GROWTH AND INNOVATION IN OCEAN ECONOMY GAPS AND … · Growth and innovation in ocean economy Gaps...

GrowthandinnovationinoceaneconomyGapsandprioritiesinseabasinobservationanddata

D2.3.5Version:V6Date:06/02/2017

GROWTHANDINNOVATIONINOCEANECONOMY

GAPSANDPRIORITIESINSEABASINOBSERVATIONANDDATA

D2.3.5MedSeaCheckpointChallenge1(WindFarmSiting):DescriptionofTargetedProducts,themethodologyandthe

expertevaluationoffitnessforpurpose

Totalnumberofpages:27

Workpackage: 2 Challenge1:WindfarmsitingAuthor(s): GeorgeKallos NKUA

GeorgeGalanis NKUA PlatonPatlakas NKUA ChristinaKalogeri NKUA Jean-FrancoisFilipot FranceEnergies

Marines RuiDuarte FranceEnergies

Marines SimonaSimoncelli INGV,Bologna

Aprojectfundedby:

EUROPEANCOMMISSION,DIRECTORATE-GENERALFORMARITIMEAFFAIRSANDFISHERIES,MARITIMEPOLICYATLANTIC,OUTERMOSTREGIONSANDARCTIC



DocumentLog

Date Author Changes Version Status19/01/2016 PlatonPatlakas

(NKUA),GeorgeGalanis(NKUA)

created

27/01/2016 PlatonPatlakas(NKUA),GeorgeGalanis(NKUA)

revised

08/02/2016 Jean-FrancoisFilipotPlatonPatlakas(NKUA),GeorgeGalanis(NKUA)

revised

25/03/2016 S.Simoncelli(INGV) revision

04/02/2017 S.Simoncelli(INGV) Lastrevision V4 05/02/2017 GeorgeGalanis

(NKUA)corrections V5

06/02/2017 R.Duarte(FEM) corrections 06/02/2017 S.Simoncelli(INGV) Lastcheck V6



Glossary ............................................................................................................................................... 4Executive Summary ............................................................................................................................. 5Targeted Products catalogue for this Challenge .................................................................................. 6Description of Characteristics and Data sources used by Targeted products ...................................... 6

MEDSEA_CH1_Product_1 ............................................................................................................. 6MEDSEA_CH1_Product_2 ............................................................................................................. 6MEDSEA_CH1_Product_3 ............................................................................................................. 7

Description of methodology to produce the Targeted Products .......................................................... 7MEDSEA_CH1_Product_1 ............................................................................................................. 7

Spatial analysis ............................................................................................................................. 9In situ analysis ............................................................................................................................ 17

MEDSEA_CH1_Product_2 ........................................................................................................... 18MEDSEA_CH1_Product_3 ........................................................................................................... 20

Expert evaluation of Targeted Product quality and gaps in the input data sets ................................. 23MEDSEA_CH1_Product_1 ........................................................................................................... 23MEDSEA_CH1_Product_2 ........................................................................................................... 24MEDSEA_CH1_Product_3 ........................................................................................................... 24

Annex 1: Definitions .......................................................................................................................... 26References ......................................................................................................................................... 27



Glossary CMCC-CentroEuro-MediterraneoperiCambiamentiClimaticiS.c.ar.l.(IT)

CLS-CollecteLocalisationSatellites(FR)

CLU-CLUs.r.l.(IT)

EDFEN(FR)

ENEA-AgenziaperleNuoveTecnologie,l'EnergiaeloSviluppoEconomicoSostenibile(IT)

FEM-FranceEnergiesMarines

HCMR-HellenicCentreforMarineResearch(GR)

IFREMER-InstitutFrançaisdeRecherchepourl'ExploitationdelaMer(FR)

INGV-IstitutoNazionalediGeofisicaeVulcanologia(IT)

NKUA–NationalKapodistrianUniversityofAthens(GR)

OCEANS-CAT-OCEANSCataloniaInternationalSL(ES)

SOCIB-BalearicIslandsCoastalObservingandForecastingSystem(ES)

UCY-UniversityofCyprus(CY)

FEM-AssociationdePréfigurationdel’IEEDFranceEnergiesMarines(FR)

IHCantabria-FundaciónInstitutodeHidráulicaAmbientaldeCantabria(ES)



