Uncertainty quantification and predictability of wind speed over the Iberian Peninsula

Abstract

During recent decades, the use of probabilistic forecasting methods has increased markedly. However, these predictions still need improvement in uncertainty quantification and predictability analysis. For this reason, the main aim of this paper is to develop tools for quantifying uncertainty and predictability of wind speed over the Iberian Peninsula. To achieve this goal, several spread indexes extracted from an ensemble prediction system are defined in this paper. Subsequently, these indexes were evaluated with the aim of selecting the most appropriate for the characterization of uncertainty associated to the forecasting. Selection is based on comparison of the average magnitude of ensemble spread (ES) and mean absolute percentage error (MAPE). MAPE is estimated by comparing the ensemble mean with wind speed values from different databases. Later, correlation between MAPE and ES was evaluated. Furthermore, probability distribution functions (PDFs) of spread indexes are analyzed to select the index with greater similarity to MAPE PDFs. Then, the spread index selected as optimal is used to carry out a spatiotemporal analysis of model uncertainty in wind forecasting. The results indicate that mountainous regions and the Mediterranean coast are characterized by strong uncertainty, and the spread increases more rapidly in areas affected by strong winds. Finally, a predictability index is proposed for obtaining a tool capable of providing information on whether the predictability is higher or lower than average. The applications developed may be useful in the forecasting of wind potential several days in advance, with substantial importance for estimating wind energy production.

Large‐Scale Drivers and Seasonal Predictability of Extreme Wind Speeds Over the North Atlantic and Europe

“…The spread of the interquartile range of System 4 appears fairly sharp, implying that at least half of the ensemble members predict wind speeds relatively close to each other. This is in accordance with Fernández‐González et al () who determined the interquartile range as the most balanced uncertainty quantification for wind speeds over Spain in the ECMWF EPS forecasting system. They also note that the ensemble mean should be accompanied by some measure of uncertainty.…”

“…Overall, the PP value proves to be a valid measure of uncertainty quantification. Fernández‐González et al () investigated the uncertainty of wind speeds for the ECMWF ensemble prediction system (EPS) operational weather forecasting ensemble with a predictability index that evaluates the average interquartile range for a 30‐day period against the current forecast. In a way, this is similar to the PP as it estimates in how far the uncertainty of the climatology can be reduced by a forecast, thus whether or not the predictability is better than “usually.” Overall, the conclusions they draw from their analysis of the ECMWF EPS are in line (see below) with System 4 although the forecasts are on different time scales and in their case on a much smaller spatial scale.…”

Numerical Simulation of a Heavy Precipitation Event in the Vicinity of Madrid-Barajas International Airport: Sensitivity to Initial Conditions, Domain Resolution, and Microphysics Parameterizations

“…An increase in ensemble spread may be attained by combining different physics parameterizations and initial conditions [41]. In this way, the underdispersive nature of the ensembles detected in the results can be partially corrected [42]. Therefore, although the best scores from the validation were obtained by the D1-GFS1-M configuration, the use of an ensemble provides additional information about the uncertainty associated with the spatiotemporal evolution of precipitation as well as the accumulated precipitation.…”

Multi‐criteria analysis and Geographic Information Systems methods for wind turbine siting in a North Aegean island

“…Toft et al 12 assess the uncertainty of the wind climate parameters at each turbine position by using the local wind measurements, the speed‐up factors, and by calculating the distance between the position of the measurements and the position of the wind turbine. Fernández‐González et al 13 develop a predictability index as a tool that can forecast the wind potential for several days in advance providing estimations of wind energy production. Katopodis et al 14 investigated the impact of climate changes on wind resources in Greece.…”