Wind Data Sources

  • Stefan Emeis
Part of the Green Energy and Technology book series (GREEN)


The resource for energy generation by wind turbines—the wind—is a vector, i.e. characterized by an amount (wind speed) and a direction (wind direction). Generally, (apart from small-scale and convective processes and flows over steep topography) the vertical wind component is much smaller than the horizontal wind components. Therefore, frequently, only horizontal wind speed is measured. Furthermore, wind is a highly variable atmospheric parameter.


  1. Abreu, V.J., J.E. Barnes, P.B. Hays: Observations of wind with an incoherent lidar detector. Appl. Opt. 31, 4509–4514 (1992)Google Scholar
  2. Adams, A.S., D.W. Keith: Are global wind power resource estimates overstated? Environ. Res. Lett., 8, 015021 (2013)Google Scholar
  3. Asimakopoulos, D.N., Helmis C.G., Michopoulos J.: Evaluation of SODAR methods for the determination of the atmospheric boundary layer mixing height. Meteor. Atmos. Phys. 85, 85–92 (2004)Google Scholar
  4. Banakh, V.A., I.N. Smalikho, F. Köpp, C. Werner: Representativeness of wind measurements with a cw Doppler lidar in the atmospheric boundary layer. Appl. Opt. 34, 2055–2067 (1995)Google Scholar
  5. Beaucage, P., M.C. Brower, J. Tensen: Evaluation of four numerical wind flow models for wind resource mapping. Wind Energy, vol. 17, pp. 197–208 (2014)Google Scholar
  6. Beyrich, F.: Mixing height estimation from sodar data—a critical discussion. Atmos. Environ. 31, 3941–3954 (1997)Google Scholar
  7. Boers, R., Spinhirne, J.D., Hart, W.D.: Lidar Observations of the Fine-Scale Variability of Marine Stratocumulus Clouds. J. Appl. Meteorol. 27, 797–810 (1988)Google Scholar
  8. Böttcher, F., S. Barth, J. Peinke: Small and large scale fluctuations in atmospheric wind speeds. Stoch. Environ. Res. Risk Assess. 21, 299–308 (2007)Google Scholar
  9. Bowen, A.J., N.G. Mortensen: WAsP prediction errors due to site orography. Risø-R-995(EN), 65 pp. (available at: (2004)
  10. Bradley, S., A. Strehz, S. Emeis: Remote sensing winds in complex terrain—a review. Meteorol. Z., 24, 547–555 (2015)Google Scholar
  11. Bryan, G.H., Wyngaard, J.C., Fritsch, J.M.: Resolution Requirements for the Simulation of Deep Moist Convection. Mon. Wea. Rev., 131, 2394–2416 (2003)Google Scholar
  12. Busch, N.E., L. Kristensen: Cup anemometer overspeeding. J. Appl. Meteorol., 15, 1328–1332 (1976)Google Scholar
  13. Calhoun, R., Heap, R., Princevac, M., Newsom, R., Fernando, H., Ligon, D.: Virtual towers using coherent Doppler lidar during the Joint Urban 2003 dispersion experiment. Journal of Applied Meteorology and Climatology, 45, 1116–1126 (2006)Google Scholar
  14. Cannon, D. J., Brayshaw, D. J., Methven, J., Coker, P. J., Lenaghan, D.: Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain. Renewable Energy, 75, 767–778 (2015)Google Scholar
  15. Carta, J.A., S. Velázquez, P. Cabrera: A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site. Renew. Sustain. Energy Rev., 27, 362–400 (2013)Google Scholar
  16. Carter, D.J.T.: Estimating extreme wave heights in the NE Atlantic from GEOSAT data. Health and Safety Executive—Offshore Technology Report. Her Majesty’s Stationary Office OTH 93 396. 28 pp. (1993)Google Scholar
