Editorial Type:
Article
Article Type:
Research Article

Surface Wind Regionalization over Complex Terrain: Evaluation and Analysis of a High-Resolution WRF Simulation

Pedro A. Jiménez Departamento de Astrofísica y Ciencias de la Atmósfera, UCM, Madrid, Spain
División de Energías Renovables, CIEMAT, Madrid, Spain

Search for other papers by Pedro A. Jiménez in
Current site
Google Scholar
PubMed Close
,
J. Fidel González-Rouco Departamento de Astrofísica y Ciencias de la Atmósfera, UCM, Madrid, Spain

Search for other papers by J. Fidel González-Rouco in
Current site
Google Scholar
PubMed Close
,
Elena García-Bustamante Departamento de Astrofísica y Ciencias de la Atmósfera, UCM, Madrid, Spain
División de Energías Renovables, CIEMAT, Madrid, Spain

Search for other papers by Elena García-Bustamante in
Current site
Google Scholar
PubMed Close
,
Jorge Navarro División de Energías Renovables, CIEMAT, Madrid, Spain

Search for other papers by Jorge Navarro in
Current site
Google Scholar
PubMed Close
,
Juan P. Montávez Departamento de Física, UM, Murcia, Spain

Search for other papers by Juan P. Montávez in
Current site
Google Scholar
PubMed Close
,
Jordi Vilà-Guerau de Arellano Meteorology and Air Quality Group, Wageningen University, Wageningen, Netherlands

Search for other papers by Jordi Vilà-Guerau de Arellano in
Current site
Google Scholar
PubMed Close
,
Jimy Dudhia Mesoscale and Microscale Meteorological Division, National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by Jimy Dudhia in
Current site
Google Scholar
PubMed Close
, and
Antonio Muñoz-Roldan *CIEMAT, Madrid, Spain

Search for other papers by Antonio Muñoz-Roldan in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Feb 2010
DOI:
https://doi.org/10.1175/2009JAMC2175.1
Page(s):
268–287
Restricted access

Abstract

This study analyzes the daily-mean surface wind variability over an area characterized by complex topography through comparing observations and a 2-km-spatial-resolution simulation performed with the Weather Research and Forecasting (WRF) model for the period 1992–2005. The evaluation focuses on the performance of the simulation to reproduce the wind variability within subregions identified from observations over the 1999–2002 period in a previous study. By comparing with wind observations, the model results show the ability of the WRF dynamical downscaling over a region of complex terrain. The higher spatiotemporal resolution of the WRF simulation is used to evaluate the extent to which the length of the observational period and the limited spatial coverage of observations condition one’s understanding of the wind variability over the area. The subregions identified with the simulation during the 1992–2005 period are similar to those identified with observations (1999–2002). In addition, the reduced number of stations reasonably represents the spatial wind variability over the area. However, the analysis of the full spatial dimension simulated by the model suggests that observational coverage could be improved in some subregions. The approach adopted here can have a direct application to the design of observational networks.

++ The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: J. Fidel González-Rouco, UCM, Facultad de Ciencias Físicas, Avd. Complutense s/n, 28040 Madrid, Spain. Email: fidelgr@fis.ucm.es

Abstract

This study analyzes the daily-mean surface wind variability over an area characterized by complex topography through comparing observations and a 2-km-spatial-resolution simulation performed with the Weather Research and Forecasting (WRF) model for the period 1992–2005. The evaluation focuses on the performance of the simulation to reproduce the wind variability within subregions identified from observations over the 1999–2002 period in a previous study. By comparing with wind observations, the model results show the ability of the WRF dynamical downscaling over a region of complex terrain. The higher spatiotemporal resolution of the WRF simulation is used to evaluate the extent to which the length of the observational period and the limited spatial coverage of observations condition one’s understanding of the wind variability over the area. The subregions identified with the simulation during the 1992–2005 period are similar to those identified with observations (1999–2002). In addition, the reduced number of stations reasonably represents the spatial wind variability over the area. However, the analysis of the full spatial dimension simulated by the model suggests that observational coverage could be improved in some subregions. The approach adopted here can have a direct application to the design of observational networks.

++ The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: J. Fidel González-Rouco, UCM, Facultad de Ciencias Físicas, Avd. Complutense s/n, 28040 Madrid, Spain. Email: fidelgr@fis.ucm.es

Save
Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Anderson, J. R., E. E. Hardy, J. T. Roach, and R. E. Witmer, 1976: A land use and land cover classification system for use with remote sensor data. U.S. Geological Survey Professional Paper 964, 41 pp.

