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Bringing satellite winds to hub-height
Badger, Merete; Pena Diaz, Alfredo; Bredesen, Rolv Erlend; Berge, Erik; Hahmann, Andrea N.; Badger, Jake; Karagali, Ioanna; Hasager, Charlotte Bay; Mikkelsen, Torben Krogh Published in: Proceedings of EWEA 2012 - European Wind Energy Conference & Exhibition
Publication date: 2012 Document Version Publisher final version (usually the publisher pdf) Link to publication
Citation (APA): Badger, M., Pena Diaz, A., Bredesen, R. E., Berge, E., Hahmann, A. N., Badger, J., ... Mikkelsen, T. (2012). Bringing satellite winds to hub-height. In Proceedings of EWEA 2012 - European Wind Energy Conference & Exhibition. European Wind Energy Association (EWEA).
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Bringing satellite winds to hub-height Merete Badger, DTU Wind Energy, Denmark
Rolv Erlend Bredesen, Erik Berge Kjeller Vindteknikk, Norway Alfredo Peña, Andrea Hahmann, Jake Badger, Ioanna Karagali, Charlotte Hasager, Torben Mikkelsen DTU Wind Energy, Denmark
Ocean wind fields from satellites Scatterometer
Synthetic Aperture Radar (SAR)
Wind speed and direction
Wind speed
Spatial resolution
0.25°lat/lon
500 m
Spatial coverage
Global
Selected areas
Up to 70 km from coastline
None
Temporal resolution
Twice daily
Variable – less than one per day
Temporal coverage
Systematically since 1991
ScanSAR since 1995
Current sensors
ASCAT, OSCAT, HY2A, MetOp-B
Envisat ASAR, Radarsat-1/2
Rain sensitivity
High – rain flags provided
Low
Retrieved parameters
Coastal mask
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Level of detail for scatterometer and SAR winds
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QuikScat mean wind speed
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Envisat ASAR mean wind5/9/2012 speed
From radar backscatter to wind
A geophysical model function is applied to retrieve 10-meter ocean winds from radar backscatter.
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Application 1: Wind resource mapping from SAR wind fields
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Application 2: Wind farm wake analyses and wake model validation
Wind speed from ERS-2 SAR, February 25, 2003
From: Christiansen, M. B. & Hasager, C. B. 2005, Wake effects of large offshore wind farms identified 6 DTU Wind Energy, Technical University of Denmark 5/9/2012 from satellite SAR. Remote Sensing of Environment, 98, 251-268
Application 3: Characterizing mesoscale wind phenomena - and validation of mesoscale models
Strait of Gibraltar 7
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The Azores 5/9/2012
Challenges for the application of SAR wind fields in offshore wind energy
Challenge 1:
Dealing with a limited number of samples See: Badger et al. 2010, Wind class sampling of satellite SAR imagery for offshore wind resource mapping. J. Appl. Meteor. Climat., 49, 2474-2491.
Challenge 2:
Bringing satellite winds from the 10-m vertical level to the hub-height of modern wind turbines - the topic of this presentation
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Recent advances - which make the lifting of SAR wind fields possible • A validated description of vertical wind profiles at high levels is available (Peña, A. et al. 2008, Measurements and Modelling of the Wind Speed Profile in the Marine Atmospheric Boundary Layer, Bound.-Layer Meteor., 129, pp. 479-495)
• Wind retrieval algorithms can produce Equivalent Neutral Winds (ENW) (Hersbach, H. 2010, Comparison of C-band Scatterometer CMOD5.N Equivalent Neutral Winds with ECMWF, J. Atmos. Oceanic Technol., 27, pp. 721-736)
• Satellite SAR imagery is available in larger quantities (500-1,000 overlapping scenes over sites in the European Seas) • Mesoscale modeling has been performed for significant areas and time periods • Offshore measurements are available for validation (masts and LiDAR)
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Bringing satellite winds to hub-height (100 m)
• Friction velocity, u* from the satellite ENW: u 10 SAR
u 10 = ∗ SAR ln κ z0
,
u∗2SAR z0 = α c g
• Obukhov length, L - using WRF parameters T2 and HFX:
LWRF / SAR
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− u∗3SAR T2WRF = g κ HFX WRF
DTU Wind Energy, Technical University of Denmark
LWRF/SAR > 0: stable LWRF/SAR ≤ 0: unstable
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Bringing satellite winds to hub-height (100 m) • Stability function, ψ(z/L): LWRF/SAR > 0 :
ψ
(
z LWRF / SAR
LWRF/SAR ≤ 0 : ψ
z L WRF / SAR
)
= − 4.7
z LWRF / SAR
1/ 3 3 1+ x + x2 2x +1 π z − 3 arctan = ln + , x = 1 − 12 2 3 3 3 L WRF / SAR
• Wind speed at 100 m, u100 - using WRF parameters T2, HFX, PBLH: LWRF/SAR > 0 :
u 100 SAR
u∗ SAR 100 100 ln = −ψ 100 1 − κ z0 2 PBLH L WRF / SAR
LWRF/SAR ≤ 0 : u 100 SAR = u∗ SAR ln 100 −ψ 100 κ z0 LWRF / SAR 11
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100-m winds at Fino-1
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100-m Weibull fit at Fino-1
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Spatial wind variability over the North Sea
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Sampling effects over the North Sea
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Special situation: Low boundary-layer height
• The applied wind profile equations are valid within the atmospheric boundary-layer • Data are discarded when the boundary-layer height is <50 m • Up to 4% of the WRF samples are discarded over the North Sea over a year • None of the 80 SAR scenes, and concurrent WRF samples, are discarded
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Conclusions • Satellite SAR data lifted to 100 m under-estimate the wind speed at Fino-1 • Concurrent WRF simulations also under-estimate the 100-m wind speed at Fino-1 • SAR-WRF agreement is generally good over the North Sea with the largest differences near the coast of Germany • The number of SAR samples (80) is insufficient to describe the mean wind climate accurately • Work is in progress to improve the accuracy of lifted satellite wind fields • Satellite observations represent a valuable source of information for offshore wind energy applications (e.g. wind resource mapping, wind farm wake analyses) 17
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Acknowledgements Satellite data: The European Space Agency (ESA) Remote Sensing Systems (RSS) SAR wind field retrieval: Collecte Localisation Satellites (CLS) The Johns Hopkins University, Applied Physics Laboratory (JHU/APL) Fino-1 and Fino-2 mast data: Bundesministerium für Umwelt (BMU), Projektträger Juelich (PTJ), Deutsches Windenergie Institut (DEWI) Funding: EU-NORSEWInD (TREN-FP7EN-21908)
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