No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Calibration and combination of a regional multi model ensemble for predicting offshore wind speeds: Presentation held at European Wind Energy Conference, 20. - 23. April 2010, Warsaw, Poland
ResearchGate has not been able to resolve any citations for this publication.
Oldenburg, University, Diss., 2003 (Nicht für den Austausch).
Wind power generation is directly linked to weather conditions and thus the first aspect of wind power forecasting is the prediction of future wind values. The new EU-Project "SafeWind" takes advantage of existing operational forecasting platforms at various European end-users. In this sense, the skill of ECMWF IFS (Integrated Forecast System) and EPS (Ensemble Prediction System) at various model levels is estimated for a period of two years (2008-09). Overall, both the IFS and EPS provide useful forecast guidance at different heights in the lower surface layer (∼0.5 to 1km height). In probabilistic mode, probabilities and uncertainty estimation based on EPS are more reliable over "synthetic" probabilities produced by EPS Ensemble Mean (EM), Control Forecast (CF) or IFS High-Resolution (HR) forecast for both the Short-Range (S-R: 0.5 to 2.5 days) and the Early Medium-Range (EM-R: 3 to 5 days). In deterministic mode, HR forecasts show considerable skill during S-R. EPS EM although better than CF, represents a less skilful option than HR during S-R. On the other hand, EM becomes more skilful than HR during EM-R. That is why Useful Forecast Interval (UFI) values for EM are higher than corresponding HR's. Spread/skill relationship of EPS spread and EM's error, although not perfect, proved to be very useful. The ability of EPS to link relatively skilful forecasts to small spread values was used to construct COBOS (COeficients Based On Spread) scheme, based on a weighted combination of EM & HR components. Overall, COBOS showed considerable superiority over EM and HR, during both the S-R and EM-R intervals.
The DEMETER multi-model ensemble system is used to investigate the enhancement in seasonal predictability that can be achieved by calibrating single-model ensembles and combining them to issue multi-model predictions. The forecast quality of both deterministic and probabilistic predictions is assessed and compared to the skill of a simple multi-model ensemble where all the single models are equally weighted. Both calibration and combination are carried out using cross-validation. Single-model seasonal ensembles are calibrated using canonical correlation analysis for model adjustment and variance inflation for reliability enhancement. Results indicate that both model adjustment and inflation increase the skill of tropical predictions for single-model ensembles, provided that the training time series are long enough. Some improvements are also found for extratropical areas, although mostly due to an increase of reliability associated with the inflation. The beneficial impact of calibration is smaller for the simple multi-model than for the single-model ensembles due to the relatively high reliability of the former. The raw single-model predictions are also linearly combined using grid-point multiple linear regression to create an optimized multi-model system. Results indicate that the forecast quality of the simple multi-model ensemble is generally difficult to improve using multiple linear regression due to the lack of robustness of the regression coefficients. As in the case of the calibration, longer time series would be preferred to achieve a significant forecast quality improvement. Over the tropics, a multiple linear regression, that uses the principal components of the model anomalies for the target area as predictors indicates a substantial gain in skill even with the available sample size. The implications of these results in an operational context are discussed.
Integration erneuerbarer Energien in die deutsche Stromversorgung im
- Ii Dena-Netzstudie
dena-Netzstudie II: " Integration erneuerbarer
Energien in die deutsche Stromversorgung im
Zeitraum 2015 – 2020 mit Ausblick 2025 ", dena,
p.379-401, 2010
SRNWP-PEPS A regional multi-model ensemble in Europe The European Forecaster
- Heizenreder
Heizenreder et al.: " SRNWP-PEPS
A regional multi-model ensemble in Europe ", The
European Forecaster, No.11, p. 29-36, 2006
Ensemble Verification I & II
- Hagedorn
Hagedorn: " Ensemble Verification I & II ", NWP-
PR Training Course, ECMWF, Reading, UK, 2009
Estimation of wind power prediction intervals using stochastic methods and artificial intelligence model ensembles
- Dobschinski
Dobschinski et al.: " Estimation of wind power
prediction intervals using stochastic methods and
artificial
intelligence
model
ensembles ",
Proceedings DEWEK 2008, Bremen, 2008