AWST Using Me So Scale Model Data in MCP May20091

8/4/2019 AWST Using Me So Scale Model Data in MCP May20091

1/10

USINGSIMULATEDWINDDATAFROM

AMESOSCALEMODELINMCP

M.Taylor

J.Freedman

K.Waight

M.Brower

May2009


2/10

Page|2 UsingSimulatedWindDatafromaMesoscaleModelinMCP

May2009

ABSTRACT

Sincefieldmeasurementcampaignsforproposedwindprojectstypicallylastnomorethanafewyears,

theobservedmeteorologicaldata collectedon siteoftendeviate from the longterm climatology.To

reduce the uncertainty associated with climate variability, a statistical relationship is typically

establishedbetweenamonitoringsiteandoneormorereferencestationsusingatechniqueknownas

MeasureCorrelatePredict(MCP).

ProperuseofMCPrequiresreferencestationswithlongdatarecordscollectedatthesamelocationand

height, with the same equipment, and in relatively unchanging surroundings. Identifying reference

stationsmeetingthesestringentcriteriaisbecomingincreasinglydifficult,especiallyconsideringthatthe

NationalWeatherService(NWS)isreplacingcupanemometerswithicefreeultrasonicanemometersat

itsAutomatedSurfaceObservingSystem(ASOS)stations.

Oneapproach thatofferspromiseofmitigating theseproblems is todriveamesoscalemodelwitha

consistent setofobservationaldata. Though the concept is similar to reanalysis, the combinationof

highermodelresolutionandcareintheselectionofstationsusedinthesimulationsshouldimproveon

thosedata.Towardsthisend,wehavecreatedwindTrends,amodeleddatasetcoveringNorthAmerica

at 20km resolution from 1997 to the present, and have conducted a thorough evaluation of its

suitabilityforMCPandotherapplications.Thisstudysummarizesourfindings.

INTRODUCTION

Predictingthefuturerequiresinsightintothepast.Untilrecently,mostresourceassessmentsforwind

energyprojectshavereliedonstandardmeteorologicaltowerstoestablishhistoricalclimateconditions.

But with intermittent changes in surroundings, locations, and equipment, the continuity and

representativeness of these reference data is a growing concern. Other commonlyused reference

sources include rawinsonde (instrumentedballoon) and reanalysisdata,but these sources alsohave

potentiallysignificantshortcomingsaswell.The relatively few rawinsondestationsoftenexhibitpoor

correlationswithwindprojectsites,whereasreanalysisdataarevulnerabletoinconsistenciescausedby

changesintheobservationaldatausedtodrivethemodels.

Oneapproachthatmayovercometheseproblemsistodriveamesoscalemodelwithaconsistentsetof

observationaldata.Wehavecreatedsuchadataseries,knownaswindTrends.Thisreportdetailsour

validationofwindTrendsagainstASOS,rawinsonde,NCEP/NCARGlobalReanalysis(NNGR)[1]andNorth

AmericanRegionalReanalysis (NARR)[2]with respect to itsconsistencyover timeand itsviabilityasa

longtermreferenceforwindresourceassessment.

BACKGROUNDANDMETHODS

InterannualVariabilityandMeasureCorrelatePredict(MCP)

Mostwindresourceassessmentprogramsspanroughlyonetotwoyears. Whenexaminingtheannual

meanwindspeedsfromreferencesiteswithlongperiodsofrecord,wefindthattheyvaryconsiderably

from yeartoyear. Figure 1 shows a time series of the annualmeanwind speeds at the Chicago

Midway(KMDW)AutomatedSurfaceObservingSystem(ASOS)[3]stationbetween1998and2007. The

annualmeanspeedwaswithin1%ofthetenyearaverageinonlyfourofthetenyearsanddeviatedby

asmuchas4.0%. Givensuchvariability,itisprudenttoadjustobserveddataatashorttermresource

assessmentsitetolongtermconditions.


