A direct comparison of hydrometeor types (HMT) from state-of-the-art hydrometeor classification schemes (HMC) with modelled hydrometeors (ICOL-LAM, operational weather predictions model of the German Weather Service) is challenging, e.g. due to different HMT definitions and numbers and difficulties to identify dominant types in mixtures of hydrometeors. A comparison of published HMCs even revealed significant differences between the membership functions used for the same hydrometeor types (Figure 1), emphasizing again the high uncertainty in scattering simulations for ice hydrometeors because of their complex geometries, dielectric properties, and largely unknown size and orientation distributions. The HMCs were applied to perturbed polarimetric variables observed by the X-band Radar in Bonn (BoXPol) to test their robustness against measurement errors and show that especially in the regions with solid precipitation misclassification in hydrometeor typing occurs often. Thus, a dual strategy to evaluate the hydrometeor type representation in ICON-LAM is presented: i) Classification after clustering of the data is assumed to reduce the sensitivity of the decision to the uncertainty of scattering simulations. First an agglomerative hierarchical clustering of the radar pixels based on their similarity in multi-dimensional polarimetric signatures is applied, and afterwards for each identified cluster a comparison of the distributions of polarimetric moments with scattering simulations or membership functions for different HMT is performed. ii) A direct comparison of multivariate simulated and observed distributions of polarimetric moments. These comparisons will be performed for different heights and/or space-time subsets, and for clusters with similar HMT in the model and the observations as identified with the advanced radar-based hydrometeor classification scheme. Results for a set of case studies observed with the polarimetric X-band radar composite in Bonn, Germany, will be presented.

Conventionally, radar-based nowcasting methods have been conducted by shifting or extrapolating the most recent estimated precipitation field along the estimated motion field to lead times up to 3 hr. However, such methods assume that these fields do not evolve during the lead-time period. A more recent nowcasting approach is the short-term ensemble prediction system known as STEPS. STEPS aims to filter small spatial scales of rain as they have an expected short life-time compared to large spatial scales. Besides, it perturbs the precipitation field during extrapolation so that the prediction uncertainties related to the evolution of precipitation is considered, resulting in an ensemble nowcast. In this work, the configuration, implementation, and performance of STEPS are studied for its radar-based ensemble nowcasting application in Germany. Attention is given to the spatial localization (i.e., the adjustment of parameters that control the spatio-temporal evolution of precipitation in large domains). For such purpose, the capability of STEPS is analyzed using multiple rain events collected by the German radar network. Preliminary analysis regarding the spatial scale filtering of STEPS (without perturbation) shows an improvement against conventional extrapolation of 20 to 30 % on the critical success index starting at lead times of 30 min at a rainfall threshold of 0.1 mm hr-1. In terms of the spatial structure, STEPS (without perturbation) provides a consistent nowcast for lead times up to 3 hr. Although small spatial structures are filtered, the spatial structure at scales of 32 km or larger is maintained. The skill of a probabilistic nowcast (STEPS with perturbation) was also analyzed. It is shown that for the probability of exceeding the 5.0 mm hr-1 threshold, the predictability of the nowcast is compromised for lead times on the order of 1 hr. However, the capability of identifying rain and non-rain areas is still valuable: the hit rates are still larger than the false alarm rates. Our preliminary results highlight aspects needed in the configuration of STEPS such as the evolution model and the perturbation model. In this manner, this study can serve as a basis for an improved nowcasting system in Germany and as a reference for forecasters to understand the characteristics of the examined nowcast system.

The Global Precipitation Measurement core satellite (GPM) has been collecting high quality precipitation data since 2014 over the globe with its Dual-frequency Precipitation Radar (DPR; Ku-band and Ka-band). Specificly over Germany, GPM provides data with typically two daily overpasses. Thus providing a unique opportunity to have a satellite based standard for estimation of precipitation in order to compare and evaluate ground-based radar network counterpart products. The German national weather service (DWD, Deutscher Wetterdienst) provides precipitation observations from its operational radar network RADOLAN as a composite products derived from 17 dual-pol C-band radars. The RADOLAN (RY) regular products are Germany-wide composites of precipitation estimates based on a set of precipitation type dependent Z-R relationships derived for liquid hydrometeors applied to radar reflectivity after clutter- and beam blockage-corrections. In this contribution we focus to compare three years of GPM DPR and RADOLAN precipitation products. This allows us to evaluate at which extend these two Near Surface products are consistent when observed from different geometries and obtained by independent instruements and retrieval methods. We quantify the uncertainties when directly comparing the DPR near surface product with RY. It is shown that a direct comparisons might not take into account a set of uncertainties originated from the scans geometry from DPR and RADOLAN, precipitation types, and sampling volumes. Therefore we suggest an adjusted DPR product, which is extracted from the DPR vertical profiles and adapted to fit the specific RY measurement configuration e.g. scans height and beam width. This allows a much more detailed classification of the hydrometoer phases per measuring volume, which we define as non-uniform phase beam filling (NPBF). The NPBF gives information about the ratio of liquid, solid or mixed hydrometeors in a given volume. Orographic, synoptic, microphysical influences as well as NPBF effects are examined and their uncertainties introduced on a direct comparison of satellite with ground-based producs are put into consideration. The adaptation of the DPR precipitation products to the specific scan geometry of the individual ground radars improves the correlation and reduce the RMSE.