Calibrated Probabilistic Hub-Height Wind Forecasts in Complex Terrain

David Siuta University of British Columbia, Vancouver, British Columbia, Canada

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Gregory West University of British Columbia, Vancouver, British Columbia, Canada

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Roland Stull University of British Columbia, Vancouver, British Columbia, Canada

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Thomas Nipen Norwegian Meteorological Institute, Oslo, Norway

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Abstract

This work evaluates the use of a WRF ensemble for short-term, probabilistic, hub-height wind speed forecasts in complex terrain. Testing for probabilistic-forecast improvements is conducted by increasing the number of planetary boundary layer schemes used in the ensemble. Additionally, several prescribed uncertainty models used to derive forecast probabilities based on knowledge of the error within a past training period are evaluated. A Gaussian uncertainty model provided calibrated wind speed forecasts at all wind farms tested. Attempts to scale the Gaussian distribution based on the ensemble mean or variance values did not result in further improvement of the probabilistic forecast performance. When using the Gaussian uncertainty model, a small-sized six-member ensemble showed equal skill to that of the full 48-member ensemble. A new uncertainty model called the pq distribution that better fits the ensemble wind forecast error distribution is introduced. Results indicate that the gross attributes (central tendency, spread, and symmetry) of the prescribed uncertainty model are more important than its exact shape.

Denotes content that is immediately available upon publication as open access.

This article is licensed under a Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0/).

© 2017 American Meteorological Society.

Corresponding author e-mail: David Siuta, dsiuta@eos.ubc.ca

Abstract

This work evaluates the use of a WRF ensemble for short-term, probabilistic, hub-height wind speed forecasts in complex terrain. Testing for probabilistic-forecast improvements is conducted by increasing the number of planetary boundary layer schemes used in the ensemble. Additionally, several prescribed uncertainty models used to derive forecast probabilities based on knowledge of the error within a past training period are evaluated. A Gaussian uncertainty model provided calibrated wind speed forecasts at all wind farms tested. Attempts to scale the Gaussian distribution based on the ensemble mean or variance values did not result in further improvement of the probabilistic forecast performance. When using the Gaussian uncertainty model, a small-sized six-member ensemble showed equal skill to that of the full 48-member ensemble. A new uncertainty model called the pq distribution that better fits the ensemble wind forecast error distribution is introduced. Results indicate that the gross attributes (central tendency, spread, and symmetry) of the prescribed uncertainty model are more important than its exact shape.

Denotes content that is immediately available upon publication as open access.

This article is licensed under a Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0/).

© 2017 American Meteorological Society.

Corresponding author e-mail: David Siuta, dsiuta@eos.ubc.ca
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  • Ahlstrom, M., and Coauthors, 2013: Knowledge is power: Efficiently integrating wind energy and wind forecasts. IEEE Energy Power Mag., 11, 4552, doi:10.1109/MPE.2013.2277999.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 1996: A method for producing and evaluating probabilistic precipitation forecasts from ensemble model integrations. J. Climate, 9, 15181530, doi:10.1175/1520-0442(1996)009<1518:AMFPAE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berner, J., G. J. Shutts, M. Leutbecher, and T. N. Palmer, 2009: A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF Ensemble Prediction System. J. Atmos. Sci., 66, 603626, doi:10.1175/2008JAS2677.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bludszuweit, H., J. A. Dominguez-Navarro, and A. Llombart, 2008: Statistical analysis of wind power forecast error. IEEE Trans. Power Syst., 23, 983991, doi:10.1109/TPWRS.2008.922526.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bourdin, D. R., T. N. Nipen, and R. B. Stull, 2014: Reliable probabilistic forecasts from an ensemble reservoir inflow forecasting system. Water Resour. Res., 50, 31083130, doi:10.1002/2014WR015462.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., and S. Park, 2009: A new moist turbulence parameterization in the Community Atmosphere Model. J. Climate, 22, 34223448, doi:10.1175/2008JCLI2556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buizza, R., P. L. Houtekamer, G. Pellerin, Z. Toth, Y. Zhu, and M. Wei, 2005: A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Mon. Wea. Rev., 133, 10761097, doi:10.1175/MWR2905.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Candille, G., 2009: The multiensemble approach: The NAEFS example. Mon. Wea. Rev., 137, 16551665, doi:10.1175/2008MWR2682.1.

