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Contract No. DE-AC36-08GO28308

Wind Resource Assessment 

of Gujarat (India)

C. Draxl, A. Purkayastha, and Z. Parker

Technical Report

NREL/TP-5000-61741

July 2014
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Wind Resource Assessment

of Gujarat (India)

C. Draxl, A. Purkayastha, and Z. Parker

Prepared under Task No. IGIN.1401

Technical Report

NREL/TP-5000-61741

July 2014
NOTICE

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Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.
1 

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Acknowledgments

The authors would like to extend sincere gratitude to the Centre for Wind Energy Technology, 

India, for providing wind data from ground measurement stations in Gujarat that we used to 

validate the modeled data in this analysis, especially to Dr. Gomathinayagam and Mr. Boopathi. 

We would also like to thank Jason Knievel of the National Center for Atmospheric Research 

(NCAR) and Andrew Clifton of the National Renewable Energy Laboratory (NREL) for their 

thoughtful reviews and comments, Julie Lundquist of NREL for her guidance at the beginning of 

the study, Sarah Booth of NREL for providing valuable input and comments during the editing 

and production process, and Jennifer Melius of NREL for the data visualization efforts. The wind 

resource and forecasting simulations were performed on NREL’s high-performance computing 

system Peregrine. Any errors or omissions are solely the responsibility of the authors. 

This analysis was funded by the U.S. Department of Energy’s Office of Energy Efficiency & 

Renewable Energy and developed in coordination with India’s Ministry of New and Renewable 

Energy under the U.S.-India Energy Dialogue’s New Technology and Renewable Energy 

Working Group. 
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Nomenclature or List of Acronyms

ABL Above ground level

CWET Centre for Wind Energy Technology

CRMSE Centered root-mean-squared error

ECMWF European Centre for Medium-Range Weather 

Forecasts

GFS Global Forecast System

GWh Gigawatt-hours

IRENA International Renewable Energy Agency

MAE Mean absolute error

NCAR National Center for Atmospheric Research

NCEP National Centers for Environmental Prediction

NREL National Renewable Energy Laboratory

NWP Numerical weather prediction

PBL Planetary boundary layer

QNSE Quasi-normal scale elimination

RMSE Root-mean-squared error

SST Sea surface temperature

USGS United States Geological Service

WDE Wind distribution error

WRF Weather Research and Forecasting Model

YSU Yonsei University
3 

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Executive Summary

India is one of the largest wind energy markets in the world. The first Indian state to install a 

wind power project was Gujarat in 1986. In February 2013, the installed wind capacity in 

Gujarat was 3,093 MW. Multiple contradicting wind resource assessments exist for India. Owing

to the uncertainty around these existing wind energy assessments, this analysis uses the Weather 

Research and Forecasting (WRF) model to simulate the wind at current hub heights for one year 

to provide more precise estimates of wind resources in Gujarat. The WRF model allows for 

accurate simulations of winds near the surface and at heights important for wind energy 

purposes. While previous resource assessments published wind power density, we focus on 

average wind speeds, which can be converted to wind power densities by the user with methods 

of their choice.

Prior to the one-year simulations, we carried out a sensitivity study using two weeks each in 

January, April, and August 2011. This sensitivity study covers the main seasons, and served to 

find the optimal boundary conditions, grid spacings, and physics settings in the model that give 

best results for Gujarat. The sensitivity study showed that a finer horizontal resolution results in 

better resource estimates, but that the increase in accuracy between a grid spacing of 3.3 km and 

1.1 km might not be worth the additional computational expense. 

We compared our simulations of both the sensitivity study and the year-long simulations to 

measurements at five locations throughout the country. We validated the model using traditional 

error metrics such as root-mean-squared error (RMSE), mean absolute error (MAE), and bias; 

but also against rank correlation, CRMSE, normalized RMSE, and the wind distribution error 

(WDE). Annual and diurnal cycles, wind roses, and histograms shed further light onto the 

behavior of the WRF model. Validation results showed a very good agreement of modeled to 

observed winds. A positive bias was found, leading to an estimated overprediction of less than 

0.8 m/s for the annual average. These and the other results presented in this report give us 

confidence in an accurate wind resource assessment.

The wind resource estimates in this study show regions with average annual wind speeds of more 

than 8 m/s. These areas are concentrated in the Gulf of Kutch and on the south coast of the 

peninsula. Previous literature found that the highest wind energy potentials are concentrated in 

the northwestern part of the state, north of the Gulf of Kutch. In our analysis, the yearly average 

for 2011 shows the highest potential at the same location but also shows a similarly high 

potential along the south coast of the peninsula (offshore and on the coast between Sarakhadi 

Gam and Gadhula). Likewise, the International Renewable Energy Agency identified the highest 

wind speeds to be in the Gulf of Kutch and the southern tip of the peninsula. They also identified 

the Gulf of Khambhat to have average wind speeds of 9 m/s, which is higher than our estimate.

Conforming with the usual seasonal cycle that can be expected in India and with validation 

results at five measurement sites, the wind speed is highest in the summer from May to August, 

with maximum average wind speeds of higher than 10 m/s, especially along the coast lines 

during these months. The wind speeds are lowest in October and November with average wind 

speeds not exceeding 7 m/s at coastal sites.
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Table of Contents

List of Figures ............................................................................................................... 5

List of Tables ................................................................................................................. 7

1 Introduction.............................................................................................................. 8

2 Data and Methods.................................................................................................... 9

2.1 Mesoscale Model Simulations......................................................................................................... 9

2.2 Observations .................................................................................................................................. 11

2.3 Climate in Gujarat.......................................................................................................................... 11

2.4 Validation Methods........................................................................................................................ 12

3 Sensitivity Study.................................................................................................... 12

3.1 Model Setup................................................................................................................................... 13

3.2 Results from Sensitivity Study and Implications........................................................................... 13

4 Gujarat’s Wind Resource ...................................................................................... 18

4.1 Results............................................................................................................................................ 18

4.2 Wind Resource Validation............................................................................................................. 22

4.2.1 Histograms......................................................................................................................... 22

4.2.2 Error Metrics ..................................................................................................................... 22

4.2.3 Impact of Temporal Resolution......................................................................................... 24

4.2.4 Diurnal Cycles................................................................................................................... 24

4.2.5 Wind Roses........................................................................................................................ 25

4.2.6 Annual Cycle ..................................................................................................................... 25

4.3 Comparisons of Simulations with Existing Wind Resource Estimates for Gujarat....................... 29

5 Summary and Conclusion..................................................................................... 30

References................................................................................................................... 33

Appendix...................................................................................................................... 36
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List of Figures

Figure 1. WRF simulation domains. Terrain elevation from domain 1 (33.3 km grid) is 

shown in domains 1–3, and from domain 4 (1.1 km grid) in domain 4. The crosses 

denote observation locations of the 5 measurement sites: purple - Balava, red - 

Jikiali, yellow - Virewa, blue - Sarva, brown - Jegawa.....................................................10

Figure 2: Histograms of wind speed for all the experiments and five locations, as 

indicated by the legend and title, respectively. Modeled wind speeds are at 80 m, 

observed at 50 m.................................................................................................................15

Figure 3: Wind roses for modeled wind speeds in meters per second at 112 m and 

observed at 50 m (bottom right) for Balava......................................................................16

Figure 4: Annual average wind speeds at 80 m. ....................................................................19

Figure 5: Annual average wind speeds at 112 m. ..................................................................20

Figure 6: Average wind speeds for every month during 2011..............................................21

Figure 7: Histograms of observed (dashed lines) and simulated (solid lines) wind 

speed at 50 m at the five measurement sites as indicated in the title of each 

panel.....................................................................................................................................23

Figure 8: Average diurnal cycle of wind speeds at different heights for Jegawa as 

indicated in the panel titles. Simulated wind speeds are in blue, observed in 

green. ...................................................................................................................................26

Figure 9: Wind roses for observed wind speeds in meters per second at 50 m (left 

column) and simulated wind speeds at 80 m (right column) for the five measurement 

sites......................................................................................................................................27

