Subcontract Report

NREL/SR-550-46764

December 2009

Development of Eastern 

Regional Wind Resource and 

Wind Plant Output Datasets

March 3, 2008 — March 31, 2010

Michael Brower

AWS Truewind, LLC

Albany, New York
National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393

303-275-3000 • www.nrel.gov

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

Office of Energy Efficiency and Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

Contract No. DE-AC36-08-GO28308

Subcontract Report

NREL/SR-550-46764

December 2009

Development of Eastern 

Regional Wind Resource and 

Wind Plant Output Datasets

March 3, 2008 — March 31, 2010

Michael Brower

AWS Truewind, LLC

Albany, New York

NREL Technical Monitor: David Corbus

Prepared under Subcontract No. ACO-8-88500-01
NOTICE

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recommendation, or favoring by the United States government or any agency thereof. The views and 

opinions of authors expressed herein do not necessarily state or reflect those of the United States 

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Table of Contents

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

2 Develop Wind Resource Datasets Based on Mesoscale Modeling ..................................... 2

2.1 Mesoscale Model Validation........................................................................................ 2

2.2 Mesoscale Runs............................................................................................................ 9

3 Selection of Wind Sites...................................................................................................... 12

3.1 Onshore Sites.............................................................................................................. 12

3.2 Offshore Sites............................................................................................................. 17

4 Generate Wind Plant Output.............................................................................................. 18

4.1 Conversion Procedure................................................................................................. 18

4.2 Validation ................................................................................................................... 21

5 Forecasts ............................................................................................................................ 23

5.1 Forecast Synthesis Procedure ..................................................................................... 23

5.2 Validation ................................................................................................................... 24

5.3 Adjustments to Forecasts............................................................................................ 27

6 One-Minute Output............................................................................................................ 27

7 Conclusions........................................................................................................................ 29
1

Development of Eastern Regional Wind Resource 

and Wind Plant Output Datasets

Subcontract No. ACO-8-88500-01

Final Report

1 Introduction

In March 2008, AWS Truewind was engaged by the National Renewable Energy Laboratory 

(NREL) to develop a set of wind resource and plant output data for the eastern United States. 

(The study region is shown in Figure 1.1.) The objective of this project was to provide wind 

resource inputs to the Eastern Wind Integration and Transmission Study (EWITS), an NREL-led 

effort to evaluate the potential impacts on the electric power system of the deployment of wind 

power plants meeting up to 30% of retail electric energy sales in a region spanning a large part of 

the Eastern Interconnect. To carry out this study, NREL required a set of data that would capture 

in a realistic fashion both the temporal and spatial variability of the wind resource and associated 

wind power generation of onshore and offshore projects totaling 300 GW. These data were to be 

based on high-resolution simulations of the historical climate performed by a mesoscale 

numerical weather prediction (NWP) model covering 2004 to 2006. 

Figure 1.1. Eastern Wind Integration and Transmission Study (EWITS) region.

AWS Truewind performed this work over a period of nine months from March 2008 to 

November 2008. The work was divided into the following five technical tasks: 

• Develop wind resource datasets based on mesoscale modeling

• Participate in the selection of wind sites
2

• Generate wind plant output 

• Simulate forecasts

• Simulate one-minute samples of wind generation

This document presents AWS Truewind’s final technical report on the methods used and results 

achieved for each of these tasks. A separate validation report is provided as an appendix.

2 Develop Wind Resource Datasets Based on Mesoscale Modeling

This task was divided into two subtasks: validation of the mesoscale model and selection of the 

“best” model configuration, and the mesoscale modeling (main runs). 

2.1 Mesoscale Model Validation

The objective of the validation portion of this task was to determine the best mesoscale model 

and model configuration for performing the main runs. This was achieved by testing a number of 

different configurations covering one full year of simulations and comparing the results with 

wind measurements from ten tall towers in the region. 

2.1.1 Validation Sites and Method

A preliminary list of seven tall towers with good data recovery in the three study years was 

identified by AWS Truewind. Data for the towers were extracted from AWS Truewind’s 

archives and time-matched. Twenty-seven periods of approximately two weeks each in which 

data recovery was high at all seven towers were identified. Subsequently, at NREL’s request, 

three additional towers (including one offshore) were chosen to provide better geographic 

coverage, bringing the total number to ten. For the offshore tower, data were not available for all 

three years, so a single year was used. Table 2.1 lists the periods.

The ten validation towers are located in the following general areas:

Maine (inland forested ridge top)

New York (upstate, mixed forest/cropland, complex terrain)

Kentucky (eastern mixed open/forested mountaintop)

West Virginia (mixed open/forested ridgeline)

Texas (open mesa)

Indiana (northwest, rolling hills, cropland)

Minnesota (south-central, open, flat)

North Dakota (eastern escarpment, open)

Kansas (west-central, open)

Offshore (Atlantic)

As agreed with NREL, no further information about these masts will be provided to protect the 

confidential client information.
3

Table 2.1. Dates of simulations for the ten validation sites. Each simulation was conducted from 0000 

Coordinated Universal Time (UTC) of the first day to 2359 UTC of the last day.

Sites Dates

NY, WV, IN, KY, 

SD, KS, ME, TX, 

MN

20040114-20040128

20040128-20040211

20040311-20040325

20040423-20040507

20040618-20040630

20040811-20040825

20041006-20041014

20041014-20041028

20040825-20040908

20041028-20041111

20041223-20041231

20050225-20050311

20050325-20050409

20050507-20050521

20050604-20050618

20050714-20050728

20050728-20050811

20050908-20050922

20051125-20051209

20051209-20051223

20051231-20060114

20060211-20060225

20060409-20060423

20060521-20060604

20050922-20051006

20060630-20060714

20061211-20061225

OFFSHORE 

ATLANTIC

20031231-20040114

20040114-20040128

20040128-20040211

20040211-20040225

20040225-20040311

20040311-20040325

20040325-20040408

20040408-20040422

20040422-20040506

20040506-20040520

20040520-20040603

20040603-20040617

20040617-20040701

20040701-20040715

20040715-20040729

20040729-20040812

20040812-20040826

20040826-20040909

20040909-20040923

20040923-20041007

20041007-20041021

20041021-20041104

20041104-20041118

20041118-20041202

20041202-20041216

20041216-20041230

Two models were selected for testing: the Mesoscale Atmospheric Simulation System (MASS), 

a proprietary model developed by AWS Truewind partner MESO, Inc., and the Weather 

Research and Forecasting (WRF) model, a community weather model. Each model was run in a 

variety of configurations, which, depending on the model, included different turbulence 

parameterization schemes, numbers of vertical levels, data sources for initialization of the model 

runs, and data sources for assimilation. 

Table 2.2 lists the configurations that were tested. Unless otherwise noted, the MASS runs 

employed 25 vertical levels, assimilated rawinsonde data every 12 hours, and used a turbulent 

kinetic energy (TKE) planetary boundary layer (PBL) scheme and one-way nesting from coarser 

to finer grids. The WRF runs employed 28 vertical levels and used the Yonsei University (YSU)

PBL scheme and two-way interactive nesting between grids. All runs employed a horizontal grid 

resolution of 2 km for the innermost grid. The runs were initialized either by the NCEP/NCAR 

Global Reanalysis (NNGR) dataset or by the North American Regional Reanalysis (NARR) 

dataset. “Sfc” refers to the assimilation of surface data. MYJ refers to the Mellor-Yamada-Janjic 

PBL scheme. Further information about these configurations can be found in the validation 

report in the Appendix.
4

Table 2.2 Validation experiments. 

Experiment Model Gridded Data, Res Other

1. MASS/NNGR MASS 6.8 NNGR, 190 km

2. MASS/NARR NARR, 32 km

3. MASS/NNGR/sfc NNGR, 190 km Surface data

4. MASS/NNGR/35 levels NNGR, 190 km 35 vertical levels

5. WRF/NARR WRF 2.2.1 NARR, 32 km

6. WF/NNGR NNGR, 190 km

7. WRF/NARR/MYJ NARR, 32 km MYJ PBL scheme

2.1.2 Results

AWS Truewind compared the simulated and observed 80 m wind speeds for each tower and 

model configuration. (Hours for which the observed data were missing or invalid were excluded 

from the comparison.) For the simulations, the values were hourly samples extracted from the 

runs, i.e., the state of the model was extracted at the top of each hour. For the observations, the 

values were hourly averages. For the purposes of this study, this distinction has no practical 

impact. 

To give the reader a sense of the diversity of results, Figures 2.1-2.3 present graphical 

comparisons of the diurnal and seasonal patterns for one of the configurations 

(MASS/NNGR/sfc) at three locations (Kansas, Maine, Minnesota). The red line on each chart is the 

observed average for that hour of the day or for that month. The green line is the raw model speed 

averaged for the hour or month. The blue line is the model speed scaled by a constant multiplicative 

factor so that the model average over all records equals the observed average. This last represents

the scaling that will be done to match the expected average wind speed at a wind project site. 

It was observed that at most sites all model configurations tended to underestimate the wind 

speed. The main reason for this is that the spatial resolution of 2 km was not adequate to fully 

resolve the elevated features on which most of the masts are located. In addition, it was noted 

that at several sites the models (after scaling) tended to overestimate the wind speed at night and 

underestimate it during the day (especially visible in Figure 2.2). This may reflect limitations of 

PBL schemes, which have difficulty accurately simulating the nocturnal boundary layer. 

