• Arbez, C., M. Clément, C. Godreau, N. Swytink-Binnema, K. Tete, and M. Wadham-Gagnon, 2016: Development and validation of an ice prediction model for wind farms. Natural Resources Canada Tech. Rep., 134 pp., https://nergica.com/wp-content/uploads/3.NRCan_Report_EN.pdf.

  • Benjamin, S. G., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 16691694, https://doi.org/10.1175/MWR-D-15-0242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bergström, H., E. Olsson, S. Söderberg, P. Thorsson, and P. Undén, 2013: Wind power in cold climates: Ice mapping methods. Elforsk Rep. 13:10, 244 pp., http://www.diva-portal.org/smash/get/diva2:704372/FULLTEXT01.pdf.

  • Bernstein, B. C., J. Hirvonen, E. Gregow, and I. Wittmeyer, 2012: Experiences from real-time LAPS-LOWICE runs over Sweden: 2011-2012 icing season. Winterwind Int. Wind Energy Conf. 2012, Skellefteå, Sweden, Swedish Wind Power Association, http://www.slideshare.net/WinterwindConference/3a-bernstein-lapslowice.

  • Bredesen, R., M. Drapalik, and B. Butt, 2017a: Understanding and acknowledging the ice throw hazard—Consequences for regulatory frameworks, risk perception and risk communication. J. Phys.: Conf. Ser., 926, 012001, https://doi.org/10.1088/1742-6596/926/1/012001.

    • Search Google Scholar
    • Export Citation
  • Bredesen, R., and Coauthors, 2017b: Wind energy projects in cold climates. IEA Wind TCP Task 19 Rep., 2nd ed. 49 pp., https://community.ieawind.org/task19/viewdocument/iea-wind-tcp-task-19-recommended-pr.

  • Colarco, P., A. da Silva, M. Chin, and T. Diehl, 2010: Online simulations of global aerosol distributions in the NASA GEOS-4 model and comparisons to satellite and ground-based aerosol optical depth. J. Geophys. Res., 115, D14207, https://doi.org/10.1029/2009jd012820.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Combitech, 2016: IceMonitor product sheet. Combitech, 2 pp., accessed 27 July 2020, http://www.rwis.net/res/pdffiles/IceMonitor_Product_Sheet.pdf.

  • Davis, N., A. N. Hahmann, N.-E. Clausen, and M. Žagar, 2014: Forecast of icing events at a wind farm in Sweden. J. Appl. Meteor. Climatol., 53, 262281, https://doi.org/10.1175/JAMC-D-13-09.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, N., Ø. Byrkjedal, A. N. Hahmann, N.-E. Clausen, and M. Žagar, 2016a: Ice detection on wind turbines using the observed power curve. Wind Energy, 19, 9991010, https://doi.org/10.1002/we.1878.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, N., P. Pinson, A. N. Hahmann, N.-E. Clausen, and M. Žagar, 2016b: Identifying and characterizing the impact of turbine icing on wind farm power generation. Wind Energy, 19, 15031518, https://doi.org/10.1002/we.1933.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Déqué, M., 2012: Deterministic forecasts of continuous variables. Forecast Verification: A Practitioner’s Guide in Atmospheric Science, 2nd ed. I. T. Jolliffe and D. B. Stephenson, Eds., John Wiley & Sons, 77–94, https://doi.org/10.1002/9781119960003.

    • Crossref
    • Export Citation
  • Drage, M. A., and G. Hauge, 2008: Atmospheric icing in a coastal mountainous terrain. Measurements and numerical simulations, a case study. Cold Reg. Sci. Technol., 53, 150161, https://doi.org/10.1016/j.coldregions.2007.12.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • ECMWF, 2020: IFS documentation. Accessed 27 July 2020, https://www.ecmwf.int/en/publications/ifs-documentation.

  • Forbes, R., A. Tompkins, and A. Untch, 2011: A new prognostic bulk microphysics scheme for the IFS. ECMWF Tech. Memo. 649, 22 pp., https://www.ecmwf.int/en/elibrary/9441-new-prognostic-bulk-microphysics-scheme-ifs.

  • Froidevaux, P., S. Bourgeois, and R. Cattin, 2019: Intercomparison of blade-based ice detection systems. Winterwind Int. Wind Energy Conf. 2019, Umeå, Sweden, Winterwind, https://winterwind.se/wp-content/uploads/2019/02/09_03_Froidevaux_Benchmark_of_four_Blade-based_Ice_Detection_Systems_Pub_v2.pdf.

