Extended Abstract

4. Blending of Rad-TRAM and COSMO-DE-EPS probabilitiesIn this study, the two forecast methods are combined additively. Therefore,weighting func-tions have to be defined. This is conducted following Kilambi and Zawadzki(2005). As the standard deviations are smallest in CSRR, the temporal development ofRad-TRAM's quality using this score defines its weighting function:As the weights should sum to 1, the weighting function for all 22 COSMO-DE-EPS forecastsis calculated withFigure 2 displays the resulting weighting functions. While for lead times up to 5.75 h moreweight is given to Rad-TRAM, the ensemble forecasts dominate the weighting afterwards.However, at the maximum lead time of 8 hours, Rad-TRAM still contributes to theblended forecast due to the small differences in quality of both forecast sources for longlead times (cf. Fig.1). This is in contrsat to very short lead times when only one component(Rad-TRAM) is used.Fig.2: Weighting functions for Rad-TRAM and COSMO-DE-EPS for the blending procedure.The blending procedure multiplies, depending on lead time, the respective weight on theprobabilistic forecasts and sums the products:For all 22 COSMO-DE-EPS forecasts, the same weight is applied as the differencesbetween the approaches are small (cf. Fig.1).Figure 3 illustrates an example of the blending procedure with the components andthe resulting combined fields for two different lead times. In the upper row, the maximumweight is at Rad-TRAM and in the lower at the COSMO-DE-EPS forecasts. Only thefraction method is shown due to clarity. It can be seen that for short lead times (upper row,τ=1.25h), the sharp structures of the Rad-TRAM forecasts (Fig.3a) can be clearly identified-304-