Abstract

A novel methodology is introduced for processing and presenting polarimetric data collected by weather surveillance radars. It involves azimuthal averaging of radar reflectivity Z, differential reflectivity ZDR, cross-correlation coefficient ρhv, and differential phase ΦDP at high antenna elevation, and presenting resulting quasi-vertical profiles (QVPs) in a height-versus-time format. Multiple examples of QVPs retrieved from the data collected by S-, C-, and X-band dual-polarization radars at elevations ranging from 6.4° to 28° illustrate advantages of the QVP technique. The benefits include an ability to examine the temporal evolution of microphysical processes governing precipitation production and to compare polarimetric data obtained from the scanning surveillance weather radars with observations made by vertically looking remote sensors, such as wind profilers, lidars, radiometers, cloud radars, and radars operating on spaceborne and airborne platforms. Continuous monitoring of the melting layer and the layer of dendritic growth with high vertical resolution, and the possible opportunity to discriminate between the processes of snow aggregation and riming, constitute other potential benefits of the suggested methodology.

1. Introduction

Polarimetric radars provide unique insight into microphysical processes in clouds and precipitation. This knowledge will create opportunities for better microphysical parameterizations in numerical weather prediction (NWP) models and for assimilation of dual-polarization data into storm-scale models. Different microphysical processes (size sorting, evaporation, melting, freezing, riming, aggregation, diffusional growth, etc.) are characterized by specific “polarimetric fingerprints” (e.g., Kumjian 2012). These can be used to evaluate and eventually improve the models via converting their outputs into fields of polarimetric radar variables and adjusting parameterizations in a way that the observed polarimetric fingerprints are adequately reproduced.

The routine presentation of radar data in the plan position indicator (PPI) mode is not convenient for linking polarimetric radar signatures aloft, associated microphysical processes, and precipitation near the surface. Most convenient for the latter are scans along vertical planes in the so-called range–height indicator (RHI) mode; however, such scans typically are not available from operational systems due to time constraints. In principle, vertical cross sections of different radar variables can be obtained from a series of PPIs at different elevations (i.e., reconstructed RHIs), but their quality and vertical resolution are usually worse than that of “genuine” RHIs. On the other hand, genuine RHIs yield information only in selected azimuthal directions and thus do not represent the general structure of the storm or its evolution.

To get a better understanding of the microphysical processes that govern precipitation generation and evolution, it is beneficial to complement polarimetric scanning radar measurements with multifrequency observations by other remote sensors, such as wind profilers, cloud radars, spaceborne or airborne radars, lidars, and radiometers—all of which are mostly vertically pointing. Observations from these instruments are usually presented in a height-versus-time format. Polarimetric weather radars, however, are rarely used to probe the atmosphere in the vertical direction because key polarimetric variables, including differential reflectivity ZDR, total differential phase ΦDP, and specific differential phase KDP, exploit the signals produced by preferentially horizontally aligned nonspherical hydrometeors. These require antenna elevations, say, below 20°–30° to capitalize on the contrasts between orthogonally polarized components of the radar returns. One exception is the common practice to use vertical soundings for the absolute calibration of ZDR. Some operational polarimetric radars, such as the U.S. WSR-88Ds, cannot even look vertically due to antenna mechanical constraints. The area-covering scanning strategies of weather radars and the usual vertical scanning modes of other atmospheric remote sensing instruments might contribute to the current lack of multiplatform, synergistic observational studies.

Dwell times adopted for the scanning strategies of operational radars are usually quite short (typically about 50 ms for operational WSR-88D radars). Such short dwell times often result in significant statistical errors in estimates of the polarimetric radar variables, especially in areas characterized by low cross-correlation coefficients ρhv, the magnitude of which determines the statistical accuracy of radar moment estimates. The magnitude of ρhv is particularly low in the melting layer of stratiform clouds and, as a result, the key polarimetric variables ZDR, ΦDP, and ρhv are usually quite noisy there. For a long time, the noisiness of ΦDP in the melting layer has impeded the separation of the contributions of the forward scattering associated with KDP and the backscattering related to the backscatter differential phase δ to total ΦDP. The enhanced backscatter differential phase shows up as a “bump” in the slant profiles of ΦDP, which usually monotonically increases with range with the slope determined by KDP. The quantity δ characterizes local scattering properties of hydrometeors in the radar resolution volume, such as non-Rayleigh or resonance scattering, by large hydrometeors. In contrast, KDP characterizes the cumulative differential phase shift attributed to the propagation of electromagnetic wave through the medium filled with nonspherical particles. To reduce the noisiness of the differential phase within the melting layer, Trömel et al. (2013, 2014) suggest azimuthal averaging at high antenna elevation angles for the estimation of δ. Similarly, Kumjian et al. (2013) recommended such azimuthal averaging to quantify rather small enhancements of ZDR and KDP within the layer of melted hydrometeors freezing into ice pellets.