  17. Carvalho, D., A. Rocha, C. Silva Santos, R. Pereira: Wind resource modelling in complex terrain using different mesoscale–microscale coupling techniques. Applied Energy, 108, 493–504 (2013)Google Scholar
  18. Ching, J., Rotunno, R., LeMone, M., Martilli, A., Kosovic, B., Jimenez, P.A., Dudhia, J.: Convectively Induced Secondary Circulations in Fine-Grid Mesoscale Numerical Weather Prediction Models. Mon. Wea. Rev., 142, 3284–3302 (2014)Google Scholar
  19. Christiansen, M.B., C.B. Hasager: Wake effects of large offshore wind farms identified from satellite SAR. Remote Sens. Environ., 98, 251–268 (2005)Google Scholar
  20. Comerón, A., M. Sicard, F. Rocadenbosch: Wavelet Correlation Transform Method and Gradient Method to Determine Aerosol Layering from Lidar Returns: Some Comments. J. Atmos. Oceanic Technol., 30, 1189–1193 (2013)Google Scholar
  21. Contini, D., Cava, D., Martano, P., Donateo, A., Grasso, F.M.: Comparison of indirect methods for the estimation of Boundary Layer height over flat-terrain in a coastal site. Meteorol. Z. 18, 309–320 (2009)Google Scholar
  22. Cook, N.J.: Towards better estimation of extreme winds, J. Wind Eng. Ind. Aerodyn. 9, 295–323 (1982)Google Scholar
  23. Cooney, J.: Measurement of atmospheric temperature profiles by Raman backscatter. J. Appl. Meteorol. 11, 108–112 (1972)Google Scholar
  24. Davies, F., C.G. Collier, K.E. Bozier, G.N. Pearson: On the accuracy of retrieved wind information from Doppler lidar observations. Quart. J. Roy. Meteor. Soc. 129, 321–334 (2003)Google Scholar
  25. Davis, F.K., H. Newstein: The Variation of Gust Factors with Mean Wind Speed and with Height. J. Appl. Meteor. 7, 372–378 (1968)Google Scholar
  26. Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N. and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q.J.R. Meteorol. Soc., 137: 553–597 (2011)Google Scholar
  27. de Haij, M., Wauben, W., Klein Baltink, H.: Determination of mixing layer height from ceilometer backscatter profiles. In: Slusser JR, Schäfer K, Comerón A (eds) Remote Sensing of Clouds and the Atmosphere XI. Proc. SPIE 6362, 63620R (2006)Google Scholar
  28. Draxl, C., A. Clifton, B.-M. Hodge, J. McCaa: The wind integration national dataset (wind) Toolkit. Appl. Energy, 151, 355–366 (2015)Google Scholar
  29. Eresmaa, N., Karppinen, A., Joffre, S.M., Räsänen, J., Talvitie, H.: Mixing height determination by ceilometer. Atmos. Chem. Phys., 6, 1485–1493 (2006)Google Scholar
  30. Elliott, D., M. Schwartz, S. Haymes, D. Heimiller, G. Scott, M. Brower, E. Hale, and B. Phelps: New wind energy resource potential estimates for the United States. Presentation at the Second Conf. on Weather, Climate, and the New Energy Economy, 27 Jan 2011, Seattle, WA. (2011)Google Scholar
  31. Emeis, S.: Measurement Methods in Atmospheric Sciences. In situ and remote. Series: Quantifying the Environment Vol. 1. Borntraeger Stuttgart. XIV + 257 pp. (2010)Google Scholar
  32. Emeis, S.: Surface-Based Remote Sensing of the Atmospheric Boundary Layer. Series: Atmospheric and Oceanographic Sciences Library, Vol. 40. Springer Heidelberg etc., X + 174 pp. (2011)Google Scholar
  33. Emeis, S.: Observational techniques to assist the coupling of CWE/CFD models and meso-scale meteorological models. J. Wind Eng. Industr. Aerodyn., 144, 24–30 (2015)Google Scholar
