Evaluation of weather research and forecasting model for the assessment of wind resource over Gharo, Pakistan

INTERNATIONAL JOURNAL OF CLIMATOLOGYInt. J. Climatol. (2014)Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/joc.4089

Evaluation of weather research and forecasting modelfor the assessment of wind resource over Gharo, Pakistan

Muhammad Amjad,* Qudsia Zafar, Firdos Khan and Muhammad Munir SheikhClimatology Section, Global Change Impact Studies Centre (GCISC), National Centre for Physics (NCP), Islamabad, Pakistan

ABSTRACT: Weather research and forecasting (WRF) model is the state-of-the-art mesoscale model that could be used as aguideline to effectively assess the wind resource of Gharo wind station lying in the coastal belt of Pakistan. The anemometerheights of 10 and 30 m for the year 2005 have been used to study the wind profile of the region for summer (June, July, August,September) and winter (December, January, February, March). The study uses an innovative approach for model comparisons,i.e. an eta-half level is added in the model on 60 m height and is interpolated to 30 m height by using well known power law.This is done by studying the diurnal variation of wind shear for the whole year of 2005 in order to reduce maximum possibleinterpolation error. For both seasons, the error measures of mean bias error (MBE), mean absolute error (MAE) and root meansquare error (RMSE) of 30 m interpolated data were found lower than 10 m height data with increased correlation (r). Abias correction methodology (best easy systematic estimator) was further applied over the model output showing a significantimprovement toward MBE, MAE and RMSE reduction, i.e. up to 99%, 73% and 68% on 10 m height and 99%, 51% and46% on 30 m height. Errors were reduced more for summer than winter. The selected bias correction methodology was thusfound to be highly applicable for both model heights. The wind energy assessment of Gharo wind station from the correctedmodel simulation showed summer having more potential for wind energy than winter with an estimated energy of up to1000 MWh.

KEY WORDS wind energy; statistical error estimation; bias correction; best easy systematic (BES) estimator; power law;WRF model

Received 31 January 2013; Revised 21 May 2014; Accepted 28 May 2014

1. Introduction

Under the subject of renewable energy in Pakistan, fea-sibility studies for on-shore wind resource assessmenthave been carried out as national projects and numerousnumerical, empirical and analytical methodologies havebeen developed with the help of in-situ measurements(Elliott, 2011; United Nations Development Programme(UNDP), 2008). Under such studies, Pakistan Meteoro-logical Department (PMD) in the year 2003 found out acorridor of wind-swept area (Chaudhry and Hussain, 2003)as around 60-km wide and 180-km long (Khan et al., 2012)extending upwards to the city of Hyderabad with a totalarea of approximately 24,450 km2, which was reaffirmedby National Renewable Energy Laboratories (USA) in theyear 2007 (Mirza et al., 2010).

‘Gharo’ wind station (24.74∘N, 67.59∘E) was selectedfor the initial wind energy development because of itslocation within the wind corridor having excellent windconditions with an annual average of more than 6 m s–1

at 30 m height (Mirza et al., 2008). Also the potentialsources of wind data out of 11 wind farm sites, currently

* Correspondence to: M. Amjad, Climatology Section, Global ChangeImpact Studies Centre (GCISC), National Centre for Physics (NCP)Complex Near Quaid-i-Azam University, P.O. QAU – 45320, Islamabad,Pakistan. E-mail: [email protected]; [email protected]

being pursued by the wind power projects in the Sindhcoastal belt of Pakistan, have been identified as Gharo andMirpur Sakro, out of which Mirpur Sakro has not beenconsidered because of the faulty recordings at 30 m (Mirzaet al., 2010). Gharo wind station has therefore been studiedvigorously up till now and is found to provide a total annualwind power potential of more than 1000 MWh (Chaudhryand Hussain, 2003; Chaudhry, 2009).

Lying in the Sindh province of Pakistan, the prevail-ing meteorological conditions of the Gharo wind site offergenerous wind harvesting conditions but are also depen-dent on the variability of wind. Gharo receives sum-mer precipitation through monsoon winds from July toSeptember associated with the monsoon depressions andsouthwesterly winds coming from the Arabian Sea, whilewestern disturbances originating from the Mediterraneangive rise to the winter precipitation.

The use of mesoscale models for the assessment of windpower potential can be one of the useful methodologies,as these models are independent of high-quality surfacewind data, required otherwise, and provide high resolu-tion wind maps and assessment information (Shimada andOhsawa, 2011). However, due to the model data incom-patibility caused by large systematic model biases (Chenet al., 2000) along with the lack of accuracy and complete-ness of the in-situ measurements that can be used for vali-dation, there are still many unknowns about the reliability