ExecutiveSummaryTheprimaryaimofthewindfarmsitingchallengeistoassesswhetherthecurrentlyavailabledataon theMediterranean Sea are appropriate for the preliminary assessment required to identifypotentialnewfarmsites.Avarietyoffactorsareconsideredtobeimportantforwindfarmsiting.Thesefactorsincludethenaturalresourcesdatathatcanbeusedtodefinetheenergypotentialoftheareaandthelocalclimaticcharacteristics,andtheintenseanthropogenicactivity,biodiversity,andsedimentcharacteristicsthatmayaffecttheinstallationofwindturbinesintheareaofinterest.Usefulinformationonthefactorsthatmightexertconstraintscanresultinthesitebeingclassifiedasunsuitablefordevelopment.Toprovidethisinformation,threetargetedproductsweredefinedasoutputsofthischallengeandaredescribedindetailinthefollowingpages:

• MEDSEA_CH1_Product_1:Awindandwavedataset

• MEDSEA_CH1_Product_2: A suitability index for a wind farm in the NWMediterraneanbasedontheenvironmentalresources

• MEDSEA_CH1_Product_3:AsuitabilityindexforawindfarmintheNWMedbasedontheenvironmentalresources,naturalbarriers,humanactivities,MarineProtectedAreas(MPA)andfisheries.

Abroadrangeofdatawereidentified,downloadedwherepossibleandreviewedforthechallenge.The discoverability and accessibility of the data and their format and usability varied a lot,dependingontheonlinesource,whichincluded:

• National and Kapodistrian University of Athens, Department of Physics, AtmosphericModelingandWeatherForecastinggroup

• AgencedesAiresMarinesProtegees• SHOM• GEBCO• EMODnet



TargetedProductscatalogueforthisChallenge NameofTargetedproduct Shortdescription FormatMEDSEA_CH1_product_1 Awind-wavedataset SQLdatabaseMEDSEA_CH1_product_2 AsuitabilityindexofawindfarmintheNWMed

basedontheenvironmentalresourcesGISshapefiles

MEDSEA_CH1_product_3 AsuitabilityindexofawindfarmintheNWMedbasedonthenaturalresources,naturalbarriers,humanactivities,MPAandfisheries.

GISshapefiles

DescriptionofCharacteristicsandDatasourcesusedbyTargetedproductsMEDSEA_CH1_Product_1Nb Characteristicsname(P02) EnvironmentalMatrix Datasource(URL)1 EWSB Zonalwindcomponent/ESEWZZXX forecast.uoa.gr2 EWSB Meridionalwindcomponent/ESNSZZXX forecast.uoa.gr3 CAPH Airpressure/CAPHZZ01 forecast.uoa.gr4 NotexistinginP02 Airdensity forecast.uoa.gr

5 CHUMSpecifichumidityoftheatmosphere/CHUMSS01

forecast.uoa.gr

6 CDTA Airtemperature/ATEMP2MM forecast.uoa.gr7 ASLV Sealevel/ASLVZZ01 forecast.uoa.gr8 TEMP Watertemperature/TEMPPR01 forecast.uoa.gr9 PSAL Watersalinity/ODSDM021 forecast.uoa.gr

10 RFVLWaterzonalvelocitycomponent/LCEWZZ01

forecast.uoa.gr

11 RFVLWatermeridionalvelocitycomponent/LCNSZZ01

forecast.uoa.gr

12 WVSP

Two-dimensionalWavespectraoverfrequenciesanddirectionsmodeloutput

forecast.uoa.gr

13 WVST SignificantWaveHeightmodeloutput forecast.uoa.gr14 GWDR Meanwavedirectionmodeloutput forecast.uoa.gr

15 WVSTMean(Energy)waveperiodmodeloutput

forecast.uoa.gr

16 WVST Peakwaveperiodmodeloutput forecast.uoa.gr17 WVST Swellwaveheightmodeloutput forecast.uoa.gr

18 WVSTMaximumexpectedwaveheightmodeloutput

forecast.uoa.gr

MEDSEA_CH1_Product_2Nb Characteristicsname(P02) EnvironmentalMatrix Datasource(URL)1 EWSB Zonalwind

component/ESEWZZXXforecast.uoa.gr

2 EWSB Meridionalwindcomponent/ESNSZZXX

forecast.uoa.gr



MEDSEA_CH1_Product_3Nb Characteristicsname(P02) EnvironmentalMatrix Datasource(URL)1 EWSB Zonalwind

component/ESEWZZXXforecast.uoa.gr

2 EWSB Meridionalwindcomponent/ESNSZZXX

forecast.uoa.gr

3 BRDA Birds:species www.aires-marines.fr4 BRDA Birds:abundance www.aires-marines.fr5 GP088 Birds:migratorypatterns www.aires-marines.fr6 GP04 Birds:reproductionarea www.aires-marines.fr7 FABD Marinemammals:species. www.aires-marines.fr8 FABD Marinemammals:size. www.aires-marines.fr9

