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35.75 4.7 4.9 5.1 5.3 5.4 5.7 5.8 5.5 5.1 4.9 4.7 4.7

35.50 5.0 5.1 5.4 5.2 5.1 5.1 4.8 4.5 4.7 4.7 4.6 4.6

35.25 4.8 4.8 4.3 3.7 2.8 2.4 2.2 2.5 3.9 4.3 4.5 4.6

35.00 4.6 4.1 3.0 2.0 1.8 1.8 2.0 2.8 4.2 4.6 4.6 4.7

34.75 4.8 4.3 3.3 2.5 2.6 3.5 4.1 4.7 4.9 4.8 4.9 4.9

34.50 5.1 5.0 4.9 4.4 4.5 4.7 5.2 5.1 5.2 5.0 5.0 5.1

32.00 32.25 32.50 32.75 33.00 33.25 33.50 33.75 34.00 34.25 34.50 34.75

1.50 2.05 2.60 3.15 3.70 4.25 4.80 5.35 5.90 6.45 7.00

wind speed average (m/s) at 10 m

Off-shore wind potential in Cyprus: Towards an integrated representative

geospatial database

Evangelos Akylas1,2, Elias Gravanis1,2, Andreas Nikolaidis1,3, Constantinos F. Panagiotou1,2, Christodoulos Mettas1,2, Phaedon Kyriakidis1,2 and Diofantos Hadjimitsis1,2

1 Cyprus University of Technology, Dept. of Civil Engineering and Geomatics, Limassol, Cyprus (evangelos.akylas@cut.ac.cy) , 2 Eratosthenes Centre of Excellence of the Cyprus University of Technology, 3 Oceanography Centre of the University of Cyprus

Introduction

Cyprus' energy balance today depends to a large

extent on imports of petroleum products for energy

production. This has an impact on both the economy

and the environment of the island. The contribution of

renewable energy sources (RES) in Cyprus, although

exhibiting considerable potential, still remains limited.

Specifically, renewable energy sources today account

for less than 9% of the country's total gross energy

consumption.

This work contributes to the study of the off-shore

wind power being present the island. It focuses on

the creation of an integrated geospatial database for

the study of wind characteristics above the coasts

and offshore of Cyprus using measurements from

meteorological stations, data from the European

database with horizontal analysis 25x25 km, and 24-

hour forecasts from the Open Skiron meteorological

model in 5x5 km spatial resolution. In particular, the

analysis take advantage of wind measurement from

meteorological stations in coastal Cyprus areas (fig.

1), as well as information on wind values from

forecasting models and databases (fig. 1) to record

an initial reference distribution in space and time.

Re-analysis data ERA5/ECMWF

The standard deviation of the wind speed is about half

the average value and the maxima about 10 m/s above

the land and 20 m/s above the sea. In fact, Cyprus is

located in the eastern Mediterranean between latitudes

of 34.6o and 35.6o north, and longitudes of 32o and 34.5o

east. During the rainy season (November to March)

Cyprus is frequently suffered by depressions moving

eastwards across the Mediterranean Sea, in addition to

troughs that extend from the continental depression

commonly centered over Asia during the dry season.

Prevailing winds in Cyprus are mostly low to medium in

speed; very strong winds (wind speeds faster than 34

knots) are rare, mainly seen in windward areas.

One of the main sources of information are the re-

analysis wind data obtained from ERA5/ECMWF. The

data covers the period from 2000 – present on an

hourly basis with a horizontal resolution of 25x25 km

(72 grid points within the analysis area), at 10 m AGL

as shown in the figures below. The typical average

speed is about 4.5 m/s above the sea and 2 m/s

above continental areas. The southern coastal parts

appear slightly more windy compared to the rest

areas.

Comparison with meteorological measurements

Selected bibliography and references

Regarding the mean wind speed over the sea, the

meteorological model provides fair predictions,

estimating 5 to 10% lower speed values, except for

the east coast where the model underestimates the

speed up to 20% compared to the re-analysis data.

In contrast to ECWMF estimations of higher values

at the North part of the sea region than the South

Part, Open-Skiron reveals a more uniform behavior.

However, much larger discrepancies are observed

over the land. Actually, an inverse behavior is

observed, in which the general circulation is very

high above the land and mildly weaken above

the sea. This picture has to be further tested

using meteorological observations in the future.

Comparisons with in-situ wind measurements by the

Cyprus meteorological service exhibits a reasonable

agreement in some cases, given the low spatial

resolution of the modeled data.