© 2014 Royal Meteorological Society

M. AMJAD et al.

of the mesoscale models for wind simulation (Shimada andOhsawa, 2011). In particular, their performance for simu-lating wind over Pakistani coastal terrain over surface andlevels above the surface has hardly ever been discussed.Keeping the uncertainty of these numerical models in view,an alternate famous approach of power law (Panofsky andDutton, 1984) for obtaining model level above the modelsurface to the desired height is used to minimize the sys-tematic errors induced by the model. Power law is the pre-ferred method in the wind resource assessment, for datainterpolation to heights above the surface, as it is easilyderived and gives the optimum performance (Kubik et al.,2013). The onshore coastal wind station of Gharo wasselected because of its reliable and almost complete in-situdata availability along with its previous wind speed andwind power assessments. The mesoscale weather researchand forecasting (WRF) model was evaluated over Gharowith the application of power law, in order to investigatethe reliability of the onshore simulated wind. A statisticalbias correction technique, the best easy systematic estima-tor (BES) (Wonnacott and Wonnacott, 1972) was imple-mented afterwards to remove the model bias. The biascorrection methodology is almost 40-years old but is stillpopularly used nowadays in the bias correction of tem-perature, precipitation, evaporation and sunshine variables(Woodcook and Engel, 2005; Doug and Stull, 2008; Engeland Ebert, 2012). In order to reduce the systematic biasesgenerated in the WRF simulated output, this study usesBES for the purpose of wind assessment, which has notbeen done up till now, in order to see its ability of gettingoptimum model output.

Our first focus is to evaluate the performance of WRFon annual and seasonal basis, for simulating wind speedsand second is to assess the effectiveness of bias correctionmethodology applied over the model data in the assess-ment of wind energy output. The analysis in the study hasbeen carried out more prominently on seasonal basis, inwhich summer season represents the months of June, July,August and September (JJAS); while winter season repre-sents December, January, February and March (DJFM).

2. Materials and methodology

For wind resource assessment applications, the recordedwind data of minimum 1 year is prepared and evaluatedagainst the historical climatic data for the region (Wolar,2008; Sultan Al-Yahyai et al., 2010) for which the year2005 was selected owing to its continuous and good qualityobservational data, simulating through WRF.

2.1. WRF model configuration

The Weather Research and Forecasting model version3.0 (Skamarock et al., 2008) is a three-dimensional,non-hydrostatic, mass-based terrain following coordinatemodel with parameterization schemes for microphysics,long- and shortwave radiation, surface processes, plane-tary boundary layer and cumulus processes. The modelintegrates the equations for air motion and uses physical

Domain - 1

Domain - 2

Figure 1. Model domains at 27 km resolution (domain 1) and 9 kmresolution (domain 2) including Gharo wind station (black dot).

parameterizations for unresolved, nonlinear processesto predict temperature, pressure, wind velocities, watervapour (mixing ratios, rainfall and other forms of pre-cipitation such as snow, ice, etc.) for three-dimensionaldomains (Prabha and Hoogenboom, 2008). The modelalso includes multiple nesting and four-dimensional dataassimilation (FDDA) options using grid nudging, whichenables it to hindcast meteorological conditions realisti-cally (Shimada and Ohsawa, 2011). This study makes useof the WRF model for simulation and evaluation of windspeeds for Gharo wind resource assessment.

The model was setup with the two-way nesting option forthe two domains (domain-1 at 27 km resolution; domain-2at 9 km resolution) focused over the Gharo wind sta-tion (Figure 1) with model configuration summarized inTable 1. Vertical levels selected were 31, with the lowesttwo-model levels maintained at the heights of 10 and 60m approximately above ground level, between the surfaceand 50 hPa pressure level. The initial and lateral boundaryconditions were provided using NCEP Final (FNL) Anal-ysis (www.dss.ucar.edu/datasets/ds083.2) available at 1∘× 1∘ spatial grid resolution and 6 h temporal resolution,having 24 vertical levels from surface to 10 hPa. The eleva-tion and land-use data for both domains were derived fromUnited States Geological Survey (USGS), 2 min topo-graphic for the outer domain and 30 s data for the innerdomains (www.wrf-model.org). Domain-1 (27 km resolu-tion) was simulated with a time slice of 3 h while domain-2(9 km resolution) was simulated with a time slice of 1 h.

The WRF model simulation for the year 2005 wasconducted on the Global Change Impact Studies Cen-tre’s (GCISC) High Performance Computing (HPC) Linuxcluster. The model output was stored at 3-hourly intervalsfor domain 1 and hourly intervals for domain 2.

2.2. Data preparation for validation of simulated windspeed

Wind speed simulated by WRF was evaluated against thein-situ measurements recorded at Gharo wind station. The

© 2014 Royal Meteorological Society Int. J. Climatol. (2014)

ASSESSMENT OF WIND RESOURCE OVER GHARO USING WRF MODEL

Table 1. WRF model configuration.