FABDMarinemammals:migratoryroutes.

www.aires-marines.fr

10 FABD Fishes:species. www.aires-marines.fr11 FAXT Fishes:abundance www.aires-marines.fr12 FREP Fishes:reproductionarea www.aires-marines.fr13

MBANSDNP01SFHTNPESBathymetry

www.emodnet.eu

14

SSTR

SDNP01SEDTYCATDescriptionoflithologyofsedimentbyvisualestimation

www.emodnet.eu

DescriptionofmethodologytoproducetheTargetedProductsMEDSEA_CH1_Product_1Ahigh-resolutiondatabasewasdeveloped,basedontheoutcomesoftheFP7MARINAPlatformproject (http://www.marina-platform.info/). The database covers a 10-year period (2001–2010)andcontainshourlydataonawiderangeofatmospheric,waveandtidalinformation.Thefollowingparametersareprovided:

1. Zonalwindcomponent

2. Meridionalwindcomponent

3. Airpressure

4. Airdensity

5. Specificatmospherichumidity

6. Airtemperature

7. Watertemperature

8. Watersalinity



9. Waterzonalvelocitycomponent

10. Watermeridionalvelocitycomponent

11. Dimensionalwaveheightmodeloutput

12. Significantwaveheightmodeloutput

13. Meanwavedirectionmodeloutput

14. Mean(energy)waveperiodmodeloutput

15. Peakwaveperiodmodeloutput

16. Swellwaveheightmodeloutput

17. Maximumexpectedwaveheightmodeloutput

Theatmosphericparametersareavailableatdifferentverticallevels(10,40,80,120and180m).The database was developed under the framework of the European FP7 “Marina RenewableIntegratedApplicationPlatform”,themainobjectiveofwhichwastosupportthedevelopmentofoffshore structures to exploitwind,wave, tidal andocean current energy around theEuropeancoastline. A 10-year re-analysis was performed, combining wind- and wave-induced motions.The regional atmospheric model, SKIRON (Kallos et al.,1997, 2006; Spyrou et al., 2010), wascombinedwiththethird-generationoceanwavemodel,WAM(Bidlotetal.,2007;Galanisetal.,2011;WAMDIG,1988)andtheglobaloceancirculationmodel,HYCOM(HybridCoordinateOceanModel)(Chassignetetal.,2003).

SKIRON uses 45 vertical levels and a time step of 15 seconds. The initial and lateral boundaryconditionswerepreparedusingtheLAPS3-Ddataassimilationmodel,basedonECMWF0.5ox0.5ogriddedfieldsandsurfaceandupperairobservations.Thelateralconditionsareupdatedevery3hours. Thegeomorphologicaldatasetsused for theatmospheric andwavemodelwere30”x30”global elevation, 30”x30” land use and vegetation cover, 2’x2’ soil classification and 1’x1’bathymetry. The SST fields were derived from NCEP with a resolution of 0.5°x0.5°. For oceancirculation,theresultsfromtheglobalmodelHybridCoordinateOceanModel-HYCOM(Chassignetetal.,2003)wereinterpolatedfromtheoriginal0.07ox0.07ogridtothemodeldomainofSKIRONandWAM.Themodelsoperatedatahighspatialresolutionof0.05°x0.05°latitude/longitudeandtheproducedoutputisavailableforthe2001-2010periodonadailybase.

TheconfigurationsoftheatmosphericmodelandthewavemodelarepresentedinFigures1and2.



Figure1ThesetupanddomainsoftheSkironatmosphericmodel.

Figure2ThesetupanddomainsoftheWAMwavemodel.

Themodelingsystemoperated ina reanalysismode, thusexploiting theadvantagesof thedataassimilationprocedureusingtheavailableobservationsandmeasurementsfromthearea(satelliterecords,meteorologicalobservations,shipreports).Thisproducedanoptimumrepresentationoftheenvironmentalparametersandadetailedwaveclimatologymapofthearea. SpatialanalysisAnumberofstatisticalindicesandmeasureswereusedforthestatisticalanalysis.• Meanvalue(μ):themeanisusedasanindicatoroftheenergypotentialofthestudyarea,and

canbecalculatedas

𝜇 =1𝛮 𝑥(𝑖)

)

*+,

wherexdenotestheparameterunderstudy(windspeed,windenergy,significantwaveheight,etc.)andNisthesizeofthesample.Foramorecomprehensiveanalysis,afundamentaltaskistodescribethewindspeedandwindenergyprobabilitydistributioncharacteristics. This canbeachievedusing the skewnessandkurtosisofthevariableunderstudy.



