Sónia Liléo, PhD Wind resource analyst - R&D manager O2 Vind AB Stockholm, Sweden

1. Investigation on the use of NCEP/NCAR, MERRA and NCEP/CFSR reanalysis data in wind resource analysis Sónia Liléo, PhD Wind resource analyst - R&D manager O2 Vind AB Stockholm, Sweden sonia.lileo@o2.se Olga Petrik Master thesis student Royal Institute of Technology Stockholm, Sweden opetrik@kth.se

2. Why the need of reanalysis data in wind resource analysis? • Interannual variability of the wind speed Need to long-termcorrect the windmeasurements Long-term series of wind data are needed

3. Reanalysis datasets may be used as reference dataseries for the long-term correction of wind measurements. The reanalysis datasets analyzed in this study are the following, (1) The 0.995 sigma levelcorresponds to a level of 99.5% of the surface pressure, that is equivalent to approximately 42m a.g.l. for standard atmosphericconditions.

4. There are two essential requirements that reanalysis datasets have to fulfil in order to be used as long-term reference data in wind resource analysis. 1. Good degree of correlation with windmeasurements 2. Temporal consistency Theseaspectshavebeeninvestigated for the reanalysis datasets NCAR, MERRA and CFSR.

5. 1. Correlation analysis of NCAR, MERRA and CFSR reanalysis wind data with wind measurements • Wind speed measurements from 24 masts (13 met masts and 11 co-locations on telecommunication masts) distributed rather uniformly over Sweden have been used in this analysis.

6. 1. Correlation analysis of NCAR, MERRA and CFSR reanalysis wind data with wind measurements • Wind speed measurements from 24 masts (13 met masts and 11 co-locations on telecommunication masts) distributed rather uniformly over Sweden have been used in this analysis.

7. 1. Correlation analysis of NCAR, MERRA and CFSR reanalysis wind data with wind measurements • Wind speed measurements from 24 masts (13 met masts and 11 co-locations on telecommunication masts) distributed rather uniformly over Sweden have been used in this analysis. • The correlation coefficient, R, of the linear regression fit between wind speed measurements from each mast and wind speed data from the nearest located reanalysis NCAR, MERRA and CFSR grid points have been analyzed.

9. 2. Analysis of the temporal consistency of NCAR, MERRA and CFSR reanalysis wind speed data

10. 2.1. Procedure kmin = k-value of the CFSR 64.5⁰N 21⁰E grid point. Corresponds to the minimum k-value of all the NCAR, MERRA and CFSR grid points. k/kmin for each of the NCAR, MERRA and CFSR grid points. NCAR, MERRA and CFSR consistency maps

11. NCAR k/kmin 2500 2.2. NCAR, MERRA and CFSR consistency maps -500

12. 2.2. NCAR, MERRA and CFSR consistencymaps NCAR k/kmin 2500 -500 k/kmin MERRA 100 • MERRA data show predominantly weak downward long-term trends. • This result is in accordance with the downward long-term trend observed in the mean wind speed in Sweden during the period of 1951-2008 as reported by Wern et al. • Wern, L. and Bärring L., “Sveriges vindklimat 1901-2008. Analys av förändring i geostrofisk vind”, Meteorologi Nr 138/2009 SMHI, 2009 -400

13. NCAR k/kmin 2500 2.2. NCAR, MERRA and CFSR consistency maps -500 k/kmin CFSR k/kmin MERRA 1500 100 -400 -1000

14. 2.3. Results MERRA wind speed data show significantly weaker long-term trends than NCAR and CFSR. 80% weaker long-term trend in average than NCAR. 50% weaker long-term trend in average than CFSR. How does temporal inconsistency of reference wind data influence the estimate of energy production?

15. 3. Influence of the choice of reanalysis data on the energy production estimate - Case Study Mainly due to the difference in temporal consistency Due to the closer location of the grid point and to the higher temporal consistency of the reanalysis data Higher correlation coefficients for closer located grid points Low temporal consistency

16. Conclusions There are two essential requirements that reanalysis datasets have to fulfil in order to be used as long-term reference data in wind resource analysis. 1. Good degree of correlation with windmeasurements The higher spatial and temporal resolutions of MERRA and CFSR reanalysis wind data allow a better representation of the local wind climate. An average improvement of 16% in correlation coefficient with local wind measurements is obtained for MERRA and 15% for CFSR when compared to NCAR. The use of MERRA and CFSR reanalysis wind data represents a relevant improvement in accuracyfor energy production estimates. 2. Temporal consistency NCAR data show for some grid points large temporal inconsistencies that affect considerably the energy production estimates. The relative difference in energy estimate is for a specific analyzed case about 14%, caused mainly by the difference in temporal consistency of the reanalysis data used.

17. Future Work Similar analysis performed on the reanalysis wind direction would be of great interest. • How to correctlyjudge the uncertaintyinferred by long-term trends in the energyestimateshould be furtherinvestigated. • The causes of the large temporal inconsistencyobserved in somegrid data should be analyzed in moredetail. • The analysis of the reanalysis dataset ERA-Interim (not publiclyavailable for commercialuses) developed by ECMWF (European Centre for Medium Range Weather Forecasts), wouldalso be of great interest.

18. Acknowledgements The NCEP/NCAR reanalysis data used in this investigation was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA. • The NCEP/CFSR data are from the NOAA’s National Operational Model Archive and Distribution System (NOMADS) which is maintained at NOAA’s National Climatic Data Center (NDCD). • The authors would also like to acknowledge the Global Modeling and Assimilation Office (GMAO) and the GES DISC (Goddard Earth Sciences Data and Information Services Center) for the dissemination of MERRA. Thank you!