  34. Emeis, S., Türk, M.: Frequency distributions of the mixing height over an urban area from SODAR data. Meteorol. Z. 13, 361–367 (2004)Google Scholar
  35. Emeis, S., M. Türk: Wind-driven wave heights in the German Bight. Ocean Dyn. 59, 463–475. (2009)Google Scholar
  36. Emeis, S., M. Harris, R.M. Banta: Boundary-layer anemometry by optical remote sensing for wind energy applications. Meteorol. Z., 16, 337–347 (2007a)Google Scholar
  37. Emeis, S., K. Baumann-Stanzer, M. Piringer, M. Kallistratova, R. Kouznetsov, V. Yushkov: Wind and turbulence in the urban boundary layer—analysis from acoustic remote sensing data and fit to analytical relations. Meteorol. Z. 16, 393–406 (2007b)Google Scholar
  38. Emeis, S., Jahn, C., Münkel, C., Münsterer, C., Schäfer, K.: Multiple atmospheric layering and mixing-layer height in the Inn valley observed by remote sensing. Meteorol. Z. 16, 415–424 (2007c)Google Scholar
  39. Emeis S., Schäfer K., Münkel C.: Surface-based remote sensing of the mixing-layer height—a review. Meteorol. Z. 17, 621–630 (2008)Google Scholar
  40. Flamant, C., Pelon, J., Flamant, P.H., Durand, P.: Lidar determination of the entrainement zone thickness at the top of the unstable marin atmospheric boundary-layer. Bound.-Lay. Meteorol. 83, 247–284 (1997)Google Scholar
  41. Foken, T.: Micrometeorology. Springer, 308 pp. (2008)Google Scholar
  42. Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L.,, Randles, C.A., Darmenova, A., Bosilovicha, M.G., Reichle, R., Wargan, K., Coya, L., Richard Cullather, R., Clara Draper, C., Santha Akella, S., Virginie Buchard, V., Austin Conaty, A., Arlindo M. da Silva, A.M., Wei Gu, W., Gi-Kong Kim, G.-K., Randal Koster, R., Robert Lucchesia, R., Dagmar Merkova, D., Jon Eric Nielsen, J.E., Gary Partyka, G., Steven Pawson, S., William Putman, W., Michele Rienecker, M., Siegfried D. Schubert, S.D., Meta Sienkiewicz, M., Zhao, B.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J. Climate, 30, 5419–5454 (2017)Google Scholar
  43. González-Aparicio, I., Monforti, F., Volker, P., Zucker, A., Careri, F., Huld, T., & Badger, J.: Simulating European wind power generation applying statistical downscaling to reanalysis data. Applied Energy, 199, 155–168 (2017)Google Scholar
  44. Gomes, L., & Vickery, B. J.: On the prediction of extreme wind speeds from the parent distribution. Journal of Wind Engineering and Industrial Aerodynamics, 2(1), 21–36 (1977)Google Scholar
  45. Gottschall, J., Gribben, B., Stein, D., Würth, I.: Floating lidar as an advanced offshore wind speed measurement technique: current technology status and gap analysis in regard to full maturity. WIREs Energy Environ, 6: n/a, e250. (2017)
  46. Gross, G.: Das dreidimensionale, nichthydrostatische Mesoscale-Modell FITNAH. Meteorol. Rdsch, 43, 97–112 (1991)Google Scholar
  47. Grund, C.J., R.M. Banta, J.L. George, J.N. Howell, M.J. Post, R.A. Richter, A.M. Weickmann: High-resolution Doppler lidar for boundary layer and cloud research. J. Atmos. Oceanic Technol. 18, 376–393 (2001)Google Scholar
  48. Gumbel, E.J.: Statistics of extremes. Columbia University Press, New York and London, 375 pp. (1958)Google Scholar