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Figure1.AnnualMeanWindSpeedsatChicagoMidwayASOSStation

TheMCPmethod of estimating longtermwind speeds correlates shortterm data from a sitewith

concurrent data from a longterm reference station (Figure 2). A regression or other relationship

between the two stations is derived, and the longtermmean speed or speed distribution at the

reference station is applied to estimate the longterm speed or speed distribution at the site. The

accuracyofthe longtermprojections isdirectlytiedtotheconsistencyof thereferencedataandthe

qualityofthecorrelationbetweenthetargetmastandreferencesite. Problemssuchasalimitedperiod

of referencedata,poor correlationwithonsitemeasurements,and changingobservationalmethods

andplatformscancausesignificanterrorsinenergyproductionestimates.

Figure2.ComparisonofTypicalMonitoringMastandReferenceSitePeriodsofRecord

CommonReferenceDataSources

Surface Stations The most widely used source of reference data is surface weather stations. They offer several

advantages.Surfacestationsarerelativelynumerousandcollectfrequent(usuallyhourly)observations.

3.0

3.2

3.4

3.6

3.8

4.0

4.2

4.4

4.6

4.8

5.0

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

WindSpee

d(m/s)

Year

AnnualMean

OverallMean

YearSpeed

(m/s)

Deviation

From Normal

1998 4.29 -4.0%1999 4.48 0.1%

2000 4.54 1.5%

2001 4.45 -0.5%

2002 4.57 2.3%

2003 4.49 0.5%

2004 4.55 1.7%

2005 4.41 -1.3%

2006 4.38 -2.0%

2007 4.48 0.2%

Mean 4.47

0

1

2

3

4

5

6

7

8

9

Jan98 Jan99 Jan00 Jan01 Jan02 Jan03 Jan04 Jan05 Jan06 Jan07 Jan08

WindSpeed(m/s)

Month

MonitoringMast

ReferenceSite


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Additionally, insomecountries,suchastheUnitedStates,sitehistories,datacollectionprotocols,and

equipment maintenance records are well documented. Unfortunately, weather stations are often

locatedfarfromwindprojectsitesorindifferentwindregimes,andthereforecanexhibitunsatisfactory

correlationswithonsitemeasurements.Thewindmonitoringheightsatsurfacereferencestationsare

alsousuallylow(10mistheASOSandinternationalstandard);theresultisthatthemeasurementsare

verysensitivetochangesinthesurroundings,suchastreegrowthorbuildingconstruction. Finally,as

has been the case with the ASOS network, instrumentation and data collection protocols change

occasionally,oftenintroducingshiftsintheobservedwindspeedsthatmayjeopardizethesitesviability

asalongtermreference. OnesuchchangeoccurredintheUnitedStateswhentheASOSstandardwas

implementedatweatherstationsstarting in themid1990sandrenderedpriormeasurementsuseless

forMCP. Very recently, ASOS cup anemometerswere replacedwith ultrasonic icefreewind (IFW)

sensors.Testdata indicateasmallbutmeasurabledifference inwindspeeds recordedby IFW,which

addsuncertaintytotheMCPprocess.[4]

Rawinsonde Stations Rawinsonde data are acquired using instrumented balloons to sample the vertical profile of the

atmosphere.[5]Comparedtosurfacestations,thismeasurementtechniquehaschangedrelatively little

in thepast twoor threedecades,resulting ina longdatarecord that is typicallyquiteconsistentand

reliableaswellasforheightswellabovegroundfreeofimpactsfromchangingsurfaceconditions.

However, rawinsondestationsare far lessnumerous thansurface stations,and theygenerally collect

soundingsonlyonceevery12hours.1 Inaddition,thelowestusefulheightofmeasurement(definedby

standardpressure levels) isoftenabove theplanetaryboundary layer.For thesereasons,rawinsonde

dataoftendonotcorrelateaswellassurfaceweatherdatawithmeasurementstakenatwindproject

sites.