Figure 10: Sarva’s annual cycle of simulated wind speed at 50 m, 80 m, and 112 m, 

and observed wind speeds at 50 m (panel a), wind speed bias for winds at 50 m 

(simulated minus observed; panel b), and normalized RMSE of wind speeds at 

50 m (panel c). .....................................................................................................................28

Figure 11: Wind power density at 50 m (left) and 80 m (right). From CWET (CWET 

2014).....................................................................................................................................29

Figure 12: Screenshot of wind speed estimates from Irena 

(http://irena.masdar.ac.ae/?map=178, accessed May 2014)............................................30

Figure 13: Histograms for each month in 2011 of 50 m observed (dashed lines) and 

simulated (solid lines) wind speeds for Balava ...............................................................36

Figure 14: Histograms for each month in 2011 of 50 m observed (dashed lines) and 

simulated (solid lines) wind speeds for Jegawa. .............................................................37

Figure 15: Histograms for each month in 2011 of 50 m observed (dashed lines) and 

simulated (solid lines) wind speeds for Virewa. ..............................................................38

Figure 16: Histograms for each month in 2011 of 50 m observed (dashed lines) and 

simulated (solid lines) wind speeds for Sarva. ................................................................39

Figure 17: Histograms for each month in 2011 of 50 m observed (dashed lines) and 

simulated (solid lines) wind speeds for Jikiali. ................................................................40

Figure 18: Average diurnal cycle of wind speeds at different heights as indicated in 

the panel titles for Virewa. Simulated wind speeds are in blue, observed in green.....41

Figure 19: Average diurnal cycle of wind speeds at different heights as indicated in 

the panel titles for Sarva. Simulated wind speeds are in blue, observed in green.......42

Figure 20: Average diurnal cycle of wind speeds at different heights as indicated in 

the panel titles for Jikiali. Simulated wind speeds are in blue, observed in green. .....43

Figure 21: Average diurnal cycle of wind speeds at different heights as indicated in 

the panel titles for Balava. Simulated wind speeds are in blue, observed in green.....44

Figure 22: Wind roses for every month of the year 2011 as modeled by WRF for 80 m 

for Balava.............................................................................................................................45
6 

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Figure 23: Wind roses of measured wind speed and direction for every month of the 

year 2011 for 50 m for Balava. ...........................................................................................46

Figure 24: Annual cycle of simulated wind speed at 50 m, 80 m, and 112 m, and 

observed wind speeds at 50 m (panel a), wind speed bias for winds at 50 m 

(simulated minus observed; panel b), and normalized RMSE of wind speeds at 

50 m (panel c) for Jikiali. ....................................................................................................47

Figure 25: Annual cycle of simulated wind speed at 50 m, 80 m, and 112 m, and 

observed wind speeds at 50 m (panel a), wind speed bias for winds at 50 m 

(simulated minus observed; panel b), and normalized RMSE of wind speeds at 

50 m (panel c) for Balava....................................................................................................48

Figure 26: Annual cycle of simulated wind speed at 50 m, 80 m, and 112 m, and 

observed wind speeds at 50 m (panel a), wind speed bias for winds at 50 m 

(simulated minus observed; panel b), and normalized RMSE of wind speeds at 

50 m (panel c) for Jegawa. ................................................................................................. 49

Figure 27: Annual cycle of simulated wind speed at 50 m, 80 m, and 112 m, and 

observed wind speeds at 50 m (panel a), wind speed bias for winds at 50 m 

(simulated minus observed; panel b), and normalized RMSE of wind speeds at 

50 m (panel c) for Virewa....................................................................................................50

Figure 28: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the 

winter months (January, February, March, October, November, December) for 

Jegawa. ................................................................................................................................51

Figure 29: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the 

winter months (January, February, March, October, November, December) for 

Virewa...................................................................................................................................51

Figure30: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the 

winter months (January, February, March, October, November, December) for 

Sarva. ...................................................................................................................................52

Figure 31: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the 

winter months (January, February, March, October, November, December) for 

Jikiali. ...................................................................................................................................52

Figure 32: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the 

winter months (January, February, March, October, November, December) for 

Balava...................................................................................................................................53

Figure 33: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the 

summer months (April, May, June, July, August, September) for Jegawa. ..................53

Figure 34: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the 

summer months (April, May, June, July, August, September) for Virewa.....................54

Figure 35: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the 

summer months (April, May, June, July, August, September) for Sarva. .....................54

Figure 36: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the 

summer months (April, May, June, July, August, September) for Jikiali. .....................55

Figure 37: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the 

summer months (April, May, June, July, August, September) for Balava.....................55
7 

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List of Tables

Table 1: Observation Locations ..............................................................................................11

Table 2: Error Metrics Used in the Analysis...........................................................................13

Table 3. Experiments for Sensitivity Study ............................................................................14

Table 4. Error Metrics for the Sensitivity Study for 50 m Wind Speeds Averaged for 

All the Sites..........................................................................................................................17

Table 5: Error Metrics for the Sensitivity Study for 112 m Wind Speeds Averaged for 

All the Sites..........................................................................................................................17

Table 6. Error Metrics for 2011 for 10-min Model Output for 50 m in meters per 

second..................................................................................................................................24

Table 7. Error Metrics for 2011 for Hourly Model Output for 50 m in Meters per 

Second. (A more detailed breakdown of RMSE and bias into diurnal cycles per 

month for all the 5 stations is in the appendix, Figure 28 through Figure 37). .............24
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1 Introduction

India is one of the largest wind energy markets in the world. Renewable energy sources represent 

12.2% of India’s installed capacity, with 70% of this contribution coming from wind energy 

(International Renewable Energy Agency, 2014). The first Indian state to install a wind power 

project was Gujarat in 1986 (Centre for Wind Energy Technology [CWET], 2014). In February 

2013, the installed wind capacity in Gujarat was 3,093 MW. Tamil Nadu, in southeast India, is 

the only state with more installed wind capacity than Gujarat. The installed capacity in Gujarat is 

expected to increase as future projects are planned, including an offshore wind farm in the Gulf 

of Khambhat in the southeastern region of the state (Patel et al, 2011). Accurate wind resource 

estimates are crucial to support the development of wind energy in Gujarat. 

There have been multiple wind resource assessments conducted for India. The first surveys, 

which used data from wind monitoring stations with towers that were 20–25 meters tall, showed 

promising wind resources in Gujarat (Mani and Mooley, 1983); however, that height is too low 

for current wind turbines. Additional wind resource assessments or mention of wind power for

India can be found in publications by Jagadeesh (1985), Jagadeesh (1987), Rao (1986), Amin 

(1999), Bakshi (2002), Mooley (1983), and Jaswal and Koppar (2013). The wind resource 

estimates in India varied depending on the methodology, data, and hub heights used in these 

reports (Phadke et al, 2011). To address this variation, Phadke et al (2011) analyzed wind 

resource data across the country. They conducted an extended reassessment study, estimating 

land-based wind energy potential for 80 m, 100 m, and 120 m. Phadke et al conclude that high 

quality wind energy sites have more than five times the current official estimate, and 30 times 

that of the official Indian estimate published in the 2010 Wind Energy Atlas (CWET, 2010). The 

authors theorize that the most likely reason for the discrepancies between these estimates is the 

difference in assumed land availability.

Due to the uncertainty around existing wind energy assessments in India, this analysis uses the 

Weather Research and Forecasting (WRF) model to simulate the wind at current hub heights to 

provide more precise estimates of wind resources in Gujarat. The wind resource estimates are 

limited to wind speeds and direction. The WRF model is a community numerical weather 

prediction (NWP) model maintained by the National Center for Atmospheric Research (NCAR) 

in the United States, and has been successfully applied to wind energy related studies and wind 

resource assessments (e.g., Draxl et al, 2012; Draxl et al, 2013; Storm and Basu, 2010; Phadke et 

al, 2011; Dvorak et al, 2012; Carvalho et al, 2013; Carvalho et al, 2014; Santos-Alamillos et al, 

2013; Garcia-Diez et al, 2012; Ji-Hang et al, 2014; Lundquist et al, 2014). The WRF model 

allows for accurate simulations of winds near the surface and at heights that are important for 

wind energy purposes. WRF’s ability to downscale to resolutions of tens of meters allows the 

resolution of small-scale features, such as fronts, sea breezes, or winds influenced by orography, 

which are all important factors in describing the wind characteristics. WRF is thus an optimal 

tool to predict the distributions of the wind over a certain area, in this case Gujarat.

