Search

Quasi-vertical profiles (QVPs) of polarimetric radar data have emerged as a powerful tool for studying precipitation microphysics. Various studies have found enhancements in specific differential phase Kdp in regions of suspected secondary ice production (SIP) due to rime splintering. Similar Kdp enhancements have also been found in regions of sublimating snow, another proposed SIP process. This work explores these Kdp signatures for two cases of sublimating snow using nearly collocated S- and Ka-band radars. The presence of the signature was inconsistent between the radars, prompting exploration of alternative causes. Idealized simulations are performed using a radar beam-broadening model to explore the impact of nonuniform beam filling (NBF) on the observed reflectivity Z and Kdp within the sublimation layer. Rather than an intrinsic increase in ice concentration, the observed Kdp enhancements can instead be explained by NBF in the presence of sharp vertical gradients of Z and Kdp within the sublimation zone, which results in a Kdp bias dipole. The severity of the bias is sensitive to the Z gradient and radar beamwidth and elevation angle, which explains its appearance at only one radar. In addition, differences in scanning strategies and range thresholds during QVP processing can constructively enhance these positive Kdp biases by excluding the negative portion of the dipole. These results highlight the need to consider NBF effects in regions not traditionally considered (e.g., in pure snow) due to the increased Kdp fidelity afforded by QVPs and the subsequent ramifications this has on the observability of sublimational SIP. Significance Statement Many different processes can cause snowflakes to break apart into numerous tiny pieces, including when they evaporate into dry air. Purported evidence of this phenomenon has been seen in data from some weather radars, but we noticed it was not seen in data from others. In this work we use case studies and models to show that this signature may actually be an artifact from the radar beam becoming too big and there being too much variability of the precipitation within it. While this breakup process may actually be occurring in reality, these results suggest we may have trouble observing it with typical weather radars.

This study evaluates ice particle size distribution and aspect ratio φ Multi-Radar Multi-Sensor (MRMS) dual-polarization radar retrievals through a direct comparison with two legs of observational aircraft data obtained during a winter storm case from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. In situ cloud probes, satellite, and MRMS observations illustrate that the often-observed Kdp and ZDR enhancement regions in the dendritic growth layer can either indicate a local number concentration increase of dry ice particles or the presence of ice particles mixed with a significant number of supercooled liquid droplets. Relative to in situ measurements, MRMS retrievals on average underestimated mean volume diameters by 50% and overestimated number concentrations by over 100%. IWC retrievals using ZDR and Kdp within the dendritic growth layer were minimally biased relative to in situ calculations where retrievals yielded −2% median relative error for the entire aircraft leg. Incorporating φ retrievals decreased both the magnitude and spread of polarimetric retrievals below the dendritic growth layer. While φ radar retrievals suggest that observed dendritic growth layer particles were nonspherical (0.1 ≤ φ ≤ 0.2), in situ projected aspect ratios, idealized numerical simulations, and habit classifications from cloud probe images suggest that the population mean φ was generally much higher. Coordinated aircraft radar reflectivity with in situ observations suggests that the MRMS systematically underestimated reflectivity and could not resolve local peaks in mean volume diameter sizes. These results highlight the need to consider particle assumptions and radar limitations when performing retrievals. significance statement Developing snow is often detectable using weather radars. Meteorologists combine these radar measurements with mathematical equations to study how snow forms in order to determine how much snow will fall. This study evaluates current methods for estimating the total number and mass, sizes, and shapes of snowflakes from radar using images of individual snowflakes taken during two aircraft legs. Radar estimates of snowflake properties were most consistent with aircraft data inside regions with prominent radar signatures. However, radar estimates of snowflake shapes were not consistent with observed shapes estimated from the snowflake images. Although additional research is needed, these results bolster understanding of snow-growth physics and uncertainties between radar measurements and snow production that can improve future snowfall forecasting.

Radar dual-wavelength ratio (DWR) measurements from the Stony Brook Radar Observatory Ka-band scanning polarimetric radar (KASPR, 35 GHz), a W-band profiling radar (94 GHz), and a next-generation K-band (24 GHz) micro rain radar (MRRPro) were exploited for ice particle identification using triple-frequency approaches. The results indicated that two of the radar frequencies (K and Ka band) are not sufficiently separated; thus, the triple-frequency radar approaches had limited success. On the other hand, a joint analysis of DWR, mean Doppler velocity (MDV), and polarimetric radar variables indicated potential in identifying ice particle types and distinguishing among different ice growth processes and even in revealing additional microphysical details. We investigated all DWR pairs in conjunction with MDV from the KASPR profiling measurements and differential reflectivity (ZDR) and specific differential phase (KDP) from the KASPR quasi-vertical profiles. The DWR-versus-MDV diagrams coupled with the polarimetric observables exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. In fallstreaks, the 35–94 GHz DWR pair increased with the magnitude of MDV corresponding to the scattering calculations for aggregates with lower degrees of riming. The DWR values further increased at lower altitudes while ZDR slightly decreased, indicating further aggregation. Particle populations with higher rime degrees had a similar increase in DWR but a 1–1.5 m s−1 larger magnitude of MDV and rapid decreases in KDP and ZDR. The analysis also depicted the early stage of riming where ZDR increased with the MDV magnitude collocated with small increases in DWR. This approach will improve quantitative estimations of snow amount and microphysical quantities such as rime mass fraction. The study suggests that triple-frequency measurements are not always necessary for in-depth ice microphysical studies and that dual-frequency polarimetric and Doppler measurements can successfully be used to gain insights into ice hydrometeor microphysics.

