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winds from 10 m to 100 m, relevant for wind turbine hub heights. Furevik et al. (2011) used eight years of QuikSCAT for wind resource mapping in the Mediterranean Sea, concluding that the satellite observations are valuable for the first phase of wind farm planning, e.g. during the identification of promising sites. Karagali et al. (2012) used the 10-year long QuikSCAT L3 product from RSS to study the seasonal 10 m wind characteristics in the North Sea and the Baltic Sea. They reported higher spatial variability of the mean wind speed in the North Sea compared to the Baltic Sea. Regarding the spatially coherent nature of the L3 product, they used the location of an offshore met. mast to calculate the spatial correlation between the grid cell containing the mast and all other grid cells. Correlation coefficients higher than 0.9 were reported for an area covering ∼4% of the North Sea. QuikSCAT winds have been used for data assimilation in atmospheric and oceanic models. Comparisons between QuikSCAT and Numerical Weather Prediction (NWP) model reanalysis from NCEP/NCAR performed by Kolstad (2008), showed a 0.9 correlation of daily and monthly mean wind speeds. Root mean square (RMS) differences were 1.1–1.81 m s −1 for the daily and 0.75–1.00 m s −1 for the monthly means. Ruti et al. (2008) reported a lower accuracy of modelled winds compared to buoy data than that of QuikSCAT compared to the same buoy data in the Mediterranean Sea. Karagali et al. (2013) compared the QuikSCAT L3 RSS product with modelled winds and found biases up to 1 m s −1 in the North Sea. Using the intra annual wind indices, they studied the temporal wind variability and concluded that QuikSCAT captures the wind variability and its amplitude as observed from 10-minute measurements at an offshore location in the North Sea. Modelled winds captured the overall seasonal trends but discrepancies were identified in the amplitude of the wind index and the wind variability. 16.7 Spatial Resolution of Scatterometer Winds The effective spatial resolution of scatterometer winds has been examined through their spectral properties. Vogelzang et al. (2011) used QuikSCAT and ASCAT products with different characteristics, along with ECMWF model forecasts and buoy measurements to evaluate the quality of the scatterometer winds, concluding that the ASCAT-25 km product contains more intermediate scale information than the QuikSCAT product processed at the Royal NetherlandsMeteorologicalInstitute(KNMI).Theadvantagesofthescatterometerspatialresolution compared to modelled wind fields have been studied using ERS (Chin et al., 1998; Halpern et al., 1999) and QuikSCAT (Zecchetto and De Biasio, 2003; Stoffelen et al., 2010). Recently, Karagali (2012) examined the spectral properties of the ENVISAT ASAR winds of varying resolutions and RSS L3 (v3) QuikSCAT winds. A significant advantage of the SAR winds processed with the same resolution as QuikSCAT (25 km) was observed indicating the ability of SAR winds to resolve more small scale variability. 16.8 Contemporary Scatterometers So far, emphasis on QuikSCAT has been given due to its unique mission lifetime which ended in 2009, after 10 years of operation. Currently, ASCAT is the longer available scatterometer in orbit. It is a C-band instrument onboard the platforms MetOp-A (operational since 2007) and MetOp-B (operational since April 2013). Due to its C-band nature, operating at 5.255 GHz with a longer wavelength, ASCAT is much less sensitive to rain compared to Ku band instruments like QuikSCAT. Because of this, C-band instruments are also less responsive to very small changes of the surface roughness. At an altitude of 837 km, ASCAT has a 500 km wide swath on each side of the platform ground track. An example from an ASCAT wind retrieval is shown in Figure 203. The data are obtained from the coastal product developed at KNMI (http://www.knmi.nl/ scatterometer/ascat_osi_co_prod/ascat_app.cgi).Themissingdata intheNorthSea are due to the nadir gap.Note the high resolution features, showing an area of high winds 304 <strong>DTU</strong> Wind Energy-E-Report-0029(EN)
Figure 203:Example of an ASCAT coastal product from KNMI over the European Seas, taken from 01/06/2007 at 20:00. The wind speed is in m s −1 . between Denmark and Norway. ISRO launchedOceansat-2(OSCAT) in 2009,carrying a Ku band (13.515GHz) scatterometer with a ground resolution of 50×50 km. Data can be viewed online, available from KNMI at 50 km resolution(http://www.knmi.nl/scatterometer/oscat_50_prod/oscat_app. cgi) and from NOAA–NESDIS/Center for Satellite Applications and Research (http:// manati.star.nesdis.noaa.gov/products/OSCAT.php), processed at 12, 25 and 50 km. See Figure 204 for an example of the available 12 km product. Figure 204: Example of the ascending pass of OSCAT, processed at 12 km, from the 15th of May 2013. The wind speed is in knots. Image taken from NOAA-NESDIS-STAR (2013) <strong>DTU</strong> Wind Energy-E-Report-0029(EN) 305
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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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Height (m) 160 140 120 100 80 60 40
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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
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Page 287 and 288: Satellites in sun-synchronous polar
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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
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Page 303: QuikSCAT 25 20 15 10 5 Horns Rev Fi
Page 307 and 308: References Bourassa M.A., Legler D.
Page 309: DTU Wind Energy Technical Universit
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