A Comparison between the GPM Dual-Frequency Precipitation Radar and Ground-Based Radar Precipitation Rate Estimates in the Swiss Alps and Plateau

Abstract

The Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR) provides a unique set of three-dimensional radar precipitation estimates across much of the globe. Both terrain and climatic conditions can have a strong influence on the reliability of these estimates. Switzerland provides an ideal testbed to evaluate the performance of the DPR in complex terrain: it consists of a mixture of very complex terrain (the Alps) and the far flatter Swiss Plateau. It is also well instrumented, covered with a dense gauge network as well as a network of four dual-polarization C-band weather radars, with the same instrument network used in both the Plateau and the Alps. Here an evaluation of the GPM DPR rainfall rate products against the MeteoSwiss radar rainfall rate product for the first two years of the GPM DPR’s operation is presented. Errors in both detection and estimation are considered, broken down by terrain complexity, season, precipitation phase, precipitation type, and precipitation rate. Errors are considered both integrated across the entire domain and spatially, and consistent underestimation of precipitation by GPM is found. This rises to −51% in complex terrain in the winter, primarily due to the predominance of DPR measurements wholly in the solid phase, where problems are caused by lower reflectivities. The smaller vertical extent of precipitation in winter is also likely a cause. Both detection and estimation performance are found to be significantly better in summer than in winter, in liquid than in solid precipitation, and in flatter terrain than in complex terrain.

Measurement of Precipitation in the Alps Using Dual-Polarization C-Band Ground-Based Radars, the GPM Spaceborne Ku-Band Radar, and Rain Gauges

“…The Ka-band (35 GHz) radar matches the scan of the Ku-band radar across the central 125 km of its swath, allowing for the application of dual-frequency algorithms (obviously reducing the coverage to the central swath). Hence, on the one hand, the use of the dual-frequency algorithm and observations would limit the number of available overpasses to 393, as can be seen in [25] (Table 2, p. 1252), whereas, on the other hand, the use of the second frequency slightly improves the GPM precipitation estimates in the Swiss Alps and plateau, as can be seen in [25] Table A1 (p. 1266) and A2 (p. 1267). Consequently, in this study we opted to maximize the number of overpasses and synchronous observations (528, see Table 2 in [25]), and this implies that the 35 GHz measurements are not considered.…”

“…Two additional radars have been installed, by necessity, at high altitudes (3000 m), to improve the coverage in the inner parts of the Alps. The radar locations (latitude, longitude, altitude) are listed in [25] (Table 1, p. 1250) and shown in [25] (Figure 1, p. 1250). During the period covered by this study, the three radars were installed and operational all time long; the high altitude radars in Valais (above Crans-Montana) and above Davos (close to Austria) entered the mosaic in May 2014, and at the end of 2015, respectively.…”

“…A small MFB adjustment was introduced in the network at the end of November 2014 (t1), and an even smaller one at the end of June 2015 (t2). For this reason, a back correction of +0.5 (+0.3) dB was applied to RADAR-derived precipitation rates before t1 (t2) in the accurate ground-based versus spaceborne radar presented in [25]. On the contrary, in the present note no back compensation is applied to RADAR quantitative precipitation estimations (QPE) because this study is not focused on the bias among the different precipitation products, but on the dispersion of the differences around the mean (see Section 3.2), as well as on correlations (see Section 3.1).…”

Multifractal evaluation of simulated precipitation intensities from the COSMO NWP model

“…This indicates that during this event COSMO had a tendency to underestimate extreme values, which might be caused by its difficulty to accurately simulate snowfall events, since COSMO does not consider partially melted snow (Frick and Wernli, 2012). Note that QPE of snow is very difficult and it is likely that the radar QPE itself is already underestimating precipi- tation intensities (Speirs et al, 2016), which would make this difference in γ s even more noteworthy.…”

A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations

“…Such a comprehensive set of observations should allow us to refine our retrieval approach and to gain insights into the state-dependence of snowfall microphysics. In turn, these improved snowfall estimates could be used to explain observed differences between ground-based radar, satellite-based radar, and snow gauge estimates of snowfall (Cao et al, 2014;Smalley et al, 2014;Norin et al, 2015;Saltikoff et al, 2015;Speirs et al, 2017) or as input for weather and climate studies.…”