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Multi-model seasonal forecasts for the wind energy sector

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Abstract

An assessment of the forecast quality of 10 m wind speed by deterministic and probabilistic verification measures has been carried out using the original raw and two statistical bias-adjusted forecasts in global coupled seasonal climate prediction systems (ECMWF-S4, METFR-S3, METFR-S4 and METFR-S5) for boreal winter (December–February) season over a 22-year period 1991–2012. We follow the standard leave-one-out cross-validation method throughout the work while evaluating the hindcast skills. To minimize the systematic error and obtain more reliable and accurate predictions, the simple bias correction (SBC) which adjusts the systematic errors of model and calibration (Cal), known as the variance inflation technique, methods as the statistical post-processing techniques have been applied. We have also built a multi-model ensemble (MME) forecast assigning equal weights to datasets of each prediction system to further enhance the predictability of the seasonal forecasts. Two MME have been created, the MME4 with all the four prediction systems and MME2 with two better performing systems. Generally, the ECMWF-S4 shows better performance than other individual prediction systems and the MME predictions indicate consistently higher temporal correlation coefficient (TCC) and fair ranked probability skill score (FRPSS) than the individual models. The spatial distribution of significant skill in MME2 prediction is almost similar to that in MME4 prediction. In the aspect of reliability, it is found that the Cal method has more effective improvement than the SBC method. The MME4_Cal predictions are placed in close proximity to the perfect reliability line for both above and below normal categorical events over globe, as compared to the MME2_Cal predictions, due to the increase in ensemble size. To further compare the forecast performance for seasonal variation of wind speed, we have evaluated the skill of the only raw MME2 predictions for all seasons. As a result, we also find that winter season shows better performance than other seasons.

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References

  • Acharya N et al (2013) On the bias correction of general circulation model output for Indian summer monsoon. Meteorol Appl 20:349–356

    Article  Google Scholar 

  • Alessandri A et al (2010) The INGV–CMCC seasonal prediction system: Improved ocean initial conditions. Mon Weather Rev 138:2930–2952

    Article  Google Scholar 

  • Amin M (2013) Energy: the smart-grid solution. Nature 499:145–147

    Article  Google Scholar 

  • Barnston AG, Mason SJ, Goddard L, DeWitt DG, Zebiak SE (2003) Multimodel ensembling in seasonal climate forecasting at IRI. Bull Am Meteorol Soc 84:1783–1796. https://doi.org/10.1175/BAMS-84-12-1783

    Article  Google Scholar 

  • Brocker J, Smith LA (2007) Increasing the reliability of reliability diagrams. Weather Forecast 22:651–661

    Article  Google Scholar 

  • Buontempo C et al (2014) Climate service development, delivery and use in Europe at monthly to inter-annual timescales. Clim Risk Manag 6:1–5

    Article  Google Scholar 

  • Charles A et al (2011) Comparison of techniques for the calibration of coupled model forecasts of Murray Darling Basin seasonal mean rainfall. CAWCR Tech Rep No. 040. http://www.cawcr.gov.au/technical-reports/CTR_040.pdf

  • Chevallier M, Salas-Mélia D (2012) The role of sea ice thickness distribution in the Arctic sea ice potential predictability: A diagnostic approach with a coupled GCM. J Clim 25(8):3025–3038

    Article  Google Scholar 

  • Clark RT, Bett PE, Thornton HE, Scaife AA (2017) Skilful seasonal predictions for the European energy industry. Environ Res Lett 12:024002

    Article  Google Scholar 

  • Coelho CAS, Costa SMS (2010) Challenges for integrating seasonal climate forecasts in user applications. Curr Opin Environ Sustain 2:317–325. https://doi.org/10.1016/j.cosust.2010.09.002

    Article  Google Scholar 

  • Daan H (1985) Sensitivity of verification scores to the classification of the preditand. Mon Weather Rev 113:1384–1392

