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COSMO SkyMED Constellation of small Satellites for the Mediterranean basin Observation (Italy) DLR German Aerospace Center E energy density Envisat Environmental Satellite (ESA) ERS European Research Satellite (ESA) ESA European Space Agency EUMETSAT European Organisation for the Exploitation of Meteorological Satellites FFT fast Fourier transformation GMF geophysical model function HH SAR operating at horizontal polarization in transmit and receive HV SAR operating at horizontal polarization in transmit and vertical in receive H-pol horizontally polarized radiation HJ-1C Huanjing (China) IEEE Institute of Electrical and Electronics Engineers ISRO Indian Space Research Organisation JAXA Japanese Space Agency JERS Japanese Earth Resource Satellite JHU/APL Johns Hopkins University Applied Physics Laboratory k Weibull shape parameter KSAT Kongsberg Satellite Service MDA MacDonald, Dettwiler and Associates Ltd METOP Meteorological polar orbiting satellites NASA National Aeronautics and Space Administration (USA) NCAR National Center for Atmospheric Research (USA) NCEP National Centers for Environmental Prediction (USA) NEST Next ESA SAR toolbox NRCS normalised radar cross section NRT Near real time NOAA National Oceanic and Atmospheric Administration NOGAPS navy operational global atmospheric prediction system OCEANSAT Ocean Satellite (India) OSCAT Ocean Scatterometer PALSAR Phased Array type L-band Synthetic Aperture Radar (Japan) PR polarization ratio QuikSCAT Quick Scatterometer (USA) RADARSAT Canadian Radar Satellite Risø DTU Risø National Laboratory for Sustainable Energy (Technical University of Denmark) SAR synthetic aperture radar SEASAT Sea Satellite (USA) S-WAsP satellite WAsP TerraSAR-X Terra Synthetic Aperture Radar X-band (Germany) TanDEM-X TerraSAR-X add-on for Digital Elevation Measurement (Germany) U wind speed at 10 m height VH SAR operating at vertical polarization in transmit and horizontal in receive V-pol vertically polarized radiation VV SAR operating at vertical polarization in transmit and receive WAsP Wind Atlas Analysis and Application Program WSM wind swath mode γ function of wind speed and local incident angle in a GMF Γ Gamma function θ radar’s local incident angle λ wavelength ρ air density σ 0 normalised radar cross section φ wind direction with respect to the radar look direction References Badger, M. (2009), Satellite SAR wind resource mapping in China (SAR-China), Risø-R-1706(EN), 17 Badger M., Badger J., Hasager C., Nielsen M., (2010b), Sampling of SAR imagery for wind resource assessment, In: Proceedings (on CD-ROM), SEASAR 2010, Frascati (IT), 25-29 Jan 2010, (European Space Agency, Paris, 2010) (ESA-SP-679), 6 292 DTU Wind Energy-E-Report-0029(EN)
Badger M., Hasager C. B., Thompson D., Monaldo F. (2008), Ocean winds from synthetic aperture radar, Ocean Remote Sensing: Recent Techniques and Applications, 31-54 Badger M., Badger J., Nielsen M., Hasager C.B., Peña P. (2010a), Wind class sampling of satellite SAR imagery for offshore wind resource mapping, J. of Applied Meteorology and Climatology Beal R. C., Young G. S., Monaldo F., Thompson D. R., Winstead N. S., Schott C. A. (2005), High Resolution Wind Monitoring with Wide Swath SAR: A User’s Guide, U.S. Department of Commerce, Washington, DC, USA, 1-155 Barthelmie R. J., Badger J., Pryor S. C., Hasager C. B., Christiansen M. B., Jørgensen B. H. (2007), Offshore coastal wind speed gradients: Issues for the design and development of large offshore windfarms, Wind Engineering: The International Journal of Wind Power, 31(6), 369-382 Barthelmie R. J. & Pryor S. C. (2003), Can satellite sampling of offshore wind speeds realistically represent wind speed distributions, Journal of Applied Meteorology, 42, 83-94 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 Christiansen M. B. & Hasager C. B. (2006), Using airborne and satellite SAR for wake mapping offshore, Wind Energy, 9, 437-455 Christiansen M. B., Koch W., Horstmann J., Hasager C. B. (2006), Wind resource assessment from C-band SAR, Remote Sensing of Environment, 105, 68-81 Dagestad K. F., Horstmann J., Mouche A., Perrie W., Shen H., Zhang B., Li X., Monaldo F., Pichel W., Lehner S., Badger M., Hasager C. B., Furevik B., Foster R.C., Falchetti S., Caruso M.J., Vachon P. (2013), Wind retrieval from Synthetic Aperture Radar – an overview, Proceedings of the SEASAR 2012, White paper, ESA (in press) Du Y., Vachon P. W., Wolfe J. (2002), Wind direction estimation from SAR images of the ocean using wavelet analysis, Canadian Journal of Remote Sensing, 28, 498-509 Elfouhaily T., Chapron B., Katsaros K., Vandemark D. (1997), A unified directional spectrum for long and short wind-driven waves, J. Geophys. Res., 102(C7), 15, 781–796 Fichaux N. & Ranchin T. (2002), Combined extraction of high spatial resolution wind speed and direction from SAR images: a new approach using wavelet transform, Canadian Journal of Remote Sensing, 28, 510-516 Frank H. P., Rathmann O., Mortensen N. G., Landberg L., (2001), The numerical wind atlas - the KAMM/WAsP method, Risø-R-1252(EN), RisøNational Laboratory, Roskilde, Denmark, 60 Furevik B., Johannessen O., Sandvik A. D. (2002), SAR-retrieved wind in polar regions - comparison with in-situ data and atmospheric model output, IEEE Transactions on Geoscience and Remote Sensing, 40, 1720-1732 Gerling T. W. (1986), Structure of the surface wind field from the SEASAT SAR, Journal of Geophysical Research, 91, 2308-2320 Hasager C. B., Barthelmie R. J., Christiansen M. B., Nielsen M., Pryor S. C. (2006), Quantifying offshore wind resources from satellite wind maps: study area the North Sea, Wind Energy, 9, 63-74 Hasager C. B., Dellwik E., Nielsen M., Furevik B. (2004), Validation of ERS-2 SAR offshore wind-speed maps in the North Sea, International Journal of Remote Sensing, 25, 3817-3841 Hasager C. B., Nielsen M., Astrup P., Barthelmie R. J., Dellwik E., Jensen N. O., Jørgensen B. H., Pryor S. C., Rathmann O., Furevik B. (2005), Offshore wind resource estimation from satellite SAR wind field maps, Wind Energy, 8, 403-419 Hasager C. B., Badger M., Peña A., Larsé n X. G. (2011a), SAR-based wind resource statistics in the Baltic Sea, Remote Sens., 3(1), 117-144 Hasager C. B., Peña A., Christiansen M. B., Astrup P., Nielsen N. M., Monaldo F., Thompson D., Nielsen P., (2008), Remote sensing observation used in offshore wind energy, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1(1), 67-79 Hasager C. B., Badger M., Mouche A., Stoffelen A., Driesenaar T., Karagali I., Bingöl F., Peña A., Astrup P., Nielsen M., Hahmann A. N., Costa P., Berge E., Bredesen R. E. (2012), NORSEWInD satellite wind climatology, DTU Wind Energy, (DTU Wind Energy E; No. 0007) DTU Wind Energy-E-Report-0029(EN) 293
