*2.1. Data Sources and Tools*

We obtained raw rain drop size distribution measurements from the German Meteorological Service (Deutscher Wetterdienst, DWD), operating a network of Thies disdrometers in Bavaria, in the southeast of Germany (Figure 1). We analyzed measurements at ten sites spanning a period of three years (January 2014–December 2016) with a temporal resolution of one minute. The disdrometers locations cover a distance of 167 km from north to south and 185 km from east to west.

**Figure 1.** Disdrometer locations in Bavaria (SE Germany) that were used to measure rain microstructure, covering a total of 18,600 h of rain in a period of 36 months.

Since raw disdrometer data requires some statistical data cleaning procedures to remove erroneous readings, we followed the filtering procedure of Friedrich et al. [56] and the additional steps of Ghada et al. [17] to remove unrealistically large particles, margin fallers, splashing effects, or readings of insect and spider webs. The filtering procedure removed: (1) All measured particles with a diameter larger than 8 mm; (2) All particles which had a falling velocity less than 60% or greater than 140% of the terminal velocity associated with rain drops of the corresponding diameter [57,58] (Figure 2); (3) Intervals marked by a damaged laser signal or as non-rain intervals by the disdrometer; (4) Intervals which included large drops (D > 5 mm) with low velocities (V < 1 m/h) as an indicator of high wind speed; (5) Intervals with rain intensity lower than 0.1 mm/h [59,60]; (6) Intervals with three or less diameter bins to insure the existence of a drop size distribution. After filtering, the dataset contained a total of 21,705 mm of accumulated rain over a period of 18,633 h.

**Figure 2.** Raindrop count in each diameter-velocity range after the filtering process. The dotted line represents the terminal velocity of each diameter value. The solid lines represent the 60% and the 140% of the terminal velocity.

The DWD classifies large-scale synoptic weather patterns into 40 classes of weather types. The weather type is provided on a daily time scale and is applicable to all of Germany and its

surroundings. The classification is based on an operational numerical weather prediction system, i.e., modelling different atmospheric fields such as geopotential height, temperature, relative humidity, and the zonal and meridional components of the wind for several elevations. A detailed explanation of the classification procedure is available online [61], and the full record of weather types is provided by the DWD [62]. Since this classification is performed on daily basis, it would be operationally feasible to associate a separate configuration of the radar rain rate estimate for each weather type class. However, in order to simplify the classification for the purpose of this exploratory case, we grouped all possible classes according to their wind direction index. This index takes one of five possible values: northeasterly (NE), southeasterly (SE), southwesterly (SW), northwesterly (NW), and no prevailing direction (XX). Determining the specific wind direction is based on the number of grid points over Germany with a specific wind direction which needs to exceed 2/3 of the total number of grid points. In case this threshold was not exceeded, the wind direction index is assigned to XX.

For data filtering, analysis, and production of visual and statistical results, we used R [63], RStudio [64], and the packages caret [65], e1071 [66], reshape2 [67], raster [68], Rmisc [69], ggplot2 [70], and rnaturalearth [71].