  • Courtney, J., P. Lynch, and C. Sweeney, 2013: High resolution forecasting for wind energy applications using Bayesian model averaging. Tellus, 65A, 19669, doi:10.3402/tellusa.v65i0.19669.

    • Search Google Scholar
    • Export Citation
  • Cutler, N. J., H. R. Outhred, and I. F. MacGill, 2012: Using nacelle-based wind speed observations to improve power curve modeling for wind power forecasting. Wind Energy, 15, 245258, doi:10.1002/we.465.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deppe, A. J., W. A. Gallus Jr., and E. S. Takle, 2013: A WRF ensemble for improved wind speed forecasts at turbine height. Wea. Forecasting, 28, 212228, doi:10.1175/WAF-D-11-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Draxl, C., A. N. Hahmann, A. Peña, and G. Giebel, 2014: Evaluating winds and vertical wind shear from Weather Research and Forecasting Model forecasts using seven planetary boundary layer schemes. Wind Energy, 17, 3955, doi:10.1002/we.1555.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eckel, F. A., and C. F. Mass, 2005: Aspects of effective mesoscale, short-range ensemble forecasting. Wea. Forecasting, 20, 328350, doi:10.1175/WAF843.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giebel, G., R. Brownsword, G. Kariniotakis, M. Denhard, and C. Draxl, 2011: The state-of-the-art in short-term prediction of wind power—A literature overview. 2nd ed. Risø DTU Tech. Rep., 109 pp. [Available online at http://orbit.dtu.dk/files/5277161/GiebelEtAl-StateOfTheArtInShortTermPrediction_ANEMOSplus_2011.]

  • Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., 133, 10981118, doi:10.1175/MWR2904.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gneiting, T., F. Balabdaoui, and A. E. Raftery, 2007: Probabilistic forecasts, calibration and sharpness. J. Roy. Stat. Soc., 69B, 243268, doi:10.1111/j.1467-9868.2007.00587.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grenier, H., and C. S. Bretherton, 2001: A moist PBL parameterization for large-scale models and its application to subtropical cloud-topped marine boundary layers. Mon. Wea. Rev., 129, 357377, doi:10.1175/1520-0493(2001)129<0357:AMPPFL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimit, E. P., and C. F. Mass, 2002: Initial results of a mesoscale short-range ensemble forecasting system over the Pacific Northwest. Wea. Forecasting, 17, 192205, doi:10.1175/1520-0434(2002)017<0192:IROAMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimit, E. P., and C. F. Mass, 2007: Measuring the ensemble spread–error relationship with a probabilistic approach: Stochastic ensemble results. Mon. Wea. Rev., 135, 203221, doi:10.1175/MWR3262.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grubišić, V., R. K. Vellore, and A. W. Huggins, 2005: Quantitative precipitation forecasting of wintertime storms in the Sierra Nevada: Sensitivity to the microphysical parameterization and horizontal resolution. Mon. Wea. Rev., 133, 28342859, doi:10.1175/MWR3004.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hersbach, H., 2000: Decomposition of the continuous ranked probability score for ensemble prediction systems. Wea. Forecasting, 15, 559570, doi:10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoeting, J. A., M. Madigan, A. E. Raftery, and C. T. Volinsky, 1999: Bayesian model averaging: A tutorial. Stat. Sci., 14, 382417, doi:10.1214/ss/1009212519.

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., and H.-L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 23222339, doi:10.1175/1520-0493(1996)124<2322:NBLVDI>2.0.CO;2.

    • Crossref
    • 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, 23182341, doi:10.1175/MWR3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hopson, T. M., 2014: Assessing the ensemble spread–error relationship. Mon. Wea. Rev., 142, 11251142, doi:10.1175/MWR-D-12-00111.1.

  • Hu, X.-M., P. M. Klein, and M. Xue, 2013: Evaluation of the updated YSU planetary boundary layer scheme within WRF for wind resource and air quality assessments. J. Geophys. Res. Atmos., 118, 10 41010 505, doi:10.1002/jgrd.50823.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jambunathan, M. V., 1954: Some properties of beta and gamma distributions. Ann. Math. Stat., 25, 401405, doi:10.1214/aoms/1177728800.

  • Janjić, Z. I., 1994: The step-mountain Eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927945, doi:10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Juban, J., N. Siebert, and G. N. Kariniotakis, 2007: Probabilistic short-term wind power forecasting for the optimal management of wind generation. Proc. 2007 Power Tech Conf., Lausanne, Switzerland, IEEE, 683–688, doi:10.1109/PCT.2007.4538398.

    • Crossref
    • Export Citation
  • Junk, C., S. Späth, L. Von Bremen, and L. Delle Monache, 2015: Comparison and combination of regional and global ensemble prediction systems for probabilistic predictions of hub-height wind speed. Wea. Forecasting, 30, 12341253, doi:10.1175/WAF-D-15-0021.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumaraswamy, P., 1980: A generalized probability density function for double-bounded random processes. J. Hydrol., 46, 7988, doi:10.1016/0022-1694(80)90036-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lange, M., 2005: On the uncertainty of wind power predictions—Analysis of the forecast accuracy and statistical distribution of errors. J. Sol. Energy Eng., 127, 177184, doi:10.1115/1.1862266.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahoney, W. P., and Coauthors, 2012: A wind power forecasting system to optimize grid integration. IEEE Trans. Sustainable Energy, 3, 670682, doi:10.1109/TSTE.2012.2201758.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marquis, M., J. Wilczak, M. Ahlstrom, J. Sharp, A. Stern, J. C. Smith, and S. Calvert, 2011: Forecasting the wind to reach significant penetration levels of wind energy. Bull. Amer. Meteor. Soc., 92, 11591171, doi:10.1175/2011BAMS3033.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mass, C. F., D. Ovens, K. Westrick, and B. A. Colle, 2002: Does increasing horizontal resolution produce more skillful forecasts? Bull. Amer. Meteor. Soc., 83, 407430, doi:10.1175/1520-0477(2002)083<0407:DIHRPM>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCollor, D., and R. Stull, 2008a: Hydrometeorological accuracy enhancement via postprocessing of numerical weather forecasts in complex terrain. Wea. Forecasting, 23, 131144, doi:10.1175/2007WAF2006107.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCollor, D., and R. Stull, 2008b: Hydrometeorological short-range ensemble forecasts in complex terrain. Part I: Meteorological evaluation. Wea. Forecasting, 23, 533556, doi:10.1175/2008WAF2007063.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCollor, D., and R. Stull, 2008c: Hydrometeorological short-range ensemble forecasts in complex terrain. Part II: Economic evaluation. Wea. Forecasting, 23, 557574, doi:10.1175/2007WAF2007064.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monteiro, C., R. Bessa, V. Miranda, A. Botterud, J. Wang, and G. Conzelmann, 2009: Wind power forecasting: State-of-the-art 2009. Argonne National Laboratory Rep. ANL/DIS-10-1, 216 pp. [Available online at http://www.ipd.anl.gov/anlpubs/2009/11/65613.pdf.]

  • Nakanishi, M., and H. Niino, 2006: An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog. Bound.-Layer Meteor., 119, 397407, doi:10.1007/s10546-005-9030-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nipen, T. N., 2012: A component-based probabilistic weather forecasting system for operational usage. Ph.D. dissertation, University of British Columbia, 101 pp. [Available online at https://open.library.ubc.ca/collections/24/items/1.0053570.]

