###### Abstract
This is, on one hand, logical, since well-functioning single radars are the necessary condition of any radar networking. On the other hand, this is a topic that requires a more elaborate approach mainly in the light of increased demands for radar data within pan-European data exchange and co-operation [7; 8; 9]. One of the basic dilemmas when evaluating data from more than one radars (\(N>1\)) at a given point (pixel, bin) is how to treat the multiplicity of the reflectivity information \(Z_{i}\), \(i=1,\,\ldots\,\,N\). There are different, generally accepted methods: to take the maximum value of the available reflectivity \(Z_{i}\), to estimate the mean reflectivity, or to compute some weighted average of the \(Z_{i}\) values. In the latter case, the definition of the appropriate weights offers plenty of further alternatives to cope with the problem: for instance, with the weighting factors being inversely proportional to the distance of the given pixel from each radar, with the inclusion of the actual height of the radar beam above the ground within the target pixel, or with the combination of these or further variables. [PERSON] et al. [13] presented a substantial overview of the pros and cons of different compositing methods and emphasized that the weights might also express the quality of the data from the contributing radars. The quality of the radar measurements may be expressed through quality information or quality indices (QI, [14]). Beyond the meteorological targets and its effects (e.g., bright band), the radar signal is affected by a number of further factors stemming from the technological construction of the radar itself, the geomorphological character of the surrounding area (e.g., ground clutter, beam blocking), different non-meteorological targets (e.g., birds, insects, interfering wi-fi signals), the parameter settings of the particular radar scan (height of the radar beam above the target points, the age of the reflectivity information within a given scan), etc. The effect of all these factors may be converted into mathematical form as quality indices, and the combination of these phenomena-related, particular QIs might be understood as the overall quality index of the given radar [15]. There is no general convention regarding how to define and/or how to use the different QIs--their usage depends on the goals of the research teams or the operational requirements of the hydrometeorological services [13; 16; 17; 18; 19; 20; 21; 22; 23]. A common measure of the quality of the QI-based estimation is some visual improvement in the layout of the radar composites. Visual impressions are subjective, and they are hard to quantify; thus, the benefits of the QI-based approaches are usually evaluated indirectly, through the improvement of the performance of various statistical, weather forecasting, or hydrological models. Such a category of statistical models is quantitative precipitation estimation (QPE). It is beyond the goals of the current study to present an overview of the methods of radar-based QPE [4]; however, we can mention a couple of works especially devoted to QI-based QPE. [PERSON] et al. [20; 21] developed a method of QPE in a probabilistic framework. Their concept assumes that the probability density function (PDF) of the rainfall amounts (rates) can be approximated by the two-parameter gamma distribution, where the parameters of the PDF are functions of the radar deterministic measurement \(R\) and the quality index QI. With the estimated PDF parameters for each pixel, ensembles of precipitation fields can be generated, which further serve as inputs for hydrological modeling [21]. [PERSON] et al. [24] developed QI-based QPE for the national network of polarimetric radars in China with QIs based mostly on polarimetric parameters. The current paper was motivated by the development of two inter-related software packages, \(qRad\) and \(qPrec\), that were built to take advantage of the quality information from the available C-band radar measurements.