Simulation period Start: 00:00 UTC 31 December 2004End: 24:00 UTC 31 December 2005

Nesting Two-way nestingInput data NCEP FNL Analysis 6-hourly, 1∘×1∘Domain 1 Resolution: 27× 27 km2

Latitude: 18∘N to 34∘NLongitude: 57∘E to 74∘E

Domain 2 Resolution: 09× 09 km2

Latitude: 22∘N to 28∘NLongitude: 62∘E to 71∘E

Vertical layer Thirty-one levels, lowest level 1: 10 m;lowest level 2: 60 m (surface to50 hPa)

Physics options RRTM long wave radiationDudhian short wave radiationKain-Fritsch cumulusparameterizationYSU PBL parameterizationNoah land surface model

FDDA option Without FDDA options

station lies at an elevation of 6 m above sea level in aterrain that ranges from sparsely populated semi-desertarea with rare vegetation found in the western and north-ern directions of the Indus delta, while in eastern andsouthern directions, the region provides agricultural landwith a higher population density (Gose, 2008). The windspeed data collection process at a particular wind map-ping site involves an erected 30-m high tower, over which,the anemometers are installed at the heights of 10 and30 m, respectively with the data recording provision of1-min average for wind speed and 5- to 10-min averagefor other variables like temperature, maximum and mini-mum wind speed, etc. (Chaudhry and Hussain, 2003). Theacquired wind speed data was filtered for missing data val-ues and adjusted for model temporal resolution by con-version to Universal Time Coordinated (UTC) for modelcomparisons. The data was averaged out for hourly and3-hourly intervals as needed for model evaluation. A com-mon practice to interpolate model data to the anemometerheight level is to calculate the height of the eta-half coordi-nates and add an eta-half level at approximately this height.However this approach involves the model uncertainties atall height levels in simulating a wind profile limiting thescope of model validation only to the simulated wind pro-file. This work adds innovation toward model validation bystudying the real time wind shear characteristics of Gharowind station for the whole year of 2005 using it for modelheight level interpolations from model eta-half level to thedesired height level. For this purpose, a model height levelwas added at 60 m height and the interpolation was donefrom 60 to 30 m height of model level using a well knownapproach of power law (Equation (1)). For model valida-tion purposes, the vertical resolution of the model in theboundary layer was set according to the observations, lim-ited to only two height levels (10 and 30 m).

vvref

=(

zzref

)𝛼

(1)

Here, v is the desired wind speed at height z above groundlevel, vref is the reference wind speed at the referenceheight zref and 𝛼 is the wind shear coefficient. A straight-forward calculation of the wind shear coefficient involvesthe wind speed at two heights, in this case 10 and 30 m, andcan be determined by rearranging Equation (1) in terms of𝛼 (alpha):

𝛼 =ln(

vvref

)

ln(

zzref

) (2)

According to the 1/7th power law, 𝛼 is taken asapproximately 1/7 or 0.143 representing neutral stabilityconditions of the atmospheric by neglecting the effectsof buoyancy. However this constant exponent value canlead to conservative estimates of wind speed (Gipe, 2004).Previous studies have shown that the coefficient is depen-dent upon factors like, atmospheric conditions, time ofday, seasons of the year, topography, mean wind speedand direction which often leads to the greater coefficientvalues in comparison to the 1/7th power law resulting inerroneous energy output calculations (Firtin et al., 2011;Kubik et al., 2011).

For such reason, the interpolation of wind speed to 30m height level was done by calculating alpha at all timeslices representing the true variation of wind shear withheight at Gharo wind station, named as ‘true values’ ofalpha (denoted by ‘alpha_tv’), in the rest of this paper. It isunderstood that as long as power law is able to interpolatethe data with minimum errors to the desired height level,the choice of the model eta-half height level selection doesnot matter.

2.3. Wind shear analysis of Gharo wind station

The analysis of wind shear was done by choosing alphawith true values (Equation (2)) and alternate values such as0.143 (1/7th power law), 0.2 and 0.36 (Firtin et al., 2011).The coefficient of correlation (Willmott, 1981) was cal-culated in order to find out alpha that gives wind speedsimilar to the observations. However ‘correlation is nota very appropriate measure for error evaluation as highor statistically significant values of correlation (r) areoften unrelated to the sizes of the differences between theobserved and the estimated values with a good possibilityfor “small” differences to occur with low or even nega-tive values of “r”’ (Willmott and Wicks, 1980). Therefore‘r’ alone should not be considered for the error estima-tion. Accordingly root mean square error (RMSE) wasestimated which is also among the ‘best’ overall measuresof error estimation, as it summarizes the mean difference inthe units of the estimation and the observation (Willmott,1981).

The diurnal variation of simulated wind speeds inter-polated to 30 m height by using above obtained differentalpha coefficients, compared with the observed wind speedis shown in Figure 2. The lowest RMSE was obtained bythe wind speed having alpha coefficient of 0.36 but witha negative correlation with the observed wind depicting


