The distinction between riming and aggregation is of high relevance for model microphysics, data assimilation, and warnings of potential aircraft hazards due to the link between riming and updrafts as well as the presence of supercooled liquid water in the atmosphere. Even though the polarimetric fingerprints for aggregation and riming are qualitatively similar, we hypothesize that it is feasible to implement an area-wide discrimination algorithm based on national polarimetric weather radar networks only. Quasi-vertical profiles (QVPs) of reflectivity (ZH), differential reflectivity (ZDR), and estimated depolarization ratio (DR) are utilized to learn about the information content of each individual polarimetric variable and their combinations for riming detection. High-resolution Doppler spectra from the vertical (birdbath) scans of the C-band radar network of the German Meteorological Service serve as input and ground truth for algorithm development. Mean isolated spectra profiles (MISPs) of the Doppler velocity are used to infer regions with frozen hydrometeors falling faster than 1.5 m s−1 and accordingly associated with significant riming. Several machine learning methods have been tested to detect riming from the corresponding QVPs of polarimetric variables. The best-performing algorithm is a fine-tuned gradient-boosting model based on decision trees. The precipitation event on 14 July 2021, which led to catastrophic flooding in the Ahr valley in western Germany, was selected to validate the performance. Considering balanced accuracy, the algorithm is able to correctly predict 74 % of the observed riming features; thus, the feasibility of reliable riming detection with national radar networks has been successfully demonstrated.

Polarimetric microphysical retrievals reveal a great potential for the evaluation of numerical models and data assimilation. However, the accuracy of ice microphysical retrievals is still poorly explored. To evaluate these retrievals and assess their accuracy, polarimetric radar measurements are spatially and temporally collocated with in situ aircraft measurements obtained during the OLYMPEX campaign (Olympic Mountain Experiment). Retrievals for ice water content (IWC), total number concentration N-t, and mean volume diameter D-m of ice particles are assessed by comparing an in situ dataset obtained by the University of North Dakota (UND) Citation II aircraft with X-band Doppler on Wheels (DOW) measurements. Sector-averaged range height indicator (RHI) scans are used to derive vertical profiles of microphysical retrievals. The comparison of these estimates with in situ data provides insights into strengths, weaknesses, and the accuracy of the different retrievals and quantifies the improvements in polarimetry-informed retrievals compared to conventional, non-polarimetric ones. In particular, the recently introduced hybrid ice water content retrieval exploiting reflectivity Z(H), differential reflectivity Z(DR), and specific differential phase KDP outperforms other retrievals based on either (Z(H), Z(DR)) or (Z(H), K-DP) or non-polarimetric retrievals in terms of correlations with in situ measurements and the root mean square error.

Polarimetric microphysical retrievals bear great potential for the evaluation of numerical models and data assimilation. However, a solid database is still lacking to evaluate their accuracy. In order to evaluate these retrievals and assess their accuracy, ground based polarimetric radar measurements are collocated spatially and temporally with airborne in-situ microphysical data collected during the OLYMPEX campaign (Olympic Mountain Experiment). Retrievals for ice water content, total number concentration, and mean volume diameter of ice particles are assessed exploiting both X-band Doppler on Wheels (DOW) measurements and an in-situ measurements obtained by the University of Dakota (UND) Citation aircraft. Vertical profiles of the microphysical retrievals are derived from sector-averaged RHI scans. The comparison of the retrievals with in-situ data above the freezing level reveals new insights into the strengths, weaknesses, and accuracies of the different retrievals, as well as the advantages using polarimetric retrievals rather than non-polarimetric ones. Results clearly demonstrate the superiority of the polarimetric retrievals. Furthermore, the recently introduced hybrid ice water content retrieval exploiting reflectivity ZH, differential reflectivity ZDR and specific differential phase KDP outperforms other retrievals based on either (ZH, ZDR) or (ZH, KDP) or non-polarimetric retrievals in terms of correlations with in-situ measurements and the root mean square error. ZH-based retrievals for the mean volume diameter partly exhibit significant deviations from airborne in-situ measurements, while polarimetric retrievals show a good agreement.

Cloud and precipitation processes are still a main source of uncertainties in numerical weather prediction and climate change projections. The Priority Programme "Polarimetric Radar Observations meet Atmospheric Modelling (PROM)", funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), is guided by the hypothesis that many uncertainties relate to the lack of observations suitable to challenge the representation of cloud and precipitation processes in atmospheric models. Such observations can, however, at present be provided by the recently installed dual-polarization C-band weather radar network of the German national meteorological service in synergy with cloud radars and other instruments at German supersites and similar national networks increasingly available worldwide. While polarimetric radars potentially provide valuable in-cloud information on hydrometeor type, quantity, and microphysical cloud and precipitation processes, and atmospheric models employ increasingly complex microphysical modules, considerable knowledge gaps still exist in the interpretation of the observations and in the optimal microphysics model process formulations. PROM is a coordinated interdisciplinary effort to increase the use of polarimetric radar observations in data assimilation, which requires a thorough evaluation and improvement of parameterizations of moist processes in atmospheric models. As an overview article of the inter-journal special issue "Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes", this article outlines the knowledge achieved in PROM during the past 2 years and gives perspectives for the next 4 years.