Verification of the ECMWF ensemble forecasts of wind speed against analyses and observations

Pinson, Pierre; Hagedorn, Renate

doi:10.1002/met.283

Cited by 63 publications

(67 citation statements)

References 38 publications

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“…NWP 10-m winds are still the main input in many applications such as, for example, wind power forecasts (Pinson and Hagedorn 2012), since ''most if not all operational NWP wind products that can be 'purchased' for wind power prediction are at 10 m'' (P. Pinson 2010, personal communication). Motta et al (2005) show that the extrapolation of 10-m wind may result in large errors, however, especially in (very) stable conditions.…”

Section: Introductionmentioning

confidence: 99%

“…The common practice, when estimating model skill by comparing forecast with analysis, is denoted as ''fair'' by Pinson and Hagedorn (2012), because the temporal and spatial scales are consistent. They point out, however, that the ''end user'' actually is interested in the skill revealed by comparison with the ''truth,'' that is, the observation at a point location.…”

Section: Introductionmentioning

confidence: 99%

“…Increasing resolution has little impact for locations dominated either by synoptic-scale processes or by subdiurnal and thus unpredictable small scales. Using 10-m wind from the ECMWF Ensemble Prediction System (EPS), Pinson and Hagedorn (2012) quantified the model performance for short-range forecasts of 0-12 h by the root-mean-square error (RMSE) of ;2.2 m s 21 , averaged over more than 600 stations. When model resolution is increased from about 50 km to a value of 33 km, the error decreased significantly for all verification measures applied, with, for example, an improvement in RMSE by 2%-3%.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wind Speeds at Heights Crucial for Wind Energy: Measurements and Verification of Forecasts

Drechsel¹,

Mayr²,

Messner³

et al. 2012

Journal of Applied Meteorology and Climatology

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Wind speed measurements from one year from meteorological towers and wind turbines at heights between 20 and 250 m for various European sites are analyzed and are compared with operational short-term forecasts of the global ECMWF model. The measurement sites encompass a variety of terrain: offshore, coastal, flat, hilly, and mountainous regions, with low and high vegetation and also urban influences. The strongly differing site characteristics modulate the relative contribution of synoptic-scale and smaller-scale forcing to local wind conditions and thus the performance of the NWP model. The goal of this study was to determine the bestverifying model wind among various standard wind outputs and interpolation methods as well as to reveal its skill relative to the different site characteristics. Highest skill is reached by wind from a neighboring model level, as well as by linearly interpolated wind from neighboring model levels, whereas the frequently applied 10-m wind logarithmically extrapolated to higher elevations yields the largest errors. The logarithmically extrapolated 100-m model wind reaches the best compromise between availability and low cost for data even when the vertical resolution of the model changes. It is a good choice as input for further statistical postprocessing. The amplitude of measured, height-dependent diurnal variations is underestimated by the model. At low levels, the model wind speed is smaller than observed during the day and is higher during the night. At higher elevations, the opposite is the case.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wind Speeds at Heights Crucial for Wind Energy: Measurements and Verification of Forecasts

Drechsel¹,

Mayr²,

Messner³

et al. 2012

Journal of Applied Meteorology and Climatology

View full text Add to dashboard Cite

show abstract

“…against a number of relevant benchmarks, namely the persistence [16], climatology [28], HIA [16] and BELM [17]. Persistence is a benchmark that is difficult to outperform when considering very short lead times, especially if issuing point forecasts only [29].…”

Section: Empirical Investigationmentioning

confidence: 99%

Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation— With Application to Solar Energy

Golestaneh

Pinson

Gooi

2016

IEEE Trans. Power Syst.

Self Cite

230

107

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Abstract-Due to the inherent uncertainty involved in renewable energy forecasting, uncertainty quantification is a key input to maintain acceptable levels of reliability and profitability in power system operation. A proposal is formulated and evaluated here for the case of solar power generation, when only power and meteorological measurements are available, without skyimaging and information about cloud passages. Our empirical investigation reveals that the distribution of forecast errors do not follow any of the common parametric densities. This therefore motivates the proposal of a nonparametric approach to generate very short-term predictive densities, i.e., for lead times between a few minutes to one hour ahead, with fast frequency updates. We rely on an Extreme Learning Machine (ELM) as a fast regression model, trained in varied ways to obtain both point and quantile forecasts of solar power generation. Four probabilistic methods are implemented as benchmarks. Rival approaches are evaluated based on a number of test cases for two solar power generation sites in different climatic regions, allowing us to show that our approach results in generation of skilful and reliable probabilistic forecasts in a computationally efficient manner.

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“…Most ensemble studies for winds have been based in Europe (Pinson and Hagedorn 2012) in association with the wind power industry in that region. Thorarinsdottir and Johnson (2012) used a nonhomogeneous Gaussian regression applied to 48-h-ahead forecasts of wind speed obtained from the University of Washington Mesoscale Ensemble to probabilistically forecast daily maximum sustained wind, probability of wind gust, and daily maximum wind gust over the U.S. Pacific Northwest.…”

Section: A Backgroundmentioning

confidence: 99%

Climatology and Ensemble Predictions of Nonconvective High Wind Events in the New York City Metropolitan Region

Layer

Colle

2015

Weather and Forecasting

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Damaging wind events not associated with severe convective storms or tropical cyclones can cause significant problems with transportation, infrastructure, and public safety over the northeastern United States. These nonconvective wind events (NCWEs) are difficult to forecast in New York City and surrounding regions as revealed by the relatively poor probability of detection (POD) and false alarm ratio (FAR) in recent years. This paper investigates the climatology of NCWEs between 15 September and 15 May over 13 cool seasons from 2000/01 through 2012/13. The events are separated into three distinct synoptic patterns: precold-frontal (PRF), post-cold-frontal (POF), and nor'easter/coastal storm (NEC) cases. Relationships between observed winds and some atmospheric parameters such as 900-hPa geopotential height gradient, 3-h mean sea level pressure (MSLP) tendency, low-level wind profile, and stability are also presented. Overall, PRF and NEC have the largest FAR, because several events with a low-level jet at 1-2 km above the surface have relatively strong low-level stability that limits vertical momentum mixing. The POD is lowest for the POF events, which have a strong diurnal influence given the relatively deep mixed layer. Verification is also conducted over the cool seasons from 2009/10 to 2013/14 using the Short Range Ensemble Forecast (SREF) system from the National Centers for Environmental Prediction (NCEP). Both deterministic and probabilistic verification metrics are used to evaluate the performance of the ensemble during NCWEs. Although the SREF has more forecast skill than any of the deterministic SREF control members, it is rather poorly calibrated and exhibits a significant overconfidence given its positive wind speed bias in the lower troposphere.

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Verification of the ECMWF ensemble forecasts of wind speed against analyses and observations

Cited by 63 publications

References 38 publications

Wind Speeds at Heights Crucial for Wind Energy: Measurements and Verification of Forecasts

Wind Speeds at Heights Crucial for Wind Energy: Measurements and Verification of Forecasts

Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation— With Application to Solar Energy

Climatology and Ensemble Predictions of Nonconvective High Wind Events in the New York City Metropolitan Region

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