M. AMJAD et al.

an unrealistic wind variation. The highest correlation of0.55 was obtained for the wind speed, interpolated usingthe true values of alpha, with RMSE of 1.36 m s–1 (sec-ond lowest). The possible cause of this RMSE could bethe clear difference of 2–3 m s–1 winds between the obser-vations and the interpolated wind using ‘alpha_tv’, henceeliminating this difference could further reduce the error,making it the best representable data for wind energy esti-mates. This also shows that it is useful to consider thediurnal profile of alpha on daily, monthly or yearly basisinstead of a constant value for reliable energy estimates.The diurnal variation of alpha at Gharo wind station for theyear 2005 is shown in Figure 3 which displays the hourlywind shear coefficients in a two-dimensional array form(Kubik et al., 2013). The alpha values on the horizontalaxis are presented in Pakistan standard time (PST) (00:00UTC is equivalent to 05:00 PST) for the whole year onhourly basis. A prominent diurnal pattern of shear coef-ficient showing greater values of alpha in the night time(6 p.m.–4 a.m.) than in the day time (5 a.m.–5 p.m.) canbe seen with a distinct central whiter area of lower alphavalues sometimes called the ‘solar shadow’ believed to becaused by the air parcel mixing due to sun heating in theday time (Kubik et al., 2013) as the warmer surface windrelative to the cooler upper level wind decreases the windshear in the daytime heating. Figure 3 also reveals a sea-sonal change in alpha values, i.e. alpha in summer months(June, July, August, September) is lower than in wintermonths (December, January, February, March) which isagain attributed to the longer day time in summer andincreased solar heating. The extreme wind shear valueswere very few with maximum value as high as 7.13 andthe minimum as low as –2.70. The distribution of alphavalues was found to be positively skewed (Figure 4) withan annual mean alpha of 0.36 on hourly data basis hav-ing a standard deviation of ±0.19 and an annual meanalpha of 0.40 on 3-hourly basis having the same stan-dard deviation of ±0.19 having maximum values of alphalying in the range 0.1–1.5 for Gharo wind station. Thewhite colour represents the missing or undefined alpha val-ues as a result of missing or zero values of the observedwind (Equation (2)), during alpha calculations. Keepingthe above in view true values of alpha were used to inter-polate the model wind speed from 60 to 30 m height level.

3. Bias correction

Direct model output usually has the systematic bias dueto the model resolution and physical parameterizationsused in the model and may contain additional biases wheninterpolated spatially and temporally for a specific siteand time. In their work, Stensrud and Skindlov, 1996 andMao et al., 1999, showed that bias was a major source ofdirect model output error, which is removed by adoptinga suitable bias correction methodology. The scope of thisstudy is focused over a robust bias correction technique,which should be able to withstand missing/extreme errors,permit modularity for detailed analysis and provide a

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Figure 2. Annual diurnal variation of wind speed (m s–1) for differ-ent alpha coefficients, over Gharo for the year 2005. ‘obs’ repre-sents observed wind speed at 30 m height. ‘alpha_tv’, ‘alpha_0.14’,‘alpha_0.2’ and ‘alpha_0.36’ represent interpolated wind speeds to 30

m height using true, 0.14, 0.2 and 0.36 values of alpha.

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Figure 3. Two-dimensional plot of true values of alpha (alpha_tv) overGharo in PST for the year 2005. White colour represents undefined alpha

values as a result of missing or zero values of the observed wind.

clear picture of the amplitude of errors affecting the finaloutcome (Woodcook and Engel, 2005).

For the selection of bias correction algorithm, we testedsimple linear regression, single exponential smoothingand Kalman filter on small samples. The simple run-ning BES (Wonnacott and Wonnacott, 1972; section 7.3)(Equation (3)) was finally chosen as a measure of the cen-tre of our historical sample as it performed better than allother algorithms. It was simple to implement and robustwith respect to the extreme values (Woodcook and Engel,2005).

BES = (Q1 + 2Q2 + Q3)4

(3)

Q1, Q2 and Q3 are the first, second and third quartiles,respectively. In literature, sample sizes or bias windowsof 7 days (Stensrud and Skindlov, 1996), 21 days (Maoet al., 1999) and 30 days (Young, 2002) have been taken.This study includes 11 bias windows in order to see the



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Figure 4. Distribution of true values of alpha (alpha_tv) over Gharo, forthe year 2005.

effect of bias correction beyond the chosen window sizeof 30 days. The algorithm works by performing quartilecalculations over the given running window data, whichis the difference between the simulated and the observeddata. The resultant value of BES obtained, is the correctionfactor for the next day’s data which is then subtractedfrom the simulated data for that day to get the correctedmodel data in order to compare it with observations. Thealgorithm was applied for summer and winter separatelyin order to estimate its performance at seasonal scale, forboth resolutions and heights.

4. Results and discussion

4.1. Model performance in simulating wind speedbefore bias correction

In order to see the accuracy of the model simulated wind,the annual wind speed obtained from large scale forcingdata of NCEP FNL (1∘ × 1∘) was compared with WRF (9×9 km) to see the added value of the model over NCEP FNL(Figure 5). It is clearly seen that the fine scale resolutionof WRF has a clear advantage over NCEP correspond-ing to the coastline. The high resolution simulationshows that Gharo is included in the small zone of5.5 m s–1 annual mean winds along with other similarzones stretched over the coastline, extending upwards to6 m s–1 wind zones and above. This detail cannot be seenin the NCEP FNL which shows large zones of higherwind speeds than the model, over the land and oceandistribution (Shimada and Ohsawa, 2011). The mean windclimatologies, mean bias error (MBE), mean absoluteerror (MAE), root mean square error (RMSE) and corre-lation coefficient (r) statistics were used for the statisticalcomparisons. For the purpose, the wind speed time serieswas extracted from WRF over the nearest grid-point valuefrom the Gharo wind station using the weighted area aver-age (WAA) approach over both domains. Two windowsizes of neighbouring 16-grid points (432 × 432) km2 and4-grid points (108 × 108) km2 were selected and testedfor averaging. It was found that there was no significanteffect on the averaged time series no matter what window

size was selected, considering which, the smaller windowwas selected to keep away from complexities. For theweighted averaging approach, we used the distances ofthe neighbouring grid points from the station locations asweights and calculated the WAA using Equation (4).

