**1. Introduction**

Understanding rain microstructure can provide us with an insight into the prevailing rain formation processes leading to it. This understanding can be employed in improving quantitative estimation of rain intensity using weather radar, especially in flat regions with high altitude values of the zero degree isotherm [1–4]. Furthermore, the parametrization of the microphysical processes in numerical weather and climate models can be improved [5,6]. Rain microstructure varies on different spatial scales ranging from few meters [7], to few hundreds of meters [8], to regional [9,10] and global extents [11,12]. This variation also occurs with seasons [13], rain types [14], and large-scale weather types [15–17].

A clear example of the different rain formation processes leading to variations in rain drop size distribution is the discrepancy between convective and stratiform rain. This has been quantified in a number of studies [5,14,18,19]. The reason for the difference is the relative importance of cold and warm rain formation processes [20]. Stratiform rain is formed mainly by processes involving ice crystals and interactions of ice with liquid water, while convective rain formation comprises both warm and cold processes. Factors and processes that influence the rain drop size distribution as observed on the ground include rimming and aggregation (above the 0 ◦C isotherm), condensation (below the 0 ◦C isotherm), collision, coalescence, turbulence, cloud thickness, electric field, evaporation, and drop fragmentation [21,22]. The difference in rain drop size distribution between convective rain and stratiform rain has been used for the classification of both rain types on the ground. Most of these methods use two rain drop size distribution parameters and a linear discrimination between the regions of rain types [19,23–26]. Recent methods employed machine learning and reached higher performance levels when using four rain drop size distribution parameters [27,28].

Large-scale weather types denote atmospheric conditions such as the high and low pressure distribution, the position and paths of frontal zones, and the existence of cyclonic or anticyclonic circulation types over a sequence of days [29]. Indirectly, they also influence stream flows [30], floods [31–33], debris-flow events [34], forest fires [35,36], air quality, and pollen distribution [37–39].

Weather type classification is an important part of statistical climatology [40,41], because these types explain many local weather phenomena. Weather types influence local near-surface temperatures and precipitation [42–46]. They also affect the diurnal cycle of precipitation in terms of frequency and amount [47–49], and they impact the occurrence and the magnitude of meteorological extreme events [50–54]. Large-scale weather types may therefore also influence rain microstructure by different rain formation processes being more prevalent under different synoptic scale conditions.

Quantifying rain microstructure under different large-scale weather types may have practical applications for radar-based estimation of rain intensity, because the microstructure influences the relationship between radar reflectivity Z and rain intensity R. For this reason, separate equations are used to estimate rain intensity of convective and stratiform rain type [10,55], instead of using one equation that fits both rain types. A similar improvement of the radar estimation of rain might be possible when considering specific Z–R relations for each of the weather types. We previously reported weather type specific Z–R models with lower errors in estimating rain intensity in Lausanne, Switzerland [17]. Similarly, the influence of weather types on Z–R relationships was also reported for the Cévennes-Vivarais Region, France [16]. However, parameterizing Z–R equations for many weather types definitively requires large amounts of data to represent each class.

Here, we contribute an analysis of the relationship between Z–R parameters and weather types in Central Europe, based on a comprehensive regional dataset of rain microstructure measurements at ten sites in the federal state of Bavaria, Germany. We ask: (1) What is the effect of weather types on rain microstructure, considering both types of rain? and (2) Is there consistent variation in the Z–R parameters between weather types that would suggest opportunities to improve QPE with radar-based methods? To address these questions, we investigate disdrometer records under different large-scale wind direction patterns at a daily scale, and rain type classifications at one-minute intervals over a period of three years.
