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Implementing Agreement on Ocean Energy Systems International Energy Agency

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IINN IIEEAA--OOEESS MMEEMMBBEERR CCOOUUNNTTRRIIEESS

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A report prepared by INETI to the IEA-OES under

ANNEX I - Review, Exchange and Dissemination

of Information on Ocean Energy Systems

IEA-OES Document No: T0103

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WAVE DATA CATALOGUE FOR RESOURCE ASSESSMENT IN IEA-OES MEMBER COUNTRIES Final Technical Report IEA-OES Document No: T0103 Author(s) Teresa Pontes Delegate Member of the IEA-OES Principal Researcher INETI, Department of Renewable Energies Estrada do Paço do Lumiar 1649-038 Lisboa Collaborators André Candelária, INETI

Customer

This report was prepared to the IEA-OES under ANNEX I: Review, Exchange and Dissemination of Information on Ocean Energy Systems.

Disclaimer

The IEA-OES also known as the Implementing Agreement on Ocean Energy Systems functions within a framework created by the International Energy Agency (IEA). Views, findings and publications of the IEA-OES do not necessarily represent the views or policies of the IEA Secretariat or of all its individual member countries.

Availability of Report

A PDF file of this report is available at: www.iea-oceans.org

Suggested Citation

The suggested citation for this report is: T. Pontes and A. Candelária (2009). Wave Data Catalogue for Resource Assessment of IEA-OES Member Countries, Report from INETI for the IEA-OES.

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I. Index I. Index ........................................................................................................................................... 0

II. List of Figures........................................................................................................................... 4

III. List of Tables........................................................................................................................... 5

IV. List of Acronyms..................................................................................................................... 6

V. List of Symbols ......................................................................................................................... 8

1. Introduction............................................................................................................................... 9

2. Waves and Wave Energy ....................................................................................................... 10

3. Wave Information Sources..................................................................................................... 14

3.1 Visual Observations………………………………………… ....................................... 15

3.2 In-situ Measurements………………………………………. ........................................ 15

3.3. Remote Sensed Measurements……………………………….. .................................... 16

3.3.1 Satellite based radars……………………………………………………….………….17 3.3.2 Ground based radars………………………………………………………….………..21

3.4. Wind-Wave Numerical Models…………………………............................................ 22

3.4.1 Global models…………………………………………………………………………23 3.4.2 Regional and Local models……………………………………………………………24

4. Atlases and Databases ............................................................................................................ 28

4.1. Ocean Wave Statistics, by Hogben & Lumb (1967)………… ..................................... 28

4.2. WERATLAS - European Wave Energy Resource Atlas (1996) ................................... 28

4.3. World Wave Atlas (1996)…………………………………......................................... 29

4.4. EUROWAVES (2000)……………………………………… ...................................... 30

4.5. ONDATLAS (2003)……………………………………… ......................................... 30

4.6. Atlas of UK Marine Renewable Energy Resources (2004).......................................... 31

4.7. Accessible Wave Energy Resource Atlas: Ireland (2005)…….. ................................... 31

4.8. WORLDWAVES (2005)…………………………………. ......................................... 32

5. Country – by – Country Review............................................................................................ 33

5.1. Belgium……………………………………………………......................................... 33

5.1.1 In-situ data ……………………..…………………………………………..…………33 5.1.2 Wind-wave models……………………………………………………………………34

5.2. Canada……………………………………………………. ......................................... 34

5.2.1 - In-situ and remote sensed data………………………...…………………..…………34 5.2.2 Wind-wave models……………………………………………………………………36

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5.3. Ireland……………………………………………………. .......................................... 36

5.1.1 In-situ data ………………………………………………………………..…………36 5.1.2 Wind-wave models……………………………………………………………………37 5.4 Portugal…………………………………………………….......................................... 38

5.4.1 - In-situ data ………………………………………………………………..…………38 5.4.2 Wind-wave models……………………………………………………………………38 5.5 Spain………………………………………………………. ......................................... 39

5.5.1 - In-situ data ………………………………………………………………..…………39 5.5.2 Wind-wave models……………………………………………………………………41 5.6 United Kingdom……………………………………………......................................... 42

5.7 United States of America…………………………………........................................... 43

6. References................................................................................................................................ 44

Appendix – In-situ Wave Measuring Devices ......................................................................... 48

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II. List of Figures Figure 2.1 Wave data types........................................................................................................... 12

Figure 3.1 Sub-image of an ERS-2 SAR scene showing swell waves………………………... . 18

Figure 3.2 ENVISAT ASAR Wide Swatch medium resolution……………………………….. 20

Figure 3.3 Schematic representation of an ensemble forecast………………………………… . 23

Figure 3.4 DMI-WAM models ..................................................................................................... 25

Figure 4.1 Annual wave power roses for the northernmost part of the Northeastern Atlantic

covered by WERATLAS. ..................................................................................................... 29

Figure 4.2 The 85 ONDATLAS data sites.................................................................................... 31

Figure 5.1 Wave buoys off Belgian coast………………… ………………………………….. 34

Figure 5.2 Canada wave buoys ................................................................................................... 35

Figure 5.3 Ireland wave measuring stations ................................................................................ 37

Figure 5.4 MAR3G wave forecast map………………………………………………………….39

Figure 5.5 Buoys networks in Spain …….……..………………………………………………..40

Figure 5.6 REDMAR tide gauge and radar networks in Spain…………………………………..41

Figure 5.7 Coastal buoys off Catalan coast, Spain…………………………………………….. 41

Figure 5.8 UK waters wave mapping ......................................................................................... 42

Figure 5.9: Northwest of USA and its wave measuring stations .................................................. 43

Figure A1.1: The Wavec Buoy.. ................................................................................................... 51

Figure A1.2: Directional Waverider Buoy.................................................................................... 51

Figure A1.3: Triaxys Directional Buoy. ....................................................................................... 51

Figure A1.4: Seawatch Buoy and variety of its deployment configurations. .............................. 52

Figure A1.5 - ODAS Data Acquisition Buoy. .............................................................................. 52

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III. List of Tables Table 2.1 Classification of seastate distributions and parameters ................................................ 14

Table 3.1 Wave data sources and types ........................................................................................ 15

Table 3.2 Most important satellite missions concerning wave data……………………………..18

Table 3.3 Main global numerical wind-wave models................................................................... 24

Table 3.4 Regional and local wave models nested in WAM and WW3 models .......................... 26

Table 3.5 Regional/local wind wave models (not nested in WAM nor in WW3)........................ 27

Table 5.1 In-situ wave measuring stations on Belgian coastal area……………………………..33

Table 5.2 Ireland in-situ data ........................................................................................................ 36

Table 5.3 Portugal buoy locations ................................................................................................ 38

Table 5.4 Spanish network of wave and tidal measuring systems................................................ 40

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IV. List of Acronyms

CM - Clima Marítimo (Puertos del Estado, Spain)

C-MAN - Coastal Marine Automated Network

DER - Departamento de Energias Renováveis (Department of Renewable Energies, INETI, Portugal)

DLR - Deutsche Zentrum für Luft- und Raumfahrt (German Aerospace Center, Germany)

DMI - Danish Meteorological Institute

DWD - Deutscher Wetterdienst (Germany National Meteorological Service)

ECAWOM - European Coupled Atmosphere-Wave-Ocean Model

ECMWF - European Centre for Medium-range Weather Forecast

ERP - Satellite Exact Repeat Period

ESA - European Space Agency

EUMETSAT - European Organisation for the Exploitation of Meteorological Satellites

GKSS –Research Centre, Germany

GOES - Geostationary Operational Environmental Satellites

GRIB - Gridded Information in Binary Form

GWS - Global Wave Statistics

IEA-OES - Implementing Agreement on Ocean Energy Systems

IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer (French Research Institute for Exploitation of the Sea, France)

IH - Instituto Hidrográfico (Hydrographic Institute), Portugal

INETI - Instituto Nacional de Engenharia, Tecnologia e Inovação, I.P.

LNEG - Laboratório Nacional de Energia e Geologia, Portugal

MEDS - Marine Environmental Data Services (Canada)

MSC - Meteorological Service of Canada

NCAR - National Centre for Atmospheric Research, USA

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NDBC - National Data Buoy Center (USA)

NCEP - National Centers for Environmental Prediction (USA)

NESDIS - National Environmental Satellite, Data, and Information Service (USA)

NOAA - National Oceanic and Atmospheric Administration (USA)

NRT - Near real time

NWS - National Weather Service (USA)

NWSTG - NWS Telecommunications Gateway

POES - Polar Operational Environmental Satellites

SAR - Synthetic Aperture Radar

UKCS - United Kingdom Continental Shelf

WERATLAS - European Wave Energy Resource Atlas

WMO - World Meteorological Organization

WW3 – WAVEWATCH III

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V. List of Symbols

E(f) – Energy (or frequency) spectrum

f - Frequency (Hz); f = 1/T

Hs - Significant wave height (m)

P- Wave power or flux of energy per unit crest length (kW/m)

S(f, θ) - Directional spectrum

T - Period (s)

Te - Energy (mean) period (s)

Tp - Peak period (s)

Tz - Mean zero-crossing period (s)

U10 – mean wind velocity at 10m height (m/s)

θm – Mean wave direction (deg)

θ (f) - Mean direction per frequency band (deg/Hz)

σ(f) - Direction standard deviation per frequency band (deg/Hz)

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1. INTRODUCTION This catalogue is being developed within the framework of the Annex I – Review, Exchange and

Dissemination of Information on Ocean Energy Systems of the Implementing Agreement on

Ocean Energy Systems (IEA-OES) of the International Energy Agency.