In this study, we follow a similar approach and routinely obtain the so-called high-resolution quasi-vertical profiles (QVPs) of Z, ZDR, ρhv, and ΦDP at relatively high elevation angles. We represent these profiles in a time-versus-height format to investigate the temporal evolution of key microphysical processes in clouds and precipitation.

2. QVP methodology

The QVPs of the polarimetric radar variables are obtained by azimuthal averaging of the radar data collected during standard conical scans at antenna elevation angles exceeding 10°–20°. Such high elevations minimize the effects of radar beam broadening and horizontal inhomogeneity. The values of ZDR, ΦDP, and KDP generally decrease only slightly with increasing elevation if the elevation angle remains below 20°–30°. Theoretical dependencies of ZDR on elevation angles for oblate spheroidal hydrometeors described by the formula (Ryzhkov et al. 2005)

 
formula

are illustrated in Fig. 1. In Eq. (1), linear scale Zdr(0) and Zdr(θ) are the differential reflectivities at elevation angles 0° and θ°, respectively. If ZDR (expressed in log scale) at grazing angles is below 1 dB, then it decreases by less than 0.15 dB at an elevation of 20°, which is within common measurement error of ZDR (0.1–0.2 dB). For higher values of ZDR and higher elevations, Eq. (1) and Fig. 1 can be used for calculating the equivalent value of ZDR at 0° elevation [ZDR(0)].

Fig. 1.

Dependence of the ZDR of oblate spheroidal hydrometeors on elevation angle for different values of intrinsic ZDR at zero elevation angle.

Fig. 1.

Dependence of the ZDR of oblate spheroidal hydrometeors on elevation angle for different values of intrinsic ZDR at zero elevation angle.

Azimuthal averaging reduces the statistical errors of the radar estimates without compromising temporal resolution and assigns their average vertical profiles to a conical volume with a vertical axis (Fig. 2). The standard deviations of the ΦDP and ZDR estimates are given by the following formulas (Melnikov 2004):

 
formula

and

 
formula

where

 
formula

M is the number of samples, σv is the Doppler spectrum width, T is the pulse repetition period (PRT), and λ is the radar wavelength. For S-band WSR-88D radars operating with T = 3.1 × 10−3 s (long PRT) and M = 16 in the surveillance scan mode (Ice et al. 2013) and for a typical value of σv = 3 m s−1, the magnitudes of SD(ΦDP) and SD(ZDR) measured within the melting layer with ρhv = 0.94 are 4.73° and 0.68 dB, respectively. If the melting layer is horizontally uniform, then azimuthal averaging of ΦDP and ZDR over 360 azimuths would result in the statistical error reduction by a factor of 3601/2, which yields standard deviations of azimuthally averaged estimates as low as 0.25° and 0.036 dB for ΦDP and ZDR, respectively. These errors are even smaller outside the melting layer, where the cross-correlation coefficient is usually higher than 0.94.

Fig. 2.

Conical volume representing azimuthally averaged quasi-vertical profiles of radar variables.

Fig. 2.

Conical volume representing azimuthally averaged quasi-vertical profiles of radar variables.

If the cloud is not horizontally uniform within the cone, then the errors of the azimuthally averaged estimates are larger, but a significant reduction in the statistical uncertainty compared to the case of a single radial still remains. For an antenna elevation of 20°, the diameter of the circle encompassing the averaging area increases from 16.5 km at a height of 3 km (the frequently observed height of the melting layer; see Fig. 2) to 55 km at a height of 10 km. The varying diameter of the averaging circle has to be taken into account in the interpretation of the QVP data.

