Example For MSSCP (Multiple Synoptic Scale Correlate Predict)
WindDataSuite ("WDS" in the following) implements the new MCP method MSSCP (Multiple Synoptic Scale Correlate Predict). MSSCP has been developed by WindDataSuite and is based on an evaluation of the wind field variability on the synoptic scale. All in the following described (and not described) separate processing steps, which are necessary for an MSSCP calculation, will be performed by WDS in each MSSCP session automatically in the background. The time series data of the reference stations (longterm and shortterm) will be processed with a digital lowpass filter thus eliminating all wave signals below the synoptic scale. Clusters will be determined from the resulting wind field dynamics according to the NavierStokes equations. The clusters comprise the entire measured dynamics and enter into the MSSCP technique as the matrix basic dimension. MSSCP can use multiple reference stations, which can be weighted for the longterm extrapolation appropriate to their distances, and/or in the particular wind direction sectors appropriate to their positions relative to the site of the shortterm wind measurement ("measurement station" in the following), and/or in the particular wind direction sectors appropriate to their correlations with the measurement station data. The longterm time range and the shortterm time range common with the measurement time range at the measurement station can be selected for each reference station individually. Longterm time range and shortterm time range may include each other, overlap, or exclude each other. MSSCP searches in each reference wind direction sector within each cluster for the one phase shift between the time series which shows the highest positive correlation. Thus, the real time delay of the wind transport as well as the different correlations within the wind field due to the actual wind field dynamics will be captured. The regression parameters will be calculated for each cluster and each reference wind direction sector individually. Inappropriate/invalid regression parameters will be conveniently replaced/interpolated. Every MCP method (including MSSCP) produces more or less erroneous predicts. This is an inevitable problem inherent to the system of every statistical model. The main problem of, e.g., regressionbased MCP methods is that the longterm extrapolations at the measurement station mostly produce wind speed frequency distributions with an overestimated Weibull shape parameter. Hence, the resulting mean wind power density (WPD) will be underestimated, though the error in the resulting mean wind speed may be less by far. In contrary, matrixMCP methods, which are based on a direct transformation of the respective shortterm and longterm wind speed frequency distributions, may produce longterm extrapolated wind speed frequency distributions at the measurement station with a underestimated Weibull shape parameter. The resulting wind power density then will be clearly overestimated. WindDataSuite has performed a lot of hindcast tests with different MSSCP variants and their combinations: regression with orthogonal distances instead of with vertical, regression through the origin of coordinates, including standard deviations, different regression models in different wind speed classes, and many more. All these statistical variations have not improved the results really (actually, most of them have deteriorated the results). Instead, they just cause confusion due to the numerous degrees of freedom and confront the user with the problem to have to make a decision (on basis of what?) on which of the methods to be used. A really essential improvement of the extrapolation results, however, is achieved by the MSSCP technique in that MSSCP analyzes and embeds the actual wind field dynamics on the synoptic scale and thereby resolves the variability of a physical process which is essential for the accuracy of the regression model. With reference to the abovementioned "more or less erroneous", MSSCP definitely contributes to the "less": the reduction of the extrapolation errors by MSSCP is enormous and is for the wind power density resulting from the extrapolations compared to a pure directional MCP method about 50% (averaged over numerous hindcast tests).
In the following example, an hindcast was performed with MERRA2
(Modern Era Retrospectiveanalysis for Research and Analysis  Version 2, NASA GEOS5 model)
reanalysis data at
MERRA2point (J287,I306), 11.25°E, 53.5°N, as measurement station.
As the shortterm measurement, the
time series data of the time range from 2015/08/01 to 2016/07/31 were selected.
As longterm 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 longterm
time range and from 2015/08/01 to 2016/07/31 for the shorttem time range common with
the shortterm measurement.
