The PROM project “Understanding Ice Microphysical Processes by combining multi-frequency and spectral Radar polarImetry aNd super-parTicle modelling” (IMPRINT) improves ice microphysical process understanding by using spectral multi-frequency and radar polarimetric observations in combination with Monte Carlo Lagrangian super-particle modelling (Brdar and Seifert, 2018). Mid-latitude stratiform clouds, which occur frequently during wintertime over JOYCE-CF, are the main focus. Radar polarimetric variables are well known to be particularly sensitive to the presence of asymmetric ice particles (e.g. Kumjian, 2013). Only recently have polarimetric cloud radars operating at the Ka- or W-band also become routinely available (Oue et al., 2018; Myagkov et al., 2016; Bühl et al., 2016; Matrosov et al., 2012). Some polarimetric variables are wavelength dependent (KDP is inversely proportional to the wavelength), which provides enhanced sensitivity to ice particle concentration at higher frequencies. Multi-frequency approaches are complementary to radar polarimetry as they are sensitive to larger ice particles. Most commonly, the dual wavelength ratio (DWR), defined as the logarithmic difference of the effective reflectivity Ze at two frequencies, is used. When ice particles transition from Rayleigh into non-Rayleigh scattering from one wavelength to a higher one, the DWR increases, which allows one to infer the characteristic size of the underlying size distribution. The use of three radar frequencies (e.g. X, Ka, W) extends the discernable size range; for example, the DWR of the Ka–W combination saturates for very large particles (Kneifel et al., 2015; Ori et al., 2021). The information content can be further extended when the Doppler spectral information is also explored. The different fall velocities allow for the separation of different hydrometeors; the high differential reflectivity (ZDR) signal originating from small, slowly falling ice crystals can be distinguished from the also low ZDR signal of faster falling snow aggregates, which usually dominate the total ZDR. Only a few studies so far have used spectral polarimetric observations for ice and snow microphysical studies (Luke et al., 2021; Oue et al., 2018; Pfitzenmayer et al., 2018; Spek et al., 2008). The observations collected during the first multi-month winter campaign carried out at JOYCE-CF as part of the IMPRINT project provide the opportunity to investigate both polarimetry and multi-frequency observations in the Doppler spectra space for the first time. An example is the analysis of the dendritic growth layer (DGL) illustrated in Fig. 1 for a snowfall event observed on 22 January 2019 at JOYCE-CF. Especially in the upper half of the cloud, ZDR is enhanced while KDP values are low (Fig. 1b–c). Starting at the -15 ∘C isotherm, ZDR sharply decreases and shows an anti-correlation with the enhanced DWR (Fig. 1a) and KDP values. These polarimetric signatures have been reported by previous studies (e.g. Moisseev et al., 2015, among others), and the DWR increase below the -15 ∘C level also resembles the examples shown in Oue et al. (2018). Oue et al. (2018) concluded, in agreement with findings in Moisseev et al. (2015), that an increasing concentration of asymmetric aggregates is partly responsible for enhanced KDP values because the number of small ice particles decreases due to aggregation. The spectrally resolved ZDR (sZDR, Fig. 1e), however, reveals that high-ZDR-producing, slowly falling ice particles are still present down to the -5 ∘C level. The spectrally resolved DWR (Fig. 1d) shows that the particles falling from above into the DGL are already partly aggregated. At -17 ∘C, the spectra are much wider, and a new spectral mode appears which is linked to the rapid sZDR increase (Fig. 1e). The new ice particle mode increases in Doppler velocity and sDWR until 20 dB is reached. Unlike ZDR, KDP (Fig. 1c and f) remains at values between 1 and 2 ∘ km-1 down to the -5 ∘C level. A possible explanation of the bimodal spectra – increased sZDR and KDP – might be secondary ice processes such as collisional fragmentation (Field et al., 2017). The few existing laboratory studies indicate that the number of fragments rapidly increases at -20 ∘C, reaching a maximum at -17 ∘C and decreasing again towards -10 ∘C (Takahashi et al., 1995; Takahashi, 2014). This temperature dependence fits well to the observed radar signatures in the DGL, although the laboratory studies only considered collisions of solid ice spheres. As we can exclude strongly rimed particles in the snowfall case shown in Fig. 1, fragile dendritic structures growing on the surface of aggregates might be responsible, which precipitate into the DGL and might easily break into smaller pieces during particle collisions (Fig. 1d). Monte Carlo Lagrangian super-particle model (Brdar and Seifert, 2018) simulations were recently extended in IMPRINT by a habit prediction scheme and a parameterization of ice collisional fragmentation following Phillips et al. (2017). The role of ice fragmentation and other ice microphysical processes is currently investigated with a radar observation operator for explaining the observed radar signatures of intense aggregation shown in Fig. 1.

