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Contract No. DE-AC36-08GO28308

Advancements in Wind 

Integration Study Data Modeling: 

The Wind Integration National 

Dataset (WIND) Toolkit

Preprint

C. Draxl, B.-M. Hodge, K. Orwig, W. Jones, 

K. Searight, and D. Getman

National Renewable Energy Laboratory

S. Harrold and J. McCaa

3TIER

J. Cline and C. Clark

U.S. Department of Energy

To be presented at the 12th International Workshop on the LargeScale Integration of Wind Power into Power Systems

London, England

October 22—24, 2013

Conference Paper

NREL/CP-5D00-60269

October 2013
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Advancements in Wind Integration Study 

Data Modeling

The Wind Integration National Dataset (WIND) Toolkit

Caroline Draxl, Bri-Mathias Hodge, Kirsten Orwig,

Wesley Jones, Keith Searight, Dan Getman

National Renewable Energy Laboratory

Golden, CO, USA

Sara Harrold, Jim McCaa

3TIER

Seattle, WA, USA

Joel Cline, Charlton Clark

U.S. Department of Energy

Washington, D.C., USA

Abstract—Regional wind integration studies in the United States 

require detailed wind power output data at many locations to 

perform simulations of how the power system will operate under 

high-penetration scenarios. The wind data sets that serve as inputs 

into the study must realistically reflect the ramping characteristics, 

spatial and temporal correlations, and capacity factors of the 

simulated wind plants, as well as be time synchronized with 

available load profiles. The Wind Integration National Dataset 

(WIND) Toolkit described in this paper fulfills these requirements. 

A wind resource dataset, wind power production time series, and 

simulated forecasts from a numerical weather prediction model run 

on a nationwide 2-km grid at 5-min resolution will be made 

publically available for more than 110,000 onshore and offshore 

wind power production sites.

Keywords-wind power; wind resource assessment; wind 

integration; numerical weather prediction

I. INTRODUCTION

One of the difficulties in conducting integration studies of 

high-penetration renewable energy futures is the requirement for 

high-resolution wind and solar power data. To conduct 

simulations of power system operations at these high 

penetrations, wind power output at a minimum of 1-h resolution 

is needed for a very large number of disparate locations. Because

these future wind plants do not yet exist, the data used for the 

study must be simulated. Another requirement is that the wind 

power output should reflect the same weather conditions as 

historical load data which are also heavily influenced by the 

prevailing weather conditions. As part of the first phase of the 

Western Wind and Solar Integration Study, a data set was created 

by 3TIER using a mesoscale numerical weather prediction 

(NWP) model [1]. This dataset provided hourly wind power data 

for approximately 30,000 sites in the Western Interconnection for 

the three-year period from 2004 to 2006. These data sets were the 

first of their kind, and their output has subsequently been used in 

many different types of research. However, improvements to the 

dataset were needed as the state of the art in renewable 

integration studies advanced. Some of the user-requested 

improvements included more-recent years of simulated data, a 

larger number of years for the evaluation of interannual 

variability, a minimization of false ramps and spatial seams, a 

more-thorough incorporation of solar power inputs, and higher 

temporal resolution data. To address these needs, the U.S. 

Department of Energy wind and solar programs funded two 

projects to develop updated data sets: the Wind Integration 

National Dataset (WIND) Toolkit and the Solar Integration 

National Dataset (SIND) Toolkit. The WIND Toolkit spans the 

years from 2007 to 2013 using advanced NWP methods run on a 

nationwide 2-km grid with 5-min resolution, providing data at

more than 110,000 onshore and offshore wind power production 

sites. This manuscript provides an overview of the WIND Toolkit 

modeling advancements, site selection, data accessibility, and 

some preliminary validation results.

II. MODELING BACKGROUND

A dearth of high-quality wind resource data for future wind 

sites is one of the largest challenges in wind integration studies. 