Somewhat surprisingly, this problem seems insensitive to the choice of PBL scheme. (The 

experiments tested three: the MASS TKE scheme and two WRF schemes.)

At NREL’s request, AWS Truewind investigated certain features observed in the comparison 

charts: (i) It was observed that the relative model speeds of the sites in Kentucky and Maine were 

similar, although it would normally be expected that the Maine site would be windier. However, 

this was explained by the fact that the Maine site is at a much lower elevation than the Kentucky 

site. (ii) A marked dip in the wind speeds at some sites at 1200 UTC, rather than 1300 UTC as 

expected based on the timing of the rawinsonde assimilation, was ascribed to an error in the 

AWS Truewind data assimilation script. This was later corrected in the main runs. (iii) Although 

the modeled and observed monthly patterns were quite similar at most towers, there was a 
5

Figure 2.1. Comparison of diurnal (top) and monthly (bottom) patterns of average wind speed at the 

Kansas tower for the MASS/NNGR/sfc configuration.
6

Figure 2.2. Same as Figure 2.1 for the Maine tower.
7

Figure 2.3. Same as Figure 2.1 for the Minnesota tower.
8

greater-than-normal discrepancy in some months at the Minnesota tower for all model 

configurations. AWS Truewind found no error in the data or analytical procedures, and also 

found a similar discrepancy at the nearest Automated Surface Observing Systems (ASOS)

station. It was concluded that certain weeks in the validation data sample were poorly forecast at 

this location, and that the discrepancy would diminish with a larger sample of data. 

In addition to making such qualitative comparisons, AWS Truewind calculated mean error (or 

bias) and mean absolute error (MAE) statistics for each tower and model configuration. It was 

primarily on the basis of the MAE for the scaled model speeds that the best configuration was 

chosen. Figure 2.4 compares the annual error statistics for all the configurations.

Figure 2.4. Mean error (top) and mean absolute error (bottom) over all simulations for each tower 

location and for all locations.

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

NY WV IN KY SD MA KS ME TX MN Mean 

Mean Error (m/s)

MASS/NNGR

MASS/NARR

MASS/NNGR/sfc

MASS/NNGR/35 levels

WRF/NARR

WRF/NNGR

WRF/NARR/MYJ

0

0.5

1

1.5

2

2.5

3

NY WV IN KY SD MA KS ME TX MN Mean 

Mean Absolute Error (m/s)

MASS/NNGR

MASS/NARR

MASS/NNGR/sfc

MASS/NNGR/35 levels

WRF/NARR

WRF/NNGR

WRF/NARR/MYJ
9

It is evident from these charts that while some model configurations performed relatively poorly 

at some sites (e.g., WRF/NNGR in South Dakota, Kansas, and Texas), most configurations 

exhibited similar errors at most sites. This reflects the underlying similarity of the models, which 

solve the same physical equations. It also suggests that most errors are due not to inadequacies in 

the model equations (e.g., turbulence parameterization) but to an insufficient amount of 

observational data available for initializing and updating the simulations.

2.1.3 Findings

By a small margin, it was found that the best configuration was MASS using NNGR as the 

initializing dataset and assimilating both surface data and rawinsonde data (i.e., 

MASS/NNGR/sfc).

MASS initiated with NARR data produced substantially worse results. It is suspected that this is 

because of an inconsistency in the definitions of land surface properties between the MASS 

model and the Eta model used to produce the NARR data. MASS with 35 levels did not 

significantly improve on MASS with the standard 25 levels, and was in some cases worse. WRF 

with NARR performed better than WRF with NNGR, and almost as well as MASS with NNGR. 

Little difference was observed in the quality of the results between the MYJ and YSU PBL 

schemes. Unfortunately, given the tight schedule for this project, it was not possible to configure 

WRF to assimilate rawinsonde and surface observations, as MASS is able to do. In addition, it 

was noted that the WRF model is not able to use more than four cores on a single eight-core 

workstation, thus making it effectively much slower than MASS. Using WRF would have 

therefore entailed significant additional delays in the project schedule.

2.2 Mesoscale Runs

The main runs were carried out over a five-month period from May to September 2008. The 

simulations covered the period 1 January 2004 to 31 December 2006. The following table 

summarizes the run configuration. 

Table 2.3. Model configuration for main runs

Model MASS v. 6.8

Initialization data source NNGR

Data to be assimilated in the course of 

simulations (30 km and 8 km grids only)

Rawinsonde, surface observations (temperature, dew 

point, wind direction and speed, pressure)

Sea-surface temperatures MODIS (Moderate Resolution Imaging Spectroradiometer)

High-resolution terrain and land cover (2 km 

grid only)

US Geological Survey National Elevation Dataset and 

Land-Use Land-Cover Dataset, both 30 m grid spacing

Cumulus scheme (30 km and 8 km grids

only)

Kain-Fritsch

Spin-up 12 hours before start of valid run

Length of run 15-16 day series (e.g., Jan 1-15, Jan 16-31) 

Frequency of data sampling Every 10 min.

Data to be stored U, V, temperature, pressure, TKE at five heights; surface 

temperature and pressure, specific humidity, incoming 

long-wave and short-wave radiation, precipitation
10

The runs employed a nested grid scheme with a horizontal resolution of 30 km for the parent grid 

and 8 km and 2 km for the child grids. The grid layout is shown in Figure 2.5. The largest black 

rectangle defines the 30-km parent grid (A). The three smaller black rectangles are the 8-km grids (B-1, 

B-2 and B-3), and the small red rectangles are the 2-km grids (C-KK).

Figure 2.5. MASS grid definitions. 

The simulations were carried out on a network of 640 cores on 80 dual-CPU quad-core Penguin 

workstations. In principle, each year should have taken about 21 days of real time on this system. 

However, inadequate cooling, network data congestion, and storage limits initially slowed 

progress to varying degrees. The cooling problem was solved with the installation of an 

additional 5-ton AC unit to supplement two existing 3-ton units. The network traffic issue was 

solved by writing more data to local scratch disks. Data storage was rectified by the installation 

of an additional 40 TB online RAID storage system to supplement the approximately 50 TB 

initially available; in addition, a high-speed tape backup system was installed to take unneeded 

data offline. 
11

Once the 2004 runs were completed, three sets of mesoscale time-series files were extracted for 

review by AWS Truewind and NREL. The three datasets consisted of: (i) grid cells near each of 

the 10 tall towers used in the validation and selection of the model configuration; (ii) 22 grid 

cells randomly selected from 11 different grids; and (iii) grid cells associated with sites near the 

three existing project sites for which data were provided by NREL. The data for sets (i) and (ii) 

were forwarded directly to NREL. The data for (iii) were used to test the power conversion 

program (described below).

After NREL had reviewed and approved the sample files, the data extraction was carried out for 

all the grid cells associated with project sites. For each grid cell, four files were produced: (i) 

surface pressure, (ii) 2-m temperature, (iii) 80-m speed, direction, air density, and TKE, and (iv) 

100-m speed, direction, air density, and TKE. Each file for 2004 (a leap year) contains 52704 

records spanning the year in 10-minute increments. The naming convention is as follows: 

GRID_XX_IIIJJJ_HHHH.TXT, where XX refers to the grid number (from one to 36, except 18, 

one of the B grids, which was mistakenly included in the grid numbering), III and JJJ to the cell 

column and row numbers, and HHHH to the height (0000M = surface pressure, 0002M = 2-m 

temperature, 0080M = 80-m speed, and 0100M = 100-m speed). An example file is provided in 

Table 2.4. These files (which for each year number close to 120,000 and occupy about 300 GB 

of disk space) were not forwarded to NREL, but may be provided at a future date.

Table 2.4. Example time-series file for the 80-m wind at a single grid cell. The first row indicates the 

latitude and longitude of the grid cell. The other rows contain the data in the following columns: (i) date 

in YYYYMMDD format, (ii) time (GMT) in HHMM format, (iii) speed in m/s, (iv) direction in degrees 

from true north, (v) air density in kg/m3

, and (vi) TKE in m2/s2.

47.02225 -68.80990

20040101 0010 4.89790 270.09622 1.25625 0.03305

20040101 0020 4.94108 268.34360 1.25527 0.02336

20040101 0030 4.81025 267.33597 1.25509 0.01175

20040101 0040 4.89001 267.15210 1.25468 0.00649

20040101 0050 4.55865 265.43286 1.25415 0.00473

20040101 0100 4.70651 265.82401 1.25480 0.00252

20040101 0110 4.84289 269.14575 1.25461 0.00214

20040101 0120 4.85045 266.78668 1.25462 0.00247

20040101 0130 4.76209 266.21219 1.25440 0.00268

20040101 0140 4.74387 263.26474 1.25424 0.00220

20040101 0150 4.89790 260.24161 1.25384 0.00246

20040101 0200 4.93185 256.34119 1.25321 0.00351

20040101 0210 4.87496 252.86868 1.25324 0.00413

The 2004 runs were completed by July 2008. With space and cooling issues resolved, the pace of 

production accelerated, and 2005 was completed in August and 2006 in September. For each 

year, a set of data files similar to that shown in Table 2.4 was extracted. 
12

3 Selection of Wind Sites

3.1 Onshore Sites

The goal of this task was to select a large number of potential wind project sites with a total rated 

capacity of at least 300 gigawatts (GW). For this purpose, a site is defined as a near-contiguous 

area meeting certain exclusion criteria (described below) and having a locally maximum wind 

resource. Initially, roughly two-thirds of the capacity was to be onshore and one-third offshore. 