The PROM project “Investigation of the initiation of convection and the evolution of precipitation using simulations and polarimetric radar observations at C- and Ka-band” (IcePolCKa) combines observations of the C-band Polarization Diversity Doppler Radar (POLDIRAD) at the German Aerospace Center (DLR), Oberpfaffenhofen, with those of the Ka-band, Milimeter-wave cloud RAdar of the Munich Aerosol Cloud Scanner (miraMACS) at Ludwig-Maximilians-Universität (LMU), Munich. While IMPRINT combines triple-frequency zenith-pointing observations with spectral cloud radar polarimetry, IcePolCKa explores the life cycle of convective precipitation with spatially separated weather and cloud radars in order to quantify ice crystal properties in precipitation formation. The project focuses on ice particle growth and its role in precipitation formation within convective cells. Coordinated range–height indicator (RHI, varying elevation at constant azimuth) scans along the 23 km long cross section between both radars allow observation of DWR (Fig. 2a) and ZDR (Fig. 2b) fingerprints of individual convective cells. While the deviation from Rayleigh scattering with increasing ice crystal size at the cloud radar wavelength allows one to distinguish regions dominated by aggregation from regions with depositional growth, the slanted perspective of the weather radar helps to narrow down the aspect ratio of ice crystals. Although the DWR technique to infer ice crystal size is well established (e.g. Kneifel et al., 2015), assumptions about the unknown ice crystal shape are necessary. Here, simultaneous polarimetric measurements, like ZDR, help to narrow down estimates of the average asphericity of ice crystals and reduce ambiguities in retrieving ice crystal size and ice water content. IcePolCKa develops an algorithm, which uses ZH, ZDR, and DWR measurements from the two radars to retrieve ice water content (IWC), the mean particle diameter Dm, and the aspect ratio of ice crystals using a least-squares fit between measurements and T-matrix scattering simulations. The model of horizontally aligned spheroids in combination with an effective medium approximation following Hogan et al (2012) is used to find the simplest ice particle model which explains the multi-wavelength polarimetric measurements. The approach allows the study of the covariance of DWR and ZDR while varying particle density, mean particle diameter Dm, and aspect ratio. More sophisticated models, such as discrete dipole approximation (DDA) simulations of specific ice crystals, would require the knowledge of the aspect ratio, and they make it hard to identify ice shape collections along these free variables. The multi-wavelength polarimetric measurements are also used as a benchmark for convective precipitation formation in NWP models, where cloud microphysics introduce substantial uncertainty (e.g. Morrison et al., 2020; Xue et al., 2017). In IcePolCKa, simulated microphysical processes in NWP models are compared to fingerprints in radar observations: a nested WRF setup covering the overlap area of both radars is used to simulate convective events with microphysical schemes of varying complexity while the Cloud-resolving model Radar SIMulator (CR-SIM; Oue et al., 2020) produces synthetic radar observations, such as DWR (Fig. 2c) and ZDR (Fig. 2d). Figure 2 illustrates that the Predicted Particle Properties (P3) scheme (Morrison and Milbrandt, 2015) is able to produce DWR features of similar magnitude and variability compared to the observations, while a realistic ice particle asphericity is still missing. IcePolCKa compiled over 30 convective days of polarimetric measurements and simulations with five different schemes over a 2-year period, which is currently used to analyse how well these different microphysical schemes reproduce the polarimetric observations. A cell-tracking algorithm (TINT; Fridlind et al., 2019) facilitates the comparison on a cell object basis. Comparison of macrophysical cloud characteristics, such as echo top height or maximum cell reflectivity, shows that the model simulates too few weak and small-scale convective cells, independent of the microphysics scheme. In ongoing studies, the P3 scheme seems to better represent radar signatures within the ice phase, while a spectral bin scheme tends to better simulate radar signatures within rain, where all other schemes are not able to correctly reproduce observed ZDR features.