This document aims at providing an overview of the available wave data appropriate for

assessing and characterizing the wave energy resource in the IEA-OES member-countries,

observers and elsewhere. This was collected through a questionnaire and was complemented by a

web search. In addition to collating and comparing such information in a single report, it will

serve to inform discussion on whether an Annex on resource assessment should be started within

this Implementing Agreement.

This report begins with an overview of the various wave data sources which include in-situ and

remote sensed measurements, and results of wind-wave numerical models that compute wave

conditions taking as input wind fields over the ocean produced by numerical atmospheric

models. Remote sensed data are mostly obtained from radar altimeter and Synthetic Aperture

Radar (SAR) flying on board of satellites. A review of the various satellite missions relevant for

this purpose and their characteristics is included. The most relevant wind-wave models

implemented in the routine operation of institutes and centres worldwide are presented. Details

of the availability of wave results produced by such models implemented globally or regionally

are described. The various wave and wave energy resource atlases and databases useful for wave

energy resource assessment are also reviewed.

Finally, a country-by-country review of the existing wave information, namely the national in-

situ measurement programmes and the available wind-wave model results, is presented.

In addition, an analysis including a short description of wave measuring devices, their location

and water-depth, data type and availability is made. This analysis focuses:

• In-situ data: wave data collected by moored buoys and measurements made from fixed

platforms and their characteristics (location, water depth, availability);

• Remote sensed data: satellite radar and their characteristics (sensor, coverage, availability,

Exact Repeat Period);

• Wind-wave numerical model results: identification, description, coverage, as well as other

technical information.

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2. WAVES AND WAVE ENERGY

Created by winds blowing over large ocean surface areas (hundred or thousands of kilometres)

for long periods of time (days), ocean waves are a complex random phenomenon thus requiring

the use of statistical methods for its characterization. Waves exhibit significant variability at all

time scales, from wave-to-wave (order of seconds), groups-to-groups (minutes), sea state–to-sea

state (hours), season to season (months) to inter-annual variations. A sea state can be defined as

stationary wave conditions on a scale of tenths of kilometres and few hours.

The generation and propagation of wind sea waves is a complex nonlinear process, in which

energy is slowly exchanged between different components. However, on a scale of tens of

kilometres and minutes, in deep water a stationary Gaussian random process describes quite

accurately the local state of the sea surface. Thus the local behaviour of the waves is determined

by the spectrum of the sea state S(f,θ) that specifies how the wave energy, proportional to the

variance of the surface elevation, is distributed in terms of frequency f and direction θ (Figure

2.1). This spectrum is usually summarized by a small number of wave parameters, namely wave

height H, period T (f = 1/T) and direction θ. Directional spectra and statistics of these wave

parameters are the basic information currently used to characterize the wave energy resource, to

design wave power converters and forecast their performance by means of mathematical or

numerical modelling, and wave tank tests of scaled models. It should be taken into consideration

that directional or 2D wave energy spectra S(f,θ) which provide an (almost) complete sea state

description have been increasingly used in ocean engineering applications. However often only

frequency or energy (1D) spectrum E(f) and mean direction distribution θ(f) are available. E(f) is

related to S(f,θ) by

( ) ( )∫=π

θdfθSfE2

0, . (1)

To obtain wave height and period parameters use is generally made of spectral moments that are

given at the nth order by

( ) ( )∫ ∫∫ ==∞∞π

nnn dffEfθdfdθfSfm

2

0 00, . (2)

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For wave height, the most widely used parameter is the significant wave height, defined as the

average of the highest one third of the up-crossing trough to crest wave heights that matches

reasonably well one’s visual impression of wave height. It can be computed from the wave

elevation variance density spectrum by

04 mHs = . (3)

For wave period several parameters are commonly used. In this context the energy (mean) period

Te and the peak period Tp are most used. The energy period is defined by

0

1

mmTe

−= . (4)

Te depends mainly on the lower frequency band of the spectrum where most of the power is

contained. In addition it provides a straightforward way of computing sea state power level in

deep water (see below).

The peak period Tp is the inverse of the peak frequency, fp, which corresponds to the highest

spectral energy density

1

pp f

T = (5)

such that E(fp) = max(E). Tp is most useful for sea states with only one wave system (single-

peaked spectra) since it provides information on the frequency band where most of the energy is

contained. Thus it is not appropriate for multi-peaked spectra, i.e. sea states that include more

than one wave system (usually wind-sea and one or more swells), which represent 20% or more

of occurrences in the North-eastern Atlantic (e.g. Guedes-Soares, 1984), with higher percentage

in the Pacific Ocean where two swells and wind-sea are often present.

The traditional mean zero-up-crossing period Tz (the average time elapsed between two

sequential crests) can also be obtained from spectra as (m0/m2)1/2. Its dependence on m2 makes it

very sensitive to the high frequency spectral tail that exhibits high variability and minute energy

contents.

Figure 2.1 illustrates the wave statistics that are most commonly used and their

interrelationships.

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Figure 2.1 - Wave data processing (sea surface (a); directional spectrum: polar (b) and 3D (d) plots; frequency spectrum and mean direction against frequency (c); example of (Hs,Te) scatter table (e)). In Saulnier and Pontes, 2006.

Spectral analysis

S(f,θ)

Sea surface elevation measurements

y

η

x

Long-term statistics

Spectral parameters Hs, Te, Tp, θm, P, ...

(a)

(e)

or E(f) and θ(f)

(d)

(b) (c)

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Several wave direction parameters can be used. Taking the directional spectrum, mean wave

direction is computed by

d d)cos(),(

d d)sin(),( arctan 2

0 0

2

0 0m

⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜

⎝

⎛

∫ ∫

∫ ∫= ∞

∞

π

π

fθθθfS

fθθθfSθ . (6)

Directional buoys often provide frequency spectra E(f), in addition to the mean direction )( fθ

and its spreading )( fσ for each frequency band. In this case mean direction is computed by

( ) ( )( )

( ) ( )( ) ⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

∫

∫=

∞

∞

0

0

d cos

d sin arctan

ffθfE

ffθfEθb . (7)

Often an oceanic sea state will include both locally generated wind sea, whose dominant

direction should be that of the local wind, and swell, i.e. generally long period far travelled

waves generated up to several days earlier by distant weather patterns. They may have a quite

different dominant direction. In this case an adequate summary of the sea state will require

separate height, period and mean direction of wind-sea and (occasionally more than one) swell

components.

In deep-water, i.e when the water depth is larger than half of the wave length, wave power P can

be computed in terms of sH and eT as

es THπ

gρP 2

2

64= . (9)

Sea-water mass density is taken as 1025=ρ kg/m3 and gravity acceleration g = 9.81 m/s2. Then

the wave energy flux per unit crest length or power level in deep-water is given by

es THP 249.0≅ kW/m (10)

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if sH is expressed in meters and eT in seconds.

Table 2.1 presents a classification for wave information as proposed by Saulnier and Pontes

(2006).

Table 2.1: Classification of sea state distributions and parameters

Directional Data Non-Directional Data

Type Type

I

II

III

),( θfS

)(),( fθfE and )( fσθ

mpes θTTH ,,,

IV

V

)( fE

sH (and possibly pe TT , )

3. WAVE INFORMATION SOURCES The two basic sources of wave information are data obtained from direct or indirect

measurement techniques i.e. in-situ or using remote sensing (both ground and satellite based),

and results of numerical wind-wave models. However, the first global source of wave data is

visual observations carried out for meteorological purposes on board of commercial ships, which

started to be archived by 1850 e.g. by British Meteorological Office. A summary of such

information is presented e.g. in Pontes et al., 2007.

In-situ measurements provide realistic data but are not widely available. Remote sensed data,

namely satellite data, are becoming increasingly accurate and available. Numerical wind-wave

models take as input wind fields over an ocean basin (or globally) and compute directional

spectra at the nodes of a grid extending over the considered basin(s). Although they cannot be

considered as the truth, model results present advantages, namely their proven accuracy for

extended oceanic areas and a very low ratio working costs/computational velocity. Measured

data and model results show good complementarities. A common practice to improve the

15

accuracy of these models is to calibrate their results against wave data, namely in-situ and

satellite data.

