Skip to main content
SpringerLink
Log in

Wind Field Probabilistic Forecasting

  • Chapter
  • First Online:
Wind Field and Solar Radiation Characterization and Forecasting

Part of the book series: Green Energy and Technology ((GREEN))

Abstract

Probabilistic wind forecasting is a methodology to deal with uncertainties in numerical weather prediction models (NWP). In this chapter, we describe the need for ensemble forecasting, the different techniques used to generate the different initial conditions, and the operational ensemble models that are used nowadays in meteorological agencies. Then, we develop an ensemble method designed for the downscaling wind model described in Chap. 4 coupled with the AROME–HARMONIE mesoscale model, a non-hydrostatic dynamic forecast model described in Chap. 5. As we have explained in Chap. 4, some parameters need to be estimated since we do not know its exact value. These parameters are, basically, the roughness length and the zero plane displacement (explained in Chap. 2), as well as the Gauss moduli parameter (\(\alpha \)) used in the diagnostic wind model. This estimation is the main source of uncertainties in the model; therefore we will estimate some of these parameters using different forecast values of the AROME–HARMONIE. Finally, an example of the approach is applied in Gran Canaria island with a comparison of the ensemble results with experimental data from AEMET meteorological stations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Barkmeijer J, Bouttier F, Gijzen MV (1998) Singular vectors and estimates of the analysis-error covariance metric. Q J Roy Meteorol Soc 124(549):1695–1713. https://doi.org/10.1002/qj.49712454916

    Article  Google Scholar 

  2. Bosart LF (1975) Sunya experimental results in forecasting daily temperature and precipitation. Mon Weather Rev 103(11):1013–1020. https://doi.org/10.1175/1520-0493(1975)103<1013:SERIFD>2.0.CO;2

    Article  Google Scholar 

  3. Bougeault P, Toth Z, Bishop C, Brown B, Burridge D, Chen DH, Ebert B, Fuentes M, Hamill TM, Mylne K, Nicolau J, Paccagnella T, Park YY, Parsons D, Raoult B, Schuster D, Dias PS, Swinbank R, Takeuchi Y, Tennant W, Wilson L, Worley S (2010) The THORPEX interactive grand global ensemble. Bull Am Meteorol Soc 91(8):1059–1072. https://doi.org/10.1175/2010bams2853.1

    Article  Google Scholar 

  4. Buizza R, Houtekamer PL, Pellerin G, Toth Z, Zhu Y, Wei M (2005) A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Mon Weather Rev 133(5):1076–1097. https://doi.org/10.1175/mwr2905.1

    Article  Google Scholar 

  5. Buizza R, Milleer M, Palmer TN (1999) Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Q J Roy Meteorol Soc 125(560):2887–2908. https://doi.org/10.1002/qj.49712556006

    Article  Google Scholar 

  6. Buizza R, Tribbia J, Molteni F, Palmer T (1993) Computation of optimal unstable structures for a numerical weather prediction model. Tellus A 45(5):388–407. https://doi.org/10.1034/j.1600-0870.1993.t01-4-00005.x

    Article  Google Scholar 

  7. Candille G (2009) The multiensemble approach: The NAEFS example. Mon Weather Rev 137(5):1655–1665. https://doi.org/10.1175/2008mwr2682.1

    Article  Google Scholar 

  8. Diaconescu EP, Laprise R (2012) Singular vectors in atmospheric sciences: A review. Earth Sci Rev 113(3–4):161–175. https://doi.org/10.1016/j.earscirev.2012.05.005

    Article  Google Scholar 

  9. Doblas-Reyes FJ, Hagedorn R, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting—II. calibration and combination. Tellus A 57(3), 234–252. https://doi.org/10.1111/j.1600-0870.2005.00104.x

    Google Scholar 

  10. Ehrendorfer M, Tribbia JJ (1997) Optimal prediction of forecast error covariances through singular vectors. J Atmos Sci 54(2):286–313. https://doi.org/10.1175/1520-0469(1997)054<0286:OPOFEC>2.0.CO;2