Reanalysis Data The term reanalysis data refers to a gridded data base ofweather observations that have been

assimilated at a later time into a global or regionalweathermodel.NNGR andNARR, two publicly

availablereanalysisdatasets,arethefocusofourdiscussion;othersincludeaglobaldatasetdeveloped

bytheEuropeanCentreforMediumRangeWeatherForecasts[6].Observationaldatasourcesassimilated

into the reanalysismodels include surface, rawinsonde, satelliteandaircraft. Themainadvantageof

reanalysisdataisthattheyprovideaconvenientsourceofweatherdataextendingbackseveraldecades

(to1948, in the caseofNNGR).However, thequantityand sourcesofweatherobservationsused to

drivethereanalysismodelhavevariedgreatlyoverthepastseveraldecades,leadingtofalsetrendsand

variationsinsomeregions.[7]Inaddition,duetothecoarsemodelresolution(210kmforNNGR),thereis

oftenapoorcorrelationwithwindmonitoringsites.

windTrends:ControlledRegionalReanalysis

windTrends is a controlled regional reanalysis dataset developed by AWS Truewind. Though

conceptuallysimilartoreanalysis,theprocessdiffersintwokeyways:thegridresolutionisfinerandthe

sourceofobservationaldata rawinsondeonly hasbeenkeptfixedthroughoutthesimulationperiod.

Thesemodifications should allow for amore consistent data set that correlates betterwith onsite

observationsthanstandardreanalysisdata.

1According to theNWSrawinsonde factsheetpostedathttp://www.ua.nws.noaa.gov/factsheet.htm,

there are 69 operational rawinsonde stations in the conterminousUnited States. Observations are

usuallycollectedtwicedaily.


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May2009

ThewindTrendsdatawereproduced fromMesoscaleAtmosphericSimulationSystem(MASS)[8]

model

simulationsonagridcoveringtheconterminousUnitedStatesandsouthernCanadastartinginJanuary

1997. The simulationsweremadeona60kmoutergridanda20kmnestedgrid,withNNGRdata

supplying the initial and lateralboundary conditions. Rawinsonde data from the IntegratedGlobal

RawinsondeArchive(IGRA)werealsousedintheobjectiveanalysesoftheinitialfields. Foreachmonth,

twoseriesofrunsweremade,beginningwithacoldstart12hoursbeforethefirstofthemonthand12

hoursbeforeeitherthe15thor16thofthemonth,followedbytwoweeksofsimulationswithrawinsonde

observationsassimilatedat0000and1200UTC.Thissimulationstrategywasdesignedtominimizethe

influenceoftheinitialconditionsfromNNGR.

Validation

TrendVerif ication The selectionof validation stations for thispartof the analysisbeganwith a listof371highquality

stations used by the Goddard Institute of Space Studies in their analysis of longterm surface

temperature trends.[9] Stations outside the conterminousUnited Stateswere eliminated. Next, the

availability of surface data for each station was verified. Stations were retained if they had 95%

availabilityforeachof10years(19972006),resulting ina listofabout185stations. Plottingthose

stationsonamap,thereweresomeareaswithsparsestationcoverage:NewYork,Vermont,theUpper

PeninsulaofMichigan, coastalNorthandSouthCarolina,coastalLouisiana,and centralandsouthern

California. Fifteenstationsinthoseareaswithalmost95%coveragewereselectedandaddedtothelist,

resulting in198 stations,ofwhich30were rawinsonde stations.Using these sites,we compared the

longtermwind speed trendsandanomalies from theNNGR,NARR,andwindTrendswith concurrent

observeddataateachoftheselectedstationsonhourly,monthly,andannualtemporalperiods.

MCPVerif ication Inearlierwork,Tayloretal.[10]developedamethodwherebytheaccuracyofMCPwastestedforwind

resourceassessmentsiteshavingperiodsofrecordspanningatleastfouryears.Theonsite(target)data

were divided into a sequence of shorter periods typical of what would be available for MCP. A

regressionwitha referencedatasetwasperformed foreachsubperiodand themeanspeed for the

entireperiodwaspredicted.Theerrormarginoftheprocedurewasderivedfromthebiasesbetween

thepredictedandactualmeanspeedsforallthesubperiods.

