Journal cover Journal topic
Annales Geophysicae An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 1.585 IF 1.585
  • IF 5-year value: 1.698 IF 5-year
    1.698
  • CiteScore value: 1.62 CiteScore
    1.62
  • SNIP value: 0.820 SNIP 0.820
  • IPP value: 1.52 IPP 1.52
  • SJR value: 0.781 SJR 0.781
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 83 Scimago H
    index 83
  • h5-index value: 24 h5-index 24
Volume 34, issue 3
Ann. Geophys., 34, 347-356, 2016
https://doi.org/10.5194/angeo-34-347-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Ann. Geophys., 34, 347-356, 2016
https://doi.org/10.5194/angeo-34-347-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Regular paper 21 Mar 2016

Regular paper | 21 Mar 2016

Three-model ensemble wind prediction in southern Italy

Rosa Claudia Torcasio1, Stefano Federico2, Claudia Roberta Calidonna1, Elenio Avolio1, Oxana Drofa3, Tony Christian Landi3, Piero Malguzzi3, Andrea Buzzi3, and Paolo Bonasoni3 Rosa Claudia Torcasio et al.
  • 1CNR-ISAC, Zona Industriale Comparto 15, 88046 Lamezia Terme, Italy
  • 2CNR-ISAC, Via del Fosso del Cavaliere 100, 00133 Rome, Italy
  • 3CNR-ISAC, Via Gobetti 101, Bologna, Italy

Abstract. Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council – Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3h up to 48h of forecast lead time.

Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30%, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

Publications Copernicus
Download
Short summary
Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. This work shows the application of a technique to improve wind forecasting. The study area is southern Italy.
Quality of wind prediction is of great importance since a good wind forecast allows the...
Citation
Share