Table 3.1: Wave data sources and types

Data Source Device / Model Data Type

In-situ Buoys and pressure, laser and

acoustic probes II, IV

Satellite altimeter V

Satellite SAR I

Measurements

Remote sensed

Radar I, IV

Wind-wave models

3rd generation models (WAM, WaveWatch III and other models), and also UK Met Office model (2nd generation)

I

3.1 Visual Observations

The accuracy of visual observations has been assessed by various authors. It is considered good

for direction, acceptable for wave height and of lower quality for period. Long term visual

observations data sets are presently more useful to identify whether a certain period of time

during which accurate data are available can be considered representative of long term

conditions.

3.2 In-situ Measurements In-situ measurements mean wave data collected by moored buoys, measurements made from

fixed platforms or bottom-mounted sensors. These data are not widely available because such

measurements are expensive and difficult to make due to the harsh environment. A wide range of

wave measuring systems exist, whose selection depends on depth, access, wave conditions of the

measurement site and of the required data details, namely directionality.

Concerning Europe, the available in-situ data is still far from sufficient for describing the

offshore wave climate along the continent. In fact, there is not yet a co-coordinated European-

wide wave data collection paralleling the network operated by NOAA in the US and MEDS in

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Canada, for example. Neither there is a central repository and/or inventory of European wave

measurements. Most European countries have, however, over the years, carried out data

collection from buoys and offshore installations for different purposes, with responsibility for

data banking resting within the individual countries. Offshore, the most common in-situ

measurements come from wave-recording buoys, which can be scalar (with no directional

component) or directional which are more common nowadays. Their results include time series

of sea surface elevation from which wave height and period (and direction in the case of

directional data) parameters can be derived by spectral or direct analysis of the time series.

Considering nearshore (water-depth h smaller than half wave-length) wave measurements,

different kinds of devices can be used besides buoys: submerged pressure and acoustic probes,

wave staffs, current meters, pressure cells, pressure sensors and acoustic probes bottom-mounted

or suspended above the sea. When used individually, these devices provide non-directional

information, which is often appropriate since refraction tends to reduce the wave directional

spreading close to the shore. When grouped in arrays or coupled to another suitable instrument

(for instance, an electromagnetic current-meter), they can provide directional information. A

short description of the above wave measuring devices is included in the Appendix. For a

detailed description of most commonly used wave measuring sensors see e.g. Holthuijsen

(2007).

3.3. Remote Sensed Measurements

As the ocean is a rough environment and in-situ data are rare, remote sensing techniques play an

increasingly important role in this context. Sensors such as the radar altimeter and the Synthetic

Aperture Radar (SAR) have the clear advantage that they penetrate clouds and are not dependent

on sun illumination of the remotely sensed objects. A disadvantage in their use is low frequency

of measurements which makes the resource statistics useful only in pre-feasibility studies or in

combination with classical offshore measurements and modelling results.

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3.3.1. Satellite Radars Remote sensed wave data are obtained by radar altimeters that provide accurate sH (and also

wind velocity at 10m height), and SAR from which estimates of directional spectra ),( θfS are

obtained.

There has been a limitation in the accuracy of SAR measurements for long waves with period

larger than 8-9s. However, development of methods to compute ),( θfS from SAR data are

underway at various institutions it being expected that in a near future it would be possible to

obtain reliable detailed wave description based on remote sensed wave data. In Pontes and Bruck

(2008) an assessment of the quality of such remote sensed data showed good prospects for their

utilization for the assessment of wave energy resource.

Altimeter

This instrument measures, from an orbit at typically 1000 km height, the oceans surface

elevation from which the wind speed and the significant wave height are estimated. The accuracy

of the significant wave height sH measurements is very close to the accuracy of buoys.

Measurements are provided each second as the satellite covers a repeated net of ground tracks at

about 6 km/s. This provides enormous amounts of wave data worldwide.

Since the beginning of the 1990s, altimeter measurements have been performed continuously by

ESA and NOAA satellites. The series of ESA’s satellites starting producing reliable data since

1991 (ERS-1) that was continued by ERS-2 and ENVISAT satellites. Since 1992 the US/French

satellite Topex/Poseidon (T/P) mission provided high quality data: it was followed by JASON-1

in 2002, and Jason-2 in June 2008 it being a product of European and US agencies (NASA,

CNES, Eumetsat, NOAA). The NASA Geosat Follow-on (GFO) satellite carrying a radar

altimeter was launched in 2000. Table 3.2 presents the most important satellite missions

concerning wave data.

Studies have shown that is possible to apply algorithms to compute a mean wave period from

altimeter data. Models to compute zT from altimeter data have been proposed by Davies et al

(1998), Gommenginger et al (2003) complemented by Caires et al (2005), and Quilfen et al

(2004). In Bruck and Pontes (2008) the good accuracy of such estimates is shown.

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Table 3.2: Most important satellite missions concerning wave data

Satellite Sensor Parameters Coverage Availability ERP Source

Altimeter

Hs, U10

JASON 1

SAR

S(f,θ)

2.8 deg long between tracks

(66 S-66 N 0 W-360 E)

Since 2002.02.01

(NRT, 4 hours) 10 days

NASA http://sealevel.jpl.nasa.

gov/mission/jason-1.html

Altimeter Hs, U10

Narrow swath 0.8 deg long

between tracks (81 S-81 N 0 W-360 E) ERS-1 and

ERS-2

SAR S(f,θ)

Snapshots at 200km intervals

along track (81 S-8 N

0 W-360 E)

Since 1991, (NRT)

35 days ESA

http://earth.esa.int/ers/

TOPEX/ Poseidon

Altimeter Hs, U10

Narrow swath 2.8 deg long

between tracks (66 S-66 N 0 W-360 E)

Since 1992.11.23 Semi NRT (8

hours)

10 days NASA

http://sealevel.jpl.nasa.gov/mission/topex.html

RADARSAT SAR S(f,θ)

Swath width of

5000 km Since 2002

24 days (sub-cycles

of 7 and 14

days)

Canadian Space Agency http://www.space.gc.ca

Altimeter Hs, U10

Narrow swath 0.8 deg longitude between tracks

(81 S-81N 0 W-360 E) ENVISAT

SAR wave mode

S(f,θ)

Snapshots at 200 km intervals along

track

Since late 2002 (NRT)

35 days ESA

http://envisat.esa.int/

Geosat Follow-on

Altimeter Hs, U10

Narrow swath, 1.7 deg longitude

between tracks (72 S-72 N 0 W-360 W)

Since late 2000 (Not NRT)

17 days US Navy

http://gfo.bmpcoe.org/Gfo/

TerraSAR-X SAR S(f,θ)

- January 2008 11 days http://www.terrasar.de/

Synthetic Aperture Radar (SAR)

Since the launch of the European remote sensing satellite ERS-2 in 1995, SAR images have been

acquired over the oceans on a continuous basis. The resolution is about 30 m in range (across

flight direction) and 10 m in azimuth (along flight direction); wavelength ranges from 20 to 1000

m in 24 logarithmic steps.

Full swath scenes of 100 x 100 km size are taken where receiving stations are in line of sight

(image mode), whereas 6 x 10 km imagettes are acquired every 100 km along the orbit (wave

19

mode). Whereas full swath scenes are well suited for detailed studies of coastal areas, wave

mode scenes are very valuable for global analysis, e.g., assessment of wave energy on a global

scale.

As an example, Figure 3.1 shows a ERS-2 SAR image acquired over the coast of Spain with

swell waves of about 200 m wavelength approaching the harbour of Gijon (Schulz-Stellenfleth et

al, 2006). Refraction phenomena as the waves enter into shallower water can be clearly

observed.

Figure 3.1: 20 by 20 km sub-image of an ERS-2 SAR scene acquired over the coast of Spain showing swell waves. At the lower part is the harbor of Gijon. In Schulz-Stellenfleth et al, 2006.

The Canadian satellite RADARSAT as well as the European satellite ENVISAT launched in

2002 have an additional Scan SAR mode providing images with a swath width of 500 km and

reduced spatial resolution. This mode gives an ideal opportunity to get a synoptic overview of

large areas, e.g., nearly the entire North Sea. With the new ENVISAT Wide Swath SLC product,

which has a range resolution of about 20 m it is even possible to look at ocean waves at these

large scales.

20

Figure 3.2 ENVISAT ASAR Wide Swath Medium Resolution

The ERS-2 and ENVISAT satellites have a revisit time of 35 days, i.e., a particular location is

imaged at these intervals with exactly the same imaging geometry. The revisit time at different

incidence angles is dependent on the imaging mode and latitude. For wide swath, the time

elapsed between two successive visits can be of 3-4 days.

A centre for satellite data distribution is held by IFREMER in France in relationship to the ESA.

This institute has developed an online database (http://www.ifremer.fr/cersat/).

The use of SAR spectra can be directly useful for wave energy resource assessment in areas

dominated by long swells, but to the authors’ knowledge this has not been done so far. However,

SAR spectra are being indirectly useful for wave energy resource assessment through their use in

the assimilation procedure of numerical wind-wave models contributing in this way to the

increase of their accuracy.

