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2022 | OriginalPaper | Buchkapitel

7. Quellen für Winddaten

verfasst von : Stefan Emeis

Erschienen in: Windenergie Meteorologie

Verlag: Springer International Publishing

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Zusammenfassung

Die Ressource für die Energieerzeugung durch Windkraftanlagen ist der Wind. Dieses Kapitel nennt Quellen für Winddaten. Dabei werden verschiedene Windmessmethoden und Simulationsdaten vorgestellt. Zudem werden wichtige Methoden für Windstatistiken präsentiert.

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Vorheriges Kapitel Physik der Windparks
Nächstes Kapitel Geräuscherzeugung und Geräuschausbreitung
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Literatur
Abreu, V.J., J.E. Barnes, P.B. Hays: Observations of wind with an incoherent lidar detector. Appl. Opt. 31, 4509–4514 (1992)CrossRef
Adams, A.S., D.W. Keith: Are global wind power resource estimates overstated? Environ. Res. Lett., 8, 015021 (2013)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
Beyrich, F.: Mixing height estimation from sodar data – a critical discussion. Atmos. Environ. 31, 3941–3954 (1997)CrossRef
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)CrossRef
Böttcher, F., S. Barth, J. Peinke: Small and large scale fluctuations in atmospheric wind speeds. Stoch. Environ. Res. Risk Assess. 21, 299–308 (2007)CrossRef
Bradley, S., A. Strehz, S. Emeis: Remote sensing winds in complex terrain – a review. Meteorol. Z., 24, 547–555 (2015)CrossRef
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)CrossRef
Busch, N.E., L. Kristensen: Cup anemometer overspeeding. J. Appl. Meteorol., 15, 1328–1332 (1976)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
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)
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)CrossRef
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)CrossRef
Christiansen, M.B., C.B. Hasager: Wake effects of large offshore wind farms identified from satellite SAR. Remote Sens. Environ., 98, 251–268 (2005)CrossRef
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)CrossRef
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)CrossRef
Cook, N.J.: Towards better estimation of extreme winds, J. Wind Eng. Ind. Aerodyn. 9, 295–323 (1982)CrossRef
Cooney, J.: Measurement of atmospheric temperature profiles by Raman backscatter. J. Appl. Meteorol. 11, 108–112 (1972)CrossRef
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)CrossRef
Davis, F.K., H. Newstein: The Variation of Gust Factors with Mean Wind Speed and with Height. J. Appl. Meteor. 7, 372–378 (1968)CrossRef
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)CrossRef
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)
Draxl, C., A. Clifton, B.-M. Hodge, J. McCaa: The wind integration national dataset (wind) Toolkit. Appl. Energy, 151, 355–366 (2015)CrossRef
Eresmaa, N., Karppinen, A., Joffre, S.M., Räsänen, J., Talvitie, H.: Mixing height determination by ceilometer. Atmos. Chem. Phys., 6, 1485–1493 (2006)CrossRef
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)
Emeis, S.: Measurement Methods in Atmospheric Sciences. In situ and remote. Series: Quantifying the Environment Vol. 1. Borntraeger Stuttgart. XIV + 257 pp. (2010)
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)
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)CrossRef
Emeis, S., Türk, M.: Frequency distributions of the mixing height over an urban area from SODAR data. Meteorol. Z. 13, 361–367 (2004)CrossRef
Emeis, S., M. Türk: Wind-driven wave heights in the German Bight. Ocean Dyn. 59, 463–475. (2009)CrossRef
Emeis, S., M. Harris, R.M. Banta: Boundary-layer anemometry by optical remote sensing for wind energy applications. Meteorol. Z., 16, 337–347 (2007a)CrossRef
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)CrossRef
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)CrossRef
Emeis S., Schäfer K., Münkel C.: Surface-based remote sensing of the mixing-layer height – a review. Meteorol. Z. 17, 621–630 (2008)CrossRef
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)CrossRef
Foken, T.: Micrometeorology. Springer, 308 pp. (2008)
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)CrossRef
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)CrossRef
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)CrossRef
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. https://​doi.​org/​10.​1002/​wene.​250 (2017)
Gross, G.: Das dreidimensionale, nichthydrostatische Mesoscale-Modell FITNAH. Meteorol. Rdsch, 43, 97–112 (1991)
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)CrossRef
Gumbel, E.J.: Statistics of extremes. Columbia University Press, New York and London, 375 pp. (1958)
Hardesty, R.M., L.S. Darby: Ground-based and airborne lidar.– Encyclopedia of Hy-drologic Sciences. M.G. Anderson (Ed.), Wiley, 697–712 (2005)
Harris, M., G. Constant, C. Ward: Continuous wave bistatic laser Doppler wind sensor. Appl. Opt. 40, 1501–1506 (2001)CrossRef
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)
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)CrossRef
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)CrossRef
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)CrossRef
ISO 28902-2: Air quality – Environmental meteorology – Part 2: Ground-based remote sensing of wind by heterodyne pulsed Doppler lidar. (2017)
Jackson, P.S., Hunt, J.C.R.: Turbulent wind flow over a low hill. Quart. J. Roy. Met. Soc., 101, 929–55 (1975)CrossRef
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)CrossRef
Jensen, N.O., L. Kristensen: Gust statistics for the Great Belt Region. Risoe-M-2828, 21 pp. (1989)
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)CrossRef
Justus, C.G., W.R. Hargraves, A. Yalcin: Nationwide Assessment of Potential Output from Wind-Powered Generators. J. Appl. Meteor. 15, 673–678 (1976)CrossRef