• Skewness(g1)isameasureofthesymmetry/asymetryofthedataandindicateswhetherhigher

valuestendtobeskewedtotherightorleftofthemeanvalue.Skewnessiscalculatedusingthesamplemean(μ)andstandarddeviation(σ):

𝑔, =,.

𝑥 𝑖 − 𝜇 0.1+,

𝜎0

• Kurtosis(g2)isameasureofthepeakednessandtailweightofthedistribution:

𝑔3 =,.

𝑥 𝑖 − 𝜇 4.1+,

𝜎4 − 3Thecombinationofthesestatisticalindexesprovidesusefulinformationabouttheoccurrenceandpotentialimpactofnon-frequentvaluesinthewindparkoperation.The variability of the produced energy is critical for the functionality of the electrical network.Therefore,afourthstatisticalparameter,theindexofvariation,isintroducedtodepictthetemporalvariation of wind energy. The index of variation is equal to the standard deviation (𝜎 =1𝑁 (𝑥 𝜄 − 𝜇)3)

*+, )dividedbythesamplemean,toobtainadimensionlessoutcome.

The spatial analysis of themain parameters under study over the entire period is presented inFigures3,4,5,6,7and8.



(a)

(b)

(c)

(d) Figure3Windspeedstatisticsat10mcomputedoverthe2001-2010period:a)mean,b)skewness,c)kursotis,d)indexofvariation.



(a)

(b)

(c)

(d) Figure4Windspeedstatisticsat80mcomputedoverthe2001-2010period:a)mean,b)skewness,c)kursotis,d)indexofvariation.



(a)

(b)

(c)

(d)

Figure5Windpowerstatisticsat10mcomputedoverthe2001-2010period:a)mean,b)skewness,c)kursotis,d)indexofvariation.



(a)

(b)

(c)

(d)

Figure6Windpowerstatisticsat10mcomputedoverthe2001-2010period:a)mean,b)skewness,c)kursotis,d)indexofvariation.



(a)

(b)

(c)

(d)

Figure7Significantwaveheightstatisticscomputedoverthe2001-2010period:a)mean,b)skewness,c)kursotis,d)indexofvariation.



(a)

(b)

(c)

(d) Figure8Waveenergyperiodstatisticscomputedoverthe2001-2010period:a)mean,b)skewness,c)kursotis,d)indexofvariation.



Themeanwindspeedisusuallymuchlowerclosetotheshoreduetothewindshadoweffectoftheland.Thereishighspatialandslightlylowertemporalvariability.Theeffectofthewindspeedspatialdistributionisevidentinthemapsthatdepictwindpower,whichshowsimilarbehavior.Themeanwindpowervaluesat10and80mintheGulfofLionsandtheSpanish-Frenchwatersshow rather increasedmeanvalues, comparing toother regionsof theNWMediterraneanSea,associatedwithlowvariability.Thelowskewnessvaluesindicatethatthedataarequitesymmetric.The correspondingmeanwind speed values are between4-7m/sec,which, combinedwith therelativelylowvariationandasymmetryvalues,furtherhighlightsthesuitabilityoftheareaforwindfarmdevelopment.IntheseaareabetweenFrench-Italianwaters,thewindpowervaluesshowlimitedpotentialatbothheightsofinterest.Themeanwindspeedislessthan4m/sec,associatedwithnontrivialvariationandkurtosisvalues.Thesevaluescharacterizetheareaasunstableingeneralandwithincreaseduncertaintyinthewindspeedvalues,comparedwithotherregionsoftheNWMediterraneanSea.However,higher indexofvariationvaluesareobservedwherethemeanwindspeedisgenerallylower.The wave height estimations, both in terms of mean and variability, do not pose importantrestrictionsonthedevelopmentofwindfarmstructures.Moreover,extremewaveheightvalues,asmeasuredbykurtosis,donothaveacriticaleffectonthegeneraldistribution.Thisbehaviorisstableforallparametersacrossthe10yearsofavailabledata.InsituanalysisInadditiontothespatialanalysis,aninsitustudywasperformedintwoselectedlocationsinFrench-SpanishandFrench-Italianwaters.Theanalysisinvolvedadirectionalstudyofwindspeedat10mandaWeibulldistributionfit.TheWeibulldistributionwaschosenasitisconsideredtorepresentwind speed and other left-skewed data sets well. The Weibull distribution probability densityfunction(PDF)ispositiveonlyforpositivevaluesofx,andzerootherwise,andisgivenby

𝑓 𝑥 =𝑏𝑎𝑥𝑎

;<,𝑒<(>/@)A

Theshapeparameter(b)andthescaleparameter(a)areestimatedusingthemaximumlikelihoodmethod.