According to the initial version of the QVP methodology, only the gates with ρhv > 0.6 and Z > −10 dBZ are used for azimuthal averaging and the number of valid gates (or azimuths) along the circle should exceed 30 in order to make an estimate of the QVP variable. Such averaging is performed at every radial gate with a typical slant range resolution Δr of 250 m. The resulting vertical resolution of the QVP data is determined by two factors: the size of radial range gate Δr and the width of the radar beam rΔθ, where r is the slant range and Δθ is the antenna beamwidth. Therefore, effective vertical resolution of QVP can be roughly estimated as the larger of the two terms:

 
formula

and

 
formula

where α is the antenna elevation angle and h is the height above ground. The term (5) increases with increasing α, whereas Eq. (6) decreases with higher antenna elevations. For α = 20°, Δr = 0.25 km, and Δθ = 1°, Δh2 exceeds Δh1 if h > 1.78 km; that is, the beam broadening term becomes dominant at higher altitudes. The corresponding vertical resolution determined by the term Δh2 is about 100 m at h = 2 km and 400 m at h = 8 km for α = 20°. The vertical resolution degrades with decreasing antenna elevation proportionally to cot α. Nevertheless, it remains much better than the one yielded by RHI reconstructed from the series of PPI scans as will be shown in the next section.

Any horizontal heterogeneity of the storm introduces the uncertainty in the QVPs. The measure of “tolerable” heterogeneity is difficult to quantify and further investigation is needed. The initial QVP methodology will be modified in the course of its future exploration using extended datasets. Possible refinements may include separation of the stratiform and convective echo to avoid contamination from spatially nonuniform convective elements. Another possibility is to utilize multiple (but relatively close) elevations to further reduce the statistical uncertainty as was done by Giangrande et al. (2008) for polarimetric detection of the melting layer.

3. QVP examples at S, C, and X bands

The advantages of the QVP methodology are first demonstrated using polarimetric data collected by the KVNX WSR-88D radar (Vance Air Force Base, Oklahoma), for a mesoscale convective system (MCS) observed in northern Oklahoma on 20 May 2011, during the Midlatitude Continental Convective Clouds Experiment (MC3E). Typical reconstructed RHIs of Z, ZDR, and ρhv are shown in Fig. 3. The ρhv depression and ZDR enhancement are visible in the melting layer of the stratiform part of the MCS, but the polarimetric data assembled in the reconstructed RHI format are quite noisy and have low native spatial resolution. The same radar data are utilized for generating the quasi-vertical profiles from the 19.5° elevation angle (the highest antenna elevation in the standard WSR-88D scanning strategy). The QVPs are presented in Fig. 4 in a time-versus-height format for the period 1000–1200 UTC, which spans 28 standard volume scans updated every 5 min. Figure 4 illustrates the evolution of the stratiform part of the MCS as it passed over the radar with high vertical resolution. At about 1100 UTC (i.e., in the middle of the observation period), the melting-layer signature is characterized by Z exceeding 42 dBZ, ZDR exceeding 2 dB at the bottom of the melting layer, ρhv reduced down to 0.94, and an extremely high δ reaching 40° in the middle of the melting layer (bottom-right panel). This backscatter differential phase in the melting layer is in agreement with similarly high values of δ at S band reported by Trömel et al. (2014).

Fig. 3.

Reconstructed composite RHIs of Z, ZDR, ρhv, and ΦDP measured by the KVNX WSR-88D radar in the case of an MCS observed in northern Oklahoma on 20 May 2011.

Fig. 3.

Reconstructed composite RHIs of Z, ZDR, ρhv, and ΦDP measured by the KVNX WSR-88D radar in the case of an MCS observed in northern Oklahoma on 20 May 2011.

Fig. 4.

The height-vs-time representation of quasi-vertical profiles of (a) Z, (b) ZDR, (c) ρhv, and (d) ΦDP retrieved from the KVNX WSR-88D radar data collected at elevation 19.5° in the case of an MCS observed in northern Oklahoma on 20 May 2011. Overlaid are contours of Z.

Fig. 4.

