The DPR onboard the GPM Core Observatory comprises two radars: the Ku-band Precipitation Radar (KuPR, 13.6 GHz) and the Ka-band Precipitation Radar (KaPR, 35.5 GHz). The DPR provides measurements with a vertical resolution of 250 m, over-sampled every 125 m, and a horizontal resolution of approximately 5 km due to the satellite's altitude of 407 km before the orbit boost in November 2023 .

Initially, the KuPR operated across a 245 km wide swath (49 beams), while the KaPR was limited to a narrower central swath of 125 km (25 beams), nested within the KuPR swath. The KaPR employed two distinct scanning modes: Measurements with a vertical resolution of 250 m, fully overlapping the central part of the KuPR swath (High-Resolution Mode). Measurements with a vertical resolution of 500 m, where the scan pattern was laterally shifted by half a footprint (24 beams) in the cross-track direction Shifted Scan Mode,.

On 21 May 2018, the scanning strategy was updated to extend the KaPR swath to 245 km, matching the KuPR swath width. The 24 beams were moved to the outer parts of the swath. This adjustment ensured that all footprints in the extended KaPR swath included DF measurements, significantly enhancing data consistency and coverage . Single-frequency beams are not considered in this study. To derive parameters of the drop size distribution, precipitation rates and attenuation corrected Ku-band and Ka-band reflectivities in logarithmic space, the measured Ka-band (ZKam) and Ku-band (ZKum) reflectivities are processed in various modules described in more detail in . The DF ratio 1DFR=ZKum-ZKam is the difference between ZKum and ZKam reflectivities in logarithmic space. In stratiform precipitation DFR is mainly affected by non-Rayleigh scattering effects and path-integrated attenuation. In the solid phase, attenuation by frozen hydrometeors is negligible for both frequencies and does not significantly change DFR. In contrast, the non-Rayleigh scattering effects play a major role and lead to an increase of the DFR with increasing hydrometeor diameters . Also, DFR in the solid region depends on the density of hydrometeors and their degree of riming. According to the Mie theory an increase in DFR is expected with decreasing density of fluffy non-rimed solid hydrometeors with low ZKum . For a fixed DFR ZKum increases with the ice particles degree of riming. However, this is only valid in stratiform precipitation and if DFR>1 dB . In the melting layer (ML) we observe an increase in ZKum due to the changes in the refractive index, particle size and concentration . As a consequence, both non-Rayleigh scattering effects and attenuation increase the DFR and result in a pronounced “bump”, called the DFR bright band, in the vertical profile of the DFR . In the liquid phase, attenuation mainly controls the DFR. Ka-band measurements are much more affected by attenuation compared to measurements at Ku-band and lead to an increase in DFR towards the ground. This increase is even more pronounced in convective precipitation where higher precipitation rates, thus the attenuation values, are observed. Furthermore, convection promotes the presence of large hydrometeors such as graupel, hail or drop diameter exceeding 0.8 mm that contribute to non-Rayleigh related DFR increase. In deep convective cores, the typical vertical profile of DFR can be distorted by multiple scattering at Ka-band. In extreme multi-scattering conditions, attenuation of the high frequency radar observations is compensated by multiple scattering effects in the upper part of the atmosphere which results in the so-called DFR-knee, i.e. a decrease in DFR towards the ground .

The overall vertical structure of the DFR is used to categorize the measurements into different rain types (RT, stratiform, convective, other, see ) and to determine the ML thickness and height, and is used to distinguish between liquid, solid and melting precipitation regions .

DP measurements of the NEXRAD WSR-88D S-band weather radars are exploited for this study. In total, 757 volume scans measured between 2014 and 2023 of the radar sites shown in Fig.  are considered. Measurements were selected to ensure that the GPM overflight took place at the closest point in time. A balanced number of convective and stratiform events is maintained to ensure a good representation of less frequently occurring hydrometeors like hail. The range resolution of the utilized NEXRAD radars is 250 m with a maximum elevation angle of 19.5° and 1° degree azimuthal resolution for higher elevation. Only quality-controlled GR observations provided by NASA's GPM Ground Validation program (GPM-GV) are used. The eastern GR sites of the NEXRAD network are predominantly used in the GPM-GV. GPM-GVs quality control includes the removal of non-precipitating echoes with different thresholds and phase unfolding. In addition, GPM-GV also provides vertical temperature information from model soundings .

