Wind-climate estimation based on mesoscale and microscale modeling: Statistical-dynamical downscaling for wind energy applications

Journal of Applied Meteorology and Climatology

Volumn 53, Issue 8, 2014, Pages 1901-1919

  Badger, Jake ^a   Frank, Helmut ^b   Hahmann, Andrea N ^a   Giebel, Gregor ^a  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^b DEUTSCHER WETTERDIENST   (Germany)

Author keywords

Mesoscale processes; Microscale processes variability; Regional effects; Renewable energy; Wind

Indexed keywords

ELECTRIC UTILITIES; WIND;

MESOSCALE MODEL SIMULATION; MESOSCALE PROCESS; MICROSCALE PROCESSES/VARIABILITY; POSTPROCESSING METHODS; REGIONAL EFFECTS; RENEWABLE ENERGIES; STATISTICAL-DYNAMICAL; WIND RESOURCE ESTIMATION;

WIND POWER;

ALTERNATIVE ENERGY; CLIMATE MODELING; DOWNSCALING; MESOSCALE METEOROLOGY; RENEWABLE RESOURCE; STATISTICAL ANALYSIS; WIND FARM; WIND POWER; WIND TURBINE;

SPAIN;

EID: 84905989194     PISSN: 15588424     EISSN: 15588432     Source Type: Journal    
DOI: 10.1175/JAMC-D-13-0147.1     Document Type: Article

References (25)