  49. Hardesty, R.M., L.S. Darby: Ground-based and airborne lidar.—Encyclopedia of Hy-drologic Sciences. M.G. Anderson (Ed.), Wiley, 697–712 (2005)Google Scholar
  50. Harris, M., G. Constant, C. Ward: Continuous wave bistatic laser Doppler wind sensor. Appl. Opt. 40, 1501–1506 (2001)Google Scholar
  51. Hasager, .CB., P. Vincent, R. Husson, A. Mouche, M. Badger, A. Peña, P. Volker, J. Badger, A. Di Bella, A. Palomares, E. Cantero, P.M.F. Correia: Comparing satellite SAR and wind farm wake models. Journal of Physics: Conference Series, 625, 012035 (2015)Google Scholar
  52. 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., McKendry, I.G.: The Vertical Chemical and Meteorological Structure of the Boundary Layer in the Lower Fraser Valley during Pacific ’93. Atmos. Environ. 31, 2089–2105 (1997)Google Scholar
  53. Hennemuth, B., Kirtzel, H.-J.: Towards operational determination of boundary layer height using sodar/RASS soundings and surface heat flux data. Meteorol. Z. 17, 283–296 (2008)Google Scholar
  54. Hooper, W.P., Eloranta, E.: Lidar measurements of wind in the planetary boundary layer: the method, accuracy and results from joint measurements with radiosonde and kytoon. J. Clim. Appl. Meteorol. 25, 990–1001 (1986)Google Scholar
  55. IEC: IEC 61400-12-1: RLV Redline version, Wind energy generation systems—Part 12-1: Power performance measurements of electricity producing wind turbines. International Electrotechnical Commission. (2017)
  56. ISO 28902-2: Air quality—Environmental meteorology—Part 2: Ground-based remote sensing of wind by heterodyne pulsed Doppler lidar. (2017)Google Scholar
  57. Jackson, P.S., Hunt, J.C.R.: Turbulent wind flow over a low hill. Quart. J. Roy. Met. Soc., 101, 929–55 (1975)Google Scholar
  58. James, E.P., S.G. Benjamin, M. Marquis: A unified high-resolution wind and solar dataset from a rapidly updating numerical weather prediction model. Renewable Energy, 102, 390–405 (2017)Google Scholar
  59. Jensen, N.O., L. Kristensen: Gust statistics for the Great Belt Region. Risoe-M-2828, 21 pp. (1989)Google Scholar
  60. Jones, P. D., Harpham, C., Troccoli, A., Gschwind, B., Ranchin, T., Wald, L., Goddess, C.M., Dorling, S.: Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables. Earth System Science Data, 9, 471–495 (2017)Google Scholar
  61. Justus, C.G., W.R. Hargraves, A. Yalcin: Nationwide Assessment of Potential Output from Wind-Powered Generators. J. Appl. Meteor. 15, 673–678 (1976)Google Scholar
  62. Justus, C.G., W.R. Hargraves, A. Mikhail, D. Graber: Methods for Estimating Wind Speed Frequency Distributions. J. Appl. Meteor. 17, 350–353 (1978)Google Scholar
  63. Kaimal, J.C., S.F. Clifford, R.J. Lataitis: Effect of finite sampling on atmospheric spectra. Bound.-Lay. Meteorol. 47, 337–347 (1989)Google Scholar
  64. Kindler, D., A. Oldroyd, A. MacAskill, D. Finch: An 8 month test campaign of the QinetiQ ZephIR system: preliminary results. Meteorol. Z., 16, 463–473 (2007)Google Scholar
  65. Klaas, T., Pauscher, L., Callies, D.: LiDAR-mast deviations in complex terrain and their simulation using CFD. Meteorol. Z, 24, 591–603 (2015)Google Scholar
  66. Kljun, N., P. Calanca, M.W. Rotach, H.P. Schmid: A simple two-dimensional parameterisation for flux footprint prediction (FFP). Geosci. Model Develop. 8, 3695–3713 (2015)Google Scholar
