EXAMPLES
MSSCP
 order 

Example
for MSSCP

 

WindDataSuite implements the new Measure Correlate Predict method MSSCP (Multiple Synoptic Scale Correlate Predict). MSSCP has been developed by WIND DATA SUiTE and is based on an evaluation of the wind field variability on the synoptic scale. Hereby MSSCP achieves an essential improvement of the extrapolation results.

MSSCP can use multiple reference stations, which can be weighted for the long-term extrapolation appropriate to their distances, and/or in the particular wind direction sectors appropriate to their positions relative to the site of the short-term wind measurement and/or in the particular wind direction sectors appropriate to their correlations with the measurement station data. The long-term time range and the short-term time range common with the measurement time range at the measurement station can be selected for each reference station individually. Long-term time range and short-term time range may include each other, overlap, or exclude each other.

Besides miscellaneous frequency distributions and WAsP-TABs, MSSCP can also generate fictitious time series of the long-term extrapolated data.

In the following example, an hindcast was performed with MERRA-2 (Modern Era Retrospective-analysis for Research and Analysis - Version 2, NASA GEOS-5 model) re-analysis data at MERRA-2-point (J287,I306), 11.25E, 53.5N, as "measurement" station. As the short-term measurement, the time series data of the time range from 2015/08/01 to 2016/07/31 were selected. As long-term reference stations, the time series data of two DWD (Deutscher Wetterdienst) weather stations, Boltenhagen, 11.19E, 54.00N, and Schwerin, 11.39E, 53.64N, were selected, from 1995/01/01 to 2015/12/31 for the long-term time range and from 2015/08/01 to 2016/07/31 for the short-term time range common with the short-term measurement.
The long-term extrapolation was performed with MSSCP and for comparison also with a wind direction matrix method (directional MCP).

The example illustrates the reliability and the robustness of the MSSCP technique: area-averaged (and temporal smoothed) quantities will be predicted from point measurements. So, the correlations with the reference stations in particular wind direction sectors are very weak (coefficient of determination minimum R2min = 0.32 and R2min = 0.30 for Boltenhagen and Schwerin, respectively).

In the following figures, the real "measured" long-term time range from 1995/01/01 to 2015/12/31 at MERRA-2-point (J287,I306) is depicted in black, the long-term extrapolation with the directional MCP method in blue, and the long-term extrapolation with MSSCP in red.

 

Fig.1:
Frequency distributions of the wind speed
Fig.2:
Percentage distributions of the wind power density
Fig.3:
Sectorial frequency distributions of the wind speed
Fig.4:
Sectorial percentage distributions of the wind power density

 

Results
real measured
directional MCP
MSSCP
mean wind speed v (m/s)
6.9
6.9
6.9
mean wind power density WPD (W/m2)
321
302
319

 

 

All input- and output-parameters for MSSCP are user-definable in WindDataSuite

Data range:
1 year for the short-term measurement, sampling rate 1 hour
21 years for both of the long-term references, sampling rate 1 hour
Processed channels:
1 height level of wind speed and wind direction, respectively
Matrix:
2 reference sites
25 clusters for the synoptic scale per reference site
12 reference wind direction sectors per cluster
12 measurement station wind direction sectors per reference wind direction sector
4 wind speed classes per reference wind direction sector
Output:
31 measurement station wind speed bins
12 measurement station wind direction bins
Computing time:
approx. 10 seconds
Processing time:
approx. 1 minute
© WIND DATA SUiTE - Dr. Helmut Frey      Last modified on 2017/12/01