y y good cult. Land dominates W to E
CAMA Crop-L Range-M y y good river basin~1.5 km to S
CATO Range-M Range-M y y fair dense trees from N to E
CENT Range-M Range-M y y fair dense trees from E to W; hillock to N
CHAN Range-M Range-M y y good trees NE to SE; clear W to NW
CHER Crop-L Crop-L y y fair/good bldgs to E & SW; good fetch to S
CHEY Range-M Range-M y y good short trees and bldgs to SW
CHIC Crop-L Crop-L y y fair/good Range-M dominates SW to NE; trees NW to SE; cult land in all dir.
CLAR Urban-L Pasture-H y y y poor too many trees around site; don't use in Windmap
CLAY Pasture-H Forest-H y y y fair to poor trees 100m to S?.; lake dominates SSW to NNE; Pasture-H dominates NNE and E to S (note 3)
CLOU Forest-L Forest-H y y poor trees dense to N and S; poor fetch, but representative of area
COOK Pasture-H Forest-H y y poor trees all around; poor fetch, but representative of area
COPA Range-M Range-M y y fair/good trees W to E; fetch to S good
DURA Pasture-H Pasture-H y y fair trees and bldgs from N to S; clear SW to NW; lake ~ 5 km to W
ELRE Range-M Crop-L y y good trees in distance from NE to SE; good fetch to S
ERIC Crop-L Crop-L y y good trees in distance to the N; good fetch to S
EUFA Pasture-H Pasture-H y y fair Lake dominates from NE to SW; trees dense from the NW to E
FAIR Crop-L Crop-L y y fair few bldgs to N; trees to S
FOR A Range-M Range-M y y good few trees to ENE; Range-M completely dominates 10 km area
FREE Crop-L Range-M y y excellent
FTCB Crop-L Crop-L y y good trees and dam to N; lake dominates to NNW for ~8km.
GOOD Crop-L Crop-L y y excellent Range-M significant from E to S and to N.
GRAN Crop-L Crop-L y y good building to NE other wise excellent
GUTH Range-M Range-M y y fair/good
HASK Pasture-H Pasture-H y y good bldg to NE and SE
HECT Pasture-H Pasture-H y y fair/good site is elevated in comparsion to surroundings; ridge to N surrounded by shortbrush; ridge to S
HINT Crop-L Crop-L y y good trees from SW to W
HOBA Crop-L Crop-L y y y excellent sm. arprt bldgs to W; concrete runways; fields all around (note site not urban-L)
HOLL Crop-L Crop-L y y good excellent fetch to the N; small bldg to SSE
HOOK Crop-L Crop-L y y y good town from N to E; (but ESE to NNW is cropland) (note site not urban-L)
HUGO Pasture-H Pasture-H y y fair urban-L 1/2 km to S; trees and bldgs to N and S
IDAB Crop-L Pasture-H y y fair crop-L dominates S in 10 km region; pasture-H dominates N in 10 km
JAYX Pasture-H Forest-H y y poor/fair trees in all dir, but representative of the area
KENT Range-M Range-M y y excellent sm.(~3m?) ledge to S.; S winds may be underestimated
KETC Range-M Range-M y y good
KING Crop-L Crop-L y y good urban-L to SSE; cultivated land from N to E
LAHO Crop-L Crop-L y y good
LANE Pasture-H Pasture-H y y fair Forest-H dominates NNE; trees In all dir
MADI Crop-L Crop-L y y fair dense veg from N to E
MANG Crop-L Crop-L y y good
MARE Range-M Range-M y y good sloping topography
MARS Crop-L Crop-L y y fair/good trees from W to N; clear from NE to SE; few trees to S
MAYR Range-M Range-M y y excellent can't tell what dominates to N, but probably Range-M
MCAL Pasture-H Pasture-H y y fair pasture, undeveloped from E to SW; airport to N; (note site not urban-L)
MEDF Crop-L Crop-L y y fair/good Urban-L to NE; fetch good from W to N; bldg and trees from NE to SW
MEDI Range-M Range-M y y y excellent fetch great but rugged terrain all around; Forest-L from NW to NE at ~2 to 5 km.