In Pontes and Bruck (2008) a verification of satellite altimeter and SAR directional spectra

against wave buoy data is presented, it being found that the quality of Hs, Tz and Te obtained from

altimeter data is good. The verification of ENVISAT SAR directional spectra for buoys in the

Equator and Hawaii area (the only buoy data with appropriate quality) also showed acceptable

accuracy.

In 2007 the German TerraSAR-X mission was launched. The X-band SAR system onboard that

satellite has a couple of very interesting features for the renewable energy sector namely spatial

resolution of up to 1m, and polarimetric and interferometer capabilities. The interferometer mode

of the satellite in particular enables the derivation of surface current information. Another

21

attractive feature of these radar instruments is their ability to provide wind, wave and current

information at the same time. This is interesting for example in the context of recent

developments to combine different systems, e.g., wind and wave converters to have a more

efficient use of the expensive sea cables.

3.3.2. Ground-based Radar

In addition to SAR and altimeters used with satellites, ground-based remote sensing radar

alternatives are described below.

MIROS Microwave Radar

This device is made by the Norwegian company MIROS for use on platforms, and provides good

measurements, including directional wave spectra and surface currents (http://www.miros.no/).

.It can be very useful for assessment of the nearshore wave conditions.

WaMoS II Radar

This wave monitoring system is connected to conventional nautical X-band radar. The system

detects, in real-time, complete wave information such as full directional spectra and statistical

sea state parameters such as significant wave height, peak wave period and peak wave direction,

as well as surface current speed (U) and current direction (θu) in a high spatial and temporal

resolution. New features for single wave detection, high-resolution current measurements and the

estimation of the water depth are available. The maximum wave height can be estimated from

spatial wave data, and therefore extreme wave events can be studied

(http://www.oceanwaves.de/start.htm)

HF Radar

The HF radar is well established as a powerful tool for sea current measurements up to a range of

about 30 km. It operates in the MHz frequency band corresponding to a radar wavelength in the

range of 10 to 300m. Although it has been claimed that HF radars can measure wave spectra at

very long ranges, the success as wave sensors has yet been limited. A great disadvantage of

many HF radars is the size of their antennas. Attempts have been made to make the antennas

smaller, but probably at the cost of greatly reduced antenna efficiency.

22

3.4. Wind-Wave Numerical Models

Modelling is the numerical solution of the equations that describe the physical processes of wave

growth, decay and propagation on the oceans, taking as input the wind fields over the ocean that

are produced by numerical atmospheric models. Steady improvements both in theory and

computer power have led over the last 20 years to very sophisticate atmospheric and wave

models, now commonly used to produce daily forecasts on global, regional and also site specific

scales.

Such models can be run in Forecast mode using the forecast wind fields or in Hindcast mode

taking the past wind fields. To run atmospheric and wave models in Forecast mode, an

assimilation procedure known as Analysis is used to produce the initialization field. Analysis

integrates measurements taken in a network of measurement points all over the world.

Reanalysis procedures have produced long time-series of consistent analyses using a single and

state-of-the-art version of the model. Two most relevant reanalysis programmes have produced

data sets available for research. These are the reanalysis from the joint effort of the NCEP and

NCAR (Kalnay, et al., 1996 ) and the other is from ECMWF. These reanalysis data sets present

the advantage of being over a long term period suitable for meaningful climatological analyses.

ECMWF reanalysis datasets include ERA-40 producing wave data from 1957 to 2001 over a

basic resolution of 1.5° by 1.5°. The online Global Wave Climatology Atlas was derived from

this 45-year of ECMWF reanalysis data (www.knmi.nl/waveatlas).

There are two main types of forecasts. The first is the traditional deterministic forecast where the

numerical wind-wave model is run once taking as input the wind-field over the ocean basin of

interest computed by the atmospheric model. A single deterministic forecast uses only one

numerical method and one set of physical parameterizations.

The ensemble forecast is obtained from multiple runs of both atmospheric and wind-wave

models each based on slightly different initial conditions or using slightly different model

configurations and/or parameterizations. Various statistical and graphical methods to combine

these multiple model runs are used. By doing so, they can include information about the level of

uncertainty, the most likely forecast outcomes, and probabilities of those outcomes.

Figure 3.3 presents a schematic representation of an ensemble forecast.

23

Figure 3.3: Schematic representation of an ensemble forecast. Lines with arrows represent the evolution as time goes by.

3.4.1. Global Models The first third-generation wave model was the WAM Model (The WAMDI Group, 1988) that

was firstly implemented at ECMWF, it being described by Komen et al. (1994). It was further

implemented in many centres, being distributed by the German Research Centre GKSS.

WAVEWATCH III (WW3) developed by NOAA/NCEP differs in governing equations,

numerical methods and physical parameterizations (http://polar.ncep.noaa.gov/waves), see e.g.

Tolman (2006). Verifications and intercomparisons of wave models have shown that model

accuracy has continuously increasing namely due to data assimilation and statistical forecasts

(ensemble prediction), see e.g., Janssen (2004). Other relevant wind-wave model is the second

generation UK Met Office wave model first developed by Golding (1983) and further updated.

Finally, the AES40 model, whose detailed description and validation is presented in Swail et al

(2000) has produced a 40-year (1958-1997) wind and wave hindcast for the North Atlantic.

Table 3.3 presents an overview of the main global wind-wave numerical models.

Initial moment Intermediate forecast

Final forecast

24

Table 3.3: Main global numerical wind-wave models

Model ID

Description Coverage (Lat/Long)

(deg)

Grid (Lat x Long)

(deg)

Available Data Type

Data Format

Availability

WW3

Global

(Tolman, 2006)

3rd generation, data

assimilation, deterministic +

ensemble

78 S-78 N 0 W-258.75 E

1.00 x 1.25 Hs, Tp, Tm θm,

S(f, θ)

GRIB and Spectral

text bulletins in ACSII

Since 03.2000

WAM (ECMWF)

(Komen et al., 1994)

3rd generation, data

assimilation, deterministic +

ensemble

81 S-81 N 0 W-259.5E

0.25 x 0.25 Hs, Tp, θm

S(f, θ),

Real-time forecast / Archived forecast

WAM

GSM (DWD)

(DMI, 2008)

3rd generation, shallow water

0.75 x 0.75 Hs, Tp, θm

(sea & swell)

GRIB

-

UKMO

Global Wave Model

(Met Office, 2006)

2nd generation 80.28 N- 79.17 S

5/9 x 5/6 Hs, Tp, θm - 2000.06.01 2003.09.30

AES40

(Swail et al, 2000)

40-year (1958-1997) wind and wave hindcast of

the North Atlantic; OWI-3G

model

0-70 N 82 W-20 E

0.625 x 0.833

Hs, Tp, θm for sea and swell

BINARY 1958-1997

3.4.2. Regional and Local Models

Usually, global-scale wave models are only designed to provide general wave pattern over the

deep ocean, and do not describe information accurate enough to describe small-scale, complex

wave patterns near the coastal areas. Therefore, both WAM, WW3 and UKMO have regional or

local models, which have higher resolution in space and time in order to predict wave conditions

adequately over the continental shelf and near land boundaries.

Besides WAM, WW3 and UKMO regional/local models, there are other successful models with

applications at different parts of the globe. SWAN model (Booij et al, 1999) for example is a

spectral wave model based on the action density balance equation that describes the evolution of

two-dimensional wave energy spectra under specified conditions of winds, currents, and

bathymetry. It simulates wave propagation, accounting for refraction due to variations in seabed

and currents; shoaling, blocking and reflection due to opposing currents; and blockage, reflection

25

or transmission due to obstacles. SWAN can be used on any scale, although this model is

specifically designed for coastal applications where reflection and diffraction are not significant.

SWAN is described in detail in Holthuijsen (2007).

MAR3G is a third-generation wind-wave model, complemented by an inverse-ray refraction

model that computes the directional spectra transformation from open-ocean to the nearshore. It

is implemented in the routine operation of Instituto de Meteorologia, Portugal where it is used

for operational forecast (www.meteo.pt). Shoaling, refraction, bottom dissipation, and shelter by

the coastline and/or neighbouring islands are taken into account (Oliveira-Pires, 1993, further

updated).

Table 3.4 presents regional/local wave models nested in WAM and WW3. The remaining

regional models are shown in Table 3.5.