Figure9LocationsofstudysitesAandB.

ThedistributionofwindspeedintheFrench-Spanishwatersisa2-parameterWeibullwithashapeparameterof1.80andscaleof8.92,andthemaindirectionisNW(Figure10).ThedistributionofwindspeedintheFrench-Spanishwatersisa2-parameterWeibullwithashapeparameterof1.54andscaleof6.68,andthemaindirectionsareNEandSW(Figure11).



Figure10(Left)Windspeeddistributionand(right)windroseplotatsiteA,asshowninFigure9.

Figure11(Left)Windspeeddistributionand(right)windroseplotatsiteB,asshowninFigure9.

TheGulfofLion ishighlyaffectedbytwo localwinds, theMarinandtheMistral.TheMarin isastrongSEwindthatisusuallyaccompaniedbywarm,cloudyweatherandrain.TheMistralblowsfromtheN/NWandcanlastuptoacoupleofdayswithspeedsreaching100km/h.ItoccurswhenadepressioniscenteredoverNWItalyandaridgeofhighpressureextendsnortheastwardacrosstheBayofBiscay.Thelatterisinagreementwiththeintensityoftheannualand10-yearmeanwindspeedandthewinddirectionobservedinthewindroseoflocationA.ThemainwinddirectionsoverlocationBareNEandSW,duetothechannelingbetweenCorsicaandNWItaly/NEFrancealongsidethedepressioncenteredovernorthwestItaly.MEDSEA_CH1_Product_2ThemethodusedforthewindfarmsitingchallengeisbasedontheapproachusedbyHRWallingfordincommercialprojectstohelpcompaniesselectpotentialwindfarmlocations.Themainaimofthestudy istoassesswhetherasite isasuitable locationforawindfarm.Theapproachclassifiesdataaccordingtotheir levelofsuitability,rangingfromgrade5forexclusionzones,toagrade1forareasdeemedappropriateforwindfarmdevelopment.ThissuitabilityscaleisdescribedindetailinTab.1.



Site

availabilityCategory Symbol Description

Verylow 5 Thepresenceofavariablethatmakestheareaunsuitablefor

windfarmdevelopment

Low 4 Thesuitabilityoftheareaunderstudyforwindfarm

developmentislowduetonearbyreceptorsormarineactivities

Medium 3

Theinstallationandpresenceofawindfarmmayadverselyaffect

themarineactivityorsensitivereceptor,althoughthesitemay

besuitablefordevelopment

High 2

Thesiteissuitablefordevelopmentandonlyminoradverse

impactsonthesensitivereceptorormarineactivityare

anticipated

Veryhigh 1 Thesiteissuitablefordevelopmentandnoadverseimpactson

thesensitivereceptorormarineactivityareanticipated

Tab.1Levelsofsuitabilityforwindfarmsitingusedtoclassifytheareaunderinvestigation.

Themeanwindspeedanddirectionvaluesandtheassociatedvariabilitywereusedtocomputeasuitability indexof sitesonly in termsofenvironmental resources.Theappliedmethodology isdescribedinTab.2.Figure12showsamapofthesuitabilityoftheareaforwindenergyplatformdevelopmentbasedonenvironmentalresourcesonly.



Tab.2Sitesuitabilityscoringindexbasedonenvironmentalresourcesonly.

Figure12Suitabilityindexmapbasedonenvironmentalresourcesonly.

MEDSEA_CH1_Product_3Inadditiontotheenvironmentalconsiderations, intenseanthropogenicactivity,biodiversityandsedimentcharacteristicsmayleadtoseveralconstraintsontheinstallationofwindturbinesintheareaofinterest.Asecondsuitabilityindexwasthereforedeveloped,whichcombinestheavailableenvironmental resources with the potential constraints set by local activities and naturalcharacteristics.Astheavailableinformationanddatawereaccessedinvariousformats,aGIStoolwasusedforthequantitativeanalysis.Theestimationsthatwereusedwerebasedon:

1. Seadepth



2. Distancefromtheshore

3. Seabedcharacterization

4. MarineProtectedAreas

ThedatawerepreparedusingtheWGS84WorldReferencesystemandtheareacharacteristicsaredescribedinTable3.Table3Siteavailabilityforwindfarmsitingintermsofwaterdepthrange,distancefromshore,MarineProtectedAreasandSeabedcharacterization.