The intrinsic uncertainty of radar-based retrievals in snow originates from a large diversity of snow growth habits, densities, and particle size distributions, all of which can make interpreting radar measurements of snow very challenging. The application of polarimetric radar for snow measurements can mitigate some of these issues. In this study, a novel polarimetric method for quantification of the extinction coefficient and visibility in snow, based on the joint use of radar reflectivity at horizontal polarization Z and specific differential phase KDP, is introduced. A large 2D-video-disdrometer snow dataset from central Oklahoma is used to derive a polarimetric bivariate power-law relation for the extinction coefficient, . The relation is derived for particle aspect ratios ranging from 0.5 to 0.8 and the width of the canting angle distribution ranging from 0° to 40°, values typical of aggregated snow, and validated via theoretical and analytical derivations/simulations. The multiplier of the relation is sensitive to variations in particles’ densities, the width of the canting angle distribution, and particles’ aspect ratios, whereas the relation’s exponents are practically invariant to changes in the latter two parameters. This novel approach is applied to polarimetric S-band WSR-88D data and verified against previous studies and in situ measurements of the extinction coefficient for four snow events in the eastern United States. The polarimetric radar estimates of the extinction coefficient exhibit smaller biases in comparison to previous studies concerning the ground measurements. The results indicate that there is good potential for reliable radar estimates of visibility from polarimetric weather radars, a parameter inversely proportional to the extinction coefficient.

Cloud microphysics significantly impact tropical cyclone precipitation. A prior polarimetric radar observational study by the authors revealed the ice-phase microphysical processes as the dominant microphysics mechanisms responsible for the heavy precipitation in the outer rainband of Typhoon Nida (2016). To assess the model performance regarding microphysics, three double-moment microphysics schemes (i.e., Thompson, Morrison, and WDM6) are evaluated by performing a set of simulations of the same case. While these simulations capture the outer rainband’s general structure, microphysics in the outer rainbands are strikingly different from the observations. This discrepancy is primarily attributed to different microphysics parameterizations in these schemes, rather than the differences in large-scale environments due to cloud–environment interactions. An interesting finding in this study is that the surface rain rate or liquid water content is inversely proportional to the simulated mean raindrop sizes. The mass-weighted raindrop diameters are overestimated in the Morrison and Thompson schemes and underestimated in the WDM6 scheme, while the former two schemes produce lower liquid water content than WDM6. Compared with the observed ice water content based on a new polarimetric radar retrieval method, the ice water content above the environmental 0°C level in all simulations is highly underestimated, especially at heights above 12 km MSL where large concentrations of small ice particles are typically prevalent. This finding suggests that the improper treatment of ice-phase processes is potentially an important error source in these microphysics schemes. Another error source identified in the WDM6 scheme is overactive warm-rain processes that produce excessive concentrations of smaller raindrops.

Polarimetric radar observations from the NEXRAD WSR-88D operational radar network in the contiguous United States, routinely available since 2013, are used to reveal three prominent microphysical signatures in landfalling tropical cyclones: 1) hydrometeor size sorting within the eyewall convection, 2) vertical displacement of the melting layer within the inner core, and 3) dendritic growth layers within stratiform regions of the inner core. Size sorting signatures within eyewall convection are observed with greater frequency and prominence in more intense hurricanes, and are observed predominantly within the deep-layer environmental wind shear vector-relative quadrants that harbor the greatest frequency of deep convection (i.e., downshear and left-of-shear). Melting-layer displacements are shown that exceed 1 km in altitude compared to melting-layer altitudes in outer rainbands and are complemented by analyses of archived dropsonde data. Dendritic growth and attendant snow aggregation signatures in the inner core are found to occur more often when echo-top altitudes are low (≤10 km MSL), nearer the −15°C isotherm commonly associated with dendritic growth. These signatures, uniquely observed by polarimetric radar, provide greater insight into the physical structure and thermodynamic characteristics of tropical cyclones, which are important for improving rainfall estimation and the representation of tropical cyclones in numerical models.