Forthisassessment,weselectedtheentireASOSnetworkastargetsitesandrepeatedthesameMCP

analysisusing,asreference,thenearestASOSsiteandnearestwindTrendsgridpointwiththestrongest

correlations.TheASOSnetworkyielded629sitesthat,untiltheinstallationoftheIFWsensors,operated

usingthesamewindmeasurementsystemforseveralyears.Wefocusedontheresultswitha12month

subperiod.

We also identified ten wind resource assessment sites that have high data recovery and fit the

establishedcriteria,and repeated thesameanalysisusingdirectobservations (ASOSandrawinsonde)

andmodeledwindTrendsandNNGRdataasreferences. Foranadditional12siteswith lessthanfour

yearsofdata,wecomparedonlythecoefficientofdeterminationorsquareofthecorrelationcoefficient

(r2)valuesoftheregressions.ForASOSreferencesitesthatwereconvertedtoIFWsensors,weuseda

preliminarycorrectiontothedatadevelopedbyAWSTruewindfromsidebysidetestdataprovidedby

theNWS.


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RESULTS

TrendVerification

Figure3containsatimeseriescomparingtheannualmeanwindspeedanomalies(deviationsfromthe

average) forNNGR,NARR,andwindTrendswith theobservedanomalies.windTrendsandNNGRdata

both

correlate

well

with

the

observations.

However,

the

NNGR

data

exhibit

a

downward

trend

that

deviates from windTrends and the observed data. The NARR data show a noticeable discontinuity

startingin2002,whichmayberelatedtoachangeinproceduresforassimilatingrawinsondedata.

Figure3.ComparisonofAnnualMeanWindSpeedAnomaliesfor198SurfaceBasedMonitoringStations

TimeseriesoftheannualmeanwindspeedsfromwindTrendsinterpolatedtoseveralmastlocationsand

comparedwith nearbyASOS or rawinsonde data suggest good agreement. Two examples of these

comparisonsarecontainedinFigure4.

Figure

4.

Annual

Mean

Wind

Speeds

from

windTrends

and

Nearby

Reference

Stations

foranEasternRidgetop(left)andCentralWashington(right)

Figure5 containsa time series comparing themonthlymeanwind speedanomalies fromNNGRand

windTrends with the observed anomalies at the 198 surface and rawinsonde stations. windTrends

correlates very well with the observed data; the NNGR anomalies also correlate well, but are

systematicallylarger,likelybecausetheywerecalculatedat25mheightaboveground. Ofimportance

is that a close examination of both datasets reveals periods in the NNGR dataset that deviate

0.5

0.4

0.3

0.2

0.1

0.0

0.1

0.2

0.3

0.4

0.5

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

WindS

peedAnomaly(m/s)

Year

ASOS

windTrends

NNGR

NARR

0

2

4

6

8

10

12

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

WindSpeed(m/s)

Year

windTrends10I

windTrends50I

Elkins,WV

Dulles,VA

Blacksburg,VA

Pittsburgh,PA

0

1

2

3

4

5

6

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

WindSpeed(m/s)

Year

windTrends10I

windTrends50I

Ellensburg,WA

Yakima,WA


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May2009

substantiallyfromtheobservedvalues. ThosediscrepanciesdonotappearinwindTrends,oraremuch

smaller.

Figure5.ComparisonofMonthlyMeanWindSpeedAnomaliesfor198SurfaceBasedMonitoringStations

MCPVerification

Forthe629ASOSsites,theASOStoASOSregressionshadastrongercorrelationinabout80%ofcases;

theaverager2valuewas0.71betweenASOSsites,whileitwas0.65betweentheASOStargetsiteand

the best windTrends grid point. Figure 6 shows the geographic distribution of the differences in r2

betweentheASOStoASOSandASOStowindTrendscorrelations.Greencontoursonthismapindicate

thewindTrendsgridpointhasthesuperiorr2,whereasredindicatestheASOSstationdoes.windTrends

generallydoesbetter inthecomplexterrainoftheAppalachianandRockyMountains. Conversely, in

coastal regions and the southeastern United States, ASOS data provide the stronger correlation.

