Solar Energy Vol. 66, No. 3, pp. 201–213, 1999
 1999 Elsevier Science Ltd
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WIND ENERGY POTENTIAL OF COASTAL ERITREA: AN ANALYSIS OFSPARSE WIND DATAKAREN ROSEN* , †, ROBERT VAN BUSKIRK** and KARINA GARBESI***Lawrence Berkeley National Laboratory, MS 90-2000, Berkeley, CA 94720, USA**Department of Environmental Studies, San Jose State University, San Jose, CA 95192, USA
Received 8 July 1998; revised version accepted 15 February 1999
Communicated by DAVID MILBORROW

Abstract—This paper describes an analysis of historical surface wind data for the small country of Eritrea, in
northeastern Africa. Winds in this region are directed by summer and winter monsoons in addition to diurnalland–sea effects. An analysis of national Eritrean and historical Italian wind records indicated marginal windresources in the central highlands near the Eritrean capital of Asmera. An analysis of wind speed records
recorded at two sites in the southern port city of Aseb indicate mean annual 10-m wind speeds of 9.5 m s 21 at
the windier site. Surface wind speed records for the Red Sea suggest that similar potential may be found along
the lower 200 km of the Eritrean coastline. Based on these findings, wind-generated electricity in this region
should be substantially cheaper than the current supply generated from imported diesel.  1999 Elsevier
Science Ltd. All rights reserved.

speeds over the southern Red Sea is provided toassist in predicting wind potential along theEritrean coastline.

1. INTRODUCTION

The Eritrean Department of Energy is currently
investigating the availability of a variety of powersources needed to rebuild the Eritrean infrastructure after several decades of war with Ethiopia.
This report is based on a study that was undertaken to supply the Eritrean Department ofEnergy with information about wind-energy resources in Eritrea (Rosen, 1998).
Some introductory studies of wind resources inand around Eritrea have previously been conducted. Studies of the region surrounding the Red
Sea and Gulf of Aden have issued promising
results, especially for coastal areas (Elliott andRenne, 1987; Pallabazzer and Gabow, 1991;
Radwan, 1987). Mulugetta and Drake (1996)
conducted a study of wind potential for Ethiopiathat included Eritrea. Their analysis included four
Eritrean sites: three in the central highlands, andone at Aseb on the southern coast.
This study reports mean annual wind speed
estimates for nine sites not included in previous
studies, including a detailed wind assessment
based on a combined total of 13 years of winddata for two sites at the windy southern port cityof Aseb. In addition, an analysis of surface wind

2. STUDY AREA

†Author to whom correspondence should be addressed.
Tel.: 11-510-486-5784; fax: 11-510-486-4673; e-mail:kbrosen@lbl.gov

Eritrea occupies the southernmost 1200 km of
the western Red Sea shoreline, including hundreds
of islands near the major ports of Mitsiwa’e(Massawa) in central Eritrea and Aseb (Assab) inthe south (see Fig. 1). The capital, Asmera(Asmara), is located in central Eritrea on a plateau
at over 2000 m elevation. This high plateau andthe surrounding hillsides are known as the central
highlands. To the west and east are the westernand coastal lowlands, respectively. The western
lowlands have a semiarid climate and consist
chiefly of desert hills and savanna. On the easternborder with the Red Sea lies a wide arid coastal
plain that stretches the entire length of Eritrea.
About 100 km south of the main Eritrean port at
Mitsiwa’e, the coastal plain dips to 100 m below
sea level at the Danakil Depression, an uninhabitable desert that divides the central highlands inthe west from a much smaller range of mountains
in the east. This second mountain range parallels
the shoreline from the Danakil Depression to
beyond the Djibouti border, reaching almost 2000
m at its highest point.
The estimated population of Eritrea is 3.6
million (Central Intelligence Agency, 1997). Of

201

202

K. Rosen et al.

Fig. 1. Map of Eritrea showing topography, major cities, the national electrical grid, surrounding countries, and major bodies ofwater.