After conversations with NREL, the desired rated capacity was increased and specific minimum 

targets for each state (onshore and offshore) were established. 

To provide a consistent set of resource estimates for ranking and selecting sites, a seamless map 

of predicted mean wind speeds at 80 m height for the EWITS region was prepared from AWS 

Truewind’s proprietary wind maps. The seamless map has a horizontal spatial resolution of 200 

m, which is sufficiently fine to reflect the influence of most terrain features and to identify 

specific locations for wind projects. AWS Truewind has developed a method of adjusting its 

wind maps using a wide array of wind resource measurements to ensure good accuracy. After 

considerable discussion, it was decided that AWS Truewind would use a version of its maps that 

had been adjusted using only publicly available wind data. 

A map of the estimated net capacity factor (CF) for a composite IEC Class 2 wind turbine was 

then created using the seamless wind speed map and speed-frequency distributions compiled 

from 10 years of historical mesoscale model runs previously performed by AWS Truewind at 20 

km resolution. Although IEC Class 2 turbines are not suitable for every site, the use of a single 

curve allowed for an objective ranking of resource potential. The composite power curve was 

created by taking the average of three commercial megawatt-class wind turbine power curves 

which had been normalized to their rated capacity. The normalized average curve was then 

rescaled to a rated capacity of 2 MW. The composite curve is shown in Table 3.1.

Maps of exclusion areas, including parks, wetlands, and urban areas, were created as well. The 

site screening took into account the following exclusions: 

• From USGS National Land Cover Database (2001):

- Open Water

- 200-m buffer of Developed Low Intensity 

- 500-m buffer of Developed Medium Intensity

- 500-m buffer of Developed High Intensity 

- Woody Wetlands 

- Emergent Herbaceous Wetland 

• From ESRI data base:

- Parks

- Parks Detailed

- Federal Lands (non-public)

- 10,000-ft buffer of small airports (all hub sizes) 

- 20,000-ft buffer of large airports (hub sizes; medium and large)
13

Table 3.1. Composite power curves 

for IEC Class 1, 2, and 3 turbines 

at standard air density (1.225 kg/m3

).

Speed Power (kW)

(m/s) IEC-1 IEC-2 IEC-3

0 0 0 0

1 0 0 0

2 0 0 0

3 0 0 12.6

4 39 56.6 82.4

5 136.2 176.8 204

6 280.2 347.8 378

7 474.2 574.6 621.4

8 732.6 867.8 943

9 1046.6 1213.2 1325.8

10 1404.2 1553.6 1676.6

11 1712.8 1810 1892.8

12 1911.2 1943.4 1974.2

13 1974.8 1985.2 1995.2

14 1989 1995.8 1999

15 1996.4 1999.6 1999.8

16 1998 2000 2000

17 2000 2000 2000

18 2000 2000 2000

19 2000 2000 2000

20 2000 2000 2000

21 2000 2000 2000

22 2000 2000 0

23 2000 2000 0

24 2000 2000 0

25 2000 2000 0

• Other:

- Slopes greater than 20%

- Areas outside the study region

At NREL’s request, data from the Conservation Biology Institute (CBI) were added. The 

excluded layers had GAPCAT values of 1, 2, 7, and 8, which are defined as areas managed to 

maintain a natural state or for natural values and managed for conservation to varying degrees. 

Many of the areas defined in the CBI data base coincided with exclusions already defined in the 

other data bases. In those cases, no changes in the exclusions occurred.
14

The site screening occurs in two steps. In the first, the program finds all sites with a maximum 

output in the immediate vicinity (i.e., a local maximum) with sufficient area to support a project 

of the desired 100-MW rated capacity and no closer than 2 km to any neighboring site. In the 

second step, the program allows each of these 100 MW sites to expand so long as the output does 

not decrease by more than 5%. If the site encounters another site, the site that has a higher mean 

output is retained and the other is dropped.

In the first round of site screening, over 2000 GW of sites with a maximum capacity of 2000 

MW each were selected. Upon review by NREL, it was concluded that many of the sites, 

particularly in the western part of the study region, were too large. At NREL’s request, a second 

round of site screening was done, with the maximum project size set to 1000 MW. This was also 

deemed unsatisfactory. AWS Truewind then modified its site-screening program so that it would 

select sites with a range of rated capacities, even in areas where very large sites could be 

supported. This final approach, which produced over 7800 sites totaling over 3000 GW of rated 

capacity, was deemed acceptable by NREL.

An additional concern raised by NREL was the small number of sites chosen in four states: 

Connecticut, Rhode Island, New Jersey, and Delaware. To address this concern, AWS Truewind

ran a separate site screening with a lower capacity factor threshold for those states alone, and the 

sites chosen were added to the others. In the last step, NREL manually selected a subset of the 

sites to arrive at the final site selection. This manual selection was done to ensure that there was 

enough wind capacity in different states and regions for the development a diverse set of wind 

scenarios for the EWITS analysis. The criteria used for this selection included wind capacity 

factor and geographic diversity. The goal was to have a “super set” of wind sites (579 GWs) 

from which lesser amounts of wind sites could be chosen to build the 20% and 30% wind 

scenarios.

The final site list contains 1326 sites totaling 580 GW. Figure 3.1 shows the locations of the 

sites, color-coded by predicted annual mean capacity factor, and Table 3.2 summarizes their 

distribution by state. In the Great Plains, the predicted capacity factor typically exceeds 40%, 

whereas in most eastern states it is below 30%. Figure 3.2 shows the distribution by rated 

capacity. The bulk of the sites fall between 100 MW and 600 MW in size. A small number of 

“megasites” with rated capacities exceeding 1000 MW were also chosen. All of these are in the 

Great Plains.
15

Table 3.2. State totals of onshore and offshore sites. Sizes of onshore sites 

range from 100 MW to over 1000 MW. Each offshore site is 20 MW.

Onshore Offshore

State Count Total MW Count Total MW

Arkansas 20 4049

Colorado 8 3760

Connecticut 7 919 84 1680

Delaware 7 1018 221 4420

Illinois 79 42029 99 1980

Indiana 63 32591 112 2240

Iowa 92 52575

Kansas 66 46069

Kentucky 6 1490

Maine 42 5863 64 1280

Maryland 9 1114 496 9920

Massachusetts 19 2166 1006 20120

Michigan 57 23944 788 15760

Minnesota 121 61480

Missouri 19 10138

Montana 12 5830

Nebraska 89 48471

New Hampshire 21 2371 1 20

New Jersey 8 1328 1343 26860

New Mexico 24 10524

New York 66 14859 502 10040

North Carolina 10 1999 2244 44880

North Dakota 60 32138

Ohio 34 17445 1479 29580

Oklahoma 82 40253

Pennsylvania 56 6988 257 5140

Rhode Island 7 1039 65 1300

South Dakota 91 48547

Tennessee 8 886

Texas 48 31896

Vermont 17 2019

Virginia 16 2097 1507 30140

West Virginia 18 2376

Wisconsin 44 20494 162 3240

Total 1326 580762.5 10430 208600
16

Figure 3.1. Locations of 1326 onshore sites. Light blue points show the locations of existing projects.

Figure 3.2. Distribution of plant sizes for 1326 onshore sites.

0

50

100

150

200

250

300

100200300400500600700800900

100011001200130014001500

Number of Sites

Rated Capacity (MW)

Plant Size Distribution
17

3.2 Offshore Sites

For the offshore site screening, NREL requested sites with a maximum water depth of 30 m and 

minimum distance from shore of 5 miles (8 km). In the initial round of offshore site screening, 

AWS Truewind selected all offshore areas meeting these criteria that were also outside federal or 

state protected areas and had an estimated net annual CF of at least 32%. The CF was determined 

from the mean speed at 80 m from AWS Truewind’s wind map, speed distributions from AWS 

Truewind’s historical 20-km resolution simulations, and the default composite IEC Class II wind 

turbine power curve. (The net CF is not meant to be realistic for offshore projects but was used 

for ranking purposes only.) The selected areas were forwarded to NREL in shape file (GIS) 

format.

After NREL’s review, the selected regions were converted to a list of offshore “sites.” The sites 

were presented somewhat differently from the onshore sites. Because there is so little spatial 

variation in the wind resource well offshore, rather than attempt to identify contiguous areas 

meeting a minimum capacity threshold such as 100 MW, it was decided to select points spaced 

approximately 2 km apart. Each point represents roughly a single mesoscale grid cell 4 km2 in 

area, which is assumed to be capable of supporting 20 MW of offshore wind capacity (i.e., a 

mean density of 5 MW/km2). 

The list included just over 16,000 such points. In addition to latitude and longitude and nearest 

state, each point was characterized by the predicted mean speed at 80 m and 100 m and the 

predicted net CF at 80 m height. Included in the spreadsheet was a running sum of the gigawatts 

of wind capacity associated with each state, in order of decreasing net CF, and a table in a 

separate worksheet showing the grand total for each state. 

In its review, NREL detected inconsistencies between the estimated site mean speed and net CF. 

