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Gard Hauge g ard.hauge@stormgeo.com Knut Lisæter Knut.lisaeter@stormgeo.com

WRF modelling at StormGeo. Gard Hauge g ard.hauge@stormgeo.com Knut Lisæter Knut.lisaeter@stormgeo.com. Who we are. History & facts. Founded in 1997, official start in 1998 Founded by meteorologist Siri Kalvig and TV 2

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Gard Hauge g ard.hauge@stormgeo.com Knut Lisæter Knut.lisaeter@stormgeo.com

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  1. WRF modelling at StormGeo Gard Hauge gard.hauge@stormgeo.com Knut Lisæter Knut.lisaeter@stormgeo.com

  2. Who weare History & facts • Founded in 1997, official start in 1998 • Founded by meteorologist Siri Kalvig and TV 2 • Worldwide operations in the Renewables, Offshore and Media industries • Headquarters in Bergen, Norway • Owned by: • IDEKAPITAL AS: 42.5 % • TV 2 Invest AS: 42.5 % • Orkan Invest AS 9.0 % • Management/Employees: 6.0 % • Board and CEO • Erik Langaker, Axel Dahl, Siri M Kalvig, Endre Solem • CEO Kent Zehetner • Turnover 2010 NOK 85 million (15 M USD) • Compoundedy-o-ygrowth in excessof 30 % since 2004 • Invested MNOK 100 in R&D over P&L sinceinception • The leading weather services provider in Scandinavia • and the North Sea region

  3. Industries and services Renewables Media Offshore Shipping Industries • PowerWeather • PreCast /InstantCast • Windsightplanner • Wind consultancy • Wind Forecasts • Hydro Power • Energy Consultancy • MetOceanforecasts • Offshore Consultancy • UK observationcourse • StormDriftOilspill • Offshore Statistics • Aviation • Seaware Routing • Seaware EnRoute • Seaware EnRoute Live • Seaware LNG Live • Seaware Fleet Manager • Seaware PVA • Internet Weather Portals • TV Weatherservices • Print • Telecom Services Offices Copen- hagen Baku Houston Bergen Stavanger Oslo Aberdeen Stockholm

  4. How do weuse WRF?

  5. ECMWF deterministicmodel – global 16km resolution Global Models 16-50 km EuropeanCentrefor MediumWeather Forecasting Used as boundaryconditions for regional and localscaleStormGeomodels. Localscalenumericalmodelling is strongly dependent on Initial Values! Initializationonpressure or hybrid levels from ECMWF

  6. Real time prediction range at StormGeo StormGeo data External Data Global Models 16-100 km Regional Models 1-9 km Observations MM5 SWAN 9 km 3 km 1 km ECMWF is thebasic fundament for all products at StormGeo

  7. WRF at StormGeo Planning ofwind parks LocalScale Regional Scale 1-2 km resolution 1-2 dayperspecitve 5-9 km resolution 2-5 dayperspecitve REAL TIME ~ 40 areas world wide per day Historicalmodel runs

  8. Regional scale Global model ECMWF • WRF v 2.2 • - in transitiontowards 3.2 now • Physics: • Thompsonmicrophysics • RRTM Long waveradiation • Dudhiashortwaveradiation • MYJ pbl • Eta surface layer • KF Cumulus • 39 vertical layers • Grid nudging +0-12 hr • Two cycles per day • NCEP postprocessor to grib WRF 9km resolution WRF 5km Holland Regional model WRF 5-9km Global observations WRF 9km Korea WRF 6km Caspian

  9. Forecasting in theCaspian - tailored forecasting system for BP Regional 6km Localscale 2km WRF twowaynested

  10. Real time challenges at regional scales • Tradeoffbetween ”optimum” configuration and theneed for computationalefficency • Tradeoffbetweenavailabilityof ECMWF fields and increased WRF forecasting skill. • ECMWF with 91 verticallayersaretoobig to be used for RT purposes • ECMWF has a consistenthighqualitywhichobjectively is difficult to outperform at a general level • … And doeshighresolutionalwaysmeanhigher forecasting skill? We have seensubstantialqualityimprovementswithhighresolutiononpredictedwindonthe 0-48 hour time range

  11. Highresolution forecasting systems

  12. Localscalepredictions Used to windenergy forecasting and virtualmeasurements Global model ECMWF Aim: To capturelocalscalewindvariations and transformthis to predictedenergyfor thewindenergycommunity Regional model WRF 1km Global observations 9 km 1 km 3 km S Large variancewithin a wind park

  13. Forecasting and planning ofwind farms • Wind ResourceMapping • Virtualmeasurements • Wind Energy Forecasting Net Production

  14. Completewind forecasting system Input Met Data & Observations (ECMWF and observastions) Post Processing Energy conversion Verification Data Mining WRF Forecast Data Storage Operational Output Power Predictions GUI Wind Farm Data (Wind powergeneration, availabiblityetc) Real Time Data stream

  15. Wind energy forecasting challenges 15. July 2008 15. July 2008 12:55 UTC Predictedwind speeds Predictedproduction Obs windsoneturbine Phaseerrors a majorchallenge!

  16. Creatingvirtualmeasurementswith WRF ECMWF 00 +12 WRF18 hourforecast Keeps +6 - +17 +12 ECMWF 12 WRF18 hourprediction Keeps +6 - +17 Large variance within park! Hindcast ERA = Climatological Perspective

  17. 1992 1993 1989 1991 1990 StormGeo wind farm planning tool Data handling Annual Energy Production WRF Hindcast Park layout Long term climate 1994 1995 1998 1996 1997 Net production Wake Loss 1999 2000 2002 2003 2001 Park layout 2004 2005 2006 2007 2008

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