Multi-decadal variability in a centennial reconstruction of daily wind

Kirchner-Bossi, Nicolas; Prieto, Luís; García‐Herrera, Ricardo; Carro-Calvo, L.; Salcedo‐Sanz, Sancho

doi:10.1016/j.apenergy.2012.11.072

Cited by 13 publications

(19 citation statements)

References 54 publications

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“…As already discussed in the context of the mean wind speed, it is important to realise that these trends are those seen at the large scales of the 20CR data, and detailed physical or statistical modelling is required to downscale to a specific location. Considering again the example of Kirchner-Bossi et al (2013), they find that the site in Spain they describe has a statistically significant negative trend in wind speed of around −0.01 m s −1 decade −1 . In our results, the corresponding grid cell has a trend of around +0.01 m s −1 decade −1 , and is consistent with zero according to our test.…”

Section: The European Context: Maps Of the Long-term Average Variabimentioning

confidence: 88%

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Using the Twentieth Century Reanalysis to assess climate variability for the European wind industry

Bett

Thornton

Clark

2015

Theor Appl Climatol

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We characterise the long-term variability of European near-surface wind speeds using 142 years of data from the Twentieth Century Reanalysis (20CR), and consider the potential of such long-baseline climate data sets for wind energy applications. The low resolution of the 20CR would severely restrict its use on its own for wind farm site-screening. We therefore perform a simple statistical calibration to link it to the higher-resolution ERA-Interim data set (ERAI), such that the adjusted 20CR data has the same wind speed distribution at each location as ERAI during their common period. Using this corrected 20CR data set, wind speeds and variability are characterised in terms of the long-term mean, standard deviation and corresponding trends. Many regions of interest show extremely weak trends on century timescales, but contain large multidecadal variability. Since reanalyses such as ERAI are often used to provide the background climatology for wind farm site assessments, but contain only a few decades of data, our results can be used as a way of incorporating decadalscale wind climate variability into such studies, allowing investment risks for wind farms to be reduced. Electronic supplementary materialThe online version of this article

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Section: The European Context: Maps Of the Long-term Average Variabimentioning

confidence: 88%

“…Palutikof et al 1992), often combined with complex statistical procedures to relate to the wind speed at a site (e.g. Kirchner-Bossi et al 2013, 2014. Around Europe, indices based on the North Atlantic Oscillation (NAO) have often been used (e.g.…”

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confidence: 99%

Using the Twentieth Century Reanalysis to assess climate variability for the European wind industry

Bett

Thornton

Clark

2015

Theor Appl Climatol

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“…Interdecadal and multidecadal changes in surface wind have been evidenced in several works during the last decades (e.g. Palutikof, 1985;Earl et al, 2013;Kirchner-Bossi et al, 2013). They have been associated with the long-term changes of large-scale circulation forcings (e.g.…”

Section: Introductionmentioning

confidence: 97%

A long-term perspective of wind power output variability

Kirchner-Bossi¹,

García‐Herrera

Prieto

et al. 2014

Int. J. Climatol

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The study of the wind power output variability comprises different time scales. Among them, low-frequency variations can substantially modify the performance of a wind power plant during its lifetime. Although in recent years other scales, as the short-term variability or the climatological conditions of wind and the corresponding generated power have been investigated in depth, the study of the decadal and multidecadal variations is still placed in its early stages.In this work the wind power output long-term variability is analyzed for two locations of wind power relevance in Spain, during the period 1871-2009. This is attained by computing the annual wind speed Probability Density Functions (PDF) from an ensemble of atmospheric Sea Level Pressure (SLP) data set from the 20th Century Reanalysis through a statistical downscaling based in Evolutionary Algorithms. Results reveal significant trends and periodicities in multidecadal bands including 13, 25 and 46 years, as well as significant differences among both sites. The impact of the leading large-scale circulation patterns (NAO, EA, SCAND and AMO) on wind power output and its stationarity is analyzed. Results on both locations show significant and opposite seasonal couplings with these forcings. Finally, the long-term variability of the reconstructed Weibull parameters of the annual wind speed distributions are employed to derive a linear model to estimate the annual wind power.

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“…Different methods have used SLP as a predictive variable for meteorological prediction. We can find in the literature works dealing with physical approaches, such as machine learning techniques or hybrid approaches involving both physical and machine learning approaches . Physical approaches refer to those methodologies that solve atmospheric equations to construct the prediction/reconstruction system.…”

Section: Introductionmentioning

confidence: 99%

Surface wind speed reconstruction from synoptic pressure fields: machine learning versus weather regimes classification techniques

et al. 2014

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This paper tackles a problem of surface wind speed reconstruction based on synoptic-scale meteorological fields. Specifically, two different approaches are discussed and compared: a pure Machine Learning method, formed by a Support Vector Regression and a genetic algorithm that only considers synoptic pressure as input variable, and a Weather Regimes Classification Technique, based on a k-means clustering of the main three principal components of the geopotential height field and a simple, but efficient, linear regression between the surface pressure gradient and the observed surface wind. Both algorithms are shown to be accurate enough for wind speed reconstruction at medium latitude regions, even when there are only a few years of observations. These methodologies can also be used for filling gaps in wind speed series and, with some modifications and further research, they could be used for wind speed forecasting. The algorithms proposed are fully described and compared in this paper, and their performance has been comparatively evaluated in several real problems of wind speed reconstruction at three sites (Cabauw (The Netherlands), Capel (Wales, UK) and Kaegnes (Denmark)), obtaining excellent results in terms of wind speed reconstruction with moderate complexity in data processing and algorithms.

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Multi-decadal variability in a centennial reconstruction of daily wind

Cited by 13 publications

References 54 publications

Using the Twentieth Century Reanalysis to assess climate variability for the European wind industry

Using the Twentieth Century Reanalysis to assess climate variability for the European wind industry

A long-term perspective of wind power output variability

Surface wind speed reconstruction from synoptic pressure fields: machine learning versus weather regimes classification techniques

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