  67. Koch W., F. Feser: Relationship between SAR-derived wind vectors and wind at 10-m height represented by a mesoscale model. Mon. Wea. Rev., 134, 1505–1517 (2006)Google Scholar
  68. Kristensen, L.: The cup anemometer and other exciting instruments. Doctor thesis at the Technical University of Denmark in Lyngby. Risø National Laboratory, Roskilde, Denmeark. Risø-R-615 (EN), 83 pp. (1993)Google Scholar
  69. Lammert, A., Bösenberg, J.: Determination of the Convective Boundary-Layer Height with Laser Remote Sensing. Bound.-Lay. Meteorol. 119, 159–170 (2006)Google Scholar
  70. Lenschow, D. H., J. Mann, L. Kristensen: How long is long enough when measuring fluxes and other turbulence statistics?.—J. Atmos. Oceanic Technol., 11, 661–673 (1994)Google Scholar
  71. Liu, Y.S., Miao, S.G., Zhang, C.L., Cui, G.X., Zhang, Z.S.: Study on micro-atmospheric environment by coupling large eddy simulation with mesoscale model. J. Wind Eng. Ind. Aerodyn., 107–108, 106–117 (2012)Google Scholar
  72. MacCready, P.B.: Mean Wind Speed Measurements in Turbulence. J. Appl. Meteorol., 5, 219–225 (1966)Google Scholar
  73. Mann, J., N. Angelou, J. Arnqvist, D. Callies, E. Cantero, R. Chávez Arroyo, M. Courtney, J. Cuxart, E. Dellwik, J. Gottschall, S. Ivanell, P. Kühn, G. Lea, J. C. Matos, J. M. L. M. Palma, L. Pauscher, A. Peña, J. Sanz Rodrigo, S. Söderberg, N. Vasiljevic, C. Veiga Rodrigues: Complex terrain experiments in the New European Wind Atlas. Phil. Trans. R. Soc. A, 375(2091), 20160101 (2017)Google Scholar
  74. Martucci, G., Srivastava, M.K., Mitev, V., Matthey, R., Frioud, M., Richner, H.: Comparison of lidar methods to determine the Aerosol Mixed Layer top. In: Schäfer K, Comeron A, Carleer M, Picard RH (eds.): Remote Sensing of Clouds and the Atmosphere VIII. Proc of SPIE 5235, 447–456 (2004)Google Scholar
  75. MEASNET: Evaluation of site-specific wind conditions. Version 2 April 2016. (5.5.17)
  76. Melfi, S.H., Spinhirne, J.D., Chou, S.H., Palm, S.P.: Lidar Observation of the Vertically Organized Convection in the Planetary Boundary Layer Over the Ocean. J. Clim. Appl. Meteorol. 24, 806–821 (1985)Google Scholar
  77. Menut, L., Flamant, C., Pelon, J., Flamant, P.H.: Urban Boundary-Layer Height Determination from Lidar Measurements Over the Paris Area. Appl. Opt. 38, 945–954 (1999)Google Scholar
  78. Mitsuta, Y., O. Tsukamoto: Studies on Spatial Structure of Wind Gust. J. Appl. Meteor. 28, 1155–1161 (1989)Google Scholar
  79. Mochida, A., Iizuka, S., Tominaga, Y., Lun, I.Y.-F.: Up-scaling CWE models to include mesoscale meteorological influences. J. Wind Eng. Ind. Aerodyn., 99, 187–198 (2011)Google Scholar
  80. Morales, A., M. Wächter, J. Peinke: Advanced characterization of wind turbulence by higher order statistics. Proc. EWEC 2010 (2010)Google Scholar
  81. Münkel, C.: Mixing height determination with lidar ceilometers—results from Helsinki Testbed. Meteorol. Z. 16, 451–459 (2007)Google Scholar
  82. Münkel, C., Räsänen, J.: New optical concept for commercial lidar ceilometers scanning the boundary layer. Proc. SPIE 5571, 364–374 (2004)Google Scholar
  83. Newitt, T.: An Offshore Wind Resource Assessment for Guernsey. PhD thesis, University of Exeter. Available from: (accessed on November 14, 2017)