6 Figure 3.4: DMI-WAM models; a) North Atlantic; b) North Sea and Baltic Sea; c) Inner Danish Waters;

d) Mediterranean Sea. In DMI, 2009.

a b

c

d

26

Table 3.3: Regional and local wave models nested in WAM and WW3 models

Model ID/ Countries

Description Coverage

(Lat/Long) (º)

Grid

Lat x Long

(º)

Data Type Data

Format Availability

WW3

W & N Atlantic (USA, Canada)

0.25 S-50.25N 98.25 W-29.75 W

01.2000

WW3

Eastern North Pacific

(USA, Canada)

4.75 N-62.25 N 170.25 W-76.75W

0.25 x 0.25

05.2002

WW3 Alaska Waters

(USA)

3rd generation, data assimilation, deterministic +

ensemble

45 N-75 N 160 E-124 W

0.25 x 0.50

Hs, Tm, Tp, θm

GRIB and Spectral

text bulletins in

ASCII

01.2000

WAVEWATCH Strait Gibraltar

(Spain) 3rd generation

35.75 N-36.75 N 6.5 W-5.25 W

1’ Hs, Tp, θm,

S(f, θ) -

WAM (ECMWF) Several

European Countries

3rd generation, data assimilation, deterministic +

ensemble, shallow water

Regional Europe (North Atlantic),

Baltic, North, Norwegian,

Med & Black Seas

0.25 x 0.25 Hs, Tp, θm,

S(f, θ) -

MSM Med Sea

0.25 x 0.25 - WAM (DWD)

European Countries

3rd generation, shallow water LSM-North, Baltic

& Adriatic 0.1 x 0.167

Hs, Tp, θm,

S(f, θ)

GRIB

-

North Atlantic (Figure 3.4a) 30 N-78 N 69 W-30 E

0.5 x 0.5

North Sea and Baltic Sea (Figure

3.4b) 36 N-75 N 20 W-30 E

1/10 x 1/6

Inner Danish Waters (Figure

3.4c) 53 N-60 N 7 E-16 E

1/50 x 1/30

WAM (DMI)

/ Ireland &

others

3rd generation, deterministic

Med. Sea (Figure 3.4d) 30.5 N-46 N

6 W-36 E

1/6 x 1/6

WAM Regional Irish

model (Met Éireann)

Ireland

3rd generation, deterministic,

nested in ECMWF WAM model

48 N-57 N 15 W-0 W

0.25 x 0.25

Hs, Tp, θm,

S(f, θ) GRIB

Archived forecast

WAM Atlantic Coast Puertos del

Estado Spain

18 N-69 N 60 E-9 E

From 3x3 (offshore)

to

0.25 x 0.25 (nearshore)

-

WAM Med Sea

(Puertos del Estado) Spain

Version 4, 3rd generation

34 N-45 N 7 E-17 E

From 0.25 x 0.25 until 0.125 x 0.125

Hs, Tp, θm,

S(f, θ) GRIB

-

27

WAM Cantabric

Coast (Puertos del

Estado) Spain

42 N-44 N 9.5 W-1 W

From 5’ to 2.5’ (coast)

-

WAM (DWD)

Germany

3rd generation, deterministic

80 S-60 N 0.25 E-100 W

0.9 x 0.9 Hs, Tp, θm,

S(f, θ) GRIB 06.1992

Table 3.5: Regional/local wind wave models (not nested in WAM nor in WW3)

Model ID/ Countries

Description Coverage

(Lat/Long) (º)

Grid (Lat x Long)

(º) Data Type

Data Format

Availability

SWAN

Belgium

3rd generation deterministic

125x 39 km parallel to the Belgian Coast

250 x 250m

S(f, θ), E(f), θ(f), σ(f), Hs , Te, Tp, Tz ,

θm

ASCII 01/01/1994-

UKMO

UK waters l, UK Met office

2nd generation data assimilation

deterministic

48 N -63 N

12 W-12 E 1/9 x 1/6

Hs, Tp, θm,

S(f, θ)

Real-time forecast

/Archived forecast

European Wave Model

2nd generation 30.75 N-67 N 14.46W-1.14E

Circa 35 x 35 km

Hs, individual (wind-sea and

swell) periods,

heights and directions

GRIB

-

MAR3G

Instituto de Meteorologia

Portugal

3rd generation model coupled to raytracing model

(shoaling, refraction,

bottom dissipation &

shelter)

Portuguese mainland and Madeira an

Azores Islands

1 x 1 Hs, Te, Tp, Tz,

θm HTML

Since 1989

28

4. ATLASES AND DATABASES

A preliminary assessment on ocean energy potential requires the compilation of realistic wave

and wind conditions, which can be embodied in the form of an atlas or other kinds of databases.

Since 1854, visual observations of commercial ships have been collected and compiled into

databases. Since the 1990s, hindcasts on one hand and satellite measurements on the other hand

have led to the development of new types of databases. The most important historical and actual

compilations of ocean information follow.

4.1. Ocean Wave Statistics, by Hogben & Lumb (1967)

Visual observations of commercial ships are done all over the oceans and seas being collected by

meteorological institutions. The first archival of observations started in Britain in 1854. They

have systematically been collected since 1961 according to the Resolution 35 of the Worldwide

Meteorological Organisation. The most well-known compilations of these observations are the

OWS (Ocean Wave Statistics, Hogben & Lumb, 1967) and after Global Wave Statistics (GWS -

Hogben, Dawnka & Olliver, 1986) that takes advantage of the experience of detected biases in

OWS to correct them.

The main advantages of GWS/OWS are the duration of the collection period. This information is

most interesting to shipping applications, because it takes into account bad weather avoidance

and it is well-documented for the major shipping routes. However for wave climate assessment it

presents important drawbacks namely the lack of information outside the main routes, the poor

accuracy for periods (whereas heights are well estimated by these experienced observers), and

some deficiencies in seasonal variations modelling and in reporting extremes.

4.2. WERATLAS - European Wave Energy Resource Atlas (1996)

The European offshore wave energy resource is described in WERATLAS, developed within a

R&D European project (INETI et al., 1998, Pontes, 1998). This atlas was the first attempt to

assess the offshore European wave energy resource using a common methodology and

homogeneous data sets whose accuracy was carefully evaluated. This wave information is result

of the numerical wind-wave WAM model, run at ECMWF, as well as from buoy data for the

North Sea, Norwegian Sea and Barents Sea.

The verification of the WAM results was made by comparison against buoy data and satellite

altimeter data. It revealed that the accuracy of the results was very good for the North Atlantic,

29

but the quality was lower for the Mediterranean, probably due to poorer accuracy of the input

wind fields. This lead to further improvement of WAM model results for the Med Sea.

WERATLAS covers the Northeastern Atlantic Ocean, the North Sea, the Barents Sea, and the

Mediterranean Sea. Results for the North Africa coastline are also included.

The atlas includes a wide range of wave-climate and wave-power statistics, presented as tables

and plots. The basic resource statistics are the long-term annual value of wave power P and its

directional distribution (wave power rose). Other relevant statistics for the resource assessment

are power exceedance curve (percentage of time during which each power level is exceeded);

univariate and bivariate frequency distributions of Hs, Te, Tp and seasonal variation of P are also

incorporated in this atlas .

Figure 4.1: Annual wave power roses for the northernmost part of the Northeastern Atlantic

covered by WERATLAS. The figure inside the rose represents the annual power level in kW/m.

4.3. World Wave Atlas (1996)

WWA 2.0 was developed following the successful release of WWA 1.0 in 1995

http://www.oceanor.no/products/ wwa.htm. It contains both GEOSAT (1986-89) and TOPEX

(1992-97) significant wave height and wind speed measurements at full resolution, quality

controlled and calibrated to provide wave data close to the accuracy of buoy data. Geographical

and statistical presentation modules allows computation of univariate and bivariate frequency

30

distributions, exceedence curves, extreme statistics for significant, maximum and crest heights,

spatial and temporal variability, seasonal and inter-annual variability.

WWA 2.0 also allows the presentation of in-situ wave and meteorological time series data from

buoys and other offshore data installations.

4.4. EUROWAVES (2000)

EUROWAVES is a tool to assess the wave climate at a coastal or shallow water location, more

or less anywhere in Europe, with acceptable accuracy and spatial resolution for most users.

EUROWAVES integrates several modules, including extensive offshore wave statistics, detailed

bathymetry of the considered area, wave models to transfer the wave conditions to the desired

nearshore location, and a statistical package for the evaluation of the nearshore wave statistics.

See Eurowaves (2001) http://www.oceanor.no/projects/eurowaves/.

4.5. ONDATLAS (2003)

ONDATLAS is a nearshore electronic atlas for Portugal containing comprehensive wave climate

and wave energy statistics for 78 points at about 20 m water depth spaced variably ca. 5–30 km,

five points at deep water, and two points at open ocean locations (Figure 4.2). The data were

produced by model MAR3G (see Table 3.5). ONDATLAS statistics comprise yearly and

monthly values, variability and probability data for significant wave height, energy (mean)

period, peak period and wave power, and directional histograms for wave and power direction.

Joint probability distributions for various combinations of the above parameters are also

available, as well as extreme values and return period for wave height and period parameters. A

summary of the detailed verification of this model using long-term buoy measurements at four

sites is presented. The strong spatial variability that wave conditions exhibit at the coastal area is

illustrated and a brief assessment of the nearshore resource at the Portugal mainland is presented

in Pontes et al, 2005. An ONDATLAS version for Madeira Islands was developed being

available at http://www.aream.pt/ondatlas/.

31

Figure 4.2: The 85 ONDATLAS data sites. In Pontes et al., 2005.

