Sub-grid peaks in localized intense rain events using high-resolution operational radar data in Switzerland

Localized intense rain events can cause significant societal and economic damages. Their accurate identification is key in order to best mitigate their impacts on people and infrastructures. By collecting distributed space-time observations, weather radars can provide useful information for the analysis and forecasting of such events. In Switzerland, five operational dual-polarization C-band radars are scanning the atmosphere with a half-power beam-width of 1 degree up to a height of 18 km, using 20 interleaved sweeps that are updated every 5 minutes. Data are generated at a native radial resolution of 83 m. This information is then integrated at 500 m combining all clutter-free 83-m gates and quality-checked before being used in subsequent product chains. This integration may smooth out several peaks of those localized intense rain events and, the information from high-resolution radar data might become valuable.

In this study, we analyze how the spatial integration of the native data at coarser polar radial resolutions of 500 m and 1000 m influences the captured spatial variability of rainfall peaks. We investigate the potential added information that would result from the use of high-resolution data for the analysis of such localized intense rain events. We focus on a case-study from June 11, 2018 when a localized intense rain event hit the city of Lausanne causing significant damages and leading to the largest 10-min rainfall accumulation ever recorded by the Swiss rain gauge network (41 mm) and was associated with reflectivity values up to 60 dBz at 83 m (as presented during the last AMS radar conference). Using all available PPI data from the five scanning radars, the analyses are conducted in the 3D space by selecting all radar gates at the native resolution that spatially overlap with the catchment of interest. We quantify the impact of the spatial integration by successively averaging all selected clutter-free high-resolution native data to the coarser 500 m and 1000 m radial resolutions, and then downscaling the integrated data back to the same 83 m resolution. We compare the native data with the integrated-downscaled ones at different timesteps and elevations, and we apply a single Z-R relationship (Z=316R1.5) to derive a corresponding “equivalent” rain rate at the gate level for the different spatial resolutions.

Preliminary results indicate that the difference between the rain estimates at the native resolution and after integration to 500 m and 1000 m exhibits a strong spatial and temporal variability. By selecting gates with high reflectivity values (i.e. native reflectivity > 50 dBz), about 2% of the gates show differences of more than 50 mm/h between the “equivalent” rain rate estimated at 83 m and at 500 m resolution. The largest differences are generally observed at gates with the highest intensity values. Looking at the ratio between the “equivalent” rain rate at 83 m and at 500 m, about 19%, 5% and 0.3% of the selected gates have a rain rate at 83 m that is respectively 1.25, 1.5 and 2 times larger than the integrated one at 500 m. These values vary over the different 5-min timesteps as a function of the event intensity. A similar but amplified behavior is observed at the 1000 m resolution, due to the largest number of gates being averaged. Despite the limitations associated to the application of a Z-R relationship independently of the drop size distribution and hydrometeor types, these differences already provide initial indications on the added value of high-resolution radar data for the analysis of those localized intense rain events. Given the non-linearity of many hydrological processes and runoff generation mechanisms across space, such differences in rainfall peaks and spatial variability can play a key role on flooding processes and related damages.