The height-vs-time representation of quasi-vertical profiles of (a) Z, (b) ZDR, (c) ρhv, and (d) ΦDP retrieved from the KVNX WSR-88D radar data collected at elevation 19.5° in the case of an MCS observed in northern Oklahoma on 20 May 2011. Overlaid are contours of Z.

Quasi-vertical profiles have been generated for a number of storms observed with operational WSR-88D radars and research dual-polarization radars operating at C and X bands with overlaid temperature contours retrieved from the regional NWP models. All examples demonstrate obvious benefits of QVPs for various practical applications, which are now discussed.

Figures 5 and 6 show QVPs of Z, ZDR, ρhv, and ΦDP generated from the KLWX WSR-88D radar (Sterling, Virginia) data collected throughout Hurricane Sandy at elevations of 19.5° on 29 October and 9.9° on 30 October 2012. Each figure illustrates the QVP evolution during 24 h and reveals the complicated internal structure of the storm. There are abrupt variations of the melting-layer height throughout time, as illustrated by elevated Z, ZDR, and δ, and depressed ρhv. All four of these variables can be effectively monitored using the height-versus-time format while preserving vertical resolution. One can examine these observations against output from the High-Resolution Rapid Refresh (HRRR) model (Alexander et al. 2010). This model has a grid spacing of 3 km, 51 vertical levels, and is updated hourly. Note the abrupt lifting of the melting layer by about 2 km detected by the radar between 2000 and 2400 UTC (Fig. 5) that was not captured by the model (Fig. 7). However, the general trends in the evolution of the height of the melting layer are consistent between the QVP and HRRR, including the drop of the melting layer almost to the surface after the 0800 UTC scan in Fig. 6 during the second day of observations. Note that monitoring the height of the freezing level is critical for timing and spatial variability of surface winter mixed-precipitation types. As in the case of the MCS illustrated in Fig. 4, the values of δ within the melting layer vary between 0° and 40°. Trömel et al. (2014) hypothesize that the magnitude of δ within the melting layer is determined by the maximal size of snowflakes falling through it. The values of δ approaching 40° are indicative of melting snowflakes well exceeding 1 cm.

Fig. 5.

As in Fig. 4, but for data during Hurricane Sandy on 29 Oct 2012 and with contours of wet-bulb temperature (°C) retrieved from the HRRR model. The signature of riming is illustrated in the top panel inset corresponding to the white rectangle in (b).

Fig. 5.

As in Fig. 4, but for data during Hurricane Sandy on 29 Oct 2012 and with contours of wet-bulb temperature (°C) retrieved from the HRRR model. The signature of riming is illustrated in the top panel inset corresponding to the white rectangle in (b).

Fig. 6.

As in Fig. 5, but for elevation 9.9° during Hurricane Sandy on 30 Oct 2012.

Fig. 6.

As in Fig. 5, but for elevation 9.9° during Hurricane Sandy on 30 Oct 2012.

Fig. 7.

Temporal evolution of the height of the freezing level retrieved from the HRRR model (solid line) and the KLWX WSR-88D radar (dashed line) during 48 h of the Hurricane Sandy event.

Fig. 7.

Temporal evolution of the height of the freezing level retrieved from the HRRR model (solid line) and the KLWX WSR-88D radar (dashed line) during 48 h of the Hurricane Sandy event.

Other important and persistent features, 2–3 km above the freezing level particularly well displayed in Fig. 5 at times between 1100 and 2300 UTC, are an increase of ZDR (up to 1.5–2 dB), a reduction of ρhv, and a strong vertical gradient of Z; these are associated with vigorous dendritic growth and onset of aggregation at temperatures between −10° and −15°C (Kennedy and Rutledge 2011; Ryzhkov et al. 2011; Andrić et al. 2013; Bechini et al. 2013; Thompson et al. 2014; Schrom et al. 2015). The signature strongly varies in time and is occasionally accompanied by noticeable vertical gradients of ΦDP caused by non-zero KDP. Note that while the backscatter differential phase (which is significant in the melting layer) shows up as a bump in the slant profiles of ΦDP, the forward propagation part of ΦDP determined by KDP manifests itself as a monotonic increase of ΦDP with height. Interestingly, at certain times, very strong increases of KDP and ZDR accompanied by the reduction of ρhv are observed near the very top of the cloud, where anisotropic pristine ice crystals are likely generated (e.g., between 1430 and 1630 UTC in Fig. 5) (Kumjian et al. 2014).