Differences in atmospheric environments can have a significant impact on microphysical processes of precipitation. Dominant warm (cold) rain processes in East Asia (southern Great Plains of the United States) are implied by a large (small or constant) gradient of reflectivity at low levels in vertical reflectivity profiles. Long-term ground observations using two-dimensional video disdrometers were conducted in the southern Korean Peninsula (KOR) and Norman, Oklahoma, United States (OKL). Raindrop size distributions (RSD) and their moments in the two regions were analyzed in the framework of scaling normalized RSDs. Results show that the concentrations of small (big) raindrops were higher (smaller) in KOR than in OKL. KOR RSDs were also found to be characterized by relatively high characteristic number concentrations and small characteristic diameters when compared to OKL RSDs. The increases with increasing in both KOR and OKL at lower Z with the opposite trend at higher Z. In addition, OKL RSDs with indicate the existence of an equilibrium between coalescence and breakup processes. Rainfall estimation relationships between the rain rate R and radar variables were retrieved from scattering simulations at S-, C-, and X-band wavelengths. KOR RSDs showed relatively small horizontal reflectivity and specific differential phase shift at the same R and same wavelength when compared to OKL RSDs. The regional dependency was significant due to the different microphysical process in KOR and OKL. The specific attenuation of KOR was, however, similar to that of OKL only at S band, indicating an advantage of using specific attenuation in S band in rainfall estimation.

A new dual-polarization (DP) radar synthetic quantitative precipitation estimation (QPE) product was developed using a combination of specific attenuation A, specific differential phase KDP, and reflectivity Z to calculate the precipitation rate R. Specific attenuation has advantages of being insensitive to systematic biases in Z and differential reflectivity ZDR due to partial beam blockage, attenuation, and calibration while more linearly related to R than other radar variables. However, the R(A) relationship is not applicable in areas containing ice. Therefore, the new DP QPE applies R(A) in areas where radar is observing pure rain, R(KDP) in regions potentially containing hail, and R(Z) elsewhere. Further, an evaporation correction was applied to minimize false light precipitation related to virga. The new DP QPE was evaluated in real time over the conterminous United States and showed significant improvements over previous radar QPE techniques that were based solely on R(Z) relationships. The improvements included reduced dry biases in heavy to extreme precipitation during the warm season. The new DP QPE also reduced errors and spatial discontinuities in regions impacted by partial beam blockage. Further, the new DP QPE reduced wet bias for scattered light precipitation in the southwest and north central United States where there is significant boundary layer evaporation.

A novel way to process polarimetric radar data collected via plan position indicator (PPI) scans and display those data in a time–height format is introduced. The columnar vertical profile (CVP) methodology uses radar data collected via multiple elevation scans, limited to data within a set region in range and azimuth relative to the radar, to create vertical profiles of polarimetric radar data representative of that limited region in space. This technique is compared to others existing in the literature, and various applications are discussed. Polarimetric ice microphysical retrievals are performed on CVPs created within the stratiform rain region of two mesoscale convective systems sampled during two field campaigns, where CVPs follow the track of research aircraft. Aircraft in situ data are collocated to microphysical retrieval data, and the accuracy of these retrievals is tested against other retrieval techniques in the literature.

A polarimetric radar–based method for retrieving atmospheric ice particle shapes is applied to snowfall measurements by a scanning Ka-band radar deployed at Oliktok Point, Alaska (70.495°N, 149.883°W). The mean aspect ratio, which is defined by the hydrometeor minor-to-major dimension ratio for a spheroidal particle model, is retrieved as a particle shape parameter. The radar variables used for aspect ratio profile retrievals include reflectivity, differential reflectivity, and the copolar correlation coefficient. The retrievals indicate that hydrometeors with mean aspect ratios below 0.2–0.3 are usually present in regions with air temperatures warmer than approximately from −17° to −15°C, corresponding to a regime that has been shown to be favorable for growth of pristine ice crystals of planar habits. Radar reflectivities corresponding to the lowest mean aspect ratios are generally between −10 and 10 dBZ. For colder temperatures, mean aspect ratios are typically in a range between 0.3 and 0.8. There is a tendency for hydrometeor aspect ratios to increase as particles transition from altitudes in the temperature range from −17° to −15°C toward the ground. This increase is believed to result from aggregation and riming processes that cause particles to become more spherical and is associated with areas demonstrating differential reflectivity decreases with increasing reflectivity. Aspect ratio retrievals at the lowest altitudes are consistent with in situ measurements obtained using a surface-based multiangle snowflake camera. Pronounced gradients in particle aspect ratio profiles are observed at altitudes at which there is a change in the dominant hydrometeor species, as inferred by spectral measurements from a vertically pointing Doppler radar.

This study demonstrates an implementation of the prototype quantitative precipitation R estimation algorithm using specific attenuation A for S-band polarimetric radar. The performance of R(A) algorithm is assessed, compared to the conventional algorithm using radar reflectivity Z, at multiple temporal scales. Because the factor α, defined as the net ratio of A to specific differential phase, is a key parameter of the algorithm characterized by drop size distributions (e.g., differential reflectivity Zdr dependence on Z), the estimation equations of α and a proper number of Zdr–Z samples required for a reliable α estimation are examined. Based on the dynamic estimation of α, the event-based evaluation using hourly rain gauge observations reveals that the performance of R(A) is superior to that of R(Z), with better agreement and lower variability. Despite its superiority, the study finds that R(A) leads to quite consistent overestimations of about 10%–30%. It is demonstrated that the application of uniform α over the entire radar domain yields the observed uncertainty because of the heterogeneity of precipitation in the domain. A climatological range-dependent feature of R(A) and R(Z) is inspected in the multiyear evaluation at yearly scale using rain totals for April–October. While R(Z) exposes a systematic shift and overestimation, each of which arise from the radar miscalibration and bright band effects, R(A) combining with multiple R(Z) values for solid/mixed precipitation shows relatively robust performance without those effects. The immunity of R(A) to partial beam blockage (PBB) based on both qualitative and quantitative analyses is also verified. However, the capability of R(A) regarding PBB is limited by the presence of the melting layer and its application requirement for the total span of differential phase (e.g., 3°), which is another challenge for light rain.