MIAM Pasture-H Pasture-H y y good urban area SW to NW; pasture, cult. fields most directions; (note site not urban-L)
MINC Crop-L Crop-L y y good fetch excellent from W to NE; fetch good to S (distant trees)
MTHE Forest-H Forest-H y n y poor fetch especially bad to North; still rep. of area though
NEWK Range-M Range-M y y excellent
NINN Range-M Range-M y y fair/good Pasture-H dominates S; varying bldgs and cult land in all dir, but good exposure to S
NORM Pasture-H Urban-L y y y fair/good Bldgs from W to N(fetch fair); pasture from NE to SSW (good); (note site not urban-L)
NOWA Crop-L Range-M y y good
OILT Pasture-H Forest-L y y fair close trees N to NE; far trees and short hill S to SW
OKEM Range-M Range-M y y good few trees from E to S; fetch good to N
OKMU Crop-L Pasture-H y y fair Urban-L to NW; trees to N and S
PAUL Range-M Range-M y y fair Urban-L dominates N to E; Range-M dominates S; trees from S to N
PAWN Range-M Range-M y y good Urban-L to SW; good fetch to S and N
PERK Range-M Range-M y y fair trees from W to N; fetch good to S
PRES Forest-L Pasture-H y y poor/fair Forest-L dominates N and S in 10 km region; varying classes of veg in all dir
PRYO Pasture-H Pasture-H y y fair/good trees in distance from N to S; clear WNW
PUTN Crop-L Range-M y y good
REDR Range-M Range-M y y good fetch excellent from N to SE; bldg and trees to SW
RETR Range-L Crop-L y y fair terrain high to N. (could cause low est. of S winds)
RING Range-M Range-M y y good photos poor but seemed to have good fetch
SALL Pasture-H Pasture-H y y fair Urban-L to N; Lake to SW; trees all around especially dense from NW to E
SEIL Crop-L Crop-L y y good distant trees from SW to SE
SHAW Pasture-H Pasture-H y y fair Urban-L to SE; distant trees and bldgs from S to NW
SKIA Range-H Forest-L y y fair trees dense from NE to E, but representative of area
SLAP Crop-L Crop-L y y good small bldgs to N; even mix over large area - Crop-L and Range-L&M
SPEN Range-H Urban-L y y y fair low bldg nearby, trees all around; (note site not urban-L)
STIG Pasture-H Pasture-H y y fair/good dense veg in distance NW and SSW
STIL Crop-L Range-M y y fair mixture of bldgs and trees in all dir; bldg complex SE to S
STUA Pasture-H Forest-L y y fair trees dense E to SSE, but representative of area
SULP Pasture-H Pasture-H y y fair/good
TAHL Pasture-H Pasture-H y y fair trees in all dir. Especially dense from S to NW
TALI Pasture-H Pasture-H y y fair/good
TIPT Crop-L Crop-L y y good cult land in all dir
TISH Range-M Range-H y y fair/good
TULL Crop-H Pasture-H y y fair trees all around
VINI Range-L Pasture-H y y good few trees ENE; fetch good N,S,and W
WALT Crop-L Crop-L y y good fetch great from the N to E; mixture of bldgs from S to W
WASH Range-M Pasture-H y y good
WATO Crop-L Crop-L y y fair/good fetch good from S to W; distant trees and mixture of bldgs from N to E
WAUR Range-L Range-L y y y good town close to W. but N,S & E mostly clr.;(note site not urban-L but low bldgs to N, S-SW))
WEAT Crop-L Crop-L y y excellent
WEBB Crop-L Pasture-H y y fair/good mixture of cult fields and short brush in all dir
WEST Forest-H Pasture-H y y fair/good
WILB Pasture-H Forest-H y y fair/good Forest-H significant to N and S in 10 km region; dense veg to SW
WIST Pasture-H Pasture-H y y fair/good
WOOD Range-M Range-L y y good distant trees to the N; fetch good S to NW
WYNO Pasture-H Range-H y y good
LULC = Landuse/lancover classification grid
Pan. Pics = Panorama pictures (of Mesonet sites)
DOQ = digital ortho-photo quads (aerial photos)
note 3: CLAY shown as "Urban-H" based on LULC: this is clearly not correct, so prevailing LU class in E to S directions ("Pasture-H") was used for site class
Appendix B. Figures
Figure 1.: Wind Power Density (10 meter) output for Hobart area
Figure 2.: Merged Digital Elevation Model data for state.
