## 3 Results  
### Correlation between Spaceborne Radar Amounts and the Three MeteoSwiss Products Amounts  
For correlation, the standard Pearson correlation coefficient is used. It is applied to 221 total precipitation amounts at rain gauge sites (219 degrees of freedom); results are shown in Table 1. The first and third line show the values after the precipitation amounts were Log-transformed in order to minimize the influence of the asymmetry of the distributions of total precipitation amounts. In these cases, the probability distribution functions are close to being normal as a result of the monotonic logarithmic transformation. In both cases, for Log-transformed and linear amounts, the outcome is clear: inside the Swiss domain (221 points out of \(\sim\)1600 GPM pixels) the best correlation is between GPM and the CombiPrecip precipitation product, followed by RADAR, and, as expected, by gauges. This is valid for both versions of GPM products: V04A and V05A. Using the new version V05A, the correlation slightly increases in the case of linear amounts.  
Figure 2 shows the scatterplot of the 221 Log-transformed data pairs from which the Pearson correlation coefficients were derived (first line in Table 1). The annual GPM estimates (augmented by 2 dB) are on the abscissa, while the three MeteoSwiss precipitation estimates are on the ordinate; Combirecip values are in blue, RADAR in red, and GAUGE in green. It can easily be observed that the blue marks are the one closest to a straight regression line (best correlation), while the green ones show the worst correlation. Among the green marks, one is of particular interest, namely the one with the largest precipitation amount: 76.8 mm/h equivalent (actually, 12.8 mm/10 min) during 212 wet GPM overpasses, which corresponds approximately to an annual estimate of 3173 mm (76.8 \(\times\) 24 \(\times\) 365/212). This \(\sim\)3170 mm/a in situ point estimate was derived using gauge measurements acquired at the \"Passo del Gran San Bernardo\" at 2472 m altitude (GSB, using MeteoSwiss nomenclature). It is a high altitude pass connecting Switzerland (Valais) to Italy (Valle d'Aosta), where precipitation measurements are challenging, especially when there is still snow. The snow is often blown into the gauge by a strong wind which is accelerated because of the Venturi effect. More details and in depth analysis will be given in Section 4; here, it is enough to underline that the 600/1.6 mm/a GPM estimate should not be compared with the 3170 mm/a estimate, but rather with a smaller value. At the moment, at MeteoSwiss we have no quantitative estimate of the amount of false precipitations blown by the wind. However, since the beginning of the year, a second rain gauge has been installed at a better site, and comparison analyses are ongoing; we are confident that we will be able to give a preliminary answer to this interesting question in a couple of years, assuming that the amounts for 2017 and for the subsequent years are sufficiently representative of the past years.  
Figure 2: GPM spaceborne radar (V05A) annual precipitation estimates (multiplied by 1.6) versus the estimates from three MeteoSwiss products: RADAR (red), GAUGE (green), and the geostatistical merging of the two (CombiPrecip). Note that the multiplication of the GPM values by 1.6 on the \(x\) axis means a visual compensation of 2 dB (out of \(\sim\)3 dB overall underestimation by GPM) in the log-log scatter plot.