Downscaling allows a grid spacing of 1.1 km, finer than previous analyses. Likewise, increased 

temporal resolution with output every 10 minutes allows the calculation of highly resolved error 

metrics to validate our wind resource estimates. The results obtained in our study can therefore 

be used to help assess the accuracy of previous wind resource assessment studies for the area of 

Gujarat.
9 

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This report presents the results of the Gujarat wind resource assessment study. The WRF model, 

observations, and methods applied are described in Section 2. Results from a sensitivity study are 

presented in section 3. Section 4 covers wind resource maps based on yearly and monthly model 

simulations, a validation with observations, and a comparison with previous and other currently 

available resource assessments. Findings are summarized in section 5. The appendix provides 

additional graphs for the interested reader.

2 Data and Methods

WRF model simulations are validated with observations from five sites in Gujarat. This section 

describes the simulations, observations, the climate in Gujarat, and the validation process. 

2.1 Mesoscale Model Simulations

The model simulations were carried out with the WRF model (Skamarock et al, 2008). WRF is a 

current-generation mesoscale numerical weather prediction system designed to serve both 

atmospheric research and operational forecasting needs (www.wrf-model.org). WRF features 

two dynamical cores, a data assimilation system, and a software architecture allowing for parallel 

computation and system extensibility. The model serves a wide range of meteorological 

applications across scales ranging from meters to thousands of kilometers. WRF is used 

operationally in the United States at the National Centers for Environmental Prediction (NCEP), 

the Air Force Weather Agency, and other centers. WRF is a community model that has been 

widely used for wind resource estimates as mentioned in the Introduction. 

In WRF simulations the user has the choice of many different physical models that describe the 

interaction and processes between and in the land surface, the atmospheric boundary layer, and 

the middle and upper atmosphere. The physics options used for this study include the Lin et al. 

microphysics scheme, the Rapid Radiative Transfer Model for longwave radiation, the Dudhia 

scheme for shortwave radiation, the Eta similarity surface layer model, the Noah land surface 

model, the Mellor-Yamada-Janjic (MYJ) planetary boundary layer (PBL) scheme, the Yonsei 

University (YSU) PBL scheme, the quasi-normal scale elimination (QNSE) PBL scheme, and 

the Betts-Miller-Janjic cumulus parameterization. Land use categories come from the United 

States Geological Service (USGS).

Because modeling results are dependent on the inputs chosen by the user, a sensitivity analysis 

was carried out to yield the best model setup. That best model setup was then used to conduct the 

final one-year wind resource assessment. For the sensitivity analysis and the final simulations, 

atmospheric parameters were output every 10 min; except for one experiment in the sensitivity 

study, where it was every 1 min. That experiment served as a test for whether a higher temporal 

resolution would improve wind resource estimates. WRF output from every minute was then 

averaged to 10 minutes to see if that would yield more accurate wind estimates. The average was 

made from minute 6 to 14, thus centered around every 10 min to be comparable to the 10 min 

instantaneous output from the other experiments. 

The WRF model is run at several different horizontal scales. These include a coarse outer 

domain, intermediate domains, and a finer-resolution inner domain. Feedback from the finer 

domains to the coarser domains was turned off for all simulations in order to study the effect of 

horizontal resolution in the two innermost domains. We used the nested setup to dynamically 
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downscale to 1.1 km in the finest domain (Figure 1). The grid spacings for each of the four 

domains are 33.3 km, 11.1 km, 3.3 km, and 1.1 km. In the finest domain, the number of grid 

points was 901 x 601. The 41 vertical levels for most sensitivity experiments and the year-long 

runs have their lowest model levels at 15 m, 47 m, 80 m, 112 m, 145 m, and 177 m above ground 

level (AGL). (Measurements of height are AGL unless otherwise noted.) The 69 vertical levels 

used for one experiment have their lowest model levels at 15, 36, 46, 57, 68, 78, 88, 99, 110, 

120, 131, 141, 152, 162, 173, and 183 m. 

Figure 1. WRF simulation domains. Terrain elevation from domain 1 (33.3 km grid) is shown in 

domains 1–3, and from domain 4 (1.1 km grid) in domain 4. The crosses denote observation 

locations of the 5 measurement sites: purple - Balava, red - Jikiali, yellow - Virewa, blue - Sarva, 

brown - Jegawa.

The model was initialized at 00 UTC each day (which corresponds to 5:00 and 5:30 local time, 

depending on the location in India). The first 6 hours were regarded as model spin-up, so forecast 

hours 6–30 were used in the wind resource assessment and sensitivity studies. The model 

simulations are initialized and forced at the boundaries by 1/12 degree sea surface temperature 

(SST) fields and 1 degree U.S. NCEP Global Forecast System (GFS) analyses; one experiment 

was run with European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis 

(ERA-Interim) analyses at 0.7 degrees.
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2.2 Observations

Observations at turbine hub heights are rarely made public. However, in Gujarat, data from five 

meteorological towers were available during 2011. The measured data were provided by the 

Centre for Wind Energy Technology (CWET). Model runs were validated with wind 

measurements from these towers, which were deployed across Gujarat (Figure 1 and Table 1). 

These towers measured wind speed and wind direction at 30 m and 50 m, and temperature at 10 

m; the maximum and minimum values and standard deviation of these quantities were recorded 

as well. The latter were particularly useful in the quality control of the observations, which was 

carried out with the software Windographer (Windographer, 2014). We found through the 

quality control that the wind direction measurements of Sarva are dubious. 

The observations were used for validation in both the six-week long sensitivity study and the 

one-year long wind resource assessment runs. The sensitivity analysis was carried out for three 

two-week periods in 2011, and the year-long simulations to estimate the wind resource 

assessment was run for the entire year 2011. The year 2011 was partly chosen because 

measurements from five towers were available to validate the wind resource modeled data. 

Table 1: Observation Locations 

Location Latitude Longitude 

Balava 21.86906 69.96097

Sarva 22.22444 71.49161

Jegawa 22.95919 71.50833

Jikiali 21.27897 71.26333

Virewa 22.03747 71.46733

2.3 Climate in Gujarat

Knowledge about Gujarat’s climate is important because the model validation should also look at 

how well the model captures climate characteristics. The Tropic of Cancer bisects Gujarat’s 

northern border, leading to a tropical and subtropical steppe climate with occasional droughts 

and floods (National Disaster Management Authority of India, www.ndma.gov.in/en/gujaratsdma-office). Cyclones occur occasionally along the coastline (Patel et al, 2011). Although most 

of Gujarat is flat, the northeast region of the state is covered by mountains and desert, with the 

highest elevation being 1,145 m. 

While India has four seasons (winter, summer, monsoon, and post-monsoon), Gujarat has three 

primary seasons: winter, from November through March, summer from March through June, and 

the monsoon season from June (Pai et al, 2013) or sometimes May (Patel et al., 2011) through 

September. October is considered an intervening month. While the north region of Gujarat is arid 

and semi-arid, the southern region is humid. The seasonal cycle leads to strong winds from 

March through August and relatively weak winds from November through March. Because 

Gujarat has a long coast line, sea breezes also influence the wind characteristics in this region. 

The El Niño Southern Oscillation plays an important role in India. El Niño affects the monsoon. 