Justus, C.G., W.R. Hargraves, A. Mikhail, D. Graber: Methods for Estimating Wind Speed Frequency Distributions. J. Appl. Meteor. 17, 350–353 (1978)CrossRef
Kaimal, J.C., S.F. Clifford, R.J. Lataitis: Effect of finite sampling on atmospheric spectra. Bound.-Lay. Meteorol. 47, 337–347 (1989)CrossRef
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)CrossRef
Klaas, T., Pauscher, L., Callies, D.: LiDAR-mast deviations in complex terrain and their simulation using CFD. Meteorol. Z, 24, 591–603 (2015)CrossRef
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)CrossRef
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)CrossRef
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)
Lammert, A., Bösenberg, J.: Determination of the Convective Boundary-Layer Height with Laser Remote Sensing. Bound.-Lay. Meteorol. 119, 159–170 (2006)CrossRef
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)
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)CrossRef
MacCready, P.B.: Mean Wind Speed Measurements in Turbulence. J. Appl. Meteorol., 5, 219–225 (1966)CrossRef
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)
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)
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)CrossRef
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)CrossRef
Mitsuta, Y., O. Tsukamoto: Studies on Spatial Structure of Wind Gust. J. Appl. Meteor. 28, 1155–1161 (1989)CrossRef
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)CrossRef
Morales, A., M. Wächter, J. Peinke: Advanced characterization of wind turbulence by higher order statistics. Proc. EWEC 2010 (2010)
Münkel, C.: Mixing height determination with lidar ceilometers – results from Helsinki Testbed. Meteorol. Z. 16, 451–459 (2007)CrossRef
Münkel, C., Räsänen, J.: New optical concept for commercial lidar ceilometers scanning the boundary layer. Proc. SPIE 5571, 364–374 (2004)CrossRef
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)CrossRef
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)CrossRef
Panchang, V., Zhao, L., Demirbilek. Z.: Estimation of extreme wave heights using GEOSAT measurements. Ocean Eng. 26, 205–225 (1999)CrossRef
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)
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)CrossRef
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)
Pedersen, T.F.: Development of a Classification System for Cup Anemometers – CLASSCUP. Risø Nat. Lab., Roskilde, Report Risø-R-1348(EN), 45 pp. (2003)
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)CrossRef
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)CrossRef
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)CrossRef
Probst, O., D. Cárdenas: State of the Art and Trends in Wind Resource Assessment. Energies, 3, 1087–1141 (2010)CrossRef
Rockel, B., Will, A., & Hense, A.: The regional climate model COSMO-CLM (CCLM). Meteorologische Zeitschrift, 17(4), 347–348 (2008)CrossRef
Rojowsky, K.: The Global Wind Trends Bulletin. DEWI Mag. 50, 50–54 (2017)
Rienecker, M., M. Suarez, R. Gelaro, R. Todling, J. Bacmeister E. 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)
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)CrossRef
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)
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)
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)CrossRef
Schmid, H. P.: Source Areas for Scalars and Scalar Fluxes, Bound.-Lay. Meteorol., 67, 293–318 (1994)CrossRef
Schroers, H., H. Lösslein, K. Zilich: Untersuchung der Windstruktur bei Starkwind und Sturm. Meteorol. Rdsch. 42, 202–212 (1990)
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)CrossRef
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)
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)CrossRef
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)
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)CrossRef
Stiperski, I., Rotach, M. W.: On the measurement of turbulence over complex mountainous terrain. Boundary-Layer Meteorology, 159(1), 97–121 (2016)CrossRef
Taylor, P.A., Teunissen, H.W.: The Askervein Hill Project: Overview and background data. Boundary-Layer Meteorology 39, 15–39 (1987)CrossRef
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)
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)
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)
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)
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)CrossRef
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)
Wagner, R., B. Cañadillas, A. Clifton, S. Feeney, N.A 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)
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)CrossRef
Weitkamp, C. (Ed.): Lidar. Range-Resolved Optical Remote Sensing of the Atmos-phere. Springer Science + Business Media Inc. New York. 455 pp. (2005)
Wieringa, J.: Gust factors over open water and built-up country. Bound.-Lay. Meteorol. 3, 424–441 (1973)CrossRef
Wieringa, J.: Shapes of annual frequency distributions of wind speed observed on high meteorological masts. Bound.-Lay.Meteorol. 47, 85–110 (1989)CrossRef
Wulfmeyer, V.: Investigation of turbulent processes in the lower troposphere with water-vapor DIAL and Radar-RASS. J. Atmos. Sci. 56, 1055–1076 (1999)CrossRef
Wyngaard, J.C.: Toward Numerical Modeling in the “Terra Incognita”. J. Atmos. Sci., 61, 1816–1826 (2004)CrossRef
Yamada, T., Koike, K.: Downscaling mesoscale meteorological models for computational wind engineering applications. J. Wind Eng. Ind. Aerodyn., 99, 199–216 (2011)CrossRef
Zhou, B., Simon, J.S., Chow, F.K.: The Convective Boundary Layer in the Terra Incognita. J. Atmos. Sci., 71, 2545–2563 (2014)CrossRef
Metadaten
Titel
Quellen für Winddaten
verfasst von
Stefan Emeis
Copyright-Jahr
2022
Buch
Windenergie Meteorologie

Print ISBN: 978-3-031-22445-4

Electronic ISBN: 978-3-031-22446-1

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-031-22446-1_7