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Gard Hauge Knut Lisæter WRF modelling at StormGeo.

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Presentation on theme: "Gard Hauge Knut Lisæter WRF modelling at StormGeo."— Presentation transcript:

1 Gard Hauge Knut Lisæter WRF modelling at StormGeo

2 Who we are 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) - Compounded y-o-y growth in excess of 30 % since 2004 - Invested MNOK 100 in R&D over P&L since inception - The leading weather services provider in Scandinavia and the North Sea region

3 Industries and services Renewables PowerWeather PreCast /InstantCast Windsight planner Wind consultancy Wind Forecasts Hydro Power Energy Consultancy Offshore Media Industries Offices BergenStavangerOsloAberdeen Copen- hagen Stockholm Services Shipping MetOcean forecasts Offshore Consultancy UK observation course StormDrift Oilspill Offshore Statistics Aviation Internet Weather Portals TV Weather services Print Telecom Seaware Routing Seaware EnRoute Seaware EnRoute Live Seaware LNG Live Seaware Fleet Manager Seaware PVA Houston Baku

4 How do we use WRF?

5 ECMWF deterministic model – global 16km resolution Global Models 16-50 km E uropean C entre for M edium W eather F orecasting Used as boundary conditions for regional and local scale StormGeo models. Local scale numerical modelling is strongly dependent on Initial Values! Initialization on pressure or hybrid levels from ECMWF

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

7 WRF at StormGeo Regional Scale Local Scale Planning of wind parks 5-9 km resolution 2-5 day perspecitve 1-2 km resolution 1-2 day perspecitve REAL TIME ~ 40 areas world wide per day Historical model runs

8 Regional scale Global observations Global model ECMWF Regional model WRF 5-9km WRF v 2.2 - in transition towards 3.2 now Physics: -Thompson microphysics -RRTM Long wave radiation -Dudhia shortwave radiation -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 6km Caspian WRF 9km Korea WRF 5km Holland

9 Forecasting in the Caspian - tailored forecasting system for BP Regional 6km Local scale 2km WRF two way nested

10 Real time challenges at regional scales Tradeoff between ”optimum” configuration and the need for computational efficency Tradeoff between availability of ECMWF fields and increased WRF forecasting skill. ECMWF with 91 vertical layers are too big to be used for RT purposes ECMWF has a consistent high quality which objectively is difficult to outperform at a general level … And does high resolution always mean higher forecasting skill? We have seen substantial quality improvements with high resolution on predicted wind on the 0-48 hour time range

11 High resolution forecasting systems

12 Aim: To capture local scale wind variations and transform this to predicted energy for the wind energy community 9 km 3 km 1 km Local scale predictions Used to wind energy forecasting and virtual measurements Global observations Global model ECMWF Regional model WRF 1km Large variance within a wind park S

13 Forecasting and planning of wind farms Wind Energy Forecasting Wind Resource Mapping Virtual measurements P99P90P75P50 233265279298 Net Production

14 Complete wind forecasting system WRF Forecast Input Met Data & Observations (ECMWF and observastions) Wind Farm Data (Wind power generation, availabiblity etc) Post Processing Energy conversion Verification Data Mining Post Processing Energy conversion Verification Data Mining Operational Output Power Predictions GUI Operational Output Power Predictions GUI Data Storage Real Time Data stream

15 Predicted production Wind energy forecasting challenges Predicted wind speeds Obs winds one turbine 15. July 2008 Phase errors a major challenge! 15. July 2008 12:55 UTC

16 Creating virtual measurements with WRF ECMWF 00 ECMWF 12 WRF 18 hour forecast WRF18 hour prediction Keeps +6 - +17 +12 Hindcast ERA = Climatological Perspective Large variance within park!

17 1991 1992 1993 1994 1995 19961997 1998 1999 2000 2001 2002 2004 2003 20052006 2007 2008 1989 1990 Long term climate Park layout StormGeo wind farm planning tool Data handling WRF Hindcast Annual Energy Production Wake Loss P99P90P75P50 240265279298 Net production Park layout

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