The ZDR decreases and Z increases below the layer of dendritic growth, which is commonly attributed to aggregation and/or riming. The ZDR decreases to a few tenths of a decibel just above the level where melting starts. However, this ZDR reduction occurs at different paces for riming and aggregation, so that ZDR is generally 0.1–0.3 dB lower just above the freezing level in the case of riming, which makes ice particles more spherical (Vogel et al. 2015; Kumjian et al. 2015, manuscript submitted to J. Geophys. Res. Atmos.; Giangrande et al. 2015, manuscript submitted to J. Geophys. Res. Atmos.). Such small differences are clearly visible in the QVP time series of ZDR (Figs. 5 and 6) because azimuthal averaging greatly reduces statistical errors of the ZDR estimates, allowing for subtle differences to stand out. Other commonly observed manifestations of riming are the radar reflectivity enhancement and the “sagging” of the melting layer because rimed snow particles have higher density, fall faster, and completely melt at lower altitudes compared to nonrimed snow (Zawadzki et al. 2005; Ryzhkov et al. 2008; Trömel et al. 2014; Kumjian et al. 2015, manuscript submitted to J. Geophys. Res. Atmos.). The riming signature is visible during the period between 1440 and 1600 UTC within the height interval from 1.5 to 3.5 km in Fig. 5 (see inset).

The link between downward extension (or sagging) of the melting layer and riming has been confirmed by model simulations (e.g., Zawadzki et al. 2005; Ryzhkov et al. 2008; Trömel et al. 2014; Kumjian et al. 2015, manuscript submitted to J. Geophys. Res. Atmos.) and polarimetric radar observations combined with the measurements of vertical velocities by wind profilers (Teshiba et al. 2009) although riming was not explicitly mentioned in the latter study. The association of the ZDR depression with riming is less obvious. On the one hand, riming makes ice particles more spherical, which results in the decrease of ZDR. On the other hand, increasing snow density caused by riming tends to increase ZDR. The fact that all our observations indicate a reduction of ZDR in snow aloft during the sagging episodes, points to the fact that the impact of the particle shape is stronger than the impact of density, so that the net effect is the decrease of ZDR in the case of heavy riming (see also Vogel et al. 2015).

The QVPs in Figs. 5 and 6 do not reach the surface because the lowest eight 250-m range gates of the WSR-88D radar data are censored out by the signal processor. This is not a problem for grazing angles (i.e., antenna < 5°) but at higher elevation of 19.5° this results in a loss of data within the first 700 m above the surface. This may prevent identification of the time when the melting layer descends to the ground and surface precipitation changes to snow at the surface, as in the second day of Hurricane Sandy observations illustrated in Fig. 6. For this reason, the data shown in Fig. 6 were taken from the lower elevation of 9.9° to reduce the depth of the “blank” layer down to 350 m. One has to keep in mind, however, that decreasing antenna elevations degrade the vertical resolution of QVPs (as specified in section 2) and increase the uncertainty associated with azimuthal averaging over larger and potentially more heterogeneous areas of the storm.

Restoring radar data in the first eight range gates would also be beneficial for capturing low-level microphysical processes and signatures, such as the “refreezing signature” described by Kumjian et al. (2013) and Ryzhkov et al. (2014). The refreezing signature is commonly marked by an increase of ZDR within a narrow layer where supercooled liquid drops freeze to ice pellets so that freezing rain changes to sleet. An example of the refreezing layer at about 500 m above the surface observed by the KTLX WSR-88D radar in Oklahoma City, Oklahoma, is shown in the QVP height–time plot in Fig. 8. The melting layer associated with an elevated temperature inversion between 2 and 3 km is identified by the typical increase of Z, ZDR, and δ as well as the drop in ρhv. The detection of the refreezing signature becomes possible using the QVP retrieved at elevation 6.4°. The series of vertical profiles of wet-bulb temperature retrieved from the HRRR model (Fig. 9) for this event indicates a classic temperature stratification favorable for a freezing rain–ice pellets transition. A deep, warm layer lies aloft and a very cold subfreezing layer remains next to the surface around 2100 UTC, when the refreezing signature becomes evident in the QVP. Note that the refreezing signature is often transient and poorly visible at the lowest radar elevations. Hence, the QVP provides a unique opportunity to observe its onset and demise, which has important practical implications for radar classification of winter precipitation types.