4.6. Atlas of UK Marine Renewable Energy Resources (2004)

The United Kingdom has developed an atlas of its Marine Renewable Energy Resources under

the responsibility of the former Department of Trade and Industry. The purpose of the atlas was

to quantify and spatially map the potential wave, tidal and offshore wind resource at a regional

scale across the limits of the UK Continental Shelf (UKCS). The atlas has been built from the

best source of wave, tide and offshore wind information presently available across the UKCS

(ABPmer et al, 2004).

4.7. Accessible Wave Energy Resource Atlas: Ireland (2005)

The Irish Wave Power Atlas 2005 is based on initial comparison between several years of hourly

wave forecasts (using WAM) on a grid of points located off the Irish coast with corresponding

records from a number of buoys installed in recent years.

32

Based on the level of agreement found the wave forecasts were then modified slightly and used

to estimate and map the mean annual power and energy resources at the theoretical, technical,

practicable and accessible levels.

The work builds on previous studies to advance understanding of the factors that influence the

scale and distribution of these resources. It also places them in context with other users of these

waters to facilitate decision making and minimize possible hindrance to future resource

utilization. For more detailed information see Marine Institute & Sustainable Energy Ireland

(2005).

4.8. WORLDWAVES (2005)

WorldWaves is an offshore database, including bathymetric data, raytracing and SWAN wave

models, statistical analysis package for offshore and nearshore analyses. The package is a

complete wave analysis and modeling packages for any country or region worldwide

(http://www.oceanor.no/products/software/wwa/index.htm). It follows EUROWAVES referred

to above.

33

5. COUNTRY – BY – COUNTRY REVIEW

A review of the available wave information at the IEA-OES member-countries is based on

questionnaires filled at national level in addition to a review of available information carried out

by the authors.

5.1. Belgium

5.1.1 In situ data The Flemish Ministry of Transport and Public Works (Agency for Maritime and Coastal

Services – Coastal Division) disposes of wave measurements between 1984 and 2007 on several

locations on the Belgian Continental Shelf (Beels et al., 2007). Table 5.1 provides an overview

of the six operational buoys deployed at the Belgian Continental Shelf. Figure 5.1 maps the

location of the buoys (Beels et al., 2007A).

Table 5.1 In-situ wave data off Belgian coast

Location

/Measuring Device

Data type Location (º ' '' )

Water depth (m)

Availability Source

Westhinder

(Wavec)

E(f),θ(f),σ(f),

Hs, Tz, Tp

51 23 12 N 2 26 52 E

28.8

Since 1/7/1990

ZW-Akkaert (Waverider)

E(f),

Hs, Tz, Tp

51 24 29 N 2 48 12 E

22.7

Since 1/1/1984

Trapegeer

(Waverider)

E(f),

Hs, Tz, Tp

51 08 15 N 2 34 59 E

6.6

Since 1/1/1994

Oostende

(Directional Waverider)

E(f),θ(f),σ(f), Hs, Tz, Tp

51 14 34 N 2 55 14 E

6.2

Since 1/6/1997

Wandelaar (Waverider)

E(f),

Hs, Tz, Tp

51 23 32 N 3 03 02 E

12.6

Since 1/1/1995

Bol van Heist

(Wavec)

E(f),θ(f),σ(f),

Hs, Tz, Tp

51 23 25 N 3 11 43 E

11.7

Since 1/1/1985

Flemish Ministry

of Transport and

Public Works

(Agency for

Maritime and

Coastal Services

– Coastal

Division)

http://www.vlaa

msehydrografie.

be/welkom.aspx

34

Fig. 5.1 – Location of wave measuring buoys off Belgian coast

5.1.2 Wind-wave model

To increase the wave dataset, Flanders Hydraulics Research (http://watlab.lin.vlaanderen.be)

uses the SWAN model (Simulating Waves Nearshore – version 40.11) for calculating directional

wave spectra for the entire Belgian Continental Shelf. Table 3.5 gives more details about the

data obtained with this wind-wave model.

5.2. Canada

5.2.1 In-situ and remote sensed data

The Marine Environmental Data Services (MEDS) of the Department of Fisheries and Oceans of

Canada maintains an online archive of wave data measured in Canadian waters, dating back to

35

the early 1970’s (e.g. Cornett, 2006). The available data can be found at http://www.meds-

sdmm.dfo-mpo.gc.ca/MEDS/Databases/Wave/WAVE_e.htm.

The sources of wave data are

• Buoys operated by the Meteorological Service of Canada (MSC);

• Selected buoys operated by the U.S. National Data Buoy Center (NDBC);

• Data submitted by researchers, universities, regional institutes and the oil and gas

industry.

These direct wave measurements are obtained at over 60 stations. MEDS databases contain over

six million wave spectra obtained from measurements from roughly 500 locations in the

Canadian area of interest (Figure 5.2). However, many of the stations are located in inland waters

or contain data for relatively short periods.

Current measurements are also being made under the responsibility of MEDS.

Figure 5.2: Canada wave buoys

The above buoys are able to measure wave height and period, sea temperature, pressure and the

wind speed. For more detailed information check the MSC webpage for the offshore buoy

network (http://weather.ec.gc.ca/marine/).

The following types of buoys and other wave instruments (described in the Appendix) are used

in Canada.

• Directional AES ODAS buoys

• Directional WAVEC buoys

36

• Non-directional WRIPS buoys

• Non-directional Waverider buoys

• Miros Radar

• Wave staffs

• Pressure sensors.

5.2.2. Wind-wave models

Two numerical wind-wave models are used in Canada (see Table 3.3). These are AES40 for the

North Atlantic generated using the OWI-3G model and WAVEWATCH-III model for the

Northeast Pacific and Northwest Atlantic.

5.3. Ireland

5.3.1 In-situ data

The Irish Marine Weather Buoy Network, constituted by six buoys and the Gas Platform Twin

Wire probe FS1 (Figure 5.3) measures the significant wave height, peak period, wind direction

and speed, atmospheric pressure, air temperature and the relative humidity

(http://www.marine.ie/home/publicationsdata/data/buoys/). This site displays the data measured

in the previous 24h. Table 5.2 summarizes the buoy network in Ireland.

Table 5.2. Ireland in-situ data

Measuring Device Data Type

Location (deg min)

Water Depth (m)

Availability

Six Buoys (MK2 Datawell Heave sensor)

Names: M1 to M6

Figure 5.3 70 – 3000 Since 2000

Twin Wire probe FS1 Gas Platform

Hs, Tz

51 23.60 N 07 54 W

100 Since

2003.02.01

Buoy 52 39 N 9 47 W

60 2003.12.01-2005.03.31

Buoy (Waverider, Datawell

Mark 1)

Hs, Tz, full surface elevation 53 13.7 N

9 16 W 20

Since 2005.11.10

37

Figure 5.3: Wave buoys and FS1 platform deployed off Ireland (http://www.marine.ie/home/publicationsdata/data/buoys/).

5.3.2 Wind-wave Models

The Irish meteorological office Met Éireann runs a regional Irish WAM model that is nested

inside the global European model run at the ECMWF. This model calculates wave directional

spectra over 30 frequencies and 24 directions with grid spacing of 0.25º. The wave model output

includes hourly predictions of significant wave height and direction, mean wave period, peak

period, significant height, direction and mean period of primary swell and sea/wind waves; and

wave spectra 6-hourly outputs. Waves can be forecast up to 2 days ahead on the Irish model and

up to 6 days ahead on the European global model (Met Éireann, 2009).

Besides the Met Éireann’s WAM model, Ireland also acquires forecasts from other models

namely WAM (ECMWF, WAM Nowcasting-International (DMI), WaveWatch III (NOAA) and

UK waters wave model (UK Met Office). See Table 3.2 for more detailed information about the

wave models used in Ireland.

38

5.4 Portugal

5.4.1 In-situ data

Wave measurements in Portugal are carried out by Instituto Hidrográfico (IH) that has the

responsibility of determining the oceanographic conditions of the Portuguese coast. Table 5.4

presents the most relevant buoy data sets. See http:// www.hidrografico.pt for more details on

available data products.