that number, roughly 20% inhabit the capital,
Asmera. Although accurate population counts forother major cities are not available, it is estimatedthat more than half of the Eritrean populace lives
in the towns and cities of the highlands, due inpart to the wetter and cooler climate found there.
Because of the heat and aridity of the lowland
areas, settlements below 1000 m are relatively
sparse. The largest city in the coastal lowlands isAseb, a port city of about 50 000 people locatedat the southernmost tip of the Eritrean coastline,
where summer temperatures average 35–458C andannual rainfall averages about 4 cm.
The main national electric grid has a formal
capacity of 40 MW. Transmission lines stretch

from Mitsiwa’e to Asmera, and then continue tothe south for a combined length of about 500 km(see Fig. 1). A second grid with a formal capacityof 17 MW is located at Aseb. In practice,
however, the actual generation capacity in Eritrea
is approximately one half the formal generation
capacity because many generators are old andinefficient.
Wind patterns in the Red Sea region arecontrolled to a large extent by two distinct
monsoons that are separated by 30 to 45 days
each. During the Northeast Monsoon from
November to March, the combined effects of theAfrican Equatorial Low and the Siberian Highpressure systems create a strong southeast wind

Wind energy potential of coastal Eritrea: an analysis of sparse wind data
Table 1. Approximate (61 m s 21 ) mean annual 10-m windspeeds (MAWS) based on data recorded at seven stations in
the Eritrean highlands and western lowlands, 1994–1995
StationGeographical coordinates

AsmeraFilfilKerkebet
Omhajer
Shambiko
Sh’eb
Tsorena

158 179 N
158 369 N
168 189 N
148 209 N
148 579 N
158 519 N
148 399 N

388 559 E
388 579 E
378 249 E
368 399 E
378 539 E
398 039 E
398 139 E

MAWS(m s 21 )
3
1
3
2
2
2
2

that is pulled through the narrow Bab el Mandebat the southern mouth of the Red Sea. This windslows as it continues toward Mitsiwa’e where it
converges with the ever-present northwest wind, aresult of the Azores High, at about 168 northlatitude. Much of this air mass is drawn southwestward across the Eritrean highlands toward theAfrican Low centered in southern Sudan. During
the Southwest Monsoon from May to September,
the Azores High and the Pakistani Low cause
surface winds along the entire length of the Red
Sea to blow from the northwest down the lengthof the Red Sea and out into the Gulf of Aden
(United States Naval Oceanography Command
Detachment, 1993).
3. STUDY OVERVIEW

In our initial search, four sources of wind speed
data were found: recent Eritrean data, historical
Italian data, United States Air Force (USAF)
surface records, and a US Naval climatic study of
the Red Sea. These data sources are described
below.
Eritrean data (1994 – 1995). In 1994, the Eritrean Department of Water Resources began recording 15 min wind speed averages at sevenstations located in towns and cities scatteredthroughout the highlands and western lowlands ofEritrea. Less than one year of data was available
for this study; however, rough (61 m s 21 ) meanannual wind speed estimates were derived and aresummarized in Table 1.
Italian data (1930 – 1933). During the Italian203

occupation in the early 1930’s, daily average windspeeds were recorded at stations along the Italianbuilt railroad line from Mitsiwa’e to Keren. Mean
annual wind speeds at four of these stations areprovided in Table 2.
US Air Force Surface Records (1973 – 1992).
Twenty years of hourly meteorological records
were available for several Eritrean cities, including Aseb (428 439 E, 128 589 N), for which a data
set containing 13 discontinuous years of observations (1973–1980, 1982–1991) was obtained. The
arithmetic mean of wind speeds in this data set
indicated a mean annual wind speed of 8 m s 21 at
Aseb.
US Navy Climatic Study of the Red Sea. A
1982 climatic study of the Red Sea conducted by
the United States Navy mapped monthly averages
with 18 spatial resolution. For seven of the 12months (October–April), the report shows meanannual speeds of between 6 and 10 m s 21 off thesouthern half of the Eritrean coastline. For theremaining months (May–September) the report
indicates mean wind speeds between 3 and 5
m s 21 .
While these estimates suggest that wind resources in the highlands may be inadequate for
utility-scale wind generation, one must keep inmind that the highland stations were not sited forwind prospecting purposes, and so are unlikely tohave recorded the highest wind speeds in theregion. VanBuskirk et al. (1997) argue that thechanneling of wind through the hills and valleysof the highlands is likely to create areas in whichwind speeds are significantly enhanced. The Eritrean Department of Energy has been collecting
additional wind data in the mountain passes near
Asmera in an attempt to locate such sites. Estimates based on preliminary measurements indicate the existence of sites with mean annual windspeeds exceeding 5 m s 21 .
Wind data obtained for Aseb and the Red Sea
produced more promising results than did data for
central Eritrea. The USAF surface records for
Aseb indicated a mean annual wind speed of 8
m s 21 , while the Naval report for the Red Sea
indicated mean monthly wind speeds as high as

