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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.

Konstantinos Gkarakis HWEA - TEI Athens, Greece
Co-authors:
Konstantinos Gkarakis (1) F P
(1) HWEA - TEI Athens, Marousi, Attica, Greece

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Abstract

ConWx mesoscale data and MERRA reanalysis data in comparison to thirty ground based meteorological masts in Greece

Introduction

The onsite measurement periods, typically ranging from 1 to 5 years, are short when compared with the standard period for average climate definition of 20 to 30 years and the lifetime of a windfarm. The annual variability of the wind regime, if not accounted for, adds to the uncertainty of the site’s resource assessment and can lead to serious misevaluations. Long-term data considered representative of the site’s long-term wind regime is therefore needed in order to estimate the long-term wind conditions at the site.

Approach

The objective of this study is to evaluate the use of different mesoscale/reanalysis data sets as long-term references for energy production estimation in comparison with the measurements of thirty meteorological masts all over Greece (majority in complex terrain). A database composed by data recorded at 30 met masts (10-80m height) placed in sites potentially suitable for windpower development (10min data - high availability), with time duration 1-5 years. Mesoscale wind data ConWx and MERRA (Modern-Era Retrospective analysis for Research and Applications) reanalysis datasets from the nearest grid points to met masts are analyzed and compared to the ground based measurements.

Main body of abstract

ConWx are meso-scale data set which are modeled and computed in-house in ConWx and EMD. The model is run at high spatial resolution of 0.03° x 0.03° (appr. 3x3 km) with 1h temporal resolution. Interim data from ECMWF are the global boundary data. The temporal coverage is from 1993 until present and the available data are from the heights of 10/25/50/75/100/150/200m agl. MERRA dataset originates from the Global Modeling and Assimilation Office of NASA / Goddard Space Flight Center. The MERRA analysis is being conducted with the GEOS-5 Atmospheric Data Assimilation System (ADAS). The model grid is 0.5 degree latitude and 2/3 degree longitude. It holds 1 hourly values in a period from 1979 until present. The available height of data is 50m agl.
Three different Measure-Correlate-Predict (MCP) methods have been used in this study: Linear Regression, Matrix MCP and Wind Index MCP. The strength of the relationship has been measured by means of the correlation coefficient R and the extraction of standard error of the prediction. Concurrent data at the 1h temporal resolution and monthly values have been used.
The ConWx mesoscale data show a higher spatial resolution in comparison with the MERRA which allows a better representation of the local wind climate but not always. There are cases that the MERRA data have better correlation with the ground based measurements than the ConWx and describes better the area wind variation. The strengths and the weaknesses of the two longterm databases through in-depth comparison with the high quality data of 30 met masts in Greece are examined.


Conclusion

In general, the ConWx mesoscale data lead to an improvement in correlation coefficient but there are some limitations in the choice of the dataset. Moreover, the distance between the met mast and the grid point of longterm data, the agreement of site and reference wind roses and the choice of MCP method are very important factors. Each long-term correction is a different case and should be examined using carefully the most appropriate dataset. The use of MERRA reanalysis and ConWx mesoscale wind data could be a relevant improvement in accuracy for the energy longterm production estimation of windfarms.


Learning objectives
The evaluation and comparison of the two state-of-the-art longterm wind databases through a great number of met masts in terrain with high complexity extracts useful conclusions for the use of them in estimation of the expected longterm wind climate. The study provides guidance for the use of the two wind datasets in complex terrain areas (practicality and uncertainty) through three worldwide accepted MCP methods.