    • Search Google Scholar
    • Export Citation

  • Baker, R. W., E. W. Hewson, N. G. Butler, and E. J. Warchol, 1978: Wind power potential in Pacific Northwest. J. Appl. Meteor., 17 , 1814–1826.

    • Search Google Scholar
    • Export Citation

  • Belserene, E. P., 1988: Rhythms of a variable star. Sky Telesc., 76 , 288–290.

  • Black, T. L., 1994: The new NMC mesoscale Eta Model: Description and forecast examples. Wea. Forecasting, 9 , 265–278.

  • Bliss, N. B., and L. M. Olsen, 1996: Development of a 30-arc-second digital elevation model of South America. Proc. Pecora 13th Symp. Human Interactions with the Environment—Perspectives from Space, Sioux Falls, SD, U.S. Geological Survey.

    • Search Google Scholar
    • Export Citation

  • Buckley, R. L., A. H. Weber, and J. H. Weber, 2004: Statistical comparison of Regional Atmospheric Modelling System forecasts with observations. Meteor. Appl., 11 , 67–82.

    • Search Google Scholar
    • Export Citation

  • Cairns, M. M., and J. Corey, 2003: Mesoscale model simulations of high-wind events in the complex terrain of western Nevada. Wea. Forecasting, 18 , 249–263.

    • Search Google Scholar
    • Export Citation

  • Conil, S., and A. Hall, 2006: Local regimes of atmospheric variability: A case study of southern California. J. Climate, 19 , 4308–4325.

    • Search Google Scholar
    • Export Citation

  • Cotton, W. R., and Coauthors, 2003: RAMS 2001: Current status and future directions. Meteor. Z., 82 , 5–29.

  • Cox, R., B. L. Bauer, and T. Smith, 1998: A mesoscale model intercomparison. Bull. Amer. Meteor. Soc., 79 , 265–283.

  • Deeming, T. J., 1975: Fourier analysis with unequally-spaced data. Astrophys. Space Sci., 36 , 137–158.

  • Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46 , 3077–3107.

    • Search Google Scholar
    • Export Citation

  • Dudhia, J., 1996: A multilayer soil temperature model for MM5. Preprints, Sixth PSU/NCAR Mesoscale Model Users’ Workshop, Boulder, CO, PSU/NCAR, 49–50.

    • Search Google Scholar
    • Export Citation

  • Endlich, R. M., F. L. Ludwig, C. M. Bhumralkar, and M. A. Estoque, 1982: A diagnostic model for estimating winds at potential sites for wind turbines. J. Appl. Meteor., 21 , 1441–1454.

    • Search Google Scholar
    • Export Citation

  • Foufoula-Georgiou, E., and P. Kumar, Eds.,. 1995: Wavelets in Geophysics. Academic Press, 373 pp.

  • García, A., J. L. Torres, E. Prieto, and A. de Francisco, 1998: Fitting wind speed distributions: A case study. Sol. Energy, 2 , 139–144.

    • Search Google Scholar
    • Export Citation

  • García-Bustamante, E., J. F. González-Rouco, P. A. Jiménez, J. Navarro, and J. P. Montávez, 2008: The influence of the Weibull assumption in monthly wind energy estimation. Wind Energy, 11 , 483–502.

    • Search Google Scholar
    • Export Citation

  • García-Bustamante, E., J. F. González-Rouco, P. A. Jiménez, J. Navarro, and J. P. Montávez, 2009: A comparison of methodologies for monthly wind energy estimations. Wind Energy, 12 , 640–659.

    • Search Google Scholar
    • Export Citation

  • Gesch, D. B., and K. S. Larson, 1996: Techniques for development of global 1-kilometer digital elevation models. Proc. Pecora 13th Symp. Human Interactions with the Environment—Perspectives from Space, Sioux Falls, SD, U.S. Geological Survey.

    • Search Google Scholar
    • Export Citation

  • Goodin, W. R., G. J. McRae, and J. H. Seinfeld, 1980: An objective analysis technique for constructing three-dimensional urban scale wind fields. J. Appl. Meteor., 19 , 98–108.

    • Search Google Scholar
    • Export Citation

  • Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 122 pp.

    • Search Google Scholar
    • Export Citation

  • Hanna, S. R., and R. Yang, 2001: Evaluations of mesoscale models’ simulations of near-surface winds, temperature gradients, and mixing depths. J. Appl. Meteor., 40 , 1095–1104.

    • Search Google Scholar
    • Export Citation

  • Hong, S-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134 , 2318–2341.

    • Search Google Scholar
    • Export Citation

  • Jackson, P. S., and J. C. R. Hunt, 1975: Turbulent wind flow over a low hill. Quart. J. Roy. Meteor. Soc., 101 , 929–955.

