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(a) Ascending mode (morning) (b) Descending mode (evening) Figure 200: Examples of the ascending, in the morning (top) and descending, in the afternoon (bottom) swath modes of QuikSCAT on the 4th of July 2006. Images are courtesy of RSS. (2002). The study concluded that biases were negligible for all stations and that the mission requirements were met. AstudyforwindresourceassessmentfromHasageretal.(2008)comparedtheRSSproduct with in situ measurements from an offshore met. mast with standard errors ∼1.3 m s −1 for the wind speed and ∼15 ◦ for the direction. Moreover, there was a very small difference in their estimation of the mean wind speed and the Weibull A and k parameters from ∼3.200 QuikSCAT observations and ∼260.000 in situ measurements. Karagalietal.(2012)usedthefullQuikSCATarchivefromRSSprocessedwiththeKu-2001 GMF, which is a descendant of the NSCAT-2 GMF, the improved version of the NSCAT-1 (GMF) (Wentz and Smith, 1999). σ0 values were mapped to a 0.25 ◦ Earth grid. This L3 product was compared with in situ derived ENW in the North Sea. Overall biases for the wind speed were found in the order of zero (±1.2 m s −1 ), and 2.3 ◦ (±15 ◦ ) for the wind direction. Figure 201 shows the scatterplots for the wind speed and wind direction bewteen In situ observationsfrom4meteorologicalmastsintheNorthSeaandQuikSCAT.insituobservations have been adjusted to ENW using the in situ wind speed and stability information. Wind speeds lower than 3 m s −1 have been excluded due to the higher QuikSCAT uncertainties at very low wind speeds. Note the higher scatter and the fewer observations for winds above 15 m s −1 . For wind energy related purposes the Weibull estimates are very relevant for site identification. Typically, 10-min in situ observations are used but at offshore locations, these are rarely available. Karagali et al. (2012) used twice daily QuikSCAT observations for the estimation 302 DTU Wind Energy-E-Report-0029(EN)
QuikSCAT 25 20 15 10 5 Horns Rev Fino 1 GG EAZ MB=−0.21 m s −1 σ=1.3 m s −1 r=0.93 r.m.s.=1.3 m s −1 N=2208 0 0 5 10 15 In Situ 10min 20 25 (a) Wind Speed QuikSCAT 350 300 250 200 150 100 50 Horns Rev Fino 1 GG EAZ MB=−3 ° σ=15.3 ° r=0.98 r.m.s.=15.6 ° N=2189 0 0 50 100 150 200 250 300 350 In Situ 10min (b) Wind Direction Figure 201: Scatterplots and statistics of in situ and QuikSCAT wind speed and direction. Measurements from 4 meteorological masts in the North Sea have been used. The RSS v3 QuikSCAT data are used. The work was performed within the NORSEWInD project frame. of the wind power and Weibull parameters and compared those estimates with the ones derived from offshore met. masts. For two offshore locations, a maximum 14% deviation in the wind power and 2.7% deviation in the mean wind speed was found between 1601 QuikSCAT observations and 137717 10-min measurements. 16.6 Applications of QuikSCAT Surface Winds Due to their frequent, long and global nature, QuikSCAT data are ideal for an evaluation of the wind resources. A description of the mean spatial wind characteristics and the climatology of the Mediterranean Sea is available from Zecchetto and De Biasio (2007), for the Nordic Seas from Kolstad (2008) and for the global ocean from Risien and Chelton (2006). 68 o N 64 o N 60 o N 56 o N 52 o N 6 o W 0 o 6 o E 12 o E 18 o E 24 o 48 E o N All Seasons (a) 7000 6000 5000 4000 3000 2000 1000 0 N. Rain−free Observations 68 o N 64 o N 60 o N 56 o N 52 o N 6 o W 0 o 6 o E 12 o E 18 o E 24 o 48 E o N 11/99−10/09 Figure 202: a) Number of rain-free QuikSCAT observations from 11/99–10/09.b)Mean wind speed using rain-free QuikSCAT observations for the period 1999-2009. Taken from Karagali et al. (2013). Within the context of offshore wind energy, Liu et al. (2008) estimated the wind power distribution over the ocean from eight years of QuikSCAT measurements. Capps and Zender (2010)estimatedtheglobaloceanwindpowerpotentialfromQuikSCATandliftedthesatellite DTU Wind Energy-E-Report-0029(EN) 303 (b) 10 9.5 9 8.5 8 7.5 7 Wind Speed (m/s)
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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.
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Wexler (1968), where the limitation
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13.2.1 Systematic turbulence errors
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where x is the center of the scanni
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The theoretical systematic errors a
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Page 275 and 276: References s point in space Sν(s)
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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
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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
Page 301: sea ice mask was applied and σ0 me
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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