# Weather Radar in Complex Orography
## 4 Solutions and Results
### Design Considerations  
A number of important decisions have to be made at an early stage when the overall design of a radar network over complex orography is necessary. A first important question is related to network design. Do we opt for (i) a dense network of short-range X-band radars deployed on both mountains and within the valleys, (ii) a limited number of long-range C-band or S-band radars sited on mountain crests or (iii) a combination of the two? After careful consideration of all the pros and cons, Switzerland decided to use long-range volume-scanning C-band radars with identical hardware for all of their five sites. However, in a country which has different types of terrain, different sizes, climate and needs, the weather service may opt for another solution. The design of a network also depends on the presence of other observational data, in particular rain and river gauges and satellite imagery, and on the strategy of how these different sources of information will be combined to fulfil the needs of the users. Another important issue is the design of the data flow and processing stages. Because of the increased bandwidth, it is possible to transmit the radar measurements at an early processing stage from the mountain site to the central computing and compositing server. This allows new sophisticated multiradar approaches to be obtained, for instance, for clutter cancellation and QPE. In the Swiss network, polarimetric moments in polar coordinates are transmitted to the central server at a radial resolution of 83 m. QPE is performed at the central server by combining, for each ground pixel, in a simultaneous manner, raw polar data from all the radars and all the elevation sweeps that provide information on the vertical column above that pixel and for that moment in time. The availability of valid radar precipitation measurements in a vertical column varies considerably in space and time. There are several reasons for this: shielding, ground clutter, variations in the refraction of the radar beam, weather, microphysical processes and the vertical stratification of hydrometeors and, obviously, interruptions of radar operation during preventive and corrective maintenance activities. The more measurements are available in the vertical column above a ground pixel, the higher the quality of the final product. For any given situation, the Swiss QPE algorithms aim to make the best possible combination of all the measurements from different radars and different elevation angles that are available for a given location by assigning different weights to each measurement, depending on the height above sea level, the level of shielding and the distance from the radar. This results in a dynamic and smooth quality transition in space and time, which is a major improvement, compared to the design of the QPE processing of the previous radar generation. The combination of measurements from different elevation angles also compensates for temporal undersampling, which is a major issue when data from only one elevation are considered. The testing of new algorithms can easily be done offline with historical data sets. However, when evaluating new radar products, together with forecasters and the users of automatic heavy precipitation warnings, it is essential to be able to run new software in real time on a test chain that is identical to the operational chain. There are four parallelreal-time processing chains on the central radar server of the Swiss network, two for testing, one for deployment and one for operational production. There is also a second digital signal processing unit on one of the radar sites (Albis) that runs in parallel with the operational one. It is used for testing purposes and as a spare part in the case of failure. The spatial and temporal resolution of product generation is another important design aspect. In the case of QPE, and most other radar products, the uncertainties are large and it is recommended to integrate values over time and/or space to reduce the stochastic errors.