WAA =v1t,nw1 + v2t,nw2 + v3t,nw3 + v4t,nw4

w1 + w2 + w3 + w4

where, w1 =(d2

2d23d2

4

), w2 =

(d2

1d23d2

4

),

w3 =(d2

1d22d2

4

), and w4 =

(d2

1d22d2

3

)(4)

where, v1t,n, v2t,n, v3t,n and v4t,n are the time series for thefour-grid points selected with t= (0, 1, 2, … ) up to nvalues. d1, d2, d3 and d4 are the distances of the gridpoints from the station locations for calculating weightsw1,w2, w3 and w4. This method was extensively tested overdifferent data sets before its application and was found togive optimum weighted averages for the time series data.

The annual wind that blows most of the time overthe station is presented in Figure 6 for WRF-simulatedwind speed against the observations (station wind) andthe forcing wind (NCEP FNL). The comparison has beenmade possible by converting the hourly and three-hourlyobserved wind and the model wind to 6-hourly windaccording to the model input forcing wind (which is 6hourly). It can be seen that all three wind distributionsexhibit similar spread with overlapping peaks and tails.The observations and the forcing wind show that mostfrequent winds at the heights of 10 and 30 m obtainedafter conversion (from hourly to 3–6 hourly), blow overthe region in the range of 5–6 m s–1 most of the year,with forcing wind showing a prominent difference of morewinds in this range than the observed wind. Looking at thesimulated wind, the peaks of the distributions in all cases,are shifted to the right of observations by approximately1–2 m s–1, depicting that WRF over estimates the windspeed in all the cases while producing the spread of dis-tribution similar to the observations, and the forcing wind,showing its ability to capture Gharo region wind climatol-ogy realistically but inability toward simulating the exactwind speeds.

While looking at MBE (Figure 7(a)), the 10 m heighttime series at both resolutions showed larger biases thanthe 30 m height time series. On annual, summer and win-ter basis, a decrease in the bias from 10 to 30 m height was(0.99, 0.87 and 1.61) m s–1 for hourly data, and (0.87, 0.71and 1.58) m s–1 for 3-hourly data. These estimates of thelower biases in 30 m height data further improve our confi-dence in the right selection of the coefficient ‘alpha_tv’ forthe 30 m model height level interpolation. Summer monthsshow relatively larger biases than the winter months sug-gesting that WRF was not able to simulate the complexmonsoon wind system as effectively as the winter westerndisturbance system. The larger bias distance between thetwo height levels for winter months in comparison to thesummer months further confirm the existence of the solarshadow (Figure 5) which strengthens in summer owing tolower alpha values which in turn reduces the differencebetween the summer biases obtained from both heights.


M. AMJAD et al.

62E 63E 64E 65E 66E 67E 68E 69E 70E 71E62E 63E 64E 65E 66E 67E 68E 69E 70E 71E

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Figure 5. Annual mean wind speeds (m s–1) (a) NCEP FNL (1∘ × 1∘) and (b) WRF (9 × 9 km) at 10 m height, for the year 2005. Black dots showocean grids in both the illustrations.

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Figure 6. Annual frequency polygons for wind speed (m s–1) over Gharo for the year 2005. WRF and ‘Obs’ (station) winds converted from hourly(h) and 3- (3h) to 6-hourly (6h) winds for comparison with NCEP FNL winds (6h) at the heights of 10 and 30 m. (a) h to 6h wind at 10 m height (b)

h to 6h wind at 30 m height (c) 3h to 6h wind at 10 m height (d) 3h to 6h wind at 30 m height.

Reduced warming in winter increases the alpha valuesincreasing the overall difference of bias between the twoheights. The error estimates for all four time series is alsopresented and an error bar is used to show the variationof standard error, which is larger for 3-hourly data dueto a smaller dataset than the hourly data. Looking at thecorrelation coefficients (Figure 7(b)), 10 m height is less

correlated than 30 m height, on annual, summer and win-ter basis, and summer month shows the best correlationsbetween both heights. Looking at the RMSE (Figure 7(c)),larger errors were found for the interpolated data at 10 mheight for both resolutions. When seen on seasonal basis,larger mean error was found for summer than for winter inall the cases with still greater errors found at 10 m height.