    Article  Google Scholar 

  • Daget N, Weaver AT, Balmaseda MA (2009) An ensemble three-dimensional variational data assimilation system for the global ocean: sensitivity to the observation- and background-error variance formulation. Q J R Meteorol Soc 135:1071–1094

    Article  Google Scholar 

  • Dee DP et al (2011) The ERA-interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597

    Article  Google Scholar 

  • Déqué M et al (1999) ARPEGE version 3, documentation algorithmique et mode d’emploi (in French). CNRM/GMGEC, Toulouse

    Google Scholar 

  • 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 57:234–252

    Google Scholar 

  • Ebinger J, Vergara W (2011) Climate impacts on energy systems: key issues for energy sector adaptation. World Bank. https://openknowledge.worldbank.org/handle/10986/2271

  • Epstein ES (1969) A scoring system for probability forecasts of ranked categories. J Appl Meteorol 8:985–987

    Article  Google Scholar 

  • Feddersen H, Navarra A, Ward MN (1999) Reduction of model systematic error by statistical correction for dynamical seasonal predictions. J Clim 12:1974–1989

    Article  Google Scholar 

  • Ferro CAT (2014) Fair scores for ensemble forecasts. Q J R Meteorol Soc 140:1917–1923

    Article  Google Scholar 

  • Ferro CAT, Richardson DS, Weigel AP (2008) On the effect of ensemble size on the discrete and continuous ranked probability scores. Meteorol Appl 15:19–24

    Article  Google Scholar 

  • Foley AM et al (2012) Current methods and advances in forecasting of wind power generation. Renew Energy 37:1–8. https://doi.org/10.1016/j.renene.2011.05.033

    Article  Google Scholar 

  • Frankfurt School-UNEP Collaborating Centre (2016) Global trends in renewable energy investment 2016, pp 1–84. http://fs-unep-centre.org/publications/global-trends-renewable-energy-investment-2016

  • 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 57:219–233

    Google Scholar 

  • IPCC (2012) Renewable energy sources and climate change mitigation: special report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA

    Google Scholar 

  • Jeong HI, Lee DY, Ashkok K, Ahn JB, Lee JY, Luo JJ, Schemm JK, Hendon HH, Braganza K, Ham YG (2012) Assessment of the APCC coupled MME suite in predicting the distinctive climate impacts of two flavors of ENSO during boreal winter. Clim Dyn 39:475–493

    Article  Google Scholar 

  • Jeong HI, Ahn JB, Lee JY, Alessandri A, Hendon HH (2015) Interdecadal change of interannual variability and predictability of two types of ENSO. Clim Dyn 44:1073–1091

    Article  Google Scholar 

  • Johnson C, Bowler N (2009) On the reliability and calibration of ensemble forecasts. Mon Weather Rev 137:1717–1720

    Article  Google Scholar 

  • Jolliffe IT, Stephenson DB (2003) Forecast verification: a practitioner’s guide in atmospheric science. Wiley, Hoboken (ISBN: 0-471-49759-2)

    Google Scholar 

  • Kharin VV, Zwiers FW (2002) Climate predictions with multimodel ensembles. J Clim 15:793–799

    Article  Google Scholar 

  • Kharin VV, Zwiers FW, Teng Q, Boer GJ, Derome J, Fontecilla JS (2009) Skill assessment of seasonal hindcasts from the Canadian Historical Forecast Project. Atmos Ocean 47:204–223

    Article  Google Scholar 

  • Kirtman BP, Min D, Infanti JM et al (2014) The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull Am Meteorol Soc 95:585–601. https://doi.org/10.1175/BAMS-D-12-00050.1

    Article  Google Scholar 

  • Koletsis I, Kotroni V, Lagouvardos K, T.Soukissian (2016) Assessment of offshore wind speed and power potential over the Mediterranean and the Black Seas under future climate changes. Renew Sustain Energy Rev 60:234–245

    Article  Google Scholar 

  • Krishnamurti TN et al (2000) Multimodel ensemble forecasts for weather and seasonal climate. J Clim 13:4196–4216