This study analyzes the microphysics and precipitation pattern of Hurricanes Harvey (2017) and Florence (2018) in both the eyewall and outer rainband regions. From the retrievals by a satellite red–green–blue scheme, the outer rainbands show a strong convective structure while the inner eyewall has less convective vigor (i.e., weaker upper-level reflectivities and electrification), which may be related to stronger vertical wind shear that hinders fast vertical motions. The WSR-88D column-vertical profiles further confirm that the outer rainband clouds have strong vertical motion and large ice-phase hydrometeor formation aloft, which correlates well with 3D Lightning Mapping Array source counts in height and time. From the results from this study, it is determined that the inner eyewall region is dominated by warm rain, whereas the external rainband region contains intense mixed-phase precipitation. External rainbands are defined here as those that reside outside of the main hurricane circulation, associated with surface tropical storm wind speeds. The synergy of satellite and radar dual-polarization parameters is instrumental in distinguishing between the key microphysical features of intense convective rainbands and the warm-rain-dominated eyewall regions within the hurricanes. Substantial amounts of ice aloft and intense updrafts in the external rainbands are indicative of heavy surface precipitation, which can have important implications for severe weather warnings and quantitative precipitation forecasts. The novel part of this study is to combine ground-based radar measurement with satellite observations to study hurricane microphysical structure from surface to cloud top so as to fill in the gaps between the two observational techniques.

In a 2018 paper by Bukovčić et al., polarimetric bivariate power-law relations for estimating snowfall rate S and ice water content (IWC), and , were developed utilizing 2D video disdrometer snow measurements in Oklahoma. Herein, these disdrometer-based relations are generalized for the range of particle aspect ratios from 0.5 to 0.8 and the width of the canting angle distribution from 0° to 40° and are validated via analytical/theoretical derivations and simulations. In addition, a novel S(KDP, Zdr) polarimetric relation utilizing the ratio between specific differential phase KDP and differential reflectivity Zdr, , is derived. Both KDP and are proportionally affected by the ice particles’ aspect ratio and width of the canting angle distribution; therefore, the variables’ ratio tends to be almost invariant to the changes in these parameters. The S(KDP, Z) and S(KDP, Zdr) relations are applied to the polarimetric S-band WSR-88D data obtained from three geographical locations in Virginia, Oklahoma, and Colorado, and their performance is compared with estimations from the standard S(Z) relations and ground snow measurements. The polarimetric estimates of snow accumulations from the three cases exhibit smaller bias in comparison with the S(Z), indicating good potential for more reliable radar snow measurements.

Time series of quasi–vertical profiles (QVPs) from 52 stratiform precipitation events observed with the polarimetric X-band radar in Bonn, Germany (BoXPol), between 2013 and 2016 have been statistically analyzed to infer microphysical processes shaping the dendritic-growth-layer (DGL) and melting-layer (ML) signatures including surface rainfall. Specific differential phase KDP in the ML shows an average correlation of 0.65 with surface rainfall for these cases. Radar reflectivity decreases below the ML by about 2 dB on average while differential reflectivity ZDR is hardly affected, which suggests rain evaporation as the dominating effect. Estimated ice water content or snow water equivalent precipitation rate S in the DGL is correlated with surface rain rates with lead times of 30 min and longer, which opens a pathway for radar-based nowcasting of stratiform precipitation tendencies. Trajectories of snow generated aloft down to the surface are constructed from wind profiles derived both from the nearest radiosounding and radar-based velocity azimuth displays (VAD) to narrow down the location at which the DGL-generated snow reaches the surface as rain. The lagged correlation analysis between KDP in the DGL and reflectivity ZH at that location demonstrates the superiority of the VAD information. Correlation coefficients up to 0.80 with lead times up to 120 min provide a proof of concept for future nowcasting applications that are based on DGL monitoring. Statistical relations found in our QVP dataset provide a database for estimating intrinsic polarimetric variables from the usual azimuth and elevation scans within and in the vicinity of the ML.

Polarimetric radars make it possible to retrieve information on hydrometeors types, sizes and concentrations. Additional information on cloud electrification can be obtained from Lightning Mapping Arrays (LMAs). To study the development time and height of the hydrometeors and electrification require tracking their evolution within the lifecycle of convective cells. A new methodology for multi-cell identification and tracking (MCIT) is presented in this study. The algorithm in this study is different from traditional tracking methods; this new algorithm is applied to time series of radar volume scans. It tracks local maxima of vertically integrated liquid (VIL) water by identifying the two cells in consecutive radar scans that have maximum common VIL. The vertical profile of the polarimetric variables is used for constructing the time-height cross section of the cells' microphysical properties around the peak reflectivity as a function of height. The LMA sources that occur within the cell area are integrated as a function of height as well for each time step, as determined by the radar volume scans. The result of the tracking can provide insights on the evolution of storms, hydrometer types, precipitation initiation and cloud electrification under different thermodynamic and aerosol conditions. The details of the MCIT algorithm, its products and their performance for different type of storms are described in this paper.

Diabatic cooling from hydrometeor phase changes in the stratiform melting layer is of great interest to both operational forecasters and modelers for its societal and dynamical consequences. Attempts to estimate the melting-layer cooling rate typically rely on either the budgeting of hydrometeor content estimated from reflectivity Z or model-generated lookup tables scaled by the magnitude of Z in the bright band. Recent advances have been made in developing methods to observe the unique polarimetric characteristics of melting snow and the additional microphysical information they may contain. However, to date no work has looked at the thermodynamic information available from the polarimetric radar brightband signature. In this study, a one-dimensional spectral bin model of melting snow and a coupled polarimetric operator are used to study the relation between the polarimetric radar bright band and the melting-layer cooling rate. Simulations using a fixed particle size distribution (PSD) and variable environmental conditions show that the height and thickness of the bright band and the maximum brightband Z and specific differential phase shift are all sensitive to the ambient environment, while the differential reflectivity is relatively insensitive. Additional simulations of 2700 PSDs based on in situ observations above the melting layer indicate that the maximum Z, , and within the melting layer are poorly correlated with the maximum cooling rate while is strongly correlated. Finally, model simulations suggest that, in addition to riming, concurrent changes in aggregation and precipitation intensity and the associated cooling may plausibly cause observed sagging brightband signatures.

A prototype quantitative precipitation estimate (QPE) algorithm that utilizes specific attenuation A and specific differential phase KDP was developed for inclusion into the Multi-Radar Multi-Sensor (MRMS) system and the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. Special attention is given to the optimization of the factor α used for computation of a path-integrated attenuation from a total span of differential phase along the propagation path in rain. It is suggested to estimate α from a slope of the ZDR dependence on Z in rain. The use of real-time adjusted α allows us to capture the variations of the drop size distributions, and therefore improve the QPE accuracy. It is demonstrated that the factor α is generally higher for tropical rain type compared to continental rain. Since the R(A) approach is only valid for pure rainfall, the R(KDP) relation is suggested as a complement in areas of hail contamination. The paper contains a description of the basic version of the R(A) and R(KDP) algorithm and recommendations for its further optimization.

The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

After decades of research and development, the WSR-88D (NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data (PRD) that have the potential to improve weather observations, quantification, forecasting, and warnings. The weather radar networks in China and other countries are also being upgraded with dual-polarization capability. Now, with radar polarimetry technology having matured, and PRD available both nationally and globally, it is important to understand the current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction (NWP) models. In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation and forecasting based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation.

Spectral polarimetry has the potential to be used to study microphysical properties in relation to the dynamics within a radar resolution volume by combining Doppler and polarimetric measurements. The past studies of spectral polarimetry have focused on using radar measurements from higher elevation angles, where both the size sorting from the hydrometeors’ terminal velocities and polarimetric characteristics are maintained. In this work, spectral polarimetry is applied to data from the 0° elevation angle, where polarimetric properties are maximized. Radar data collected by the C-band University of Oklahoma Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME) during a hailstorm event on 24 April 2011 are used in the analysis. The slope of the spectral differential reflectivity exhibits interesting variations across the hail core, which suggests the presence of size sorting of hydrometeors caused by vertical shear in a turbulent environment. A nearby S-band polarimetric Weather Surveillance Radar-1988 Doppler (KOUN) is also used to provide insights into this hailstorm. Moreover, a flexible numerical simulation is developed for this study, in which different types of hydrometeors such as rain and melting hail can be considered individually or as a combination under different sheared and turbulent conditions. The impacts of particle size distribution, shear, turbulence, attenuation, and mixture of rain and melting hail on polarimetric spectral signatures are investigated with the simulated Doppler spectra and spectral differential reflectivity.

Radar dual wavelength ratio (DWR) measurements from the Stony Brook Radar Observatory Ka-band Scanning Polarimetric Radar (KASPR, 35 GHz), a profiling W-band (94 GHz) and a next generation K-band (24-GHz) Micro Rain Radar (MRRPro) were exploited for ice particle identification using triple frequency approaches. The results indicated that two of the radar frequencies (K- and Ka-band) are not sufficiently separated, thus, the triple radar frequency approaches had limited success. On the other hand, a joint analysis of DWR, mean vertical Doppler velocity (MDV), and polarimetric radar variables indicated potential in identifying ice particle types and distinguishing among different ice growth processes and even in revealing additional microphysical details.We investigated all DWR pairs in conjunction with MDV from the KASPR profiling measurements and differential reflectivity (ZDR) and specific differential phase (KDP) from the KASPR quasi-vertical profiles. The DWR-versus-MDV diagrams coupled with the polarimetric observables exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. In fallstreaks, the 35–94 GHz DWR pair increased with the magnitude of MDV corresponding to the scattering calculations for aggregates with lower degrees of riming. The DWR values further increased at lower altitudes while ZDR slightly decreased, indicating further aggregation. Particle populations with higher rime degrees had a similar increase of DWR, but the 1–1.5 m s−1 larger magnitude of MDV and rapid decreases in KDP and ZDR. The analysis also depicted the early stage of riming where ZDR increased with the MDV magnitude collocated with small increases of DWR. This approach will improve quantitative estimations of snow amount and microphysical quantities such as rime mass fraction.

The utilization of polarimetric weather radars for optimizing cloud models is a next frontier of research. It is widely understood that inadequacies in microphysical parameterization schemes in numerical weather prediction (NWP) models is a primary cause of forecast uncertainties. Due to its ability to distinguish between hydrometeors with different microphysical habits and to identify “polarimetric fingerprints” of various microphysical processes, polarimetric radar emerges as a primary source of needed information. There are two approaches to leverage this information for NWP models: (1) radar microphysical and thermodynamic retrievals and (2) forward radar operators for converting the model outputs into the fields of polarimetric radar variables. In this paper, we will provide an overview of both. Polarimetric measurements can be combined with cloud models of varying complexity, including ones with bulk and spectral bin microphysics, as well as simplified Lagrangian models focused on a particular microphysical process. Combining polarimetric measurements with cloud modeling can reveal the impact of important microphysical agents such as aerosols or supercooled cloud water invisible to the radar on cloud and precipitation formation. Some pertinent results obtained from models with spectral bin microphysics, including the Hebrew University cloud model (HUCM) and 1D models of melting hail and snow coupled with the NSSL forward radar operator, are illustrated in the paper.

Mechanisms of formation of differential reflectivity columns are investigated in simulations of a midlatitude summertime hailstorm with hailstones up to several centimeters in diameter. Simulations are performed using a new version of the Hebrew University Cloud Model (HUCM) with spectral bin microphysics. A polarimetric radar forward operator is used to calculate radar reflectivity and differential reflectivity ZDR. It is shown that ZDR columns are associated with raindrops and with hail particles growing in a wet growth regime within convective updrafts. The height and volume of ZDR columns increases with an increase in aerosol concentration. Small hail forming under clean conditions grows in updrafts largely in a dry growth regime corresponding to low ZDR. Characteristics of ZDR columns are highly correlated with vertical velocity, hail size, and aerosol concentration.

This study implements a new quasi-vertical profile (QVP) methodology to investigate the microphysical evolution and significance of intriguing winter polarimetric signatures and their statistical correlations. QVPs of transitional stratiform and pure snow precipitation are analyzed using WSR-88D S-band data, alongside their corresponding environmental thermodynamic High-Resolution Rapid Refresh model analyses. QVPs of KDP and ZDR are implemented to demonstrate their value in interpreting elevated ice processes. Several fascinating and repetitive signatures are observed in the QVPs for differential reflectivity ZDR and specific differential phase KDP, in the dendritic growth layer (DGL), and at the tops of clouds. The most striking feature is maximum ZDR (up to 6 dB) in the DGL occurring near the −10-dBZ ZH contour within low KDP and during shallower and warmer cloud tops. Conversely, maximum KDP (up to 0.3° km−1) in the DGL occurs within low ZDR and during taller and colder cloud tops. Essentially, ZDR and KDP in the DGL are anticorrelated and strongly depend on cloud-top temperature. Analyses also show correlations indicating larger ZDR within lower ZH in the DGL and larger KDP within greater ZH in the DGL. The high-ZDR regions are likely dominated by growth of a mixture of highly oblate dendrites and/or hexagonal plates, or prolate needles. Regions of high KDP are expected to be overwhelmed with snow aggregates and crystals with irregular or nearly spherical shapes, seeded at cloud tops. Furthermore, QVP indications of hexagonal plate crystals within the DGL are verified using in situ microphysical measurements, demonstrating the reliability of QVPs in evaluating ice microphysics in upper regions of winter clouds.

Accurate measurements of snow amounts by radar are very difficult to achieve. The inherent uncertainty in radar snow estimates that are based on the radar reflectivity factor Z is caused by the variability of snow particle size distributions and snow particle density as well as the large diversity among snow growth habits. In this study, a novel method for snow quantification that is based on the joint use of radar reflectivity Z and specific differential phase KDP is introduced. An extensive dataset of 2D-video-disdrometer measurements of snow in central Oklahoma is used to derive polarimetric relations for liquid-equivalent snowfall rate S and ice water content IWC in the forms of bivariate power-law relations S = and along with similar relations for the intercept N0s and slope Λs of the exponential snow size distribution. The physical basis of these relations is explained. Their multipliers are sensitive to variations in the width of the canting angle distribution and to a lesser extent the particles’ aspect ratios and densities, whereas the exponents are practically invariant. This novel approach is tested against the S(Z) relation using snow disdrometer measurements in three geographical regions (Oklahoma, Colorado, and Canada). Significant improvement in snow estimates relative to the traditional Z-based methods is demonstrated.

Achieving accurate storm-scale analyses and reducing the spinup time of modeled convection is a primary motivation for the assimilation of radar reflectivity data. One common technique of reflectivity data assimilation is using a cloud analysis, which inserts temperature and moisture increments and hydrometeors deduced from radar reflectivity via empirical relations to induce and sustain updraft circulations. Polarimetric radar data have the ability to provide enhanced insight into the microphysical and dynamic structure of convection. Thus far, however, relatively little has been done to leverage these data for numerical weather prediction. In this study, the Advanced Regional Prediction System’s cloud analysis is modified from its original reflectivity-based formulation to provide moisture and latent heat adjustments based on the detection of differential reflectivity columns, which can serve as proxies for updrafts in deep moist convection and, subsequently, areas of saturation and latent heat release. Cycled model runs using both the original cloud analysis and above modifications are performed for two high-impact weather cases: the 19 May 2013 central Oklahoma tornadic supercells and the 25 May 2016 north-central Kansas tornadic supercell. The analyses and forecasts of convection qualitatively and quantitatively improve in both cases, including more coherent analyzed updrafts, more realistic forecast reflectivity structures, a better correspondence between forecast updraft helicity tracks and radar-derived rotation tracks, and improved frequency biases and equitable threat scores for reflectivity. Based on these encouraging results, further exploration of the assimilation of dual-polarization radar data into storm-scale models is warranted.

A new methodology for estimating the depolarization ratio (DR) by dual-polarization radars with simultaneous transmission/reception of orthogonally polarized waves together with traditionally measured differential reflectivity ZDR, correlation coefficient ρhυ, and differential phase ΦDP in a single mode of operation is suggested. This depolarization ratio can serve as a proxy for circular depolarization ratio measured by radars with circular polarization. The suggested methodology implies the use of a high-power phase shifter to control the system differential phase on transmission and a special signal processing to eliminate the detrimental impact of differential phase on the estimate of DR. The feasibility of the suggested approach has been demonstrated by retrieving DR from the standard polarimetric variables and the raw in-phase I and quadrature Q components of radar signals and by implementing the scheme on a C-band radar with simultaneous transmission/reception of horizontally and vertically polarized waves. Possible practical implications of using DR include the detection of hail and the determination of its size above the melting layer, the discrimination between various habits of ice aloft, and the possible identification and quantification of riming, which is associated with the presence of supercooled cloud water. Some examples of these applications are presented.

Advancing understanding of deep convection microphysics via mesoscale modeling studies of well-observed case studies requires observation-based aerosol inputs. Here, we derive hygroscopic aerosol size distribution input profiles from ground-based and airborne measurements for six convection case studies observed during the Midlatitude Continental Convective Cloud Experiment (MC3E) over Oklahoma. We demonstrate use of an input profile in simulations of the only well-observed case study that produced extensive stratiform outflow on 20 May 2011. At well-sampled elevations between −11 and −23 °C over widespread stratiform rain, ice crystal number concentrations are consistently dominated by a single mode near ∼ 400 µm in randomly oriented maximum dimension (Dmax). The ice mass at −23 °C is primarily in a closely collocated mode, whereas a mass mode near Dmax ∼ 1000 µm becomes dominant with decreasing elevation to the −11 °C level, consistent with possible aggregation during sedimentation. However, simulations with and without observation-based aerosol inputs systematically overpredict mass peak Dmax by a factor of 3–5 and underpredict ice number concentration by a factor of 4–10. Previously reported simulations with both two-moment and size-resolved microphysics have shown biases of a similar nature. The observed ice properties are notably similar to those reported from recent tropical measurements. Based on several lines of evidence, we speculate that updraft microphysical pathways determining outflow properties in the 20 May case are similar to a tropical regime, likely associated with warm-temperature ice multiplication that is not well understood or well represented in models.

On 23rd December 2012, a warm frontal atmospheric system caused significant freezing rain at the Vienna International Airport. Analyses of Austrian C‑band polarimetric weather radar data reveal cloud microphysical insights in the internal structure of such systems accompanied by the transition from snow toward ice pellets and subsequent freezing rain and warm rain. Polarimetric radar data are exploited using a recently suggested quasi-vertical profiles (QVP) technique. This method allows to analyze the evolution of the melting layer height and other important microphysical signatures as well as the following precipitation development with high temporal and vertical resolution. Because the QVP polarimetric radar data are represented in a height vs time format which is fully compatible with the format the data from vertically pointing remote sensors such as cloud radars or wind profilers are displayed, their combined use is very efficient for understanding and monitoring microphysical processes leading to precipitation formation. This is demonstrated by the joint analysis of the polarimetric and profiler data at the Vienna airport. The QVP methodology is particularly effective for monitoring weather in the terminal areas of airports because of its local coverage and high precision as well as its potential for nowcasting. The methodology is very easy to implement and its use along with traditional techniques for weather radar data analysis using PPIs and reconstructed RHIs looks very promising.