Fig. 8.

As in Fig. 4, but for data collected at elevation 6.4° during the freezing rain event in central Oklahoma on 2 Mar 2014. The layers of enhanced differential reflectivity indicating the melting and refreezing layers are shown by arrows in (b).

Fig. 8.

As in Fig. 4, but for data collected at elevation 6.4° during the freezing rain event in central Oklahoma on 2 Mar 2014. The layers of enhanced differential reflectivity indicating the melting and refreezing layers are shown by arrows in (b).

Fig. 9.

Vertical profiles of the HRRR-analyzed wet-bulb temperature at KTLX from 0000–2100 UTC 2 Mar 2014: 0000, 0300, 0600, 0900, 1200, 1500, 1800, and 2100 UTC.

Fig. 9.

Vertical profiles of the HRRR-analyzed wet-bulb temperature at KTLX from 0000–2100 UTC 2 Mar 2014: 0000, 0300, 0600, 0900, 1200, 1500, 1800, and 2100 UTC.

The QVP time series can also conveniently be used for continuous monitoring of ZDR calibration. Ryzhkov et al. (2005) suggested using ZDR measured in snow just above the freezing level for absolute calibration of differential reflectivity because the intrinsic ZDR in heavily aggregated dry snow is less than 0.25 dB and does not vary much. We assume that the ZDR of dry aggregated snow is slightly positive, whereas most of rimed snow has a ZDR very close to zero (especially if a higher antenna elevation is used). This was confirmed in the recent studies by Vogel et al. (2015), Kumjian et al. (2015, manuscript submitted to J. Geophys. Res. Atmos.), and Giangrande et al. (2015, manuscript submitted to J. Geophys. Res. Atmos.), where riming of snow was recognized by using either vertically pointing Doppler radar or in situ aircraft measurements. Vogel et al. (2015) and Giangrande et al. (2015, manuscript submitted to J. Geophys. Res. Atmos.) distinguish between the cases of rimed snow with unimodal and bimodal spectra of vertical Doppler velocities within the temperature interval between 0° and −10°C. The bimodal spectra indicate secondary ice production of very anisotropic particles like needles or plates, which contribute to the increase of ZDR. Vogel et al. (2015, p. 15) report that “cases with a bimodal spectra can have ZDR values of about 0.2 dB up to 0.4 dB higher than non-riming cases while riming cases without a bimodal spectra can have ZDR values up to 0.2 dB lower than non-riming events.” The bimodal spectra seem to be typical for the cases of weaker riming when the contribution to ZDR by the primary ice with a quasi-spherical shape and highly anisotropic, smaller-size secondary ice are comparable. For heavier riming, ZDR is dominated by quasi-spherical larger-size ice, which has intrinsic ZDR very close to 0 dB. Kumjian et al. (2015, manuscript submitted to J. Geophys. Res. Atmos.) show that heavier riming is usually associated with the “sagging bright band.”

Summarizing these latest findings, one can conclude that heavily rimed snow characterized by relatively high Z and sagging bright band has ZDR very close to zero slightly above the freezing level. Dry aggregated (unrimed) snow has slightly positive ZDR, which is 0.1–0.25 dB higher than heavily rimed snow. Weakly rimed snow may have higher ZDR than aggregated, unrimed snow if secondary ice is produced. Therefore, one can identify minimal values of ZDR just above the freezing level (likely associated with heavier riming) in the QVP time series and make sure that the measured ZDR is equal to zero there. Figure 8 shows that ZDR measured by the KTLX WSR-88D radar above the 0°C level is stable and close to −0.5 dB for 2 March 2014 (with minimal values of about −0.6 or −0.7 dB), which points to the known negative ZDR bias of 0.6–0.7 dB for this WSR-88D radar.