Figure 5.: OWPAI web-site home page
Appendix C. Tutorials
Lesson Number 1. in an Oklahoma Wind Power Tutorial Series
By Tim Hughes, Environmental Verification and Analysis Center, The University of Oklahoma
Calculation of Wind Energy and Power
Calculating the energy (and later power) available in the wind relies on knowledge of basic
geometry and the physics behind kinetic energy. The kinetic energy (KE) of an object (or
collection of objects) with total mass M and velocity V is given by the expression:
KE = ½ * M * V2 (1)
Now, for purposes of finding the kinetic energy of
moving air molecules (i.e.:wind), let's say one has
a large air parcel with the shape of a huge hockey puck:
that is, it has the geometry of a collection of air molecules
passing though the plane of a wind turbine's blades (which
sweep out a cross-sectional area A), with thickness (D)
passing through the plane over a given time. A Air flow
The volume (Vol) of this parcel is determined
by the parcel's area multiplied by its thickness:
Vol = A * D
Let ρ (the greek letter 'rho') represent the density
of the air in this parcel. Note that density is mass D
per volume and is expressed as:
ρ = M / Vol
and a little algebra gives: M = ρ * Vol
Now let's consider how the velocity (V) of our air parcel can be expressed. If a time T is required
for this parcel (of thickness D) to move through the plane of the wind turbine blades, then the
parcel's velocity can be expressed as V = D / T, and a little algebra gives D = V * T.
Let's make some substitutions in expression no. 1 ( KE = ½ * M * V2 )
Substitute for M ( = ρ * Vol ) to obtain: KE = ρ
½ * (ρ * Vol) * V2
And Vol can be replaced by A * D to give: KE = ρ
½ * (ρ * A * D) * V2
And D can be replaced by V * T to give: KE = ρ
½ * (ρ * A * V * T) * V2
Leaving us with: KE = ½ * ρ * V3 * A * T
Now, power is just energy divided by time, so the power available from our air parcel can be
Pwr = KE / T = (½ * ρ * V3 * A * T) / T = ½ * ρ * V3 * A
And if we divide Pwr by the cross-sectional area (A) of the parcel, then we are left with the
Pwr / A = ½ * ρ * V3
Note two important things about this expression: one is that the power is proportional to the cube of
the wind speed. The other is that by dividing power by the area, we have an expression on the right
that is independent of the size of a wind turbine's rotor. In other words, Pwr/A only depends on (1)
the density of the air and (2) the wind speed. In fact, there is no dependence on size, efficiency or
other characteristics of wind turbines when determining Pwr/A.
The term for the quotient Pwr/A is called the "Wind Power Density" (WPD) and has units of
watts/m2. WPD will be discussed more in later lessons.
An excellent treatment of Wind Power Density and other wind power related concepts can be found
at this web location: http://www.windpower.org/tour/wres/enerwind.htm
1. For a wind turbine with rotor diameter 43 meters (a typical size for a 600 kW turbine), calculate
the volume and mass of a 1 meter thick parcel of air passing through the plane of the turbine
blades (for this exercise, assume a value for the air density of 1.225 kg/m3).