The vertical temperature profiles are interpolated linearly at the beam center (Tc) and at the respective outer beam edges (3 dB beam width). From now on referred to Tt as temperature at the top beam edge and Tb as the temperature at the bottom beam edge. All radar bins with Tb<0 °C are classified as solid and all those with Tt>4 °C as liquid. All other radar measurements are considered as partly melted. Additional GR processing e.g. phase processing, GR calibration and attenuation correction are explained in more detail in the Appendix .

The applied standard HMC HMCZ; to identify the dominant hydrometeor type in a resolved radar volume and used to estimate the qHPRs is based on two dimensional membership functions (MSF) defined in with slightly modified hydrometeor types and MSF-parameters. The predefined hydrometeor types are light rain, moderate rain, heavy rain, big drops, rain/hail, graupel, crystals, dry snow, wet snow, plates/dendrites and hail. The hydrometeor classes are generally abbreviated to HMk where k=1,…,n with n=11. For more information on HMCZ, we refer to the Appendix .

The volume matching method (VMM) is performed with ωradlib and represents a well-known method for transferring SR and GR measurements to comparably sized volumes. In a first step all DP measurements of all GR bins within the SR footprint are averaged (DP‾, Fig. , left, plan view). Secondly, the DF observations of all SR bins (vertical resolution 125 m) within the GR beamwidth are averaged (DF‾, Fig. , right, side view). For more details see or . This results in equally sized superobbed volumes described by averaged DP variables Z‾H, Z‾DR, K‾DP and ρ‾HV and averaged DF variables Z‾Kum, Z‾Kam and DFR‾, from now on called sample Si (Fig. , top center and Fig. , blue box). Furthermore, each Si contains a mean temperature (T‾) and a rain type index (RT‾). RT‾ is convective if more then 10 % of the GR pixels in a Si are defined as convective (RT‾=2), otherwise RT‾ is defined as stratiform (RT‾=1). Si also includes the number of dominant hydrometeor classes N(HMk) classified with HMCZ on the original GR radar grid (Fig. , blue box). For each Si the N(HMk) are used to calculate the qHPRk via 2qHPRk=NHMk∑k=0nNHMk.

Note that qHPRs only represent estimators for the HPRs. E.g., due to their disproportionate influence on the polarimetric moments, hail or graupel may be classified as the dominant hydrometeor class in radar volumes despite low HPR. This can lead to overestimated qHPR for graupel and hail. In this study only Si with at least 50 valid GR pixels, well-defined stratiform or convective SR profiles and DPR detected precipitation (flagPrecip) are considered. Si showing strong differential attenuation due to hot spots above the ML or depolarization streaks leading to negative ZDR stripes are excluded. Furthermore, SR observations below 15.5 dBZ at Ku-band and 18 dBZ at Ka-band are not considered.

80 % of the Si, including DF‾, DP‾ measurements and qHPRs, serve as training data for the HMCP (Fig. , center) and the remaining 20 % of the Si are utilized as test dataset for the evaluation. Section  presents results for one case study entirely independent of the test and training dataset used.

The p̃k of the DP (Fig. ) and DF variables (Fig. d, e and f), as well as of RT (Fig. a, b and c) for each hydrometeor class k are derived based on the training data set (Fig. , center) as described in Sect.  and represent the basis for the HMCP. The hydrometeor classes are analyzed separately in the regions where they are most likely to occur, e.g. light rain, moderate rain, heavy rain and big drops in the liquid region, plates/dendrites, ice crystals and snow in the solid region and wet snow, graupel, hail and rain/hail in the solid, liquid and melting region (mixed). Note that the figures mentioned above illustrate only two-dimensional representations of the multidimensional p̃k, normalized according to Eq. () without weighting Wk(T).

