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The use of satellite data assimilation methods inregional NWP for solar irradiance forecasting

Frederik Kurzrock, Sylvain Cros, Nicolas Sébastien, Fabrice Chane-Ming,Laurent Linguet, Roland Potthast, Gilles Lajoie

To cite this version:Frederik Kurzrock, Sylvain Cros, Nicolas Sébastien, Fabrice Chane-Ming, Laurent Linguet, et al.. Theuse of satellite data assimilation methods in regional NWP for solar irradiance forecasting. EuropeanGeosciences Union General Assembly 2016, Apr 2016, Vienna, Austria. 18, pp.EGU2016-15821-1,2016, 2016, Geophysical Research Abstracts. �hal-01483991�

Frederik Kurzrock1,2,3, Sylvain Cros², Nicolas Sébastien2, Fabrice Chane-Ming3, Laurent Linguet4, Roland Potthast5 and Gilles Lajoie1

1UMR 288 ESPACE-DEV, Université de La Réunion, France, ²Reuniwatt SAS, France, 3UMR 8105 Laboratoire de l’Atmosphère et des Cyclones (LACy), Université de La Réunion, France, 4UMR 288 ESPACE-DEV, Université de Guyane, France, 5University of Reading, UK

References• Schomburg,A.,Schraff,C.,&Potthast,R.(2015).AconceptfortheassimilationofsatellitecloudinformationinanEnsembleKalmanFilter:singleobservationexperiments.QuarterlyJournaloftheRoyalMeteorologicalSociety,141(688),893-908.• Mathiesen,P.,Collier,C.,&Kleissl,J.(2013).Ahigh-resolution,cloud-assimilatingnumericalweatherpredictionmodelforsolarirradianceforecasting.SolarEnergy,92,47-61.• PourBiazar,A.,T.McNider,R.,&Doty,K.,(2011).AssimilationofSatelliteObservedCloudsinWRF,InternationalWorkshoponAirQualityForecastingResearch,WashingtonDC,USA.• Schipper,J.andMathiesen,P.,(2015).AdvancedCloudSimulationsforImprovedDayaheadSolarPowerForecasts,5thSolarIntegrationWorkshop,Brussels,Belgium.• Stengel,M.,Lindskog,M.,&Gustafsson,N.(2013).TheimpactofcloudaffectedIRradiancesonforecastaccuracyofalimitedareaNWPmodel.QuarterlyJournaloftheRoyalMeteorologicalSociety,139(677),2081-2096.

The use of satellite data assimilation methods in regional NWP for solar irradiance forecasting

Incommonstate-of-the-artdataassimilationprocedures,primarilyappliedtoglobalNWPmodels,alltypesofavailableobservationsareassimilatedusingtheestablishedmethods(e.g.3D-Var,4D-Var,ENKF).Theultimategoalistofindtheoptimalinitialconditionsregardingthemodel’sstatevariables.Thetypicallyassimilatedsatelliteobservationsareradiancesandatmosphericmotionvectors(AMVs).AssimilatingsuchobservationsusingvariationaldataassimilationorEnsembleKalmanFiltermethodsaffectsthemodelstatevariablesinthefirstplaceandthereforeinfluencestherepresentationandevolutionofcloudsindirectly.

2. Advantages of satellite observations

4. Recent developments and their shortcomings

Comparedtoothersourcesofobservations,geostationarysatellitesprovidemanyadvantagesregardingcontinuoussolarirradianceforecasting:

•largespatialcoverage;highspatialandtemporalresolution

•lackofmeasureddataaroundislands

•satelliteradiancesarereliableradiometricsignaturesofcloudpresence,propertiesandevolution

•constantaccuracyinspaceandtime

•comprehensiveobservationsforlargepartsoftheworld

•future-oriented

Intherecentyears,differentapproachesaimedatderivingatmosphericanalysesforlimited-areamodelswithmostrealisticcloudfeatures,usinggeostationarysatelliteobservations.Thefocusismostlysetontheassimilationofcloud-topinformation(e.g.cloudtoptemperature),sinceopticalandthermalsensorsarenotabletocaptureinformationinsideclouds(Figure2).

