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Uncertainty quantification and predictability of wind speed over the Iberian Peninsula

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Fernández González, S. and Martín, M. L. and Merino, A. and Sánchez, J. L. and Valero Rodríguez, Francisco (2017) Uncertainty quantification and predictability of wind speed over the Iberian Peninsula. Journal of geophysical research-atmospheres, 122 (7). pp. 3877-3890. ISSN 2169-897X

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Official URL: http://dx.doi.org/10.1002/2017JD026533




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.


Item Type:Article
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© 2017 American Geophysical Union. This work was partially supported by research projects METEORISK (RTC-2014-1872-5), CGL2011-25327, AYA2011-29967-C05-02, PCIN-2014-013-C07-04 (UE ERA-NET Plus NEWA Project), ESP2013-47816-C4-4-P, CGL2010-15930, and CGL2016-78702, and by the Instituto de Matemática Interdisciplinar (IMI) of the Universidad Complutense. Special thanks are due to Roberto Weigand, Steven Hunter, and Analisa Weston. The authors also thank the Deutscher Wetterdienst (DWD) and European Centre for Medium-Range Weather Forecasts (ECMWF) for providing the gridded daily mean near-surface (10 m) wind speed for Europe (DWD), EPS, and ERA-Interim databases (ECMWF). To request the data, please contact S. Fernández González (sefern04@ucm.es).

Uncontrolled Keywords:Ensemble prediction system; Spread-error relationship; Data assimilation system; Model output statistics; Kalman filter; Weather forecasts; Variability; Methodology; Reliability; Regression
Subjects:Sciences > Physics > Astrophysics
Sciences > Physics > Astronomy
ID Code:43078
Deposited On:06 Jun 2017 14:32
Last Modified:10 Dec 2018 15:04

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