Extended Abstract

3. DataFor the investigation the weather radar data for the time period March 2000 – December 2007,provided by the German Weather Service (DWD), were taken into account. The advantage of thisradar product is the high temporal (5 min) and spatial (1 km) resolution. Disadvantages are someshadowing effects caused by the highest mountain peaks due to the precipitation scan of the radar,which allows only the scan of the lowest layer. For further investigations, the output of convectivecells and tracks has been subdivided into different stream directions, based on the objective weatherclassification of the DWD.4. MethodBased on an existing tracking algorithm (STEINACKER et al. 2001) a simple algorithm was created tofilter radar images, to identify convective cell cores, and to determine the tracks for every single cellcore.A 2-dimensional Gauss filter was used to smooth the radar images with a standard deviation σ of 0.85.The filter matrix consists of 7 x 7 grid points. The necessity of the filter is explained by remainingclutter and the precipitation structure in general, which can have weak secondary cell cores. Afterfiltering they merge with primary cell cores and this make the identification of convective cell coresmore clearly for the automatic algorithm.To identify convective cell cores, two basic conditions have been defined to distinguish betweenconvective and stratiform precipitation. On the one hand an intensity threshold for convective cellcores has to be considered, which was set to 8.12 mm/h. On the other hand the spatial variability ofconvective precipitation has to be considered. To check these conditions a 7 x 7 grid point matrix wasagain taken into account. The algorithm checks the differences in precipitation from the central gridpoint to the surroundings. Potential cell cores must have a larger precipitation intensity than its directsurrounding grid cells, and grid cells in larger distance to the central grid point of the matrix must havea certain precipitation difference to them.For tracing convective cell cores from one time step to the next time step, additional data were needed.Thus, six hourly means of NCEP/NCAR reanalysis data for u- and v- wind vectors have been used(HARRIS 2008). To recover a cell core in the next time step from a radar image, a displacement vector,based on the reanalysis wind data, will indicate the expected position of the cell core. From the end ofthis displacement vector a searching area is defined to look for cell cores. In the case the algorithmfinds a cell core within this area an identical ID number is assigned, otherwise the cell core isdissolved or a cell core beyond the area is assigned with a new ID number. A shortcoming of thealgorithm is that merging and splitting of convective cells cannot be considered and the assumptionthat convective cell cores always shift with the mean stream direction of the troposphere.5. ResultsThe whole dataset consists of 89671 cell cores, which mostly occur under southwesterly flow direction(59.6%). The second most frequently-occuring weather type for convective cells is the northwesterlyflow (18.1%), followed by the undefined flow direction (17.3%) and the southeast (3.9%) andnortheast (1.1%) flows. To get an overview of the large dataset of convective cells and their tracks, aline density per km 2 was calculated within a radius of 1.5km. Figure 2 shows the most interestingstream directions for the whole period of investigation. First of all the results show different regionsfor the highest densities of convection, highly depending on the stream direction. Southwesterly flowsshow a very high density of convective tracks caused by the eastern side ridge of the TaunusMountains (c), which moves to the Wetterau Basin (g) and Lahn Valley (h) east of the TaunusMountains (Fig. 2). The western part of the Taunus Mountains (d) shows significantly lower densities.Northwesterly flows show a significant high density of convective tracks at the Odenwald Foothills (f)-387-