2. Assume there is a wind blowing with a constant velocity V of 10.0 m/s through the blades of the
turbine described in no. 1. What is the wind power density? (again, assume ρ = 1.225 kg/m3)
3. A smaller wind turbine which produces about 50kW has a blade rotor radius of 7 m. (14 m.
diameter). Calculate WPD for this wind turbine, assuming the same conditions as given in
Exercises 1 and 2.
Future lessons will cover these topics.
• Air Density
• Determining Estimates of Wind Power Density (WPD) and Wind Power Classes
• Determining More Accurate Estimates of Wind Power Density
• How Turbine Differences Affect Power Output
Answers to sample exercises
1.) Ans.: the volume of this disk of air equals its cross-sectional area A ( = ¼ PI * Diameter2 ) times
the disk's depth (D): Vol = ¼ * 3.1416 * (43 m)2 * 1 m = 1451 m3.
And its mass equals the volume of air times air density:
M = ρ * Vol = 1.225 kg/m3 * 1451 m3 = 1780 kg. (or ~3900 pounds - about
the weight of an intermediate car!)
2.) Ans.: WPD = Pwr / A = ½ * ρ * V3
= ½ * 1.225 kg/m3 * (10.0 m/s)3
= 613 watts/m2 (this is an excellent value for WPD, as will be
discovered in future lessons)
3.) Ans.: Easy! Remember that WPD is independent of turbine type or size, and only depends on
wind speed and density. Hence, the WPD is still 613 watts/m2, the same as calculated in exercise
Lesson Number 2. in an Oklahoma Wind Power Tutorial Series
By Tim Hughes, Environmental Verification and Analysis Center, The University of Oklahoma
Estimating and Calculating Air Density
As determined in Lesson 1., the wind power density term is directly proportional to air density:
WPD = ½ * ρ * V3
where the greek letter ρ stands for air density, which
is defined by:
ρ = M / Vol
Air density can be defined as the mass of a given
sample of air, divided by that sample's volume.
Example: for the cube on the right, measuring 1 meter
on each side, the density of the air inside would be 1 m.
determined by its mass divided by 1 cubic meter (or
per m3). At sea level and under standard conditions
(temperature of 25 degrees C and pressure = 1
atmosphere), the mass of air in this cube would be
1.225 kg, so the density of the air is 1.225 kg per m3.
Air density can be approximated or calculated in the following ways.
Approximating air density.
Method 1. If your area of study is close to sea level, and is in a region with moderate climate, using
the value of 1.225 kg./m3 will not introduce a huge error into your estimate of WPD. This is
especially so if you are looking at annual average values, so that changes in air density due to
seasonal changes in air temperature are averaged over time and a variety of conditions.
Method 2. However, if the area is much above sea level, or if you just want to be more precise, you
can use this approximation to account for elevation change:
ρ = 1.225 - (1.194 * 10-4) * z (z=the location's elevation above sea level in meters)
This approximates the U.S. Standard Atmosphere profile for air density and will give a good long-
term average value of air density in temperate areas.
Calculating air density more exactly.
Method 3. If your region of interest has a less temperate climate (i.e.: it is either quite hot or quite
cold most of the time), or if you are interested in seasonal estimates of wind power density, the
following expression gives a much better value for air density.
ρ = P / RT (kg/m3) where P = air pressure (in units of Pascals or Newtons/m2)
R = the specific gas constant (287 J kg-1 Kelvin-1)
T = air temperature in degrees Kelvin (deg. C + 273)
Method 4. While air temperature data is not too hard to come by (and is relatively cheap to measure
even if you don't have data already), air pressure data can be tougher to find. If air pressure data
for your region is unobtainable, you can estimate density as just a function of site elevation and
temperature with the following expression.
ρ = (Po / RT) exp(-g*z/RT) (kg/m3)
where Po = standard sea level atmospheric pressure (101,325 Pascals) [or you can use a sea-
level adjusted pressure reading from a nearby weather station];
g= the gravitational constant (9.8 m/s2); and
z= the region's elevation above sea level (in meters)
Another treatment of air density as it relates to wind power can be found at this web site:
1. If you were responsible for calculating the annual average WPD for a potential wind farm to go
offshore in the Gulf of Mexico, but weather data was scarce, which method would you use to
estimate air density? Why?