Table 2. Mean annual 10-m wind speeds (MAWS) based on historical Italian data for four stations in the Eritrean highlands,
1930–1933
StationGeographical coordinates

AsmeraFaghena
Keren
Mitsiwa’e

158 179 N
158 109 N
158 469 N
158 379 N

MAWS(m s 21 )388 559 E
398 289 E
388 279 E
398 279 E

2.7
2.1
1.9
3.7

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K. Rosen et al.

10 m s 21 just off the southern coast. Althoughelectricity demand in Eritrea is highest in thecapital city of Asmera, we chose to pursue a moredetailed study of the Aseb area based on the windspeeds recorded there. Analyses of winds at two
Aseb sites and of winds over the Red Sea arepresented below.
4. ASEB DATA AND ANALYSISThe Eritrean Civil Aviation Department maintains two meteorological stations at Aseb, one at
the Aseb Airport about 15 km northwest of Aseband one at the Seaport within the city.1 The
Airport station is located in a flat, open plain at
about 10 m elevation. The Seaport station is at theshoreline, sheltered by buildings on the northwestside (see Fig. 2).
The wind speed observations in the USAF data
set for Aseb were estimated and recorded bypersonnel of the Eritrean and / or Civil Aviation
Department. Although these wind speed estimates
were based on the rotation of standard 10-m
anemometers, wind speeds were visually estimated by personnel rather than mechanicallyestimated by a data logger. A recent comparison
between simultaneous human wind speed estimates and 10-m anemometer readings showed anaverage error of 3% and an average absolute errorof 11%.
Until 1991, weather observations recorded
every 3 h were archived by the United States Air
Force Environmental Technical Application Center, 1986 (USAFETAC). Observations reported
before 1986 were taken at the Airport station,
while observations reported after 1986 were taken
at the Seaport station. The National Climatic Data
Center (NCDC) provided a data set containing
thirteen years (1977–1979, 1982–1991) of US
Air Force surface observations for Aseb. Afterremoving incomplete or otherwise inconsistent
data records, over 5000 observations remained foreach station.
As discussed above, weather patterns at Aseb
can be divided into two distinct seasons. For thisanalysis, mean monthly wind speeds calculated by
the United States Naval Oceanography Command
Detachment (1993) were used to divide the entire
1

The US Air Force database did not indicate the existence of
two separate stations. An apparent change in mean annual
wind speed and observation patterns late in 1986 alerted us
to the situation. The existence of the two stations and dateof transition were affirmed by visiting the two stations andreviewing the logs.