➡ Two (2) floats were operational:

OPTIONAL

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OPTIONAL

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[1] Akylas E., Tombrou M., Panourgias J. and Lalas D. (1997). The use of common meteorological predictions in estimating short term wind

energy production in complex terrain. In: Watson R, editor. Proceedings of European wind energy conference, Dublin Castle, Ireland, 1997. p.

329-32.

[2] Akylas E., Lalas D. P., Pesmajoglou S., Sakellario N. and Tombrou M. (1999). Investigation of the effects of wind speed forecasts and

economic evaluation of the increased penetration of wind energy for the island of Crete. In EWEC’99, p. 1074-1077.

[3] Akylas E. and Tombrou M., Reconsidering a generalized interpolation between the Kansas type formulae and free convection forms,

Boundary-Layer Met. 15, 2005, 381

[4] Akylas E., Zavros P., Skarlatos D. and M. Fyrillas (2010). Weibull distribution and wind speed timeseries, 3rd International Conference of the

ERCIM, 10-12 December 2010, London, UK, abstract E756.

[5] Beller C. (2009). Urban wind energy-state of the art 2009. Danmarks Tekniske Universitet, Risø Nationallaboratoriet for Bæredygtig Energi.

[6] Chompoo-inwai C., Leelajindakrairerk M., Banjongjit P. F., and Wei-Jen L. (2008). Design optimization of Wind Power planning for a country

with low– medium–wind–speed profile. IEEETrans. Indus. Applic. 44:1341–1347.

[7] Cohen C., (1973). The Reflected Weibull Distribution. Technometrics,Vol. 15, No. 4 (Nov., 1973), pp. 867-873

[8] Jacovides C. P., Theophilou C., Tymvios F. S. & Pashiardes S. (2002). Wind statistics for coastal stations in Cyprus. Theoretical and applied

climatology, 72(3-4), 259.

[9] Kastanas I., Georgiou A., Zavros P. and E. Akylas (2014). An integrated GIS-based method for wind-power estimation: Application to

western Cyprus, in Open Geosciences 6(1):79-87 DOI: 10.2478/s13533-012-0162-3

Acknowledgment: This research is supported by the

project “Cross-Border Cooperation for Implementation of

Maritime Spatial Planning” referred as “THAL-CHOR 2”

and it is co-funded by the European Regional

Development Fund (ERDF) and by national funds of

Greece and Cyprus, under the Cooperation Programme

“INTERREG V-A Greece-Cyprus 2014-2020”.

Figure 3. Comparison between mean wind speed for 2017, as obtained by ECMWF re-

analysis data and Open-Skiron forecasts.

Figure 5. Comparisons of the Weibull distributions of two coastal –stations (green curves) with the

closest land (red curves) and sea (blue curves) grid point for 25km resolution.

Conclusions

➡ ECMWF re-analysis data show reasonable

agreement with forecasts of Open-Skiron and

meteorological stations in some cases

➡ Increasing the resolution through the use of the

5 km analysis data will provide more detailed

information in the future

➡ The inclusion of the in-situ measurements will be

extrapolated using flow models and used to increase

the accuracy of the picture.

34.375

34.625

34.875

35.125

35.375

35.625

35.875

31.875 32.375 32.875 33.375 33.875 34.375

Figure 1. Area of application and

positions of the meteorological

stations.

