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λa,B wavelength of sound wave fulfilling the Bragg condition λe wavelength of electro-magnetic wave σw standard deviation of the vertical wind component References Angevine W.,White A.B.,andAveryS.K.(1994) Boundary layerdepth andentrainment zone characterization with a boundary layer profiler. Bound.-Layer Meteorol. 68: 375–385 Asimakopoulos D. N., Helmis C. G., and Michopoulos J (2004) Evaluation of SODAR methods for the determination of the atmospheric boundary layer mixing height. Meteorol. Atmos. Phys. 85: 85–92 Beyrich F. (1995) Mixing height estimation in the convective boundary layer using sodar data. Bound.-Layer Meteorol. 74: 1–18 Beyrich F. (1997) Mixing height estimation from sodar data - a critical discussion. Atmosph. Environ. 31: 3941–3954 Beyrich F. and Görsdorf U. (1995) Composing the diurnal cycle of mixing height from simultaneous SODAR and Wind profiler measurements. Bound.-Layer Meteorol. 76: 387–394 Boers R., Spinhirne J. D., and Hart W. D. (1988) Lidar Observations of the Fine-Scale Variability of Marine Stratocumulus Clouds. J. Appl. Meteorol. 27: 797–810 Bösenberg J. and Linné H. (2002) Laser remote sensing of the planetary boundary layer. Meteorol. Z. 11: 233–240 Brooks I.M.(2003) Finding boundary layertop: application ofawaveletcovariance transform tolidar backscatter profiles. J. Atmos. Oceanic Technol. 20: 1092–1105 Cohn S. A. and Angevine W. M. (2000) Boundary Layer Height and Entrainment Zone Thickness Measured by Lidars and Wind-Profiling Radars. J. Appl. Meteorol. 39: 1233–1247 Contini D., Cava D., Martano P., Donateo A., and Grasso F. M. (2009) Comparison of indirect methods for the estimation of Boundary Layer height over flat-terrain in a coastal site. Meteorol. Z. 18: 309–320 Davis K. J., Gamage N., Hagelberg C. R., Kiemle C., Lenschow D. H., and Sullivan P. P. (2000) An objective method for deriving atmospheric structure from airborne lidar observations. J. Atmos. Oceanic Technol. 17: 1455–1468 de Haij M., Wauben W., and Klein Baltink H. (2006) Determination of mixing layer height from ceilometer backscatter profiles. In: Slusser J. R., Schäfer K., Comerón A. (eds) Remote Sensing of Clouds and the Atmosphere XI. Proc. SPIE Vol 6362: paper 63620R. Devara P. C. S., Ernest Ray P., Murthy B. S., Pandithurai G., Sharma S., and Vernekar K. G. (1995) Intercomparison of Nocturnal Lower-Atmospheric Structure Observed with LIDAR and SODAR Techniques at Pune, India. J. Appl. Meteorol. 34: 1375–1383 Emeis S. (2010) Measurement methods in atmospheric sciences. In situ and remote. Vol. 1 of Quantifying the Environment. Borntraeger, Stuttgart, XIV+257 pp., 103 Figs, 28 Tab. ISBN 978-3-443-01066-9. Emeis S. (2011) Surface-Based Remote Sensing of the Atmospheric Boundary Layer. Series: Atmospheric and Oceanographic Sciences Library, Vol. 40. Springer Heidelberg etc., X+174 pp. 114 illus., 57 in color., H/C. ISBN: 978-90-481-9339-4, e-ISBN 978-90-481-9340-0, ISSN 1383-8601, DOI: 10.1007/978-90-481-9340-0 Emeis S. (2012) Wind Energy Meteorology - Atmospheric Physics for Wind Power Generation. Springer Heidelberg etc., XIV-196 pp. ISBN 978-3-642-30522-1, e-book: ISBN 978-3-642-30523-8 Emeis S. and Schäfer K. (2006) Remote sensing methods to investigate boundary-layer structures relevant to air pollution in cities. Bound.-Layer Meteorol. 121: 377–385 Emeis S. and Türk M. (2004) Frequency distributions of the mixing height over an urban area from SODAR data. Meteorol. Z. 13: 361–367 Emeis S., Jahn C., Münkel C., Münsterer C., and Schäfer K (2007) Multiple atmospheric layering and mixinglayer height in the Inn valley observed by remote sensing. Meteorol. Z. 16: 415–424 Emeis S., Schäfer K., and Münkel C (2008) Surface-based remote sensing of the mixing-layer height - a review. Meteorol. Z. 17: 621–630 Emeis S., Schäfer K., and Münkel C (2009) Observation of the structure of the urban boundary layer with different ceilometers and validation by RASS data. Meteorol. Z. 18: 149–154 140 <strong>DTU</strong> Wind Energy-E-Report-0029(EN)
