## 4 Discussion  
Radar composites of high quality are not only needed in the international exchange of radar data but also locally in geographically complex terrain. In such cases, a locally installed smaller meteorological radar (such as an X-band radar, [36; 37; 38]) could supplement the information on the lower layers of the atmosphere that remain invisible for other radars. Moreover, with their higher temporal and spatial resolution, these can contribute to a more detailed characterization of precipitation systems, particularly in the case of detection and nowcasting of severe weather phenomena [38]. One of the most important drawbacks of the X-band radars is their limited maximum range (80-100 km); however, this disadvantage is balanced by the network of C-band radars with a much wider spatial resolution. Figure 10: Scatter plots of the measured water film height on the runway (RWY) and the 4-min QPE for two summer periods (June/July/August) of the years 2019 and 2020 at the Poprad Airport, Slovakia. Top: traditional QPE, based on a simple Marshall-Palmer relationship, with no quality information included (CMAX). Bottom: novel QPE, based on the _qPrc_ package. The logarithmic color scale indicates the frequency of occurrence (10 \(\times\) log\({}_{10}(N)\)) of the pairs of the values in the bins with a width of 0.1.  
coverage. Therefore, developing correct methodologies for creating composites based on a hybrid network of X-band and C-band radars is of a great importance [39; 40]. The quality-based approach to the correction of the radar data offers a high degree of flexibility for the researchers. The selection of individual QIs, their definition (e.g., whether one uses linear or exponential relationships, as well as the selection thresholds and constants), the method of scaling, and the technique of combination are rather subjective. There is not a unique, objective, and standardized way of making use the QIs themselves. Their benefits depends on the user's application and on the particular method of their implementation (weighting, thresholding, using the maximum of the QIs, etc.). The effect of various QI algorithms are usually evaluated through different use-cases. For example, when the radar data are used for QPE, the benefits of the QI algorithm should be evaluated as the improvement of the QPE performance. Similarly, when the radar data are used in numerical weather prediction models, the QI algorithm should be evaluated by its influence on the model outputs. Nowcasting based on cleaner radar images should be more reliable, as the non-meteorological echoes--which can cause unrealistic motion vector patterns and subsequently wrong extrapolation of the rainfall field--are suppressed or filtered out. As soon as the first versions of the \(qRad\) software became available for internal use, we experienced positive feedback from the forecasters and internal users of SHMU. They emphasized the quality of our visualization where the majority of non-meteorological and false echoes were removed or at least minimized by using the quality information. Therefore, when presenting interesting meteorological situations to the official channels of SHMU, they clearly preferred the \(qRad\) outputs over the products from the official software of the radar manufacturer. The software \(qPrec\) offers a wide range of benefits from the point of view of climatology. The ground-based network of pluviographs can never be sufficiently dense to catch the high temporal and spatial variability of the local intensive thunderstorms. \(qPrec\) is able to detect these phenomena and allows for a more precise reconstruction of the meteorological conditions of their occurrence, evolution and decay. This is particularly important in the case of the evaluation of insurance issues and other expert opinions. Hydroological validation is also a part of the iterative development process of the \(qPrec\) software. The help of hydrologists of SHMU is kindly appreciated in this way.