multi-year data set into two subsets corresponding
to the two seasons. From this point on, May–
September will be referred to as the summer
season while October–April is referred to as thewinter season.
Each seasonal subset was further divided according to whether the observations were recorded at the Airport or the Seaport station. The
resulting four subsets were analyzed independently. A preliminary statistical analysis of the four
data subsets is presented in Table 3.
Although the number of observations did not
differ significantly on a month to month basis,
observation patterns did vary widely on an hourly
basis. Midnight and early morning observations
were comparatively few, and for the first eight
years were practically non-existent. To compensate for the fact that more observations were madeduring the daytime when wind speeds are highest,
the four data subsets were each divided into eight
3-h bins that were analyzed separately. Because
records for late evening and early morning hours
were not recorded on a regular basis until after theobservation site was moved to the Seaport, six ofthe 16 data subsets for the Airport contained
insufficient data for analysis. Fig. 3 illustrates thedata divisions and the number of observations ineach subset.
To derive the mean annual wind speed at the
Airport, it was necessary to estimate mean windspeeds for the 21:00, 0:00 and 3:00 data subsets.
First, mean values were calculated for the Airportand Seaport data subsets that contained sufficientdata. A scatter plot was then created using the
Airport values as the abscissas and the Seaportvalues as the ordinates (see Fig. 4). Using regression analysis, a linear correlation was derived
between the time-correlated pairs of mean windspeeds at the two stations (R 2 5 0.96). Each of thethree missing values for the two seasons werethen calculated as ma 5 mms 1 b, where ma and ms
are the mean wind speeds at the Airport andSeaport, respectively, and m 5 1.043, and b 5
2.179 are the slope and y-intercept, respectively,
found in the regression analysis.
To provide accurate annual wind speed distributions for the two Aseb sites, it was necessary
once again to estimate values for the empty data
bins at the Airport. Therefore, we planned to
calculate Weibull parameters for the subsets with
sufficient data, and then use them to estimate theWeibull parameters for the subsets with insufficient data. From these estimated parameters,
model distributions for the empty subsets could
then be constructed. At this point, however, itWind energy potential of coastal Eritrea: an analysis of sparse wind data

205

Fig. 2. Map showing the Airport and Seaport meteorological stations at Aseb, where the US Air Force data were recorded.

became clear that the wind speed database for
Aseb contained an unusually large number ofcalms. This presented a problem because thestandard Weibull distribution does not allow for awind speed of 0. To resolve this problem, we
employed a method described by Mulugetta andDrake (1996) in which a Weibull distribution is fit

to the observed distribution above 0 m s 21 , andcalms are considered separately. This method
tends to provide a more conservative estimate ofmean wind speed by assuming that the reported
frequency of calms is correct, whereas the standard Weibull distribution assumes that the frequency of calms is 0. This is particularly im-

Table 3. Results of a preliminary statistical analysis of US Air Force wind speed data for Aseb
AirportNo. of observations
Minimum (m s 21 )Maximum (m s 21 )Arithmetic mean (m s 21 )Variance (m s 21 )Standard deviation (m s 21 )Standard error
Skewness
Kurtosis

SeaportSummer

WinterSummer

Winter

2593
0.0
31.0
7.7
18.3
4.3
0.08
0.26
0.19

3258
0.0
42.2
12.1
25.0
5.0
0.09
0.14
1.33

1899
0.0
30.0
4.8
13.1
3.6
0.08
0.92
1.64

3244
0.0
44.0
9.1
20.8
4.6
0.08
0.21
0.92

206

K. Rosen et al.

Fig. 3. Division of the US Air Force data for Aseb, showing number of data points in each seasonal 3-h bin.

portant in the case of the Aseb data, where theobserved frequency drops abruptly between 0 and
1 m s 21 , and then follows the expected Weibull
distribution closely.
To the standard Weibull parameters k and A is
added a variable s denoting the frequency ofcalms. The equation of the three-parameter
Weibull probability density function is:Hs, at v 5 0f(v) 5k(1 2 s)(kv k 21 /Ak ) exp 2(v / A) , for v . 0(1)

where v is the wind speed, s is the observed
frequency of calms, A is the magnitude parameter,
k is the shape parameter, and f(v)dv is the fractionof time the velocity data is in a bin centered at v,
with bin size dv. Note that for v.0, the model