El Niño years are typically characterized by week monsoons and droughts, whereas La Niña 
12 

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typically leads to strong monsoons. According to the Indian Meteorological Institute, 2011 was

characterized as a normal monsoon year in Gujarat―another reason this year was chosen for the 

assessment of the wind resource in addition to the availability of tower measurements at five 

sites across the state. 2011 was a moderate La Niña year for Gujarat, and the monsoon began on 

June 22 and concluded between September 23 and October 11, 2011. 

2.4 Validation Methods

We validated the model simulations at five measurement stations across Gujarat. The validation 

includes visual and computational methods. Wind roses, frequency distributions, and time series 

of diurnal cycles and annual cycles provided a good visual aid in assessing model performance. 

Metrics used to validate the model were the root-mean-squared error (RMSE), bias, centered 

root-mean-squared error (CRMSE), the normalized RMSE to take the error dependency with 

wind speed into account (Drechsel et al, 2012), rank correlation, mean absolute error (MAE), 

wind distribution error (Windographer, 2014), and expected gigawatt-hours (GWh) per year

(Table 2) with an example wind turbine with a 66-m rotor diameter. Observations were available 

only at 30 m and 50 m. Because current turbine heights are around 80 m and 100 m, metrics 

were computed at 80 m and 112 m. For 112 m the observations were extrapolated with the power 

law (Peterson and Hennessey, 1978) from 50 m to 112 m. For the long-term (one year) wind 

resource estimate, seasonal behavior (monsoon) and directional dependence were also analyzed.

The model output is 10-min instantaneous values, whereas the observations are 10-min averages. 

However, we do not expect the modeled variation of wind speed within 10 min to be significant 

enough to cause a major discrepancy to the observed average wind speeds. To minimize any 

potential concerns, the model was validated with hourly values as well. This also indicates

whether the computational expense of having more frequent model output can be justified. Wind 

profiles were not evaluated because the maximum height of the measurements is only 50 m.

3 Sensitivity Study

The results of the WRF simulations are dependent on the inputs or the choices made by the user. 

Moreover, the more confidence there is in the configuration of the NWP model, the more 

valuable wind resource assessments are. Therefore, a sensitivity analysis was carried out to 

identify the best model setup. That best model setup was then used to conduct the final one-year 

wind resource assessment.

The main setup was motivated by a previous successful WRF modeling of wind resource 

estimates of the United States of America (Draxl et al, 2013). However, because the atmospheric 

peculiarities are different in Gujarat than in the United States, the WRF setup for the U.S.A. was 

taken only as a basis, and a sensitivity analysis was carried out.
13 

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Table 2: Error Metrics Used in the Analysis

Metric Description 

Root-mean-squarederror (RMSE) 

RMSE2 = 1

Np

� (Fk − Ok)2 = CRMSE2 + BIAS Np 2

k=0 ; Np is the number of 

available forecast (F) – observation (O) pairs. The RMSE can be split into the 

systematic and random components (BIAS and CRMSE) of the RMSE

(Taylor, 2001).

Bias (BIAS) 

BIAS = F� − O�; F� and O� are the forecast and observation averages over Np

values. The bias is the systematic component of the error and describes the 

differences in the means of two time series.

Centered-root-meansquared-error (CRMSE) 

CRMSE = � 1

Np

∑ [(Fk − F� Np

k=0 ) − (Ok − O�)]2; The CRMSE is the random 

component of the error, and describes the centered pattern of the error, the 

differences in wind speed variations around the mean.

Rank Correlation The rank correlation reflects the strength of a monotone relationship between 

two variables.

Normalized RMSE RMSE

O� � ; The RMSE is divided by the observation average over all the values.

Wind distribution error 

(WDE) 

The WDE is a measure of how closely the distribution of wind speed of a set 

of predicted values matches the distribution of the observed values. 

WDE = ∑ (Ḟ

i−Fi)2

Fi

Nf

k=0 ; Nfis the number of bins in the frequency distribution, Fi

is the frequency of the i

th bin of the observed distribution, and Ḟ

i is the 

frequency of the i

th bin of the predicted distribution (Windographer, 2014). 

3.1 Model Setup

A sensitivity study was carried out to find the best model setup for the long-term high resolution 

model runs. For that purpose, WRF was run in hindcast mode for 2 weeks in each of the 3 

seasons in Gujarat (i.e., 6 weeks), 1–15 Jan, 1–15 April, and 1–15 August. Eight different 

experiments were chosen to determine the best setup in terms of horizontal and vertical 

resolution, PBL scheme, temporal resolution, and boundary conditions. The different 

experiments are summarized in Table 3. The differences between the simulations of each 

experiment are listed in this table. All the simulations have the same first model level to exclude 

differences in output based on the lowest model level (Shin et al, 2012).

3.2 Results from Sensitivity Study and Implications

We evaluated the simulations at five measurement stations in Gujarat. The histograms for each 

of the five sites can be seen in Figure 2. In general, the tail of the model distributions shows 

higher values than observed, whereas in the center of the distribution, the modeled wind speeds 

are lower than observed. This results in a positive bias for all the stations and experiments. This 

behavior may be a result of the observed wind speeds being measured at 50 m, while the 

modeled data is for 80 m. WRF is also generally known to have a positive bias in wind speed at 

low levels, mainly because it underpredicts the frequency of light winds. It is also observed that 

the distribution from the simulations using the ECMWF ERA Interim data as boundary 
14 

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conditions is very different and positively biased. The simulations using 69 vertical levels and 

the modeled wind speeds that were averaged to 10 min from 1 min model output show similar 

inaccuracies: distributions that are too wide and wind speeds that are too high. Using the wind 

distribution error, the simulations with experiment MYJ_41L perform best for 50-m wind 

speeds.

Comparing average diurnal cycles of wind speed and temperature (not shown), the experiments 

with 69 levels, ECMWF boundary conditions, and 1-min model output show the same behavior 

as described above, i.e., worse than the other experiments.

Wind roses for the observations show mainly three wind directions: southwest, northwest, and 

northeast (Figure 3 shows an example for Balava). The simulations with ECMWF boundary 

conditions miss the northwest component completely, while the runs using 1-min model output 

and 69 levels capture only the southwesterly component. All the other runs capture the wind 

direction signal satisfactorily. 

Table 3. Experiments for Sensitivity Study 

Experiment Vertical 

Levels

Boundary 

Conditions 

PBL 

Scheme 

Output 

Frequency 

Horizontal 

Grid 

Spacing 

MYJ_41L 41 GFS MYJ 10 min 1.1 km

MYJ_41L_ECMWF 41 ECMWF MYJ 10 min 1.1 km

MYJ_41_1min 41 GFS MYJ 1 min 1.1 km

QNSE_41L 41 GFS QNSE 10 min 1.1 km

YSU_41L 41 GFS YSU 10 min 1.1 km

MYJ_41L_3km 41 GFS MYJ 10 min 3.3 km

MYJ_69L 69 GFS MYJ 10 min 1.1 km
15 

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Figure 2: Histograms of wind speed for all the experiments and five locations, as indicated by the 

legend and title, respectively. Modeled wind speeds are at 80 m, observed at 50 m. 

