Bringing satellite winds to hub-height

1
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

2
Ocean wind fields from satellites
Scatterometer Synthetic Aperture Radar (SAR)
Retrieved parameters Wind speed and direction Wind speed
Spatial resolution 0.25lat/lon 500 m
Spatial coverage Global Selected areas
Coastal mask 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
3
Level of detail for scatterometer and SAR winds
QuikScat mean wind speed
Envisat ASAR mean wind speed
4
From radar backscatter to wind
A geophysical model function is applied to
retrieve 10-meter ocean winds from radar
backscatter.
5
Application 1Wind resource mapping from SAR
wind fields
6
Application 2Wind 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 from satellite SAR. Remote Sensing of
Environment, 98, 251-268
7
Application 3Characterizing mesoscale wind
phenomena - and validation of mesoscale models
Strait of Gibraltar
The Azores
8
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

9
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)

10
Bringing satellite winds to hub-height (100 m)

• Friction velocity, u from the satellite ENW
• ,
• Obukhov length, L
• - using WRF parameters T2 and HFX
• LWRF/SAR gt 0 stable
• LWRF/SAR 0 unstable

11
Bringing satellite winds to hub-height (100 m)

• Stability function, ?(z/L)
• LWRF/SAR gt 0
• LWRF/SAR 0 ,
• Wind speed at 100 m, u100
• - using WRF parameters T2, HFX, PBLH
• LWRF/SAR gt 0
• LWRF/SAR 0

12
100-m winds at Fino-1
13
100-m Weibull fit at Fino-1
14
Spatial wind variability over the North Sea
15
Sampling effects over the North Sea
16
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 lt50 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

17
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)

18
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)

Add Presentation Title in Footer via Insert
Header Footer