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Hasager C. B., Bingöl F., Badger M., Karagali I., Sreevalsan E. (2011b), Offshore Wind Potential in South India from Synthetic Aperture Radar, Risoe-R; No. 1780(EN), Danmarks Tekniske Universitet, RisøNationallaboratoriet for Bæredygtig Energi, Roskilde, Danmark He Y. J., Perrie W., Zou Q. P., Vachon P. W. (2005), A new wind vector algorithm for C-band SAR, IEEE Transactions on Geoscience and Remote Sensing, 43, 1453-1458 Hersbach H., Stoffelen A., de Haan S. (2007), An improved C-band scatterometer ocean geophysical model function: CMOD5, J. Geophys. Res., 112, C03006 Hersbach H (2010), Comparison of C-band scatterometer CMOD5.N equivalent neutral winds with ECMWF, J. Atm. Oceanic Tech., 27, 721–736 Horstmann J., Koch W., Lehner S., Tonboe R. (2000), Wind retrieval over the ocean using synthetic aperture radar with C-band HH polarization, IEEE Transactions on Geoscience and Remote Sensing, 38, 2122–2131 Horstmann J., Schiller H., Schulz-Stellenfleth J., Lehner S. (2003), Global wind speed retrieval from SAR, IEEE Transactions on Geoscience and Remote Sensing, 41, 2277–2286 Isoguchi O. & Shimada M. (2009), An L-band ocean geophysical model function derived from PALSAR, IEEE Trans. Geosci. Remote Sens., 43, 1925–1936 Karagali I. (2012), Offshore wind energy: wind and sea surface temperature from satellite observations, PhD dissertation, DTU Wind Energy, Roskilde, Denmark Karagali I. (2013), Scatterometer for wind energy, PhD dissertation, DTU Wind Energy, Roskilde, Denmark Koch W. (2004), Directional analysis of SAR images aiming at wind direction, IEEE Transactions on Geoscience and Remote Sensing, 42, 702–710 Kudryavtsev V., Hauser D., Caudal G., Chapron B. (2003), A semiempirical model of the normalized radar cross-section of the sea surface - 1. Background model, Journal of Geophysical Research - Oceans, 108 Lehner S., Horstmann J., Koch W., Rosenthal W. (1998), Mesoscale wind measurements using recalibrated ERS SAR images, Journal of Geophysical Research - Oceans, 103, 7847–7856 Li X. & Lehner S. (2012), Sea surface wind field retrieval from TerraSAR-X and its applications to coastal areas, Presented at IGARSS 2012, Munich, Germany, 2059–2062 Monaldo F. M. & Beal R. (2004), Wind speed and direction, In C. R. Jackson & J. R. Apel (Eds.), Synthetic Aperture Radar Marine User’s Manual, U.S. Department of Commerce, National Oceanic and Atmospheric Administration, Washingon, DC, 305–320 Monaldo F. M., Thompson D. R., Beal R. C., Pichel W. G., Clemente-Coló n P. (2001), Comparison of SAR-derived wind speed with model predictions and ocean buoy measurements, IEEE Transactions on Geoscience and Remote Sensing, 39, 2587–2600 Monaldo F. M., Thompson D. R., Pichel W. G., Clemente-Colon P. (2004), A systematic comparison of QuikSCAT and SAR ocean surface wind speeds, IEEE Transactions on Geoscience and Remote Sensing, 42, 283–291 Mortensen N. G., Heathfield D. N., Myllerup L., Landberg L., Rathmann O. (2005), Wind Atlas Analysis and Application Program: WAsP 8 Help Facility, RisøNational Laboratory, Roskilde, Denmark Mouche A. A., Hauser D., Daloze J. F., Guerin C. (2005), Dual-polarization measurements at C-band over the ocean: Results from airborne radar observations and comparison with ENVISAT ASAR data, IEEE Transactions on Geoscience and Remote Sensing, 43, 753–769 Mouche A., Collard F., Chapron B., Johannessen J.A. (2013), Doppler Centroid, Normalized Radar Cross Sections and Sea Surface Wind, Proceedings of SeaSAR 2012 Tromsø, Norway, ESA (in press) Nielsen M., Astrup P., Hasager C. B., Barthelmie R. J., Pryor S. C. (2004), Satellite information for wind energy applications, Risø-R-1479(EN), RisøNational Laboratory, Roskilde, Denmark, 1–57 Peña A., Mikkelsen T., Grynin, S-E., Hasager C. B., Hahmann A. N., Badger M., Karagali I., Courtney M. (2012), Offshore vertical wind shear: Final report on NORSEWInD’s work task 3.1, DTU Wind Energy, Roskilde, Denmark, DTU Wind Energy E; No. 0005, Pryor S. C., Nielsen M., Barthelmie R. J., Mann J. (2004), Can satellite sampling of offshore wind speeds realistically represent wind speed distributions? Part II Quantifying uncertainties associated with sampling strategy and distribution fitting methods, Journal of Applied Meteorology, 43, 739–750 Quilfen Y., Chapron B., Elfouhaily T., Katsaros K., Tournadre J. (1998), Observation of tropical cyclones by high-resolution scatterometry, Journal of Geophysical Research, 103, 7767–7786 294 DTU Wind Energy-E-Report-0029(EN)