  84. Newman, J. F., Bonin, T. A., Klein, P. M., Wharton, S., & Newsom, R. K.: Testing and validation of multi‐lidar scanning strategies for wind energy applications. Wind Energy, 19(12), 2239–2254 (2016)Google Scholar
  85. Palutikof, J.P., B.B. Brabson, D.H. Lister, S.T. Adcock: A review of methods to calculate extreme wind speeds. Meteorological Applications, 6, 119–132 (1999)Google Scholar
  86. Panchang, V., Zhao, L., Demirbilek. Z.: Estimation of extreme wave heights using GEOSAT measurements. Ocean Eng. 26, 205–225 (1999)Google Scholar
  87. Pauscher, L., Hagemann, S., Klaas, T., Callies, D., & Lange, B.: Wind over complex, forested terrain: first year of measurement with 200 m research mast. Proceedings of the EWEA conference. Vienna (2013)Google Scholar
  88. Pauscher, L., Vasiljevic, N., Callies, D., Lea, G., Mann, J., Klaas, T., Hieronimus, J., Julia Gottschall, J., Schwesig, A., Kühn, M., Courtney, M.: An Inter-Comparison Study of Multi-and DBS Lidar Measurements in Complex Terrain. Remote Sensing, 8, 782 (2016)Google Scholar
  89. Pauscher, L., D. Callies, T. Klaas, T. Foken: Wind observations from a forested hill: Relating turbulence statistics to surface characteristics in hilly and pachy terrain. Meteorol. Z., prepubl. online (2017)Google Scholar
  90. Pedersen, T.F.: Development of a Classification System for Cup Anemometers—CLASSCUP. Risø Nat. Lab., Roskilde, Report Risø-R-1348(EN), 45 pp. (2003)Google Scholar
  91. Pichugina, Y. L., Banta, R. M., Brewer, W. A., Sandberg, S. P., & Hardesty, R. M.: Doppler lidar–based wind-profile measurement system for offshore wind-energy and other marine boundary layer applications. J. Appl. Meteor. Climatol., 51, 327–349 (2012)Google Scholar
  92. Piironen, A.K., Eloranta, E.W.: Convective boundary layer depths and cloud geometrical properties obtained from volume imaging lidar data. J. Geophys. Res. 100, 25569–25576 (1995)Google Scholar
  93. Powers, J.G., J.B. Klemp, W.C. Skamarock, C.A. Davis, J. Dudhia, D.O. Gill, J.L. Coen, D.J. Gochis, R. Ahmadov, S.E. Peckham, G.A. Grell, J. Michalakes, S. Trahan, S.G. Benjamin, C.R. Alexander, G.J. Dimego, W. Wang, C.S. Schwartz, G.S. Romine, Z. Liu, C. Snyder, F. Chen, M.J. Barlage, W. Yu, and M.G. Duda: The Weather Research and Forecasting Model: Overview, System Efforts, and Future Directions. Bull. Amer. Meteor. Soc., 98, 1717–1737 (2017)Google Scholar
  94. Probst, O., D. Cárdenas: State of the Art and Trends in Wind Resource Assessment. Energies, 3, 1087–1141 (2010)Google Scholar
  95. Rockel, B., Will, A., & Hense, A.: The regional climate model COSMO-CLM (CCLM). Meteorologische Zeitschrift, 17(4), 347–348 (2008)Google Scholar
  96. Rojowsky, K.: The Global Wind Trends Bulletin. DEWI Mag. 50, 50–54 (2017)Google Scholar
  97. Rienecker, M., M. Suarez, R. Gelaro, R. Todling, J. BacmeisterE. Liu, M. Bosilovich, S. Schubert, L. Takacs, G. Kim, S. Bloom, J. Chen, D. Collins, A. Conaty, A. da Silva, W. Gu, J. Joiner, R. Koster, R. Lucchesi, A. Molod, T. Owens, S. Pawson, P. Pegion, C. Redder, R. Reichle, F. Robertson, A. Ruddick, M. Sienkiewicz, J. Woollen: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 3624–3648 (2011)Google Scholar
