ERA5: The new champion of wind power modelling?

Olauson, Jon

doi:10.1016/j.renene.2018.03.056

Cited by 368 publications

(266 citation statements)

References 21 publications

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“…We used the ERA5 reanalysis product (Copernicus Climate Change Service 2017) to represent observed historical meteorological conditions (Olauson 2018, Urraca et al 2018, Ramon et al 2019. The full ERA5 record available at time of analysis was used, 1979-2018, providing 40 years of data.…”

Section: Meteorological Datamentioning

confidence: 99%

The influence of weather regimes on European renewable energy production and demand

Wiel

Bloomfield

Lee

et al. 2019

Environ. Res. Lett.

108

109

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The growing share of variable renewable energy increases the meteorological sensitivity of power systems. This study investigates if large-scale weather regimes capture the influence of meteorological variability on the European energy sector. For each weather regime, the associated changes to wintertime-mean and extreme-wind and solar power production, temperature-driven energy demand and energy shortfall (residual load) are explored. Days with a blocked circulation pattern, i.e. the 'Scandinavian Blocking' and 'North Atlantic Oscillation negative' regimes, on average have lower than normal renewable power production, higher than normal energy demand and therefore, higher than normal energy shortfall. These average effects hide large variability of energy parameters within each weather regime. Though the risk of extreme high energy shortfall events increases in the two blocked regimes (by a factor of 1.5 and 2.0, respectively), it is shown that such events occur in all regimes. Extreme high energy shortfall events are the result of rare circulation types and smaller-scale features, rather than extreme magnitudes of common large-scale circulation types. In fact, these events resemble each other more strongly than their respective weather regime mean pattern. For (sub-) seasonal forecasting applications weather regimes may be of use for the energy sector. At shorter lead times or for more detailed system analyses, their ineffectiveness at characterising extreme events limits their potential.

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Section: Meteorological Datamentioning

confidence: 99%

The influence of weather regimes on European renewable energy production and demand

Wiel

Bloomfield

Lee

et al. 2019

Environ. Res. Lett.

108

109

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“…Olauson [28] accurately calculates the wind power generation of several countries and regions using the newly available ERA5 reanalysis data [29]. The quality of these predictions and the global availability of an unprecedented spatial and temporal resolution has motivated us to use this data to compare the wind resources available to conventional wind turbines and to AWE systems.…”

Section: Introductionmentioning

confidence: 99%

Airborne wind energy resource analysis

et al. 2019

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We compare the available wind resources for conventional wind turbines and for airborne wind energy systems. Accessing higher altitudes and continuously adjusting the harvesting operation to the wind resource substantially increases the potential energy yield. The study is based on the ERA5 reanalysis data which covers a period of 7 years with hourly estimates at a surface resolution of 31 × 31 km and a vertical resolution of 137 barometric altitude levels. We present detailed wind statistics for a location in the English Channel and then expand the analysis to a surface grid of Western and Central Europe with a resolution of 110 × 110 km. Over the land mass and coastal areas of Europe we find that compared to a fixed harvesting height at the approximate hub height of wind turbines, the wind power density which is available for 95 % of the time increases by a factor of two. (Philip Bechtle), m.schelbergen@tudelft.nl (Mark Schelbergen), r.schmehl@tudelft.nl (Roland Schmehl), zillmann@airbornewindeurope.org (Udo Zillmann), s.j.watson@tudelft.nl (Simon Watson)

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“…Although there are several ongoing initiatives in the community to produce reanalysis datasets (e.g., Copernicus Climate Change Service (C3S), 2017), only a few efforts have been devoted to comparing their quality in a coordinated way (Fujiwara et al, 2017). It is true, however, that several studies have focused on verifying reanalysis data on a regional scale (Kumar and Hu, 2012;Alvarez et al, 2014;Kaiser-Weiss et al, 2015;Sharp et al, 2015;Olauson, 2018;Rehman Tahir et al, 2018;Uotila et al, 2018). Others intended to cover the whole world by using interpolated observational datasets (Donat et al, 2014) or employing some stations distributed worldwide (Decker et al, 2012).…”

mentioning

confidence: 99%

What global reanalysis best represents near‐surface winds?

Ramón

Lledó

Torralba

et al. 2019

Quart J Royal Meteoro Soc

252

184

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Since global reanalysis datasets first appeared in the 1990s, they have become an essential tool to understand the climate of the past. The wind power industry uses those products extensively for wind resource assessment, while several climate services for energy rely on them as well. Nowadays various datasets coexist, which complicates the selection of the most suitable source for each purpose. In an effort to identify the products that best represent the wind speed features at turbine hub heights, five state‐of‐the‐art global reanalyses have been analysed: ERA5, ERA‐Interim, the Japanese 55‐year Reanalysis (JRA55), the Modern Era Retrospective Analysis for Research and Applications‐2 (MERRA2), and the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis 1 (R1). A multi‐reanalysis ensemble approach is used to explore the main differences amongst these datasets in terms of surface wind characteristics. Then, the quality of the surface and near‐surface winds is evaluated with a set of 77 instrumented tall towers. Results reveal that important discrepancies exist in terms of boreal winter seasonal means, interannual variability (IAV), and decadal linear trends. The differences in the computation of these parameters, which are mainly concentrated inland, reach up to the order of magnitude of the parameters themselves. Comparison with in situ observations shows that the ERA5 surface winds offer the best agreement, correlating and reproducing the observed variability better than a multi‐reanalysis mean in 35.1% of the tall tower sites on a daily time‐scale. However, none of the reanalyses stands out from the others when comparing seasonal mean winds. Regarding the IAV, near‐surface winds from ERA5 offer the values closest to the observed IAV.

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ERA5: The new champion of wind power modelling?

Abstract: Access to the published version may require subscription. N.B. When citing this work, cite the original published paper.

Cited by 368 publications

References 21 publications

The influence of weather regimes on European renewable energy production and demand

The influence of weather regimes on European renewable energy production and demand

Airborne wind energy resource analysis

What global reanalysis best represents near‐surface winds?

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