Although observational data is to be favored, by definition it 

cannot be available for all locations considered for future highpenetration scenarios. Observed data should be used where 

available, but care should be taken to recognize some of its 

limitations. For example, wind speed data may be available at 

one location where a future wind plant is envisioned; however, it 

may be at or near ground level. There can be significant 

differences between ground-level wind speeds and hub-height 

wind speeds, and even then the single measurement point is not 

sufficient to represent the conditions at every one of tens of 

turbines simultaneously. Some statistical techniques have been 

developed to make the best use of limited datasets. Measure, 

correlate, predict (MCP) is one method that can be used to 

compare the differences between observed winds at two locations 

during a short time period, and then produce longer time series 

based on the relationships identified. This technique is best used 

for single locations that are geographically proximate to the 

desired location.
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The use of reanalysis datasets [2] removes the need for 

observational data, but still has many of the same flaws 

concerning the discrepancy of conditions at even geographically 

proximate locations. Reanalysis datasets often contain substantial 

biases [3], and are of too-coarse resolution for integration studies.

Mesoscale NWP models can be used to downscale reanalysis 

datasets while adding additional physical phenomena as a result 

of their smaller spatial and temporal timescales, including the 

consideration of local topographical features.

Mesoscale models also have the advantage of being able to 

simulate a large number of locations while maintaining the 

correlation of weather phenomena and their influence on local 

conditions from one location to the next. This spatial and 

temporal correlation is essential for integration studies [4]. Lew et 

al. demonstrated some difficulties when utilizing NWP as the 

basis for wind integration input datasets. Specifically, mesoscale 

NWP models need to be nested/run regionally and restarted 

periodically because of computational limitations. When spliced 

together, these temporal and spatial seams had some unintended 

consequences, such as false ramps, that resulted in unrealistic 

outcomes (e.g., higher reserve requirements) during the power 

system modeling. These undesirable data characteristics had to be 

corrected or, if corrections were ineffective, chunks of the data 

had to be removed. The following sections discuss in more detail 

the current methodology and how some of these previous issues 

were addressed. 

III. SITE SELECTION

The site selection process was an important component of the 

dataset generation process. The goal of the site selection 

methodology was not to recommend future wind plant sites, but 

to select likely locations. Based on common-practice siting 

criteria, a total of 100,000 sites onshore and 10,000 offshore were 

chosen (see Fig. 1). Those included existing wind plants as well 

as previous Western Wind and Solar Integration Study and 

Eastern Wind Integration and Transmission Study locations. Each 

site was defined by a 2-km by 2-km grid cell in the NWP dataset, 

and it was assumed that eight 2-MW wind turbines was the 

maximum that could be accommodated per grid cell. Certain 

onsite locations were excluded from consideration based on 

environmental and land-use criteria: most federal lands and all 

National Park Service and U.S. Fish and Wildlife Service 

managed lands, open water areas, areas with a slope greater than 

20%, and those areas within a buffer area of developed land and 

airports. Although the location of existing transmission lines is an 

important consideration in building a new wind plant, the large 

number of locations needed precluded transmission availability as 

a feasible criterion. However, because of the large number of 

sites, users will have the ability to choose sites that most 

adequately corresponded with their expected, planned, or 

simulated transmission build-out scenarios.

The site-selection model was run utilizing 3TIER’s 90 m 

continental U.S. wind resource dataset for mean annual wind 

speeds. Based on the exclusions and the buildable land area in 

each cell, as well as the turbine type implied by the class of wind 

speed, each of the grid cells was provided with an effective MWh 

value. The sites were then ranked and the best 100,000 sites 

chosen, with care given to choose a geographically diverse 

dataset, while enabling users to define plant build-outs by 

clustering sites. For the selection of the 10,000 offshore sites, the 

main selection criteria included: the wind resource, distance from 

shore, and bathymetry. Location specification permitting was not 

considered. All of the sites were at least 8 km offshore with a 

maximum water depth of 30 m.

Figure 1. Map of the final site locations. Each dot on this graph represents a 

site.

