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Delegates are invited to meet and discuss with the poster presenters in this topic directly after the session 'Wind speed predictions: Are we at the limit of our knowledge or can we improve?' taking place on Wednesday, 12 March 2014 at 11:15-12:45. The meet-the-authors will take place in the poster area.

Ine Wijnant KNMI, The Netherlands
Ine Wijnant (1) F P Henk van den Brink (1) Andrew Stepek (1)
(1) KNMI, De Bilt, The Netherlands

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Presenter's biography

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

The presenter initiates communication between the wind experts from the diverse departments of KNMI and has put wind energy squarely on the agenda. For the past 4 years at Climate Services she is also responsible for the national archive of wind measurements, maintaining the time series of potential wind and conducting research involving the archive (trends, extremes, spatial interpolation and vertical extrapolation). She graduated as a mechanical engineer (specialism fluid dynamics), organised dredging research at Delft Hydraulics before becoming a weather forecaster for 17 years. Shortly before her current post, she enjoyed a 3 month interim at Ecofys Wind Unit.


High Quality North Sea wind climatology based on ERA-interim and ECMWF model data


Long high-quality wind time series at hub height are vital for the assessment of wind energy resources. The common approach is to transform surface observations to hub height with the MCP method. The main problem with using observations as reference data is that they are predominantly done at levels below hub height. To bridge the height difference between the reference and target locations, assumptions have to made on the atmospheric stability and the associated vertical wind profile because temperature profiles required for deriving the actual atmospheric stability are only measured at a few wind masts and for a limited period.


Here we present an alternative approach, using the 34-year ERA-interim reanalysis at a resolution of about 80 km as a starting point. The long period ensures that the whole variability of the wind climate is included. We overcome the drawback of the rather coarse ERA-interim resolution by comparing the last years of the ERA-interim period to the operational ECMWF analysis (which has a much higher resolution of 16 km). Using a Weibull-distribution, a gridbox-wise transformation can be applied to the ERA-interim set, which improves the spatial resolution of the ERA-interim data, which is especially important in the coastal zone.

Main body of abstract

The MCP method needs reference data to correlate to hub height measurements at the potential wind farm site (target site). The advantage of using model winds as reference data as opposed to observations is that model data are available at different heights around hub height and that they give a far better coverage, especially in areas where the observation network is sparse (e.g. offshore). Another great advantage of using model data is that stability information is available at every grid box, and incorporated in the wind profile.
To include the whole variability of the wind climate we need to analyse a long period of data. The only weather models that provide consistent long data sets are older models with a resolution so coarse that they represent the coastal area poorly. Our solution is to correlate wind data from a model which covers a long period, but has a coarse resolution (34 years ERA-Interim 80 km resolution) to wind data from a high resolution weather model (2 years ECMWF 16 km horizontal resolution).


On a 16 by 16 kilometre grid and at several heights (40, 60, 80, 100, 120, 140, 160, 180 and 200 metres), we provide wind speed time series and climatological information, such as the wind speed frequency distribution via the Weibull parameters and annual and decadal wind statistics (including the probability of extremes). The combination of high resolution model wind and time series over a long period provides a better description of the wind climate on the North Sea. The main improvements are of the description of the wind in the coastal area and of the variability of the wind.

Learning objectives
The technique of transforming long series of course model data to a finer resolution by comparing a subset of the course data to finer resolution model data is new. This technique enables us to provide high resolution time series and climatological information that include the whole known variability of the wind climate. One specific objective is to learn what the P90 wind power production levels are with greater certainty than earlier research has produced.