    Article  Google Scholar 

  11. Epstein ES (1969) Stochastic dynamic prediction. Tellus 21(6):739–759. https://doi.org/10.3402/tellusa.v21i6.10143

    Article  Google Scholar 

  12. Errico RM, Ehrendorfer M, Raeder K (2001) The spectra of singular values in a regional model. Tellus A 53(3):317–332. https://doi.org/10.1034/j.1600-0870.2001.01199.x

    Article  Google Scholar 

  13. Fritsch JM, Hilliker J, Ross J, Vislocky RL (2000) Model consensus. Weather Forecast 15(5):571–582. https://doi.org/10.1175/1520-0434(2000)015<0571:MC>2.0.CO;2

    Article  Google Scholar 

  14. Gleeson TA (1966) A causal relation for probabilities in synoptic meteorology. J Appl Meteorol 5(3):365–368. https://doi.org/10.1175/1520-0450(1966)005<0365:ACRFPI>2.0.CO;2

    Article  Google Scholar 

  15. Gyakum JR (1986) Experiments in temperature and precipitation forecasting for illinois. Weather Forecast 1(1):77–88. https://doi.org/10.1175/1520-0434(1986)001<0077:EITAPF>2.0.CO;2

    Article  Google Scholar 

  16. Hagedorn R, Doblas-Reyes FJ, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting— i. basic concept. Tellus A 57(3), 219–233 (2005). https://doi.org/10.1111/j.1600-0870.2005.00103.x

    Google Scholar 

  17. Krishnamurti TN (1999) Improved weather and seasonal climate forecasts from multimodel superensemble. Science 285(5433):1548–1550. https://doi.org/10.1126/science.285.5433.1548

    Article  Google Scholar 

  18. Lacarra JF, Talagrand O (1988) Short-range evolution of small perturbations in a barotropic model. Tellus A Dyn Meteorol Oceanogr 40(2):81–95. https://doi.org/10.3402/tellusa.v40i2.11784

    Article  Google Scholar 

  19. Leutbecher M (2005) On ensemble prediction using singular vectors started from forecasts. Mon Weather Rev 133(10):3038–3046. https://doi.org/10.1175/MWR3018.1

    Article  Google Scholar 

  20. Lewis JM (2005) Roots of ensemble forecasting. Mon Weather Rev 133(7):1865–1885. https://doi.org/10.1175/mwr2949.1

    Article  Google Scholar 

  21. Lorenz EN (1960) Energy and numerical weather prediction. Tellus 12(4):364–373. https://doi.org/10.3402/tellusa.v12i4.9420

    Article  Google Scholar 

  22. Lorenz EN (1962) The statistical prediction of solutions of dynamic equations. In: International Symposium on Numerical Weather Prediction in Tokyo. The Meteorological Society of Japan

    Google Scholar 

  23. Lorenz EN (1965) A study of the predictability of a 28-variable atmospheric model. Tellus 17(3):321–333. https://doi.org/10.1111/j.2153-3490.1965.tb01424.x

    Article  Google Scholar 

  24. Lorenz EN (1995) The essence of chaos. University of Washington Press

    Google Scholar 

  25. Magnusson L, Leutbecher M, Klln E (2008) Comparison between singular vectors and breeding vectors as initial perturbations for the ecmwf ensemble prediction system. Monthly Weather Rev 136(11), 4092–4104 (2008). https://doi.org/10.1175/2008MWR2498.1

    Article  Google Scholar 

  26. Murphy JM (1988) The impact of ensemble forecasts on predictability. Q J Royal Meteorol Soc 114(480):463–493. https://doi.org/10.1002/qj.49711448010

    Article  Google Scholar 

  27. Palmer TN, Doblas-Reyes FJ, Hagedorn R, Alessandri A, Gualdi S, Andersen U, Feddersen H, Cantelaube P, Terres JM, Davey M, Graham R, Délécluse P, Lazar A, Déqué M, Guérémy JF, Díez E, Orfila B, Hoshen M, Morse AP, Keenlyside N, Latif M, Maisonnave E, Rogel P, Marletto V, Thomson MC (2004) Development of a European multimodel ensemble system for seasonal-to-interannual prediction (DEMETER). Bull Am Meteorol Soc 85(6), 853–872. https://doi.org/10.1175/bams-85-6-853

