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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.

Dimitri Foussekis C.R.E.S., Greece
Co-authors:
Dimitri Foussekis (1) F P Konstantinos Garakis (2)
(1) C.R.E.S., Pikermi, Greece (2) Technological Educational Institute, Athens, Greece

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Presenter's biography

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

Dimitri FOUSSEKIS has been working at CRES, the Greek Centre for Renewable Energy Sources for more than 20 years and is currently a Senior Research Engineer. His primary research interests lie in the fields of i) wind potential studies (member of MEASNET’s Expert Group for Site Assessment), ii) LIDAR and SODAR performance evaluation in complex terrains and iii) design and implementation of remotely controlled measurement systems for mission critical applications (wind farm monitoring, load and power performance measurements of wind turbines). He has more than 50 papers, presentations and announcements in scientific journals, conferences and workshops.

Abstract

Comparison of reanalysis data with long-term measurements in complex and coastal terrains

Introduction

The objective of this study is to evaluate the use of different mesoscale/reanalysis data sets in the investigation of the stable long-term average wind speed.
Reanalysis data offer consistent spatial and temporal resolution for a duration of 20+ years, but with some suspicious shifts or trends. Consequently, it is crucial to verify the applicability of these data sources when using them to investigate inter-annual and seasonal variations.

Approach

Data from several long-term meteorological masts (from 10m to 100m a.g.l.) installed in complex and/or coastal sites are used for this purpose.
Investigations comprise sensitivity of the results i) different time-step configurations (varying from 10min to daily and monthly averages), ii) with the wind direction and iii) with the elapsed years.
Emphasis is given, not only on the time-series length, but also in the stability of the results over the years.

Main body of abstract

Mesoscale and reanalysis datasets (Blended Coastal Winds, CFSR, ConWx, MERRA, NCAR) from the nearest grid points to two reference met masts are analyzed and compared to the ground based measurements. The time step of the above mentioned datasets is 1 and 6h and the selected temporal coverage for the study is from 1979, 1983 and 1993 to present. The available heights of data are 10-200m agl.
The first reference mast of 10m height, operates since 1991 in Andros Island with >85% data availability, is used as long-term reference mast for the Cyclades islands, Attica and South Evia island. Another reference mast of 100m height operates since 2001 at CRES Test Station, in Lavrio, SE Attica.
Among the three different Measure-Correlate-Predict (MCP) methods used in this study, the Matrix MCP was mostly used, but results are given also for the other two: the Linear Regression and Wind Index. The strength of the relationship has been measured by means of the correlation coefficient R and the extraction of standard error of the prediction. Concurrency of the data was verified (and sometimes corrected) through maximization of the correlation within a given time-window frame.
Correlations show small differences concerning the global correlation coefficient regarding the Andros 10m reference mast, however results depend on the employed MCP method, the temporal resolution and the wind direction.
Very good agreement is obtained for the 100m mast especially for ConWx mesoscale data and MERRA reanalysis, leading in some cases remarkably good R values especially for monthly averages.

Conclusion

The strengths and the weaknesses of the five long-term databases, through an in-depth comparison with high quality measured data, are examined.
In general, the ConWx, MERRA and CSFR data lead to an improvement in correlation coefficient, but there are some limitations in the choice of the dataset. In any case, the distance between the met mast and the grid point of reanalysis data, the agreement of the wind roses and the choice of MCP method are very important factors. Also better results are obtained for sites with higher annual average wind speeds.



Learning objectives
Comparisons of the state-of-the-art long-term wind databases to reference masts in complex and coastal terrains, provides valuable information for the necessary criteria needed for the adoption of reanalysis data, when extrapolating short-term measured data to long-term ones.