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Wednesday, 12 March 2014
11:15 - 12:45 Wind speed predictions: Are we at the limit of our knowledge or can we improve?
Resource Assessment  

Room: Ponent
Session description

No new long-term correction methods have appeared for years and it is possible that current techniques are optimal. There are several issues which affect any long-term correction analysis:

… from the mundane: the optimum reference period? how do we measure success? re-analysis data or ground based stations? non-integer years of data?

… to the exotic: atmospheric stability, climate change decadal variations, sun spots activity and solar cycles.

It is likely that long-term correction techniques which consider these may provide more reliable predictions than has previously been possible.

This session describes new long-term correction methodologies and compares the results with those of conventional methods. Innovative techniques to improve the representativeness of long-term data series are discussed, different long-term data series are compared and conclusions on the decadal-scale variability of the wind speed are presented. The overall objective of this session is to give insight on how these developments contribute to a greater certainty in future wind speed predictions.

Learning objectives

  • Evaluate innovative methods to improve the representativeness of long-term data series and the overall accuracy of long-term extrapolations
  • Compare new long-term correction methods to traditional methods
  • Understand how a more accurate description of the decadal-scale variability of the wind speed contributes to the reduction of the uncertainty in the long-term corrected wind speed
Lead Session Chair:
Sónia Liléo, Kjeller Vindteknikk AS, Sweden

Steve Ross, 3Tier
Michelle Spillar Met Office, United Kingdom
Michelle Spillar (1) F P
(1) Met Office, Exeter, United Kingdom

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2014 and are published here unedited

Michelle graduated with a distinction in MSc-Meteorology in 2002 and joined the Met Office that year. She has worked across a range of departments since then, starting her career as a forecaster before moving to the Met Office College. Here she designed and delivered training courses to meteorological and non-metrological staff. In 2008, Michelle then transferred to the commercial team as a customer account manager and, more recently, as Head of Utilities, where she utilised her meteorological background to understand customer needs and create bespoke products and services. Michelle is now the Head of Renewable Energy at the Met Office.


State of the art wind speed assessments: seasonal forecasting and decadal variability


The financial risks around building and running wind farms can be reduced through realistic, detailed knowledge of the likely wind speeds over a range of timescales. Increases in supercomputing power result in corresponding improvements in our understanding of the physical mechanisms driving wind variability, through larger datasets and more detailed simulations. We show how recent advances allow us to make detailed assessments of wind variability on decadal timescales, reducing the financial uncertainty over the lifetime of an asset; and how advances in seasonal forecasting are allowing for the first time skillful forecasts of winter wind speeds over Europe.


Improvements in understanding decadal variability can be made using new long-baseline reanalysis data sets. We demonstrate our use of the Twentieth Century Reanalysis (20CR), which spans an unprecedented 140 years with 56 ensemble members. Because observations become more scarce at earlier times, the ensemble data must be analysed carefully, avoiding potential inhomogeneities and spurious trends. We also demonstrate the skill of the Met Office's new seasonal forecast system over Europe, through correlations of new hindcast data with ERA-Interim reanalysis data. We show correlation scores for the winter North Atlantic Oscillation (NAO), and the corresponding mean wind speed.

Main body of abstract

Use of larger historical data sets allows us to go beyond 30-year climatologies to assess the decadal-scale variability that is essential for understanding the impact of climate variability on the wind speed distribution. As well as mapping the mean wind speed from the 20CR data (1871-2010), we map possible long-term trends in the data. In fact, we find that there are almost no significant trends in wind speed on centennial scales, with the possible exception of a small increase in the North Atlantic. All areas show strong interannual and decadal variation however, and this allows us to see recent high-wind and low-wind periods in a long-term context.

Secondly, we demonstrate how greater supercomputing power also improves teleconnections between drivers of large-scale climate variability around the globe, resulting in unprecedented levels of skill in seasonal forecasts. By mapping the correlations of the model hindcast with the observation-rich ERA-Interim reanalysis, a clear region of high predictability is shown in winter over north-western Europe and the north-east Atlantic. High levels of correlation are present for the NAO: in most of the past 20 years for which hindcasts were run, the interquartile range of the ensemble members encompasses the observed value, resulting in a correlation greater than 0.6. This leads, for example, to a corresponding correlation in the winter mean wind speeds over northwest Europe and North America of ~0.6.


Advances in computing power have brought corresponding advances in our knowledge of the variability of the distribution of wind speeds. This is seen through large, long-baseline data sets such as the 20CR, which allow us to put recent observed apparent trends in annual mean wind speeds into the largely trend-free long-term context. Further understanding -- and predictive power -- is gained from high-resolution simulations of the global climate system, allowing skillful predictions of European winter winds for the first time. These improvements could feed through directly into reducing the risks of financing and operating wind farms.

Learning objectives
* Recognise the importance of understanting decadal-scale variability when assessing yield over the lifetime of a wind asset.
* Consider how skilful seasonal wind forecasts could affect financial planning
* Discuss how the continual improvements in computing power and scientific understanding allow for greater certainty in planning and running wind farms, both now and in the future.