Table1sumsuprecentadvancesandtheirmainlimitations.Althoughthelistedmethodsgenerallyprovideimprovedintraday(andevenday-ahead)cloudcoverforecastsinmid-latitudes,amajorlimitationforallmethodsisthatonlyclear-skyorcompletelycloudycasescanbeconsidered.ThisisbecausefractionalcloudscauseameasuredsignalmixingcoldcloudsandthewarmerEarthsurface.

Figure 1: Mashup of a MSG-2 RGB composite (source: EUMETSAT) and a GFS GHI forecast

(visualised using Panoply).

1. Motivation

3. Indirectly influencing cloud evolution

Photovoltaicenergyoffersanintermittentproduction.PVproductionforecastsarenecessarytopermitasafeintegrationintotheelectricitygrids.Thecorrectsimulationofcloudevolutionisessentialtoforecastaccuratelyirradiancereachingthegroundlevel.Besidessolarpowerforecasting,accurateshort-termforecastsofprecipitatingandnon-precipitatingcloudsareimportantforvariousmeteorologicalapplications.RegionalNWPisthestate-of-the-arttooltoprovidehighlyresolvedcloudforecastsforleadtimesbeyond6h.CloudsarecurrentlyratherpoorlyrepresentedinNWPmodels.Theprecisesimulationofcloudevolutionrequiresaccurateatmosphericanalyseswhichareobtainedbyexploitingatmosphericobservationsusingdataassimilationmethods.InthisworkwefocusontheassimilationofgeostationarysatellitedataintoregionalNWPmodelswiththegoalofimprovingshort-termcloudinessforecastsathighspatialresolutions.

Satellite-derived parameters

Influenced model variables

Assimilation method

Applied regional NWP model Satellite sensors Main limitation of

the procedure Reference

Cloudmask,cloudclassification,cloudtopheight

Relativehumidity,cloud-topheight,cloudfractions

Assimilationofpseudo-obser-vationsusingaLETKF

COSMO MSG-SEVIRIMethodrequiresradiosondehumiditysoundings

Schomburgetal.(2014)

Cloudmask,cloud-toptemperature

Watervapourprofile

Directmodificationofmodelfields(watervapourprofile)

WRF GOES-NImager

Modifiedmodelfieldshavetobesmoothedhorizontallyandverticallytoavoidnumericalinstability

Mathiesenetal.(2013)

Cloud-toptemperatureandpressurebyOCA(OptimalCloudAnalysisproduct)

Watervapourprofile

Directmodificationofmodelfields(watervapourprofile)

WRF MSG-SEVIRI

Multi-layeredcloudstructurescannotbereconstructedinthemodel

SchipperandMathiesen(2015)

Cloud-toptemperatureandcloudalbedo

Verticalvelocityanddivergence

1-DVarforhorizontalwind,nudgingofmodelwinds

WRF GOES-NImager

Methodtriggersdissipation/creationofmodelcloudsbutnottheirverticalextentandstructure

PourBiazaretal.(2011)

Progressbeingmaderegardingtheassimilationofcloud-affectedradiancesintolimited-areaNWPmodels(Stengeletal.,2013).However,theindirectassimilationofquantitieslikeradiancesorAMVshasnotbeenexploitedsofarinregionalNWPregardingsolarirradianceforecasting.Themainreasonforthisisanumberofproblemsthathavetobeovercomeforeachindividualmodeldomain:thechoiceofsatellitechannels,optimalthinning,biascorrection,observationandbackgrounderrorestimation.

Futureinvestigationsaddress:

•thecombinationofboththeclassical“indirect”assimilationmethodswithmethodsthatexplicitlyassimilatecloudphysicalproperties(e.g.cloudtopheight,cloudemissivity,…).

•theimpactofsuchmethodsondifferentleadtimes,sincetheinformationfromthedrivingglobalmodelisexpectedtopredominateatacertainpoint.

•theinfluenceofdomainsizeandgridspacinginconnectionwithsatellitedataassimilationinregionalNWP.

•theuseofdifferentlimited-areamodelsandsatellites,andrespectivelytheapplicationtodifferentgeographicalregions(e.g.mid-latitudesandtropics).

Theultimategoalisanassimilationstrategythatcanbeusedindependentlyofthemodelandsatelliteandwhichcan

5. Outline

Figure 2: Sketches of arbitrary satellite IR

weighting functions for different cases. (After lecture notes of Tony McNally (ECMWF))