Some sites had a lower speed than was apparently justified by the CF. AWS Truewind found that 

the wrong speed map had been used when extracting the speeds for the table. In addition, AWS 

Truewind discovered that sites closer than 5 miles (8 km) from the Atlantic coast had been 

mistakenly retained in the list. Both problems were corrected, and a new table containing about 

10,000 offshore sites was sent to NREL. The distribution by nearest state is shown in Table 3.2. 

A map of the offshore sites color-coded by CF is shown in Figure 3.3.
18

Figure 3.3. Offshore sites. 

4 Generate Wind Plant Output

4.1 Conversion Procedure

Converting the meteorological data generated by the mesoscale model to wind plant output was 

done by a program written by AWS Truewind called SynOutput. 

The program starts by reading a list of the 10 validation towers and their nearest associated grid 

cells (grid number and column and row position). It also reads a list of the grid cells associated 

with the sites in the 580-GW scenario. Up to several dozen grid cells are associated with each 

site, depending on its size and shape. For each cell, the list provides the latitude and longitude, 

expected mean speed of the part occupied by turbines, mesoscale elevation, actual mean 

elevation of the turbines, and relative proportion of the site’s total rated capacity associated with 

that cell. 1

 1 Initially there was an error in the code that truncated the proportion of the rated capacity in a grid cell to an integer. 

This caused a systematic underestimation of the total production for a given rated capacity. The error was corrected 

for the final dataset.

The mean speeds are based on AWS Truewind’s wind maps adjusted to the year of the 

simulation. Since each year was produced separately, it was not possible to scale the mean 

speeds for all three years to the expected 2004-2006 mean at each site. AWS Truewind proposed 

that its 20-km historical dataset be used to establish each year’s mean relative to the 1997-2007 

period. It is expected that this procedure will result in only minimal error. NREL accepted this 

proposal.
19

The program then imports the turbine power curves. There is one power curve for each IEC 

class. (The curves are shown in Table 2.1.) The power curves are scaled to a rated capacity of 2 

MW and are valid for the standard sea-level air density of 1.225 kg/m3

. The IEC 1 and 2 curves 

are based on a composite of three commercial turbines (GE, Vestas, and Gamesa brands). In 

consultation with NREL, it was decided to base the IEC 3 curve on just two turbines (GE 1.5xle 

and Gamesa G90) to avoid an inconsistency in the cut-out speed of the Vestas V100. In addition, 

the cut-out speed of the GE turbine was changed from 20 to 21 m/s to match that of the Gamesa 

turbine. The IEC 1 and 2 turbines are assumed to have a hub height of 80 m and the IEC 3 

turbine 100 m.

The program next reads a set of 12x24 speed matrixes, one for each of the 10 validation towers. 

These matrixes give the mean speed for each hour of the day and for each month of the year. For 

each tower there are two matrices, one for each hub height (80 m and 100 m). The program reads 

the mesoscale time series file for each of the grid cells nearest the validation towers. From the 

speed data, it creates a 12x24 mean speed matrix for each hub height. The ratio between the 

average observed speed and the average simulated speed is then calculated for each bin and 

normalized to an average of one. The result is an adjustment matrix which is used to correct 

model biases. Although the program calculates adjustments on a monthly basis, it was found 

during the validation phase that the monthly variation in speeds was accurately predicted by the 

model. Therefore, only an annual adjustment is actually performed.

The mesoscale time series file for each grid cell associated with a project site are then read. The 

speed data are scaled to match the expected mean speed and finally summed for all the grid cells 

associated with the site. In the sum, each cell’s speeds are weighted according to the proportion 

of the site area associated with that cell. The result is a time series of simulated wind speeds for 

the site as a whole at both 80 m and 100 m.

The program calculates a correlation coefficient (r

2

) between the simulated daily mean speeds for 

the site in question and the simulated daily mean speeds for each validation location. It then 

calculates a weighted average adjustment matrix for the site in which the weight given to the 

adjustment matrix for each validation location is proportional to its correlation coefficient. The 

program applies this blended adjustment matrix to the simulated data for the site. For example, if 

the time in question is 1300, the simulated speed is multiplied by the adjustment factor for 1300. 

The speeds for each grid cell are then adjusted for wake losses in a manner that depends on the 

simulated wind direction relative to the prevailing (most frequent) direction. The loss is given by 

( )sin ( ) max

2

w = wmin + wmax − wmin θ −θ , where wmin is the minimum loss (assumed to be 4%) 

when the wind is aligned with or opposite to the prevailing direction θmax, and wmax is the 

maximum loss (9%) when the wind is perpendicular to the prevailing direction. The loss factors

– which were arrived at by trial and error to conform with AWS Truewind’s estimates for actual 

wind projects – account both for wake losses and implicitly for other losses such as blade soiling 

that affect the efficiency of power conversion for a given free-stream speed without reducing the 

maximum output. The method does not account for sites where there is more than one prevailing 

wind direction or where the prevailing energy-producing direction differs from the most frequent 

direction. The speed is further adjusted by adding a random factor (from -1 to +1) multiplied by 

the predicted TKE. This adjustment is intended to reflect the impact of gusts on the speeds 

experienced by the turbines in the wind project. The frequency and intensity of such simulated 
20

gusts depends to a degree on time of day, as TKE is generally higher in the day, when the PBL is 

thermally unstable or neutral, than at night, when it is thermally stable.

Next, the adjusted speeds are applied to the turbine power curve for each IEC class. In the 

process, the power curve is corrected to the predicted air density. A time filter is then applied to 

mimic the effect of spatial averaging on the fluctuations of wind output over the area of a 

mesoscale grid cell. The time filter gives a weight of 90% to the predicted output at the current 

time, and divides the remaining 10% weight equally among the predicted output values of the 

previous 17 time records (i.e., about 3 hours of actual time). This approach has been found to 

reproduce the observed “variability” of wind plant output, as measured by the mean absolute 

deviation (MAD) as a function of time, with reasonable accuracy. Without such time filtering, 

the simulated plant output would tend to be more variable than the observed.

The program applies an additional power loss to account for turbine and plant availability. Based 

on data obtained by AWS Truewind for operating wind projects, the availability is assumed to 

follow a normal distribution with a mean of 94.8% and a standard deviation of 2.3%; the 

distribution is truncated at 100%. To avoid unrealistic rapid fluctuations in output, the 

availability is allowed to change at random intervals averaging only once per hour. An additional 

loss of 3% is subtracted from the output to represent electrical losses. For most sites, the total 

loss (relative to the gross production in the absence of any loss-related adjustments to speed or 

direct plant losses) is about 15%-17%. The range is from about 12% to about 20%.

For each site, the simulated speeds at both hub heights and the power output for all IEC classes 

are output to a single text file. In addition, the program selects the most appropriate IEC class 

based on the estimated maximum long-term annual average mean speed within the site, adjusted 

for air density. The power output for the selected IEC class is provided in the last column of the 

file. In this way, users of the data can simply import the last column rather than have to select 

themselves which IEC class to use. A sample text file is shown in Table 4.1.

Table 4.1. Sample plant-output data file.

SITE NUMBER: 4403 RATED CAP: 100.2 IEC CLASS: 3 LOSSES (%): 19.5 18.6 17.0

Date

(GMT)

Time

(GMT)

Speed

(80 m)

Speed

(100 m)

IEC1 

(80 m)

IEC2 

(80 m)

IEC3

(100 m)

Selected

20060101 10 3.876 4.062 1.2 1.8 3.3 3.3

20060101 20 3.982 4.314 1.4 2.0 4.2 4.2

20060101 30 4.844 5.204 4.0 5.3 8.3 8.3

20060101 40 5.549 5.929 7.2 9.2 12.8 12.8

20060101 50 5.999 6.471 9.8 12.2 17.0 17.0

20060101 100 6.242 6.785 11.2 14.0 20.0 20.0

20060101 110 6.085 6.687 10.1 12.7 18.7 18.7

20060101 120 5.653 6.315 7.9 10.0 15.9 15.9

20060101 130 4.852 5.563 4.1 5.4 10.5 10.5

20060101 140 3.870 4.592 1.6 2.2 5.9 5.9

... .... .... .... .... .... .... ....
21

4.2 Validation

NREL provided three years of 10-minute plant output data for three projects within the EWITS 

area: Lake Benton (MN), Storm Lake (IA), and Blue Canyon (OK). (A fourth project, Trent 

Mesa, TX, is outside the EWITS area and was therefore not used.) The power conversion 

program was tested using mesoscale data extracted for grid cells associated with the three 

projects, and the results were provided to NREL both in tabular form and as diurnal and change 

plots. A comparison of the 10-minute output for a typical period at Blue Canyon is provided in 

Figure 4.1. 

Figure 4.1. Comparison of simulated (SIM) and observed (OBS) 10-minute plant output at the Blue Canyon I 

project. The plot covers about 18 days.

By using an appropriate turbine power curve for each project and by adjusting the hub height of 

the simulated speeds and diurnal corrections to match the actual hub height, a reasonably close 

agreement between the predicted and observed net capacity factors and diurnal patterns was 

obtained (Figure 4.2). The mean absolute changes in output as a function of time lapse, an 

indication of the variability of output over a range of time scales, were also compared. The 

curves for the simulated and observed wind plant output follow similar trajectories (Figure 4.3).