Engelbart D.A.M.and Bange J. (2002) Determination ofboundary-layer parameters using wind profiler/RASS and sodar/RASS in the frame of the LITFASS project. Theor. Appl. Climatol. 73: 53–65 Eresmaa N., Karppinen A., Joffre S. M., Räsänen J., and Talvitie H. (2006) Mixing height determination by ceilometer. Atmos. Chem. Phys. 6: 1485–1493 Flamant C., Pelon J., Flamant P. H., and Durand P. (1997) Lidar determination of the entrainement zone thickness at the top of the unstable marin atmospheric boundary-layer. Bound.-Layer Meteorol. 83:247–284 Gerstengarbe F.-W., Werner P. C., and Rüge U. (Eds.) (1999) Katalog der Großwetterlagen Europas (1881 - 1998). Nach Paul Hess und Helmuth Brezowsky. 5th edition. German Meteorological Service, Potsdam/Offenbach a. M. (available from: http://www.deutscherwetterdienst.de/lexikon/download.php?file=Grosswetterlage.pdf or http://www.pik-potsdam.de/ uwerner/gwl/gwl.pdf) Grimsdell A. W. and Angevine W. M. (1998) Convective Boundary Layer Height Measurement with Wind Profilers and Comparison to Cloud Base. J. Atmos. Oceanic Technol. 15: 1331–1338 Gryning S.-E., Batchvarova E., Brümmer B., Jørgensen H., and Larsen S. (2007) On the extension of the wind profile over homogeneous terrain beyond the surface layer. Bound.-Layer Meteorol. 124:251–268 Hayden K. L., Anlauf K. G., Hoff R. M., Strapp J. W., Bottenheim J. W., Wiebe H. A., Froude F. A., Martin J. B., Steyn D. G., and McKendry I. G. (1997) The Vertical Chemical and Meteorological Structure of the Boundary Layer in the Lower Fraser Valley during Pacific ’93. J. Atmos. Environ. 31: 2089–2105 Hennemuth B. and Kirtzel H.-J. (2008) Towards operational determination of boundary layer height using sodar/RASS soundings and surface heat flux data. Meteorol. Z. 17: 283–296 Hooper W. P.and Eloranta E. (1986) Lidar measurements of wind inthe planetary boundary layer:the method, accuracy and results from joint measurements with radiosonde and kytoon. J. Clim. Appl. Meteorol. 25: 990–1001 Lammert A. and Bösenberg J. (2006) Determination of the Convective Boundary-Layer Height with Laser Remote Sensing. Bound.-Layer Meteorol. 119: 159–170 Marshall J. M., Peterson A. M., and Barnes A. A. (1972) Combined radar-acoustic sounding system. Appl. Opt. 11: 108–112 Martucci G., Srivastava M. K., Mitev V., Matthey R., Frioud M., and Richner H. (2004) Comparison of lidar methods to determine the Aerosol Mixed Layer top. In: Schäfer K., Comeron A., Carleer M., Picard R. H. (eds.): Remote Sensing of Clouds and the Atmosphere VIII. Proc of SPIE 5235: 447–456 Melfi S. H., Spinhirne J. D., Chou S. H., and Palm S. P. (1985) Lidar Observation of the Vertically Organized Convection in the Planetary Boundary Layer Over the Ocean. J. Clim. Appl. Meteorol. 24: 806–821 Menut L., Flamant C., Pelon J., and Flamant P. H. (1999) Urban Boundary-Layer Height Determination from Lidar Measurements Over the Paris Area. Appl. Opt. 38: 945–954 Münkel C. and Räsänen J. (2004) New optical concept for commercial lidar ceilometers scanning the boundary layer. Proc. SPIE Vol 5571: 364–374 NeffW.D.andCoulter R.L.(1986) Acoustic remote sensing. In:Lenschow D.H.(ed)Probing the Atmospheric Boundary Layer. Amer. Meteorol. Soc., Boston, MA, 201–239 Peña A., Gryning S.-E., and Hasager C. B. (2010) Comparing mixing-length models of the diabatic wind profile over homogeneous terrain. Theor. Appl. Climatol. 100:325–335 Piironen A. K. and Eloranta E. W. (1995) Convective boundary layer depths and cloud geometrical properties obtained from volume imaging lidar data. J. Geophys. Res. 100: 25569–25576 Schäfer K., Emeis S. M., Rauch A., Münkel C., and Vogt S. (2004) Determination of mixing-layer heights from ceilometer data. In: Schäfer K., Comeron A. T., Carleer M. R., Picard R. H., and Sifakis N. (eds.): Remote Sensing of Clouds and the Atmosphere IX. Proc. SPIE Vol 5571: 248–259 Schäfer K., Emeis S., Junkermann W., and Münkel C. (2005) Evaluation of mixing layer height monitoring by ceilometer with SODAR and microlight aircraft measurements. In: Schäfer K., Comeron A. T., Slusser J. R., Picard R. H., Carleer M. R., and Sifakis N. (eds) Remote Sensing of Clouds and the Atmosphere X. Proc. SPIE Vol 5979: 59791I-1–59791I-11 Seibert P., Beyrich F., Gryning S.-E., Joffre S., Rasmussen A., and Tercier P. (2000) Review and intercomparison of operational methods for the determination of the mixing height. Atmosph. Environ. 34: 1001-1-027 <strong>DTU</strong> Wind Energy-E-Report-0029(EN) 141
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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
Page 89 and 90: from ±VH sinδ (if the tilt is tow
Page 91 and 92: as a down draught (of the same abso
Page 93 and 94: Table 7: Combined results from 28 Z
Page 95 and 96: in Eastern Jutland between January
Page 97 and 98: Figure 56: Normalized power curves
Page 99 and 100: the concept. Developments include i
Page 101 and 102: References x horizontal position in
Page 103 and 104: Wagner R., Mikkelsen T., and Courtn
Page 105 and 106: 5.2 End-to-end description of pulse
Page 107 and 108: Scanner Coherent lidar measure the
Page 109 and 110: Figure 62: Radial wind velocity ret
Page 111 and 112: transform in order to use data obta
Page 113 and 114: This wavelength is also the most fa
Page 115 and 116: Eq.(132)isadaptedforcollimatedsyste
Page 117 and 118: 5.3.6 Existing systems and actual p
Page 119 and 120: (Gottshall et al., 2010; Albers et
Page 121 and 122: References Albers A., Janssen A. W.