Ren Y. Z., Lehner S., Brusch S., Li X. M., He M. X., (2012), An Algorithm for the retrieval of sea surface wind fields using X-band TerraSAR-X data, International Journal of Remote Sensing, 33 Romeiser R. & Alpers W. (1997a), An improved composite surface model for the radar backscattering cross section of the ocean surface. 2: Model response to surface roughness variations and the radar imaging of underwater bottom topography, Journal of Geophysical Research-Oceans, 102, 25251–25267 Romeiser R., Alpers W., Wismann, V. (1997b), An improved composite surface model for the radar backscattering cross section of the ocean surface.1: Theory of the model and optimization/validation by scatterometer data, Journal of Geophysical Research-Oceans, 102, 25237-25250 Shimada T., Kawamura H., Shimada M. (2004), Evaluation of JERS-1 SAR images from a coastal wind retrieval point of view, IEEE Trans. Geosci. Remote Sens, 42, 491–500 Stoffelen A. & Anderson D. L. T. (1997), Scatterometer data interpretation: Estimation and validation of the transfer function CMOD4, Journal of Geophysical Research, 102, 5767-5780 Takeyama Y., Ohsawa T., Kozai K., Hasager C.B., Badger M (2013), Comparison of Geophysical Model Functions for SAR Wind Speed Retrieval in Japanese Coastal Waters, Remote Sens., 5(4), 1956–1973 Thompson D. R., Horstmann J., Mouche A., Winstead N. S., Sterner R., Monaldo F. M. (2012), Comparison of high-resolution wind fields extracted from TerraSAR-X SAR imagery with predictions from the WRF mesoscale model, J. Geophys. Res., 117, C02035 Thompson D., Elfouhaily T., Chapron B. (1998), Polarization ratio for microwave backscattering from the ocean surface at low to moderate incidence angles, Proceedings International Geoscience and Remote Sensing Symposium, Seattle, WA, 1671–1676 Troen I. & Petersen E. L. (1989), European Wind Atlas, RisøNational Laboratory, Roskilde, Denmark, 1–656 Vachon P. W. & Dobson E. W. (2000), Wind retrieval from RADARSAT SAR images: selection of a suitable C-band HH polarization wind retrieval model, Canadian Journal of Remote Sensing, 26, 306–313 Valenzuela G.R.(1978), Theories forthe interaction ofelectromagnetic andoceanwaves -Areview,Boundary- Layer Meteorology, 13, 61–85 Zhang B., Perrie W., He Y. (2011), Wind speed retrieval from RADARSAT-2 quad-polarization images using a new polarization ratio model, J. Geophys. Res., 116, C08008 Zhang B. & Perrie W. (2012), Cross-polarized synthetic aperture radar: A new potential measurement technique for hurricanes, Bull. Amer. Meteor. Soc., 531–541 DTU Wind Energy-E-Report-0029(EN) 295
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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
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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
Page 247 and 248: where x is the center of the scanni
Page 249 and 250: The theoretical systematic errors a
Page 251 and 252: Height (m) 160 140 120 100 80 60 40
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
Page 257 and 258: involves interaction of all compone
Page 259 and 260: Lindelöw P. (2007) Fibre Based Coh
Page 261 and 262: plications, including meteorologica
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
Page 269 and 270: This method gives a good accuracy (
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
Page 281 and 282: educe speckle noise, a random noise
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 and 292: (with the fewest samples). The unce
Page 293: Badger M., Hasager C. B., Thompson
Page 297 and 298: tracking, climate studies, air-sea
Page 299 and 300: The modified logarithmic wind profi
Page 301 and 302: sea ice mask was applied and σ0 me
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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