Verónica Torralba

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

AbstractClimate 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...

Characterization of the near surface wind speed distribution at global scale: ERA-Interim reanalysis and ECMWF seasonal forecasting system 4

The present developments in 10 m wind seasonal forecast products have lead to a growth in the number of studies analysing different aspects of both its predictability and applicability. However, there is still a lack of global studies analysing the statistical properties of the probability distribution of 10 m wind speed comparing the seasonal forecast systems with the widely used reanalysis products. To fill this gap we have studied the properties of the probability distributions of 10 m wind speed from the ERA-Interim reanalysis and the European Centre for Medium-Range Weather Forecasts System 4 seasonal forecast system. We have focused on two seasons, JJA and DJF, considering both their interannual and intraseasonal variability. The 10 m wind speed distribution has been characterized in terms of the four main moments of the probability distribution (mean, standard deviation, skewness and kurtosis). We have also computed the coefficient of variation to identify the regions with the higher wind variability and the Shapiro–Wilks goodness of fit test to assess their normality. This set of parameters is important to provide useful climate information in wind energy decision-making processes that use simple assumptions of the wind speed frequency distribution to properly estimate the wind energy potential. Besides, this study also illustrates where the discrepancies of the distributions of the seasonal predictions and the reference dataset are higher and, thus, which might need special attention from a bias adjustment perspective.