  • Ginoux, P., M. Chin, I. Tegen, J. Prospero, B. Holben, O. Dubovik, and S. Lin, 2001: Sources and distributions of dust aerosols simulated with the GOCART model. J. Geophys. Res., 106, 20 25520 273, https://doi.org/10.1029/2000JD000053.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grell, G. A., and S. R. Freitas, 2014: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos. Chem. Phys., 14, 52335250, https://doi.org/10.5194/acp-14-5233-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hogan, R. J., and I. B. Mason, 2012: Deterministic forecasts of binary events. Forecast Verification: A Practitioner’s Guide in Atmospheric Science, 2nd ed. I. T. Jolliffe and D. B. Stephenson, Eds., John Wiley and Sons, 31–59, https://doi.org/10.1002/9781119960003.

    • Crossref
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • ISO, 2017: Atmospheric icing of structures. ISO 12494:2017(en), https://www.iso.org/obp/ui#iso:std:iso:12494:ed-2:v1:en.

  • Jokela, T., M. Tiihonen, and T. Karlsson, 2019: Validation of droplet size in the VTT icing wind tunnel test section. Winterwind Int. Wind Energy Conf. 2019, Umeå, Sweden, Winterwind, 12 pp., https://winterwind.se/wp-content/uploads/2019/02/11_01_Jokela_Validation_of_Droplet_Size_in_the_VTT_Icing_Wind_Tunnel_Test_Section_Pub_v1.pdf.

  • Jolin, N., D. Bolduc, N. Swytink-Binnema, G. Rosso, and C. Godreau, 2019: Wind turbine blade ice accretion: A correlation with nacelle ice accretion. Cold Reg. Sci. Technol., 157, 235241, https://doi.org/10.1016/j.coldregions.2018.10.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaikkonen, V. A., E. O. Molkoselka, and A. J. Makynen, 2020: A rotating holographic imager for stationary cloud droplet and ice crystal measurements. Opt. Rev., 27, 205216, https://doi.org/10.1007/s10043-020-00583-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klemp, J. B., J. Dudhia, and A. D. Hassiotis, 2008: An upper gravity-wave absorbing layer for NWP applications. Mon. Wea. Rev., 136, 39874004, https://doi.org/10.1175/2008MWR2596.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krenn, A., and Coauthors, 2018: International recommendations for ice fall and ice throw risk assessments. IEA Wind TCP Task 19 Rep., 41 pp., https://community.ieawind.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=3e92fc30-a54a-4888-e612-79126301c58e&forceDialog=1.

  • Lehtomäki, V., 2016: Emerging from the cold. Wind Power Mon., 29 July, https://www.windpowermonthly.com/article/1403504/emerging-cold.

  • Lehtomäki, V., and Coauthors, 2018: Available technologies for wind energy in cold climates. IEA Wind TCP Task 19 Rep., 129 pp., https://community.ieawind.org/task19/viewdocument/available-technologies-for-wind-ene.

  • Makkonen, L., 2000: Models for the growth of rime, glaze, icicles and wet snow on structures. Philos. Trans. Roy. Soc., A358, 29132939, https://doi.org/10.1098/rsta.2000.0690.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meteotest, 2016: Evaluation of ice detection systems for wind turbines. VGB Research Project 392 Final Rep., 111 pp., https://www.vgb.org/vgbmultimedia/392_Final+report-p-10476.pdf.

  • Molinder, J., H. Körnich, E. Olsson, H. Bergström, and A. Sjöblom, 2018: Probabilistic forecasting of wind power production losses in cold climates: A case study. Wind Energy Sci., 3, 667680, https://doi.org/10.5194/wes-3-667-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molinder, J., H. Körnich, E. Olsson, and P. Hessling, 2019: The use of uncertainty quantification for the empirical modeling of wind turbine icing. J. Appl. Meteor. Climatol., 58, 20192032, https://doi.org/10.1175/JAMC-D-18-0160.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2004: An improved Mellor-Yamada level-3 model with condensation physics: Its design and verification. Bound.-Layer Meteor., 112, 131, https://doi.org/10.1023/B:BOUN.0000020164.04146.98.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, https://doi.org/10.1007/s10546-005-9030-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NCEP/NOAA, 2020: Global Forecast System. Accessed 27 July 2020, https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php.

  • NCEP/NWS/NOAA/U.S. Department of Commerce, 2008: NCEP ADP global upper air and surface weather observations (PREPBUFR format). National Center for Atmospheric Research Computational and Information Systems Laboratory Research Data Archive, accessed 27 July 2020, https://doi.org/10.5065/Z83F-N512.

    • Crossref
    • Export Citation
  • Nygaard, B. E., 2009: Evaluation of icing simulations for “COST727 icing test sites” in Europe. 13th Int. Workshop on Atmospheric Icing of Structures (IWAIS XIII), Andermatt, Switzerland, IWAIS, https://www.compusult.com/html/IWAIS_Proceedings/IWAIS_2009/Session_3_cost_727_wg1/session_3_nygaard.pdf.

  • Nygaard, B. E. K., J. E. Kristjánsson, and L. Makkonen, 2011: Prediction of in-cloud icing conditions at ground level using the WRF Model. J. Appl. Meteor. Climatol., 50, 24452459, https://doi.org/10.1175/JAMC-D-11-054.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nygaard, B. E. K., H. Ágústsson, and K. Somfalvi-Toth, 2013: Modeling wet snow accretion on power lines: Improvements to previous methods using 50 years of observations. J. Appl. Meteor. Climatol., 52, 21892203, https://doi.org/10.1175/JAMC-D-12-0332.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Podolskiy, E. A., B. E. K. Nygaard, K. Nishimura, L. Makkonen, and E. P. Lozowski, 2012: Study of unusual atmospheric icing at Mount Zao, Japan, using the weather research and forecasting model. J. Geophys. Res., 117, D12106, https://doi.org/10.1029/2011JD017042.

    • Search Google Scholar
    • Export Citation
  • Richardson, D. S., 2012: Economic value and skill. Forecast Verification: A Practitioner’s Guide in Atmospheric Science, 2nd ed. I. T. Jolliffe and D. B. Stephenson, Eds., John Wiley and Sons, 167–184, https://doi.org/10.1002/9781119960003.

    • Crossref
    • Export Citation
  • Rydblom, S., B. Thornberg, and E. Olsson, 2019: Field study of LWC and MVD using the droplet imaging instrument. IEEE Trans. Instrum. Meas., 68, 614622, https://doi.org/10.1109/TIM.2018.2843599.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scher, S., and J. Molinder, 2019: Machine learning-based prediction of icing-related wind power production loss. IEEE Access, 7, 129 421129 429, https://doi.org/10.1109/ACCESS.2019.2939657.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and J. B. Klemp, 2008: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys., 227, 34653485, https://doi.org/10.1016/j.jcp.2007.01.037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smirnova, T. G., J. M. Brown, S. G. Benjamin, and J. S. Kenyon, 2016: Modifications to the Rapid Update Cycle Land Surface Model (RUC LSM) available in the Weather Research and Forecasting (WRF) Model. Mon. Wea. Rev., 144, 18511865, https://doi.org/10.1175/MWR-D-15-0198.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, A., N. Lott, and R. Vose, 2011: The integrated surface database: Recent developments and partnerships. Bull. Amer. Meteor. Soc., 92, 704708, https://doi.org/10.1175/2011BAMS3015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sundqvist, H., E. Berge, and J. E. Kristjansson, 1989: Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model. Mon. Wea. Rev., 117, 16411657, https://doi.org/10.1175/1520-0493(1989)117<1641:CACPSW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, G., and T. Eidhammer, 2014: A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J. Atmos. Sci., 71, 36363658, https://doi.org/10.1175/JAS-D-13-0305.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, https://doi.org/10.1175/2008MWR2387.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, G., B. E. Nygaard, L. Makkonen, and S. Dierer, 2009: Using the Weather Research and Forecasting (WRF) model to predict ground/structural icing. 13th Int. Workshop on Atmospheric Icing of Structures (IWAIS XIII), Andermatt, Switzerland, IWAIS, https://www.compusult.com/html/IWAIS_Proceedings/IWAIS_2009/Session_3_cost_727_wg1/session_3_thompson.pdf.

  • Thorsson, P., S. Söderberg, and H. Bergström, 2015: Modelling atmospheric icing: A comparison between icing calculated with measured meteorological data and NWP data. Cold Reg. Sci. Technol., 119, 124131, https://doi.org/10.1016/j.coldregions.2015.07.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vassbø, T., J. Kristjansson, S. Fikke, and L. Makkonen, 1998: An investigation of the feasibility of predicting icing episodes using numerical weather prediction model output. Proc. Eighth Int. Workshop on Atmospheric Icing of Structures (IWAIS VIII), Reykjavik, Iceland, IWAIS, 343–347.