  • Jenkins, G. J., P. J. Mason, W. H. Moores, and R. I. Sykes, 1981: Measurements of the flow structure around Ailsa Craig, a steep, tree-dimensional, isolated hill. Quart. J. Roy. Meteor. Soc., 107 , 833–851.

    • Search Google Scholar
    • Export Citation

  • Jiménez, P. A., J. F. González-Rouco, J. P. Montávez, J. Navarro, E. García-Bustamante, and F. Valero, 2008: Surface wind regionalization in complex terrain. J. Appl. Meteor. Climatol., 47 , 308–325.

    • Search Google Scholar
    • Export Citation

  • Jiménez, P. A., J. F. González-Rouco, J. P. Montávez, E. García-Bustamante, and J. Navarro, 2009: Climatology of wind patterns in the northeast of the Iberian Peninsula. Int. J. Climatol., 29 , 501–525.

    • Search Google Scholar
    • Export Citation

  • Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47 , 2784–2802.

    • Search Google Scholar
    • Export Citation

  • Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The Kain–Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 165–170.

    • Search Google Scholar
    • Export Citation

  • Klink, K., 2002: Trends and interannual variability of wind speed distributions in Minnesota. J. Climate, 15 , 3311–3317.

  • Laprise, R., 1992: The Euler equations of motion with hydrostatic pressure as an independent variable. Mon. Wea. Rev., 120 , 197–207.

  • Lin, Y-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in cloud model. J. Climate Appl. Meteor., 22 , 1065–1092.

    • Search Google Scholar
    • Export Citation

  • Lo, J. C-F., Z-L. Yang, and R. A. Pielke Sr., 2008: Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model. J. Geophys. Res., 113 , D09112. doi:10.1029/2007JD009216.

    • Search Google Scholar
    • Export Citation

  • Ludwig, F. L., J. M. Livingston, and R. M. Endlich, 1991: Use of mass conservation and critical dividing streamline concepts for efficient objective analysis of winds in complex terrain. J. Appl. Meteor., 30 , 1490–1499.

    • Search Google Scholar
    • Export Citation

  • Ludwig, F. L., D. K. Miller, and S. G. Gallaher, 2006: Evaluating a hybrid prognostic–diagnostic model that improves wind forecast resolution in complex coastal topography. J. Appl. Meteor. Climatol., 45 , 155–177.

    • Search Google Scholar
    • Export Citation

  • Mahrer, Y., and R. A. Pielke, 1977: The effects of topography on sea and land breezes in a two-dimensional numerical model. Mon. Wea. Rev., 105 , 1151–1162.

    • Search Google Scholar
    • Export Citation

  • Mahrer, Y., M. Segal, and R. Pielke, 1985: Mesoscale modelling of wind energy over non-homogeneous terrain. Bound.-Layer Meteor., 31 , 13–23.

    • Search Google Scholar
    • Export Citation

  • Martner, B. E., and J. D. Marwitz, 1982: Wind characteristics in southern Wyoming. J. Appl. Meteor., 21 , 1815–1827.

  • Mason, P. J., and R. I. Sykes, 1979: Flow over an isolated hill of moderate slope. Quart. J. Roy. Meteor. Soc., 105 , 383–395.

  • Mason, P. J., and J. C. King, 1984: Atmospheric flow over a succession of nearly two-dimensional ridges and valleys. Quart. J. Roy. Meteor. Soc., 110 , 821–845.

    • Search Google Scholar
    • Export Citation

  • Milligan, G. W., 1980: An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika, 50 , 325–342.

    • Search Google Scholar
    • Export Citation

  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102 , 16663–16682.

    • Search Google Scholar
    • Export Citation

  • Montgomery, R. B., 1950: The Taylor diagram (temperature against vapour pressure) for air mixtures. Meteor. Atmos. Phys., 2 , 163–183.

  • Pan, Z., E. Takle, W. Gutowski, and R. Turner, 1999: Long simulation of regional climate as a sequence of short segments. Mon. Wea. Rev., 127 , 308–321.

    • Search Google Scholar
    • Export Citation

  • Pielke, R. A., 2002: Mesoscale Meteorological Modeling. Academic Press, 676 pp.

  • Preisendorfer, R. W., 1988: Principal Component Analysis in Meteorology and Oceanography. Elsevier, 425 pp.

  • Qian, J-H., A. Seth, and S. Zebiak, 2003: Reinitialized versus continuous simulations for regional climate downscaling. Mon. Wea. Rev., 131 , 2857–2874.