In DP space, the centroids of the hydrometeor classes light rain, moderate rain, heavy rain (Fig. a, d and g) show in the ZH-ZDR and ZH-KDP plane an increasing ZH with increasing ZDR and KDP respectively. With increasing ZH ρHV is decreasing due to droplet growth and the associated increase in droplet flattening in liquid precipitation . The big drops centroid shows an increased ZDR compared to light rain, moderate rain, heavy rain . In DP space of the solid region (Fig. b, e and h), the centroids of plates/dendrites differ from crystals with respect to high ZDR and KDP values, which is in line with expected characteristics of ice particles especially in the dendritic growth layer (DGL). Dry snow instead is characterized by reduced ZDR and KDP values but higher ZH values, which is due to the increase in particle size and decrease in density during aggregation processes. As expected the p̃k for plates/dendrites show reduced ρHV values due to the diversity of ice particles in the DGL . With regard to the mixed hydrometeors (Fig. c, f and i) ZH of the wet snow centroid is much lower compared to the ones of graupel, rain/hail and hail. The latter shows the highest ZH. ρHV is the lowest in wet snow, followed by the two hail classes and then graupel. Hail and graupel show lower ZDR values compared to rain/hail and wet snow due the impact of tumbling of hail and potentially conical shapes of graupel .

In DF space (only ZKum-DFR space is shown) the centroids for liquid hydrometeors (Fig. d) show the typical behavior with increasing DFR and increasing ZKum due to increasing attenuation effects transitioning from light rain to heavy rain . An even more pronounced increase in DFR with rising ZKum is observed for big drops. This can be attributed to the additional influence of non-Rayleigh scattering effects when the droplet diameter exceeds 0.8 mm . DFR show an increase for both crystals and snow with increasing ZKum due to the increasing impact of non-Rayleigh effects with increasing particle diameter (Fig. e). Graupel, wet snow and rain/hail (Fig. f) show increased DFR due to a combination of increased diameters, riming and attenuation effects . Note that DF variables do not significantly differ between rain/hail and hail nor between crystals and plates/dendrites (not shown). As a consequence the hydrometeor classes are merged to rain/hail and crystals.

Big drops, graupel, rain/hail and hail are mostly restricted to convective precipitation where heavy rain has higher tendency to appear also in stratiform precipitation. Light rain, wet snow, dry snow, crystals and plates/dendrites are restricted to stratiform precipitation where wet snow, dry snow, crystals and plates/dendrites can occur with lower probabilities also in convection (Fig. a, b and c).

In order to evaluate the DF- and DP-based HPR retrievals, HMCP estimates (E) of the test dataset are compared to the qHPR serving as the reference (R), with the following statistical metrics: 10BIAS=1N∑i=1NEi-Ri,11RMSE=1N∑i=1NEi-Ri2 and12CCP=∑i=1NEi-E‾Ri-R‾∑i=1NEi-E‾2∑i=1NRi-R‾2.

R‾ and E‾ denote the mean values of Ri and Ei, respectively. A comparison between the qHPRs and HPRs based on the DF‾ and DP‾ variables results in high CCP for several hydrometeor classes. E.g., CCPs higher than 0.8, are achieved with the DP-based retrievals for light rain, moderate rain, heavy rain, wet snow, crystals and dry snow and with the DF-based retrievals for light rain, moderate rain, and dry snow (Fig. a, b, c, d, e, o, m, q and r). The lowest correlations occur with HPRkDP and HPRkDF of big drops (Fig. g and h) followed by HPRkDF of rain/hail and crystals and HPRkDP of plates/dendrites (Fig. j, n and t). The largest underestimations can be found in snow in both the DP and DF space, with a BIAS up to -5.15 % (Fig. , q and r) followed the HPRkDP of light rain and moderate rain and HPRkDF of crystals and wet snow (Fig. a, c, n and p). Pronounced HPR overestimation occurs for heavy rain, graupel for DF- and DP-based retrievals as well as for HPRkDP of big drops (Fig. e, f, g, k, and l). The comparison of hail HPRs shows an overestimation of rain/hail and hail HPRkDP and small underestimation of HPRkDF of rain/hail (Fig. i, j and s). Note that qHPR estimated from the dominant hydrometeor classes may overestimate the actual partitioning ratios due to the disproportional impact of hail on DP variables. As a consequence the biases in HPRkDP of rain/hail and hail may be even more pronounced than indicated by the qHPR-based evaluation. BIAS and RMSE values are small for the hail classes and big drops, which can be attributed to their overall low HPR values.

In summary the DP-based retrievals outperform the ones based on DF in terms of CCP and RMSE, in most cases also with respect to the BIAS values. This can be attributed to the higher information content of DP compared to DF measurements, for example, regarding the shape, orientation and homogeneity of the hydrometeors within the measurement volume.

Except for the big drops estimates, the retrievals for liquid hydrometeors in both DF- and DP-space, achieve a higher accuracy compared to the retrievals for the solid hydrometeor classes, reflecting the increased complexity and variability of DP and DF signals for solid and mixed hydrometeors.