Table 5.4. Portugal most relevant in situ data

Location /

Measuring Device Data Type

Location (deg min sec)

Water Depth (m)

Availability Source

Leixões

(Directional Waverider)

41N 19' 00" 08W 59' 00"

83 Since 07.1996

Figueira da Foz (Waverider)

40N 13’ 30” 9N 06´00”

83 1981 -1990

Figueira da Foz (Directional Waverider)

40N 11’ 08” 9N 08´44”

92 1990-1994

Sines (Directional Waverider)

37N 55' 16" 08W 55' 44"

97 Since 01.1996

Faro (Directional Waverider)

36N 54' 17" 07W 53'54"

93 Since 02.2000

Madeira/Funchal (Directional Waverider)

32N 37' 06" 16W 56' 30"

100 Since 11.1996

Madeira/Caniçal (Directional Waverider,

Datawell)

32N 43' 12" 16W 43' 42"

108 Since 02.2002

Instituto Hidrográfico

www.hidrografico.pt

oceanografia@hidrografico.pt

Açores/Praia da Vitória (Directional Waverider)

38N 44' 54" 27W 00' 54"

85 Since 02.2005

Açores/Ponta Delgada (Directional Waverider)

37N 43' 53" 25W 43' 28"

90 Since 08.2005

Açores/Flores (Directional Waverider)

E(f), Θ(f), σ(f) Hs, Tz, Tp

39N 21' 51" 31W 09' 58"

80 Since 06.2006

Universidade dos Açores,

Climaat Project

www.climaat.uac.pt

http://www.climaat.angra.uac

.pt/boias/

5.4.2 Wind-Wave Models

MAR3G model (Table 3.5) run at Instituto de Meteorologia (IM) supplies every 6-hour forecasts

of wave parameters up to 5 days. This is a 3rd generation numerical wind-wave model that for the

coastal area is linked to a shallow-water wave propagation model that takes into account

shoaling, refraction and bottom-friction. The model uses as input ECMWF wind fields.

Predictions are available at http://www.meteo.pt/en/maritima/mar3g/ (Figure 5.4).

39

Wave predictions are also made operationally by Instituto Hidrográfico (www.hidrografico.pt)

for Portuguese waters, using SWAN model that takes as boundary conditions results of WW III

results and wind fields produced by Instituto de Meteorologia using ALADIN numerical weather

prediction model (ALADIN webpage: http://www.cnrm.meteo.fr/aladin/).

Figure 5.4: MAR3G Hs and θm forecast for North Atlantic Ocean

5.5 Spain

5.5.1 In-situ data

Wave data collection and forecasting / hindcasting is under the responsibility of Clima Maritimo

(CM) belonging to the Ente Publico Puertos del Estado, a public company that carries out the co-

ordination and management of the 44 harbours constituting the Spanish Harbour System.

(http://www.puertos.es/externo/clima/clima.html). Table 5.4 summarizes the Spanish wave and

tidal measurement networks.

There is an additional intermediate water-depth network of buoys deployed along the NE

Spanish Mediterranean Coast (see www.xiom.cat). Wave, wind and circulation parameters are

recorded permanently at 4 points (see Figure 5.7).

40

Table 5.4 Spanish Network of Wave and Tidal Measuring Systems

Measuring Device Data Type Location Water Depth Availability

3 Directional Buoys (Wavescan)

300 to 1200 m Oldest: 11.1990

11 Directional Buoys (Seawatch)

Deep sea around all Spain

(Figure 5.5) 260 to 780 m Oldest: 07.1996

17 Scalar Buoys (Waverider)

10 to 90 m Oldest: 02.1981

6 Directional Buoys (Triaxys)

Hs, Tm, Tp

Coastal waters around all Spain

(Figure 5.5) 21 to 68.5 m Oldest: 07.2001

15 REDMAR Sonar Acoustic Sensors

7 REDMAR Aanderaa Pressure Sensors

Sea Level Coastal waters

around all Spain (Figure 5.6)

- Oldest: 1992

6 Directional Wamos II Radar

Hs, Tm, Tp

Coastal waters around all Spain

(Figure 5.7)

Above sea level -

Figure 5.5. Buoy networks in Spain. Left - deep sea; right- coastal http://www.puertos.es/es/oceanografia_y_meteorologia/redes_de_medida/index.html

41

Figure 5.6. REDMAR tide gauge and Spanish radar network, Spain

Figure 5.7. Coastal buoy network off the Catalan

Coast (NW Spanish Mediterranean)

5.5.2 Wind -Wave Models

Spain uses the following regional and local wind-wave mathematical models, nested to the

WAM or WW3 models: WAM for Atlantic Coast, Cantabric Coast and Mediterranean Sea, and

WAVEWATCH III for the Strait of Gibraltar.

The WAM model cycle 4 is run operationally by Clima Maritimo since 1995 providing with a

wave forecast the Spanish Harbours. CM was also involved in the work carried out to implement

the model at the ECMWF as the operational model of the Centre. Several modifications have

been introduced in the code to adapt it to the Spanish coastal waters and to improve its

performance. The output is regularly verified against the Spanish buoy network (Puertos del

Estado, 2009).

A database containing all the measured data from the Spanish network of wave measuring

stations and tidal gauges is maintained by CM. Data is subjected to a quality control process and

stored daily. Numerical data from CM's operational forecast systems is also stored in the

database. Mean and extreme analysis for selected points is regularly carried out by requirement

of the users and extreme and mean atlases are regularly issued.

42

In addition there are higher resolution nested simulations, using WAM and SWAN for Catalunya

(NE Spain) and Galicia (NW Spain). The operational, downscaled forecasted fields are also

stored and checked against observations by the local meteorological services in cooperation with

other research groups (e.g. Servei Meteorologic de Catalunya SMC in cooperation with

Catalonia University of Technology UPC for the Catalan case).

5.6 United Kingdom

The UK Met Office runs for many years a second-generation wave model (Golding, 1983 and

updated since then) providing forecasts of sea state. It supports a range of user applications

which were used in constructing the Atlas of UK Marine Renewable Energy Resources. There

are three operational wave model configurations, with different areas and resolutions, currently

in use: global, European and for UK waters (Tables 3.3 and 3.5). Figure 5.8 shows Hs mapping

in the UK waters.

Figure 5.8 - UK waters wave model (wave height in meters). http://www.metoffice.gov.uk/research/ncof/wave/regionalwave.html, 2008

43

5.7 United States of America

The United States of America buoy measuring program is in charge of the National Oceanic and

Atmospheric Administration (NOAA) National Data Buoy Center (NDBC), a part of the

National Weather Service (NWS). NDBC designs, develops, operates, and maintains a network

of data collecting buoys and coastal stations (NOAA buoy/C-MAN network).

NDBC provides hourly observations from a network of about 90 buoys and 60 Coastal Marine

Automated Network (C-MAN) stations. All stations measure wind speed, direction, and gust;

barometric pressure; and air temperature. In addition, all buoy stations, and some C-MAN

stations, measure sea surface temperature and wave height and period. Conductivity and water

current are measured at selected stations (http://www.ndbc.noaa.gov) .

Besides the NBCB moored stations and the C-MAN stations, the United States also use

information from other sources like those shown in Figure 5.9.

Figure 5.9: Northwest of USA and its stations. In http://www.ndbc.noaa.gov/, 2008

Note: Information on wind-wave models at USA is missing

44

6. REFERENCES

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the “Bundesamt für Seeschiffahrt und hydrographie”: A verification using ERS-1 altimeter and

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Booij, N., Ris, R.C., Holthuijsen, L.H., 1999, A third-generation wave model for coastal regions,

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Caires, S., A. Sterl and C. P. Gommenginger. C. P. Global Ocean Wave Period Data: Validation and Description. J. Geophys. Res., vol.110, c02003, doi:10.102 /2004JC002631, 2005.

Cornett, A.M., 2006, Inventory of Canada’s Marine Renewable Energy Resources. Technical

Report CHC-TR-041, Canadian Hydraulics Centre, National Research Council Canada.

Davies, C.G., Cotton, P.D., Challenor, P.G., Carter, D.J.T., 1998, On the Measurements of Wave

Period From Radar Altimeters, Ocean Wave Measurements and Analysis, Proc. 3rd International

Symposium Waves’97, ASCE, Reston, VA, p. 819-826.

DMI, 2006, DMI-WAM, Danish Meteorological Institute, Denmark,

http://ocean.dmi.dk/models/wam.uk.php

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Eurowaves, 2001, A user-friendly tool for the evaluation of wave conditions at any European

coastal location, http://www.oceanor.no/ projects/eurowaves/, about.htm.

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E&P Technology, Houston, Texas, in: American Meteorological Society, volume 30, number 8,

pages 1931-1943.

Fugro OCEANOR, 2009, Seawatch Buoy, http://www.oceanor.no/products/projects.htm

Golding, B., 1983, “A wave-prediction system for real-time sea state forecasting”, Quart. J. R.

Met. Soc., Vol. 109, 393-416.

Gommenginger, C. P., M.A. Sroksoz, P. G. Challenor, 2003, Measuring ocean wave period with

satellite altimeters : A simple empirical model. Geophysical Research Letters, vol. 30, Nº 22.

Guedes-Soares, C. (1984) Representation of double-peaked sea wave spectra. Ocean

Engineering, vol. 11, nº2, 185-207.

Hogben, N., Lumb, F.E., 1967, Ocean Wave Statistics, Her Majesty’s Stationery Office.

Hogben, N., Dacunha and Ollivier, 1986, Global Wave Statistics, Unwin Brothers, London.

Holthuijsen, L.H., 2007, Waves in Oceanic and Coastal Waters. Cambridge University Press.

