North Sea Wind Climatology Part 2: ERA-Interim and Harmonie model data

I.L. Wijnant, H.W. van den Brink and A. Stepek

The national government needs high quality offshore wind climatology at and around hub height to be able to determine a realistic wind power potential for the North Sea and to be able to assess whether the yields predicted by wind energy companies are reliable. One “reference” wind atlas makes procedures more transparent for all stages in the process of establishing Dutch offshore wind energy: allocation of areas, tendering procedure, allocation of wind energy producer and monitoring of the wind energy yields. It will also save time and money as individual wind energy companies would not have to make their own wind climatologies. It is however essential that the quality of this “reference” wind atlas is scientifically sound and that it has the confidence of the wind energy sector and the banks ( so they would be willing to lend the required funds).

Wind energy developers determine wind climatology at the site where they want to build the wind turbine or wind farm (target site) by transforming long series of near surface wind measurements at a nearby reference site, if available, to hub height at the target site. The transformation requires simultaneous measurements at both sites, but the measurement campaigns at the target site are relatively short. This is called the Measure Correlate Predict (MCP) method. The main problem with using measurements as reference data is that they are predominantly done at levels below hub height. To bridge the height difference between the reference and target measurements, assumptions have to be made on the atmospheric stability and the associated vertical wind profile. The temperature profiles required for deriving the actual atmospheric stability are only measured at a few wind masts and for a limited period.

Also wind atlases are used for siting purposes. Part 1 of this report gives an overview of the wind atlases that are currently used in the wind energy sector and explains what their limitations are. In this report (part 2) we present an approach similar to the MCP method and use the 34-year ERA-Interim reanalysis (a homogeneous model data set) on a horizontal grid of about 80 km as a starting point. The advantage of using weather model winds as reference data as opposed to measurements is that these data are available at different heights around hub height. Furthermore, the wind profile in the model adapts from hour to hour to incorporate the effect of varying stability in a dynamical way. Model data provide a complete and uniform space and time coverage, which can not be said of the observation network. Another advantage of using the analysis made by weather models is that it is based on many measurements at all heights in the atmosphere. For example, satellite-observed sea surface temperature is used as input for the model, providing additional information on stability, which determines the wind profile.

We overcome the drawback of the rather coarse ERA-Interim grid-spacing of 80 km by comparing the Weibull-distribution of the analysis wind speed of the last few years of the ERA-Interim period to that of the analysis of the operational weather forecasting model of Harmonie which has a finer grid-spacing of 2.5 km. This is similar to the correlate stage of the MCP method. Using the relationship between the Weibull-distributions, a grid-box-wise transformation can be applied to the ERA-Interim set. This improves the spatial representation of the ERA-Interim data, especially in coastal areas because Harmonie has a significantly better representation of the land-sea mask than ERA-Interim. On a 2.5 by 2.5 km grid and at 40, 60, 80, 100, 120, 140, 160, 180 en 200 m, we provide 34 year long time series of wind speed, as well as climatological information, e.g., the wind speed frequency distribution via the Weibull parameters, annual and decadal wind statistics (including the probability of extremes).

Validating the model-based time series of wind speed and the climatological information against observations (e.g. satellite or weather masts) is beyond the scope of this project and it is our most important recommendation that a verification is performed. Comparing them to other wind atlases seems superfluous: that there will be differences is to be expected, it is also clear why these differences will arise but until a comparison with high quality hub height measurements can be made, no strong conclusions can be drawn concerning which climatology comes closest to the truth. The offshore wind energy sector can play an important role here by making such measurements available for research.

Bibliographic data

I.L. Wijnant, H.W. van den Brink and A. Stepek. North Sea Wind Climatology Part 2: ERA-Interim and Harmonie model data
KNMI number: TR-343, Year: 2014, Pages: 52

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