The PROM project “A seamless column of the precipitation process from mixed-phase clouds employing data from a polarimetric C-band radar, a micro-rain radar and disdrometers” (HydroColumn) characterizes precipitation processes inside a vertical atmospheric column by combining polarimetric Doppler weather radar observations with co-located measurements from micro-rain radars, disdrometers, and in situ measurements and by relating these observations to the large-scale atmospheric thermodynamics derived from NWP models. To date, spectral analyses are mostly performed with cloud radars operating at shorter wavelengths (see previous paragraphs or, e.g. Shupe et al., 2004; Verlinde et al., 2013; Kalesse et al., 2016; Gehring et al., 2020; Li and Moisseev, 2020), but their implementation across the national C-band radar network offers prospects for operational area-wide applications, e.g. the identification of dominant precipitation particle growth processes such as aggregation or riming. While the operational DWD birdbath scan has so far been used primarily to monitor ZDR (Frech and Hubbert, 2020), HydroColumn now also exploits the Doppler spectra measured at C-band for the analysis of microphysical process information. Figure 3 shows quasi-vertical profiles (QVPs; Trömel et al., 2014; Ryzhkov et al., 2016) of polarimetric variables and Doppler spectra from birdbath scans for a stratiform precipitation event monitored with the Hohenpeißenberg C-band research radar (47.8014∘ N, 11.0097∘ E) of DWD together with in situ particle images obtained by the Falcon research aircraft from DLR during the BLUESKY campaign (Voigt et al., 2021) within the POLICE project (Sect. 4.2.1). In situ measurements have been performed with the Cloud, Aerosol and Precipitation Probe (CAPS; Kleine et al., 2018) integrated in a wing station on the Falcon flying within a horizontal distance of about 20 km from the radar site and within about ±15 min of the radar measurements. The dendritic growth layer (DGL; Ryzhkov and Zrnic, 2019) centred around -15 ∘C is characterized by ZDR maxima of ∼ 1 dB and KDP of ∼ 0.2 ∘ km-1 and a strong ZH increase towards lower levels (Fig. 3a). Particle images collected at temperatures below about -15 ∘C indicate mostly small irregular ice particles with the number of larger particles increasing toward -15 ∘C (see levels L1 and L2 in Fig. 3c) and further down also reveal dendrites and plates (L3, L4). In general, aggregation and riming become highly effective particle growth mechanisms at temperatures around -7 ∘C (Libbrecht, 2005), and both processes result in a reduction of ZDR (Fig. 3a). The vertically pointing Doppler measurements can be used here to gain a deeper insight into the particle growth process. In this case study, the Doppler measurements illustrated in Fig. 3b indicate typical ice-particle fall speeds increasing to about 2 m s-1 just above the melting layer and thus suggest a transition from predominantly aggregates to moderately rimed particles based on the relationship between Doppler velocity and riming degree found by Kneifel and Moisseev (2020). This conclusion is supported by the corresponding in situ images showing increasing riming of polycrystals and aggregates toward the melting layer (L6). The analysis confirms the benefit of interpreting radar signatures from polarimetric weather radar observations in combination with vertically pointing Doppler radar measurements, which was previously pointed out for higher-frequency cloud research radars (Oue et al., 2018; Kumjian et al., 2020). This novel application of radar spectral analysis to vertically pointing operational weather radar scans may provide a more detailed view into intense precipitation events, such as hailstorms, where the use of cloud radars is severely limited due to the strong attenuation at high radar frequencies.

