Bosveld, F. C., P. Baas, and A. A. M. Holtslag, 2010: The Third GABLS SCM Intercomparison and Evaluation Case. Preprints, 19th Symp. on Boundary Layer Turbulence, Keystone, CO, Amer. Meteor. Soc., 5.2. [Available online at http://ams.confex.com/ams/pdfpapers/172634.pdf.]
Bougeault, P., and P. Lacarrere, 1989: Parameterization of orography-induced turbulence in a mesobeta-scale model. Mon. Wea. Rev., 117, 1872–1890.
Buizza, R., M. Miller, and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Quart. J. Roy. Meteor. Soc., 125, 2887–2908.
Buizza, R., and Coauthors, 2005: A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Mon. Wea. Rev., 133, 1076–1097.
Cardell, J. B., and C. L. Anderson, 2009: Estimating the system costs of wind power forecast uncertainty. Proc. Power and Energy Society General Meeting, Calgary, AB, Canada, IEEE.
Costa, A., A. Crespo, J. Navarro, G. Lizcano, H. Madsen, and E. Feitosa, 2008: A review on the young history of the wind power short-term prediction. Renew. Sustain. Energy Rev., 12, 1725–1744.
Dempster, A. P., N. M. Laird, and D. B. Rubin, 1977: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc., 39B, 1–38.
Draxl, C., A. N. Hahmann, A. Pena, J. N. Nissen, and G. Giebel, 2010: Validation of boundary-layer winds from WRF mesoscale forecasts with applications to wind energy forecasting. Preprints, 19th Symp. on Boundary Layers and Turbulence, Keystone, CO, Amer. Meteor. Soc., 1B.1. [Available online at http://ams.confex.com/ams/pdfpapers/172440.pdf.]
Fabbri, A., T. G. S. Roman, J. R. Abbad, and V. H. M. Quezada, 2005: Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market. IEEE Trans. Power Syst., 20, 1440–1446.
Georgilakis, P. S., 2008: Technical challenges associated with the integration of wind power into power systems. Renew. Sustain. Energy Rev., 2, 852–863.
Giebel, G., R. Brownsword, G. Kariniotakis, M. Denhard, and C. Draxl, 2011: The state-of-the-art in short-term prediction of wind power. Project ANEMOS Deliverable Rep. D1.2, Roskilde, Denmark. [Available online at http://www.prediktor.dk/publ/GGiebelEtAl-StateOfTheArtInShortTermPrediction_ANEMOSplus_2011.pdf.]
Hacker, J. P., J. L. Anderson, and M. Pagowski, 2007: Improved vertical covariance estimates for ensemble-filter assimilation of near-surface observations. Mon. Wea. Rev., 135, 1021–1036.
Holtslag, A. A. M., G. J. Steeneveld, and B. J. H. van de Wiel, 2007: Role of land-surface temperature feedback on model performance for the stable boundary layer. Bound.-Layer Meteor., 125, 361–376.
Hong, S.-Y., and S.-W. Kim, 2008: Stable boundary layer mixing in a vertical diffusion scheme. Preprints, 18th Symp. on Boundary Layers and Turbulence, Stockholm, Sweden, Amer. Meteor. Soc., 16B.2. [Available online at http://ams.confex.com/ams/pdfpapers/140120.pdf.]
Janjić, Z. I., 1994: The step-mountain eta-coordinate model: Further developments of the convection, viscous sublayer and turbulent closure schemes. Mon. Wea. Rev., 122, 927–945.
Janjić, Z. I., 2001: Nonsingular implementation of the Mellor–Yamada level 2.5 scheme in the NCEP meso model. NOAA/NWS/NCEP Office Note 437, 61 pp. [Available online at http://www.emc.ncep.noaa.gov/officenotes/newernotes/on437.pdf.]
Kulkarni, M. A., S. Patil, G. V. Rama, and P. N. Sen, 2008: Wind speed prediction using statistical regression and neural network. J. Earth Syst. Sci., 117, 457–463.
Lei, M., L. Shiyan, J. Chuanwen, L. Hongling, and Z. Yan, 2009: A review on the forecasting of wind speed and generated power. Renew. Sustain. Energy Rev., 13, 915–920.
McLachlan, G. J., and T. Krishnan, 1997: The EM Algorithm and Extensions. John Wiley and Sons, 274 pp.
McSharry, P. E., S. Bouwman, and G. Bloemhof, 2005: Probabilistic forecasts of the magnitude and timing of peak electricity demand. IEEE Trans. Power Syst., 20, 1166–1172.
Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996: The ECMWF Ensemble Prediction System: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73–119.
Monfared, M., H. Rastegar, and H. M. Kojabadi, 2009: A new strategy for wind speed forecasting using artificial intelligent methods. Renew. Energy, 34, 845–848.
Monin, A. S., and A. M. Obukhov, 1954: Basic laws of turbulent mixing in the surface layer of the atmosphere. Contrib. Geophys. Inst. Acad. Sci. USSR, 151, 163–187.
Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. Japan, 87, 895–912.
NARUC, 2007: FERC Order 890: What does it mean for the West? National Association of Regulatory Utility Commissioners (NARUC), National Wind Coordinating Collaborative (NWCC), and the Western Governors’ Association, 8 pp. [Available online at http://www.nationalwind.org/assets/publications/ferc890.pdf.]
Oztopal, A., 2006: Artificial neural network approach to spatial estimation of wind velocity data. Energy Convers. Manage., 47, 395–406.
Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 1160–1174.
Riahy, G. H., and M. Abedi, 2008: Short term wind speed forecasting for wind turbine applications using linear prediction method. Renew. Energy, 33, 35–41.
Roquelaure, S., and T. Bergot, 2008: A local ensemble prediction for fog and low clouds: Construction, Bayesian model averaging calibration, and validation. J. Appl. Meteor. Climatol., 47, 3072–3088.
Sfetsos, A., 2000: A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renew. Energy, 21, 23–35.
Shin, H. H., and S.-Y. Hong, 2011: Intercomparison of planetary boundary layer parameterizations in the WRF model for a single day from CASES-99. Bound.-Layer Meteor., 139, 261–281, doi:10.1007/s10546-010-9583-z.
Singh, S., T. S. Bhatti, and D. P. Kothari, 2007: Wind power estimation using artificial neural network. J. Energy Eng., 133, 46–52.
Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. D. Powers, 2005: A description of the Advanced Research WRF version 2. NCAR Tech. Note TN-468+STR, 88 pp.
Sloughter, J. M., A. E. Raftery, T. Gneiting, and C. Fraley, 2007: Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Mon. Wea. Rev., 135, 3209–3220.
Sloughter, J. M., T. Gneiting, and A. E. Raftery, 2010: Probabilistic wind speed forecasting using ensembles and Bayesian model averaging. J. Amer. Stat. Assoc., 105, 25–35, doi:10.1198/jasa.2009.ap08615.
Smith, J. C., E. A. DeMeo, B. Parsons, and M. Milligan, 2004: Wind power impacts on electric power system operating costs: Summary and perspective on work to date. NREL/CP-500-35946, National Renewable Energy Laboratory, 13 pp. [Available online at http://www.nrel.gov/docs/fy04osti/35946.pdf.]
Sukoriansky, S., B. Galperin, and V. Perov, 2006: A quasi-normal scale-elimination model of turbulence and its application to stably stratified flows. Nonlinear Processes Geophys., 13, 9–22.
Sumner, J., and C. Masson, 2006: Influence of atmospheric stability on wind turbine power performance curves. J. Sol. Energy Eng., 128, 531–538.
Taylor, J. W., 2004: Forecasting weather variable densities for weather derivatives and energy prices. Modelling Prices in Competitive Electricity Markets, D. W. Bunn, Ed., Wiley, 307–330.
Taylor, J. W., P. E. McSharry, and R. Buizza, 2009: Wind power density forecasting using ensemble predictions and time series models. IEEE Trans. Power Syst., 24, 775–782.
Traiteur, J., 2011: A short-term wind speed forecasting system for wind power applications. M.S. thesis, Dept. of Atmospheric Sciences, University of Illinois at Urbana–Champaign, 76 pp.
U.S. Department of Energy, 2008: 20% wind energy by 2030: Increasing wind energy’s contribution to U.S. electricity supply. DOE/GO-102008-2567, 248 pp. [Available online at http://www.20percentwind.org/20percent_wind_energy_report_revOct08.pdf.]
U.S. Department of Energy, cited 2011: U.S. installed capacity and wind project locations. [Available online at: http://www.windpoweringamerica.gov/wind_installed_capacity.asp.]
von Storch, H., and F. W. Zwiers, 2001: Statistical Analysis in Climate Research. Cambridge University Press, 484 pp.
Wagner, R., M. S. Courtney, T. J. Larsen, and U. Schmidt Paulsen, 2010: Simulation of shear and turbulence impact on wind turbine performance. Risø DTU National Laboratory for Sustainable Energy, Riskilde, Denmark, 55 pp.