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Figure 7. Calculation of (a) mean bias error (MBE) in units of ‘m s–1’ with standard error bars (b) correlation coefficient ‘r’ (c) root mean squareerror (RMSE) in units of ‘m s–1’, from hourly and 3-hourly time series of WRF and observed at 10 m and 30 m heights, over Gharo for the year

2005.

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(b)(a)

Figure 8. Percentage of errors versus bias correction window size over Gharo, for the year 2005, obtained from hourly (h) and 3 hourly (3h) timeseries of WRF and observed at 10 m and 30 m heights (a) for summer (June, July, August, September) (b) for winter (December, January, February,

March).

This is in accordance with the MBE (Figure 7(a)) implyingthat the model was successful at simulating wind speedsmore realistically in summer having highest correlationsbut with higher MBE and RMSE, than in winter with lowercorrelations but also with lower MBE and RMSE. Thisleads to the need of correcting the bias of model data inorder to reduce the MBE and RMSE for realistic windresource assessment.

4.2. Bias window behaviour

Figure 8 presents the relationship between the differentrunning bias-correction windows and the improvement in

the next day’s corrected data generated by BES for bothsummer and winter. It was found that up till a certainwindow size, the algorithm showed a realistic behaviourfor the simulated surface winds by showing a decrease inthe percentage error of next days’ corrected data as thenumber of days included in the running mean increased,i.e. reaching an asymptotic value by 30–60 days in caseof three hourly data (on 27 km resolution) and 15–30days for hourly data (on 9 km resolution). This behaviourof the bias correction methodology was found similar forboth model height levels showing the performing abilityof BES for higher model height levels. For the selection


M. AMJAD et al.

of suitable bias window consideration was given to theadditional tolerance for missing or faulty observations inproducing the next day’s corrected data for a single siteunder operational conditions (Woodcook and Engel, 2005;Engel and Ebert, 2012). For this a 30-day bias correctionwindow was adopted for both 9 and 27 km resolutiondata at both heights for bias corrected wind speed dataanalysis.

4.3. Bias corrected data analysis

The bias corrected cumulative frequencies (Figure 9)showed that the model wind speed biases were signifi-cantly reduced with an obvious match of the shape or thevariation of corrected wind speed with the observations.The correction over both data sets significantly improvedthe representation of the surface winds for WRF data bothin the model bias and the model climatology (distribu-tion peak). The overlapped distributions confirmed thatthe model was able to simulate the climatology realisti-cally with a requirement of correction in the model windspeed bias. This has also been studied by Shimada andOhsawa, 2011, who stated that ‘improvement of the largepositive bias is the key to improve the accuracy of theWRF wind speed’. It is also noticeable that cumulativewind speed representation on both the resolutions is sig-nificantly improved with the application of this bias cor-rection algorithm over both heights with more profoundresults for 10 m height level winds. This might be due tothe fact that 30 m height level contained missing valuesafter the application of power law with true values of alpha.BES was also found to predict the missing values makingthe corrected data denser than the original data hence mak-ing it representable accordingly to the 10 m height leveldata. The model statistics were calculated afterwards byapplying bias correction (Table 2) which showed that on9 km resolution the MAE was reduced up to 73%, RMSEup to 68% and correlation improvement of 36%. On 27km resolution, MAE was reduced up to 67%, RMSE up to62% and the correlation improved up to 54% at 10 m heightonly. By observing model heights, MBE, MAE and RMSEwere reduced up to 99%, 73% and 68% on 10 m height and99%, 51% and 46% on 30 m height with error reductionof summer more than winter. These error estimates depictan average error reduction, up to 80% on 10 m heightand up to 65% on 30 m height. This shows that BES wasable to remove the model bias significantly hence makingit reliable for correction on seasonal basis among whichthe summer errors were fixed more than winter but withlower correlations. The corrected data was further used forplotting wind energy curves for both seasons (Figure 10)and an excellent coherence of bias corrected model out-put with the observed data was obtained. The maximumpower potential obtained by 9 km resolution data for sum-mer therefore is 570 MWh at 10 m which increases up to1000 MWh on 30 m whereas for winter the potential of 125MWh at 10 m increases up to 300 MWh at 30 m. Going upto a coarser resolution, the maximum potential for summeris 200 MWh at 10 m which increases up to 340 MWh on

30 m, whereas for winter the 10 m potential of 40 MWhincreases to 80 MWh on 30 m. There is a slight overestima-tion of energy by BES in winter by 10–20 MWh on 27 kmand 40–50 MWh on 9 km resolution. As these wind energyfigures have been drawn from the observations and the cor-rected data after applying our own filter measures thereforethey might vary in slight approximations from Chaudhry,2009 and the technical report by Chaudhry and Hussain,2003 published over Gharo. Most of the past studies havebeen done over the Gharo-keti-bandar wind corridor there-fore have not been considered for comparisons.

Looking at these, BES has been found to improve theerror statistics of WRF winds significantly and is foundapplicable for its application toward wind resource assess-ment of Gharo wind station.