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Remote Sensing for Wind Energy DTU
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Author: Alfredo Peña, Charlotte B.
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4 Introduction to continuous-wave D
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8 Nacelle-based lidar systems 157 8
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12 Complex terrain and lidars 231 1
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1 Remote sensing of wind Torben Mik
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Figure 2: Calibration, laboratory w
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Figure 3: Example of scatter plots
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1.2.3 Summary of sodars Most of tod
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1.3.3 Wind lidars Measuring wind wi
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Figure 6: CW wind lidars (ZephIRs)
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Further developments Furthermore, n
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2 The atmospheric boundary layer S
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Figure 9: Large spatial scale varia
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Du3 Dt Du1 Dt Du2 Dt The three mome
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Figure 13: Consensus relations betw
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ψ z L ∼ − 5 L . For unstable c
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Figure 15: Behavior of the turbulen
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Figure 17: Newly developed models t
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The value of q0 at the surface is d
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the spray is the source of icing on
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u∗2 u∗1 u1(h) = u∗1 k ln h
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Figure 26:Land-seabreeze system,whe
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Figure28:Three dimensionalpicture o
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sufficient information. Finally, we
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Mann, J. (1998) Wind field simulati
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(2010) and comparison under differe
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To get the velocity field from the
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τ(k) [Arbitrary units] 10 3 10 2 1
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(Koopmans, 1974; Bendat and Piersol
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anemometer was installed at each en
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and fSw(f) u 2 ∗ = 1.05n 1+5.3n 5
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and n = 0.468. This spectrum implie
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to be calculated. We do that on a m
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Notation A Charnock constant neutra
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Maxey M. R. (1982) Distortion of tu
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4.2 Basic principles of lidar opera
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4.2.5 Wind profiling in conical sca
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4.3.1 Behaviour of scattering parti
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the beam radius at the output lens.
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from which the value of VLOS is der
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the atmosphere. The SNR 4 for a win
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individual line-of-sight wind speed
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A general approach to mitigating th
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from ±VH sinδ (if the tilt is tow
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as a down draught (of the same abso
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Table 7: Combined results from 28 Z
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in Eastern Jutland between January
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Figure 56: Normalized power curves
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the concept. Developments include i
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References x horizontal position in
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Wagner R., Mikkelsen T., and Courtn
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5.2 End-to-end description of pulse
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Scanner Coherent lidar measure the
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Figure 62: Radial wind velocity ret
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transform in order to use data obta
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This wavelength is also the most fa
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Eq.(132)isadaptedforcollimatedsyste
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5.3.6 Existing systems and actual p
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(Gottshall et al., 2010; Albers et
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References Albers A., Janssen A. W.