1. Adrian, G., and F. Fiedler, 1991: Simulation of unstationary wind and temperature fields over complex terrain and comparison with observations. Beitr. Phys. Atmos., 64, 27-48.
2. Astrup, P., N. O. Jensen, and T. Mikkelsen, 1996: Surface roughness model for LINCOM. Risø National Laboratory Tech. Rep. Risø-R-900(EN), Roskilde, Denmark, 30 pp. [Available online at http://www.rodos.fzk.de/Documents/Public/Handbook/ Volume3/4_2_4_LINCOM.pdf.]
3. Badger, J., X. G. Larsén, A. Hahmann, N. G. Mortensen, J. C. Hansen, Z. Rong, Y. Zhenbin, and Y. Chunhong, 2011: Methods to assess uncertainty of wind resource estimates determined by mesoscale modelling. Proc. 2011 EWEA Annual Event, Brussels, Belgium, European Wind Energy Association. [Available online at http://proceedings.ewea.org/annual2011/ programme/info2.php?id25307&id5=54%20&ordre51.]
4. Badger, M., J. Badger, M. Nielsen, C. B. Hasager, and A. Peña, 2010: Wind class sampling of satellite SAR imagery for offshore wind resource mapping. J. Appl. Meteor. Climatol., 49, 2474-2491, doi:10.1175/2010JAMC2523.1.
5. Bergström, H., 2001: Boundary-layer modelling for wind climate estimates. Wind Eng., 25, 289-299, doi:10.1260/ 030952401760177864.
6. Frank, H. P., and L. Landberg, 1997: Modelling the wind climate of Ireland. Bound.-Layer Meteor., 85, 359-377, doi:10.1023/ A:1000552601288.
7. Frank, H. P., O. Rathman, N. Mortensen, and L. Landberg, 2001: The numerical wind atlas-The KAMM/WASP method. Risø National Laboratory Tech. Rep. Risø-R-1252(EN), Roskilde, Denmark, 60 pp. [Available online at http://www.risoe.dk/ rispubl/VEA/veapdf/ris-r-1252.pdf.]
8. Frey-Buness, F., D. Heimann, and R. Sausen, 1995: A statisticaldynamical downscaling procedure for global climate simulations. Theor. Appl. Climatol., 50, 117-131, doi:10.1007/ BF00866111.
9. Giebel, G., J. Badger, I. M. Perez, P. Louka, G. Kallos, A. M. Palomares, C. Lac, and G. Descombes, 2006: Short-term forecasting using advanced physical modelling-The results of the ANEMOS project. Results from mesoscale, microscale and CFD modelling. Proc. European Wind Energy Conf. Exhibition, Athens, Greece, EuropeanWind Energy Association. [Available online at http://proceedings.ewea.org/ewec2006/ allfiles2/967_Ewec2006fullpaper.pdf.]
10. Jiménez, P. A.,E.García-Bustamante, J.González-Rouco, F. Valero, J. Montávez, and J. Navarro, 2008: Surface wind regionalization in complex terrain. J. Appl. Meteor. Climatol., 47, 308-325, doi:10.1175/2007JAMC1483.1.
11. Kalnay, E., et-al., 1996: The NCEP-NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437-471.
12. Kariniotakis, G., et-al., 2004: What performance can be expected by short-term wind power prediction models depending on site characteristics? Proc. European Wind Energy Conf. Exhibition, London, United Kingdom, European Wind Energy Association.
13. Klemp, J. B., and D. R. Durran, 1983: An upper boundary condition permitting internal gravity wave radiation in numerical mesoscale models. Mon. Wea. Rev., 111, 430-444, doi:10.1175/ 1520-0493(1983)111,0430:AUBCPI.2.0.CO;2.
14. Larsén,X.G., J. Badger,A.N.Hahmann, and N. G. Mortensen, 2012: The selective dynamical downscaling method for extremewind atlases. Wind Energy, 16, 1167-1182, doi:10.1002/we.1544.
15. Leckebusch, G. C., B. Koffi, U. Ulbrich, J. G. Pinto, T. Spangehl, and S. Zacharias, 2006: Analysis of frequency and intensity of European winter storm events from a multi-model perspective, at synoptic and regional scales. Climate Res., 31, 59-74, doi:10.3354/cr031059.
16. Mengelkamp, H.-T., H. Kapitza, and U. Pflüger, 1997: Statisticaldynamical downscaling ofwind climatologies. J.Wind Eng. Ind. Aerodyn., 67-68, 449-457, doi:10.1016/S0167-6105(97)00093-7.
17. Mortensen, N. G., and Coauthors, 2005: Wind Atlas for Egypt: Measurements and Modelling 1991-2005. New and Renewable Energy Authority, Egyptian Meteorological Authority, and Risø National Laboratory, 258 pp.
18. Pinto, J. G., C. P. Neuhaus, G. C. Leckebusch, M. Reyers, and M. Kerschgens, 2010: Estimation of wind storm impacts over western Germany under future climate conditions using a statistical-dynamical downscaling approach. Tellus, 62A, 188-201, doi:10.1111/j.1600-0870.2009.00424.x.
19. Rife, D. L., E. Vanvyve, J. O. Pinto, A. J. Monaghan, C. A. Davis, and G. S. Poulos, 2013: Selecting representative days for more efficient dynamical climate downscaling: Application to wind energy. J. Appl. Meteor. Climatol., 52, 47-63, doi:10.1175/ JAMC-D-12-016.1.
20. Sempreviva, A. M., S. E. Larsen, N. G. Mortensen, and I. Troen, 1990: Response of neutral boundary layers to changes of roughness. Bound.-Layer Meteor., 50, 205-225, doi:10.1007/ BF00120525.
21. Skamarock,W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 3019-3032, doi:10.1175/MWR2830.1.
22. Sreevalsan, E., S. S. Das, R. S. Kumar, G. Arivukkodi, and J. Badger, 2010: Indian Wind Atlas. Centre for Wind Energy Technology, 351 pp.
23. Tammelin, B., et-al., 2013: Production of the Finnish wind atlas. Wind Energy, 16, 19-35, doi:10.1002/we.517.
24. Troen, I., and E. L. Petersen, 1989: European Wind Atlas. Risø National Laboratory, 656 pp.
25. Yu, W., R. Benoit, C. Girard, A. Glazer, D. Lemarquis, J. R. Salmon, and J. -P. Pinard, 2006: Wind Energy Simulation Toolkit (WEST): A wind mapping system for use by the wind-energy industry. Wind Eng., 30, 15-33, doi:10.1260/ 030952406777641450.