Seasonal Climate Prediction: A New Source of Information for the Management of Wind Energy Resources

Torralba, Verónica; Doblas‐Reyes, Francisco J.; MacLeod, Dave; Christel, Isadora; Davis, Micheal A.

doi:10.1175/jamc-d-16-0204.1

Cited by 82 publications

(80 citation statements)

References 49 publications

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“…In this climate prediction framework, reanalyses are also used as observational reference for the adjustment of systematic errors affecting these predictions (Torralba et al . ) and to assess the forecast quality (Jolliffe and Stephenson, ).…”

Section: Introductionmentioning

confidence: 99%

What global reanalysis best represents near‐surface winds?

Ramón

Lledó

Torralba

et al. 2019

Quart J Royal Meteoro Soc

Self Cite

267

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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Section: Introductionmentioning

confidence: 99%

What global reanalysis best represents near‐surface winds?

Ramón

Lledó

Torralba

et al. 2019

Quart J Royal Meteoro Soc

Self Cite

267

184

View full text Add to dashboard Cite

show abstract

“…Probabilistic multi-category events are also an alternative representation and can be constructed from the quintiles (five categories) or terciles (three categories) [159]. The temporal resolution are usually in a monthly or yearly scale.…”

Section: Long-term Portfolio Planningmentioning

confidence: 99%

“…However, in [159] it is highlighted that the main limitation of seasonal forecasts is the systematic error (bias), which requires bias correction and calibration methods for successful integration of this product in the industry practices. Also according to [159], the utilization of advanced post-processing methods in long-term forecasts has potential to reduce the uncertainty and generate valuable climate information, which was identified as a primary requirement by the wind energy industry. The Gaussian assumption for the wind speed forecast uncertainty is a subject for further work since it requires non-Gaussian post-processing methods.…”

Section: Long-term Portfolio Planningmentioning

confidence: 99%

See 1 more Smart Citation

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

Bessa

Möhrlen²,

Fundel

et al. 2017

Energies

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Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. This paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. A set of recommendations for standardization and improved training of operators are provided along with examples of best practices.

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“…CC BY 4.0 License. available to inform planning for WT and grid maintenance (Yu et al, 2015;Torralba et al, 2017). Variability on intra-annual to decadal time scales (Pryor and Barthelmie, 2011;Pryor et al, 2006) arises primarily due to the action of internal climate modes such as El Niño-Southern Oscillation (ENSO) (Schoof and Pryor, 2014;Pryor and Ledolter, 2010;KirchnerBossi et al, 2015;Bett et al, 2017;Watts et al, 2017) and climate non-stationarity (e.g.…”

Section: Introductionmentioning

confidence: 99%

Inter-annual variability of wind climates and wind turbine annual energy production

Pryor¹,

Shepherd²,

Barthelmie³

2018

Preprint

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Abstract.Inter-annual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in pre-construction projected AEP and the difference in 50 th and 90 th percentile (P50 and P90) AEP derives in part from variability in wind climates. However, the magnitude of IAV in wind speeds at/close to wind 10 turbine hub-heights is poorly constrained and maybe overestimated by the 6% standard deviation of annual mean wind speeds that is widely applied within the wind energy industry. Thus there is a need for improved understanding of the longterm wind resource and the inter-annual variability therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub-heights over the eastern USA indicate median gross capacity factors (computed using 10-minute wind speeds close to wind turbine hub-heights and the 15 power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at/near to typical wind turbine hub-heights in these simulations is lower than is implied by assuming a standard deviation of 6%. Indeed, rather than in 9 in 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP, results presented herein indicate that over 90% of the area in the eastern USA that currently has operating wind turbines simulated AEP lies within 0.94 and 1.06 of the long-term average. 20Further, IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to pre-construction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

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Seasonal Climate Prediction: A New Source of Information for the Management of Wind Energy Resources

Cited by 82 publications

References 49 publications

What global reanalysis best represents near‐surface winds?

What global reanalysis best represents near‐surface winds?

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

Inter-annual variability of wind climates and wind turbine annual energy production

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