To verify and illustrate the plausibility of the HMCP retrievals, a GPM overflight is directly compared to the KDDC NEXRAD GR and a pseudo range height indicator (RHI) is generated along DPRs along-track scan (Fig. a, blue dashed and red solid lines). The DP (Fig. b, d, f and h) and DF (Fig. c, e and g) variables are exploited to derive and compare the HPRkDP (Fig. ) and HPRkDF (Fig. ) with the HMCP.

A comparison of the GR and SR measurements (Fig. ) reveals a slight discrepancy in the STH. While GR measurements indicate a STH of approximately 15 km, SR indicate lower values due to the KuPR and KaPR sensitivity . The precipitation event can be subdivided into a stratiform and convective region. According to the GR-based RT classification, the convective area starts at a distance of 60 km from the GR, while the SR classification indicates that the convective area starts at a distance of 50 km. This discrepancy is likely attributable to the presence of a bright band located approximately at 3.5 km height characterized by increased ZH, ZDR, and reduced ρHV values, which is not properly identified by the DPR between 50 and 60 km range and thus partly classified as a convective region. Additionally, the GR has identified further convective areas up to a distance of 40 km, which may not be detected by SR due to their relatively small scale. The GR beams of higher elevation angles are affected by differential attenuation in the ML resulting in negative ZDR (Fig. d). At distances of approximately 80 km and beyond, the measurements at low elevation angles are partially affected by non-uniform beam filling (NBF), characterized by extreme high ϕDP (not shown) and low ρHV values . In the convective region, the impact of attenuation on SR near surface measurements is significant, especially at Ka-band (not shown). The signal partially drops below the 18 dBZ Ka-band threshold and is therefore excluded.

Until 50 km range, enhanced ZDR and DFR and moderate ZKum and ZH values result in corresponding increased HPRkDF and HPRkDP of moderate rain and low HPRkDF and HPRkDP of light rain (Figs. a, b and a, b). HPRkDF of big drops appears with low ratios in the convective region, whereas HPRkDP of big drops does not show a clear signal. In a range between 10 to 30 km the small-scale convective regions are not detected by the DPR resulting in no big drops HPRkDF where small proportions of HPRkDP are still estimated (Figs. d and d). DP estimates effectively illustrate the transition from solid hydrometeors such as dry snow via wet snow to liquid hydrometeors such as light rain, moderate rain and heavy rain. However, wet snow HPRkDF do not match with DPRs bright-band detection where HPRkDP of wet snow is restricted between Tb=0 °C and Tt=4 °C (Figs. h and h). Heavy rain is apparent in DP measurements within the ML, which is not the case in the DF measurements (Figs. c and c).

Both DF and DP measurements allocate the transition zone from ice to snow retrievals at approximately 8 km altitude, which corresponds to the height of the DGL (Figs. g, i and g, i) identified by increased KDP values slightly above the -15 °C isotherm (Fig. f). In the measurements obtained at ranges up to 20 km a decrease in KDP and an increase in ZH can be identified below the -15 °C isotherm (Fig. b, d and f) indicating aggregation processes . This is also supported by increasing DFR measurements in the same region (Fig. g). Increased snow HPRs above the -15 °C isotherm may be connected to the underestimation of ice HPRs, as identified in Sect. . The partial occurrence of HPRkDP of plates/dendrites in the DGL (Fig. j) is challenging to interpret due to the differential attenuation (Fig. d).

As expected rimed hydrometeors like graupel, rain/hail and hail, are primarily observed in convective regime. Due to the discrepancy between RT classifications based on GR and SR measurements (f in Figs.  and ) high graupel HPRkDF extend over a larger region compared to HPRkDP of graupel. Overall HPR of graupel in DP and DF are significantly overestimated (compare Sect. ). A comparison of hail HPRs reveal a comparable vertical distribution up to an altitude of approximately 8 km. Note that rain/hail and hail HPRkDP (Fig. e and k) have to be considered combined for a direct comparison with rain/hail HPRkDF (Fig. e). In regions with NBF, the detection of hail has to be considered with caution due the similarity of the DP signals for NBF and hail. However, SR partially confirms hail HPR in these areas. Due to the overall overestimation (underestimation) of hail HPRs in DP (DF) space, according estimates should be treated with caution.