On the surface wind speed probability density function over complex terrain

被引:13
作者
Jimenez, P. A. [1 ,2 ]
Dudhia, J. [2 ]
Navarro, J. [1 ]
机构
[1] CIEMAT, Div Energias Renovables, ES-28040 Madrid, Spain
[2] NCAR, Mesoscale & Microscale Meteorol Div, Boulder, CO 80307 USA
基金
美国国家科学基金会;
关键词
DISTRIBUTIONS;
D O I
10.1029/2011GL049669
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The physical mechanisms determining the shape of the surface wind speed probability density function over complex terrain are investigated using observations from a dense mesoscale network and a high spatial resolution mesoscale simulation for the 1992 to 2005 period. Results indicate that the atmospheric stability plays a major role in controlling the shape of the wind speed distribution but its effects are strongly modulated by the contribution of different atmospheric scales of motion such as the mesoscale or synoptic scale. The local topographic features further modulate the relative contribution of each mechanism. As a consequence of the complicated interaction of these atmospheric processes the surface wind speed distribution can present a complicated shape that is not always expected to fit a unimodal Weibull type distribution over complex terrain regions. Citation: Jimenez, P. A., J. Dudhia, and J. Navarro (2011), On the surface wind speed probability density function over complex terrain, Geophys. Res. Lett., 38, L22803, doi: 10.1029/2011GL049669.
引用
收藏
页数:5
相关论文
共 21 条
[1]  
[Anonymous], 2000, MOUNTAIN METEOROLOGY
[2]   Sea-breeze-induced mass transport over complex terrain in south-eastern France: A case-study [J].
Bastin, S ;
Drobinski, P .
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2006, 132 (615) :405-423
[3]   A review of wind speed probability distributions used in wind energy analysis Case studies in the Canary Islands [J].
Carta, J. A. ;
Ramirez, P. ;
Velazquez, S. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2009, 13 (05) :933-955
[4]  
Crawford K.C., 1973, J APPL METEOROL, V12, P127
[5]   The ERA-Interim reanalysis: configuration and performance of the data assimilation system [J].
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. ;
Holm, E. V. ;
Isaksen, L. ;
Kallberg, P. ;
Koehler, M. ;
Matricardi, M. ;
McNally, A. P. ;
Monge-Sanz, B. M. ;
Morcrette, J. -J. ;
Park, B. -K. ;
Peubey, C. ;
de Rosnay, P. ;
Tavolato, C. ;
Thepaut, J. -N. ;
Vitart, F. .
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2011, 137 (656) :553-597
[6]   Fitting wind speed distributions: A case study [J].
Garcia, A ;
Torres, JL ;
Prieto, E ;
De Francisco, A .
SOLAR ENERGY, 1998, 62 (02) :139-144
[7]   A Comparison of Methodologies for Monthly Wind Energy Estimation [J].
Garcia-Bustamante, E. ;
Gonzalez-Rouco, J. F. ;
Jimenez, P. A. ;
Navarro, J. ;
Montavez, J. P. .
WIND ENERGY, 2009, 12 (07) :640-659
[8]   Probability distributions of land surface wind speeds over North America [J].
He, Yanping ;
Monahan, Adam Hugh ;
Jones, Colin G. ;
Dai, Aiguo ;
Biner, Sebastien ;
Caya, Daniel ;
Winger, Katja .
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2010, 115
[9]  
HENNESSEY JP, 1977, J APPL METEOROL, V16, P119, DOI 10.1175/1520-0450(1977)016<0119:SAOWPS>2.0.CO
[10]  
2