2. The Oklahoma Mesonet is a statewide network of 115 weather stations. The station with the
lowest elevation is near Idabel in southeast OK and is about 110 m above sea level. The highest
station is near Kenton (near the Black Mesa in the far west part of the panhandle) and is at 1322 m.
If the annual average temperature is about the same in both areas (this is far from the case, but this
exercise is meant to just illustrate the dependence of WPD on air density alone):
a.) what would be the ratio of Kenton's air density to that of Idabel, on average?
b.) If both sites had the same winds (again, this is far from the case), what is the
ratio (as a percentage) in WPD for these two areas, based on air density alone?
3. For Kenton, typical values for daily average pressure and temperature in the coldest month of
winter are about 101392 Pascals and 11 degrees Celsius, and those for the hottest month of summer
are about 101935 Pascals and 32 degrees Celsius. Determine the ratio of air densities and WPD
under these two sets of conditions (again assuming the wind data is the same).
Answers to sample exercises
1. Ans.: Because the climate is moderate and conditions will average to close to standard
conditions over a year, and because the site will be at sea level, using Method 1.(assume
ρ = 1.225 kg/m3) will suffice.
2. Ans.: a.) Since we are told to assume the same temperature conditions, Method 2. will suffice
for reasonable estimates of air density.
For Idabel: ρ = 1.225 - (1.194*10-4 * 110 m.) = 1.212 kg/m3
For Kenton: ρ = 1.225 - (1.194*10-4 * 1322 m.) = 1.067 kg/m3
The air density at Kenton would be 1.067/1.212 = 88.0% of that at Idabel.
b.) Since WPD is dependent only on wind and air density, and we are told to assume the wind
conditions are the same at both sites, the ratio of WPD at Kenton to that at Idabel will be the
same as the ratios of air density, or 88.0 %.
3. Ans.: In the coldest months, Kenton will have air density values around:
ρcold = P / RT = (101392 N/m2) / [(287 J K-1 kg-1) * (273K + 11 K) ] = 1.244 kg/m3
In the warmest months:
ρwarm = P / RT = (101935 N/m2) / [(287 J K-1 kg-1) * (273K + 32 K) ] = 1.165 kg/m3
Since we are to assume the wind conditions are identical, all terms except the air density cancel
when we take the ratio of WPD (cold season) to WPD (warm season):
WPDcold / WPDwarm = ρcold / ρwarm = 1.244 kg/m3 / 1.165 kg/m3 ~ 1.07
Hence, cold weather could mean higher WPD values by about 7%, assuming the winds blow
the same (there are of course seasonal differences, but this will be covered in later lessons).
Lesson Number 3. in an Oklahoma Windpower Tutorial Series
By Tim Hughes, Environmental Verification and Analysis Center, The University of Oklahoma
Determining Wind Power Density and Wind Power Classes From Wind Speed
Review: In lessons 1. and 2. we determined this expression for wind power density:
WPD = ½ * ρ * V3 (1)
And that air density can be determined to varying degrees of accuracy with the following.
1.) ρ = 1.225 kg/m3 (constant value based on U.S. Std. Atmosphere, at sea level)
2.) ρ = 1.225 - (1.194 * 10-4) * z (z=the location's elevation above sea level in m.)
3.) If you have pressure and temperature data:
ρ = P / RT (kg/m3)
where P = air pressure (in units of Pascals or Newtons/m2)
R = the specific gas constant (287 J kg-1 Kelvin-1)
T = air temperature in degrees Kelvin (deg. C + 273)
4.) If you have temperature data but not pressure data:
ρ = (Po / RT) * exp(-g*z/RT) (kg/m3)
where Po = std. sea level atmospheric pressure (101,325 Pascals)
g = the gravitational constant (9.8 m/s2); and
z = the region's elevation above sea level (in meters)
New lesson: Note that the expression (1) for WPD is a simplification that held for our example in
Tutorial 1 because we made the tacit assumption that the wind blew with speed V all the time. In
reality, varying winds mean we must work a little harder to find the true WPD. To get the most
accurate estimate for Wind Power Density, one must actually perform a summation using data taken
over time, as follows.