The PROM project “Polarimetry Influenced by CCN aNd INP in Cyprus and Chile” (PICNICC) seeks to improve our understanding of aerosol effects on microphysical growth processes in mixed-phase clouds. PICNICC exploits unique remote-sensing datasets from the LACROS suite (Radenz et al., 2021) extended with ground-based remote sensing instruments installed at Leipzig University, Universidad de Magallanes (Punta Arenas), and Cyprus University of Technology (Limassol). Thus, dual-frequency polarimetric radar observations from the polluted, aerosol-burdened Northern Hemisphere and from the clean, pristine Southern Hemisphere can be contrasted for microphysical process studies as already performed in the project for stratiform mixed-phase clouds to investigate inter-hemispheric contrasts in the efficiency of heterogeneous ice formation (Radenz et al., 2021). The PICNICC project challenges the hypothesis that higher ice crystal concentrations favour aggregation, which is expected to be more frequent for high aerosol loads and accordingly higher ice-nucleating particle (INP) concentrations, while riming should prevail when supercooled liquid layers are sustained due to a scarcity of INPs. Evaluating this hypothesis requires the distinction between aggregation and riming in mixed-phase cloud systems. Figure 4 demonstrates for a deep mixed-phase cloud system passing the low-aerosol site in Punta Arenas (53∘ S, 71∘ W), Chile, on 30 August 2019, the capability of the LACROS suite to distinguish between aggregates and rimed particles when combined with a 94 GHz Doppler radar. The pattern of the 94 GHz radar reflectivity factor (Ze, Fig. 4a) underlines the complex structure of the system. The height spectrogram of the vertical-pointing 94 GHz slanted linear depolarization ratio (SLDR, Fig. 4e) from 08:30 UTC exhibits regions of changing shape signatures and multi-modality in the cloud radar Doppler spectra, where multiple hydrometeor populations coexist. The polarizability ratio ξe (Myagkov et al., 2016; Fig. 4d) obtained from the RHI scans of SLDR and the co-cross-correlation coefficient of horizontally and vertically polarized channels in the slanted basis ρs at 35 GHz (Fig. 4b, c) allows the estimation of a density-weighted hydrometeor shape. SLDR is more suited for shape classification compared to LDR. By slanting the polarization basis by 45 ∘, the returned LDR signatures are much less sensitive to the canting angle distribution of the targets, especially at low elevation angles (Matrosov et al., 2001; Myagkov et al., 2016). The polarimetric RHI scans and the Doppler spectra data enable the retrieval of the vertical profile of the hydrometeors: columnar-shaped bullet rosettes are formed between 2.5 km height and cloud top as indicated in the RHI scans by an elevation-constant SLDR (Fig. 4b) and an increase in ρs with decreasing elevation (Fig. 4c). ξe values around 1.3 (Fig. 4d) are characteristic for slightly columnar crystals. The decreasing elevation dependence of ρs already at around 3 km height (-15 to -20 ∘C) suggests more random particle orientations; here the W-band SLDR spectra (Fig. 4e) show reduced values, likely due to the co-existence of dendritic ice crystals, which are preferably formed in this temperature range. The co-location of dendrites and columnar crystals can be explained by either splintering of the arms of the dendritic crystals or a mixing of locally produced dendrites with columnar crystals from higher up, or both. Below 2.5 km, ξe decreases toward unity, indicating the growth of isometric particles. In addition, the vertical-pointing W-band SLDR slowly decreases toward the cloud base, while fall velocities increase (Fig. 4e). Both features are characteristic for riming, which is corroborated by co-located lidar observations that indicate liquid water in the cloud-base region (not shown). Doppler spectra profiles such as the one presented in Fig. 4e are also used in a new neural-network-based riming detection algorithm recently tailored by Vogl et al. (2021) for vertical-pointing cloud radar observations. This new approach is insensitive to the mean Doppler velocity, which is – especially at Punta Arenas – strongly influenced by orographic mountain waves, because the radar reflectivity factor, skewness, and edge width of the Doppler spectrum are used instead.