  98. Saha, S., S. Moorthi, X. Wu, J. Wang, S. Nadiga, P. Tripp, D. Behringer, Y. Hou, H. Chuang, M. Iredell, M. Ek, J. Meng, R. Yang, M. Mendez, H. van den Dool, Q. Zhang, W. Wang, M. Chen, and E. Becker: The NCEP Climate Forecast System Version 2. J. Climate, 27, 2185–2208 (2014)Google Scholar
  99. Schäfer, K., Emeis, S.M., Rauch, A., Münkel, C., Vogt, S.: Determination of mixing-layer heights from ceilometer data. In: Schäfer K, Comeron AT, Carleer MR, Picard RH, Sifakis N (eds.): Remote Sensing of Clouds and the Atmosphere IX. Proc. SPIE 5571, 248–259 (2004)Google Scholar
  100. Schäfer, K., Emeis, S., Junkermann, W., Münkel, C.: Evaluation of mixing layer height monitoring by ceilometer with SODAR and microlight aircraft measurements. In: Schäfer K, Comeron AT, Slusser JR, Picard RH, Carleer MR, Sifakis N (eds) Remote Sensing of Clouds and the Atmosphere X. Proc. SPIE 5979, 59791I-1–59791I-11 (2005)Google Scholar
  101. Schlünzen, H., Grawe, D., Bohnenstengel, S.I., Schlüter, I., Koppmann, R.: Joint modelling of obstacle induced and mesoscale changes—current limits and challenges. J. Wind Eng. Ind. Aerodyn., 99, 217–225 (2011)Google Scholar
  102. Schmid, H. P.: Source Areas for Scalars and Scalar Fluxes, Bound.-Lay. Meteorol., 67, 293–318 (1994)Google Scholar
  103. Schroers, H., H. Lösslein, K. Zilich: Untersuchung der Windstruktur bei Starkwind und Sturm. Meteorol. Rdsch. 42, 202–212 (1990)Google Scholar
  104. Seibert, P., Beyrich, F., Gryning, S.-E., Joffre, S., Rasmussen, A., Tercier, P.: Review and intercomparison of operational methods for the determination of the mixing height. Atmos. Environ. 34, 1001–1027 (2000)Google Scholar
  105. Senff, C., Bösenberg, J., Peters, G., Schaberl, T.: Remote Sesing of Turbulent Ozone Fluxes and the Ozone Budget in the Convective Boundary Layer with DIAL and Radar-RASS: A Case Study. Contrib. Atmos. Phys. 69, 161–176 (1996)Google Scholar
  106. Sicard, M., Pérez, C., Rocadenbosch, F., Baldasano, J.M., García-Vizcaino, D.: Mixed-Layer Depth Determination in the Barcelona Coastal Area From Regular Lidar Measurements: Methods, Results and Limitations. Bound.-Lay. Meteorol. 119, 135–157 (2006)Google Scholar
  107. Skamarock, W. C., and Coauthors: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475 + STR, 113 pp., doi: (2008)
  108. Steinfeld, G., S. Raasch, T. Markkanen: Footprints in homogeneously and heterogeneously driven boundary layers derived from a lagrangian stochastic particle model embedded into large-eddy simulation. Bound.-Layer Meteor. 129, 225–248 (2008)Google Scholar
  109. Steyn, D.G., Baldi, M., Hoff, R.M.: The detection of mixed layer depth and entrainment zone thickness from lidar backscatter profiles. J. Atmos. Ocean Technol. 16, 953–959 (1999)Google Scholar
  110. Stiperski, I., Rotach, M. W.: On the measurement of turbulence over complex mountainous terrain. Boundary-Layer Meteorology, 159(1), 97–121 (2016)Google Scholar
  111. Sumner, J., C.S. Watters, C. Masson, C.: CFD in wind energy: the virtual, multiscale wind tunnel. Energies, 3(5), 989–1013. (2010)
  112. Taylor, P.A., Teunissen, H.W.: The Askervein Hill Project: Overview and background data. Boundary-Layer Meteorology 39, 15–39 (1987)Google Scholar