Siteavailability Waterdepthrange

(m)

Distancefrom

shore(km)

MarineProtected

Areas

Seabed

characterization

Verylow >500 >200or<25 Includedinthe

Natura2000

HabitatsandBirds

Directive

ProtectedSeagrass:

posidoniaoceanica

Low 200-500 150-200 Coralpresence,

Hardsubstrate,

Rockfragment,

Seagrass

Medium 60-200 100-150 Silt,clay

High 25-60 50-100 Mud,gravelly

sediment

Veryhigh 0-25 25-50 - Sand,Sediment

TheresultsareillustratedinFigures13and14.Asafinalconcludingremark,oneshouldemphasizethe high suitability of the area between French and Spanish waters for wind farm platformdevelopment.



Figure13Suitabilityindexbasedonbiological,humanandgeophysicalconstraints.

Figure 14 Suitability index of wind farm locations in the NWMed based on environmentalresources,naturalbarriers,humanactivities,MPAandfisheries.



ExpertevaluationofTargetedProductqualityandgapsintheinputdatasetsTheobjectiveistoprovideanexpertevaluationofthe“fitnessforpurposeanduse”foreachTargetedProduct.Thecoordinatoraskedthechallengeteamstoprovidethefollowinginformation.

1. Assignanoverallproductqualityscorewithrespecttoscope(fitnessforpurpose)andexplainwhy,accordingtothescaleinTableA.

2. Identify the most important characteristic(s) for the Targeted Product quality (if allcharacteristicsareimportant,pleasesayso).

3. Identifywhichqualityelement(s)(seeAnnex1)ofthemostimportantcharacteristic(s)affectstheTargetedProductquality.

4. IdentifythelimitationsofthequalityoftheTargetedproductsduetotheinputdatasetused.5. Explainwhichofthecharacteristics“mostfails”tomeetthescopeoftheTargetedProduct.6. Provide an expert judgement of themost importantgaps in the input data sets for each

TargetedProduct.SCORE MEANING

1 EXCELLENTàcompletelymeetsthescopeoftheTargetedProduct2 VERYGOODàmeetsmorethan70%ofthescopeoftheTargetedProduct3 GOODàmeetslessthan50%ofthescopeoftheTargetedProduct4 SUFFICIENTàdoesnotadequatelymeetthescopebutisastartingpoint5 INADEQUATEàdoesnotfulfillthescopeandisnotusable

TableATargetedProductsqualityscoresandtheirmeaning.

MEDSEA_CH1_Product_11. The product quality score is excellent (1). The developed wind/wave database and the

associatedstatisticalanalysismeetthetargetssetbytheprojectforacompleteassessmentof wind farm siting. A wide range of environmental parameters (beyond the classicwind/wave information)were considered, over an area that extends the borders of thepredefinedregionunderstudy.Thedatawereanalyzedusingavarietyofconventionalandadvancedstatisticaltoolsthatprovidecriticalinformationonthedataandtheirimpactonwindfarmsiting.

2. All of the input characteristics contribute to the product quality. However, the windcomponents(zonalandmeridional)arecriticalforestimatingtheavailablewindpower.

3. Thespatialandtemporalextentandresolutioncombinedwiththeaccuracyofthedataarethemostimportantqualityelementsthatinfluencetheanalysisusedtodefinetheoptimalareasforwindfarmdevelopment.

4. Theproduct’squalityislimitedbytheverticalandhorizontalresolutionofthewinddata,whichdoesnotresolvethesub-scalephenomena,especiallyhorizontally.

5. Allofthecharacteristicscontributetotheanalysis,butthe2-dimensionalwavespectrafailthe most to meet the scope of the product because the data are limited to specificpreselectedgridpoints.Inparticular,whiletheotheratmosphericandwaveparameterdataare in one-dimensional time series, thewave spectra are in 2-dimensionalmatrices thatcouldnotbestoredinfullduetostoragelimitations.Therefore,onlythewavespectrafor



specific pre-defined pointswere stored in the data base.Nevertheless, these points areindicativeforthecoastlineofinterest.

6. Therearenoseriousgapsintheinputdatasets,butasmentionedabove,the2-dimensionalwave spectradataareonly available for fixedpre-selectedpoints rather than thewholedomainunderstudy,whichcreatessomerestrictions.

MEDSEA_CH1_Product_2

1. Theproduct’squalityscoreisexcellent (1).Thesuitability indexdevelopedforwindfarmsitingiscompleteanddetailedbecauseitcombinesstatisticalindexesandprovidesmeanvaluesandvariability.

2. Wind (zonalandmeridional components) is themost important characteristicbecause itdefinestheavailablewindpowerpotential.