distribution is simply (12s) times the standard
Weibull distribution.
For each season, this three-parameter Weibull
model was fit to the wind speed frequency
distributions for the 13 subsets containing sufficient data. A best fit was obtained by minimizing
the sum of the squared differences between points
on the observed frequency distribution and theWeibull distribution. After calculating the threeparameters of k, A and s for each subset, a scatterplot was then created using the Airport values as
the abscissas, and the Seaport values as theordinates (see Fig. 5).
As can be seen in Fig. 5(a), the scatter plot of
k-values found at the Aseb Airport and Seaport
did not indicate a clear linear correlation (R 2 5
0.6373); therefore, we used the average seasonal kvalue at the Airport to estimate the three missing
values.2 For both A and s, acceptable linear
correlations were determined between the twostations (R 2 50.9575 and R 2 50.9626, respectively). The missing values were then calculated as
y5mx1b, where y and x are the parameters at
the Airport and Seaport, respectively, and m and b
are the slopes and y-intercepts found in the
regression analyses respectively. Mean windspeeds could then be estimated as (Mulugetta andDrake, 1996):

m 5 AG (1 /k 1 1)(1 2 s)

(2)

Seasonal wind speed frequency distributions at
the Airport were calculated as the average of thefive observed and three estimated frequency distributions for each 3-h bin for each season.
Seasonal wind speed frequency distributions at
2

Fig. 4. Scatter plot showing the correlation between mean
wind speeds at the Aseb Airport and Seaport.

Many different methods were used to estimate the Weibull
parameter of k for the empty bins, including averaging,linear correlations and non-linear correlations. All methods
provided nearly identical mean annual wind speeds (60.5m s 21 ). We chose to present the averaging k-value method
because, of the methods tested, it resulted in the mostconservative wind speed estimates.

Wind energy potential of coastal Eritrea: an analysis of sparse wind data

207

Fig. 5. Scatter plots showing correlations between Airport and Seaport k, A and s values.

the Seaport were simply calculated as the averageof the eight observed frequency distributions. For
this study, we defined the summer season as thefive months from May to September and thewinter season as the seven months from October
to April; therefore, annual distributions for eachstation were calculated as:
57f(v) 5 ] fs (v) 1 ] fw (v)
12
12

(3)

where f(v) is the annual wind speed frequency

distribution, and fs (v) and fw (v) are the summer
and winter wind speed frequency distributions,
respectively. Three-parameter Weibull distributions were then fit to the resulting annual windspeed distributions to derive Weibull parameters
for the aggregated data at each site.
Wind power density is the amount of wind
power available in a plane perpendicular to thedirection of the wind. In this study we calculate
the mean annual wind power density for each 3-hdata bin as:

208

K. Rosen et al.

O ]21 rv f(v)3(4)

where r is the average observed air density, v is
the wind speed, and f(v) is the wind speed
frequency distribution. Mean annual wind power
density is calculated as the arithmetic average ofthe 3-h bin wind power densities.
5. RED SEA DATA AND ANALYSISThe Comprehensive Ocean-Atmosphere Data
Set (COADS) is a global collection of marine
observations recorded between 1854 and 1993
that is widely used for studies of ocean winds,
temperature, and heat budget. Due to insufficientspatial resolution (18), observations recorded before 1940 were discarded. The portion of data set
retained for this study contained over 650 000
observations.
To account for uneven temporal sampling in
the data set, the data were divided into seasonal
6-h bins, resulting in eight data subsets. Meanwind speeds were calculated for each position
containing data in all eight data subsets. The
arithmetic average of wind speeds in each 0.18 bin
was then calculated for each subset. Average
seasonal wind speeds were derived as the averageof the four 6-h subsets for each season for each
0.18 bin. Mean annual wind speeds were calculated for each 0.18 bin as the sum of 5 / 12 thesummer average and 7 / 12 the winter average.
6. RESULTS