The PROM project “Investigating the impact of Land-use and land-cover change on Aerosol-Cloud-precipitation interactions using Polarimetric Radar retrievals” (ILACPR) analyses polarimetric radar observations and model simulations simultaneously in order to improve our understanding of land–aerosol–cloud–precipitation interactions. The Terrestrial Systems Modelling Platform (TSMP; Shrestha et al., 2014; Gasper et al., 2014) developed under the DFG-funded Transregional Research Center TR32 (Simmer et al., 2015) is used to simulate summertime convective storms passing the polarimetric X-band radar (BoXPol; e.g. Diederich et al., 2015a, b) located in Bonn, Germany. TSMP generally underestimates the convective area fraction, high reflectivities, and width–magnitude of differential reflectivity (ZDR) columns indicative of updrafts, all leading to an underestimation of the frequency distribution for high precipitation values (Shrestha et al., 2021a). A decadal-scale simulation over the region using the hydrological component of TSMP also shows that much of the variability in the simulated seasonal cycle of shallow groundwater could be linked to the distribution of clouds and vegetation (Shrestha, 2021), which further emphasizes the importance of evaluating the representation of clouds and precipitation in numerical models. The fusion of radar observations and models with the aid of observation operators allows for an extended interrogation of the effects of anthropogenic interventions on precipitation-generating processes and the capabilities of numerical models to reproduce them. Here, findings from one simulated hailstorm observed on 5 July 2015 passing the city of Bonn, Germany, are explained. Sensitivity simulations are conducted using large-scale aerosol perturbations and different land-cover types reflecting actual, reduced, and enhanced human disturbances. While the differences in modelled precipitation in response to the prescribed forcing are below 5 %, the micro- and macrophysical pathways differ, acting as a buffered system to the prescribed forcings (Stevens and Feingold, 2009; Seifert and Beheng, 2012). Figure 5 shows vertical cross sections reconstructed from volume scans measured with BoXPol together with simulated ZH and ZDR for the TSMP simulations with actual land cover but perturbed condensation nuclei (CN) and ice-nucleating particle (INP) concentrations. CN concentrations are 100 cm-3 for maritime and 1700 cm-3 for continental aerosol. Similarly, default INP concentrations for dust, soot, and organics are 162×103, 15×106, and 177×106 m-3, respectively. For low (high) INPs, the concentration of soot and organics are decreased (increased) by 1 order of magnitude. To generate the synthetic radar observations, the Bonn Polarimetric Radar observation Operator, B-PRO (Xie et al., 2021, 2016; Heinze et al., 2017; Shrestha et al., 2021b), is applied. B-PRO is based on the non-polarimetric version of EMVORADO (Zeng et al., 2016); its code part for computing unattenuated radar reflectivity on the original model grid (Blahak, 2016) has been expanded to unattenuated polarimetric variables based on spheroidal shape assumptions (T-matrix). Because the full polarimetric version of EMVORADO (Pol-EMVORADO; see Sect. 4.1) was only released very recently, the model data in ILACPR have been processed using B-PRO. Preliminary comparisons between B-PRO and Pol-EMVORADO (not shown here) exhibit negligible differences in their results on the model grid, but Pol-EMVORADO is much more computationally efficient and takes effects of beam broadening and attenuation along the actual radar ray paths into account. The vertical cross sections are compared at different times marked by the vertical grey bars in the time series of convective area fraction (CAF, Fig. 5a), defined as the ratio of area with ZH>40 dBZ (at 2 km a.g.l.) to total storm area. On average BoXPol observations show a bit higher CAF compared to the simulations. The evolution is always similar in terms of an initial increase and intensification in the second part of the observation period, where the experiment with maritime aerosols and low INPs (Mar-lowIn) is closest to the observations. All simulations show ZH and ZDR patterns comparable to BoXPol observations; however, the experiment with continental aerosol and default INPs (Con-defIN, Fig. 5c) shows weaker ZH while Mar-lowIN (Fig. 5d) shows somewhat higher ZH values compared to BoXPol (see Fig. 5a). The simulations with maritime CN produce low cloud droplet concentrations with larger mean diameters compared to the simulations with continental CN. Accompanied by a very strong updraft, this also leads to high concentrations of supercooled raindrops above the melting layer with broader spatial extent (due to a broader updraft region) compared to the simulations with continental CN and contributes to an enhanced growth of hail resulting in higher ZH. Also, as shown in the CAF time series, simulations with continental aerosol and default/high IN tend to exhibit similar behaviour in radar space, with the latter exhibiting higher CAF only at latter stages of the storm. The continental CN simulations with default and high IN differ in terms of simulated updraft speed and total hydrometeor content, being higher for the latter one. However, Cont-highIN produces smaller graupel and hail particles compared to Cont-defIN, resulting in similar ZH. The experiment with continental aerosol and high