  113. Troen, I.: A high resolution spectral model for flow in complex terrain. Proc. Ninth Symposium on Turbulence and Diffusion. American Meteorological Society, Risø National Laboratory, Roskilde, Denmark, April 30-May 3, 417–20 (1990)Google Scholar
  114. Troen, I., Hansen, B. O.: Wind resource estimation in complex terrain: Prediction skill of linear and nonlinear micro-scale models. Poster session presented at AWEA Windpower Conference & Exhibition, Orlando, FL, United States (2015)Google Scholar
  115. Troen, I., Petersen, E.L.: European Wind Atlas. Published for the Commission of the European Communities, Brussels, Belgium, by Risø National Laboratory, Roskilde, Denmark, ISBN 87-550-1482-8, 656 pp. (1989)Google Scholar
  116. Troen, I., Bechmann, A., Kelly, M. C., Sørensen, N. N., Réthoré, P-E., Cavar, D., Ejsing Jørgensen, H.: Complex terrain wind resource estimation with the wind-atlas method: Prediction errors using linearized and nonlinear CFD micro-scale models. Proceedings of EWEA 2014 (2014)Google Scholar
  117. Tsegas, G., Barmpas, P. Douros, I., Moussiopoulos, N.: A metamodelling implementation of a two–way coupled mesoscale–microscale flow model for urban area simulations. Int. J. Environ. Poll., 47, 278–289 (2011)Google Scholar
  118. Türk, M.: Ermittlung designrelevanter Belastungsparameter für Offshore-Windkraftanlagen. PhD thesis University of Cologne (2008) (Available from:
  119. Van der Hoven, I.: Power Spectrum of Horizontal Wind Speed in the Frequency Range from 0.0007 to 900 Cycles per Hour. J. Meteorol. 14, 160–164 (1957)Google Scholar
  120. Wagner, R., B. Cañadillas, A. Clifton, S. Feeney, N. Nygaard, M. Poodt, C. St. Martin, E. Tüxen, J.W. Wagenaar: Rotor equivalent wind speed for power curve measurement—comparative exercise for IEA Wind Annex 32. J. Phys.: Conf. Ser., 524, 012108 (2014)Google Scholar
  121. Walmsley, J.L., Salmon, J.R., Taylor, P.A.: On the application of a model of boundary-layer flow over low hills in real terrain. Bound.-Lay. Meteorol., 23, 17–46 (1982)Google Scholar
  122. Weitkamp, C. (Ed.): Lidar. Range-Resolved Optical Remote Sensing of the Atmos-phere. Springer Science + Business Media Inc. New York. 455 pp. (2005)Google Scholar
  123. Wieringa, J.: Gust factors over open water and built-up country. Bound.-Lay. Meteorol. 3, 424–441 (1973)Google Scholar
  124. Wieringa, J.: Shapes of annual frequency distributions of wind speed observed on high meteorological masts. Bound.-Lay.Meteorol. 47, 85–110 (1989)Google Scholar
  125. Wulfmeyer, V.: Investigation of turbulent processes in the lower troposphere with water-vapor DIAL and Radar-RASS. J. Atmos. Sci. 56, 1055–1076 (1999)Google Scholar
  126. Wyngaard, J.C.: Toward Numerical Modeling in the “Terra Incognita”. J. Atmos. Sci., 61, 1816–1826 (2004)Google Scholar
  127. Yamada, T., Koike, K.: Downscaling mesoscale meteorological models for computational wind engineering applications. J. Wind Eng. Ind. Aerodyn., 99, 199–216 (2011)Google Scholar
  128. Zhou, B., Simon, J.S., Chow, F.K.: The Convective Boundary Layer in the Terra Incognita. J. Atmos. Sci., 71, 2545–2563 (2014)Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institut für Meteorologie und KlimaforschungKarlsruher Institut für TechnologieGarmisch-PartenkirchenGermany

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