Unless otherwise noted, results presented here
represent wind speed values at 10 m height. It is
expected that wind speeds at typical turbine hubheights of 30 m or more would be higher thanthose presented here. In some cases, however,winds have been known to decrease with height,
particularly in mountainous areas. Therefore, wehave chosen to present the majority of values at
10 m without making any assumptions about thewind speeds at higher hub heights. Mean windspeed values based on USAF surface records are
estimated to be accurate within 5% of given
values.
Table 4 shows mean wind speeds calculated
from observed wind speed values. Values in italics
at 21:00, 0:00 and 3:00 at the Airport were
estimated as described in Section 4.
Table 5 shows the parameters obtained using
three-parameter Weibull fits to the observed 3-hwind speed distributions. Again, values for 21:00,
0:00 and 3:00 are estimated as described in
Section 4, where mean wind speeds are based on
Eq. (2). The majority of mean 3-h wind speed
values presented in Table 5 are within 2% of theobserved mean wind speeds. The greatest difference between calculated values and observed
values was 3.3% at 0:00 at the Seaport.
Fig. 6 depicts the mean diurnal wind speed
variations at the Aseb Airport and Seaport. Estimated values at 21:00, 0:00 and 3:00 are circled
in Fig. 6(b). The higher midday wind speeds
shown in both graphs suggest potential for increased power supplies during the peak demand
period. Obvious similarities between Figs. 6(a)and 6(b) support the assumption that wind speeds
at the two stations are closely related.
Fig. 7 shows the three-parameter Weibull fits tothe wind speed distributions at the Aseb Airportand Seaport. In Fig. 7(a), the ‘Observed’ distribution at the Airport is actually the arithmetic
average of the five observed distributions andthree estimated distributions (see Section 4). The’Observed’ distribution in Fig. 7(b) is the arithmetic average of the eight observed 3-h distributions at the Seaport.
The amount of electricity that can be generated
at Aseb depends on several factors, including the
hub height, rotor size, and spacing of the windturbines, and also the estimated efficiencies of theTable 4. Results of the analysis of US Air Force data for Aseb, showing mean seasonal and annual 10-m wind speeds (in m s 21 )for 3-h periods at the Aseb stations
Eritrean time
6:00
9:00
12:00
15:00
18:00
21:00
0:00
3:00
Averagea

Airport

SeaportSummer

WinterAnnualSummer

WinterAnnual

3.6
6.0
8.9
9.3
8.8
7.9 a
6.1
5.2
7.0

7.8
11.2
13.1
13.6
11.8
11.6
10.9
10.1
11.3

6.0
9.0
11.3
11.8
10.5
10.1
8.9
8.0
9.5

2.1
3.8
5.6
6.2
6.1
5.2
3.9
3.2
4.5

5.8
8.0
10.2
10.8
10.6
9.2
8.4
7.6
8.8

4.3
6.2
8.3
8.9
8.7
7.5
6.5
5.8
7.0

Figures in italics represent values estimated by linear correlation between station wind speeds.

Wind energy potential of coastal Eritrea: an analysis of sparse wind data

209

Table 5. Three-parameter Weibull parameters and mean 10-m wind speeds derived from them for seasonal 3-h periods at the
Aseb stations
Eritrean time

Summer
6:00
9:00
12:00
15:00
18:00
21:00
0:00
3:00
Summer averages
Winter
6:00
9:00
12:00
15:00
18:00
21:00
0:00
3:00
Winter averages
Annual averagesaAirport

Seaport

k

A

s
(%)

Wind speed(m s 21 )

k

A

s
(%)

Wind speed(m s 21 )

2.1
1.9
2.9
2.7
3.2
2.6 a
2.6
2.6
2.3

6.6
7.5
9.8
10.3
10.0
8.7
8.1
7.9
8.8

0.41
0.10
0.00
0.00
0.00
0.06
0.11
0.25
0.12

3.5
6.0
8.7
9.2
8.9
7.3
6.4
5.2
6.9

2.0
2.0
2.2
2.2
2.0
2.0
2.1
2.3
2.0

3.8
4.5
6.1
6.7
6.9
5.6
4.8
4.5
5.5

0.49
0.21
0.07
0.02
0.05
0.10
0.16
0.32
0.18

1.7
3.2
5.0
5.8
5.8
4.5
3.6
2.7
4.0

2.4
3.2
3.3
3.5
3.8
3.2
3.2
3.2
3.4
2.4

10.0
12.8
14.7
15.3
13.4
13.3
12.1
12.1
13.2
11.5

0.10
0.02
0.00
0.00
0.01
0.00
0.02
0.02
0.02
0.06

8.0
11.2
13.2
13.7
12.0
12.0
10.6
10.6
11.4
9.6

2.0
2.5
2.6
3.0
3.4
3.2
4.1
3.2
2.4
1.8

7.6
10.0
12.4
12.9
12.6
11.1
9.7
9.6
10.3
8.9

0.12
0.06
0.03
0.01
0.02
0.03
0.06
0.05
0.05
0.10

5.9
8.3
10.7
11.4
11.1
9.6
8.2
8.2
8.7
7.1

Figures in italics represent values estimated using correlations between station parameters k, A and s.