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derived from fluctuations of the wi
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Acoustic received echo (ARE) method
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Figure 71: Sample time-height cross
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A gradient minimum is characterized
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Figure73:Bragg-relatedacoustic(belo
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stability (inversion strength) can
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Figure 76: Combined soundingwith a
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Figure 79: Favorite regions (shaded
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Direct detection of MLH from acoust
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Engelbart D.A.M.and Bange J. (2002)
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7 What can remote sensing contribut
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uyms 9.0 8.5 8.0 7.5 7.0 120 140 16
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Bottom of rotor Φ rotation r w u
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Height Height Hub 1.6 1.4 1.2 1.0 0
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PP rated PP rated 1.0 0.8 0.6 0.4 0
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KEprofileKEhub 1.2 1.1 1.0 0.9 0.8
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PP rated WS Lidarms 1.0 0.8 0.6 0.4
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8 Nacelle-based lidar systems Andre
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• Flexibletrajectories.Dependingo
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Figure 104: Sketch of simultaneous
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The normal wind direction vector nw
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Figure 109: Test site at DTU Wind E
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Figure 112: Power curve met mast an
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Notation C number of sent photons C
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9 Lidars and wind turbine control -
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for three unknowns, it is impossibl
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model of the blade pitch actuator,
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|GRL| [-] 1 0.8 0.6 0.4 0.2 10 k [r
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PSD(Ωg) [(rpm) 2 /Hz] PSD(Ωg) [
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0.04 0.03 ˆk [ rad m ] 0.02 0.63 0
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[%] 10 0 −10 −20 −30 MyT Moop
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PSD(θ1) [rad 2 /Hz] PSD(Moop1) [Nm
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Pel/Pel,max [-] 1 0.98 0.96 0.94 0.
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fL weighting function GRL transfer
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E. Hau, Windkraftanlagen, 4th ed. S
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¨¦¦§©¡§ ¥§¨¦¦§£ ¡¥
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vertical (m) 150 100 50 R d 0
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With feedback only, on the other ha
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Figure 136: Estimated preview requi
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Normalized C r = C r P Q 2 W (r) b
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Normalized C r = C r P Q 2 W (r) b
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Coherence 1 0.8 0.6 0.4 0.2 0 10
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Coherence 1 0.8 0.6 0.4 0.2 0 10
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Magnitude Squared 10 8 10 7 10 6 10
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Figure 148: During simulation, FAST
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Figure 149: Collective flap respons
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Magnitude (abs) blade pitch gen spe
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• Measurement coherence, which ca
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Jonkman, B. (2009) TurbSim user’s
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11 Lidars and wind profiles Alfredo
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z [m] 160 100 80 60 40 20 10 15 20
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z [−] zo 1 κ ln 40 38 36 34 32 3
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z [m] z [m] 1000 900 800 700 600 50
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the growth of the length scale, agr
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12 Complex terrain and lidars Ferha
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Figure 158: The ZephIR models which
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Uconst wΑx l h Φ h.tanΦ Figure 1
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Figure 162: Lavrio: The scatter plo
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Figure 163: Panahaiko: The scatter
Page 241 and 242: References Albers A. and Janssen A.
Page 243 and 244: Wexler (1968), where the limitation
Page 245 and 246: 13.2.1 Systematic turbulence errors
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Page 249 and 250: The theoretical systematic errors a
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Page 253 and 254: Height (m) Height (m) 160 140 120 1
Page 255 and 256: RMSPE (%) 70 60 50 40 30 20 10 0 vu
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Page 259 and 260: Lindelöw P. (2007) Fibre Based Coh
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Page 263 and 264: Figure 173: Rayleigh-Jeans’ appro
Page 265 and 266: Figure 176: Brightness temperature
Page 267 and 268: with rain are shown in Fig. 179. A
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Page 271 and 272: Figure 183: A recent maintenance in
Page 273 and 274: Figure 185: Temperature profiles in
Page 275 and 276: References s point in space Sν(s)
Page 277 and 278: Also the Doppler Centroid anomaly c
Page 279 and 280: Christiansen et al., 2006). The res
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Page 283 and 284: Figure 188: Envisat ASAR wind field
Page 285 and 286: Figure 190: Envisat ASAR wind field
Page 287 and 288: Satellites in sun-synchronous polar
Page 289 and 290: 500 overlapping scenes for wind res
Page 291: (with the fewest samples). The unce
Page 295 and 296: Ren Y. Z., Lehner S., Brusch S., Li
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Page 299 and 300: The modified logarithmic wind profi
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Page 303 and 304: QuikSCAT 25 20 15 10 5 Horns Rev Fi
Page 305 and 306: Figure 203:Example of an ASCAT coas
Page 307 and 308: References Bourassa M.A., Legler D.
Page 309: DTU Wind Energy Technical Universit
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