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Determining the bounds of skilful forecast range for probabilistic prediction of system-wide wind power generation

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Cannon, D., Brayshaw, D., Methven, J. and Drew, D. (2017) Determining the bounds of skilful forecast range for probabilistic prediction of system-wide wind power generation. Meteorologische Zeitschrift, 26 (3). pp. 239-252. ISSN 0941-2948

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To link to this item DOI: 10.1127/metz/2016/0751

Abstract/Summary

State-of-the-art wind power forecasts beyond a few hours ahead rely on global numerical weather prediction models to forecast the future large-scale atmospheric state. Often they provide initial and boundary conditions for nested high resolution simulations. In this paper, both upper and lower bounds on forecast range are identified within which global ensemble forecasts provide skilful information for system-wide wind power applications. The power system of Great Britain (GB) is used as an example because independent verifying data is available from National Grid. An upper bound on forecast range is associated with the limit of predictability, beyond which forecasts have no more skill than predictions based on climatological statistics. A lower bound is defined at the lead time beyond which the “resolved” uncertainty associated with estimating the future large-scale atmospheric state is larger than the “unresolved” uncertainty associated with estimating the system-wide wind power response to a given large-scale state. The bounds of skilful forecast range are quantified for three leading global forecast systems. The upper bound defined by forecasts of GB-total wind power generation at a specific point in time are found to be 6-8 days. The lower bound is found to be 1.4-2.4 days. Both bounds depend on the global forecast system and vary seasonally. In addition, forecasts of the probability of an extreme power ramp event were found to possess a shorter limit of predictability (4.5-5.5 days). The upper bound to useful forecast range can only be extended by improving the global forecast system (outside the control of most users) or by changing the metric used in the probability forecast. Improved downscaling and microscale modelling of the wind farm response may act to decrease the lower bound to useful forecast range. The potential gain from such improvements have diminishing returns beyond the short-range (out to around 2 days).

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Walker Institute
Interdisciplinary centres and themes > Energy Research
Faculty of Science > School of Mathematical, Physical and Computational Sciences > NCAS
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:45951
Publisher:Gebrueder Borntraeger Verlagsbuchhandlung

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