5. Conclusion

In order to investigate the reliability of WRF for onshorewind speed assessment, the accuracy and characteristicsof the model were investigated using the in-situ measure-ments obtained over Gharo wind station. The main conclu-sions follow:

(i) The wind shear profile of ‘Gharo’ wind stationwas studied in detail and found to vary on diurnalbasis throughout the year 2005. Day time alphaswere found lower than night time, similarly summermonths alphas were found lower than winter months.Annual mean alpha of 0.36 and 0.40 on hourly andthree hourly data basis with ±0.19 standard devi-ations were found for Gharo wind station havingmaximum values of alpha lying in the range 0.1–1.5.Model wind speed interpolations to 30 m height,done by using power law showed highest correla-tions and lower RMSE, compared to observations,when ‘alpha_tv’ was used, thus denying the idea ofusing a constant ‘alpha’ value.

(ii) This study shows that if power law is appliedfor model data interpolations having the windshear coefficient calculated for each time step(‘alpha_tv’ here), then it can give a wind profileof the model with profound accuracy. Table 2shows the model error statistics (MBE, MAE,RMSE and CC(r)), which were found lower for theinterpolated data of 30m height level by using‘alpha_tv’ than the surface data.

(iii) Summer was found to have larger biases and a bettermatch of wind speed distribution with the observa-tions (higher correlation) compared to winter, at bothheights and resolutions.

(iv) Wind speed climatology indicated that improvementof the large positive bias is the key to improve theaccuracy of the WRF wind speed, in fact if the bias isremoved, the accuracy of the wind speed was greatlyimproved, i.e. up to 80% on 10m height and up to 65%on 30m height on average for both resolutions.

(v) BES performed efficiently for simulated data at bothheights and resolutions, for both summer and winter.



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for Panel A – summer and Panel B – winter.



Table 2. Model error statistics for hourly (h) and 3-hourly (3h) data.

Season Winter (DJFM) Summer (JJAS)

Statistics MBE MAE RMSE CC (r) MBE MAE RMSE CC (r)

Height data Before bias correction (MBE, MAE, RMSE in units of ‘m s–1’)h-10m 2.58 2.60 2.84 0.36 2.84 2.84 3.03 0.68h-30m 0.97 1.28 1.60 0.44 1.96 1.99 2.26 0.643h-10m 2.47 2.48 2.75 0.27 2.47 2.48 2.69 0.633h-30m 0.89 1.25 1.53 0.47 1.76 1.81 2.09 0.61

After bias correction (MBE, MAE, RMSE in units of ‘m s–1’)h-10m 0.07 0.97 1.20 0.53 0.01 0.76 0.96 0.54h-30m –0.62 1.12 1.42 0.68 0.00 0.98 1.33 0.653h-10m 0.09 1.00 1.21 0.59 0.04 0.82 1.02 0.563h-30m –0.43 1.03 1.31 0.43 0.11 0.96 1.13 0.53

Error reduction after bias correction (MBE, MAE, RMSE, r in ‘%’)h-10m 97.4 62.8 57.9 31.9 99.7 73.3 68.4 –26.1h-30m 163.7 12.6 11.1 36.2 99.9 51.0 40.9 2.03h-10m 96.4 59.9 56.0 53.9 98.2 67.1 62.2 –10.73h-30m 147.9 18.0 14.4 –8.0 93.9 46.8 45.9 –13.7

It significantly reduced the overall bias and improvedthe representation of the model data. The study showsthat even with the use of older model version, thebias correction methodology was able to make thesimulation closer to the observations making it usefulfor the wind resource assessment studies.

(vi) Summer was found to give the highest wind speeds ofthe year with highest wind energy of ∼1000 MWh on30 m height level when studied on hourly basis dataon finer resolution of 9 km.

(vii) The WRF bias corrected data for both resolutionsat both heights was therefore found applicable forfeasibility studies such as wind energy calculationsfor further wind stations.

Acknowledgements

The authors acknowledge the support of Weather Researchand Forecasting (WRF) model team for their usefuladvices during course of the study. The authors are alsograteful to the Executive Director, Global Change ImpactStudies Centre (GCISC), Dr. Arshad M. Khan for his con-tinued support during the study period. We also appreciateMr. M. Asif (ex-GCISC colleague) for assistance in WRFsimulations and Mr. Irfan Yousuf (Alternative EnergyDevelopment Board, Islamabad) for help in acquisitionof observed wind speed data for Gharo wind station. Wethank the editor and anonymous reviewers for providingthoughtful comments and suggestions.

References

Chaudhry QZ. 2009. Preliminary investigation of wind power potentialof Gharo-Sindh, Pakistan. Pak. J. Meteorol. 6: 1–11.

Chaudhry QZ, Hussain A. 2003. Preliminary investigation of wind powerpotential of Gharo. PMD Technical Report No. 6/2003, PakistanMeteorological Department (PMD), Islamabad.

Chen D, Cane MA, Zebiak SE, Kaplan A. 2000. Bias correctionof an ocean-atmosphere coupled model. Geophys. Res. Lett. 27:2585–2588.

Doug M, Stull R. 2008. Hydrometeorological accuracy enhancementvia postprocessing of numerical weather forecasts in complex terrain.Weather Forecast. 23: 131–144.