WPD = 0.5 * 1/n * ρ
Σ (ρj * Vj3 ) (2)
where n is the number of wind speed readings and ρj and Vj are the jth (1st, 2nd, 3rd, etc.) readings of
the air density and wind speed.
Since air density ρ and wind speed V will change with every data reading, the most accurate result
would entail a calculation for every data interval. For example, to calculate the best possible value
for WPD for an Oklahoma Mesonet weather station location for a year, one would need to perform
calculations for ρ and V for 105,120 data intervals! (288 observations per day times 365 days per
year). Clearly this must involve running computer programs and is one method by which the
Oklahoma Wind Power Assessment Initiative will assess WPD for our state.
Fortunately, there are two ways to get reasonable estimates for WPD without doing all the
calculations described above:
Method 1.) The best way to approximate WPD uses the results of a wind speed frequency
distribution (this is like a histogram - a sample table of wind speed frequency occurrence from a
Mesonet weather station will be shown in the next tutorial). Using such distribution information,
the following summation can be applied:
WPD = 0.5 * Σ [ρ * (median V3 in class j) * (frequency of occurrence in class j )]
If one uses a value for air density that does not change over time (like values from methods no. 1 or
2) , then air density can come out of the expression to give:
WPD = 0.5 * ρ * Σ [ (median V3 in class j) * (frequency of occurrence in class j) ]
By using wind summary products that are available from the Oklahoma Mesonet, one can get a
reasonable estimate of WPD that does not involve hundreds of thousands of calculations. In fact, it
can be done by summing only about 8 terms in the above expression. This method will be covered
in more detail in the following tutorial (no. 4.)
Method 2.) For the rest of this tutorial, we will focus on a simpler method by which you can
estimate the WPD in your area of interest.
If one makes an assumption about how wind speeds are distributed in the wind speed frequency
diagram, one can approximate WPD with the following:
WPD = 0.5 * K * ρ * (mean wind speed)3 (3)
where K = a value determined by the shape of the distribution pattern that the wind speeds
For example, some wind speed
frequency patterns follow a "Rayleigh" Sample Rayleigh
distribution, which looks like the figure Distribution
to the right. For some wind speed
distributions, K will have the value
1.91. Inserting that into the expression
WPD = 0.955 * ρ* (mean wind speed)3
Now, all that is needed is knowledge of your nearest weather station's mean wind speed and your
elevation (to use method 2. for estimating air density) and you can find a reasonable estimate for
WPD in your area.
An important note: it may be tempting to simply take the mean wind speed for your area and
plug that in for V in the expression WPD = ½ * ρ * V3, but this will give you an erroneous
value. This is basically because the mean of the cubes of wind velocities will almost always
be greater than (mean wind speed)3. That's a mouthful. To understand this better, do
exercises 1 and 2 below.
Determining "Wind Classes"
Areas are often described by their "wind class" ranking, rather then their range of Wind Power
Densities or mean wind speeds. Below is a table that shows the ranges of WPD and associated
classes, at 10 meters height above ground (a typical wind speed measurement height and small
turbine height) and at 50 meters (the industry standard level for WPD determinations for large wind
turbines). Note that it also gives the ranges of mean wind speed for each class.