turbine, array and power system. The calculations
presented in Table 6 use the following assumptions: (i) The rotor diameter (D) is 20 m; (ii) the
turbine spacing is 10D 35D; (iii) the array andsystem efficiency is 75%.
A typical annual average wind power density
for utility scale energy production is 450 W m 22
at turbine hub height (Cavallo et al., 1993). The
wind power densities shown in Table 6 are highbecause of the very high wind speeds during thewinter months at Aseb. It should be noted,
however, that the wind power supply between
May and September is expected to be considerably lower.
Fig. 8 shows approximate (61 m s 21 ) meanannual wind speeds in the Red Sea along thesouthern Eritrean coastline. According to theseresults, mean annual wind speeds along the lower
200 km of the southern Eritrean coastline are inexcess of 6 m s 21 . This suggests that sites withhigh wind power potential may be found alongmuch of the lower Eritrean coastline.
7. DISCUSSION

The results of this study indicate that the meanannual wind speed difference between the Airport
and the Seaport is about 2.5 m s 21 . We attribute
this to the different levels of wind exposure at thetwo stations: the Airport is located outside the cityand has excellent wind exposure, whereas the
Seaport station is located in a developed area

within the town. This suggests that the Seaportestimate is not representative of the high wind
power potential in the surrounding region.
There were several confounding factors in the
US Air Force data set for Aseb, including thelocation change in 1986, the lack of nighttime
observations at the Airport and the high percentage of calms recorded at both stations. To
account for these factors, several different methods of analysis were used as described in Section
4. It is interesting at this point to note thedifferences between these results. A summary of
the results obtained using four different methodsof analysis is presented in Table 7.
As noted previously, inconsistent sampling
methods favored the higher daytime wind speeds,
resulting in the artificially high values describedas ‘Unbinned.’ Standard Weibull fits to the 3-h
distributions also produced higher values because
they failed to recognize the high percentage ofcalms recorded at Aseb. The linear correlation andthree-parameter Weibull fit methods used in thisanalysis resulted in much more conservative andprobably more accurate mean annual wind speed
values.
Wind development at Aseb depends mainly on
economic factors. Because of the uncertainties inwind speeds, cost estimates at this stage arehighly unreliable; however, the cost of windgenerated electricity at Aseb will almost certainly
be lower than the current diesel-generated electricity. The California Energy Commission (CEC)

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K. Rosen et al.

Fig. 6. Graphs showing mean diurnal wind speed variations at the Aseb Airport and Seaport.

estimates that the cost of wind power at good sites
is about US$13 per MJ (4.6 ¢ / kW h) (California
Energy Commission, 1997). All else being equal,
this cost is already less than the cost of diesel fuelat Aseb, which is about US$18 per MJ (6.5¢ / kW h). Based on the unusually high meanannual wind speed at Aseb, it is safe to assume
that the cost of wind power would be even lower
than the CEC estimate.
There are several problems associated withwind development at Aseb. The main factor
constraining wind power development near Asebis the relatively small capacity of the Aseb powersystem. Given that the combined capacity of thepower system is only 17 MW, the Aseb utility
without expansion could accommodate only about
3 MW of wind (Grubb and Meyer, 1993). This isa minute fraction of the total available wind
power at Aseb (see Table 6).
The ample land and wind power resources at
Aseb have the potential to offer more power thanthe entire country could absorb in the foreseeable
future. It therefore seems worthwhile to consider
options for maximizing utilization of this re-Wind energy potential of coastal Eritrea: an analysis of sparse wind data

211

Fig. 7. Graphs showing three-parameter Weibull fits to the observed annual wind speed distributions at the Aseb Airport andSeaport.