0 5 10 15 20 25 30 0

0.05

0.1

0.15

0.2

0.25

wind speed [m/s]

relative frequency

Balava

MYJ_41L

MYJ_41L_1min

MYJ_41L_D03

MYJ_41L_ECMWF

MYJ_69L

QNSE_41L

YSU_41L

OBS 50m

0 5 10 15 20 25 30 0

0.05

0.1

0.15

0.2

0.25

wind speed [m/s]

relative frequency

Jegawa

MYJ_41L

MYJ_41L_1min

MYJ_41L_D03

MYJ_41L_ECMWF

MYJ_69L

QNSE_41L

YSU_41L

OBS 50m

0 5 10 15 20 25 30 0

0.05

0.1

0.15

0.2

0.25

wind speed [m/s]

relative frequency

Virewa

MYJ_41L

MYJ_41L_1min

MYJ_41L_D03

MYJ_41L_ECMWF

MYJ_69L

QNSE_41L

YSU_41L

OBS 50m

0 5 10 15 20 25 30 0

0.05

0.1

0.15

0.2

0.25

wind speed [m/s]

relative frequency

Sarva

MYJ_41L

MYJ_41L_1min

MYJ_41L_D03

MYJ_41L_ECMWF

MYJ_69L

QNSE_41L

YSU_41L

OBS 50m

0 5 10 15 20 25 30 0

0.05

0.1

0.15

0.2

0.25

wind speed [m/s]

relative frequency

Jikiali

MYJ_41L

MYJ_41L_1min

MYJ_41L_D03

MYJ_41L_ECMWF

MYJ_69L

QNSE_41L

YSU_41L

OBS 50m
16 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 3: Wind roses for modeled wind speeds in meters per second at 112 m and observed at 50 

m (bottom right) for Balava. 
17 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

We further evaluated error metrics (RMSE, bias, CRMSE, rank correlation, and bias of potential 

GWh/year; bias is modeled minus observed wind speeds). The error metrics for experiments 

MYJ_69L, MYJ_41L_ECMWF, and MYJ_41L_1min are extremely bad as indicated by the 

histograms, with RMSE values on the order of 10 m/s. The experiments that performed the best 

are MYJ_41L, MYJ_41L_D03, and YSU_41L. A more detailed analysis was carried out among 

the three best performing experiments (Table 4 and Table 5). 

Table 4. Error Metrics for the Sensitivity Study for 50 m Wind Speeds Averaged for All the Sites

Experiment RMSE

[m/s]

Bias 

[m/s]

CRMSE

[m/s]

Correlation [-] MAE

[m/s]

Norm. 

RMSE [-] 

WDE

[-] 

Bias

[GWh/year] 

MYJ_41L_D03 2.08 0.35 2.04 0.59 1.61 0.33 1403 0.53

MYJ_41L 2.04 0.37 1.96 0.58 1.60 0.32 1220 0.50

YSU_41L 2.03 0.42 1.95 0.59 1.58 0.32 1704 0.54

Table 5: Error Metrics for the Sensitivity Study for 112 m Wind Speeds Averaged for All the Sites.

Experiment RMSE

[m/s]

Bias 

[m/s]

CRMSE

[m/s]

Correlation 

[-] 

MAE

[m/s]

Norm. RMSE

[-] 

WDE 

[-] 

MYJ_41L_D03 2.68 1.20 2.38 0.63 2.11 0.39 7577

MYJ_41L 2.65 1.21 2.33 0.61 2.10 0.38 8233

YSU_41L 2.57 1.06 2.32 0.62 2.03 0.37 6663

The model runs using the MYJ PBL scheme and 41 vertical levels with a grid spacing of 3.3 km 

performed nearly as well as the runs using a grid spacing of 1.1 km. For the year-long runs we 

chose the 1.1-km grid spacing, because the error metrics were slightly better. For multi-year 

simulations or follow-up projects, a reassessment of additional value versus computational 

expense might be necessary for flat terrains like Gujarat. This finding is in line with current 

research, as model runs at a horizontal resolution of ~1 km do not necessarily produce more 

accurate results (as measured through point-wise metrics such as the ones we chose) than coarser 

resolution runs (Wyngaard, 2004). Research in this area should evaluate if ramping events or 

variability signals show differences, as mean error metrics tend to be favored by coarser grid 

spacing. The WRF setup was motivated by wind resource studies in the United States and 

optimized for 41 levels. We suggest that increasing the number of levels to 69 might have altered 

the background static stability, which resulted in poorly performing simulations. 

The simulations using the YSU boundary layer scheme performed slightly better during low 

winds (0–3 m/s) using the RMSE, bias, MAE, and rank correlation. Taking the wind speeds into 

account that are most important for wind energy (i.e., 3.5–25 m/s), the wind distribution error, 

and the potential GWh/year using the whole wind speed range, the MYJ PBL scheme 

outperformed all the other schemes. Because the differences in error metrics between the YSU 

and MYJ PBL scheme are very small (tenths of meters per second), and the MYJ PBL scheme 

has an option for turbulence kinetic energy output that might be relevant for future studies, we 
18 

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used the MYJ boundary layer scheme to model the wind resource for one year. These 

simulations have a horizontal grid spacing of 1.1 km, 41 vertical levels, and use GFS boundary 

conditions. 

4 Gujarat’s Wind Resource

Based on the validation results from the sensitivity study, the WRF was run for the year 2011 

with 41 vertical levels and the MYJ PBL scheme. GFS data and sea surface temperatures were 

used as boundary conditions (experiment MYJ_41L in Table 3). The model output from domain 

4 with a 1.1-km grid spacing serves as the basis for the wind resource estimate for Gujarat. Data 

were converted into monthly and annual average wind speeds for each point on the grid, as 

required. 

4.1 Results

A hub height of greater than 80 m would most likely be better for energy production given the 

obstructions and thick ground cover in Gujarat. However, the deployment of turbines with a hub 

height closer to 80 m will be likely in the near term due to the current high costs of turbines with 

taller hub heights and any regulations that may limit the height of turbines. This report presents 

the annual averages of wind speed at both 80 m and 112 m and the monthly averages at 80 m 

only. 

Figure 4 to Figure 6 show maps of average horizontal wind speed for the State of Gujarat, 

including the area 20 miles off the coast that might initially be explored for offshore wind 

energy. Several aspects influence the wind distribution in a region, such as topography, land 

cover, distribution of land and sea, and atmospheric pressure gradients. Therefore there may be 

local variations that are not captured in these maps. In general, offshore wind speeds are higher 

than those on land, which is similar to conditions in Europe and the United States. 

Considering the usual seasonal cycle that can be expected in India (section 2.3) and the 

validation results at the five measurement sites (section 4.2), the wind speed is highest in the 

summer from May to August, with maximum average wind speeds exceeding 10 m/s, especially 

along the coasts. The wind speed is lowest in October and November with average wind speeds 

not exceeding 7 m/s at coastal sites. 

Annual average wind speeds at 112 m above the surface are higher than at 80 m (Figure 4 and 

Figure 5). Average annual wind speeds at 80 m reach 8 m/s around the coast. The majority of the 

Gulf of Khambhat experiences annual averages of 6-7 m/s. At specific sites on the mainland, 

especially in northwest Gujarat and north of the north coast of the Gulf of Kutch, wind resources 

are abundant. 

Depending on the turbine type, wind resources are usually viable for wind energy applications at 

annual average wind speeds greater than approximately 6 m/s. For wind turbines with larger 

rotors, lower wind speeds become viable. That lower limit for wind resource assessment may 

change with future advances in wind turbine technology.
19 

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Figure 4: Annual average wind speeds at 80 m. 
20 

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Figure 5: Annual average wind speeds at 112 m. 
21 

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Figure 6: Average wind speeds for every month during 2011. 
22 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

4.2 Wind Resource Validation

The one-year-long model simulations were compared to measurement data from five 

measurement sites throughout Gujarat. This enabled an estimate of the model performance and 

validation of the wind resource estimate. This validation gives a good indication of whether our 

wind maps can be trusted. Because the measurements were only available at 10 m and 50 m, we 

validated the model with the 50-m measurements. 

The validation includes various aspects: 

a) Temporal resolution: Usually, NWP models used for wind resource assessment produce 

output every hour at the most. We ran our NWP model producing 10-min output to 

determine if a higher temporal resolution would lead to more accurate results. 

b) Diurnal cycles: In addition to yearly bias and RMSE, we were interested in whether or 

not the model was able to capture a diurnal cycle in wind speed, which is associated with 

the transitions from a stable PBL at night to a convective PBL during the day. 

c) Seasonal and annual cycles: Because India’s seasons are greatly influenced by the 

monsoon, we wanted to see if the model reproduces seasonal signals in the data. This was 

checked using with wind roses and annual cycles. 

These aspects are discussed in detail in the following sections.

4.2.1 Histograms

In general, the frequency distributions of the simulated wind speeds at 50 m correspond well 

with those observed at all the five sites (Figure 7). WRF tends to exhibit lower frequencies in the 

peak region between 4 and 8 m/s and to overestimate wind speeds at the right tail of the 

distribution. 