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 5 10 15 20 25

Capacity Factor

Local Time of Day

Blue Canyon I - 65M 

MOD

OBS

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 5 10 15 20 25

Capacity Factor

Time of Day

Storm Lake - 65M - Z750

MOD

OBS
22

Figure 4.2. Comparison of simulated and observed diurnal mean wind speeds for two validation projects, Blue 

Canyon I (left) and Storm Lake (right).

Figure 4.3. Comparison of simulated and observed mean changes in plant output as a function of time lapse for Blue 

Canyon I (left) and Storm Lake (right).

Figure 4.4 compares the modeled and observed ramp frequencies (step change frequency as a 

function of step change size) for Blue Canyon I for 10-minute and 60-minute intervals. The 

profiles have a very similar shape. The importance of distinguishing between averaged and 

sampled data is illustrated by the difference between the observed 60-minute average and sample 

curves. The modeled data are intended to represent sampled plant output, which is more variable 

than output averaged over each hour.

Figure 4.4. Comparison of simulated and observed step-change frequency for Blue Canyon I for 10-minute and 60-

minute time intervals. Averaging over a 60-minute period significantly reduces the variability of plant output, as 

shown by the blue curve.

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0 50 100 150 200

Mean Absolute Deviation

Time Lapse (Minutes)

Blue Canyon I - 65M

MOD

OBS

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0 50 100 150 200

Mean Absolute Deviation

Time Lapse (Minutes)

Storm Lake - 65M - Z750

MOD

OBS
23

In reviewing the plant output files, MISO noted significant jumps at 0000 and 1200 UTC on 

many of the days. AWS Truewind concluded that the jumps were caused by the abrupt 

assimilation of rawinsonde and surface observations every 12 hours in the mesoscale runs. A 

workaround was developed, in which values spanning the affected times were replaced with 

synthesized data (Figure 4.5). Two other errors in the data processing were discovered at the 

same time, and a new set of data was produced. With these revisions, the final plant output data 

files were regenerated and delivered to NREL.

Figure 4.5. Jumps in mean speed at one site before (left) and after (right) the fix. The red curve is the mean output 

(left axis), the purple curve is the absolute change in output from one 10-minute record to the next (left axis), and the 

blue curve is the change in output (right axis).

5 Forecasts

AWS Truewind produced hourly forecasts for three different time horizons: next-day, six-hour, 

and four-hour. Each set of forecasts was synthesized by running a statistical forecast synthesis tool 

called SynForecast, which was developed by AWS Truewind. This tool uses actual forecasts and 

observed plant output to develop a set of transition probabilities. The probabilities are applied to 

simulated plant output data, stepping forward in time from a random starting point in a process 

known as a Markov chain. This process results in a synthetic forecast that imitates the statistical 

behavior of a real forecast. The procedure is described in depth in the following section.

5.1 Forecast Synthesis Procedure

The first step in the forecast synthesis process is to produce a sequence of real forecasts for one 

or more operating wind projects using a state-of-the-art wind forecasting system. It is assumed 

that these forecasts are typical or representative of what forecasts would look like at other sites in 

the region. For this purpose, AWS Truewind ran its eWind forecasting system2

 2 eWind is a commercial wind forecasting service. It takes as its main inputs weather forecasts from a mesoscale 

weather model and actual plant operating data; on-site wind observations are also often used. Over a period of 

several days or weeks, eWind builds a statistical model relating the forecasted plant output to the actual plant output. 

This model is then applied to correct the wind forecasts going forward. Over time, the model “learns” from past 

forecast errors, and its skill gradually improves. 

in “hindcast” 

mode for the four wind plants for which NREL had previously provided output data for 2004-

2006: Trent Mesa, Blue Canyon, Lake Benton, and Storm Lake. The mesoscale model feed for 
24

the forecasts was provided by 8-km resolution MASS simulations. The observed data feed was 

provided by the actual plant data up to the time each forecast was assumed to be generated. For 

the next-day forecasts, this was 5 pm local time of the day before the forecast; this effectively 

assumes the forecasts are generated once per day. For the four-hour and six-hour forecasts, the 

latest time was four hours and six hours ahead of the delivery time, which implies an hourly 

update schedule. 

Since no on-site wind measurements were available, the eWind statistical module transformed 

the mesoscale model data directly into plant output by constructing a non-linear power curve 

based on a rolling 30-day window of simulated and observed data. The forecasts were issued in 

24-hour periods from midnight to midnight of each forecast day and appended to one another to 

form a continuous time series of forecasts.

From each of these four sets of forecasts, the SynForecast program constructed a matrix of 

forecast probabilities of the following form:

( ) P At ∩ Ft−1 ∩ Ft

The probability P is the number of occurrences for which the actual output was Ai and the 

forecasted outputs were Ft-1 and Ft, where t is a particular moment in time and t-1 is the previous 

moment (one hour earlier). Before constructing this matrix, both the actual and forecasted output 

values are normalized to the rated capacity of the wind project and placed in 10 bins ranging in 

capacity factor from 0.05 to 0.95 in increments of 0.10. Both the current and previous forecasts 

are included in the probability matrix to capture the autocorrelation of forecast errors, as 

otherwise the synthesized forecasts would fluctuate randomly about the actual output in an 

unrealistic fashion.

For each wind project site, the SynForecast program selected, at random, one of the four 

transition probability matrixes. Both onshore and offshore projects made use of the same four 

matrixes; given the lack of offshore plant output data, it is not known whether forecast skill will 

be similar for offshore projects as for onshore projects. Starting with a random seed, the program 

stepped forward in time taking random draws from the transition matrix. In this manner, an 

hourly next-day forecast was synthesized. 

5.2 Validation

To verify that the program was working properly, AWS Truewind compared synthesized 

forecasts with the actual forecasts for the four validation wind projects. First, the time correlation 

of the actual and forecasted generation and the root-mean-square (RMS) forecast error were 

considered for next-day forecasts. The results of the actual and synthesized forecasts were very 

similar, as shown in Table 5.1. The RMS error depends in part on the average plant output, with 

more productive plants experiencing higher forecast errors as a fraction of rated capacity because 

they spend more time in the steeply sloping parts of their power curves.

Next, the autocorrelation of the output, the forecasts, and the forecast errors was considered. The 

autocorrelation indicates the degree to which a particular parameter tends to persist over time. A 

parameter that typically changes little would have an autocorrelation factor of nearly one, 

whereas one that fluctuates randomly would exhibit an autocorrelation factor of nearly zero. The 

following tables indicate that the observed output of the four wind projects tends to be quite 

strongly autocorrelated over a period of one to several hours. The eWind and synthesized 
25

forecasts exhibit similar degrees of autocorrelation in each case. As for the forecast errors, their 

autocorrelation is considerably lower, and the SynForecast program seems to capture the pattern 

of decreasing correlation with increasing time shift quite well. However, the synthesized 

forecasts are slightly less correlated in time than the eWind forecasts, while the synthesized

forecast errors are slightly more correlated.

Table 5.1. Comparison of correlation of forecasted and 

actual output and RMS forecast error for synthesized 

and real (eWind) forecasts.

Correlation

(Pearson r)

RMS Forecast 

Error (CF)

Plant eWind SynFcst eWind SynFcst

Trent Mesa 0.77 0.77 0.20 0.20

Blue Canyon 0.77 0.73 0.21 0.22

Storm Lake 0.79 0.81 0.16 0.16

Lake Benton 0.72 0.71 0.19 0.19

Table 5.2a. Autocorrelation of actual output, eWind forecasts, 

and synthesized forecasts and forecast errors: Trent Mesa

Time 

Shift Obs eWind SynFcst

eWind 

Error

SynFcst 

Error

0 1 1 1 1 1

1 0.93 0.94 0.92 0.78 0.81

2 0.85 0.87 0.85 0.59 0.65

3 0.77 0.80 0.79 0.46 0.51

4 0.70 0.72 0.72 0.37 0.39

5 0.63 0.65 0.66 0.30 0.30

6 0.56 0.57 0.60 0.26 0.22

7 0.50 0.50 0.55 0.22 0.15

8 0.44 0.44 0.50 0.19 0.10

Table 5.2b. Same as Table 5.2a for Blue Canyon

Time 

Shift Obs eWind SynFcst

eWind 

Error

SynFcst 

Error

0 1 1 1 1 1

1 0.93 0.95 0.92 0.77 0.82

2 0.86 0.89 0.85 0.60 0.69

3 0.80 0.83 0.79 0.48 0.57

4 0.74 0.77 0.73 0.39 0.48

5 0.68 0.71 0.68 0.33 0.40

6 0.63 0.66 0.63 0.28 0.33

7 0.58 0.61 0.59 0.24 0.27

8 0.53 0.57 0.55 0.21 0.22
26

Table 5.2c. Same as table 5.2a for Storm Lake

Time 

Shift Obs eWind SynFcst

eWind 

Error

SynFcst 

Error

0 1 1 1 1 1

1 0.92 0.94 0.91 0.73 0.76

2 0.85 0.88 0.84 0.53 0.59

3 0.78 0.83 0.78 0.41 0.46

4 0.71 0.77 0.72 0.32 0.35

5 0.65 0.72 0.66 0.26 0.27

6 0.60 0.67 0.61 0.21 0.20

7 0.54 0.62 0.56 0.18 0.15

8 0.49 0.57 0.52 0.15 0.12

Table 5.2d Same as Table 5.2a for Lake Benton

Time 

Shift Obs eWind SynFcst

eWind 

Error

SynFcst 

Error

0 1 1 1 1 1

1 0.92 0.95 0.91 0.79 0.82

2 0.84 0.89 0.85 0.62 0.68

3 0.77 0.83 0.79 0.51 0.57

4 0.70 0.77 0.73 0.42 0.47

5 0.65 0.72 0.68 0.36 0.40

6 0.59 0.66 0.63 0.32 0.34

7 0.54 0.61 0.58 0.28 0.28

8 0.49 0.57 0.54 0.24 0.23

Last, the correlation of forecast errors between projects was evaluated, particularly as a function 