3. The spatial (5 km) and temporal (hourly) resolutions guarantee a detailed and accurateanalysisofthesuitabilityofanareaforwindfarmdevelopment.

4. Theproduct’squalityislimitedbytheverticalandhorizontalresolutionofthewinddata,whichalthoughhigh,donotresolvethesub-scalephenomena,especiallyhorizontally.

5. Allthecharacteristicscontributetotheanalysisandnoneofthemfailstomeetthescopeoftheproduct.

6. Therewerenoseriousgapsintheinputdatasets.

MEDSEA_CH1_Product_31. Theproduct’squalityscoreisverygood(2).Itcoversthemostimportantresourcesavailable

in and constraints on the targeted area. It provides crucial information regarding thesuitabilityofthetargetedareaforwindfarmsiting.

2. Alloftheinputcharacteristicscontributetotheproductquality;however,thepresenceofanationalreserveorprotectedareapreventsanywindfarmdeployment.

3. Thespatialextentandresolution,togetherwiththeaccuracyandcompleteness,havethegreatestimpactontheproductquality.

4. Aswehaveveryaccurateandcomplete informationontheseadepthanddistancefromshore(withaspatialresolutionlessthan1km),theproduct’squalityismainlydrivenbythebiological or sediment dataset and the main concerns are their spatial accuracy andcompleteness.Thiswouldrequireupdatingthedatasetasoftenaspossible.

5. Allofthecharacteristicscontributetotheanalysisandmeetthescopeoftheproduct.Thebiologicaldata(marineprotectedareasandsediments)havethegreatestpotentialfortimeandspaceevolutionand,asstatedabove,theirdatabasesneedtobeupdatedasoftenaspossible.

6. Individualbiologicalspeciesdistributionsareavailable,butnotreadilyuseableinadatabaseand their presence is taken into account in the marine protected areas maps. Othercharacteristicscouldbeimportant,butinformationonthemwasnotavailable,eitherduetotheirreal-timeornon-freenature,suchascommercialshippingroutes,orduetodatapolicyreasons,particularlyregardingmilitaryareas.

TP CH1 1 1 2 1 3 2



TableBSummaryofthequalityscoresassociatedwitheachTargetedProductaccordingtotheexperts’evaluationsandtheevaluationschemepresentedinTableA.



Annex1:DefinitionsWeextractedthefollowingdefinitionsfromtheMedSeaLiteratureSurvey:CharacteristicInthisdocument,a“characteristic”isadistinguishingfeaturethatrefers

1. eithertoavariablederivedfromtheobservation,themeasurement,orthenumericalmodeloutputofaphenomenonorofanobjectpropertyintheenvironment;or

2. tothegeographicalrepresentationofanobjectonamap(i.e.,alayersuchasaprotectedarea,acoastlineorwreck)byasetofvectors(polygon,curve,point)oraraster(aspatialdatamodelthatdefinesspaceasanarrayofequallysizedcellssuchasagridoranimage).

EnvironmentalmatricesThisconceptisintroducedtoavoidambiguitieswhenusingacharacteristicnamesuchas“temperature”.Theenvironmentmatrixistheenvironmenttowhichacharacteristicisrelated,whichwedefineas

1. Air2. MarineWater3. FreshWater4. Biota/Biology5. Seabed6. Humanactivities.

Qualityprinciplesü Spatialextent

Boxorgeographicregionboundingthedatasets.ü Spatialresolution

Sizeofthesmallestobjectthatcanberesolvedontheground.Inarasterdataset,theresolutionislimitedbythecellsize.

ü SpatialaccuracyRequestedclosenessofcoordinatevaluestovaluesacceptedasorbeingtruee.g.onthebaseoftheinstrumentused.

ü TimeextentTimeintervalrepresentedbythedatasetorbythecollection.

ü TimeresolutionSizeofthesmallestintervaloftimethatcanberesolved.

ü TimeaccuracyRequestedclosenessoftemporalvaluestothevaluesthatareacceptedasoraretrue.

ü UsabilityTheextenttowhichaproductcanbeusedbyspecifieduserstoachievespecifiedgoalswitheffectiveness,efficiencyandsatisfactioninaspecifiedcontextofuse.

ü CompletenessAmountofmissingdatainadataset.

ü LogicalconsistencyDegreeofadherencetotherequiredformat.

ü ThematicaccuracyRequestedclosenessofcharacteristicvaluestothevaluesthatareacceptedasoraretrue(theso-calledattributeofadataentity,e.g.,"waveheight").Itincludesthecorrectnessoftheclassificationoffeaturesortheirassociations.