INP concentration (Con-highIN, not shown) generates similar polarimetric moments to Con-defIN. All experiments exhibit vertically extensive columns of (slightly) enhanced ZDR, collocated with intense simulated updrafts reaching up to 13 to 14 km. Indeed, ZDR columns emerged as proxies for updraft strength and ensuing precipitation enhancement (Weissmann et al., 2014; Simmer et al., 2014; Kumjian et al., 2014; Kuster et al., 2020), and research on their exploitation for nowcasting and data assimilation is ongoing. In Fig. 5c, d synthetic ZDR columns are vertically extensive, while ZDR values within the column stay below 0.3 dB. BoXPol observations show ZDR columns reaching up to 6 km height only but with ZDR values exceeding 1 dB. While ZDR values in the lower part of the columns are mostly generated by large raindrops, freezing drops and wet hail determine ZDR in the upper parts of the column (Kumjian et al., 2014; Snyder et al., 2015). The diverging appearance of observed and synthetic ZDR columns may point to deficiencies in the treatment of raindrops undergoing freezing and motivates further research. Too rapid freezing of drops combined with graupel generated from the frozen drops may generate enhanced but still low ZDR up to high altitudes. Following Ilotoviz et al. (2018) such attributes of ZDR columns are highly determined by the vertical velocity, hail size, and aerosol concentration; e.g. higher CN concentrations lead to higher columns with higher ZDR values inside and also higher ZH. In this case study and the specific time step shown, Mar-lowIN (i.e. with lower CN concentration) shows a wider and somewhat taller ZDR column together with a more intense ZH core (compare Fig. 5c, d). Further explanations require an improved representation of the ZDR columns in the model.

Within the PROM project Operation Hydrometeors, the up-to-now non-polarimetric radar observation operator EMVORADO (Zeng et al., 2016; Blahak and de Lozar, 2020; Blahak, 2016) has been extended to polarimetry (Mendrok et al., 2021). (Non-polarimetric) EMVORADO has been designed to efficiently simulate PPI volume scan measurements of entire radar networks from the prognostic model state of an NWP model for direct comparisons with the radar observations. EMVORADO is part of the executable of both the COSMO and ICON NWP models, which allows us to run the operator within a NWP model run and to access the model state and radar variables in memory. The code is MPI- and OpenMP-parallelized and thus fully exploits the computational power of modern high-performance computers (HPCs) and avoids storing and re-reading extensive model state data to and from hard drives. This enables large-scale real-time applications such as operational data assimilation and extensive NWP model verifications using whole radar networks at high temporal resolution. Its modular nature allows for relatively easy interface development to other NWP models. An offline framework is also available, which accesses model states of one model time step from hard disc. EMVORADO includes detailed modular schemes to simulate beam bending, beam broadening, and melting effects and allows users to choose for each process between computationally cheap and physically accurate options. The operator has been used for the assimilation of radar reflectivity with a positive impact on precipitation forecasts (Bick et al., 2016; Zeng et al., 2018, 2019, 2020). Currently, DWD uses EMVORADO to operationally assimilate 3D volumetric reflectivity and radial wind observations of its C-band radar network. Key for this application is also the extensive use of precomputed lookup tables that relate (Mie-theory-based) bulk reflectivity directly to hydrometeor densities and temperature. The effects of neglecting radar beam pattern and broadening and of hydrometeor fall speeds on data assimilation have been investigated in a joint effort together with the PROM project “Representing model error and observation Error uncertainty for Data assimilation of POLarimetric radar measurements” (REDPOL) (Zeng et al., 2021a).

The polarimetry-extended EMVORADO, in the following referred to as Pol-EMVORADO, has inherited all features of EMVORADO, which in turn have been expanded where necessary to calculate and handle polarimetric variables. This includes, e.g. beam bending, beam broadening, and beam smoothing schemes; effective medium approximations allowing one- and two-layered hydrometeors with different water–ice–air mixing schemes and melting topologies; and a lookup table approach for efficient access to polarimetric observables such as ZDR, LDR, ρHV, and KDP. Optionally, attenuation effects can be considered, specific and differential attenuation (AH and ADP, respectively) can be provided, and further output quantities derivable from the complex scattering amplitudes can be easily added. Pol-EMVORADO applies state-of-the-art scattering properties of spheroidal particles derived by one-layered (Mishchenko, 2000) and two-layered T-matrix approaches (Ryzhkov et al., 2011). Assumptions on spheroid shape and orientation follow parameterizations introduced in Ryzhkov et al. (2011). The lookup table approach has been revised to accommodate additional parameters necessary to derive the full set of polarimetric radar output. For a given set of parameters affecting the hydrometeor scattering properties, the lookup tables are created only once, stored in files, and re-used for subsequent runs.