    Article  Google Scholar 

  • Kug JS, Lee JY, Kang IS (2008) Systematic error correction of dynamical seasonal prediction of sea surface temperature using a stepwise pattern project method. Mon Weather Rev 136:3501–3512. https://doi.org/10.1175/2008MWR2272.1

    Article  Google Scholar 

  • Langford S, Hendon HH (2013) Improving reliability of coupled model forecasts of australian seasonal rainfall. Mon Weather Rev 141:728–741

    Article  Google Scholar 

  • Lee DY, Ashok K, Ahn JB (2011) Toward enhancement of prediction skills of multimodel ensemble seasonal prediction: a climate filter concept. J Geophys Res 116:D06116. https://doi.org/10.1029/2010JD014610

    Article  Google Scholar 

  • Lee DY, Ahn JB et al (2013) Improvement of grand multi-model ensemble prediction skills for the coupled models of APCC/ENSEMBLES using a climate filter. Atmos Sci Lett 14:139–145. https://doi.org/10.1002/asl2.430

    Article  Google Scholar 

  • Lee DY, Ahn J-B, Yoo J-H (2015) Enhancement of seasonal prediction of East Asian summer rainfall related to western tropical Pacific convection. Clim Dyn 45:1025–1042

    Article  Google Scholar 

  • Leung LR et al (1999) Simulations of the ENSO hydroclimate signals in the pacific Northwest Columbia River Basin. Bull Am Meteorol Soc 80:2313–2329

    Article  Google Scholar 

  • Madec G, Delecluse P, Imbard M, Levy C (1998) Opa 8 ocean general circulation model—reference manual. Tech. rep., LODYC/IPSL Note 11

  • Meteo France (2015a) Météo-France seasonal forecast system 5 for Eurosip: technical description, pp 1–38

  • Meteo France (2015b) Météo-France seasonal forecast system 5 versus system 4: robust scores, pp 1–5

  • Michaelsen J (1987) Cross-validation in statistical climate forecast models. J Clim Appl Meteorol 26:1589–1600

    Article  Google Scholar 

  • Min Y-M, Kryjov VN, Park C-K (2009) A probabilistic multimodel ensemble approach to seasonal prediction. Weather Forecast 24:812–828. https://doi.org/10.1175/2008WAF2222140.1

    Article  Google Scholar 

  • Molteni F et al (2011) The new ECMWF seasonal forecast system (System 4). ECMWF Technical Memoranda, No. 656

  • Morse AP et al (2005) A forecast quality assessment of an end-to-end probabilistic multi-model seasonal forecast system using a malaria model. Tellus A 57:464–475

    Article  Google Scholar 

  • Murphy AH (1971) A note on the ranked probability score. J Appl Meteorol 10:155–156

    Article  Google Scholar 

  • Murphy AH (1988) Skill scores based on the Mean square error and their relationships to the correlation coefficient. Mon Weather Rev 116:2417–2424

    Article  Google Scholar 

  • Palmer BTN (2000) A probability and decision-model analysis of PROVOST seasonal multi-model ensemble integrations. Q J R Meteorol Soc 126:2013–2033

    Article  Google Scholar 

  • Palmer TN et al (2005) Probabilistic prediction of climate using multi-model ensembles: from basics to applications. Philos Trans R Soc Lond B Biol Sci 360:1991–1998

    Article  Google Scholar 

  • Pan J, Van den Dool H (1998) Extended-range probability forecasts based on dynamical model output. Weather Forecast 13:983–996

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Peng P et al (2002) An analysis of multimodel ensemble predictions for seasonal climate anomalies. J Geophys Res 107:1–12. https://doi.org/10.1029/2002JD002712

    Article  Google Scholar 

  • Richardson DS (2001) Measures of skill and value of ensemble prediction systems, their interrelationship and the effect of ensemble size. Q J R Meteorol Soc 127:2473–2489