  • Weigel, A. P., 2012: Ensemble forecasts. Forecast Verification: A Practitioner’s Guide in Atmospheric Science, 2nd ed. I. T. Jolliffe and D. B. Stephenson, Eds., John Wiley and Sons, 141–166, https://doi.org/10.1002/9781119960003.

    • Crossref
    • Export Citation
  • Weissinger, M., 2017: Synoptische Analyse von Vereisungsfällen an Windkraftanlagen am Beispiel Ellern, Deutschland (Synoptic analysis of icing events at wind turbines in Ellern, Germany). Bachelor’s thesis, University of Vienna, 53 pp.

  • Wilks, D., 1997: Resampling hypothesis tests for autocorrelated fields. J. Climate, 10, 6582, https://doi.org/10.1175/1520-0442(1997)010<0065:RHTFAF>2.0.CO;2.

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

    • Crossref
    • Export Citation
  • Yang, J., K. F. Jones, W. Yu, and R. Morris, 2012: Simulation of in-cloud icing events on Mount Washington with the GEM-LAM. J. Geophys. Res., 117, D17204, https://doi.org/10.1029/2012JD017520.

    • Search Google Scholar
    • Export Citation
  • Zhao, Q., and F. H. Carr, 1997: A prognostic cloud scheme for operational NWP models. Mon. Wea. Rev., 125, 19311953, https://doi.org/10.1175/1520-0493(1997)125<1931:APCSFO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Skill and Potential Economic Value of Forecasts of Ice Accretion on Wind Turbines

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  • 1 Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
  • 2 MeteoServe Wetterdienst GmbH, Vienna, Austria
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Abstract

In this paper, a verification study of the skill and potential economic value of forecasts of ice accretion on wind turbines is presented. The phase of active ice formation on turbine blades has been associated with the strongest wind power production losses in cold climates; however, skillful icing forecasts could permit taking protective measures using anti-icing systems. Coarse- and high-resolution forecasts for the range up to day 3 from global (IFS and GFS) and limited-area (WRF) models are coupled to the Makkonen icing model. Surface and upper-air observations and icing measurements at turbine hub height at two wind farms in central Europe are used for model verification over two winters. Two case studies contrasting a correct and an incorrect forecast highlight the difficulty of correctly predicting individual icing events. A meaningful assessment of model skill is possible only after bias correction of icing-related parameters and selection of model-dependent optimal thresholds for ice growth rate. The skill of bias-corrected forecasts of freezing and humid conditions is virtually identical for all models. Hourly forecasts of active ice accretion generally show limited skill; however, results strongly suggest the superiority of high-resolution WRF forecasts relative to other model variants. Predictions of the occurrence of icing within a period of 6 h are found to have substantially better accuracy. Probabilistic forecasts of icing that are based on gridpoint neighborhood ensembles show slightly higher potential economic value than forecasts that are based on individual gridpoint values, in particular at low cost-loss ratios, that is, when anti-icing measures are comparatively inexpensive.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Lukas Strauss, lukas.strauss@meteoserve.at

Abstract

In this paper, a verification study of the skill and potential economic value of forecasts of ice accretion on wind turbines is presented. The phase of active ice formation on turbine blades has been associated with the strongest wind power production losses in cold climates; however, skillful icing forecasts could permit taking protective measures using anti-icing systems. Coarse- and high-resolution forecasts for the range up to day 3 from global (IFS and GFS) and limited-area (WRF) models are coupled to the Makkonen icing model. Surface and upper-air observations and icing measurements at turbine hub height at two wind farms in central Europe are used for model verification over two winters. Two case studies contrasting a correct and an incorrect forecast highlight the difficulty of correctly predicting individual icing events. A meaningful assessment of model skill is possible only after bias correction of icing-related parameters and selection of model-dependent optimal thresholds for ice growth rate. The skill of bias-corrected forecasts of freezing and humid conditions is virtually identical for all models. Hourly forecasts of active ice accretion generally show limited skill; however, results strongly suggest the superiority of high-resolution WRF forecasts relative to other model variants. Predictions of the occurrence of icing within a period of 6 h are found to have substantially better accuracy. Probabilistic forecasts of icing that are based on gridpoint neighborhood ensembles show slightly higher potential economic value than forecasts that are based on individual gridpoint values, in particular at low cost-loss ratios, that is, when anti-icing measures are comparatively inexpensive.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Lukas Strauss, lukas.strauss@meteoserve.at
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