35.75 20.4 20.6 20.4 19.4 19.0 19.8 19.6 19.5 19.5 20.8 21.9 20.4

35.50 20.5 20.3 20.4 19.1 18.7 17.9 17.0 16.8 20.2 19.9 21.4 21.8

35.25 19.0 19.7 17.4 15.4 12.7 10.7 11.0 14.1 20.0 20.1 20.4 20.9

35.00 19.4 19.0 14.9 10.3 8.8 12.3 14.2 16.4 20.2 20.2 20.3 20.3

34.75 18.9 18.1 14.8 12.4 11.8 15.5 17.9 19.6 20.5 20.1 20.3 20.4

34.50 18.7 18.4 18.0 16.3 16.8 18.4 20.4 19.5 20.1 19.7 19.8 19.8

32.00 32.25 32.50 32.75 33.00 33.25 33.50 33.75 34.00 34.25 34.50 34.75

35.75 2.8 2.7 2.8 2.8 2.9 3.1 3.2 3.1 3.0 2.8 2.6 2.6

35.50 2.8 2.8 2.8 2.7 2.7 2.6 2.5 2.5 2.7 2.5 2.5 2.5

35.25 2.7 2.7 2.4 1.9 1.5 1.3 1.2 1.5 2.3 2.5 2.5 2.5

35.00 2.6 2.4 1.8 1.3 1.0 1.1 1.2 1.6 2.4 2.6 2.6 2.6

34.75 2.5 2.3 1.8 1.5 1.5 1.9 2.3 2.6 2.7 2.6 2.6 2.6

34.50 2.5 2.4 2.4 2.3 2.3 2.5 2.7 2.7 2.7 2.6 2.6 2.6

32.00 32.25 32.50 32.75 33.00 33.25 33.50 33.75 34.00 34.25 34.50 34.75

8.00 9.40 10.80 12.20 13.60 15.00 16.40 17.80 19.20 20.60 22.00

wind speed maximum (m/s) at 10 m

1.00 1.20 1.40 1.60 1.80 2.00 2.20 2.40 2.60 2.80 3.00

wind speed standard deviation (m/s) at 10 m

Figure 2. Average, maximum, and standard deviation of the wind speed (m/s) in the area of Cyprus

based on the hourly data of ECMWF (re-analysis data) with a spatial analysis of 25 km x 25 km.

General comparisons with data from the Open Skiron

35.75 4.5 4.7 5.0 5.2 5.3 5.6 5.6 5.3 5.0 4.7 4.6

35.50 4.7 4.8 5.1 5.0 5.0 5.0 4.7 4.4 4.6 4.5 4.4

35.25 4.6 4.5 4.1 3.5 2.7 2.3 2.1 2.5 3.8 4.1 4.2

35.00 4.3 3.8 2.9 1.9 1.7 1.8 2.0 2.7 4.1 4.3 4.4

34.75 4.5 4.1 3.2 2.4 2.5 3.4 3.9 4.5 4.7 4.5 4.6

34.50 4.8 4.7 4.7 4.3 4.3 4.5 4.9 4.9 4.9 4.7 4.7

32.00 32.25 32.50 32.75 33.00 33.25 33.50 33.75 34.00 34.25 34.50

35.75 4.2 4.4 4.7 5.1 5.3 5.3 5.2 5.0 4.7 4.3 4.1

35.50 4.4 4.6 4.8 4.9 5.1 4.8 4.3 4.0 3.6 3.4 3.7

35.25 4.4 4.4 4.3 3.9 3.1 3.2 2.7 2.3 2.8 3.4 4.0

35.00 4.1 3.7 3.2 3.1 2.7 2.7 2.5 2.9 3.5 4.1 4.4

34.75 4.2 3.9 3.6 3.4 3.1 3.5 4.0 4.3 4.4 4.5 4.6

34.50 4.6 4.6 4.8 4.9 4.8 4.9 4.9 4.8 4.7 4.7 4.6

32.00 32.25 32.50 32.75 33.00 33.25 33.50 33.75 34.00 34.25 34.50

1.50 2.05 2.60 3.15 3.70 4.25 4.80 5.35 5.90 6.45 7.00

wind speed average (m/s) at 10 m

35.75 -6 -7 -4 -1 -1 -5 -7 -7 -6 -8 -11

35.50 -6 -6 -7 -1 3 -4 -9 -8 -21 -24 -17

35.25 -4 -2 6 11 18 36 26 -6 -26 -17 -6

35.00 -4 -3 11 62 57 54 27 9 -15 -6 0

34.75 -7 -5 12 42 26 3 2 -4 -7 0 -1

34.50 -5 -3 3 15 12 8 -1 -1 -4 -2 -2

32.00 32.25 32.50 32.75 33.00 33.25 33.50 33.75 34.00 34.25 34.50

-26 -17 -9 0 9 18 26 35 44 53 62

relative differences (%)

In the figures below we compare basic statistics of

the wind speed as obtained from the 24 h forecasts

of the Open-Skiron meteorological model for the

year 2017. Open-Skiron operates on 5 km

resolution and results have been scaled to 25 km

for the needs of the present comparison.

Actual Weibull distributions reveal an intermediate

behavior between modelled sea and land points.

Figure 4. Hourly maximum, average and standard deviation of the wind speed (m/s) (top to bottom) for

the period 2002-2008, obtained from three meteorological stations (green curves) and the ECWMF.

With red curves the closest gird point over land and with blue curves the closest sea grid point.