Table 6. Projected wind speed, capacity factors, power, and energy output at Aseb, at 10 and 30 m hub height a
AirportAverage annual wind speed (m s 21 )Wind power density (W m 22 )Capacity factor (%)
Wind power available per turbine (kW)Electrical power generated per turbine (kW)Annual energy output per turbine (TJ)
Annual energy output per km 2 (TJ km 22 )aSeaport

10 m

30 m

10 m

30 m

9.5
905

11.1
1454
66
457
226
7.1
356

7.0
480

8.2
769
44
242
80
2.5
126

Height extrapolations to 30 m based on the 1 / 7 power law. Capacity factors based on the Flowind AWT-27 power curve.

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K. Rosen et al.

Fig. 8. Map showing mean annual wind speeds in the southern Red Sea off the Eritrean coastline.

source. A plan that would both further exploit thewind resource and level the seasonal loads is acooperative effort with Ethiopia, in which winterwind power from Aseb is traded for summerhydropower from the Blue Nile region. However,this option may not be feasible due to recent
political conflicts with Ethiopia. Another option
for distributing excess supply may be to connect
the power system at Aseb to the national grid in
the central highlands. Although this option ispreferable in many respects, construction of trans-mission lines may be prohibitively expensive andpower losses unacceptably high. It may be possible, however, to find high-quality coastal wind
power sites that are closer to the power supply
system in the central highlands. The analysis of
Red Sea winds indicates that high wind speeds
may extend as far as 14.58 north latitude, about
300 km from the central grid at Mitsiwa’e. If
current wind prospecting efforts in this region are
successful, transmission between these two locations may be feasible. Research continues to be

Table 7. Results of different data analysis methods for the Aseb Airport and Seaport, showing Weibull parameters k and A, meanannual wind speeds (MAWS) and mean annual wind power densities (WPD), all at 10 m height
MethodAirportk

Unadjusted a
Linear correlation b
Three-parameter Weibull cStandard Weibull d
a

2.4
2.5

Seaport
A

11.6
11.7

MAWS
(m s 21 )WPD(W m 22 )

10.3
9.5
9.6
10.4

920
915
1025

k

1.8
1.8

A

8.9
9.3

MAWS
(m s 21 )WPD(W m 22 )

7.3
7.0
7.1
8.3

480
538
688

Unadjusted, unbinned arithmetic mean of all data.
Data divided into subsets as shown in Fig. 6. Linear correlation between subset means used to estimate means for empty bins.
Data divided into subsets as shown in Fig. 6. Correlations between three-parameter Weibull parameters, k, A and s, used toestimate parameters for empty bins.
dData divided into subsets as shown in Fig. 6. Correlations between standard Weibull parameters, k and A, used to estimateparameters for empty bins.
bcWind energy potential of coastal Eritrea: an analysis of sparse wind data

conducted in the Eritrean highlands, but further
investigation into power transmission from thesouthern Eritrean coast to the highland area is also
warranted.
Further funding for this project has been procured from the Global Environment Facility(GEF), a joint program between the United
Nations Development Program (UNDP), United
Nations Environment Program (UNEP) and theWorld Bank. Results of a full scale wind prospecting effort undertaken under this grant are expected in 1999.
8. CONCLUSIONS

According to these results, the Eritrean coastline has an outstanding wind resource, particularlyin the Aseb area. Of the two sites monitored at
Aseb, the Aseb Airport site appears to be themore promising. Existing data sources indicate
mean annual wind speeds of 9.5 m s 21 at the
Aseb Airport at 10 m height. Such winds can
produce more power than the Aseb power system
can absorb, but limited development may provide
Aseb with a less expensive power source than thecurrent diesel supply. The analysis of Red Sea
surface winds presented in this study suggest thatsimilar wind resource potential extends as far as
200 km north of Aseb along the Eritrean coastline.
Acknowledgements—This work was supported by San Jose
State University, San Jose, California. The authors would also
like to thank the Eritrean Department of Energy for its
cooperation and assistance.

213

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