4.2.2 Error Metrics

For the 10-min instantaneous modeled and 10-min sustained observed wind speeds, the RMSE is 

approximately 2 m/s at all the sites. The normalized RMSE is around 0.4. According to our

experience, this is a reasonable result for NWP modeling. This RMSE can be decomposed in the 

CRMSE and the bias. The bias is mostly positive for all the sites except for Jikiali, where it is 

very slightly negative. Balava has the highest bias (0.73 m/s), followed by Virewa with a bias of 

0.52 m/s. The other sites have biases lower than 0.1 m/s. The biggest part of the RMSE

constitutes the CRMSE, which describes the random component of the error and the intrinsic 

skill of the model. CRMSE ranges are between 1.83 and 2 m/s. The rank correlation shows 

reasonable values for all the sites, and the MAE reaches values between 1.5 and 1.6 m/s. The 

lowest wind distribution error is at Jegawa, the highest at Balava. Figure 7 confirms these results.
23 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 7: Histograms of observed (dashed lines) and simulated (solid lines) wind speed at 50 m at 

the five measurement sites as indicated in the title of each panel. 
24 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 6. Error Metrics for 2011 for 10-min Model Output for 50 m in meters per second

Site RMSE

[m/s]

Bias

[m/s]

CRMSE

[m/s]

Correlation 

[-] 

MAE

[m/s]

Norm. RMSE

[-] 

WDE

[m/s]

Balava 1.98 0.73 1.83 0.67 1.55 0.35 13,510

Jikiali 1.93 -0.03 1.93 0.58 1.57 0.36 3,806

Sarva 2.01 0.09 2.00 0.63 1.51 0.37 1,199

Virewa 1.98 0.52 1.91 0.62 1.56 0.36 6,727

Jegawa 1.97 0.08 1.97 0.67 1.47 0.36 898

4.2.3 Impact of Temporal Resolution

Because NWP models used for wind resource assessment usually produce hourly outputs, we 

examined whether high temporal resolution data, i.e., outputs every 10 min, would lead to more 

accurate results. As expected, using hourly values gives lower but similar error metrics (Table 7). 

For hourly model output the CRMSE was slightly reduced. This leads to a lower RMSE as well. 

The rank correlation values were degraded for hourly output, but the MAE decreased. The 

normalized RMSE is about the same. The WDE decreased considerably. This degradation might 

be due to the fact that instantaneous hourly model output is not representative of intra-hourly 

wind variations that would be captured with 10-min model output. This is a very positive aspect 

of using high temporal resolution data. The overall wind resource estimate was not altered 

significantly by using 10-min instead of hourly data. For future projects, using hourly data for 

wind resource estimates might suffice for sites with a climate similar to that of Gujarat. 

Table 7. Error Metrics for 2011 for Hourly Model Output for 50 m in Meters per Second. (A more 

detailed breakdown of RMSE and bias into diurnal cycles per month for all the 5 stations is in the 

appendix, Figure 28 through Figure 37).

Site RMSE

[m/s]

Bias

[m/s]

CRMSE

[m/s]

Correlation 

[-] 

MAE

[m/s]

Norm. RMSE

[-] 

WDE

[m/s]

Balava 1.81 0.77 1.64 0.68 1.45 0.34 783

Jikiali 1.85 -0.67 1.72 0.44 1.56 0.35 924

Sarva 1.77 0.11 1.76 0.57 1.28 0.33 252

Virewa 1.91 0.34 1.88 0.60 1.55 0.32 253

Jegawa 1.86 0.02 1.86 0.51 1.39 0.34 118

4.2.4 Diurnal Cycles

Diurnal cycles of simulated wind speed at 10 m, 50 m, 80 m, and 112 m and for observed wind 

speeds at 10 m and 50 m are shown in Figure 8 for Jegawa. Diurnal cycles for the other locations 

are shown in the appendix. The data were averaged for every hour. Plots of diurnal cycles allow

for a more detailed analysis of how well the model captures transitions from stable to convective 

boundary layers and diurnal variations throughout the day. The diurnal cycles are very well 

captured at most sites at 50 m.
25 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Diurnal cycles of modeled temperature, atmospheric stability, and boundary layer height, and of 

observed temperature were qualitatively examined (not shown). Results appeared reasonable. 

As expected, wind speeds are lower during a convective boundary layer during the day than at 

night, when low-level jets might increase wind speeds above 50 m. On the contrary, wind speeds 

near the surface at 10 m are lower during the night. Observed winds at 10 m are mostly 

overpredicted throughout the day, which might also be due to measurement errors or sensors 

being installed in non-representative locations. Diurnal cycles of wind speeds at 50 m correspond 

very well to those observed, except for Jikiali and Balava. These two locations are closest to the 

ocean and might be influenced by land-sea breezes. 

4.2.5 Wind Roses

Because the simulated wind direction at 50 m does not differ considerably from that at 80 m, 

wind roses with observed directions at 50 m are compared to those simulated for a potential hub 

height of 80 m in Figure 9. 

The wind roses capture the wind directions very well. The simulated wind direction signal show 

a stronger southwest signal than observed for all stations but Sarva. These wind roses are the 

combined wind signals for 2011. A breakdown per month for all the locations shows a strong 

monthly signal of usually one dominant wind direction (see data for Balava in Figure 22 and 

Figure 23 in the appendix). The strong southwest summer monsoon and weaker northeast winter 

monsoon can be seen clearly. The modeled wind directions correspond very well to the observed 

ones, which can be seen as an indication that the model captures seasonal signals. The slight 

overprediction of modeled wind speeds is seen through the darker colors in the modeled wind 

roses. 

4.2.6 Annual Cycle

Figure 10 shows annual cycles of simulated wind speed at Sarva at 50 m, 80 m, and 112 m, and 

observed wind speeds at 50 m in the first panel; wind speed bias for winds at 50 m (simulated 

minus observed) in the second panel; and normalized RMSE of wind speeds at 50 m in the third 

panel. Annual cycles for all the other stations can be found in the appendix (Figures 24–27).

At all locations, the modeled wind speed increases with height, and WRF tends to underpredict 

wind speeds in the winter and overpredict them in the summer. For Balava, WRF overpredicts in 

all the months except October and November. This overprediction in the summer is also 

reflected in the bias, which is highest in the summer and lower in the winter at all the sites. It is 

therefore mostly negative during winter months and positive during summer months. That cycle 

is also visible for Balava, although the bias is only negative during October and November. At 

Virewa, November, December, and April are the only months with a slightly negative bias, and 

similar to other locations, the bias is highest in a summer month. Biases are mostly below 1 m/s, 

except for Virewa in June and July and Balava from May until August. 

The RMSE values are similar at all locations, and like the bias, the annual cycle of RMSE

exhibits lower values during the winter (not shown). Because wind speeds are higher during the 

summer and high RMSE values during high winds might be an artifact, we show the normalized 

RMSE (by observed wind speed) in each plot. The normalized RMSE is highest during October 

and November for all locations (except Balava) and lowest in early summer.
26 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 8: Average diurnal cycle of wind speeds at different heights for Jegawa as indicated in the 

panel titles. Simulated wind speeds are in blue, observed in green. 

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 10m wind speed

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 50m wind speed

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 80m wind speed

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 112m wind speed

Local time [h]
27 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 9: Wind roses for observed wind speeds in meters per second at 50 m (left column) and 

simulated wind speeds at 80 m (right column) for the five measurement sites. 