    Article  Google Scholar 

  28. Palmer TN, Gelaro R, Barkmeijer J, Buizza R (1998) Singular vectors, metrics, and adaptive observations. J Atmos Sci 55(4):633–653. https://doi.org/10.1175/1520-0469(1998)055<0633:SVMAAO>2.0.CO;2

    Article  Google Scholar 

  29. Palmer TN, Zanna L (2013) Singular vectors, predictability and ensemble forecasting for weather and climate. J Phys A Math Theor 46(25):254,018. https://doi.org/10.1088/1751-8113/46/25/254018

    Article  MathSciNet  Google Scholar 

  30. Pavan V, Doblas-Reyes FJ (2000) Multi-model seasonal hindcasts over the euro-atlantic: skill scores and dynamic features. Clim Dyn 16(8):611–625. https://doi.org/10.1007/s003820000063

    Article  Google Scholar 

  31. Rajagopalan B, Lall U, Zebiak SE (2002) Categorical climate forecasts through regularization and optimal combination of multiple gcm ensembles. Mon Weather Rev 130(7):1792–1811. https://doi.org/10.1175/1520-0493(2002)130<1792:CCFTRA>2.0.CO;2

    Article  Google Scholar 

  32. Sanders F (1973) Skill in forecasting daily temperature and precipitation: some experimental results. Bull Am Meteorol Soc 54(11):1171–1178. https://doi.org/10.1175/1520-0477(1973)054<1171:SIFDTA>2.0.CO;2

    Article  Google Scholar 

  33. Simmons AJ, Hollingsworth A (2002) Some aspects of the improvement in skill of numerical weather prediction. Q J R Meteorol Soc 128(580):647–677. https://doi.org/10.1256/003590002321042135

    Article  Google Scholar 

  34. Thompson PD (1985) Prediction of the probable errors of predictions. Monthly Weather Rev 113(2):248–259. https://doi.org/10.1175/1520-0493(1985)113<0248:POTPEO>2.0.CO;2

    Article  Google Scholar 

  35. Toth Z, Kalnay E (1993) Ensemble forecasting at nmc: the generation of perturbations. Bull Am Meteorol Soc 74(12):2317–2330. https://doi.org/10.1175/1520-0477(1993)074<2317:EFANTG>$2.0.CO;2

    Article  Google Scholar 

  36. Toth Z, Kalnay E (1997) Ensemble forecasting at ncep and the breeding method. Monthly Weather Rev 125(12):3297–3319. https://doi.org/10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2

    Article  Google Scholar 

  37. Trevisan A, Legnani R (1995) Transient error growth and local predictability: a study in the lorenz system. Tellus A Dyn Meteorol Oceanogr 47(1):103–117. https://doi.org/10.3402/tellusa.v47i1.11496

    Article  Google Scholar 

  38. Wang X, Bishop CH (2003) A comparison of breeding and ensemble transform kalman filter ensemble forecast schemes. J Atmos Sci 60(9):1140–1158. https://doi.org/10.1175/1520-0469(2003)060<1140:ACOBAE>2.0.CO;2

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University Institute for Intelligent Systems and Numerical Applications in Engineering, University of Las Palmas de Gran Canaria, Edificio Central del Parque Tecnológico, Campus de Tafira, 35017, Las Palmas de Gran Canaria, Spain

    Albert Oliver, Eduardo Rodríguez & Luis Mazorra-Aguiar

Authors
  1. Albert Oliver
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eduardo Rodríguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luis Mazorra-Aguiar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Albert Oliver .

Editor information

Editors and Affiliations

  1. Atmospheric Sciences Research Center, State University of New York, Albany, New York, USA

    Richard Perez

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Oliver, A., Rodríguez, E., Mazorra-Aguiar, L. (2018). Wind Field Probabilistic Forecasting. In: Perez, R. (eds) Wind Field and Solar Radiation Characterization and Forecasting. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-76876-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-76876-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76875-5

  • Online ISBN: 978-3-319-76876-2

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation