Abstract

The National Center for Atmospheric Research (NCAR) operates a state-of-the-art S-band dual-polarization Doppler radar (S-Pol) for the National Science Foundation (NSF). This radar has some similar and some distinguishing characteristics to the National Weather Service (NWS) operational Weather Surveillance Radar-1988 Doppler Polarimetric (WSR-88DP). One key difference is that the WSR-88DP is used for operational purposes where rapid 360° volumetric scanning is required to monitor rapid changes in storm characteristics for nowcasting and issuing severe storm warnings. Since S-Pol is used to support the NSF research community, it usually scans at much slower rates than operational radars. This results in higher resolution and higher data quality suitable for many research studies. An important difference between S-Pol and the WSR-88DP is S-Pol’s ability to use customized scan strategies including scanning on vertical surfaces ([range–height indicators (RHIs)], which are presently not done by WSR-88DPs. RHIs provide high-resolution microphysical structures of convective storms, which are central to many research studies. Another important difference is that the WSR-88DP simultaneously transmits horizontal (H) and vertical (V) polarized pulses. In contrast, S-Pol typically transmits alternating H and V pulses, which results in not only higher data quality for research but also allows for the cross-polar signal to be measured. The cross-polar signal provides estimates of the linear depolarization ratio (LDR) and the co- to cross-correlation coefficient that give additional microphysical information. This paper presents plots and interpretations of high-quality, high-resolution polarimetric data that demonstrate the value of S-Pol’s polarimetric measurements for atmospheric research.

Polarimetric radar data from NCAR’s S-Pol are presented for a severe, hail-producing convective storm and for widespread migrating insects.

Research radars have led the way for many years by demonstrating new polarimetric measurement capabilities that are not possible with Doppler-only radars. Indeed, dual-polarization variables have improved rainfall estimates, made it possible to discriminate ice and rain, provided identification of hail cores and updraft regions, and in general increased data quality (Chandrasekar et al. 2013; Kumjian 2013a,b,c). Today, modern weather radar manufacturers employ dual-polarization technology because of this new proven capability. Many countries have procured dual-polarization technology, and some have upgraded or plan to upgrade their national fleet of radars to dual polarization. Such operational government radars have standard scanning strategies that emphasize fast updates for storm tracking and evolution. The fast updates are obtained with fast scan rates (∼15°–30° s–1), which translate to fewer samples that are integrated per resolution volume as compared to slower scan rates. Since S-band dual-polarization Doppler radar (S-Pol) is a research radar, it typically scans at significantly slower rates for better signal statistics: plan position indicators (PPIs), 8°–10° s–1, and range–height indicators (RHIs), 3°–6° s–1, although these rates are user selectable. The Weather Surveillance Radar-1988 Doppler polarimetric (WSR-88DP)1 is not able to scan along vertical planes (RHIs), and while this has not a been a significant disadvantage for operational purposes, it does limit research objectives. This will be illustrated in the following RHI examples from S-Pol. Also, increased range resolution is obtained by S-Pol, which normally uses a 1-μs transmit pulse length that corresponds to 150-m-range samples as compared to WSR-88DP’s 250-m resolution. The result is that S-Pol is able to obtain high-resolution, high-data-quality measurements that reveal intricate storm structure and microphysics that would be difficult to see with operational scanning strategies.

The purpose of this paper is to provide, via example, the high-resolution precipitation microphysics phenomena and insect behavior that can be revealed with data from a state-of-the-art polarimetric S-band radar like National Center for Atmospheric Research (NCAR)’s S-Pol. While the examples are from S-Pol, other research S-band radars have similar capabilities. The WSR-88DPs have potentially the same basic capabilities, but their operational mode requires faster scanning procedures. For comparable data quality measurements with a WSR-88DP that used slower scanning rates, see Melnikov et al. (2011).

Using the fact that raindrops become more oblate as they get larger and that they are well oriented as they fall, Seliga and Bringi (1976) first reported on the potential of dual-polarization measurements, that is, differential reflectivity ZDR, to describe the oblateness of raindrops and thereby improve rain-rate estimation. The first ZDR measurements were made soon after by the University of Chicago–Illinois State Water Survey (CHILL) radar facility in 1977 (Seliga et al. 1979). Several research radars soon followed and made polarization measurements mostly by transmitting alternate pulses of horizontal (H) and vertical (V) polarized waves (Seliga et al. 1990). Doviak et al. (2000) proposed using simultaneous transmission of the H and V polarized waves (SHV) to achieve dual-polarization measurements, the result of which is that today almost all polarimetric weather radars transmit both H and V polarized waves simultaneously, such as the WSR-88DP, while a few, primarily research radars, transmit fast alternating pulses of H and V (FHV) polarized waves. SHV mode works well but has been shown to produce biases under certain weather scenarios. For example, in regions of ice crystals that have been aligned and canted by an electric field, cross coupling of the H and V waves occurs, which causes radial ZDR streaks (Ryzhkov and Zrnić 2007). Also, antenna-polarization errors cause cross coupling that biases ZDR as a function of copolar differential phase ϕDP when using SHV mode (Wang and Chandrasekar 2006; Hubbert et al. 2010a,b). These errors do not occur with S-Pol since it achieves dual polarization by transmitting fast alternating pulses of H and V polarized waves. An advantage of SHV radars is that twice as many samples in each polarization are gathered in SHV mode as compared to FHV mode for the same sampling period (dwell time) and transmit pulse repetition time (PRT). More samples typically mean better signal statistics. This is a distinct advantage for SHV mode for fast scan rates with fewer samples per resolution volume. However, for slower scan rates and shorter PRTs, which S-Pol employs, this advantage of additional samples does not necessarily translate to significantly better signal statistics, which depend more on the number of independent samples [see Bringi and Chandrasekar (2001), their Figs. 6.29c and 6.30c, for a comparison of SHV and FHV statistics]. A disadvantage of SHV operations is the elimination of the capability to measure the cross-polar signals (e.g., transmit H and receive V polarization). The ratio of the cross-polar to copolar powers is termed the linear depolarization ratio (LDR), which is valuable for identifying regions of wet ice. Additionally, FHV radars can measure the correlation of the copolar to cross-polar signals ρco-x that can indicate regions of cloud electrification (Ryzhkov et al. 2002; Hubbert et al. 2015).

POLARIMETRIC VARIABLE INTERPRETATION

The reflectivity factor Z indicates the quantity of precipitation and has units of mm6 m–3 (dBZ). For particles with diameters much less than the transmitted wavelength (i.e., Rayleigh scattering), the received power is proportional to the sum of the sixth power of the particles’ equivolumetric spherical diameters per unit volume. Typical values are 50 dBZ and greater in the core of a thunderstorm and 20 dBZ in light rain.

The differential reflectivity ZDR is a measure of the reflectivity-weighted mean axis ratio of the precipitation particle distribution and is a hail versus rain discriminator. Typical values in rain are slightly positive to about 3 dB; however, for hail, ZDR is typically near 0 dB.

The differential phase ϕDP and its derivative with respect to radar range, specific differential phase KDP, in degrees per kilometer is used for rainfall estimation, with 1° km–1 yielding about 45 mm h–1.

The copolar correlation coefficient ρhv is an indicator of mixed phase, large hail, and the bright band (melting snow). Taking the correlation product of the copolar H and V received signals for a resolution volume yields ρhv. In rain ρhv is usually >0.97 while in hail and in the bright band ρhv < 0.95.

LDR is an indicator of the presence of asymmetric particles such as wet snow and ice. For rain, LDR is typically less than –28 dB, while asymmetric particles in the bright band and wet hail typically yield LDR greater than –27 dB. LDR is not measured by SHV radars such as NEXRAD.

The co- to cross-correlation coefficient ρco-x is an indicator of ice crystals that are canted away from the horizontal because of the presence of an electric field. In rain and in many ice crystal regions, ρco-x is less than 0.3, while areas containing sufficient aligned ice crystals with a nonzero mean canting angle have ρco-x greater than 0.4.

The backscatter copolar differential phase shift δ is an indicator of resonant scatterers with particle diameters greater than 8 mm (for S band) such as larger water-covered ice and hail.

See the online supplementary material for more details on polarimetric variables (https://doi.org/10.1175/BAMS-D-17-0317.2).

There are two other advantages that S-Pol enjoys: 1) S-Pol operates without a radome, which can distort signals, especially when it is wet (Salazar-Cerreno et al. 2014), and 2) S-Pol uses copolar and cross-polar receivers instead of H and V receivers. Since the copolar H and V signals use the same receiver, signal statistics are immune to any time-fluctuating receiver gain, thereby improving both bias and standard deviation of ZDR, copolar differential phase ϕDP, and copolar correlation coefficient ρhv. For a detailed discussion on weather radar, there are books such as Doviak and Zrnić (1993), Bringi and Chandrasekar (2001), Fabry (2015), and Zhang (2017). A good introductory text is Rinehart (2004). A few articles discussing polarimetric radar are Herzegh and Jameson (1992), Zrnić and Ryzhkov (1999), Ryzhkov et al. (2005), Chandrasekar et al. (2013), and Kumjian (2013a,b,c).

STORM STRUCTURE REVEALED BY AN RHI THROUGH A CONVECTIVE CELL DURING PECAN.

From 1 June to 15 July 2015, S-Pol collected data for the field campaign Plains Elevated Convection at Night (PECAN; Geerts et al. 2016; Earth Observing Laboratory 2015; Lutz et al. 1995), which was centered at Hays, Kansas. S-Pol was located 42 km southwest of Hays close to McCracken, Kansas. The standard scan strategy for S-Pol was to alternate PPI volume scans with a series of RHI scans placed at 30° increments in azimuth. The PPI surveillance scan rate was 12° s–1 with 0.75° azimuth resolution, and the RHI scan rate was 3.6° s–1 with 0.25° elevation resolution. The NCAR-developed algorithm, clutter mitigation decision (CMD; Hubbert et al. 2009b,a), is used to identify clutter-contaminated data, and a Gaussian model adaptive processing (GMAP)-like clutter filter is applied (Siggia and Passarelli 2004). Both CMD and GMAP are used by Next Generation Weather Radar (NEXRAD). S-Pol’s ZDR is very well calibrated for the PECAN dataset using a new technique based on the cross-polar power method (Hubbert et al. 2003; Hubbert 2017), which takes into account diurnal temperature-driven variation of the ZDR bias. The uncertainty of the ZDR calibration is estimated to be less than 0.1 dB at the 98% confidence level (Hubbert 2017).

On the afternoon of 25 June 2015, there was convection along a cold-frontal boundary near S-Pol. Frontal overrunning of the southerly low-level jet led to ongoing convection initiation along and north of the cold front throughout the evening and overnight. The convection grew upscale into mesoscale convective systems that led to the generation of multiple outflow boundaries, bores, and other waves and further convection initiation throughout the night. We next show data from one of the strong convective cells.

Shown in Fig. 1 is a reflectivity PPI scan at 0.5° elevation angle gathered at 0004:07 UTC 26 June 2015. The local time is UTC minus 5 h. There are two strong convective cells to the east and southeast of S-Pol. Closer to S-Pol are several boundaries or fine lines marked by higher reflectivity, which are composed of mostly insects (Wilson et al. 1994). The yellow line marks where the RHI data were collected at 0005:23 UTC. Figures 2 and 3 show RHIs of Z and ZDR, respectively, with areas marked with contour lines and labels. The 0°C isotherm, estimated from a nearby sounding, is indicated by the horizontal white line at 4.7 km MSL. This is a large hail-producing storm topping out at about 16 km MSL with peak reflectivities exceeding 65 dBZ. The height of S-Pol is about 0.65 km MSL. Large tennis ball–sized hail was reported at 38°35´24˝N, 98°46´48˝W (about 66 km due east of S-Pol) at 0028:00 UTC by a trained hail spotter [National Oceanic and Atmospheric Administration (NOAA) Storm Prediction Center website: www.spc.noaa.gov/]. At 0027:00 UTC at 38°20´24˝N, 99°6´36˝W near Ash valley and relative to S-Pol at 121° azimuth (from north) and 44-km range, the NOAA site reports, “There were several episodes of damaging hail at this location. The corn crop was shredded and the wheat was at least 50 percent destroyed.” This is the location of the RHI data reported here. There is a remarkable amount of information contained in the individual polarimetric variables, described below, and especially evident in the ZDR plot. We next discuss some of the scattering signatures seen in the RHI plots.

Fig. 1.

Reflectivity PPI scan at 0.5° elevation angle gathered at 0004:07 UTC 26 Jun 2015. The yellow line marks the location of the subsequent RHI data.

Fig. 1.

Reflectivity PPI scan at 0.5° elevation angle gathered at 0004:07 UTC 26 Jun 2015. The yellow line marks the location of the subsequent RHI data.

Fig. 2.

A reflectivity RHI of a large convective cell gathered at 0005:23 UTC 26 Jun 2015 from along the yellow line in Fig. 1. Eight regions are marked with white contour lines and are labeled. The dashed line marks the 55-dBZ contour; 60 dBZ is seen up to 13 km MSL, indicating the likely presence of large hail.

Fig. 2.

A reflectivity RHI of a large convective cell gathered at 0005:23 UTC 26 Jun 2015 from along the yellow line in Fig. 1. Eight regions are marked with white contour lines and are labeled. The dashed line marks the 55-dBZ contour; 60 dBZ is seen up to 13 km MSL, indicating the likely presence of large hail.

Fig. 3.

Differential reflectivity ZDR corresponding to Fig. 2. It is particularly effective for delineating various storm regions as indicated by the white contour lines in the plot.

Fig. 3.

Differential reflectivity ZDR corresponding to Fig. 2. It is particularly effective for delineating various storm regions as indicated by the white contour lines in the plot.

Description of echo regions.

Regions 1, 2, and 4: Insects.

Regions 1, 2, and 4, to the northwest of the storm core, are characterized by mostly low reflectivities (–10 to 6 dBZ; Fig. 2), very high ZDR (4 to 14 dB; Fig. 3), and low ρhv (<0.9; Fig. 4), which is the signature of insects (Wilson et al. 1994; Zrnić and Ryzhkov 1998; Melnikov et al. 2015). Insects are also distinguishable from weather by the increased spatial texture (variability) of the ZDR and ϕDP, which is consistent with the characteristic low ρhv (Chandrasekar et al. 2013). These regions consist primarily of horizontally oriented insects, which yield the observed high ZDR, with the likely presence of a few birds, but they are hard to distinguish in the insect-dominated returns. Birds can have distinct polarimetric signatures from insects (Zrnić and Ryzhkov 1998) and are particularly obvious when colonies emerge from roost or a cave (e.g., bats; Russell et al. 1998). Since insects are characterized by lower ρhv as compared to most precipitation, precipitation regions are typically easily distinguishable from areas of insects as seen in Fig. 4.

Fig. 4.

Copolar correlation coefficient ρhv corresponding to Fig. 2. It is a measure of the similarity of the H and V copolar radar received signals.

Fig. 4.

Copolar correlation coefficient ρhv corresponding to Fig. 2. It is a measure of the similarity of the H and V copolar radar received signals.

Where different air masses collide, updrafts are generated. Insects resist being carried aloft, and thus, the number density increases, resulting in lines of increased reflectivity (Wilson et al. 1994) as seen in Fig. 1 (lines marked as convergence lines) and as seen in region 2 of Fig. 2. Region 4 likely contains insects, indicated by the high ZDR values. It appears that a layer of insects resides above the layer marked Bragg scatter (discussed below), perhaps carried aloft in the updrafts caused by boundary layer horizontal convective rolls, and are up against the 0°C isotherm (Weckwerth et al. 1997; LeMone 1973). Insects avoid turbulent regions and freezing temperatures (Drake and Reynolds 2012). In region 1, especially close to the ground, some residual clutter remains that the clutter filter was unable to eliminate, and this is likely the source of the higher reflectivity values (>9 dBZ). Many times, ground clutter will be characterized by negative ZDR since many ground clutter targets have longer vertical than horizontal dimensions. This likely causes most of the negative ZDR close to ground in Fig. 3. There are many ZDR values in excess of 8 dB in the insect regions. WSR-88DP’s maximum measurable ZDR is 7.9 dB by design (Melnikov et al. 2015) since NEXRAD’s focus is weather; therefore, such high ZDR values will not be observable with WSR-88DPs.

Region 3: Bragg scatter.

Region 3 is characterized by low reflectivity (≲5 dBZ), high ρhv (≳0.99), and near-0-dB ZDR. This is the signature of Bragg scatter (Gossard 1977, 1990; Doviak and Zrnić 1993; Wilson et al. 1994; Melnikov et al. 2011). Bragg scatter occurs in the atmosphere when turbulence mixes air masses of different refractive indices on the scale of half the radar wavelength, and it frequently occurs at the top of the boundary layer (Gossard 1990; Melnikov et al. 2013). Some of the ρhv values in the Bragg layer are lower than its expected value of >0.99, but this is due to low signal-to-noise ratio (SNR), insect contamination, or sidelobe ground clutter. Bragg scatter is difficult to distinguish from clouds composed of very small droplets since the radar signatures are the same, and for this reason, NCAR’s particle identification (PID) algorithm combines cloud and Bragg scatter into one category.

One way to distinguish between Bragg and cloud scatter is the use of visual observations. S-Pol is equipped with four wide-angle cameras, facing north, east, south, and west, that capture images about every 30 s. For PECAN at the time of the RHI discussed here, the images show fairly clear skies out to the lower part of the convective cell and gust front that support the classification of Bragg scatter (see supplemental photographs online; https://doi.org/10.1175/BAMS-D-17-0317.3). However, the region above the gust front (labeled in the RHI) is difficult to discern from the photographs, so clouds could be present there. Thus, it is very possible that there are small cloud droplets that have increased the reflectivity. Additionally, the velocity fields from the available RHIs (sampled at every 30° in azimuth) show wind shear (e.g., Fig. 5) that would produce turbulence, which corresponds to the Bragg scatter layer. Bragg scatter at the top of the boundary layer was seen frequently during PECAN. It is also well know that Bragg scatter occurs as “mantle echoes” on the outside edges of early convective cells (Knight and Miller 1993).

Fig. 5.

An RHI of the radial velocity corresponding to Fig. 2. Positive velocities are away from the radar, while negative velocities are toward the radar. Convergence is seen in the area of the positive and negative ZDR columns. Strong divergence is seen at 42-km range and 13–16-km height. Velocity folding is evident here.

Fig. 5.

An RHI of the radial velocity corresponding to Fig. 2. Positive velocities are away from the radar, while negative velocities are toward the radar. Convergence is seen in the area of the positive and negative ZDR columns. Strong divergence is seen at 42-km range and 13–16-km height. Velocity folding is evident here.

Region 5: Ice crystals.

Region 5 is characterized by low reflectivity (<5 dBZ), high ZDR (>3 dB), and high ρhv (>0.98) (where there is sufficient SNR), and below-0°C temperatures. It is inferred to be an ice cloud with horizontally oriented ice crystals with axis ratios significantly departing from unity such as ice plates. Since insects avoid freezing temperatures and they typically have reduced ρhv, they are very unlikely to be there. Note the variation in ZDR values as a function of elevation angle in this layer. At 22-km range (about 13° elevation angle), ZDR is 6–8 dB, and at 6-km range (about 40° elevation angle), ZDR is 2–3 dB. This is likely due to the ice crystal’s reduced H-to-V aspect ratio apparent to the radar at higher elevation angles. This agrees very well with ice crystal scattering studies for plate crystals with a mean canting angle of 0° and a standard deviation of 10° (Aydin and Tang 1997; Battaglia et al. 2001) and further supports the ice crystal classification of this region.

Region 6: Positive ZDR column.

Region 6 contains a very large positive ZDR column, indicating large oblate raindrops or oriented, wet ice particles, extending to 7.5 km MSL or about 3 km higher than the 0°C level as seen in Fig. 3. The highest ZDR is about 4 dB which corresponds to scattering from a 6mm rain drop as a point of reference (see Fig.ES2 in supplementary material). The ZDR columns are typically located in or on the fringe of the storm’s updraft region (Illingworth et al. 1987; Conway and Zrnić 1993; Kumjian et al. 2014).2 Positive ZDR columns are caused by large oblate supercooled raindrops or water-covered ice, which has been verified by aircraft penetration studies (Bringi et al. 1996, 1997; Brandes et al. 1995). A small reflectivity notch is seen in Fig. 2 at 40-km range at the 0°C level that corresponds to the storm inflow region supported by the radial velocity RHI of Fig. 5, which shows positive velocities into the ZDR column with a convergence signature on the back side of the ZDR column. At the top of the ZDR column, LDR is high (–17 to –20 dB; Fig. 6), while ρhv is low (0.92–0.96). This is the well-known “LDR cap” that marks a mixed-phase transition where raindrops freeze and there may be wet growth (Bringi et al. 1996; Hubbert et al. 1998; Jameson et al. 1996; Kennedy et al. 2001; Kumjian 2012). The size and strength of the ZDR column has been linked to hail growth and hail extent at the ground (Picca et al. 2010; Kumjian et al. 2014).

Fig. 6.

An RHI of the LDR corresponding to Fig. 2. LDR in pure rain is usually low (<–29 dB). Particles that are symmetric about the H and V axes have theoretical LDR of −∞. High LDR usually results in regions of large irregular and wet ice particles. Marked is a region very likely contaminated by multipath scattering. The diagonal orientation of this region suggests possible three-body scattering effects. The signal path would be from the radar to the high-reflectivity storm core to the ground, back to the storm core, and returning to the radar. A received signal from such a scattering path would be manifest in back of the storm core. Typically, three-body scattering has been examined in terms of the copolar signals, but here, we believe that the cross-polar signal is increased because of multiple scattering, thereby increasing LDR. This is more plausible than ascribing the high LDR values >−25 dB in this region to large hail and/or wet ice particles.

Fig. 6.

An RHI of the LDR corresponding to Fig. 2. LDR in pure rain is usually low (<–29 dB). Particles that are symmetric about the H and V axes have theoretical LDR of −∞. High LDR usually results in regions of large irregular and wet ice particles. Marked is a region very likely contaminated by multipath scattering. The diagonal orientation of this region suggests possible three-body scattering effects. The signal path would be from the radar to the high-reflectivity storm core to the ground, back to the storm core, and returning to the radar. A received signal from such a scattering path would be manifest in back of the storm core. Typically, three-body scattering has been examined in terms of the copolar signals, but here, we believe that the cross-polar signal is increased because of multiple scattering, thereby increasing LDR. This is more plausible than ascribing the high LDR values >−25 dB in this region to large hail and/or wet ice particles.

Regions 7 and 8: Negative ZDR column.

Regions 7 and 8 are likely wet graupel or hail. These regions have predominately negative ZDR with very high LDR <–22 dB, which indicates asymmetric particles with high dielectric constants (i.e., wet ice; Bringi and Chandrasekar 2001). The negative ZDR above the positive ZDR column have been observed before (Zrnić et al. 1993; Kumjian et al. 2014); however, the negative ZDR region in Fig. 3 extends to 12 km MSL, which is quite unusual. Reflectivities exceeding 60 dBZ around and above the negative ZDR column and the radial velocities indicating convergence in the storm core region and in back of the negative ZDR column suggest that continued hail growth is taking place and there is either large vertically oriented hail (Zrnić et al. 1993) or very large oblate hail with resonant scattering (Balakrishnan and Zrnić 1990; Aydin and Zhao 1990; Ryzhkov et al. 2013). Interestingly, another negative ZDR signature, region 7, extends to the front edge of the positive ZDR column from about 2 to 6 km MSL, coincident with very low reflectivities (–5 to 18 dBZ) and very high LDR (–25 to –15 dB), and this suggests that a very low concentration of vertically oriented wet graupel/hail or large oblate hail could be falling out of the storm. Conical or cone-shaped ice particles are known to orient themselves vertically so that negative ZDR values result (Aydin et al. 1984). The negative ZDR signatures are not unique to this RHI and are even more dramatic in other neighboring RHIs (see the supplemental Z and ZDR figures; https://doi.org/10.1175/BAMS-D-17-0317.4).

Positive KDP column.

Figures 7 and 8 show RHIs of copolar differential phase ϕDP and its range derivative (slope), specific differential phase KDP, respectively, that correspond to Fig. 2. Note the ϕDP color-scale increments of just 1° up to 10° and 2° steps thereafter. These small increments and the spatial smoothness of the plot are indicative of S-Pol’s low ϕDP measurement error.

Fig. 7.

Differential phase ϕDP corresponding to Fig. 2; the ϕDP starting offset is set at about 0° here. Note the marked regions of vertical ice crystals where ϕDP is decreasing in range.

Fig. 7.

Differential phase ϕDP corresponding to Fig. 2; the ϕDP starting offset is set at about 0° here. Note the marked regions of vertical ice crystals where ϕDP is decreasing in range.

Fig. 8.

Specific differential phase KDP corresponding to Fig. 2; KDP is the range slope of ϕDP. Here, an interesting positive KDP column is seen extending above the 0° isotherm, indicating the presence of liquid drops and/or aligned water-covered wet ice. Higher in the figure, negative KDP is seen, indicating ice crystals aligned vertically by an electric field.

Fig. 8.

Specific differential phase KDP corresponding to Fig. 2; KDP is the range slope of ϕDP. Here, an interesting positive KDP column is seen extending above the 0° isotherm, indicating the presence of liquid drops and/or aligned water-covered wet ice. Higher in the figure, negative KDP is seen, indicating ice crystals aligned vertically by an electric field.

There is a vertical column of positive KDP extending above the 0°C level at about 41-km range that is coincident with the positive ZDR column. The KDP columns (i.e., positive KDP values that are above the 0°C level) have been well studied and documented (Hubbert et al. 1998; Loney et al. 2002; Ryzhkov et al. 2005; Kumjian et al. 2010; van Lier-Walqui et al. 2016). Many times, there is a spatial offset between the positive ZDR and KDP columns (Kumjian and Ryzhkov 2008), but here, they are coincident with the KDP column extending to nearly the top of the ZDR column. At about 6.6 km MSL, the maximum KDP is 1.7° km–1 where the temperature is –20°C, ZDR is up to 3.4 dB, the reflectivities exceed 55 dBZ, LDR is greater than –22 dB, and ρhv is between 0.94 and 0.97. This indicates there are supercooled raindrops and/or oblate-oriented water-coated ice (hail) particles that cause the high ZDR, and their concentration is high enough to cause the positive KDP. This is likely a strong updraft region where hail is experiencing wet growth, which would cause the high LDR and low ρhv (Balakrishnan and Zrnić 1990; Kumjian et al. 2010; Picca and Ryzhkov 2012). When hail experiences wet growth, the hailstones can shed raindrops, and these drops could be in part the source of the high KDP (Rasmussen and Heymsfield 1987).

Automated classification.

Much of the above classification can and has been automated with fuzzy logic. The first fuzzy logic precipitation particle identification inference we know of was done by Straka and Zrnić (1993) and then by Vivekanandan et al. (1999). The fuzzy logic algorithm in Vivekanandan et al. (1999) essentially became NCAR’s PID algorithm, the results of which are shown next. Figure 9 shows the results of the PID corresponding to Fig. 2 with human-expert classification regions overlaid. The PID algorithm inputs are the radar variables (Z, ZDR, ρhv, KDP, and LDR), the local spatial standard deviation of both ZDR and ϕDP, and a vertical temperature profile. The temperature profile can come from data sources such as soundings or models. PID includes several nonhydrometeor types such as insects and ground clutter. The PID is designed to identify the most likely dominant scatterer in the radar volume. For example, large Z and near-zero or negative ZDR is a strong indicator of hail; however, if the hail is mixed with small ice or cloud drops, only the reflectivity-dominant hail will be detected. In the example RHI shown in Fig. 9, the PID yields a tenable classification and identifies features of the storm such as the large-hail region (yellow). Below the hail signal near the ground, melting hail (light green) is identified. Melting hail (light green) and melting graupel (dark green) can also be seen in the ZDR column region. The insect clear-air and ground-clutter returns are identified near the radar, close to the ground and separated from the ice and cloud/Bragg layers. The PID erroneously identifies the sidelobe contamination at the top of the storm echo as ice crystals. The PID may be in error if unusual or contaminated radar echoes occur, but it is quite useful for identifying the general structure of convection.

Fig. 9.

An RHI of the NCAR PID algorithm corresponding to Fig. 2. The labeled regions within the contours are human-expert classifications. By and large, the PID and human-expert classifications agree well. The PID did not recognize region 4 as insects.

Fig. 9.

An RHI of the NCAR PID algorithm corresponding to Fig. 2. The labeled regions within the contours are human-expert classifications. By and large, the PID and human-expert classifications agree well. The PID did not recognize region 4 as insects.

STORM ELECTRIFICATION REVEALED BY POLARIMETRIC VARIABLES.

The topic of charge separation and storm electrification is a continued area of research interest (Korolev et al. 2017; Stough et al. 2017). It is known that when larger ice crystals (>30 µm) fall in stagnant air that is not electrified, they fall with their major axis horizontal (Zikmunda and Vali 1972; Foster and Hallett 2002). This has been reported to be true even in turbulent cumulus clouds (Cho et al. 1981). This means that nonelectrified regions in the ice phase of clouds will be characterized by KDP ≥ 0. It is also known that electric fields in clouds can orient ice crystals along the lines of the electric field (Weinheimer and Few 1987; Foster and Hallett 2002; Saunders and Rimmer 1999). This has been verified by radar studies that have shown regions where KDP is negative, indicating vertically oriented ice crystals (Hendry and McCormick 1976; Krehbiel et al. 1996; Galloway et al. 1997; Ryzhkov and Zrnić 2007; Hubbert et al. 2014b). Caylor and Chandrasekar (1996) and Metcalf (1997) reported that after observed lightning discharges, the associated negative KDP disappeared, indicating the ice crystals returned to their natural horizontal orientation state.

Indication of vertically oriented ice crystals are seen both in ϕDP of Fig. 7 and in KDP in Fig. 8 at about 45-km range and 10 km MSL. The ϕDP clearly shows a decreasing trend along the radar radial beginning at about 45-km range and 8-km height with the color scale changing from gray to light green to light blue to dark blue and then to magenta. The accompanying negative KDP is labeled in Fig. 8. This again is very likely caused by ice crystals aligned toward the vertical by a strong electric field. We note that the observed differential phase shifts would be larger in magnitude for shorter-wavelength radars (e.g., C band and X band).

The ρco-x: An indicator of storm electrification.

A quite sensitive indicator of ice crystals that are canted away from the horizontal by an electric field is the co- to cross-channel correlation coefficient ρco-x, which is measured by FHV radars and not by SHV radars. This is a radar variable that has received little attention in the literature with a few notable exceptions (Ryzhkov et al. 2002; Hubbert et al. 2015; Reimann and Hagen 2014; Hubbert et al. 2014c). It is well known via modeling that for an ensemble of precipitation particles that possess symmetry about the vertical axis, the theoretical co- to cross covariances are zero (Bringi and Chandrasekar 2001; i.e., ρco-x = 0). Experimentally, ρco-x is small but does not go to zero for several reasons: 1) estimates are based on finite-length data, 2) cross coupling of H and V signals caused by antenna imperfections, and 3) orientation distributions are not symmetric about the vertical. From experience with S-Pol data, in regions outside of canted ice crystals, ρco-x is less than about 0.30 and has a high spatial variance. In regions of ice crystals aligned by a strong electric field, ρco-x is consistently above 0.3. For example, Fig. 10 shows ρco-x, corresponding to Figs. 28, with visible radial streaks of elevated ρco-x (>0.32) located between 8 and 15 km MSL and between 42- and 70-km range. These radar radial streaks are similar to the radial streaks shown in Ryzhkov and Zrnić (2007) for SHV ZDR caused by canted ice crystals.

Fig. 10.

An RHI of ρco-x corresponding to Fig. 2. Under only the influences of aerodynamics will ice crystals align with their major axis in the horizontal, and in such cases, ρco-x is theoretically zero. Practically, ρco-x ≤ 0.25 is indicative of this. When ρco-x > 0.4 over a region, this indicates a particle population with nonzero mean canting angle. Above about 7 km, there are three discernible radial steaks of elevated ρco-x that indicate ice crystals aligned because of an inferred electric field.

Fig. 10.

An RHI of ρco-x corresponding to Fig. 2. Under only the influences of aerodynamics will ice crystals align with their major axis in the horizontal, and in such cases, ρco-x is theoretically zero. Practically, ρco-x ≤ 0.25 is indicative of this. When ρco-x > 0.4 over a region, this indicates a particle population with nonzero mean canting angle. Above about 7 km, there are three discernible radial steaks of elevated ρco-x that indicate ice crystals aligned because of an inferred electric field.

Cross coupling occurs both at backscatter and for forward scatter. When the cross coupling is caused by forward scattering, the cross-coupled signals remain in the cross channel, thus creating radial streaks in ρco-x, LDR, and SHV ZDR, if sufficient signal is cross coupled. However, among the three, ρco-x is the most sensitive to scattering from canted ice crystals since it is a cross-channel correlation product rather than a power (the co- and cross-channel signals have uncorrelated noise). For example, in Fig. 10, the 0-dB SNR cross-polar power contour is shown by the larger dashed white line. Note the ability of ρco-x to detect cross coupling even when the cross-polar power is lower than 0-dB SNR (e.g., 65-km range and 11-km height). Also, it is where ρco-x is increasing along a radar radial that it is most indicative of the presence of oriented and canted ice crystals. The elevated ρco-x values below the melting level at closer ranges are a manifestation of cross coupling caused by the insects and residual ground clutter. The high values at the very top of the storm are caused by sidelobes.

Figure 11 shows a further example of S-Pol’s ability to detect ice crystals canted by an electric field. The data are from PECAN at 0135:30 UTC 26 June in a four-panel PPI plot: Z, ZDR, ϕDP, and ρco-x at 10° elevation angle. The melting level is about 4.5 km MSL, which translates to about 26-km range (range rings are labeled in the Z plot). Thus, nearly all the observed echoes are above the 0°C level. We infer that charged particles are likely being generated in the convective core regions and then are advected out into the anvil region. We see that there is evidence of this in the ρco-x as manifest by the red radial steaks, which are indicated by white contour lines that are also superimposed on the ϕDP plot. The ρco-x is maximized for ice crystals canted at 45° where KDP would be at a minimum (Hubbert et al. 2015). The ϕDP plot also shows evidence of ice crystals that are more horizontally oriented (in yellow and red colors) and more vertically oriented (in blue and magenta colors). Again, it is where ϕDP is increasing or decreasing in range that the anisotropic, aligned ice crystals are located. The ZDR plot shows values from about 0 dB (gray color) to slightly positive [0.2 to 0.4 dB (green color)] in the regions where ϕDP and ρco-x streaks exist. Modeling studies have shown that these regions can consist of two coexisting classes of particle types: 1) small anisotropic ice crystals that can be aligned by the electric field and give rise to the observed ϕDP and ρco-x signatures and 2) larger polarimetrically isotropic particles that produce ZDR values close to 0 dB (Hubbert et al. 2014a,b; Ryzhkov and Zrnić 2007). Maximum increase (decrease) of ϕDP in range occurs when the particles are aligned horizontally (vertically). Thus, where there are adjacent radials of increasing ϕDP and decreasing ϕDP with range, we can conclude that an electric field caused ice crystals to change orientation from more horizontal to more vertical across those radials. Also, in Hubbert et al. (2015), it is shown that ρco-x detected the presence of an electric field via canted ice particles before the first electrical discharge was detected by a Lightning Mapping Array (LMA; Rison et al. 1999). Thus, ρco-x may be a useful measurement for studying the early electrification of convective cells.

Fig. 11.

A PPI at 10° elevation angle of Z, ZDR, ϕDP, and ρco-x from PECAN data gathered at 0135:30 UTC 26 Jun 2015. The radial red streaks in ρco-x are evidence of electric fields that cant the ice crystals.

Fig. 11.

A PPI at 10° elevation angle of Z, ZDR, ϕDP, and ρco-x from PECAN data gathered at 0135:30 UTC 26 Jun 2015. The radial red streaks in ρco-x are evidence of electric fields that cant the ice crystals.

INSECTS.

During the warmer months (temperatures >10°C), insects typically fill the skies over almost all land areas of the world. The literature contains numerous articles of such observations (Mueller and Larken 1985; Wilson et al. 1994; Melnikov et al. 2015; Zrnić and Ryzhkov 1998; Chilson et al. 2012; Vivekanandan et al. 2013; Lang et al. 2004; Drake and Reynolds 2012). In a recent study in the United Kingdom, researchers estimate that over the southern United Kingdom for high-flying (>150 m) insects, “that about 3.5 trillion insects (3200 tons of biomass) migrate above the region annually” (Hu et al. 2016, p. 1582). This explains why the field of radar entomology is becoming an area of increased interest.

When migrating, the insects align themselves in a common direction; thus, in a 360° PPI scan, the reflectivity will maximize in the direction where the insects are oriented perpendicular to the radar beam and minimize in the direction where they are aligned parallel to the radar beam. This gives rise to the so-called dumbbell pattern (Mueller and Larken 1985; Drake and Reynolds 2012) as observed in Fig. 12. Likewise, ZDR will maximize and minimize similarly. When insects are not migrating, they will have no preferred orientation, and thus, the dumbbell pattern is absent. Figure 12 shows PPI plots of Z, ZDR, radial velocity, and ϕDP of S-Pol data from PECAN gathered at 1111:01 UTC 20 June 2015 at 1° elevation angle. Clear skies throughout the PECAN domain dominated on 20 June 2015, which provides an opportunity to examine the details of S-Pol’s clear-air return. Winds from a low-level jet are 25 m s–1 from the southwest. The Z and ZDR values range from about 0 to 20 dBZ and 0 to 15 dB, respectively. The southwest-to-northeast black line in Fig. 12 is drawn through the highest Z and ZDR values. The other orthogonal black line then is an estimate of the insect flight orientation, which is southeast to northwest. A white dashed line is located through the highest-magnitude Doppler velocity. As shown, the insect flight orientation is about 60° from the maximum wind velocity direction. The insect ϕDP signature shows significant differential phase shift at backscatter δ as indicated by dashed contours in the plot. The intrinsic ϕDP (i.e., starting value) of the radar is about –60° (light blue color scale). The large values of δ suggest backscatter from insects is well into the resonant scattering regime, and thus, the insects are likely 1 cm or larger in length. Also, the large positive and negative δ do not align with the maximum and Z and ZDR as might be expected. We believe this indicates that the insects likely fly with a pitch angle from the horizontal and the insects are not symmetric physically. These factors can cause the asymmetric δ signature; however, we offer no supporting modeling such as was done in Melnikov et al. (2015). Figure 13 shows a four-panel RHI located along the yellow line in Fig. 12. Two distinct layers of insects are shown centered at about 1 and 2.9 km MSL. The higher reflectivities and ZDR values for the insects at 1 km indicates they are larger and more elongated than those at 2.9 km. Note these low-level insects are located at the height of the maximum winds in the low-level jet, whereas the insects at 2.9 km are located at a height of minimum wind speeds. The distinct positive δ is easily observed in the lower insect layer, suggesting that a different population of insects are in each layer. Figures 12 and 13 demonstrate the power of polarimetric radar data to potentially identify insect types and characterize their behavior.

Fig. 12.

A PPI of insect echo at 1° elevation angle of Z, ZDR, velocity, and ϕDP from PECAN data gathered at 1111:01 UTC 20 Jun. The black arrow in the Z plot shows the estimated direction of insect flight based on the maximum Z and ZDR regions. The flight direction angle is about 60° from the wind direction (white dashed line). The white dashed ovals in the ϕDP plot indicate regions with interesting phase shift at backscatter δ.

Fig. 12.

A PPI of insect echo at 1° elevation angle of Z, ZDR, velocity, and ϕDP from PECAN data gathered at 1111:01 UTC 20 Jun. The black arrow in the Z plot shows the estimated direction of insect flight based on the maximum Z and ZDR regions. The flight direction angle is about 60° from the wind direction (white dashed line). The white dashed ovals in the ϕDP plot indicate regions with interesting phase shift at backscatter δ.

Fig. 13.

An RHI of insect echo at 240° azimuth angle (yellow line in Fig. 12) of Z, ZDR, velocity, and ϕDP from PECAN data gathered at 1111:01 UTC 20 Jun. Two distinct layers can be seen at about 1 and 3 km MSL. The plot of ϕDP of the lower insect level shows a phase shift at a backscatter δ of about 20°–40°.

Fig. 13.

An RHI of insect echo at 240° azimuth angle (yellow line in Fig. 12) of Z, ZDR, velocity, and ϕDP from PECAN data gathered at 1111:01 UTC 20 Jun. Two distinct layers can be seen at about 1 and 3 km MSL. The plot of ϕDP of the lower insect level shows a phase shift at a backscatter δ of about 20°–40°.

Supplemental time-lapse figures are provided of Z, ZDR, radial velocity, and ϕDP (which shows δ) so that the insect behavior at sunrise (1112:00 UTC) is observed (https://doi.org/10.1175/BAMS-D-17-0317.5).

SUMMARY.

We have shown two examples of S-Pol polarimetric data 1) from a very strong convective storm and 2) from insects in clear air with both datasets from the PECAN field campaign centered in Kansas. S-Pol’s availability to scan slowly, execute RHIs, and transmit fast alternating H and V polarizations contribute to the data quality and the detail of storm structure and help to demonstrate the power of polarimetric data. S-Pol is operated by NCAR for the National Science Foundation (NSF) and is available to the NSF scientific community as part of the NSF-supported Lower Atmosphere Observing Facilities (LOAF; please visit www.eol.ucar.edu/laof-guidebook).

ACKNOWLEDGMENTS

The authors would like to acknowledge the EOL RSF technical staff for their time, effort, and interest in the collection of the experimental data used in this paper. Drs. Hubbert, Ellis, and Dixon as well as Mr. Loew are supported in part by the Radar Operations Center of Norman, Oklahoma, via the Centuria Corporation, Award 20140711. The authors thank the three anonymous reviewers whose comments greatly improved the manuscript. The National Center for Atmospheric Research is sponsored by the National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.

REFERENCES

REFERENCES
Aydin
,
K.
, and
Y.
Zhao
,
1990
:
A computational study of polarimetric radar observables in hail
.
IEEE Trans. Geosci. Remote Sens.
,
28
,
412
421
, https://doi.org/10.1109/TGRS.1990.572906.
Aydin
,
K.
, and
C.
Tang
,
1997
:
Millimeter wave radar scattering from model ice crystal distributions
.
IEEE Trans. Geosci. Remote Sens.
,
35
,
140
164
, https://doi.org/10.1109/36.551942.
Aydin
,
K.
,
A.
Seliga
, and
V. N.
Bringi
,
1984
:
Differential radar scattering properties of model hail and mixed phase hydrometeors
.
Radio Sci
.,
19
,
58
66
, https://doi.org/10.1029/RS019i001p00058.
Balakrishnan
,
N.
, and
D. S.
Zrnić
,
1990
:
Use of polarization to characterize precipitation and discriminate large hail
.
J. Atmos. Sci.
,
47
,
1525
1540
, https://doi.org/10.1175/1520-0469(1990)047<1525:UOPTCP>2.0.CO;2.
Battaglia
,
A.
,
O.
Sturniolo
, and
F.
Prodi
,
2001
:
Analysis of polarization radar returns from ice clouds
.
Atmos. Res.
,
59–60
,
231
250
, https://doi.org/10.1016/S0169-8095(01)00118-1.
Brandes
,
E. A.
,
J.
Vivekanandan
,
J.
Tuttle
, and
C. J.
Kessinger
,
1995
:
A study of thunderstorm microphysics with multiparameter radar and aircraft observations
.
Mon. Wea. Rev.
,
123
,
3129
3143
, https://doi.org/10.1175/1520-0493(1995)123<3129:ASOTMW>2.0.CO;2.
Bringi
,
V. N.
, and
V.
Chandrasekar
,
2001
: Polarimetric Doppler Weather Radar. Cambridge University Press, 636 pp.
Bringi
,
V. N.
,
L.
Liu
,
P.
Kennedy
,
V.
Chandrasekar
, and
S.
Rutledge
,
1996
:
Dual multiparameter radar observations of intense convective storms: The 24 June 1992 case study
.
J. Meteor. Atmos. Phys.
,
59
,
3
31
, https://doi.org/10.1007/BF01031999.
Bringi
,
V. N.
,
K.
Knupp
,
A.
Detwiler
,
L.
Liu
,
I. J.
Caylor
, and
R. A.
Black
,
1997
:
Evolution of a Florida thunderstorm during the Convection and Precipitation/Electrification Experiment: The case study of 9 August 1991
.
Mon. Wea. Rev.
,
125
,
2131
2160
, https://doi.org/10.1175/1520-0493(1997)125<2131:EOAFTD>2.0.CO;2.
Caylor
,
I.
, and
V.
Chandrasekar
,
1996
:
Time-varying ice crystal orientation in thunderstorms observed with multiparameter radar
.
IEEE Trans. Geosci. Remote Sens.
,
34
,
847
858
, https://doi.org/10.1109/36.508402.
Chandrasekar
,
V.
,
R.
Keránen
,
S.
Lim
, and
D.
Moisseev
,
2013
:
Recent advances in classification of observations from dual polarization weather radars
.
Atmos. Res.
,
119
,
97
111
, https://doi.org/10.1016/j.atmosres.2011.08.014.
Chilson
,
P. B.
, and Coauthors
,
2012
:
Partly cloudy with a chance of migration: Weather, radars, and aeroecology
.
Bull. Amer. Meteor. Soc.
,
93
,
669
686
, https://doi.org/10.1175/BAMS-D-11-00099.1.
Cho
,
H. R.
,
J. V.
Iribarne
, and
W. G.
Richards
,
1981
:
On the orientation of ice crystals in cumulonimbus cloud
.
J. Atmos. Sci.
,
38
,
1111
1114
, https://doi.org/10.1175/1520-0469(1981)038<1111:OTOOIC>2.0.CO;2.
Conway
,
J. W.
, and
D. S.
Zrnić
,
1993
:
A study of embryo production and hail growth using dual-Doppler and multiparameter radars
.
Mon. Wea. Rev.
,
121
,
2511
2528
, https://doi.org/10.1175/1520-0493(1993)121<2511:ASOEPA>2.0.CO;2.
Doviak
,
R. J.
, and
D. S.
Zrnić
,
1993
: Doppler Radar and Weather Observations. 2nd ed. Academic Press, 562 pp.
Doviak
,
R. J.
,
V.
Bringi
,
A.
Ryzhkov
,
A.
Zahrai
, and
D.
Zrnić
,
2000
:
Polarimetric upgrades to operational WSR-88D radars
.
J. Atmos. Oceanic Technol.
,
17
,
257
278
, https://doi.org/10.1175/1520-0426(2000)017<0257:CFPUTO>2.0.CO;2.
Drake
,
V. A.
, and
D. R.
Reynolds
,
2012
: Radar Entomology: Observing Insect Flight and Migration. CAB International, 489 pp.
Earth Observing Laboratory
,
2015
:
S-band/Ka-band dual polarization, dual wavelength Doppler radar
.
NCAR–UCAR
, https://doi.org/10.5065/D6RV0KR8.
Fabry
,
F.
,
2015
: Radar Meteorology: Principles and Practice. Cambridge University Press, 256 pp., https://doi.org/10.1017/CBO9781107707405.
Foster
,
T.
, and
J.
Hallett
,
2002
:
The alignment of ice crystals in changing electric fields
.
Atmos. Res.
,
62
,
149
169
, https://doi.org/10.1016/S0169-8095(02)00008-X.
Galloway
,
A.
,
A.
Pazmany
,
J.
Mead
,
R.
MacIntosh
,
D.
Leon
,
R.
Kelly
, and
G.
Vali
,
1997
:
Detection of ice hydrometeor alignment using an airborne W-band polarimetric radar
.
J. Atmos. Oceanic Technol.
,
14
,
3
12
, https://doi.org/10.1175/1520-0426(1997)014<0003:DOIHAU>2.0.CO;2.
Geerts
,
B.
, and Coauthors
,
2016
:
The 2015 Plains Elevated Convection at Night (PECAN) field project
.
Bull. Amer. Meteor. Soc.
,
98
,
767
786
, https://doi.org/10.1175/BAMS-D-15-00257.1.
Gossard
,
E.
,
1977
:
Refractive index variance and its height distribution in different air masses
.
Radio Sci
.,
12
,
89
105
, https://doi.org/10.1029/RS012i001p00089.
Gossard
,
E.
,
1990
: Radar research on the atmospheric boundary layer. Radar in Meteorology, D. Atlas, Ed., Amer. Meteor. Soc., 477–527.
Hendry
,
A.
, and
G.
McCormick
,
1976
:
Radar observations of the alignment of precipitation particles by electrostatic fields in thunderstorms
.
J. Geophys. Res.
,
81
,
5353
5357
, https://doi.org/10.1029/JC081i030p05353.
Herzegh
,
P. H.
, and
A. R.
Jameson
,
1992
:
Observing precipitation through dual-polarization measurements
.
Bull. Amer. Meteor. Soc.
,
73
,
1365
1374
, https://doi.org/10.1175/1520-0477(1992)073<1365:OPTDPR>2.0.CO;2.
Hu
,
G.
,
K. S.
Lim
,
N.
Horvitz
,
S. J.
Clark
,
D. R.
Reynolds
,
N.
Sapir
, and
J. W.
Chapman
,
2016
:
Mass seasonal bioflows of high-flying insect migrants
.
Science
,
354
,
1584
1587
, https://doi.org/10.1126/science.aah4379.
Hubbert
,
J. C.
,
2017
:
Differential reflectivity calibration and antenna temperature
.
J. Atmos. Oceanic Technol.
,
34
,
1885
1906
, https://doi.org/10.1175/JTECH-D-16-0218.1.
Hubbert
,
J. C.
,
V. N.
Bringi
,
L.
Carey
, and
S.
Bolen
,
1998
:
CSU-CHILL polarimetric radar measurements in a severe hail storm in eastern Colorado
.
J. Appl. Meteor.
,
37
,
749
775
, https://doi.org/10.1175/1520-0450(1998)037<0749:CCPRMF>2.0.CO;2.
Hubbert
,
J. C.
,
V. N.
Bringi
, and
D.
Brunkow
,
2003
:
Studies of the polarimetric covariance matrix. Part I: Calibration methodology
.
J. Atmos. Oceanic Technol.
,
20
,
696
706
, https://doi.org/10.1175/1520-0426(2003)20<696:SOTPCM>2.0.CO;2.
Hubbert
,
J. C.
,
M.
Dixon
,
S.
Ellis
, and
G.
Meymaris
,
2009a
:
Weather radar ground clutter. Part I: Identification, modeling, and simulation
.
J. Atmos. Oceanic Technol.
,
26
,
1165
1180
, https://doi.org/10.1175/2009JTECHA1159.1.
Hubbert
,
J. C.
,
M.
Dixon
, and
S.
Ellis
,
2009b
:
Weather radar ground clutter. Part II: Real-time identification and filtering
.
J. Atmos. Oceanic Technol
.,
26
,
1181
1197
, https://doi.org/10.1175/2009JTECHA1160.1.
Hubbert
,
J. C.
,
S.
Ellis
,
M.
Dixon
, and
G.
Meymaris
,
2010a
:
Modeling, error analysis, and evaluation of dual-polarization variables obtained from simultaneous horizontal and vertical polarization transmit radar. Part I: Modeling and antenna errors
.
J. Atmos. Oceanic Technol.
,
27
,
1583
1598
, https://doi.org/10.1175/2010JTECHA1336.1.
Hubbert
,
J. C.
,
S.
Ellis
,
M.
Dixon
, and
G.
Meymaris
,
2010b
:
Modeling, error analysis, and evaluation of dual-polarization variables obtained from simultaneous horizontal and vertical polarization transmit radar. Part II: Experimental data
.
J. Atmos. Oceanic Technol.
,
27
,
1599
1607
, https://doi.org/10.1175/2010JTECHA1337.1.
Hubbert
,
J. C.
,
S.
Ellis
,
W.-Y.
Chang
,
M.
Dixon
, and
Y.-C.
Liou
,
2014a
:
X-band polarimetric observations of cross coupling in the ice phase of convective storms in Taiwan
.
J. Appl. Meteor. Climatol.
,
53
,
1678
1695
, https://doi.org/10.1175/JAMC-D-13-0360.1.
Hubbert
,
J. C.
,
S.
Ellis
,
W.-Y.
Chang
,
S.
Rutledge
, and
M.
Dixon
,
2014b
:
Modeling and interpretation of S-band ice crystal depolarization signatures from data obtained by simultaneously transmitting horizontally and vertically polarized fields
.
J. Appl. Meteor. Climatol.
,
53
,
1659
1677
, https://doi.org/10.1175/JAMC-D-13-0158.1.
Hubbert
,
J. C.
,
P.
Kennedy
,
M.
Dixon
,
W.-C.
Lee
,
S.
Rutledge
,
T.
Weckwerth
,
V.
Chandrasekar
, and
E.
Loew
,
2014c
: Meteorological applications of the Font Range Observational Network Testbed. Eighth European Conf. on Radar in Meteorology and Hydrology, Garmisch-Partenkirchen, Germany, DWD and German Aerospace Center, NET.P09, www.pa.op.dlr.de/erad2014/programme/ExtendedAbstracts/153_Hubbert.pdf.
Hubbert
,
J. C.
,
W.
Deierling
, and
P.
Kennedy
,
2015
: Detection of electrification with the co-to-cross correlation coefficient with storm microphysics analysis. 37th Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 63, https://ams.confex.com/ams/37RADAR/webprogram/Paper276111.html.
Illingworth
,
A. J.
,
W.
Goddard
, and
S.
Cherry
,
1987
:
Polarization radar studies of precipitation development in convective storms
.
Quart. J. Roy. Meteor. Soc.
,
113
,
469
489
, https://doi.org/10.1002/qj.49711347604.
Jameson
,
A. R.
,
M. J.
Murphy
, and
E.
Krider
,
1996
:
Multiple-parameter radar observations of isolated Florida thunderstorms during the onset of electrification
.
J. Appl. Meteor.
,
35
,
343
354
, https://doi.org/10.1175/1520-0450(1996)035<0343:MPROOI>2.0.CO;2.
Kennedy
,
P. C.
,
S. A.
Rutledge
,
W. A.
Petersen
, and
V. N.
Bringi
,
2001
:
Polarimetric radar observations of hail formation
.
J. Appl. Meteor.
,
40
,
1347
1366
, https://doi.org/10.1175/1520-0450(2001)040<1347:PROOHF>2.0.CO;2.
Knight
,
C. A.
, and
J.
Miller
,
1993
:
First radar echoes from cumulus clouds
.
Bull. Amer. Meteor. Soc.
,
74
,
179
188
, https://doi.org/10.1175/1520-0477(1993)074<0179:FREFCC>2.0.CO;2.
Korolev
,
A.
, and Coauthors
,
2017
: Mixed-phase clouds: Progress and challenges. Ice Formation and Evolution in Clouds and Precipitation: Measurement and Modeling Challenges, Meteor. Monogr., No. 58, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-17-0001.1.
Krehbiel
,
P.
,
T.
Chen
,
S.
McCray
,
W.
Wilson
,
G.
Gray
, and
M.
Brook
,
1996
:
The use of dual channel circular-polarization radar observations for remotely sensing storm electrification
.
J. Meteor. Atmos. Phys.
,
59
,
65
82
, https://doi.org/10.1007/BF01032001.
Kumjian
,
M. R.
,
2012
:
Freezing of raindrops in deep convective updrafts: A microphysical and polarimetric model
.
J. Atmos. Sci.
,
69
,
3471
3490
, https://doi.org/10.1175/JAS-D-12-067.1.
Kumjian
,
M. R.
,
2013a
:
Principles and applications of dual-polarization weather radar. Part I: Description of the polarimetric radar variables
.
J. Oper. Meteor.
,
1
,
226
242
, https://doi.org/10.15191/nwajom.2013.0119.
Kumjian
,
M. R.
,
2013b
:
Principles and applications of dual-polarization weather radar. Part II: Warm- and cold-season applications
.
J. Oper. Meteor.
,
1
,
243
264
, https://doi.org/10.15191/nwajom.2013.0120.
Kumjian
,
M. R.
,
2013c
:
Principles and applications of dual-polarization weather radar. Part III: Artifacts
.
J. Oper. Meteor.
,
21
,
265
274
, https://doi.org/10.15191/nwajom.2013.0121.
Kumjian
,
M. R.
, and
A. V.
Ryzhkov
,
2008
:
Polarimetric signatures in supercell thunderstorms
.
J. Appl. Meteor. Climatol.
,
47
,
1940
1961
, https://doi.org/10.1175/2007JAMC1874.1.
Kumjian
,
M. R.
,
A. V.
Ryzhkov
,
V. M.
Melnikov
, and
T. J.
Schuur
,
2010
:
Rapid-scan super-resolution observations of a cyclic supercell with a dual-polarization WSR-88D
.
Mon. Wea. Rev.
,
138
,
3762
3786
, https://doi.org/10.1175/2010MWR3322.1.
Kumjian
,
M. R.
,
A. P.
Kahn
,
N.
Benmoshe
,
E.
Ilotoviz
,
A. V.
Ryzhkov
, and
V. J.
Phillips
,
2014
:
The anatomy and physics of ZDR columns: Investigating a polarimetric radar signature with a spectral bin microphysical model
.
J. Appl. Meteor. Climatol.
,
53
,
1820
1843
, https://doi.org/10.1175/JAMC-D-13-0354.1.
Lang
,
T. J.
,
S. A.
Rutledge
, and
J. L.
Smith
,
2004
:
Observations of quasi-symmetric echo patterns in clear air with the CSU–CHILL polarimetric radar
.
J. Atmos. Oceanic Technol.
,
21
,
1182
1189
, https://doi.org/10.1175/1520-0426(2004)021<1182:OOQEPI>2.0.CO;2.
LeMone
,
M.
,
1973
:
The structure and dynamics of horizontal roll vortices in the planetary boundary layer
.
J. Atmos. Sci.
,
30
,
1077
1091
, https://doi.org/10.1175/1520-0469(1973)030<1077:TSADOH>2.0.CO;2.
Loney
,
M. L.
,
D. S.
Zrnić
,
J. M.
Straka
, and
A. V.
Ryzhkov
,
2002
:
Enhanced polarimetric radar signatures above the melting level in a supercell storm
.
J. Appl. Meteor.
,
41
,
1179
1194
, https://doi.org/10.1175/1520-0450(2002)041<1179:EPRSAT>2.0.CO;2.
Lutz
,
J.
,
P.
Johnson
,
B.
Lewis
,
E.
Loew
,
M.
Randall
, and
J. V.
Andel
,
1995
: NCAR S-Pol: Portable polarimetric S-band radar. Preprints, Ninth Symp. on Meteorological Observations and Instrumentation, Charlotte, NC, Amer. Meteor. Soc., 408–410.
Melnikov
,
V.
,
R.
Doviak
,
D.
Zrnić
, and
D.
Stensrud
,
2011
:
Mapping Bragg scatter with a polarimetric WSR-88D
.
J. Atmos. Oceanic Technol.
,
28
,
1273
1285
, https://doi.org/10.1175/JTECH-D-10-05048.1.
Melnikov
,
V.
,
R.
Doviak
,
D.
Zrnić
, and
D.
Stensrud
,
2013
:
Structures of Bragg scatter observed with the polarimetric WSR-88D
.
J. Atmos. Oceanic Technol.
,
30
,
1253
1258
, https://doi.org/10.1175/JTECH-D-12-00210.1.
Melnikov
,
V.
,
M. J.
Istok
, and
J. K.
Westbrook
,
2015
:
Asymmetric radar echo patterns from insects
.
J. Atmos. Oceanic Technol.
,
52
,
2276
2285
, https://doi.org/10.1175/JTECH-D-13-00247.1.
Metcalf
,
J. I.
,
1997
:
Temporal and spatial variations of hydrometeor orientations in thunderstorms
.
J. Appl. Meteor.
,
36
,
315
321
, https://doi.org/10.1175/1520-0450(1997)036<0315:TASVOH>2.0.CO;2.
Mueller
,
E. A.
, and
R. P.
Larken
,
1985
:
Insects observed using dual-polarization radar
.
J. Atmos. Oceanic Technol.
,
2
,
49
54
, https://doi.org/10.1175/1520-0426(1985)002<0049:IOUDPR>2.0.CO;2.
Picca
,
J.
, and
A.
Ryzhkov
,
2012
:
A dual-wavelength polarimetric analysis of the 16 May 2010 Oklahoma City extreme hailstorm
.
Mon. Wea. Rev.
,
140
,
1385
1403
, https://doi.org/10.1175/MWR-D-11-00112.1.
Picca
,
J.
,
M. J.
Kumjian
, and
A. V.
Ryzhkov
,
2010
: ZDR columns as a predictive tool for hail growth and storm evolution. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., 11.3, https://ams.confex.com/ams/25SLS/webprogram/Paper175750.html.
