Revealing The Impact Of Climate Variability On The Wind Resource Using Data 

Andrew Clifton(1), Julie K. Lundquist(1,2)

1) National Renewable Energy Laboratory, Golden, CO. 2) Department of Atmospheric Sciences, University of Colorado at B

1. The wind resource 

2. Grouping wind data into clusters

Climate research tells us that large-scale climate variability can influence localconditions, from precipitation to snow cover, air temperatures and wind speeds. Teasingout these links requires careful statistical treatment of the data. Up to now, we are notaware of a technique that detects trends from wind speed and direction together.In this study, we develop a method to identify dominant wind conditions at a site andrelate those winds to larger-scale atmospheric and climate forcing. We use data fromthe 80-m tall M2 tower at the National Wind Technology Center (NWTC) near Boulder,Colorado, about 2 km west of the Front Range. The M2 tower has been collecting windspeed and direction measurements at 2, 5, 10, 20, 50 and 80 m above ground sincelate 1996. The record also includes temperature and stability data. All of this data ispublically available online (http://www.nrel.gov/midc/nwtc_m2/).

Figure 1. One of several pairs of turbines and meteorological towers at the NWTC. Inset: 14-year wind rose, 80 m above ground

Figure 3. The optimal wind clusters at 80 m height. Bold markers show cluster centroids.

Wind Cluster Positive 

Correlations

NeCo

Strong westerlies

∆Z500 Niñ

Weak westerlies

∆Z500 PN

Southerlies - ∆Z

Northerlies - ∆Z

The monthly-average wind resource a

and Niño 3.4 climate indices (Figure 7is R2 = 0.4 (p < 0.01). The residuals ar

Table 1. Significant correlatio(p<0.05)between wind clusteanomalies and climate indice

Figure 2. Cluster quality metric, BIC at each height. The knee point at k=4 indicates the same, optimal number of clusters at each height.

2 m

k=0 k=5 k=10 k=15 k=20

5 m

10 m20 m50 m80 m

k = 4 is the optimal solution

We find groups in hourly-average wind data as follows:

1. Convert wind speed and direction observations (Figure 1)

into east-west and north-south wind components (Figure 3)

2. Remove data below turbine starting speed, 3.5 m s-1

3. Classify hourly wind component data in to 2 groups using kmeans clustering with 2 clusters (k = 2)4. Score the solution using the Bayesian Information Criterion

(BIC). The BIC increases as the fit to the data improves, butdecreases as the number of clusters increases

5. Repeat steps 3) and 4) with more clusters (3 ≤ k ≤ 20) to

get a range of solutions

6. Find the knee point in BIC as k increases, using the anglebased BIC method [ZHF08]. This is the optimum solution.

7. Assign observations to the nearest cluster

At the NWTC the optimum solution for 1997-2010 is 4 clusters.This is found independently at each height (Figures 2 and 3).

[ZHF08] Qinpei Zhao, Ville Hautamaki, and Pasi Fränti. Knee point detection in BIC for detecting the number of clusters. In Jacques Blanc-Talon, Salah Bourennane, Wilfried Philips, Dan Popescu, and Paul Scheunders, editors, Advanced Concepts for Intelligent Vision Systems, volume 5259 of Lecture Notes in Computer Science, pages 664–673. Springer Berlin / Heidelberg, 2008.[Kli07] Katherine Klink. Atmospheric circulation effects on wind speed variability at turbine height. Journal of Applied Meteorology and Climatology, 46:445–456, 2007.Literature Cited

Strong

westerlies

Weak

westerlies

Northerlies

Southerlies

Calm

Prevailing winds at the NWTC are from the west through north west, over the ColoradoFront Range (Figure 1). Less frequently, winds come from the south or north, roughlyparallel to the Front Range, although these are slower than the westerly winds.

Figure 7. Changes in m

follow the pressure gra

Niño 3.4).

El Niño

La 

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4. ConclusionsLong time series of wind speed and wthe k-means clustering algorithm. Th

independently at each measurement hThe frequency of all clusters are stronphenomena show significant correlatwesterly winds is negatively correlated conditions tend to reduce the frequencThis technique could be applied to othavailable, such as at reference sites, year of data is available, the method computational fluid dynamics tools oannualized energy production in each climate variability.

The frequency of winds in each cluster follows a distinctannual pattern. Winds from the west peak during wintermonths, while summer months are dominated byweaker flows from the west, north and south (Figures 4and 6).

3. The impact of 

Our method identifies clusters from east-west and north-southwind speeds. The groups that we found partly correspond todifferent stability regimes (Figure 5). Including stability in theclustering is possible, but would be a significant increase incomputational effort. This might be beneficial for someapplications, for example selecting scenarios for mesoscalemodeling or computational fluid dynamics flow modeling.Figure 5. Mode of stability for flows in 1 m s-1 by 

1 m s-1 bins.

AO, Niño 3.4 and PNA climate index data were downloaded from the NOAA Climate Prediction Center website, http://www.cpc.ncep.noaa.gov. Pressure gradient ∆Z500 was derived from NCEP Reanalysis data provided by NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from http://www.esrl.noaa.gov/psd/Acknowledgments

Figure 6. The annual cycle of winds at 80 m above ground

1997 1999 2001 2003 2005 2007 2009 2011

0

100200

Frequency [hrs]

1997 1999 2001 2003 2005 2007 2009 2011

0

100200

Frequency [hrs]

Northerlies

Weak westerlies

Southerlies

Strong westerlies

Figure 4. Hours per month each cluster was detected at 80 m.

We compared the standardized depargradient and climate indices (Figure and p-value for each variable and an o

factor on the cluster frequency.

N

S

W E

Calm:

7.4 %

< 1m/s

024681012141618202224262830323436

80 m wind rose

Speed m s-1

Weak westerlies

Strong westerlies

Southerlies

Northerlies

Monthly Frequency [hrs] Monthly Frequency [hrs]

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