0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

N

S

W E

6 %

2 %

4 %

Calm:

0.5 %

<1m s−1

0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

N

S

W E

6 %

2 %

4 %

Calm:

0.7 %

<1m s−1

0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

N

S

W E

6 %

2 %

4 %

Calm:

1.0 %

<1m s−1

Balava

0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

N

S

W E

6 %

2 %

4 %

Calm:

0.5 %

<1m s−1

0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

N

S

W E

6 %

2 %

4 %

Calm:

1.5 %

<1m s−1

Jegawa

0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

N

S

W E

6 %

2 %

4 %

Calm:

0.7 %

<1m s−1

0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

N

S

W E

3 %

1 %

2 %

Calm:

0.5 %

<1m s−1

Virewa

0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

N

S

W E

6 %

2 %

4 %

Calm:

0.8 %

<1m s−1

0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

N

S

W E

6 %

2 %

4 %

Calm:

1.3 %

<1m s−1

Sarva

0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

N

S

W E

6 %

2 %

4 %

Calm:

1.0 %

<1m s−1

Observa ons 50 m Simula ons 80 m
28 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 10: Sarva’s annual cycle of simulated wind speed at 50 m, 80 m, and 112 m, and observed 

wind speeds at 50 m (panel a), wind speed bias for winds at 50 m (simulated minus observed; 

panel b), and normalized RMSE of wind speeds at 50 m (panel c). 

Apart from a monthly/seasonal signal in wind direction (see section above), we show histograms 

and wind speed time series per month in the Appendix to estimate wind resources in different 

months and to evaluate the model’s capability of capturing annual cycles. Figure 13 to Figure 17 

(in the appendix) show that the modeled wind speed frequency in each month is generally similar 

to the observed wind speed frequency. However, during some months the observed distributions 

do not follow the modeled ones.
29 

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4.3 Comparisons of Simulations with Existing Wind Resource 

Estimates for Gujarat 

This analysis adds to the existing literature by using the WRF model to estimate wind resource in 

Gujarat at 80 m and 112 m. The WRF model has been used in previous estimates as well, but 

with a lower temporal and horizontal resolution. While previous publications show the wind 

power density over the whole country, this report presents maps indicating wind speed, which in 

turn can be transformed to wind power densities using various methods. 

Phadke et al. (2011) found that Gujarat is one of the states with the highest wind energy 

potential, most of which is concentrated in the northwestern part of the state north of the Gulf of 

Kutch. In our analysis, the yearly average for 2011 shows the highest potential at the same 

location but also shows a similarly high potential along the south coast of the peninsula (offshore 

and on the coast between Sarakhadi Gam and Gadhula). 

The following maps are available from CWET (Gomathinayam and Boopathi, 2014). 

Qualitatively, the areas of highest wind resource in Gujarat agree with our findings (Figure 11).

 

Figure 11: Wind power density at 50 m (left) and 80 m (right). From CWET (CWET 2014). 

The International Renewable Energy Agency (IRENA, 2014) publishes worldwide wind maps. A 

5-km gridded onshore wind speed estimate is available for Gujarat at 80 m above ground. 

According to IRENA, these data were provided by 3TIER. A screenshot of the IRENA estimate 

is shown in Figure 12. 
30 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 12: Screenshot of wind speed estimates from Irena (http://irena.masdar.ac.ae/?map=178, 

accessed May 2014). 

According to the IRENA map, the highest wind resource is again in the Gulf of Kutch, but also 

in the Gulf of Khambhat, followed by the northwest of Gujarat and the southern tip of the 

peninsula. This corresponds to findings presented here of wind speeds between 6 and 7 m/s near 

the coasts, and maximum average winds of 9 m/s. We did not identify the Gulf of Khambhat as 

having the high wind potential indicated by IRENA. 

Recent ground measurements performed by CWET indicate that the wind speed north of the Gulf 

of Kutch may be higher than the wind speeds modeled in our study (K. Boopathi, CWET, 

personal communication, June 2014). However, these ground measured data were not available 

for comparison in this analysis. 

5 Summary and Conclusion 

A wind resource assessment for the Indian state of Gujarat has been carried out. This resource 

assessment was made using the WRF model and validated against data from five sites across 

Gujarat. WRF was chosen because of its applicability to a wide range of atmospheric conditions, 

and successful use for resource estimates in other locations. Furthermore, we wanted to make the 

results reproducible by using a model with open access. The model setup was chosen based on 

previous modeling experiences and the domain setup was constrained by computational limits. 

Prior to the year-long simulations, we carried out a sensitivity study using two weeks each in 

January, April, and August 2011. This sensitivity study therefore covers the main seasons, and 

serves to find the optimal boundary conditions, grid spacings, and physics settings in the model 

that give the most accurate results for Gujarat. The sensitivity study showed that finer horizontal 

resolution results in better resource estimates, but that the increase in accuracy between a grid 

spacing of 1.1 km and 3.3 km is minor, and therefore might not be worth the additional 

Gulf of Kutch

Gulf of Khambhat
31 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

computational expense. Error metrics between these two grid spacings were similar. We further 

investigated whether a temporal resolution of 1 min, 10 min, or 1 hour would yield best results 

for wind energy estimates. We found that averaging 1-min WRF model output to 10 min resulted 

in worse and inaccurate wind speeds. Hourly model output resulted in slightly better error 

metrics. Again, the higher computational expense to create higher temporal resolution model 

output is not worth the effort. We therefore used 10-min model output for the year-long 

simulations. We further found that the ERA Interim boundary conditions yield very poor wind 

speed estimates. This is surprising in that the ERA Interim is known to produce more accurate 

results in Europe (Carvalho et al, 2013). For the Indian subcontinent however, we recommend 

using GFS boundary conditions. 

We compared our simulations to measurements at five locations throughout Gujarat. We 

validated the simulations against observations using traditional error metrics such as RMSE, 

MAE, and bias; we also used rank correlation, CRMSE, normalized RMSE, and the wind 

distribution error. Annual and diurnal cycles of wind speed, wind roses, and histograms of wind 

speed shed further light onto the behavior of the WRF model. Although highly relevant in wind 

energy, we refrained from analyzing wind profiles, because observations were only available at 

10 m and 50 m. Instead, we offer wind maps at 80 m and 112 m. Sudden changes in wind speed 

(ramp events) and variability are important for grid integration purposes, but were not taken into 

account either, as grid integration topics are beyond the scope of this study.1

Validation results showed a very good agreement between modeled and observed winds. A small 

positive bias was found, leading to an estimated overprediction of less than 0.8 m/s for the 

annual average. These and the other results presented in this report are very encouraging and 

give us confidence in an accurate wind resource assessment.

For the future, error estimates would add high value to wind resource maps. Those can be done 

without access to additional observations. Methods to estimate the uncertainty of the wind 

resource include ensembles, using data assimilation techniques and/or adjoints. Statistical 

methods that relate the one-year runs to long-term estimates such as the Analog Ensemble 

method (Delle Monache, 2013) or the Bias Correction Spatial Disaggregation method (Wood et 

al. 2004) could be employed to yield a long-term resource characterization of approximately 20 

years.

Previous analyses of the wind resource in India, and specifically near Gujarat, indicated a wide 

range of estimates. This report aims at providing a further estimate to reduce the ambiguity of 

past estimates. While previous resource assessments presented wind power density, our report 

focuses on average wind speeds, which can be converted to wind power densities by the user 

with methods of their choice. 

Our wind resource estimate corresponds to existing estimates and shows regions with average 

annual wind speeds of more than 8 m/s. These areas are concentrated in the Gulf of Kutch and on 

the south coast of the peninsula. Consistent with the usual seasonal cycle in India and with the 

 

1 NREL is currently conducting a wind grid integration analysis for Gujarat. The report is scheduled to be published 

in fall 2014. 
32 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

validation results at five measurement sites, the wind speed is highest in the summer from May 

to August with maximum average wind speeds of higher than 10 m/s, especially along the coast 

lines. The wind speed is lowest in October and November with average wind speeds not 

exceeding 7 m/s at coastal sites. The topography, land cover, and distribution of land and sea 

result in varied winds in Gujarat. In general, offshore wind resources are higher than those on 

land, as expected. Annual average wind speeds are higher at 112 m than at 80 m. 
33 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

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36 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Appendix 

Figure 13: Histograms for each month in 2011 of 50 m observed (dashed lines) and simulated 

(solid lines) wind speeds for Balava 

0 10 20

0

0.1

0.2

0.3

January

relative frequency

0 10 20

0

0.1

0.2

0.3

February

0 10 20

0

0.1

0.2

0.3

March

0 10 20

0

0.1

0.2

0.3

April

relative frequency

0 10 20

0

0.1

0.2

0.3

May

0 10 20

0

0.1

0.2

0.3

June

0 10 20

0

0.1

0.2

0.3

July

relative frequency

0 10 20

0

0.1

0.2

0.3

August

0 10 20

0

0.1

0.2

0.3

September

0 10 20

0

0.1

0.2

0.3

October

relative frequency

wind speed [m/s]

0 10 20

0

0.1

0.2

0.3

November

wind speed [m/s]

0 10 20

0

0.1

0.2

0.3

December

wind speed [m/s]
37 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 14: Histograms for each month in 2011 of 50 m observed (dashed lines) and simulated 

(solid lines) wind speeds for Jegawa. 

