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Delegates are invited to meet and discuss with the poster presenters in this topic directly after the session 'The model chain: First steps towards tomorrow's technology' taking place on Thursday, 13 March 2014 at 09:00-10:30. The meet-the-authors will take place in the poster area.

Andrea Vignaroli WindSim AS, Norway
Co-authors:

(1) WindSim AS, Tønsberg, Norway (2) North Carolina State University , Raleigh, United States

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Presenter's biography

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

I am a senior site assessment engineer and i coordinate the activities the global consulting team of WindSim AS.

Abstract

Downscaling MERRA mesoscale data for the generation of microscale wind fields using CFD

Introduction

For many wind farm developers, obtaining meteorologically representative and accurate wind climatology data proves to be one of the most challenging aspects of their wind resource assessment campaign. As an alternative to the conventional technique of deploying multiple tall met masts and waiting several years for this data, we propose the use of mesoscale reanalysis model output statistically and dynamically downscaled using computational fluid dynamics (CFD).

Approach

In the proposed methodology, we use long term global mesoscale reanalysis data from NASA’s “Modern-Era Retrospective analysis for Research and Applications“ (MERRA) reanalysis dataset to scale CFD simulations, of varying complexity, in order to generate wind (speed and direction) time series comparable to those measured by met mast sensors. These ‘synthetic’ wind time series primarily rely on (1) accurate CFD simulations and (2) properly defined forcing data from the mesoscale model.

Main body of abstract

Virtual wind data available globally with very high resolution in order to take into account at best the effect of elevation and vegetation on the wind flow can be obtained using this apporach. The time series can include also other crucial meteorological parameters for effective wind farm project siting like temperature, density, stability, boundary layer height.
Relevant wind energy applications that can take advantage of such novel approach are:

• Reference dataset for long term correction (MCP)
• Identification of potential site for wind farm project
• Measurement campaign design
• Layout design
• Siting and energy assessment of small wind turbines

The procedure used for the calculation can be easily adapted to use Meso-scale data from numerical weather prediction modelling as driving data.
The validity of the downscaling methodology was verified against 7 sites of varying atmospheric stability and terrain characteristics. At each site, virtual climatologies were compared to measured wind climatologies with respect to vertical profile, speed and direction distribution, and time series. At sites where energy production data were available, AEP validations were carried out.


Conclusion

Incorporating mesoscale reanalysis data into the wind resource assessment process offers a number of unique advantages and opportunities for application which will be discussed in the presentation. The accuracy of this technique is largely sensitive to terrain complexity but wind speed errors of less than approximately ±15% are easily achievable. Based on power production data availability, AEP errors of ±15% were observed with this technique.


Learning objectives
Upon completion, participants will be able to understand the value of MERRA reanalysis data for wind resource assessment Upon completion, participants will learn about a technique to downscale mesoscale reanalysis data with the help of computational fluid dynamics to gain high resolution wind data for wind resource assessment
Upon completion, participants will be able to understand the uncertainties in using mesoscale reanalysis data for wind resource assessment compared to using direct measurements onsite