10 m (33 ft) 50 m (164 ft)
Wind Wind Mean Speed Wind Mean Speed
Power Power range (b) Power range (b)
Class Density Density
(W/m^2) m/s (mph) (W/m^2) m/s (mph)
1 <100 <4.4 (9.8) <200 <5.6(12.5)
2 100 - 150 4.4 (9.8)/5.1 (11.5) 200 - 300 5.6 (12.5)/6.4 (14
3 150 - 200 5.1 (11.5)/5.6 (12.5) 300 - 400 6.4 (14.3)/7.0 (15
4 200 - 250 5.6 (12.5)/6.0 (13.4) 400 - 500 7.0 (15.7)/7.5 (16
5 250 - 300 6.0 (13.4)/6.4 (14.3) 500 - 600 7.5 (16.8)/8.0 (17
6 300 - 400 6.4 (14.3)/7.0 (15.7) 600 - 700 8.0 (17.9)/8.8 (19
7 >400 >7.0 (15.7) >800 >8.8 (19.7)
(a) Vertical extrapolation of wind speed based on the 1/7 power law
(b) Mean wind speed is based on the Rayleigh speed distribution of equivalent
wind power density. Wind speed is for standard sea-level conditions. To maintain
the same power density, mean wind speed must increase 3%/1000 m (5%/5000 ft)
of elevation. (from the Battelle Wind Energy Resource Atlas)
This table and more explanation of wind power classes can be found on the web pages of the
American Wind Energy Association (www.awea.org/faq/basicwr.html).
1.) Imagine that you have just 2 readings of wind speed: 5 m/s and 15 m/s. Calculate the WPD
over the interval of these readings (assume ρ = 1.0 kg/m3 to make the math easier).
2.) Calculate the mean of the two readings given in exercise 1, plug that value into expression (1),
and compare to your answer above to see the error in WPD that would result.
3.) Let's say you are interested in buying and installing a small wind turbine, and that you live near
the Cheyenne Mesonet station (CHEY). But first you want to get some idea of the WPD (at 10
meters). Given that CHEY's elevation is 692 m. and its long term mean wind speed (at 10
meters) is 5.8 m/s, estimate the WPD in that area (assume a Rayleigh distribution for the winds,
with K=1.91). What wind class does this represent?
Answers to sample exercises
1.) Ans. If you do this the correct way (using expression 2), you will have:
WPD = 0.5 * 1/2 * Σ (ρj * Vj3 ) = 0.25 * [(1.0 * 53) + (1.0 * 153)] = 875 W/m2
2.) Ans. While if you mistakenly plug the mean wind speed (the mean of 5 m/s and 15 m/s is 10
m/s) into expression 1, you will get:
WPD = ½ * ρ * V3 = ½ * 1.0 * (10)3 = 500 W/m2
Clearly, the incorrect method gives a much lower value of WPD, and this will almost always be the
This is another illustration of the significance of the dependence of WPD on the cube of the wind
speeds. The more wind speeds that fall into the high end of the wind speed frequency distribution,
the higher your value of WPD will be.
3.) Ans. Use expression 3: WPD = 0.5 * K * ρ * (mean wind speed)3, where K = 1.91.
First calculate air density using the adjustment for elevation only, since you have no other data.
ρ = 1.225 - (1.194 * 10-4) * z = 1.225 - (1.194*10-4) * 692 m = 1.091 kg/m3
Then WPD = 0.5 * 1.91 * (1.091 kg/m3) * (5.8 m/s)3 = 203 W/m2.
Looking at the table above, you will see that wind class 4 at 10 meters has WPD values between
200 and 250 W/m2. Therefore, your area near Cheyenne is wind class 4. This is a very good wind
resource, by the way.
A note for later study:
The assumption that your wind resource is close to that of the Cheyenne Mesonet site depends
heavily on your elevation and vegetation being similar to that of the site. That is, if you live in a
valley (a relative low spot) while the site is at a relative high spot (as CHEY is), and/or if your site
is surrounded by trees or other heavy vegetation (while CHEY is clear), your wind resource will not
be as good. The dependence of wind resource on elevation and vegetation will be covered in later
You can learn more about the terrain and vegetation around Cheyenne and other mesonet sites by
visiting the OK Mesonet web site: http://okmesonet.ocs.ou.edu/siteinfo/
Vers. Date: 11/13/00
Appendix D. Letters