of distance between them. The correct modeling of the spatial correlation of forecast errors was 

an important consideration for this study as it looks at the aggregate impact of many wind 

projects over a large region. If the synthesized forecast errors are not correlated enough between 

projects, then the aggregate forecast error will be underestimated, and therefore also the impacts 

of those errors on system operations; overestimating the degree of correlation between projects 

will have the opposite effect. The results are presented in Table 5.3. The synthesized forecasts 

exhibit a greater degree of correlation of forecast errors for a given distance, indicating a possible 

tendency to overestimate the errors when aggregated over large numbers of projects in a given 

region. However, the difference is small and the functional dependence with distance is similar.
27

Table 5.3. Correlation of forecast errors as a

function of distance between project pairs

Distance 

(km) Output

eWind 

Error

SynFcst 

Error

168 0.61 0.21 0.27

303 0.62 0.21 0.27

913 0.23 0.03 0.08

1047 0.15 0.03 0.06

1214 0.16 0.03 0.05

1342 0.11 0.04 0.03

5.3 Adjustments to Forecasts

Following the delivery of the synthesized forecasts to NREL, it was found that the difference 

between the next-day and six-hour-ahead forecast errors in the synthesized forecasts was not as 

large as that in many real forecasts. This problem was traced to the use of the same mesoscale 

model feed to generate forecasts for all time horizons. In reality, next-day forecasts would be 

based on mesoscale simulations that were run some 24-36 hours before the simulations for the 

four- and six-hour-ahead forecasts, and therefore the errors for the next-day time horizon would 

be somewhat larger. To compensate for this, AWS Truewind generated two additional sets of 

forecasts. One reduced the forecast error margin for the four- and six-hour-ahead forecasts by 

about 12% relative to the next-day forecasts; the other increased the next-day forecast error by 

the same margin relative to the four- and six-hour forecasts. The former may be regarded as 

representing the performance of an advanced forecasting system, whereas the latter represents 

current technology.

6 One-Minute Output

In the final task, AWS Truewind simulated one-minute plant output data for the onshore and 

offshore sites. The data were produced for the following time windows selected by NREL (see 

Table 5.4).

Table 5.4. Date Ranges (MM/DD/YY) 

for One-Minute Samples

Start End

1/1/05 1/15/05

4/1/05 4/16/05

7/10/05 7/16/05

10/9/05 10/22/05

1/9/06 1/15/06

5/1/06 5/7/06

8/1/06 8/1/06

10/1/06 10/7/06
28

To produce the data, AWS Truewind employed a computer program to sample four-hour 

windows of historical one-minute data from existing wind projects. The source of the samples 

was two years of one-minute plant output data provided in a previous project by ERCOT for 17 

substations serving seven Texas wind projects.3

Figure 6.1 shows a typical sample of one-minute simulated and actual plant output data for a 

single project overlaid on 10-minute data. The synthesized one-minute data display an obvious 

similarity to the actual data.

(Because of a lack of output data from offshore 

plants, the same dataset was used to simulate fluctuations at offshore sites.) The program 

removed 10-minute trends from the data using a bicubic fitting procedure, and then added the 

residuals to the simulated 10-minute output for each site. It did not allow the same window of 

residuals to be applied to two different sites in the same time period, as this would have resulted 

in perfect correlation of the one-minute fluctuations between those sites, whereas in reality oneminute fluctuations between wind projects are entirely uncorrelated. The program excluded from 

the training data one-minute changes greater than 5% of the plant rated capacity, as they 

correspond to plant outages, curtailments, and restarts unrelated to the wind. 

Figure 6.1. Sample of one-minute data overlaid on corresponding 10-minute data. The left-hand chart shows 

simulated data for a Texas site, the right-hand chart shows actual data from a Texas wind project. (Note that 

the sites and time periods are not the same.)

Figure 6.2 compares the modeled and observed frequency distributions for one-minute step 

changes for one of the plants in ERCOT dataset. It was not possible to match the site exactly to 

this project, since the ERCOT plants are to the south of the EWITS region. Thus, two EWITS 

sites are shown to illustrate the range of results to be expected in this region. The ramp 

distributions are similar. 

 3 The files provided by ERCOT contained data for 32 substations. However, 17 of these did not have valid data. Of 

the remaining 15 substations, several represented different parts of the same project. 
29

Figure 6.2 Comparison of modeled and observed step-change frequency for two EWITS sites (Site 138 and Site 

3060) to the north of a wind plant in central Texas (Obs-1). 

7 Conclusions

AWS Truewind has produced a wind plant output dataset spanning three years at 10-minute time 

resolution for over 580 GW of onshore wind projects and 208 GW of offshore wind projects in 

the eastern United States. Comparison of the data with observed plant output at three existing 

wind projects indicate that the diurnal and seasonal patterns as well as the dynamic behavior 

(ramp rates) are represented with acceptable accuracy. Hourly synthetic wind plant forecasts for 

four, six, and 24 hours ahead were also produced using a probabilistic method based on actual 

forecasts, and the autocorrelations and correlations between projects were found to be captured 

with acceptable accuracy. An adjustment was later applied to correct for an overly optimistic 

next-day forecast error margin relative to the other time horizons. Last, one-minute plant output 

data for the same onshore and offshore sites were produced for several time windows selected by 

NREL. 
Appendix

Validation Report

 

 

 

 

Development of Eastern Regional Wind Resource

and Wind Plant Output Datasets

Subcontract No. ACO-8-88500-01

Submitted by:

AWS TRUEWIND, LLC

263 NEW KARNER ROAD 

ALBANY, NEW YORK

Michael Brower

Chief Technical Officer

Tel: 978-749-9591

Fax: 978-749-9713

mbrower@awstruewind.com

July 17, 2008
1

Development of Eastern Regional Wind Resource 

and Wind Plant Output Datasets

Subcontract No. ACO-8-88500-01

Validation Report 

1. Introduction 

To make the best possible simulations for this project, AWS Truewind tested several different 

configurations of two mesoscale models to choose the model and configuration for this project. AWS 

Truewind has successfully used the MASS (Mesoscale Atmospheric Simulation System) model to 

simulate wind resources for many years. The community WRF (Weather Research and Forecasting) 

model is widely used by many groups in the United States and around the world, and AWS Truewind has 

used it for some wind forecasting applications. Each of these models was run in a few different 

configurations and the resulting 80-m wind speeds were compared to a set of 10 tall tower locations in 10 

different states. The best combination of model and configuration was chosen for the subsequent 

production simulations of the 2004-2006 period over the entire Eastern Region.

In the second phase of the validation process, AWS Truewind extracted 10-minute data from the 

mesoscale simulations in the selected configuration for points near three wind projects for which 

measured plant output data were provided by NREL. The SynOutput program was run to convert the 

mesoscale data to simulated plant output. The simulated output was then compared to the observed 

output. With adjustments for plant size, turbine hub height, and turbine power curve, a reasonably good fit 

with the observed data was obtained, and NREL approved the program for the simulation of plant data for 

all sites.

2. Validation of the Mesoscale Model 

2.1 Experimental Configurations 

High-quality tall tower data was collected from 10 sites, each in a different state within the Eastern 

Region area of interest. Nine of the 10 sites had data for the same 2004-2006 period that will be used for 

the production runs. For those nine sites, 27 two-week periods spread throughout those years were 

chosen, based on the best dates for overlapping availability of valid tower data. The other site 

(Massachusetts) had only data for 2004, so 26 two-week periods covering the entire year (except for 

December 31) were used instead. Table 1 shows the dates of the validation simulations for each site. For 

each series, the first date is a “spin-up” day – no comparisons with observations are made for those days. 

For each location, 30-km, 8-km, and 2-km grids were created that are centered over the tall tower site. 

Table 2 shows the set of seven experiments that were tested for this project. Four experiments used 

MASS and three used WRF in various configurations. AWS Truewind has generally used gridded data 

from the NCAR-NCEP Global Reanalysis (NNGR) for initial and boundary conditions for wind mapping 

simulations. In recent years, a new dataset has been tested, the North American Regional Reanalysis 

(NARR). The NARR has much higher resolution than the NNGR (32 km instead of about 190 km), and 

incorporates more data sources, which should result in better surface fields (e.g., soil moisture). Each 

model was tested with both sources of gridded data, and the source that performed best was used for 

subsequent tests.
2

Table 1. Dates of simulations for the 10 validation sites.

Sites Dates 

NY, WV, IN, KY, 

SD, KS, ME, TX, 

MN

20040114-20040128

20040128-20040211

20040311-20040325

20040423-20040507

20040618-20040630

20040811-20040825

20041006-20041014

20041014-20041028

20040825-20040908

20041028-20041111

20041223-20041231

20050225-20050311

20050325-20050409

20050507-20050521

20050604-20050618

20050714-20050728

20050728-20050811

20050908-20050922

20051125-20051209

20051209-20051223

20051231-20060114

20060211-20060225

20060409-20060423

20060521-20060604

20050922-20051006

20060630-20060714

20061211-20061225

MA 20031231-20040114

20040114-20040128

20040128-20040211

20040211-20040225

20040225-20040311

20040311-20040325

20040325-20040408

20040408-20040422

20040422-20040506

20040506-20040520

20040520-20040603

20040603-20040617

20040617-20040701

20040701-20040715

20040715-20040729

20040729-20040812

20040812-20040826

20040826-20040909

20040909-20040923

20040923-20041007

20041007-20041021

20041021-20041104

20041104-20041118

20041118-20041202

20041202-20041216

20041216-20041230

Table 2. The list of validation experiments. Unless otherwise noted, the MASS runs employed 25 

vertical levels, assimilated rawinsonde data, and used a TKE PBL scheme and one-way nesting from 

coarser to finer grids. The WRF runs employed 28 vertical levels and used the YSU PBL scheme and 

two-way interactive nesting between grids.

Experiment Model Gridded Data, Res Other 

1. MASS/NNGR MASS 6.8 NNGR, 190 km

2. MASS/NARR NARR, 32 km

3. MASS/NNGR/sfc NNGR, 190 km Surface data

4. MASS/NNGR/35 levels NNGR, 190 km 35 vertical levels

5. WRF/NARR WRF 2.2.1 NARR, 32 km

6. WF/NNGR NNGR, 190 km

7. WRF/NARR/MYJ NARR, 32 km MYJ PBL scheme

Since the NNGR is so coarse, rawinsonde data is routinely assimilated into the initial conditions for all of 

the MASS validation tests. Surface observations are sometimes used, but there is a risk that incorporating 

surface data every 12 hours during a series of simulations might disrupt higher-resolution mesoscale 

circulations that the model is creating, and there are also concerns about data quality (the possibility that 

individual erroneous observations will negatively affect one or more simulations). Experiment 

MASS/NNGR/sfc tested the effect of surface data in MASS. WRF was used in this project without 

assimilating rawinsonde or surface data by the WRF-VAR scheme, as there was not time to obtain the 

needed data and create suitable background error fields before the validation tests were scheduled to 

begin.

Compared to conventional mesoscale simulations used for weather forecasting, we generally use a modest 

number of vertical levels – 25 for MASS and 28 for WRF, with higher resolution near the ground (MASS 

has 10 vertical levels within the lowest 1 km). With these configurations, both models have enough 

resolution in the planetary boundary layer (PBL) to reasonably resolve low level jets and other 
3

phenomena important for the correct prediction of low-level wind speed. Increasing the number of 

vertical levels should improve the overall simulation of certain types of cases; for example, added 

resolution at middle and upper levels of the troposphere should better resolve complex synoptic 

circulations such as upper level jets and cloud microphysical processes. However, additional vertical 

levels increase the computational cost, an important consideration when very large numbers of 

simulations are required, as for this project. Experiment MASS/NNGR/35 levels tests the sensitivity of 

the MASS simulations to an increase from 25 to 35 vertical levels. 

One of the advantages of the WRF system is that, as a community model, it includes a wide range of 

physics options. For wind prediction, the performance of the PBL scheme is of vital importance. Both 

the Yonsei State University (YSU) and Mellor-Yamada-Janjic (MYJ) PBL schemes are popular with 

WRF users. Experiments WRF/NARR and WRF/NNGR use the YSU scheme, while Experiment 7 uses 

the MYJ. 

2.2 Results 

For each site, hourly wind speeds were extracted at 80 m above ground level from the highest-resolution 

(2 km) simulations and compared to tall tower wind speeds at 80 m. Three statistics were used to 

compare the simulated to observed wind speeds: mean error (ME), mean absolute error (MAE), and 

scaled mean absolute error (SMAE). The SMAE is the MAE of the simulated hourly wind speeds after 

each value has been scaled by the ratio of the observed to simulated mean wind speed over the entire 

period of comparison, to remove the overall bias. Since the wind speeds from the main mesoscale runs 

will be scaled in this fashion to the expected mean wind speed at each wind project site, the SMAE is 

arguably the most important measure for determining the best model configuration.

Figure 1 shows each statistic over the entire validation period for each site. On the far right of each plot, 

the values for all 10 sites are averaged together and labeled as “Mean”. In general, both models had a 

negative bias (i.e., the simulated wind speed was lower than the observed), by an average of about 1 m/s. 

The sign and magnitude of the biases at each site were generally comparable between models and 

configurations; in only a handful of cases did one or more experiments diverge sharply from the others at 

a site. The main reason for the negative bias, we believe, is that most towers are on elevated features that 

are not fully resolved at the model resolution. 

The MAE and SMAE results show that each model performed decisively better for one gridded data 

source. MASS had a SMAE of 2.00 m/s using NNGR data, but 2.18 m/s with the higher resolution 

NARR data. This is a surprising result, but we have seen problems in earlier projects running MASS with 

NARR. 

For the WRF model however, using NARR gridded data produced clearly superior results. Since 

rawinsonde and surface observations were not assimilated into the WRF runs, perhaps the 32 km 

resolution was the most important factor in improved performance. It’s also possible that the WRF 

physics schemes were able to better utilize some of the additional surface fields that come with the NARR 

dataset (e.g., several layers of soil moisture and temperature). 

The MASS/NARR and WRF/NNGR were clearly the worst-performing configurations, but the other 

eight configurations were quite similar in SMAE values. The best was the MASS/NNGR/sfc; it had a 

SMAE value of 1.97 m/s averaged over all stations. But the next three best MASS configurations were 

close behind, with SMAE values between 1.99 and 2.00 m/s, and the two best WRF configurations 

(WRF/NARR and WRF/NARR/MYJ) were also in the same range. 
4

Figure 1a. Mean error over all simulations for each tower location, along with the mean of all the towers.

 Figure 1b. Mean absolute error over all simulations for each tower location, along with the mean of all 

the towers.

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

NY WV IN KY SD MA KS ME TX MN Mean 

Mean Error (m/s)

MASS/NNGR

MASS/NARR

MASS/NNGR/sfc

MASS/NNGR/35 levels

WRF/NARR

WRF/NNGR

WRF/NARR/MYJ

0

0.5

1

1.5

2

2.5

3

NY WV IN KY SD MA KS ME TX MN Mean 

Mean Absolute Error (m/s)

MASS/NNGR

MASS/NARR

MASS/NNGR/sfc

MASS/NNGR/35 levels

WRF/NARR

WRF/NNGR

WRF/NARR/MYJ
5

 Figure 1c. Scaled mean absolute error over all simulations for each tower location, along with the mean 

of all the towers.

 

In addition to the mean biases and absolute errors of the various model configurations, the diurnal and 

monthly mean speed patterns were evaluated qualitatively. 

Appendix A contains the validation plots for the MASS/NNGR/SFC configuration, which was judged the 

best overall. The most significant discrepancies in the diurnal patterns are seen at the Maine, New York, 

Texas, and West Virginia sites. The fact that the model cannot fully resolve the elevated features on 

which these towers are located may explain its tendency to overestimate the range of daily variation in the 

mean speed in each case. (Such patterns are caused by variations in the thermal stability and mixing of the 

planetary boundary layer, which are less pronounced on sharp peaks and ridge lines.)

By and large, the monthly patterns are more accurately simulated than the diurnal patterns. At only one 

site, in Minnesota, was a substantial discrepancy observed. After further investigation, it was concluded 

that poor model forecasts for the region in several of the two-week validation periods were to blame. It is 

expected that the discrepancy will not be as large for the full three-year period.

Among the various model configurations, no systematic differences in the error patterns were detected. 

Some model configurations did better at some sites than at others, but none were found to be clearly 

superior to the others overall. In particular, WRF produced a similar monthly error pattern at the 

Minnesota site as the MASS model, implying that the problem at that location was not model-dependent.

It was concluded that the scaled mean absolute error provided a reliable indication of the relative merit of 

the different models and their respective configurations.

0

0.5

1

1.5

2

2.5

3

NY WV IN KY SD MA KS ME TX MN Mean 

Scaled Mean Absolute Error (m/s)

MASS/NNGR

MASS/NARR

MASS/NNGR/sfc

MASS/NNGR/35 levels

WRF/NARR

WRF/NNGR

WRF/NARR/MYJ
6

2.3 Selection of Best Model Configuration 

When 80-m wind speeds from the simulations were compared to tall tower data at 10 different sites, the 

best configuration overall used the MASS model initialized by NNGR gridded data, assimilating 

rawinsonde and surface data, and with 25 vertical levels (Experiment MASS/NNGR/sfc in Table 2). 

Three other MASS configurations and the two best WRF configurations were not far behind, but some of 

them have other disadvantages. The MASS runs with more vertical levels would take significantly more 

computational time, and the WRF runs do not take full advantage of the Intel quad-core architecture, 

possibly because of the use of shared cache between each set of two cores, resulting in a much lower 

overall throughput. 

