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Open AccessArticle

Sensitivity Studies for a Hybrid Numerical–Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)

1
TECNALIA, Parque Tecnológico de Álava, Albert Einstein 28, E-01510 Vitoria-Gasteiz (Araba/Álava), Spain
2
Applied Physics II Department, Faculty of Science and Technology, University of the Basque Country, E-48940 Leioa, Spain
3
NE and Fluid Mechanics Department, Faculty of Engineering, University of the Basque Country, E-48013 Bilbao, Spain
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Joint Research Unit, BEGIK, Spanish Institute of Oceanography-University of the Basque Country, Plentzia Itsas Estazioa (PIE), E-48620 Plentzia, Spain
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Institute of Coastal Research, Helmholtz-Zentrum-Geesthacht, 21502 Geesthacht, Germany
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NE and Fluid Mechanics Department, Faculty of Engineering, University of the Basque Country, E-20600 Eibar, Spain
*
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(1), 45; https://doi.org/10.3390/atmos11010045
Received: 28 November 2019 / Revised: 24 December 2019 / Accepted: 27 December 2019 / Published: 29 December 2019
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
This study evaluates the performance of statistical models applied to the output of numerical models for short-term (1–24 h) hourly wind forecasts at three locations in the Basque Country. The target variables are horizontal wind components and the maximum wind gust at 3 h intervals. Statistical approaches such as persistence, analogues, linear regression, and random forest (RF) are used. The verification statistics used are coefficient of determination (R2) and root mean square error (RMSE). Statistical models use three inputs: (1) Local wind observations; (2) extended EOFs (empirical orthogonal functions) derived from past local observations and ERA-Interim variables in a previous 24-h period covering a domain around the area of study; and (3) wind forecasts provided by ERA-Interim. Results indicate that, for horizons less than 1–4 h, persistence is the best model. For longer predictions, RF provides the best forecasts. For horizontal components at 4–24 h horizons, RF slightly outperformed ERA-Interim wind forecasts. For gust, RF performs better than ERA-Interim for all the horizons. Persistence is the most influential factor for 2–5 h. Beyond this horizon, predictors from the ERA-Interim wind forecasts led the contribution. Hybrid numerical–statistical methods can be used to improve short-term wind forecasts. View Full-Text
Keywords: short-term forecast; wind; statistical forecast; random forest; ERA-Interim; persistence short-term forecast; wind; statistical forecast; random forest; ERA-Interim; persistence
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Carreno-Madinabeitia, S.; Ibarra-Berastegi, G.; Sáenz, J.; Zorita, E.; Ulazia, A. Sensitivity Studies for a Hybrid Numerical–Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain). Atmosphere 2020, 11, 45.

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