  • Nipen, T. N., and R. Stull, 2011: Calibrating probabilistic forecasts from an NWP ensemble. Tellus, 63A, 858875, doi:10.1111/j.1600-0870.2011.00535.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., R. Buizza, F. Doblas-Reyes, T. Jung, M. Leutbecher, G. J. Shutts, M. Steinheimer, and A. Weisheimer, 2009: Stochastic parametrization and model uncertainty. ECMWF Tech. Memo. 598, 42 pp. [Available online at http://www.ecmwf.int/sites/default/files/elibrary/2009/11577-stochastic-parametrization-and-model-uncertainty.pdf.]

  • Pinson, P., 2012: Very-short-term probabilistic forecasting of wind power with generalized logit-normal distributions. J. Roy. Stat. Soc., 61, 555576, doi:10.1111/j.1467-9876.2011.01026.x.

    • Search Google Scholar
    • Export Citation
  • Pinson, P., J. Juban, and G. N. Kariniotakis, 2006: On the quality and value of probabilistic forecasts of wind generation. Proc. 2006 Int. Conf. on Probabilistic Methods Applied to Power Systems, Stockholm, Sweden, IEEE, doi:10.1109/PMAPS.2006.360290.

    • Crossref
    • Export Citation
  • Pleim, J. E., 2007: A combined local and nonlocal closure model for the atmospheric boundary layer. Part I: Model description and testing. J. Appl. Meteor. Climatol., 46, 13831395, doi:10.1175/JAM2539.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 11551174, doi:10.1175/MWR2906.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shao, H., and Coauthors, 2016: Bridging research to operations transitions: Status and plans of community GSI. Bull. Amer. Meteor. Soc., 97, 14271440, doi:10.1175/BAMS-D-13-00245.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siuta, D., G. West, and R. Stull, 2017: WRF hub-height wind forecast sensitivity to PBL scheme, grid length, and initial-condition choice in complex terrain. Wea. Forecasting, doi:10.1175/WAF-D-16-0120.1, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., doi:10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • 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, 2535, doi:10.1198/jasa.2009.ap08615.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., 2007: Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models. Cambridge University Press, 459 pp.

    • Crossref
    • Export Citation
  • Stensrud, D. J., J.-W. Bao, and T. T. Warner, 2000: Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems. Mon. Wea. Rev., 128, 20772107, doi:10.1175/1520-0493(2000)128<2077:UICAMP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stull, R. B., 1988: An Introduction to Boundary Layer Meteorology. Kluwer Academic, 666 pp.

    • Crossref
    • Export Citation
  • Sukoriansky, S., B. Galperin, and V. Perov, 2005: Application of a new spectral theory of stably stratified turbulence to the atmospheric boundary layer over sea ice. Bound.-Layer Meteor., 117, 231257, doi:10.1007/s10546-004-6848-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X., and C. H. Bishop, 2003: A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci., 60, 11401158, doi:10.1175/1520-0469(2003)060<1140:ACOBAE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Warner, T. T., 2011: Numerical Weather and Climate Prediction. Cambridge University Press, 526 pp.

  • Whitaker, J. S., and A. F. Loughe, 1998: The relationship between ensemble spread and ensemble mean skill. Mon. Wea. Rev., 126, 32923302, doi:10.1175/1520-0493(1998)126<3292:TRBESA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilczak, J., and Coauthors, 2015: The Wind Forecast Improvement Project (WFIP): A public–private partnership addressing wind energy forecast needs. Bull. Amer. Meteor. Soc., 96, 16991718, doi:10.1175/BAMS-D-14-00107.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Academic Press, 676 pp.

    • Crossref
    • Export Citation
  • Zhang, Y., J. Wang, and X. Wang, 2014: Review on probabilistic forecasting of wind power generation. Renewable Sustainable Energy Rev., 32, 255270, doi:10.1016/j.rser.2014.01.033.

    • Crossref
    • Search Google Scholar
    • Export Citation
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