Using pre-existing lookup tables, the computations for virtual polarimetric volume scans of radar networks are very fast. For example, simulating the volume scan observations of all polarimetric parameters for all 17 German radars takes only a few seconds on a Linux workstation (8 cores) and adds only about 1 s per radar output time step to the model runtime when performed online during a run of ICON-D2 (DWD's operational convection-allowing ICON version with 2 km grid spacing) on DWD's NEC Aurora supercomputer. That is, simulating polarimetric radar data in intervals of 5 min as observed by DWD's weather radar network adds only a few percent of the total model runtime (Mendrok et al., 2021), enabling the exploitation of Pol-EMVORADO for the assimilation of high-temporal-resolution polarimetric radar data in an operational framework. Pol-EMVORADO has been incorporated into the official version of EMVORADO and can be run online (i.e. within a COSMO or ICON run) as well as offline (i.e. stand-alone with model fields from data files). Although designed as a PPI volume scan observation operator for a radar network, its output can also be provided on NWP model grids. An example of a ZDR volume scan simulated by Pol-EMVORADO for the REDPOL project is shown in Fig. 6 (see also Sect. 4.2.3).

In summary, (Pol-)EMVORADO comprises a wide set of state-of-the-art features. While each of these features is also provided by other observation operators (Pol-)EMVORADO is, to our knowledge, unique in combining them into one operator that allows the simulation of virtual observations, including instrumental effects and in formats directly comparable to real observational scans, from NWP model runs in a comparably accurate and very fast manner targeted at operational applications. Mendrok et al. (2021) give a comprehensive description of the features developed or updated for Pol-EMVORADO including details on their implementation and performance.

However, from the application of Pol-EMVORADO (or B-PRO; see Sect. 3.2) within PROM, a number of problems became evident. Modelling hydrometeors as homogeneous effective-medium particles (e.g. oblate spheroids) does not reproduce the polarimetric signatures of low-density hydrometeors like dendrites or aggregates typical for snow while keeping their microphysical properties well (e.g. aspect ratio, degree of orientation) within realistic – observed or model-predicted – ranges and consistent between different radar frequencies. This deficiency has been demonstrated and explained from electromagnetic theory by Schrom et al. (2018). It is obvious in one case study (Shrestha et al., 2021b) and in Fig. 7, where ZDR and KDP in the snow-dominated layer between 2.5 and 5 km height almost entirely lack the typical observed features, i.e. bands of enhanced ZDR and KDP in the dendritic growth layer that then smoothly decrease to mostly positive, non-zero values towards the melting layer. This deficiency can also be observed with other polarimetric observation operators applying a T-matrix approach (see simulation-to-observation comparisons in Wolfensberger and Berne (2018), Matsui et al. (2019), and Oue et al. (2020), where the lack of ZDR and KDP signatures is not discussed at all or exclusively explained by a lack of secondary ice, though), which nevertheless currently constitutes the state of the art in radar polarimetry. Orientation and shape of frozen and melting hydrometeors are very variable, both in nature and in the assumptions used in observation operators, which translates into large uncertainties in polarimetric radar signatures (e.g. Matsui et al., 2019; Shrestha et al., 2021b).

To tackle these challenges, it is planned to interface Pol-EMVORADO to scattering databases or other scattering models in order to enable more realistic cloud ice and aggregate snowflake scattering properties and allow for improvements or extensions of the polarimetry-related microphysical assumptions (shape/habit/microstructure, orientation, and their distribution, e.g. Wolfensberger et al., 2018), particularly for (partly) frozen hydrometeors. For PROM's second phase, we have proposed taking this up guided with Lagrangian particle model information as well as testing the application of Pol-EMVORADO in an operational data assimilation environment.