    Article  Google Scholar 

  • Royer JF, Cariolle D, Chauvin F, Déqué M, Douville H, Hu RM, Planton S, Rascol A, Ricard JL, Salas y Melia D, Sevault F, Simon P, Somot S, Tyteca S, Terray L, Valcke S (2002) Simulation des changements climatiques au cours du 21-e`mesie`cle incluant l’ozone stratosphe´rique (simulation of climatechanges during the 21-st century including stratospheric ozone). C R Geosci 334:147–154

    Article  Google Scholar 

  • Torralba V et al (2017) Seasonal climate prediction: a new source of information for the management of wind energy resources. J Appl Meteorol Clim 56:1231–1247. https://doi.org/10.1175/JAMC-D-16-0204.1

    Article  Google Scholar 

  • Troccoli A et al (2010) Weather and climate risk management in the energy sector. Bull Am Meteorol Soc 91:785–788

    Article  Google Scholar 

  • Troccoli A et al (2013) Promoting new links between energy and meteorology. Bull Am Meteorol Soc 94:ES36–ES40. https://doi.org/10.1175/BAMS-D-12-00061.1

    Article  Google Scholar 

  • Vladislavleva E et al (2013) Predicting the energy output of wind farms based on weather data: Important variables and their correlation. Renewable Energy 50:236–243

    Article  Google Scholar 

  • Voldoire A et al (2013) The CNRM-CM5.1 global climate model: description and basic evaluation. Clim Dyn 40:2091–2121

    Article  Google Scholar 

  • Wang B et al (2008) How accurately do coupled climate models predict the leading modes of Asian–Australian monsoon interannual variability? Clim Dyn 30:605–619

    Article  Google Scholar 

  • Weigel AP, Liniger MA, Appenzeller C (2008) Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts? Q J R Meteorol Soc 134:241–260

    Article  Google Scholar 

  • Weigel AP et al (2010) Risks of model weighting in multimodel climate projections. J Clim 23:4175–4191

    Article  Google Scholar 

  • Weisheimer A et al (2009) ENSEMBLES: a new multimodel ensemble for seasonal-to-annual predictions—skill and progress beyond DEMETER in forecasting tropical Pacific SSTs. Geophys Res Lett 36:L21711. https://doi.org/10.1029/2009GL040896

    Article  Google Scholar 

  • Wilks DS (2006) Statistical methods in the atmospheric sciences. Elsevier, Amsterdam, p 627 (ISSN 0074-6142)

    Google Scholar 

  • Yang D, Yang X-Q, Xie Q, Zhang Y, Ren X, Tang Y (2016) Probabilistic versus deterministic skill in predicting the western North Pacific-East Asian summer monsoon variability with multimodel ensembles. J Geophys Res 121:1079–1103. https://doi.org/10.1002/2015JD023781

    Article  Google Scholar 

  • Yun WT et al (2005) A multi-model superensemble algorithm for seasonal climate prediction using DEMETER forecasts. Tellus A 57:280–289

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by the Spanish Ministry of Economy (MINECO) under the framework of the RESILIENCE project (CGL2013-41055-R). We are grateful to ECMWF and Météo France as the supporting institutions that provide with climate prediction datasets.

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Authors and Affiliations

  1. Earth Sciences Department, Barcelona Supercomputing Center (BSC), C/Jordi Girona, 29, 08034, Barcelona, Spain

    Doo Young Lee, Francisco J. Doblas-Reyes, Verónica Torralba & Nube Gonzalez-Reviriego

  2. Computational Physics and Methods (CCS-2), Computer, Computational, and Statistical Sciences (CCS) Division, Los Alamos National Laboratory (LANL), Los Alamos, USA

    Doo Young Lee

  3. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

    Francisco J. Doblas-Reyes

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  1. Doo Young Lee
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Lee, D.Y., Doblas-Reyes, F.J., Torralba, V. et al. Multi-model seasonal forecasts for the wind energy sector. Clim Dyn 53, 2715–2729 (2019). https://doi.org/10.1007/s00382-019-04654-y

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