Therefore, the MASS/NNGR/sfc configuration was the final choice for the production runs.

3. Validation of the Plant Output Model 

3.1 Plant Datasets 

NREL provided three years of 10-minute plant production data for four wind projects: Lake Benton, 

Minnesota; Storm Lake, Iowa; Blue Canyon, Oklahoma; and Trew Ranch, Texas. The last was not within 

the bounds of the study area, however, so only the first three were employed in the validation of the plant 

output model. 

Table 3. Wind power projects employed in the validation.

Plant Name State Rated 

Capacity

(MW)

Turbine Type Hub Height 

(m)

Blue Canyon I Oklahoma 74.25 NM72 (1.65 MW) 67 m

Lake Benton Minnesota 103.5 Zond 750 51.2 m

Storm Lake I Iowa 112.5 Zond 750 63 m

3.2 Method 

A time series of mesoscale data were extracted from all grid cells near each wind project site and near 

each validation tower. A computer program written in FORTRAN was then employed to convert the 

mesoscale data for the cells near the project sites to plant output. This program proceeds in the following 

steps:

1. The program reads a list of the sites and the nearest associated grid cells (grid number, I and J 

position). For each cell, the list includes the latitude and longitude, expected mean speed, 

mesoscale elevation, actual elevation, and proportion of the site’s total rated capacity associated 

with that cell.

2. The program imports the turbine power curves. Normally, there is one power curve for each IEC 

class. The power curves are normalized to the rated capacity, and are valid for the standard sealevel air density of 1.225 kg/m3

. The IEC 1 and 2 curves are based on a composite of three 

commercial turbines (GE, Vestas, and Gamesa). In consultation with NREL, it was decided to 

base the IEC 3 curve on just two turbines (GE 1.5xle and Gamesa G90) to remove an 

inconsistency in the cut-out speed of the Vestas V100. In addition, the cut-out speed of the GE 

turbine was changed from 20 to 21 m/s to match that of the Gamesa turbine. (As described in the 
7

next section, the Zond 750 power curve was also employed, which improved the fit to the data 

from the Lake Benton and Storm Lake projects.)

3. The program reads a set of 12x24 speed matrixes, one for each of the 10 validation towers. These 

matrixes give the mean speed for each hour of the day and for each month of the year. For each 

tower there are two matrices, one for each hub height (80 m and 100 m). (For the plant output 

validation, additional matrices were created for 50 m and 65 m to approximately match the actual 

hub heights of the turbines.)

4. The program creates a similar 12x24 speed matrix from the mesoscale speed data for the grid 

cells nearest the validation towers. The ratio between the average annual diurnal speed observed 

at each tower and that predicted by the model is then calculated. This forms an adjustment matrix, 

which is used to correct model biases (especially a tendency for the speed to be too high from 

around midnight to 5 or 6 am local time).

5. The program reads the mesoscale time series file for each grid cell associated with a project site. 

It then scales the data to match the expected mean speed from the wind map, and finally sums the 

scaled speeds for all the grid cells associated with the site. In the sum, each cell’s speeds are 

weighted according to the proportion of the site area associated with the cell. The result is a time 

series of simulated wind speeds for the site as a whole, unadjusted for diurnal model biases.

6. The program calculates a correlation coefficient (r2

) between the simulated daily mean speeds for 

the site in question and the simulated daily mean speeds for each validation location. It then 

calculates a weighted average adjustment matrix for the site, in which the weight given to the 

adjustment matrix for each validation location is proportional to its correlation coefficient. The 

program then applies this blended adjustment matrix to the simulated data for each grid cell 

associated with the site. For example, if the time in question is 1300, the simulated speed is 

multiplied by the adjustment factor for 1300. It should be noted that although the program 

calculates adjustments on a monthly basis, only an annual adjustment is performed; the monthly 

variations in the diurnal adjustment factors are ignored. This is because it was concluded in the 

first validation phase that the MASS model reproduces the pattern of monthly variations with 

reasonable accuracy.

7. The speeds for each grid cell are then adjusted for wake and other losses in a manner that depends 

on the simulated wind direction relative to the prevailing (most frequent) direction. The method 

accounts both for wake losses and, implicitly, for other losses that affect the efficiency of power 

conversion for a given free-stream speed without reducing the maximum rated capacity, e.g., 

blade soiling and icing. 

8. The speed is further adjusted by adding a random factor scaled to the predicted TKE. This 

adjustment is intended to reflect the impact of gusts on the speeds experienced by the turbines in 

the wind project.

9. Next, the adjusted speeds are applied to the turbine power curve for each IEC class. In the 

process, the power curve is corrected to the predicted air density.

10. A time filter is then applied to mimic the effect of spatial averaging on the fluctuations of wind 

project output in large plants. This approach has been found to reproduce the observed 

“variability” of wind plant output, as measured by the mean absolute deviation (MAD) as a 

function of time, with reasonable accuracy. Without such time filtering, the simulated plant 

output would tend to be more variable than the observed.

11. The program applies an additional power loss to account for turbine and plant availability. This 

power loss varies randomly from zero to 12%, and averages 6%. The variation is intended to 
8

capture the effects of changes in plant availability over time. For most sites, the total loss is about 

14-15%. The range is from about 11% to about 18%.

3.3 Results 

The power conversion program was tested on the mesoscale data that was extracted for grid cells 

associated with the three projects for which plant data were available, and the results were provided to 

NREL both in tabular form and as diurnal plots. Minor problems were found with the first two versions 

that were submitted; version 3 appeared to be satisfactory. 

For the first try, the default power curves and hub heights were used; in addition, the rated capacity of the 

nearest site, rather than the true rated capacity of the project, was assumed. However, NREL observed 

that the predicted output at two projects, Lake Benton and Storm Lake, was substantially greater, on 

average, than the observed. By using the correct turbine power curve at each project, and by adjusting the 

hub height of the simulated speeds and diurnal corrections to match the actual hub height of each project, 

an acceptable agreement between the predicted and observed capacity factors was obtained. Furthermore, 

to provide a true test of the ramp rates of project output – which depend on plant size – the rated 

capacities were adjusted to match the actual.

Figure 2 compares the simulated and observed diurnal mean output patterns for the Blue Canyon I, Lake 

Benton, and Storm Lake I projects. There are some discrepancies. The predicted output at Blue Canyon is 

slightly below the observed, a fact we attribute to an underestimation (by about 3-4%) of the mean wind 

speed in the AWS Truewind map. The predicted output at both Lake Benton and Storm Lake is, on the 

other hand, somewhat too high. We believe this is largely the result of poor (~90%) availability and other 

operational problems at both projects. For example, although the rated capacity of Lake Benton is 

nominally 103.5 MW, the greatest output observed in 2004 was 87.4 MW. These discrepancies were 

judged to be within an acceptable range for this project. In addition, the diurnal patterns appear to be 

reasonably well modeled.

Figure 3 compares the simulated and observed mean ramp rates of the wind projects. This is measured by 

the mean absolute deviation (MAD) of the capacity factor from any time T to a time T+N in the future. 

Once again, despite some deviations, the agreement was judged to be satisfactory for the purposes of this 

study.
9

Figure 2. Comparison of simulated and observed diurnal mean output patterns for the Blue Canyon I 

(top), Lake Benton (middle), and Storm Lake I (bottom) projects.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 5 10 15 20 25

Capacity Factor

Local Time of Day

Blue Canyon I - 65M 

MOD

OBS

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 5 10 15 20 25

Capacity Factor

Time of Day

Lake Benton - 50M - Z750

MOD

OBS

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 5 10 15 20 25

Capacity Factor

Time of Day

Storm Lake - 65M - Z750

MOD

OBS
10

Figure 3. Comparison of simulated and observed mean ramp rates for the Blue Canyon I (top), Lake 

Benton (middle), and Storm Lake I (bottom) projects. 

Based on the results of the validation, NREL gave its approval to proceed with version 3 of the program 

to convert the main mesoscale runs to plant output. 

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0 50 100 150 200

Mean Absolute Deviation

Time Lapse (Minutes)

Blue Canyon I - 65M

MOD

OBS

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0 50 100 150 200

Mean Absolute Deviation

Time Lapse (Minutes)

Lake Benton - 50M - Z750

MOD

OBS

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0 50 100 150 200

Mean Absolute Deviation

Time Lapse (Minutes)

Storm Lake - 65M - Z750

MOD

OBS
11

APPENDIX A

VALIDATION PLOTS FOR THE

MASS/NNGR/SFC CONFIGURATION 
12
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F1146-E(10/2008)

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1. REPORT DATE (DD-MM-YYYY)

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4. TITLE AND SUBTITLE

Development of Eastern Regional Wind Resource and Wind Plant 

Output Datasets: March 3, 2008 – March 31, 2010

5a. CONTRACT NUMBER

DE-AC36-08-GO28308

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6. AUTHOR(S)

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WER7.7402 & WER8.5004

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ALBANY, NEW YORK, 12205

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NREL Technical Monitor: David Corbus

14. ABSTRACT (Maximum 200 Words)

The objective of this project was to provide wind resource inputs to the Eastern Wind Integration and Transmission 

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15. SUBJECT TERMS

Wind; integration; datasets; EWITS; utilities; RTO; ISO; modeling; simulation; mesoscale; AWS Truewind.

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