IV. MODEL RUNS

The WIND Toolkit was produced with the Weather Research 

and Forecasting (WRF) model [5] version 3.4.1. By the end of 

the project, it will provide high-resolution NWP model output 

throughout a seven-year timeframe (2007 to 2013) at 5-min 

temporal resolution. This data will be converted into publically 

available wind power production time series and simulated 

operational forecasts for 1-h, 4-h, 6-h, and day-ahead forecast 

horizons at the 110,000 sites selected. The available parameters 

will include power, barometric pressure, wind speed and 

direction (at 100 m), relative humidity, temperature, and air 

density. 

The WRF model setup for the wind power production time 

series consisted of a main grid with horizontal grid spacing of 54 

km and three nested domains of 18 km, 6 km, and 2 km. For the 

forecasts, only the three outermost nested grids were used, which 

corresponds well with operational forecast grid spacing. The 

model was initialized and forced at the boundaries with Interim 

Reanalyses of the European Centre for Medium-Range Weather 

Forecasts (ERA-Interim). The model terrain, roughness, and soil 

properties were obtained from the U.S. Geological Survey

GTOPO30 data. Scale-selective grid nudging with the ERAInterim data was used. Grids were continuously relaxed toward 

the large-scale reanalysis to prevent drift of the simulations away 

from the analyzed synoptic patterns, and the model was restarted 

every 30 days to avoid excessive drift. To eliminate temporal 

seams, the model was run with an overlap of ~3 days around 

these cold starts, during which the data were interpolated. The 

model physics options were based on a sensitivity study carried 

out for this project, and included the NOAH land surface model,

the YSU boundary layer scheme [6], and topographic wind 

enhancement [7]. Model output statistics and post-processing 

techniques will be applied before the data are made available. 

Creating and storing many terabytes of multiyear and highresolution wind resource output data requires innovative 

solutions. The 2-km domain consists of 200 million grid points 

(3,007 x 1,633 horizontally and 41 vertical levels), which with 

output every 5 min would result in 10.6 petabytes for a seven-
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year simulation. This would require more than three years to run 

on a standard HPC system. Therefore, parallel asynchronous I/O 

(PnetCDF combined with WRF quilt-I/O) [7] was used to keep 

pace with continuous generation of output data. This 

asynchronous I/O method improved the output speed 50:1, and 

made this project feasible. 

V. VALIDATION

Any data set is only of value if its deficiencies are known, as 

then corrective actions may be taken. Therefore, we plan to 

validate the data set against tall towers in different geographical 

areas and with different wind situations. The data will be 

validated from a meteorological point of view (e.g., diurnal and 

annual cycles, frequency distributions, error metrics), and from a 

grid integration perspective (e.g., ramps and variability of power 

output). The power validation will be done against real power 

output from existing wind plants, and cover, among others, ramps 

and variability distributions. 

The final validation will be performed against both the raw 

model data and the post-processed model output. Because the 

WIND Toolkit project is still underway, we show only 

preliminary results of the raw model output. As expected, the 

winds are modeled more accurately in homogeneous than in 

complex terrain, therefore we show results for a site in complex 

terrain: the National Wind Technology Center (NWTC) in 

Colorado, United States. The NWTC is located at the foot of the 

Rocky Mountains at an elevation of ~1,850 m above sea level. 

Winds on-site are dominated by strong westerly winds, typically 

resulting from a drainage flow out of the nearby Eldorado 

Canyon. The site itself is flat and undeveloped, and the mean 

wind speed on-site is low, but winds can be extremely gusty and 

turbulent [8]. An 80-m tower monitors the wind flow in 1-min 

resolution, and serves as the truth against which the raw model 

output of the WIND Toolkit for two years (2007 and 2008) was 

compared. 