Multisensor observations of anvil mammatus are analyzed in order to gain a more detailed understanding of their spatiotemporal structure and microphysical characterization. Remarkable polarimetric radar signatures are detected for the Pentecost 2014 supercell in Northrhine Westfalia, Germany, and severe storms in Oklahoma along their mammatus-bearing anvil bases. Radar reflectivity at horizontal polarization ZH and cross-correlation coefficient ρHV decrease downward toward the bottom of the anvil while differential reflectivity ZDR rapidly increases, consistent with the signature of crystal depositional growth. The differential reflectivity ZDR within mammatus exceeds 2 dB in the Pentecost storm and in several Oklahoma severe convective storms examined for this paper. Observations from a zenith-pointing Ka-band cloud radar and a Doppler wind lidar during the Pentecost storm indicate the presence of a supercooled liquid layer of at least 200–300-m depth near the anvil base at temperatures between −15° and −30°C. These liquid drops, which are presumably generated in localized areas of vertical velocities of up to 1.5 m s−1, coexist with ice particles identified by cloud radar. The authors hypothesize that pristine crystals grow rapidly within these layers of supercooled water, and that oriented planar ice crystals falling from the liquid layers lead to high ZDR at precipitation radar frequencies. A mammatus detection strategy using precipitation radar observations is presented, based on a methodology so far mainly used for the detection of updrafts in convective storms. Owing to the presence of a supercooled liquid layer detected above the mammatus lobes, the new detection strategy might also be relevant for aviation safety.

Convective clouds play an important role in the Earth's climate system as a driver of large-scale circulations and a primary mechanism for the transport of heat, moisture, aerosols, and momentum throughout the troposphere. Despite their climatic importance, multi-scale models continue to have persistent biases produced by insufficient representation of convective clouds. This is the result of an incomplete understanding of key processes such as convective initiation, updraft and downdraft dynamics, cloud and precipitation microphysics, and aerosol-convection interactions.The Aerosol-Cloud-Precipitation-Climate Initiative, an international research group dedicated to advancing understanding of aerosol impacts on clouds relevant to climate, has identified the Houston, Texas region as an optimal location for targeted studies of aerosol-convection interactions within frequently developing isolated deep convection. Houston lies within a humid subtropical climate regime, where onshore flow and sea-breeze convection interact with a range of aerosol conditions associated with Houston's urban and industrial emissions. Pilot studies have suggested that convective clouds in this region are potentially significantly impacted by the varying aerosol conditions.

To probe the potential value of a radar-driven field campaign to constrain simulation of isolated convection subject to a strong aerosol perturbation, convective cells observed by the operational KHGX weather radar in the vicinity of Houston, Texas, are examined individually and statistically. Cells observed in a single case study of onshore flow conditions during July 2013 are first examined and compared with cells in a regional model simulation. Observed and simulated cells are objectively identified and tracked from observed or calculated positive specific differential phase (KDP) above the melting level, which is related to the presence of supercooled liquid water. Several observed and simulated cells are subjectively selected for further examination. Below the melting level, we compare sequential cross sections of retrieved and simulated raindrop size distribution parameters. Above the melting level, we examine time series of KDP and radar differential reflectivity (ZDR) statistics from observations and calculated from simulated supercooled rain properties, alongside simulated vertical wind and supercooled rain mixing ratio statistics. Results indicate that the operational weather radar measurements offer multiple constraints on the properties of simulated convective cells, with substantial value added from derived KDP and retrieved rain properties. The value of collocated three-dimensional lightning mapping array measurements, which are relatively rare in the continental US, supports the choice of Houston as a suitable location for future field studies to improve the simulation and understanding of convective updraft physics. However, rapid evolution of cells between routine volume scans motivates consideration of adaptive scan strategies or radar imaging technologies to amend operational weather radar capabilities. A 3-year climatology of isolated cell tracks, prepared using a more efficient algorithm, yields additional relevant information. Isolated cells are found within the KHGX domain on roughly 40 % of days year-round, with greatest concentration in the northwest quadrant, but roughly 5-fold more cells occur during June through September. During this enhanced occurrence period, the cells initiate following a strong diurnal cycle that peaks in the early afternoon, typically follow a south-to-north flow, and dissipate within 1 h, consistent with the case study examples. Statistics indicate that ∼ 150 isolated cells initiate and dissipate within 70 km of the KHGX radar during the enhanced occurrence period annually, and roughly 10 times as many within 200 km, suitable for multi-instrument Lagrangian observation strategies. In addition to ancillary meteorological and aerosol measurements, robust vertical wind speed retrievals would add substantial value to a radar-driven field campaign.