Three-model ensemble wind prediction in southern Italy
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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-03-01
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| Series: | Annales Geophysicae |
| Online Access: | https://www.ann-geophys.net/34/347/2016/angeo-34-347-2016.pdf |
| 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. 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 <i>H</i> 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 3 h up to 48 h of forecast lead time.
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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. |
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| ISSN: | 0992-7689 1432-0576 |