ThroughoutmuchoftheGreatPlains,thetwoareroughlyequivalent.

0.8

0.6

0.4

0.2

0

0.2

0.4

0.6

0.8

Jan97 Ja n98 Ja n99 Ja n0 0 Ja n01 Ja n02 Ja n03 Ja n0 4 Ja n05 Ja n06

WindSpeedAnomaly(m/s)

Month

ASOS

NNGR

0.8

0.6

0.4

0.2

0

0.2

0.4

0.6

0.8

Jan97 Ja n98 Ja n99 Ja n0 0 Ja n01 Ja n02 Ja n03 Ja n0 4 Ja n05 Ja n06

WindSpeedAnomaly(m/s)

Month

ASOS

windTrends


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May2009

Figure6.GeographicDistributionofRelativeASOStoASOSandASOStowindTrendsCorrelation

In contrast to the ASOS target sites, the wind resource assessment sites generally have stronger

correlationswiththewindTrendsdatathanwiththeASOSreferencedata.Figure7containsascatterplot

ofther2valuesfor22targetsitesandtheircorrelationswithASOSandwindTrendsreferencedata.In16

cases,thewindTrendscorrelationishigher.Thispatternmayreflectthefactthatmostwindprojectsites

arebetter exposed to synopticscalewinds,which are capturedwellby thewindTrends simulations,

whereasASOSstationsareofteninshelteredlocations,suchasvalleys.

Figure7.ScatterplotofMasttoASOSandMasttowindTrendsr2valuesfor22Sites

Asshown inFigure8, thedistributionsofMCPstandarderrors forASOSandwindTrendsarevirtually

identical.ForASOStowindTrendsregressions,themeanstandarderroris0.096m/s;forASOStoASOS

regressions,itis0.095m/s.

ASOS-to-wT r2 > ASOS-to-ASOS r2

ASOS-to-ASOS r2 > ASOS-to-wT r2

MeanwT r2:0.65

MeanASOSr2:0.71

Higherr2

wT:122Sites

MeanASOSr2:506Sites

Equal:1Site

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1

ASOSr

squared

windTrendsrsquared


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May2009

Figure8.FrequencyDistributionsofASOStoASOSandASOStowindTrendsStandardErrors

Forthetenwindresourceassessmentmastswithperiodsofrecordofat leastfouryears,windTrends

performedslightlybetterthanASOSandsignificantlybetterthanNNGRforMCP.Therespectiveaverage

r2 valueswere 0.75 (masttowindTrends), 0.72 (masttoASOS), and 0.55 (masttoNNGR),while the

standarderrorswere0.12(windTrends),0.13(ASOS),and0.16(NNGR).Theresultsaresummarized in

Figure9.

Figure9.StandardErrorsofMCPAnalysesfor10MonitoringMastUsingwindTrends,ASOS,andNNGRDataasReferences

Inmostindividualcases,thewindTrendsstandarderrorsarecomparabletoorlowerthanthosederived

using theASOSorNNGR referencedata. Theonly locationwherewindTrendsperformssubstantially

worsethanASOSisatMast6,whichislocatedinaCaliforniamountainpasswherethewindclimateis

highlylocalizedandthereforenotresolvedbythe20kmwindTrendssimulations.

0%

5%

10%

15%

20%

25%

30%

35%

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

PercentofOc

currence

StandardError(m/s)

ASOStowindTrends

ASOStoASOS

wTAvgDist:13.2km

ASOSAvgDist:75.2kmwTStdErr:0.096

ASOSStdErr:0.095

LowerStdErr

wT:315Sites

ASOS:314Sites

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

1 2 3 4 5 6 7 8 9 10 Mean

StandardError(m/s)

MastNumber

windTrends

ASOS

NNGR

Meanr2

windTrends:0.75

ASOS:0.72

NNGR:0.55


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May2009

SUMMARYANDCONCLUSIONS

AWS Truewind developed windTrends in hopes of providing a consistent set of reference data for

performingMCPstudiesthatarewellcorrelatedtowindprojectsitesintheconterminousUnitedStates

andsouthernCanada.ThevalidationworkreportedhereshowsthatwindTrendsisgenerallysuperiorto

globalreanalysis(NNGR)andcomparabletoASOSstationsforthispurpose.