Elliott D. 2011. Wind Resource Assessment and Mapping for Afghanistanand Pakistan (South Asia Regional Initiative for Energy Cooperationand Development (Sari-Energy)), Retrieved June 7, 2012. http://www.sari-energy.org/PageFiles/Countries/Afghanistan_Energy_Over-view.asp.

Engel C, Ebert EE. 2012. Gridded operational consensus forecasts of2-m temperature over Australia. Weather Forecast. 27: 301–322.

Firtin E, Guler O, Akdag SA. 2011. Investigation of wind shear coeffi-cients and their effect on electrical energy generation. Appl. Energy88: 4097–4105.

Gipe P. 2004. Wind power. Wind Eng. 28: 629–631.Gose M. 2008. 50 MW FFC wind power plant Jhimpir, Pak-

istan – environmental impact assessment. TD-06-401-2, FaujiFertilizer Company Limited (FFC), Thatta, Pakistan. Retrieved May21, 2014. http://www.ffc.com.pk/uploads/docs/EIA_FFC_WPP.pdf.

Khan I, Chowdhury H, Rasjidin R, Alam F, Islam T, Islam S. 2012.Review of wind energy utilization in South Asia. Procedia Eng. 49:213–220, DOI: 10.1016/j.proeng.2012.10.130.

Kubik MI, Coker PJ, Hunt C. 2011. Using meteorological wind datato estimate turbine generation output: a sensitivity analysis. In WorldRenewable Energy Congress 2011, Linköping University, Linköping,Sweden.

Kubik MI, Coker PJ, Barlow JF, Hunt C. 2013. A study into the accuracyof using meteorological wind data to estimate turbine generationoutput. Renew. Energy 51: 153–158.

Mao Q, McNider RT, Mueller SF, Juang HH. 1999. An optimal modeloutput calibration algorithm suitable for objective temperature fore-casting. Weather Forecast. 14: 190–202.

Mirza IA, Khalil S, Khan BDNA, Yousuf I. 2008. Wind farm at Gharo,Pakistan: basis for site selection. Mehran Univ. Res. J. Eng. Technol.27: 419–428.

Mirza IA, Khan NA, Memon N. 2010. Development of benchmark windspeed for Gharo and Jhimpir, Pakistan. Renew. Energy 35: 576–582.

Panofsky H, Dutton J. 1984. Atmospheric Turbulence: Models andMethods for Engineering Applications. John Wiley & Sons: New York,NY.

Prabha T, Hoogenboom G. 2008. Evaluation of the weather research andforecasting model for two frost events. Comput. Electron. Agric. 64:234–247.

Shimada S, Ohsawa T. 2011. Accuracy and characteristics of offshorewind speeds simulated by WRF. SOLA 7: 21–24.

Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W,Powers JG. 2008. A description of the advanced research WRF Ver-sion 3. Technical Note, TN-475+STR, National Center for Atmo-spheric Research, Boulder, CO, 1–96, Retrieved May 21, 2014.http://www2.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.


M. AMJAD et al.

Stensrud DJ, Skindlov JA. 1996. Grid point predictions of hightemperature from a mesoscale model. Weather Forecast. 11:103–110.

Sultan Al-Yahyai , Yassine C, Adel G. 2010. Review of the use of numer-ical weather prediction (NWP) models for wind energy assessment.Renew. Sustain. Energy Rev. 14: 3192–3198.

United Nations Development Programme (UNDP). 2008. UNDP/GEFwind energy project – regional environmental assessment study of theGharo wind corridor in Pakistan: environmental and socio-economicbaseline report, WEP/0707/EIA, Prepared by Taller de IngenieríaAmbiental, Eolic Partners and Halcrow Pakistan (Pvt) Limited, Alter-nate Energy Development Board, Islamabad, Retrieved May 21, 2014.http://ifcext.ifc.org/ifcext/spiwebsite1.nsf/0/0111579D09F9C0198525786F0068D32C/$File/Gharo%20Wind%20Corridor%20REA%20(Section%203).pdf.

Willmott CJ. 1981. Some comments on the evaluation of model perfor-mance. Bull. Am. Meteorol. Soc. 63: 1309–1313.

Willmott CJ, Wicks DE. 1980. An empirical method for the spatialinterpolation of monthly precipitation within California. Phys. Geogr.1: 59–73.

Wolar J. 2008. Meteorological Towers and Wind Power Analysis. Alter-nate Energy Solutions Integrated Inc: Eastpointe, MI.

Wonnacott TH, Wonnacott RJ. 1972. Introductory Statistics. John Wiley& Sons: New York, NY, 510 pp.

Woodcook F, Engel C. 2005. Operational consensus forecasts. Am.Meteorol. Soc. 20: 101–111.

Young G. 2002. Combining forecasts for superior prediction. In 16thConference on Probability and Statistics in the Atmospheric Sciences,Orlando, FL, American Meteorological Society, Washington, DC,107–111.


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Evaluation of weather research and forecasting model for the assessment of wind resource over Gharo, Pakistan