IM, 2006, MAR3G Wave Model – MAR3G Wave Model Description, Instituto de Meteorologia,

http://www.meteo.pt/pt/enciclopedia/maritima/modelo_mar/index.html (in Portuguese)

InterOcean, 2006, Coastal Monitoring Stations – Complete Meteorological & Oceanographic

Data Acquisition Systems, http://www.interoceansystems.com/pdfs/ Coastal_Monitoring_System

_Brochure.pdf,

Janssen, P. , 2004,The Interaction of Ocean Waves and Wind, Cambridge University Press,

Joosten, H., 2006, Directional Wave, in: International Oceans Systems, pages 18-21, July/August

2006.

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Komen, G., L. Cavaleri, M. Donelan, K. Hasselmann, S. Hasselmann and P.A.E.M. Janssen,

Dynamics and Modelling of Ocean Waves, Cambridge Univ. Press, 532 pp., 1994.

Marine Institute and Sustainable Energy Ireland, 2005, Accessible Wave Energy Resource Atlas:

Ireland, December 2005.

Marine Institute, 2009, Irish Marine Weather Buoy Network, available at

http://www.marine.ie/home/publicationsdata/data/buoys/.

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available at http://www.meds-sdmm.dfo-mpo.gc.ca/

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(NOAA), National Data Buoy Center (NDBC), National Weather Service (NWS), available at

http://www.ndbc.noaa.gov/tour/virtr1.shtml.

Oliveira-Pires, 1993, Numerical Modeling of Wind-generated Waves. Ph.D. Thesis,

Lisbon Technical University, 222 pp.

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Mechanics and Arctic Engineering (JOMAE), volume 120, pages 226-231, 1998.

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Portugal, paper OMAE 2008- 57775.

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48

APPENDIX

In-situ Wave Measuring Devices

In-situ wave measuring devices give time series of several wave properties, the most common

being sea elevation or heave. There are two important parameters, which largely determine the

quality of time series. The first is the Sampling Interval, which is the time interval between

consecutive wave measurements. This should be small compared with the periods of interest in

the data. Sampling intervals between 0.25 and 1 second are usually used for sea waves. The

second is the Record Length, which should be as long as possible, provided that the sea state

does not change significantly during the recording. The minimum record length for a wave

record is determined by wave frequencies to be resolved by spectral analysis. For sea waves, a

length of 15 minutes to one hour is usually chosen.

A brief description of the types of in-situ wave measuring systems useful for the evaluation of

the offshore as well as shoreline resource follows.

Buoys

Accelerometer instrumented buoys are very often used for wave measurements in deep water.

The buoy moves with the waves and measures vertical acceleration which is the time integrated

twice to provide a record of wave elevations. An important parameter for buoys is the Natural

Frequency which is the frequency where the energy transference to buoys is maximal. It is the

resonant frequency which should be far away from the frequencies of interest. These buoys

generally telemeter wave measurements to a shore based receiving station (directly or via

satellite) where wave data are recorded or monitored in real-time. In the first case buoys must

generally be within roughly 40 km of the receiving station to assure good reception.

There are two kinds of buoys: scalar and directional. The description of some of their

characteristics follows.

Buoys

Scalar buoys provide heave time series obtained from wave motion acceleration measurements,

and are one of the most common ways of estimating scalar wave parameters, such as heights and

49

periods. These can be calculated by spectral techniques or estimated directly from the time

series.

This buoy senses the waves using a vertically stabilized accelerometer, and is usually used in

coastal or relatively shallow waters close to offshore installations with data telemetered to a

receiver station for storage, data analyses and dissemination. The Waverider measures only the

wave elevation, typically sampled at 1 or 2Hz for 15-30 minutes each 3hours (although

continuous measurements are often made in connection with offshore operations and for stormy

periods). The one-dimensional wave spectrum and associated wave parameters such as

significant wave height and wave period can be computed from the Waverider.

Directional buoys

For directional wave measurements there are several alternative buoy systems on the market.

Since its launch in 1989 Datawell’s Directional Waverider has become the one of the most sold

directional buoys taking over from its predecessor, the Wavec. The Wavec is a so-called

heave/pitch/roll buoy which measures the directional wave spectrum by sensing the wave

elevation, as the Waverider does, simultaneously measuring the slope of the ocean surface in two

orthogonal directions together with the buoy’s heading.

Other heave/pitch/roll buoys have been used in European waters over the last 15-20 years. The

most important of these are described in the following. The Norwegian Norwave buoy, which

uses the same sensor as the Wavec – the Datawell Hippy – and incorporates also a

meteorological station, has been used since 1980 particularly in deep offshore waters. All data

are recorded on the buoy. In addition, a directional wave analysis is carried out in near real time

on-board the buoy and a subset of directional wave parameters are transmitted by satellite to

shore. The Norwegian buoy Wavescan was developed in the mid-1980s as a second generation

directional buoy of the Norwave generic type. It is considerably lighter and has better dynamic

response in waves compared to the Norwave one. It is probably the next most popular directional

buoy after the Datawell buoys. It is being offered with an alternative wave sensor to the Datawell

Hippy, the Seatex MRU. In the UK, the oil industry used much larger discus buoys for offshore

directional measurements during the 1980s (DB2, DB3 etc). These buoys also used the Datawell

Hippy sensor.

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Fugro OCEANOR in Norway uses either the Waverider or Directional Waverider in their

Seawatch buoy. This is a multi-sensor which can incorporate in addition to wave measurements,

a meteorological station, current measurements and water quality sensors. The buoy stores all

data internally on hard disc in addition to transmitting a sub-set of parameters by either Argos or

Inmarsat satellite to shore. This means that the Seawatch buoy can be located more or less

anywhere as it does not require a receiver station within reach. Is has also been used as a pure

directional wave buoy in recent years for deep water offshore data collection.

Several of the larger offshore buoys are routinely moored for extended periods in very deep

water (thousands of meters).

Finally, a radically new approach to directional wave measurement was launched on the market

and may herald the future of wave measuring instrumentation. This is the so-called Smart-800

buoy, developed by Seatex in Norway. It uses differential GPS to determine the buoy’s motions

and computes the directional wave spectrum using an analysis similar to that from the other

buoys described above. Excellent agreement has been found in an intercomparison with the

Wavescan buoy.

Shortly, the directional buoys referred to in this report and their respective manufacturing

companies are (see more detailed information in the references)

• WAVEC (Datawell BV, Netherlands) (Figure A.1)

• DIRECTIONAL WAVERIDER (Datawell BV, Netherlands) (Figure A.2)

• NORWAVE (Seatex A/S, Norway)

• WAVESCAN (Seatex A/S, Norway) (Figure A.4)

• SEAWATCH (Oceanor, Norway) (Figure A.5)

• TRIAXYS (Axys Technologies Inc. and Canadian Hydraulics Centre, Canada) (Figure

A.3)

• OCEANOGRAPHIC DATA ACQUISITION SYSTEMS – ODAS (InterOceans Systems

Inc, USA) (Figure A.6).

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Figure A.1: The Wavec Buoy. In Joosten, 2006.

Figure A.2: Directional Waverider Buoy. In Joosten, 2006.

Figure A.3 Triaxys Directional Buoy. In Axys Technology Inc, 2008

52

Figure A.4: Seawatch Buoy and variety of its deployment configurations. In Fugro, 2004

Figure A.5 - ODAS Data Acquisition Buoy. In InterOcean, 2008

53

Wave Staff

A wave staff is attached to a fixed structure and penetrates the water surface. The staff is a

component of an electronic circuit which produces voltage changes proportional to either the

length of the staff that is or is not submerged. Biological fouling can be an important

disadvantage of some types of wave staffs.

The Baylor wave staff consists of a pair of stainless steel wire ropes separated by insulators

about 20 cm long. The transducer measures the natural frequency of the inductive loop made by

the two wires and the sea surface, from which the length of the loop is found. The instrument is

robust and relatively immune to fouling. It has been particularly popular for wave measurements

from platforms in the Gulf of Mexico. Tests have shown that it has linearity better than 1% and

that it will record changes of elevation at least as fast as 300 m s−1. Experiences from calibrating

Baylor staffs show that a quite firm short is necessary before the sensor responds. It thus seems

unlikely that it would be affected by spray (Forristal, 2000).

Pressure sensor/cell

A subsurface pressure sensor can be placed on the bottom or mounted on a structure below the

sea surface. It measures the changing wave-induced pressure beneath waves. This type of sensors

cannot normally be used at depths greater than 20 meters below the surface due to pressure

attenuation with depth. These sensors are simple and robust and generally of low cost

Acoustic sensor

An acoustic sensor transmits sound pulses towards the sea surface and receives sound pulses

reflected from this surface. The time interval between transmission and reception determines the

vertical distance to the sea surface. These systems generally allow corrections due to variation in

temperature (and salinity, when bottom mounted) which affects the speed of sound. Foam or

bubbles at the sea surface adversely affect acoustic measurements and the sensor must be

properly aligned in the vertical.

These types of sensors can be bottom mounted or suspended over the sea surface, at a safety

distance from it. They are more expensive and complicated than pressure sensors, wave staffs or

even some accelerometer buoys.