ReferencesBidlot J, JanssenP,AbdallaS,HersbachH (2007)Arevised formulationofoceanwavedissipationand itsmodel impact. ECMWF Tech.Memo. 509. ECMWF, Reading, United Kingdom, 27pp. available online at:http://www.ecmwf.int/publications/.E.P. Chassignet, Smith, L.T., Halliwell, G.R., Bleck, R., 2003. North Atlantic simulations with the HYbridCoordinate Ocean Model (HYCOM): impact of the vertical coordinate choice, reference density, andthermobaricity.J.Phys.Oceanogr.33,2504–2526

L.Cradden,C.Kalogeri,I.MartinezBarrios,G.Galanis,D.Ingram,G.Kallos,Multi-criteriasiteselectionforoffshorerenewableenergyplatforms,RenewableEnergy,2016,87pp.791-806.G. Emmanouil, Galanis G., Kallos G., Zodiatis G., Wind and sea waves analysis for the Greek area withapplication to renewable energy, American Geophysical Union (AGU) Joint Assembly 2015, Montreal,Canada,2015.

G.Galanis,P.C.ChuandG.Kallos,Statisticalpostprocessesfortheimprovementoftheresultsofnumericalwavepredictionmodels.AcombinationofKolmogorov-ZurbenkoandKalmanfilters,JournalofOperationalOceanography,Vol4(1),2011,pp.23-31.G.Galanis,G.Emmanouil,C.KalogeriandG.Kallos,MathematicalandPhysicalModelsfortheEstimationofWind-WavePowerPotentialintheEasternMediterraneanSea,MathematicsofPlanetEarthLectureNotesinEarthSystemSciences2014,pp561-564.DOI10.1007/978-3-642-32408-6_123.

G.Galanis,G.Emmanouil,M.Kafatos,P.Chu,N.HatzopoulosandG.Kallos,Anewhigh resolutionwavemodelingsystemforrenewableenergyapplicationsinCaliforniaandtheMediterraneanSea,AGU2014FallMeeting,SanFrancisco,USA,December2014.G.Kallos,G.Galanis,C.Kalogeri,P.Patlakas,Advancedatmosphericmodelingforengineeringapplications,6thInternationalSymposiumonComputationalWindEngineering-CWE2014,2014,Hamburg,Germany.

KallosG (1997), The Regionalweather forecasting system SKIRON. Proceedings, Symposiumon RegionalWeatherPredictiononParallelComputerEnvironments,15-17October1997,Athens,Greece,9pp.G. Kallos,G.Galanis, C. Spyrou, C. Kalogeri, A. Adam, and P. Athanasiadis,Offshore EnergyMapping forNortheastAtlanticandMediterranean:MARINAPLATFORMproject,GeophysicalResearchAbstractsVol.14,EGU2012-10767,2012.

G. Kallos, G.Galanis, C. Stathopoulos, C. Kalogeri andN. Barranger,Operationalwind power forecastingsystemsbasedonphysical and statisticalmodels, EWEAWindPower Forecasting TechnologyWorkshop,Rotterdam,Netherlands,2013.JanssenP(2004),TheInteractionofOceanWavesandWind.Cambridge,UniversityPress,300pp.

X.G.Larsen,C.Kalogeri,G.GalanisandG.Kallos,Astatisticalmethodologyfortheestimationofextremewave conditions for offshore renewable applications, Renewable Energy (2015), pp. 205-218 DOIinformation:10.1016/j.renene.2015.01.069.

P.Patlakas,G.Galanis.N.Barrenger,G.Kallos,Extremewindevents inacomplexmaritimeenvironment:waysofquantification,J.WindEng.Ind.Aerodyn.149(2016)89–101.

SpyrouC.,MitsakouC.,KallosG.,LoukaP.,VlastouG.,Animprovedlimitedareamodelfordescribingthedustcycleintheatmosphere,JournalofGeophysicalResearch:Atmospheres115(D17),2010.

WAMDIG, The WAM-Development and Implementation Group: Hasselmann S, Hasselmann K, Bauer E,BertottiL,CardoneCV,EwingJA,GreenwoodJA,GuillaumeA,JanssenP,KomenG,LionelloP,ReistadM,ZambreskyL(1988),TheWAMModel-athirdgenerationoceanwavepredictionmodel,JournalofPhysicalOceanography,18(12),1775–1810.

GROWTH AND INNOVATION IN OCEAN ECONOMY GAPS AND … · Growth and innovation in ocean economy Gaps...

Documents

Transcript of GROWTH AND INNOVATION IN OCEAN ECONOMY GAPS AND … · Growth and innovation in ocean economy Gaps...