The QVP methodology proves to be efficient at C and X bands as well. Figure 10 shows quasi-vertical profiles of Z, ZDR, and ρhv measured by the C-band CSAPR radar in northern Oklahoma on 27 April 2011 during the MC3E field campaign. The QVP clearly exhibits two episodes of riming associated with weak embedded convection at about 0740 and 1000 UTC. All three attributes of riming are evident in the QVP plots: enhanced Z, depressed ZDR above the freezing level, and downward excursion of the melting layer as indicated by ZDR and ρhv. For this event, riming aloft was supported by relatively high liquid water content at subfreezing temperatures measured on board the research aircraft and upward air motion measured by the vertically pointing 915 MHz wind profiler and Ka-band Doppler cloud radar (not shown).

Fig. 10.

As in Fig. 4, but for profiles retrieved from the research C-band ARM precipitation radar (CSAPR) data collected at elevation 21.4° during the storm in northern Oklahoma on 27 Apr 2011.

Fig. 10.

As in Fig. 4, but for profiles retrieved from the research C-band ARM precipitation radar (CSAPR) data collected at elevation 21.4° during the storm in northern Oklahoma on 27 Apr 2011.

Quasi-vertical profiles of polarimetric radar variables were also retrieved for a number of cases observed by the X-band dual-polarization radar at the University of Bonn, Germany (BoXPol). One QVP example is complemented with the height–time plot of vertical Doppler velocity measured every volume scan with a vertically pointing antenna (Fig. 11). The QVP plots of Z, ZDR, ΦDP, and ρhv with overlaid contours of temperature illustrate the features similar to those shown in the previous examples for S- and C-band radars. These include high-resolution representations of the melting layer with undulations attributed to embedded weak convection and associated riming, as well as a layer of dendritic growth aloft with strong vertical gradients of Z, increased ZDR, and significant vertical gradients of ΦDP. Maximal values of ZDR in the layer of dendrites do not exceed 1 dB in this example, implying the presence of more isotropically scattering dendrites or their aggregates compared to the case displayed in Figs. 5 and 6. The vertical gradient of ΦDP within the height interval between 3.5 and 5.3 km at about 2030 UTC corresponds to a KDP of about 1.1° km−1, which is consistent with typical KDP values at X band reported by Bechini et al. (2013) and Schrom et al. (2015). The backscatter differential phase within the melting layer is notable but generally lower than the δ measured at S band, as was previously documented by Trömel et al. (2014). The maximal value of δ estimated in the five cases examined at X band was 8.5°.

Fig. 11.

The height-vs-time representation of the quasi-vertical profiles of (top) (left to right) Z, ZDR, and vertical Doppler velocity and (bottom) ρhv and ΦDP, retrieved from the data collected by BoXPol at elevation 28° on 6 May 2014. Overlaid are contours of Z (thin lines) and wet-bulb temperature (thick lines) retrieved from the German Meteorological Service’s [Deutscher Wetterdienst (DWD)] version of the Consortium for Small-Scale Modeling (COSMO-DE) model.

Fig. 11.

The height-vs-time representation of the quasi-vertical profiles of (top) (left to right) Z, ZDR, and vertical Doppler velocity and (bottom) ρhv and ΦDP, retrieved from the data collected by BoXPol at elevation 28° on 6 May 2014. Overlaid are contours of Z (thin lines) and wet-bulb temperature (thick lines) retrieved from the German Meteorological Service’s [Deutscher Wetterdienst (DWD)] version of the Consortium for Small-Scale Modeling (COSMO-DE) model.

Riming signatures exhibited by the sagging of the melting layer and ZDR depressions in a 2-km-deep layer above the 0°C level are visible at about 2030 and 2200 UTC in Fig. 11. The riming signature at 2030 UTC is fairly consistent with the indication of an updraft in the vertical Doppler velocity panel at that time. A maximal updraft velocity of 0.5 m s−1 is measured near the cloud top between 6.5 and 7.0 km AGL. It is reasonable to assume that supercooled cloud water supported by the updraft produced riming, which could have led to an increase in the mass and density of snow particles that subsequently acquire larger terminal velocities, increasing the magnitude of the downward Doppler velocity to >2 m s−1 within the 1.5-km-deep layer above the 0°C level (slightly higher than 2 km AGL). This example emphasizes the importance of combining the quasi-vertical profiles of polarimetric radar variables with vertical profiles of Doppler velocities measured either by the same radar or by wind profilers or by vertically pointing cloud radars (if possible). The VAD profiles of horizontal wind or the mesoscale divergence profile obtained from the same conical scans as QVP may also contribute to the understanding of the interplay of the dynamical and microphysical processes within the clouds.

