Investigation on the use of NCEP/NCAR, MERRA and NCEP/CFSR reanalysis data in wind resource analysis

Transcription

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 4.9 MERRA 57N 14E MERRA 67N 2E Annual mean wind velocity (m/s) Need to long-term correct Long-term series of the wind measurements 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, Reanalysis dataset Institution Vintage Time interval available Horizontal resolution (⁰lat x ⁰lon) Vertical level Temporal resolution (h) NCEP/NCAR NCEP present (Monthly releases; 1 week delay) 5/2 x 5/2.995 sigma level (1) 6 (instantaneous) MERRA NASA present (Monthly releases; 1.5 months delay) 1/2 x 2/3 5 m 1 (time averaged) NCEP/CFSR NCEP Dec 29 (planned to be available on real time) 1/2 x 1/2.995 sigma level (1) 1 (instantaneous) (1) The.995 sigma level corresponds to a level of 99.5% of the surface pressure, that is equivalent to approximately 42m a.g.l. for standard atmospheric conditions.

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 wind measurements 2. Temporal consistency These aspects have been investigated 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 colocations 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 colocations on telecommunication masts) distributed rather uniformly over Sweden have been used in this analysis. Mast wind speed (m/s) Correlation with CFSR for Mast Amboke_7 2 Mast wind speed (m/s) y=.68338*x , R= CFSR wind speed (m/s)

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 colocations 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. Mast wind speed (m/s) Correlation with CFSR for Mast Amboke_7 2 Mast wind speed (m/s) y=.68338*x , R= CFSR wind speed (m/s)

8 Mast R-value NCAR data R-value MERRA data R-value CFSR data Improvement in R-value MERRA as compared to NCAR (%) Improvement in R-value CFSR as compared to NCAR (%) M M M M M M M M M M M M M M M M M M M M M M M M Mean (%) Stdev (%)

9 2. Analysis of the temporal consistency of NCAR, MERRA and CFSR reanalysis wind speed data NCEP/NCAR 6N 17.5E 42m a.g.l. NCEP/CFSR 6N 17.5E 42m a.g.l. MERRA 6N 17.3E 5m a.g.l Monthly mean wind speed (m/s) jan/8 nov/8 sep/81 jul/82 maj/83 mar/84 jan/85 nov/85 sep/86 jul/87 maj/88 mar/89 jan/9 nov/9 sep/91 jul/92 maj/93 mar/94 jan/95 nov/95 sep/96 jul/97 maj/98 mar/99 jan/ nov/ sep/1 jul/2 maj/3 mar/4 jan/5 nov/5 sep/6 jul/7 maj/8 mar/9

10 12 NCEP/NCAR 6N 17.5E 42m a.g.l. NCEP/CFSR 6N 17.5E 42m a.g.l. MERRA 6N 17.3E 5m a.g.l Procedure 1 k-value = slope of the trend line k min = 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. Monthly mean wind speed (m/s) jan/8 nov/8 sep/81 jul/82 maj/83 mar/84 jan/85 nov/85 sep/86 jul/87 maj/88 mar/89 jan/9 nov/9 sep/91 jul/92 maj/93 mar/94 jan/95 nov/95 sep/96 jul/97 maj/98 mar/99 jan/ nov/ sep/1 jul/2 maj/3 mar/4 jan/5 nov/5 sep/6 jul/7 maj/8 mar/9 k/k min for each of the NCAR, MERRA and CFSR grid points. NCAR, MERRA and CFSR consistency maps

11 2.2. NCAR, MERRA and CFSR consistency maps Latitude (degrees) NCAR map of Sweden NCAR k/k min k/k min Longitude (degrees) -5-5

12 2.2. NCAR, MERRA and CFSR consistency maps Latitude (degrees) NCAR map of Sweden NCAR k/k min k/k min Longitude (degrees) -5-5 Latitude (degrees) MERRA MERRA map of Sweden Longitude (degrees) k/k min min 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 as reported by Wern et al. Wern, L. and Bärring L., Sveriges vindklimat Analys av förändring i geostrofisk vind, Meteorologi Nr 138/29 SMHI, 29

13 2.2. NCAR, MERRA and CFSR consistency maps Latitude (degrees) NCAR map of Sweden NCAR k/k min k/k min Latitude (degrees) MERRA MERRA map of Sweden Longitude (degrees) Longitude (degrees) k/k min min Latitude (degrees) CFSR map of Sweden CFSR Longitude (degrees) k/k min

14 2.3. Results Reanalysis data Range of k/k min Mean value of k/k min Standard deviation of k/k min NCAR [ ; ] MERRA [ ; 184.5] CFSR [ ; ] MERRA wind speed data show significantly weaker longterm trends than NCAR and CFSR. 8% weaker long-term trend in average than NCAR. 5% 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 Grid points Distance from the mast (km) R-value on wind speed Trend (k/k min ) Energy correction factor Relative difference in the energy estimate compared to using NCAR 57.5⁰N 15⁰E NCAR 57.5⁰N 15⁰E MERRA 57.5⁰N 14.7⁰E CFSR 57.5⁰N 14.5⁰E % % Mainly due to the difference in temporal consistency MERRA 58.⁰N 14.7⁰E CFSR 58.⁰N 14.5⁰E % % Higher correlation coefficients for closer located grid points Low temporal consistency Due to the closer location of the grid point and to the higher temporal consistency of the reanalysis data

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 wind measurements 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. 2. Temporal consistency NCAR data show for some grid points large temporal inconsistencies that affect considerably the energy production estimates. The use of MERRA and CFSR reanalysis wind data represents a relevant improvement in accuracy for 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 correctly judge the uncertainty inferred by long-term trends in the energy estimate should be further investigated. The causes of the large temporal inconsistency observed in some grid data should be analyzed in more detail. The analysis of the reanalysis dataset ERA-Interim (not publicly available for commercial uses) developed by ECMWF (European Centre for Medium Range Weather Forecasts), would also 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!