Predicting Wind Power with Reforecasts

Dabernig, Markus; Mayr, Georg J.; Messner, Jakob W.

doi:10.1175/waf-d-15-0095.1

Cited by 9 publications

(7 citation statements)

References 19 publications

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“…Similar results are shown in Dabernig et al . (), where the EMOS results based on ensemble forecasts outperformed the forecasts using only the control run.…”

Section: Resultsmentioning

confidence: 93%

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Analog‐based post‐processing of the ALADIN‐LAEF ensemble predictions in complex terrain

Plenković

Schicker

Dabernig

et al. 2020

Quart J Royal Meteoro Soc

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The main goal of this study is to assess the performance of the analog‐based post‐processing method applied to the Austrian ALADIN‐LAEF wind speed ensemble predictions through a set of sensitivity experiments. Evaluation of several analog‐based configurations using various meteorological variables as predictors is therefore conducted. The results of those experiments are compared to the ensemble model output statistics (EMOS) baseline model. The hypothesis further investigated is that using summarized measures, such as mean and standard deviation of an ensemble for several meteorological variables, is comparable to the analog post‐processing using all of the ensemble members. Results show that both analog‐based and EMOS experiments considerably improve the raw model forecast. Even though the improvement over raw model forecast is evident, large differences among nearby stations are noticed in the highly complex terrain. The processes at lower stations seem to be better represented by the raw model, which leads to a better input forecast to the post‐processing and a better overall result than for the mountain stations. The analog‐based method is overall comparable to or even outperforms the EMOS. Assessing the post‐processing performance for high wind speeds shows that the analog experiments can improve the raw forecast, exhibiting significantly higher skill than the EMOS. The difference among all analog experiments is less pronounced, especially the experiment using all of the raw model ensemble members and the one using summarized measures. Furthermore, it is demonstrated that the usage of summarized ensemble measures is an optimal way to improve the forecast skill, compared to the other analog‐based experiments and the EMOS model. Therefore, it is suggested that it is not necessary to increase the computational costs by using the full input spectrum of a raw probabilistic model, that is, all ALADIN‐LAEF members as predictors, as the summarized metric suffices.

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“…Similar results are shown in Dabernig et al . (), where the EMOS results based on ensemble forecasts outperformed the forecasts using only the control run.…”

Section: Resultsmentioning

confidence: 93%

“…Dabernig et al . () show the value of an ensemble forecast compared to its deterministic control run. Therefore, the first experiment, the AnEnCtrl , uses the ALADIN‐LAEF control member for the six meteorological parameters available as six predictors.…”

Section: Methodsmentioning

confidence: 99%

Analog‐based post‐processing of the ALADIN‐LAEF ensemble predictions in complex terrain

Plenković

Schicker

Dabernig

et al. 2020

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

show abstract

“…The ensemble reforecast is based on the current version of the operational model but with a lighter configuration to reduce computing time. It is initialised from the ECMWF Retrospective Analysis (ERA) Interim (Dee et al, 2011) and ensemble members are obtained from initial perturbations computed with singular vectors. In contrast to the operational model, stochastic perturbations of physical processes are not applied to the ensemble members.…”

Section: Model Datamentioning

confidence: 99%

“…The reforecasts are verified against ERA-Interim reanalyses, which are available since 1979 and are computed with a horizontal grid spacing of approximately 80 km, corresponding to a 2006 version of the operational model (Dee et al, 2011). The verification is based on the 6-hourly meansea-level pressure (MSLP) for the track and intensity of the storms and on the daily maximum wind gusts for the other metrics.…”

Section: Model Datamentioning

confidence: 99%

Revisiting the synoptic-scale predictability of severe European winter storms using ECMWF ensemble reforecasts

Pantillon¹,

Knippertz²,

Corsmeier³

2017

Preprint

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Abstract. New insights into the synoptic-scale predictability of 25 severe European winter storms of the 1995–2015 period are obtained using the homogeneous ensemble reforecast dataset from the European Centre for Medium-Range Weather Forecasts. The predictability of the storms is assessed with different metrics including the track and intensity to investigate the storms’ dynamics and the Storm Severity Index to estimate the impact of the associated wind gusts. The storms are correctly predicted by the ensemble reforecasts up to 2–4 days ahead only, which restricts the use of ensemble average and spread to short lead times. At longer lead times, the Extreme Forecast Index and Shift of Tails are computed from the deviation of the ensemble reforecasts from the model climate. Based on these indices, the model has some skill in forecasting the area covered by extreme wind gusts up to 10 days, which indicates clear potential for the early warning of storms. However, a large variability is found between the predictability of individual storms and does not appear to be related to the storms’ characteristics. This may be due to the limited sample of 25 cases, but also suggests that each severe storm has its own dynamics and sources of forecast uncertainty.

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“…The NOAA ensemble reforecast could help to identify systematic links between the dynamics and predictability of storms, as it covers a longer period and offers a daily initialization (Hamill et al, 2013). However, this dataset appears not to perform as well as its ECMWF counterpart for predicting wind over central Europe (Dabernig et al, 2015). The operational ECMWF ensemble forecast is initialised twice a day and contains 50 members but Pirret et al (2017) struggled to find a relation between the predictability and the intensity, track or physical processes of storms because of the steady increase in skill with more recent model versions.…”

mentioning

confidence: 99%

Revisiting the synoptic-scale predictability of severe European winter storms using ECMWF ensemble reforecasts

Pantillon

Knippertz

Corsmeier

2017

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. New insights into the synoptic-scale predictability of 25 severe European winter storms of the 1995-2015 period are obtained using the homogeneous ensemble reforecast dataset from the European Centre for Medium-Range Weather Forecasts. The predictability of the storms is assessed with different metrics including (a) the track and intensity to investigate the storms' dynamics and (b) the Storm Severity Index to estimate the impact of the associated wind gusts. The storms are well predicted by the whole ensemble up to 2-4 days ahead. At longer lead times, the number of members predicting the observed storms decreases and the ensemble average is not clearly defined for the track and intensity. The Extreme Forecast Index and Shift of Tails are therefore computed from the deviation of the ensemble from the model climate. Based on these indices, the model has some skill in forecasting the area covered by extreme wind gusts up to 10 days, which indicates a clear potential for early warnings. However, large variability is found between the individual storms. The poor predictability of outliers appears related to their physical characteristics such as explosive intensification or small size. Longer datasets with more cases would be needed to further substantiate these points.

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Predicting Wind Power with Reforecasts

Cited by 9 publications

References 19 publications

Analog‐based post‐processing of the ALADIN‐LAEF ensemble predictions in complex terrain

Analog‐based post‐processing of the ALADIN‐LAEF ensemble predictions in complex terrain

Revisiting the synoptic-scale predictability of severe European winter storms using ECMWF ensemble reforecasts

Revisiting the synoptic-scale predictability of severe European winter storms using ECMWF ensemble reforecasts

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