As opposed to the signal processing on the WSR-88D radars, the data obtained from the C- and X-band research radars in our study were not censored in the first range gates. Therefore, the quasi-vertical profiles of the radar variables are extended down to the surface, which is an apparent advantage for detecting low-level features.

4. Summary

Several benefits of the QVP methodology are summarized herein.

  1. The procedure for generating quasi-vertical profiles of polarimetric radar variables is very simple and straightforward, and the QVP plots in the height-versus-time format can be produced in real time for operational dual-polarization weather radars as a standard radar product.

  2. QVP generation is an efficient way to examine the temporal evolution of microphysical processes governing precipitation production and to display physical links between polarimetric signatures aloft in the ice- or mixed-phase parts of the clouds, polarimetric characteristics of the melting layer, and the resulting rain near the surface. The method assumes horizontal uniformity of the radar echo and is particularly effective for stratiform-type precipitation. Decreasing antenna elevation degrades the vertical resolution of QVPs and increases the uncertainty associated with azimuthal averaging over larger and potentially more heterogeneous areas of the storm.

  3. The QVP methodology facilitates an effective integration of polarimetric data obtained from the scanning surveillance weather radars with observations made by vertically pointing remote sensors such as wind profilers, lidars, radiometers, cloud radars, and radars operating at satellite or airborne platforms because the data from all these instruments can be represented in a standard height-versus-time (or height-versus-orbit/flight path) format.

  4. The temporal evolution of the melting layer can be monitored with high vertical resolution using QVP, which presents a more reliable way to estimate δ within the melting layer compared to standard slant angle radar scans (Trömel et al. 2013, 2014). As Trömel et al. (2014) demonstrated, measurements of δ show good promise for estimating the maximal size of snow particles formed above the freezing level.

  5. Another important microphysical feature aloft that can be monitored with QVP is the layer of dendritic growth. According to Kennedy and Rutledge (2011), the intensification of the layer may serve as a predictor of precipitation enhancement at the surface (snow or rain).

  6. The height-versus-time representation of the QVP may be helpful for discrimination between snow aggregation and riming. The latter is revealed by the enhancement of Z and the decrease of ZDR above the freezing level, and the subsidence of the melting layer. The difference between ZDR values for aggregated snow and rimed snow with various degrees of riming is relatively small—a few tenths of a decibel—and using the QVP methodology is instrumental for detecting such small polarization contrasts.

  7. The QVP is efficient for continuous monitoring of the absolute calibration of differential reflectivity and for detecting any other abrupt changes in the quality of the radar signal.

  8. QVPs of polarimetric radar data can be complemented with estimates of vertical velocities or the convergence/divergence of horizontal wind from the VAD profiles obtained from the same conical radar scan and vertical velocities from collocated vertically pointing radars.

Acknowledgments

Funding for the study was provided by NOAA/Office of Oceanic and Atmospheric Research under NOAA–University of Oklahoma Cooperative Agreement NA11OAR4320072, U.S. Department of Commerce, and by the U.S. National Weather Service, Federal Aviation Administration, and U.S. Department of Defense program for modernization of NEXRAD radars. Additional support was given from the Grant ER65459 by the U.S. Department of Energy’s Atmospheric System Research program and Grant AGS-1143948 from the U.S. National Science Foundation. Research based on the Bonn X-band radar was carried out in the framework of the Hans-Ertel-Centre for Weather Research (http://www.herz-tb1.uni-bonn.de/), funded by the BMVBS (Federal Ministry of Transport, Building and Urban Development), and the SFB/TR 32 (Transregional Collaborative Research Centre 32; http://www.tr32.de/), funded by the DFG (German Research Foundation).

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