## 1 Introduction
This explains some of the difficulties in the use of radars for quantitative precipitation estimations. When gauge data are also smoothed in time, the random error and the bias are reduced. Various deterministic or statistical methods for radar-gauge merging have been proposed. Many applications of these tools have appeared in the literature, all of which provide valuable contributions to the common knowledge regarding the problem at hand (e.g., [16; 17; 18; 19; 20; 21; 22]). In Switzerland, the merging of radar and rain gauge measurements is based on spatio-temporal kriging with external drift [22]; it was implemented at MeteoSwiss five years ago and it has been run operationally since then. This product is called CombiPrecip. To summarize, in the Swiss Alps there are currently three different ground-measured precipitation products available: (1) a product based on a network of automatic telemetered rain gauges (GAUGE); (2) a product based on a state-of-the art, C-band, dual-polarization Doppler, ground-based weather radar network (RADAR); and (3) a product consisting in a real-time merging of observations from these two networks, implemented and operational at MeteoSwiss (CombiPrecip). In this paper, we compare these products with another product based on the GPM spaceborne radar, which should provide the best space-based instantanous rainfall rate estimations. Several authors (e.g., [13; 15; 23]) illustrate the problems of the two types of instruments (radars and gauges) concerning the large scatter of \"short time-scale\" values for a given location, which are caused by the huge differences in the sampling modes. In many cases, and certainly for our evaluation purposes, it is wise to integrate the measurements over time. An integration over long times also reduces the differences in the retrieved estimates between the spaceborne and the ground-based radars. Although these two sensors certainly have more similarities than a radar and a rain gauge, there are nevertheless enormous differences between the two. In this regard, mention can be made of the different operating frequencies, sampling volumes, geometrical viewing angles, attenuation, sensitivity, and temporal resolution (see Sections 2.1 and 2.2). Consequently, in this short note aimed at evaluating the performance of three Swiss precipitation products (RADAR, GAUGE, and their combination, i.e., CombiPrecip) in comparison with GPM, a 2 year integration period is used to minimize the large differences in the sampling modes between the various instruments. During this period, the same number of rain gauges (221) and ground-based radars (four) were used. Obviously, only RADAR, GAUGE, and CombiPrecip observations that were closest in time to GPM overpasses are used to derive the total precipitation amounts in every 25 km\({}^{2}\) reference pixel (see Sections 2.1 and 2.2.3 for more details). The GPM precipitation product underestimates, on average, the precipitation in Switzerland by a factor of 2 (\(-\)3 dB) with respect to RADAR, and by a factor of \(-\)3.4 dB with respect to GAUGE. However, GPM estimates are used to independently evaluate the three Swiss products in terms of dispersion of the error around the mean (as well as of correlation among the various precipitation products), rather than in terms of exact average amount of 2 year precipitation over the country (mean error). To the best of the authors' knowledge, this is the first attempt to compare in complex terrain four precipitation products that use three very different types of sensors. Using GPM as a reference, the best precipitation product results to be the combination, through a geostatistical approach, of ground-based radar and rain-gauge measurements. This fact is thoroughly described in Section 3, which presents the results. The findings and their implications are discussed Section 4, which presents also future research directions. Section 2 gives an overview of the instruments and precipitation products used, and describes the study area and methods. A conclusion is given in Section 5.