|Title:||Wind field probabilistic forecasting||Authors:||Oliver, Albert
|UNESCO Clasification:||3322 Tecnología energética||Keywords:||Numerical Weather Prediction
Daily Temperature, et al
|Issue Date:||2018||Publisher:||Springer||Journal:||Green Energy and Technology||Abstract:||Probabilistic wind forecasting is a methodology to deal with uncertainties in numerical weather prediction models (NWP). In this chapter, we describe the need for ensemble forecasting, the different techniques used to generate the different initial conditions, and the operational ensemble models that are used nowadays in meteorological agencies. Then, we develop an ensemble method designed for the down-scaling wind model described in Chap. 4 coupled with the AROME-HARMONIE mesoscale model, a non-hydrostatic dynamic forecast model described in Chap. 5. As we have explained in Chap. 4, some parameters need to be estimated since we do not know its exact value. These parameters are, basically, the roughness length and the zero plane displacement (explained in Chap. 2), as well as the Gauss moduli parameter (a) used in the diagnostic wind model. This estimation is the main source of uncertainties in the model; therefore we will estimate some of these parameters using different forecast values of the AROME-HARMONIE. Finally, an example of the approach is applied in Gran Canaria island with a comparison of the ensemble results with experimental data from AEMET meteorological stations.||URI:||http://hdl.handle.net/10553/42411||ISBN:||978-3-319-76875-5||ISSN:||1865-3529||DOI:||10.1007/978-3-319-76876-2_6||Source:||Wind Field and Solar Radiation Characterization and Forecasting. Green Energy and Technology [ISSN 1865-3529] / Perez Richard (eds), p. 129-145, (Enero 2018)|
|Appears in Collections:||Capítulo de libro|
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