# Weather Radar in Complex Orography
## 4 Solutions and Results
### Data Processing and Product Generation
Here, we limit the presentation to performance statistics routinely generated for real-time QPE products over Switzerland for hourly rainfall accumulations in the summer season. Table 3 shows the performance of two operational real-time QPE products, that is, a product based only on radar data (PRECIP) [4] and a product that combines PRECIP with rain gauge measurements using a space-time cokriging with external drift technique (CPC) [90]. The year 2011 has been excluded from these statistics, because of the installation of the first two radars of the fourth-generation network. The numbers in Table 3 show that a substantial improvement has been achieved over the years, which can be attributed to the deployment of two additional radars within the inner-Alpine regions in 2014 and 2016, improvements in the data quality control procedures and a number of innovations in the operational QPE algorithms. The rapidly growing data archives open the way towards new solutions that incorporate machine learning techniques, as demonstrated in Figure 19, which compares PRECIP and CPC with the results of RainForest, a new radar QPE algorithm currently being tested. RainForest was designed for use over complex orography and it makes use of dual-polarisation measurements and a machine learning approach [108]. The version  
Figure 18: In the Swiss radar network, clutter signals are cancelled at the radial resolution of 83-m gates, using a multiparameter decision tree, which combines single- (light blue), Doppler (dark blue) and dual-polarisation (yellow) information. The numbers at the exit of each test indicate the exit statistics for 2020. It shows the minimum and maximum values for the five radars, with the initial letter of the radar site in parenthesis. Considering all 5 radars, the algorithm has to make a decision for 51 million gates every 5 min, that is, 170,000 gates every second. It should be noted that a few strong clutter gates, erroneously classified as precipitation, may be enough to generate a false severe weather warning. of the RainForest algorithm that was used to calculate the data of Figure 19 has been optimised, simultaneously, for several different scores, including bias and scatter. If the aim is to optimise only one parameter, for instance scatter, RainForest can achieve even better scores. This demonstrates the great potential of machine learning approaches for customised applications optimised for specific needs. It should be pointed out that any attempt to verify a QPE product using rain gauge measurements underrates the accuracy of radar products. First, rain gauge measurements are subject to errors, especially in a mountainous region, where the instrument is exposed to wind and a considerable part of precipitation falls as snow. Second, there is a substantial difference in the catching surface of a rain gauge and the spatial resolution of a radar pixel (representativeness error), see, for instance, [157]. Therefore, the differences between the QPE and the reference rain gauge are partly due to instrumental errors of the gauge and representativeness errors. This is less of a problem when the aim is to compare different methods (Figure 19) or different versions of algorithms (Table 3) in _relative_ terms. However, caution is required when interpreting verification scores in _absolute_ terms, for instance in the context of hydrological applications. Figure 19: Performance of different QPE algorithms, evaluated at the automatic rain gauge locations, and expressed as the scatter in dB, a robust measure of the standard deviation [4; 91]. The smaller the scatter is, the better the QPE algorithm. (**a**) PRECIP is a QPE product that is based on single-polarisation algorithms [4]. (**b**) CPC is based on geostatistical merging of PRECIP and rain gauge data [90]; to determine the scatter for CPC, the tested rain gauge was left out of the geostatistical merging (cross-validation). (**c,d**) RainForest is a recently developed QPE algorithm that uses machine learning and incorporates dual-polarisation data.