    • Search Google Scholar
    • Export Citation

  • Ratto, C. F., R. Festa, C. Romeo, O. A. Frumento, and M. Galluzzi, 1994: Mass-consistent models for wind fields over complex terrain: The state of the art. Environ. Software, 9 , 247–268.

    • Search Google Scholar
    • Export Citation

  • Reid, S., and R. Turner, 2001: Correlation of real and model wind speeds in different terrains. Wea. Forecasting, 16 , 620–627.

  • Reynolds, O., 1895: On the dynamical theory of incompressible viscous fluids and the determination of the criterion. Philos. Trans. Roy. Soc. London, 186B , 123–164.

    • Search Google Scholar
    • Export Citation

  • Rife, D. R., C. A. Davis, Y. Liu, and T. T. Warner, 2004: Predictability of low-level winds by mesoscale meteorological models. Mon. Wea. Rev., 132 , 2533–2569.

    • Search Google Scholar
    • Export Citation

  • Sherman, C. A., 1978: A mass-consistent model for wind fields over complex terrain. J. Appl. Meteor., 17 , 312–319.

  • Simmons, A., and J. Gibson, 2000: The ERA-40 Project Plan. ECMWF ERA-40 Project Rep. 1, 63 pp.

  • Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the advanced research WRF version 2. NCAR Tech. Note TN-468+STR, 88 pp.

    • Search Google Scholar
    • Export Citation

  • Taylor, I. G., 1917: The formation of fog and mist. Quart. J. Roy. Meteor. Soc., 43 , 241–288.

  • Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106 , (D7). 7183–7192.

  • Torrence, C., and G. P. Compo, 1998: A practical guide to wavelet analysis. Bull. Amer. Meteor. Soc., 79 , 61–78.

  • Torres, J. L., A. García, E. Prieto, and A. De Francisco, 1999: Characterization of wind speed data according to wind direction. Sol. Energy, 66 , 57–64.

    • Search Google Scholar
    • Export Citation

  • Torres, J. L., A. García, M. D. Blas, and A. D. Francisco, 2005: Forecast of hourly average wind speed with ARMA models in Navarre (Spain). Sol. Energy, 79 , 65–77.

    • Search Google Scholar
    • Export Citation

  • Troen, I., and E. L. Petersen, 1989: The European Wind Atlas. Risoe National Laboratory, 656 pp.

  • Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis. Quart. J. Roy. Meteor. Soc., 131 , 2961–3012.

  • Verdin, K. L., and S. K. Greenlee, 1996: Development of continental scale digital elevation models and extraction of hydrographic features. Proc. Third Int. Conf./Workshop on Integrating GIS and Environmental Modeling, Santa Fe, NM, National Center for Geographic Information and Analysis.

    • Search Google Scholar
    • Export Citation

  • Volmer, J. P., M. Deque, and D. Rousselet, 1984: EOF analysis of 500 mb geopotential: A comparison between simulation and reality. Tellus, 36A , 336–347.

    • Search Google Scholar
    • Export Citation

  • von Storch, H., 1995: Inconsistencies at the interface of climate impact studies and global climate research. Meteor. Z., 4 , 72–80.

  • von Storch, H., and F. W. Zwiers, 1999: Statistical Analysis in Climate Research. Cambridge University Press, 499 pp.

  • Walmsley, J. L., J. R. Salmon, and P. A. Taylor, 1982: On the application of a model of boundary-layer flow over low hills to real terrain. Bound.-Layer Meteor., 23 , 17–46.

    • Search Google Scholar
    • Export Citation

  • Walter, A., and Coauthors, 2006: A high resolution reference data set of German wind velocity 1951–2001 and comparison with regional climate model results. Meteor. Z., 15 , 585–596.

    • Search Google Scholar
    • Export Citation

  • Wendland, W. M., 1982: Wind power as an electrical power source in Illinois. J. Appl. Meteor., 21 , 423–428.

  • Whiteman, C. D., 2000: Mountain Meteorology: Fundamentals and Applications. Oxford University Press, 355 pp.

  • Zagar, N., M. Zagar, J. Cedilnik, G. Gregoric, and J. Rakovec, 2006: Validation of mesoscale low-level winds obtained by dynamical downscaling of ERA40 over complex terrain. Tellus, 58A , 445–455.

    • Search Google Scholar
    • Export Citation

  • Zhong, S., and J. Fast, 2003: An evaluation of the MM5, RAMS, and Meso-Eta models at subkilometer resolution using VTMX field campaign data in the Salt Lake valley. Mon. Wea. Rev., 131 , 1301–1322.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 2900 1910 29
PDF Downloads 820 117 10