for MSSCP

The measure-correlate-predict method MSSCP
(**M**ultiple **S**ynoptic **S**cale **C**orrelate **P**redict)
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.

Download WDS report on MSSCP (PDF)

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.

MSSCP can also be reduced to the common matrix method.

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), 11.25°E, 53.5°N, 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, 11.19°E, 54.00°N, and Schwerin, 11.39°E, 53.64°N, 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 R^{2}_{min} = 0.32 and
R^{2}_{min} = 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

real measured

directional MCP

MSSCP

mean wind speed v (m/s)

6.9

6.9

6.9

mean wind power density WPD (W/m^{2})

321

302

319