The absolute error metrics for the 5-min model output 

(instantaneous value) and the 1-min wind speed observations at 

the same time, and those averaged over one hour, vary only 

slightly. A root-mean-squared-error (RMSE) for hourly averaged 

(5-min) values of 4.2 (4.5) m/s and a bias of 1.1 m/s show the 

need for post-processing for complex wind sites. The values for a 

site on the U.S. East Coast and one in the Columbia River Gorge 

in the northwest United States are much lower (2.2 and 2.8 m/s 

for RMSE, respectively). The model is prone to underpredict 

during times of low wind speeds and overpredict during high 

winds at the NWTC; however, the opposite is true for the other 

two sites. The post-processing will be done using a regional 

approach because of the number of sites and will nudge the 

model closer to measured values. The error values throughout the 

year and day are shown in Fig. 2 and Fig. 3, respectively. 

The annual cycle for the wind speed RMSE (Fig. 2 d) shows 

lower RMSE values during the summer months and higher ones 

during the winter months. The RMSE can be split into the bias 

and the centered root-mean-squared error (CRMSE) [9]. The 

CRMSE considers the random component of the error and 

indicates the intrinsic skill of the model. The annual cycle of the 

RMSE is clearly dominated by the annual cycle of the CRMSE, 

which exhibits lower intrinsic skill at the NWTC during the 

winter months. The bias is also higher during the winter. 

Although a zig-zag pattern is apparent during the summer 

months, its amplitude is very small. Looking at seasonal and 

diurnal cycles (not shown), we found this to be a result of 

variability in wind speed. The annual cycle of rank correlation 

follows the behavior of RMSE, which reinforces the findings 

above. 

The distribution of the error metrics (Fig. 3) throughout a day 

shows lower values for bias, CRMSE, and RMSE during the 

morning hours (14 – 21 UTC = 7 – 14 local time). This is the 

time when the mountains to the west of the NWTC heat up, and 

the winds change to easterly. This is also the time when the wind 

speeds are often lower than at other times of the day. We suggest 

a combination of lower errors during low wind speeds and local 

forcing to be responsible for this pattern. The maximum of 

RMSE, bias, and CRMSE around 11 UTC to 12 UTC (4 to 5 

local time) could be attributed to problems in capturing the 

transition from stable boundary layers at night to convective 

boundary layers during the day. The rank correlation is highest 

when the errors are lowest, but is not high in general and does not 

change much throughout a day. Values of higher correlation 

indicate similar patterns of wind speed in the model and the 

observations. 

Figure 2. (a) Bias, (b) CRMSE, (c) RMSE, and rank correlation of 80-m wind 

speed at the NWTC for every month averaged during the years 2007 and 2008. 
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Figure 3. Same as Fig. 2, but as a function of time of the day (UTC). 

VI. POWER CONVERSION

The conversion from wind to power will be done in the 

following steps: 1) Bias removal from wind speeds, 2) wind 

speed adjustment for wakes with an empirical function, 3) 

application of power curves using different power curves for 

offshore and Class 1 to Class 4 wind sites (Fig. 4), and 4) 

statistical adjustment to power. 

Figure 4. Power curves used to convert modeled wind speeds to power for 

offshore and Class 1 to Class 4 locations. 

VII. CONCLUSION

The WIND Toolkit will be a freely available wind speed and 

power data set that can be used for wind resource assessments, as 

well as grid integration and grid planning studies. It will allow 

users to perform detailed simulations of how the future power 

system will operate under high-penetration scenarios. The WIND 

Toolkit dataset will realistically reflect ramping characteristics, 

spatial and temporal correlations, and capacity factors of 

simulated wind plants, as well as having the ability to be time 

synchronized with available load profiles. In this manuscript, we 

described the importance of creating this dataset, the challenges 

associated with creating NWP simulations for a large 

geographical area such as the continental United States, how the 

simulated wind speeds will be converted to power, and initial 

validation results from the raw data set. The latter confirms the 

need for regional post-processing approaches. 

ACKNOWLEDGMENT

This work was supported by the U.S. Department of Energy 

under Contract No. DE-AC36-08-GO28308 with the National 

Renewable Energy Laboratory. The authors would like to thank 

all of the technical review committee participants who helped to 

shape the WIND Toolkit project. 

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