# Weather Radar in Complex Orography
## 5 Concluding Remarks  
In complex orography scientists, engineers, technicians and the users of weather radars are faced with several major problems. The solutions developed for flat regions do not work, if not adapted to the particular situation, and the sources of errors are manifold. In this paper, it has been shown how the problems can be addressed in an operational context. The presented solutions and practical applications demonstrate the large potential and practical value of weather radars in a mountainous region, in spite of the harsh conditions and limitations. Diversity has turned out to be beneficial, for several aspects. With a combination of different types of radar sites (\"plain\", \"offsetide\", \"peak\" and \"valley\"), one has a better chance of properly sampling precipitating clouds over the foreland, the foothills, the upslope region and in the inner parts of a major mountain range. It is crucial to combine different sources of information in parallel for hardware acceptance testing, monitoring and calibration. For QPE, a weighted combination of all the measurements in the vertical column above a ground pixel, which come from different elevation angles and radars, results in more accurate estimates of ground-level precipitation rates than single-sweep and single-radar approaches. It is recommended, for hydrological applications and the issuing of automatic heavy rainfall warnings, to combine, in parallel, in the sense of an ensemble, different types of QPE products (single polarisation, dual polarisation, radar rain gauge merging). For automatic hail reports at an airport, it is recommended to combine single-polarisation hail products, such as POH and MESHS, dual-polarisation hydrometeor classification and, ideally, also ground measurements of hail. Machine learning approaches have great potential to _complement_ algorithms based on physics, as demonstrated by many recent publications, see, for instance, [58; 107; 108; 173]. The use of artificial intelligence has been accelerated by the continuously increasing computer power, easy access to libraries with advanced algorithms, rapidly growing archives of high quality data, and the ever-increasing demands of the users. An area with great potential for development is that of _customised_ machine learning solutions designed and trained for specific applications. However, these data-centric approaches come with some drawbacks, which need to be addressed in future research. Other innovation paths concern the integration of crowdsourcing data, a first step of which was shown in [98], distributed adaptive sensing [141]  
Figure 20: LEHA is an arithmetic rule that is used to transform an MESHS hail diameter (\(x\)-axis), referring to a 1 km\({}^{2}\) pixel, to a smaller reference area (\(y\)-axis), which is typically the size of a building. The abbreviation LEHA stands for largest expected hail size on a reference area. For instance, a LEHA value of about 3.5 centimeters is obtained for an MESHS value of 5.5 cm and a house roof with a size of 100 m\({}^{2}\). and new radar technologies, such as phased-array antennas. It remains to be seen how these can successfully be used in complex orography. Conceptualization, writing--original draft preparation, U.G. ; writing--review and editing, methodology, investigation, M.B., L.C., M.G., U.G., A.H., M.S. and I.V.S. ; supervision, B.C. All authors have read and agreed to the published version of the manuscript. This work was funded by the Federal Office of Meteorology and Climatology MeteoSwiss. The research on the usage of dual-polarisation measurements was cofunded by and done in close collaboration with the [PERSON] group at EPFL. The research by [PERSON] and [PERSON] was funded by SNF, under Ambizione grant PZ00P2_161316. The network of 80 automatic hail sensors was funded by Mobiliar and deployed by inNET Monitoring AG. Research on hail was cofunded by Mobiliar and conducted in close collaboration with the [PERSON] group at University of Bern.