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

Why the need of reanalysis data in wind resource analysis?

Interannual variability of the wind speed

Need to long-term correct

the wind measurements

Long-term series of

wind data are needed

Reanalysis datasets may be used as reference dataseries for the

long-term correction of wind measurements.

Reanalysis dataset

Institution VintageTime interval

available

Horizontal resolution( lat x lon)⁰ ⁰

Vertical level

Temporal resolution

(h)

NCEP/NCAR NCEP 19951948 – present

(Monthly releases; 1 week delay)

5/2 x 5/20.995 sigma

level(1)

6(instan-taneous)

MERRA NASA 20091979 – present

(Monthly releases; 1.5 months delay)

1/2 x 2/3 50 m1

(time averaged)

NCEP/CFSR NCEP 2009

1979 - Dec 2009(planned to be

available on real time)

1/2 x 1/20.995 sigma

level(1)

1(instan-taneous)

(1) The 0.995 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.

The reanalysis datasets analyzed in this study are the following,

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.

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.

0 5 10 15 20 250

2

4

6

8

10

12

14

16

18

Correlation with CFSR for MastAmboke_70

CFSR wind speed (m/s)

Mas

t w

ind

sp

eed

(m

/s)

Mast wind speed (m/s)y=0.68338*x + 1.6753, R=0.86898

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.

0 5 10 15 20 250

2

4

6

8

10

12

14

16

18

Correlation with CFSR for MastAmboke_70

CFSR wind speed (m/s)

Mas

t w

ind

sp

eed

(m

/s)

Mast wind speed (m/s)y=0.68338*x + 1.6753, R=0.86898

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.

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 (%)

M1 0.731 0.872 0.870 19.3 19.0M2 0.716 0.874 0.865 22.0 20.8M3 0.642 0.821 0.806 28.0 25.6M4 0.715 0.835 0.825 16.8 15.3M5 0.807 0.885 0.895 9.6 10.9M6 0.672 0.880 0.855 31.0 27.2M7 0.799 0.873 0.869 9.3 8.8M8 0.738 0.841 0.850 14.0 15.3M9 0.826 0.856 0.865 3.6 4.7M10 0.701 0.799 0.819 13.9 16.7M11 0.806 0.858 0.880 6.4 9.2M12 0.733 0.826 0.804 12.6 9.6M13 0.762 0.853 0.863 12.0 13.3M14 0.773 0.860 0.841 11.2 8.8M15 0.670 0.850 0.822 26.9 22.6M16 0.799 0.849 0.853 6.2 6.8M17 0.635 0.843 0.833 32.7 31.2M18 0.762 0.848 0.848 11.3 11.2M19 0.675 0.817 0.805 21.0 19.3M20 0.700 0.815 0.813 16.3 16.1M21 0.703 0.814 0.797 15.8 13.5M22 0.759 0.815 0.826 7.4 8.8M23 0.695 0.814 0.797 17.1 14.7M24 0.636 0.748 0.629 17.6 -1.0

Mean (%) 15.9 14.5

Stdev (%) 7.7 7.3

2. Analysis of the temporal consistency of NCAR, MERRA and CFSR

reanalysis wind speed data

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.

NCAR, MERRA and CFSR consistency maps

k/kmin for each of the NCAR, MERRA and CFSR grid points.

2.1. Procedure

10 12.5 15 17.5 20 22.5 2552.5

55

57.5

60

62.5

65

67.5

70

Longitude (degrees)La

titud

e (d

egre

es)

NCAR map of Sweden

-500

0

500

1000

1500

2000

2500

k/kmin

2500

-500

NCAR

2.2. NCAR, MERRA and CFSR consistency maps

k/kmin

10 12.5 15 17.5 20 22.5 2552.5

55

57.5

60

62.5

65

67.5

70

Longitude (degrees)La

titud

e (d

egre

es)

NCAR map of Sweden

-500

0

500

1000

1500

2000

2500

k/kmin

2500

-500

NCAR

10 10.7 11.3 12 12.7 13.3 14 14.7 15.3 16 16.7 17.3 18 18.7 19.3 20 20.7 21.3 22 22.7 23.3 2455

55.5

56

56.5

57

57.5

58

58.5

59

59.5

60

60.5

61

61.5

62

62.5

63

63.5

64

64.5

65

65.5

66

66.5

67

67.5

68

68.5

69

69.5

70

Longitude (degrees)

Latit

ude

(deg

rees

)

MERRA map of Sweden

-400

-300

-200

-100

0

100

k/kmin

100

-400

MERRA

2.2. NCAR, MERRA and CFSR consistency maps

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

k/kmin

k/kmin

10 12.5 15 17.5 20 22.5 2552.5

55

57.5

60

62.5

65

67.5

70

Longitude (degrees)La

titud

e (d

egre

es)

NCAR map of Sweden

-500

0

500

1000

1500

2000

2500

k/kmin

2500

-500

NCAR

10 10.7 11.3 12 12.7 13.3 14 14.7 15.3 16 16.7 17.3 18 18.7 19.3 20 20.7 21.3 22 22.7 23.3 2455

55.5

56

56.5

57

57.5

58

58.5

59

59.5

60

60.5

61

61.5

62

62.5

63

63.5

64

64.5

65

65.5

66

66.5

67

67.5

68

68.5

69

69.5

70

Longitude (degrees)

Latit

ude

(deg

rees

)

MERRA map of Sweden

-400

-300

-200

-100

0

100

k/kmin

100

-400

MERRA

10 10.5 11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17 17.5 18 18.5 19 19.5 20 20.5 21 21.5 22 22.5 23 23.5 24 24.5 2555

55.5

56

56.5

57

57.5

58

58.5

59

59.5

60

60.5

61

61.5

62

62.5

63

63.5

64

64.5

65

65.5

66

66.5

67

67.5

68

68.5

69

69.5

70

Longitude (degrees)

Latit

ude

(deg

rees

)

CFSR map of Sweden

-1000

-500

0

500

1000

1500

k/kmin

1500

-1000

CFSR

2.2. NCAR, MERRA and CFSR consistency maps

k/kmin

k/kmin

k/kmin

2.3. Results

Reanalysis data

Range of k/kmin Mean value of |k/kmin| Standard deviation of |k/kmin|

NCAR [-755.6 ; 2784.8] 939.9 806.8

MERRA [-488.9 ; 184.5] 198.4 111.1

CFSR [-1136.8 ; 1719.5] 394.0 309.7

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?

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/kmin)

Energy correction factor

Relative difference in the energy estimate compared to using NCAR 57.5 N 15 E ⁰ ⁰

NCAR57.5 N 15 E⁰ ⁰

66 0.806 +1393 0.93 -

MERRA 57.5 N 14.7 E⁰ ⁰

61 0.817 -412 1.06 +14%

CFSR57.5 N 14.5 E⁰ ⁰

60 0.852 -106 1.10 +18%

MERRA58.0 N 14.7 E⁰ ⁰

10 0.858 -458 1.07 +15%

CFSR58.0 N 14.5 E⁰ ⁰

0 0.880 -79 1.09 +17%

Higher correlation coefficients for closer located grid points

Low temporal consistency

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

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

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 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.

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.

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.

Conclusions

Similar analysis performed on the reanalysis wind direction would be of great

interest.

Future Work

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.

The NCEP/NCAR reanalysis data used in this investigation was provided by the

NOAA/OAR/ESRL PSD, Boulder, Colorado, USA.

Acknowledgements

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!