Combined Data Sets

DNICast Advances:

- Combined nowcasts in almost all caes have lower error than single nowcasts.

- Summer month show better results due to less clouds and better coincidence with measurements.

- Combined nowcasts are used to evaluate benefits of power plant operation using nowcasting systems.

The combined nowcast uses multiple nowcast data sets to produce a best estimate based on the uncertainty information of the single nowcasts. To calculate the optimal combination of the different provided nowcasts, the method of Meyer et al. (2008) is applied.

In a first step, nowcasted DNI in Wm-2 is extracted for each available data set.For each set of refresh time and forecast time for the combined nowcast data set, it is verified if a nowcasted DNI from the different data sets is available.

The combined DNI is calculated from all available nowcasted DNIi from the data set i for each set of refresh time and forecast time:

Combined datasets_equation1_Demonstrator.png

Where DNIcombined is the resulting combined DNI and DNIi is the nowcasted DNI of the data set i, both given in Wm-2 for one time stamp. ?i is the absolute error for each data set I and this time stamp. ?i is provided by WP3.1-3.3 and WP4 as a function of the forecast horizon and the meteorological conditions.

The estimated absolute uncertainty of combined DNI ?DNIcombined is calculated with:

Combined datasets_Equation2_Demonstrator.png

given in Wm-2.

If no absolute uncertainty is given for one set of refresh time and forecast time and one data set, a default uncertainty is estimated dependent on the data set. If the absolute uncertainty of Meteotest (clim post-processed) is available, the absolute uncertainty of the persistence nowcast is estimated to be the same.


Meyer, R., Geuder, N., Lorenz, E., Hammer, A., Ossietzky, C. Von, Oldenburg, U., Beyer, H.G., 2008. Combining solar irradiance measurements and various satellite-derived products to a site-specific best estimate, in: SolarPaces Conference. pp. 1–8.

Turlapaty, A.C., Younan, N.H., Anantharaj, V.G., 2012. A linear merging methodology for high-resolution precipitation products using spatiotemporal regression. International Journal of Remote Sensing 33, 7844–7867. doi:10.1080/01431161.2012.703345