0 10 20

0

0.1

0.2

0.3

January

relative frequency

0 10 20

0

0.1

0.2

0.3

February

0 10 20

0

0.1

0.2

0.3

March

0 10 20

0

0.1

0.2

0.3

April

relative frequency

0 10 20

0

0.1

0.2

0.3

May

0 10 20

0

0.1

0.2

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June

0 10 20

0

0.1

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July

relative frequency

0 10 20

0

0.1

0.2

0.3

August

0 10 20

0

0.1

0.2

0.3

September

0 10 20

0

0.1

0.2

0.3

October

relative frequency

wind speed [m/s]

0 10 20

0

0.1

0.2

0.3

November

wind speed [m/s]

0 10 20

0

0.1

0.2

0.3

December

wind speed [m/s]
38 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 15: Histograms for each month in 2011 of 50 m observed (dashed lines) and simulated 

(solid lines) wind speeds for Virewa. 

0 10 20

0

0.1

0.2

0.3

January

relative frequency

0 10 20

0

0.1

0.2

0.3

February

0 10 20

0

0.1

0.2

0.3

March

0 10 20

0

0.1

0.2

0.3

April

relative frequency

0 10 20

0

0.1

0.2

0.3

May

0 10 20

0

0.1

0.2

0.3

June

0 10 20

0

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0.3

July

relative frequency

0 10 20

0

0.1

0.2

0.3

August

0 10 20

0

0.1

0.2

0.3

September

0 10 20

0

0.1

0.2

0.3

October

relative frequency

wind speed [m/s]

0 10 20

0

0.1

0.2

0.3

November

wind speed [m/s]

0 10 20

0

0.1

0.2

0.3

December

wind speed [m/s]
39 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 16: Histograms for each month in 2011 of 50 m observed (dashed lines) and simulated 

(solid lines) wind speeds for Sarva. 

0 10 20

0

0.1

0.2

0.3

January

relative frequency

0 10 20

0

0.1

0.2

0.3

February

0 10 20

0

0.1

0.2

0.3

March

0 10 20

0

0.1

0.2

0.3

April

relative frequency

0 10 20

0

0.1

0.2

0.3

May

0 10 20

0

0.1

0.2

0.3

June

0 10 20

0

0.1

0.2

0.3

July

relative frequency

0 10 20

0

0.1

0.2

0.3

August

0 10 20

0

0.1

0.2

0.3

September

0 10 20

0

0.1

0.2

0.3

October

relative frequency

wind speed [m/s]

0 10 20

0

0.1

0.2

0.3

November

wind speed [m/s]

0 10 20

0

0.1

0.2

0.3

December

wind speed [m/s]
40 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 17: Histograms for each month in 2011 of 50 m observed (dashed lines) and simulated 

(solid lines) wind speeds for Jikiali. 

0 10 20

0

0.1

0.2

0.3

January

relative frequency

0 10 20

0

0.1

0.2

0.3

February

0 10 20

0

0.1

0.2

0.3

March

0 10 20

0

0.1

0.2

0.3

April

relative frequency

0 10 20

0

0.1

0.2

0.3

May

0 10 20

0

0.1

0.2

0.3

June

0 10 20

0

0.1

0.2

0.3

July

relative frequency

0 10 20

0

0.1

0.2

0.3

August

0 10 20

0

0.1

0.2

0.3

September

0 10 20

0

0.1

0.2

0.3

October

relative frequency

wind speed [m/s]

0 10 20

0

0.1

0.2

0.3

November

wind speed [m/s]

0 10 20

0

0.1

0.2

0.3

December

wind speed [m/s]
41 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 18: Average diurnal cycle of wind speeds at different heights as indicated in the panel titles 

for Virewa. Simulated wind speeds are in blue, observed in green. 

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 10m wind speed

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 50m wind speed

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 80m wind speed

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 112m wind speed

Local time [h]
42 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 19: Average diurnal cycle of wind speeds at different heights as indicated in the panel titles 

for Sarva. Simulated wind speeds are in blue, observed in green. 

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 10m wind speed

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 50m wind speed

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 80m wind speed

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 112m wind speed

Local time [h]
43 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 20: Average diurnal cycle of wind speeds at different heights as indicated in the panel titles 

for Jikiali. Simulated wind speeds are in blue, observed in green. 

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 10m wind speed

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 50m wind speed

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 80m wind speed

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 112m wind speed

Local time [h]
44 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 21: Average diurnal cycle of wind speeds at different heights as indicated in the panel titles 

for Balava. Simulated wind speeds are in blue, observed in green. 

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 10m wind speed

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 50m wind speed

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 80m wind speed

9 12 15 18 21 24 3 6

2

3

4

5

6

7

8

9

wind speed [m/s]

Average diurnal cycle of 112m wind speed

Local time [h]
45 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 22: Wind roses for every month of the year 2011 as modeled by WRF for 80 m for Balava. 
46 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 23: Wind roses of measured wind speed and direction for every month of the year 2011 for 

50 m for Balava. 
47 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 24: Annual cycle of simulated wind speed at 50 m, 80 m, and 112 m, and observed wind 

speeds at 50 m (panel a), wind speed bias for winds at 50 m (simulated minus observed; panel b), 

and normalized RMSE of wind speeds at 50 m (panel c) for Jikiali. 
48 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 25: Annual cycle of simulated wind speed at 50 m, 80 m, and 112 m, and observed wind 

speeds at 50 m (panel a), wind speed bias for winds at 50 m (simulated minus observed; panel b), 

and normalized RMSE of wind speeds at 50 m (panel c) for Balava. 
49 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 26: Annual cycle of simulated wind speed at 50 m, 80 m, and 112 m, and observed wind 

speeds at 50 m (panel a), wind speed bias for winds at 50 m (simulated minus observed; panel b), 

and normalized RMSE of wind speeds at 50 m (panel c) for Jegawa. 
50 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 27: Annual cycle of simulated wind speed at 50 m, 80 m, and 112 m, and observed wind 

speeds at 50 m (panel a), wind speed bias for winds at 50 m (simulated minus observed; panel b), 

and normalized RMSE of wind speeds at 50 m (panel c) for Virewa. 
51 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 28: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the winter months 

(January, February, March, October, November, December) for Jegawa. 

Figure 29: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the winter months 

(January, February, March, October, November, December) for Virewa. 
52 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 30: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the winter months 

(January, February, March, October, November, December) for Sarva. 

Figure 31: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the winter months 

(January, February, March, October, November, December) for Jikiali. 
53 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 32: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the winter months 

(January, February, March, October, November, December) for Balava. 

Figure 33: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the summer months 

(April, May, June, July, August, September) for Jegawa. 
54 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 34: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the summer months 

(April, May, June, July, August, September) for Virewa. 

Figure 35: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the summer months 

(April, May, June, July, August, September) for Sarva. 
55 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 36: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the summer months 

(April, May, June, July, August, September) for Jikiali. 

Figure 37: Diurnal cycle of (a) bias and (b) RMSE of wind speeds at 50 m for the summer months 

(April, May, June, July, August, September) for Balava.