Page 123 and 124: derived from fluctuations of the wi
Page 125 and 126: Acoustic received echo (ARE) method
Page 127 and 128: Figure 71: Sample time-height cross
Page 129 and 130: A gradient minimum is characterized
Page 131 and 132: Figure73:Bragg-relatedacoustic(belo
Page 133 and 134: stability (inversion strength) can
Page 135 and 136: Figure 76: Combined soundingwith a
Page 137 and 138: Figure 79: Favorite regions (shaded
Page 139: Direct detection of MLH from acoust
Page 143 and 144: 7 What can remote sensing contribut
Page 145 and 146: uyms 9.0 8.5 8.0 7.5 7.0 120 140 16
Page 147 and 148: Bottom of rotor Φ rotation r w u
Page 149 and 150: Height Height Hub 1.6 1.4 1.2 1.0 0
Page 151 and 152: PP rated PP rated 1.0 0.8 0.6 0.4 0
Page 153 and 154: KEprofileKEhub 1.2 1.1 1.0 0.9 0.8
Page 155 and 156: PP rated WS Lidarms 1.0 0.8 0.6 0.4
Page 157 and 158: 8 Nacelle-based lidar systems Andre
Page 159 and 160: • Flexibletrajectories.Dependingo
Page 161 and 162: Figure 104: Sketch of simultaneous
Page 163 and 164: The normal wind direction vector nw
Page 165 and 166: Figure 109: Test site at DTU Wind E
Page 167 and 168: Figure 112: Power curve met mast an
Page 169 and 170: Notation C number of sent photons C
Page 171 and 172: 9 Lidars and wind turbine control -
Page 173 and 174: for three unknowns, it is impossibl
Page 175 and 176: model of the blade pitch actuator,
Page 177 and 178: |GRL| [-] 1 0.8 0.6 0.4 0.2 10 k [r
Page 179 and 180: PSD(Ωg) [(rpm) 2 /Hz] PSD(Ωg) [
Page 181 and 182: 0.04 0.03 ˆk [ rad m ] 0.02 0.63 0
Page 183 and 184: [%] 10 0 −10 −20 −30 MyT Moop
Page 185 and 186: PSD(θ1) [rad 2 /Hz] PSD(Moop1) [Nm
Page 187 and 188: Pel/Pel,max [-] 1 0.98 0.96 0.94 0.
Page 189 and 190: 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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Height (m) Height (m) 160 140 120 1
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RMSPE (%) 70 60 50 40 30 20 10 0 vu
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involves interaction of all compone
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Lindelöw P. (2007) Fibre Based Coh
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plications, including meteorologica
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Figure 173: Rayleigh-Jeans’ appro
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Figure 176: Brightness temperature
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with rain are shown in Fig. 179. A
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This method gives a good accuracy (
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Figure 183: A recent maintenance in
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Figure 185: Temperature profiles in
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References s point in space Sν(s)
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Also the Doppler Centroid anomaly c
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Christiansen et al., 2006). The res
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educe speckle noise, a random noise
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Figure 188: Envisat ASAR wind field
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Figure 190: Envisat ASAR wind field
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Satellites in sun-synchronous polar
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500 overlapping scenes for wind res
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(with the fewest samples). The unce
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Badger M., Hasager C. B., Thompson
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Ren Y. Z., Lehner S., Brusch S., Li
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tracking, climate studies, air-sea
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The modified logarithmic wind profi
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sea ice mask was applied and σ0 me
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QuikSCAT 25 20 15 10 5 Horns Rev Fi
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Figure 203:Example of an ASCAT coas
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References Bourassa M.A., Legler D.
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DTU Wind Energy Technical Universit
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