Rasmussen
,
R. M.
, and
A.
Heymsfield
,
1987
:
Melting and shedding of graupel and hail. Part I: Model physics
.
J. Atmos. Sci.
,
44
,
2754
2763
, https://doi.org/10.1175/1520-0469(1987)044<2754:MASOGA>2.0.CO;2.
Reimann
,
J.
, and
M.
Hagen
,
2014
: Detection of electric fields using full polarimetric C-band radar data. Eighth European Conf. on Radar in Meteorology and Hydrology, Garmisch-Partenkirchen, Germany, DWD and German Aerospace Center, 12b.4, www.pa.op.dlr.de/erad2014/programme/ShortAbstracts/229_short.pdf.
Rinehart
,
R.
,
2004
: Radar for Meteorologists. Rinehart Publications, 482 pp.
Rison
,
W.
,
R. J.
Thomas
,
P. R.
Krehbiel
,
T.
Hamlin
, and
J.
Harlin
,
1999
:
A GPS-based three-dimensional lightning mapping system: Initial observations in central New Mexico
.
Geophys. Res. Lett.
,
26
,
3573
3576
, https://doi.org/10.1029/1999GL010856.
Russell
,
K. R.
,
D. S.
Mizrahi
, and
S. A.
Gauthreaux
,
1998
:
Large-scale mapping of Purple Martin pre-migratory roosts using WSR-88D weather surveillance radar
.
J. Field Ornithol.
,
69
,
316
325
.
Ryzhkov
,
A.
, and
D.
Zrnić
,
2007
:
Depolarization in ice crystals and its effect on radar polarimetric measurements
.
J. Atmos. Oceanic Technol.
,
24
,
1256
1267
, https://doi.org/10.1175/JTECH2034.1.
Ryzhkov
,
A.
,
D.
Zrnić
,
J. C.
Hubbert
,
V. N.
Bringi
,
J.
Vivekanandan
, and
E. A.
Brandes
,
2002
:
Polarimetric radar observations and interpretation of co-cross-polar correlation coefficients
.
J. Atmos. Oceanic Technol.
,
19
,
340
354
, https://doi.org/10.1175/1520-0426-19.3.340.
Ryzhkov
,
A.
,
T. J.
Schuur
,
D. W.
Burgess
, and
D. S.
Zrnić
,
2005
:
Polarimetric tornado detection
.
J. Appl. Meteor.
,
44
,
557
570
, https://doi.org/10.1175/JAM2235.1.
Ryzhkov
,
A.
,
M. R.
Kumjian
,
S. M.
Ganson
, and
A. K.
Khan
,
2013
:
Polarimetric radar characteristics of melting hail. Part I: Theoretical simulations using spectral microphysical modeling
.
J. Appl. Meteor. Climatol.
,
52
,
2849
2870
, https://doi.org/10.1175/JAMC-D-13-073.1.
Salazar-Cerreno
,
J. L.
,
V.
Chandrasekar
,
J. M.
Trabal
,
P.
Siquera
,
R.
Medina
,
E.
Knapp
, and
D. J.
McLaughlin
,
2014
:
A drop size distribution (DSD)-based model for evaluating the performance of wet radomes for dual-polarized radars
.
J. Atmos. Oceanic Technol.
,
31
,
2409
2430
, https://doi.org/10.1175/JTECH-D-13-00208.1.
Saunders
,
C.
, and
J.
Rimmer
,
1999
:
The electric field alignment of ice particles in thunderstorms
.
J. Atmos. Res.
,
51
,
337
343
, https://doi.org/10.1016/S0169-8095(99)00018-6.
Seliga
,
T. A.
, and
V. N.
Bringi
,
1976
:
Potential use of radar differential reflectivity measurements at orthogonal polarizations for measuring precipitation
.
J. Appl. Meteor.
,
15
,
69
76
, https://doi.org/10.1175/1520-0450(1976)015<0069:PUORDR>2.0.CO;2.
Seliga
,
T. A.
,
V. N.
Bringi
, and
H.
Al-Khatib
,
1979
:
Differential reflectivity measurements in rain: First experiments
.
IEEE Trans. Geosci. Electron.
,
17
,
240
244
, https://doi.org/10.1109/TGE.1979.294652.
Seliga
,
T. A.
,
R. G.
Humphries
, and
J. I.
Metcalf
,
1990
: Polarization diversity in radar meteorology: Early developments. Radar in Meteorology, D. Atlas, Ed., Amer. Meteor. Soc., 109–114.
Siggia
,
A.
, and
R.
Passarelli
Jr.
,
2004
: Gaussian model adaptive processing (gmap) for improved ground clutter cancellation and moment calculation. Proc. Third European Conference on Radar in Meteorology and Hydrology, Visby, Sweden, Swedish Meteorological and Hydrological Institute, 67–73.
Stough
,
S. M.
,
L. D.
Carey
,
C. J.
Schultz
, and
P. M.
Bitzer
,
2017
:
Investigating the relationship between lightning and mesocyclonic rotation in supercell thunderstorms
.
Wea. Forecasting
,
32
,
2237
2259
, https://doi.org/10.1175/WAF-D-17-0025.1.
Straka
,
J.
, and
D.
Zrnić
,
1993
: An algorithm to deduce hydrometeor types and contents from multi-parameter radar data. Preprints, 26th Int. Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 513–515.
van Lier-Walqui
,
M.
, and Coauthors
,
2016
:
On polarimetric radar signatures of deep convection for model evaluation: Columns of specific differential phase observed during MC3E
.
Mon. Wea. Rev.
,
144
,
737
758
, https://doi.org/10.1175/MWR-D-15-0100.1.
Vivekanandan
,
J.
,
D.
Zrnić
,
S. M.
Ellis
,
R.
Oye
,
A.
Ryzhkov
, and
J.
Straka
,
1999
:
Cloud microphysics retrieval using S-band dual-polarization radar measurements
.
Bull. Amer. Meteor. Soc.
,
80
,
381
388
, https://doi.org/10.1175/1520-0477(1999)080<0381:CMRUSB>2.0.CO;2.
Vivekanandan
,
J.
,
J.
Hubbert
,
S.
Ellis
, and
J.
Wilson
,
2013
: Radar observations and radar model computations of biological scatterers. 36th Conf. on Radar Meteorology, Breckenridge, CO, Amer. Meteor. Soc., 13A.1, https://ams.confex.com/ams/36Radar/webprogram/Paper228799.html.
Wang
,
Y.
, and
V.
Chandrasekar
,
2006
:
Polarization isolation requirements for linear dual-polarization weather radar in simultaneous transmission mode of operation
.
IEEE Trans. Geosci. Remote Sens.
,
44
,
2019
2028
, https://doi.org/10.1109/TGRS.2006.872138.
Weckwerth
,
T. M.
,
J. W.
Wilson
,
R. M.
Wakimoto
, and
N. A.
Crook
,
1997
:
Horizontal convective rolls: Determining the environmental conditions supporting their existence and characteristics
.
Mon. Wea. Rev.
,
125
,
505
526
, https://doi.org/10.1175/1520-0493(1997)125<0505:HCRDTE>2.0.CO;2.
Weinheimer
,
A.
, and
A.
Few
,
1987
:
The electric field alignment of ice particles in thunderstorms
.
J. Geophys. Res.
,
92
, 142833–142844, https://doi.org/10.1029/JD092iD12p14833.
Wilson
,
J.
,
T.
Weckwerth
,
J.
Vivekanandan
,
R.
Wakimoto
, and
R.
Russell
,
1994
:
Boundary layer clear-air radar echoes: Origin of echoes and accuracy of derived winds
.
J. Atmos. Oceanic Technol.
,
11
,
1184
1206
, https://doi.org/10.1175/1520-0426(1994)011<1184:BLCARE>2.0.CO;2.
Zhang
,
G.
,
2017
: Weather Radar Polarimetry. CRC Press, 304 pp.
Zikmunda
,
J.
, and
G.
Vali
,
1972
:
Fall patterns and fall velocities of rimed ice particles
.
J. Atmos. Sci.
,
29
,
1334
1347
, https://doi.org/10.1175/1520-0469(1972)029<1334:FPAFVO>2.0.CO;2.
Zrnić
,
D. S.
, and
A. V.
Ryzhkov
,
1998
:
Observations of insects and birds with a polarimetric radar
.
IEEE Trans. Geosci. Remote Sens.
,
36
,
661
668
, https://doi.org/10.1109/36.662746.
Zrnić
,
D. S.
, and
A. V.
Ryzhkov
,
1999
:
Polarimetry for weather surveillance radars
.
Bull. Amer. Meteor. Soc.
,
80
,
389
406
, https://doi.org/10.1175/1520-0477(1999)080<0389:PFWSR>2.0.CO;2.
Zrnić
,
D. S.
,
V. N.
Bringi
,
N.
Balakrishnan
,
K.
Aydin
,
V.
Chandrasekar
, and
J. C.
Hubbert
,
1993
:
Polarimetric measurements in severe hailstorm
.
Mon. Wea. Rev.
,
121
,
2223
2238
, https://doi.org/10.1175/1520-0493(1993)121<2223:PMIASH>2.0.CO;2.

Footnotes

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

1

NEXRAD refers to the network of NWS radars, while WSR-88DP is the type of radar in the NEXRAD network, though they are used interchangeably.

2

For a detailed study of ZDR columns and more references, see Kumjian et al. (2014).