Seasonal Climate Prediction: A New Source of Information for the Management of Wind Energy Resources

Climate predictions tailored to the wind energy sector represent an innovation in the use of climate information to better manage the future variability of wind energy resources. Wind energy users have traditionally employed a simple approach that is based on an estimate of retrospective climatological information. Instead, climate predictions can better support the balance between energy demand and supply, as well as decisions relative to the scheduling of maintenance work. One limitation for the use of the climate predictions is the bias, which has until now prevented their incorporation in wind energy models because they require variables with statistical properties that are similar to those observed. To overcome this problem, two techniques of probabilistic climate forecast bias adjustment are considered here: a simple bias correction and a calibration method. Both approaches assume that the seasonal distributions are Gaussian. These methods are linear and robust and neither requires parameter estimation—essential features for the small sample sizes of current climate forecast systems. This paper is the first to explore the impact of the necessary bias adjustment on the forecast quality of an operational seasonal forecast system, using the European Centre for Medium-Range Weather Forecasts seasonal predictions of near-surface wind speed to produce useful information for wind energy users. The results reveal to what extent the bias adjustment techniques, in particular the calibration method, are indispensable to produce statistically consistent and reliable predictions. The forecast-quality assessment shows that calibration is a fundamental requirement for high-quality climate service. The authors acknowledge funding support from the RESILIENCE (CGL2013-41055-R) project, funded by the Spanish Ministerio de Economía y Competitividad (MINECO) and the FP7 EUPORIAS (GA 308291) and SPECS (GA 308378) projects. Special thanks to Nube Gonzalez-Reviriego and Albert Soret for helpful comments and discussion. We also acknowledge the COPERNICUS action CLIM4ENERGY-Climate for Energy (C3S 441 Lot 2) and the New European Wind Atlas (NEWA) project funded from ERA-NET Plus, topic FP7-ENERGY.2013.10.1.2. We acknowledge the s2dverification and SpecsVerification R-based packages. Finally we would like to thank Pierre-Antoine Bretonnière, Oriol Mula and Nicolau Manubens for their technical support at different stages of this project.