WhetherASOSorwindTrendsshouldbeusedinaparticularMCPanalysiscanbedecidedonacaseby

casebasis.Where the correlationsare similar and there areno evident consistencyproblems in the

ASOSdata,eitherdataset(orboth)canbeused.Whereoneexhibitsamuchstrongercorrelationthan

theother,thatdatasetislikelytobethebetterchoice.Theconsistencyproblemswehaveobservedin

bothNNGRanditsregionalcounterpart,NARR,suggestthattheyarenotsuitableinmostsituationsfor

MCP.

ONGOINGANDFUTUREWORK

Validationwork continues. One goal is to identify and assess additionalwind resource assessment

mastswithsufficiently longperiodsofrecord toprovideamoreextensivetestofwindTrends inMCP.

The

present

sample

of

10

such

masts

is

fairly

small.

Another

goal

is

to

gauge

the

impact

of

the

IFW

correctionontheaccuracyofMCPusingASOSstations.

We are also working to improve the windTrends data set. Since the current 20km resolution is

insufficient to resolve highly localized wind climates such asmountain passes, we are planning to

increasethegridresolutionto5km.Thisshould increasethequalityofthecorrelationsanddecrease

thestandarderrorsinthelongtermpredictions.

ThewindTrendsapproachisalsobeingextendedtotherestofCanada,India,andothercountries.

References:

[1] Kistler,R.etal.,2001:TheNCEP/NCAR50YearReanalysis:MonthlyMeansCDROMandDocumentation.BulletinoftheAmericanMeteorologicalSociety,82,No.2,247268

[2] Mesinger,F.etal.,2006:NorthAmericanRegionalReanalysis.BulletinoftheAmericanMeteorologicalSociety,87,No.3,343360

[3] NOAA, 1998:ASOS users guide.NationalOceanographic andAtmosphericAdministration, 61 pp. [Available online athttp://www.nws.noaa.gov/asos]

[4] Lewis,R.and J.Dover,2004:FieldandOperationalTestsofaSonicAnemometer fortheAutomatedSurfaceObservingSystem,EighthSymposiumon IntegratedObservingandAssimilationSystemsforAtmosphere,Ocean,andLandSurface,

AmericanMeteorological.Society,Seattle,WA.

[5] ImkeDurre,RussellS.VoseandDavidB.Wuertz.2006:OverviewoftheIntegratedGlobalRadiosondeArchive.JournalofClimate:Vol.19,No.1,pp.5368.Brower,M.C.,2006:TheUseofReanalysisDataforClimateAdjustments.AWSTruewind

ResearchNote#1.

[6] Uppala,etal.,2006:TheERA40reanalysis.QuarterlyJournaloftheRoyalMeteorologicalSociety.,131,29613012.[7] Brower,M.C.,2006:TheUseofReanalysisDataforClimateAdjustments.AWSTruewindResearchNote#1.[8] Manobianco,J.,J.W.ZackandG.E.Taylor,1996:WorkstationbasedRealtimeMesoscaleModelingDesignedforWeather

Support to Operations at the Kennedy Space Center and Cape Canaveral Air Station. Bulletin of the American

Meteorological

Society,

77,

No.

4,

653

672

[9] Hansen,J.,R.Ruedy,J.Glascoe,andM.Sato,1999:GISSanalysisofsurfacetemperaturechange.JournalofGeophysicalResearch.104,3099731022.

[10] Taylor,Mark,etal.,2004:AnAnalysisofWindResourceUncertainty inEnergyProductionEstimates,ProceedingsoftheEuropeanWindEnergyConference.

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AWST Using Me So Scale Model Data in MCP May20091