Abstract

Polarimetric radar observations of convective storms routinely reveal positive differential reflectivity Z _DR extending above the 0°C level, indicative of the presence of supercooled liquid particles lofted by the storm’s updraft. The summit of such “Z _DR columns” is marked by a zone of enhanced linear depolarization ratio L _DR or decreased copolar cross-correlation coefficient ρ _hv and a sharp decrease in Z _DR that together mark a particle freezing zone. To better understand the relation between changes in the storm updraft and the observed polarimetric variables, it is necessary to first understand the physics governing this freezing process and the impact of freezing on the polarimetric variables.

A simplified, one-dimensional explicit bin microphysics model of stochastic drop nucleation by an immersed foreign particle and subsequent deterministic freezing is developed and coupled with an electromagnetic scattering model to explore the impact of the freezing process on the polarimetric radar variables. As expected, the height of the Z _DR column is closely related to the updraft strength and initial drop size distribution. Additionally, the treatment of the stochastic nucleation process can also affect the depth of the freezing zone, underscoring the need to accurately depict this process in parameterizations. Representation of stochastic nucleation and deterministic freezing for each drop size bin yields better agreement between observations and the modeled vertical profiles of the surface reflectivity factor Z_H and Z _DR than bulk microphysics schemes. Further improvements in the representation of the L _DR cap, the observed Z_DR gradient in the freezing zone, and the magnitude of the ρ _hv minimum may require inclusion of accretion, which was not included in this model.

Abstract

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 Z _DR. It is shown that Z _DR columns are associated with raindrops and with hail particles growing in a wet growth regime within convective updrafts. The height and volume of Z _DR 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 Z _DR. Characteristics of Z _DR columns are highly correlated with vertical velocity, hail size, and aerosol concentration.

Abstract

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 K _DP, 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, ${\sigma }_{\mathit{e}}\left(K_{\mathrm{DP}},Z\right)=\gamma K_{\mathrm{DP}}^{\alpha }{Z}^{\beta }$ . 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.

Abstract

A polarimetric radar–based method for retrieving atmospheric ice particle shapes is applied to snowfall measurements by a scanning K_a-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.

Abstract

Snow sublimating in dry air is a forecasting challenge and can delay the onset of surface snowfall and affect storm-total accumulations. Despite this fact, it remains comparatively less studied than other microphysical processes. Herein, the characteristics of sublimating snow and the potential for nowcasting snowfall reaching the surface are explored through the use of dual-polarization radar. Twelve cases featuring prolific sublimation were analyzed using range-defined quasi-vertical profiles (RDQVPs) and were compared with environmental model analyses. Overall, reflectivity Z significantly decreases, differential reflectivity Z _DR slightly decreases, and copolar-correlation coefficient ρ _hv remains nearly constant through the sublimation layer. Regions of enhanced specific differential phase K _dp were frequently observed in the sublimation layer and are believed to be polarimetric evidence of secondary ice production via sublimation. A 1D bin model was initialized using particle size distributions retrieved from the RDQVPs using numerous novel polarimetric snow retrieval relations for a wide range of forecast lead times, with the model environment evolving in response to sublimation. It was found that the model was largely able to predict the snowfall start time up to 6 h in advance, with a 6-h median bias of just −18.5 min. A more detailed case study of the 8 December 2013 snowstorm in the Philadelphia, Pennsylvania, region was also performed, demonstrating good correspondence with observations and examples of model fields (e.g., cooling rate) hypothetically available from such a tool. The proof-of-concept results herein demonstrate the potential benefits of incorporating spatially averaged radar data in conjunction with simple 1D models into the nowcasting process.

Abstract

Quasi-vertical profiles (QVPs) obtained from a database of U.S. WSR-88D data are used to document polarimetric characteristics of the melting layer (ML) in cold-season storms with high vertical resolution and accuracy. A polarimetric technique to define the top and bottom of the ML is first introduced. Using the QVPs, statistical relationships are developed to gain insight into the evolution of microphysical processes above, within, and below the ML, leading to a statistical polarimetric model of the ML that reveals characteristics that reflectivity data alone are not able to provide, particularly in regions of weak reflectivity factor at horizontal polarization Z _H . QVP ML statistics are examined for two regimes in the ML data: Z _H ≥ 20 dBZ and Z _H < 20 dBZ. Regions of Z _H ≥ 20 dBZ indicate locations of MLs collocated with enhanced differential reflectivity Z _DR and reduced copolar correlation coefficient ρ _hv, while for Z _H < 20 dBZ a well-defined ML is difficult to discern using Z _H alone. Evidence of large Z _DR up to 4 dB, backscatter differential phase δ up to 8°, and low ρ _hv down to 0.80 associated with lower Z _H (from −10 to 20 dBZ) in the ML is observed when pristine, nonaggregated ice falls through it. Positive correlation is documented between maximum specific differential phase K _DP and maximum Z _H in the ML; these are the first QVP observations of K _DP in MLs documented at S band. Negative correlation occurs between minimum ρ _hv in the ML and ML depth and between minimum ρ _hv in the ML and the corresponding enhancement of Z _H (ΔZ _H = Z _Hmax − Z _Hrain).

Abstract

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 K _DP and Z _DR are implemented to demonstrate their value in interpreting elevated ice processes. Several fascinating and repetitive signatures are observed in the QVPs for differential reflectivity Z _DR and specific differential phase K _DP, in the dendritic growth layer (DGL), and at the tops of clouds. The most striking feature is maximum Z _DR (up to 6 dB) in the DGL occurring near the −10-dBZ Z _H contour within low K _DP and during shallower and warmer cloud tops. Conversely, maximum K _DP (up to 0.3° km⁻¹) in the DGL occurs within low Z _DR and during taller and colder cloud tops. Essentially, Z _DR and K _DP in the DGL are anticorrelated and strongly depend on cloud-top temperature. Analyses also show correlations indicating larger Z _DR within lower Z _H in the DGL and larger K _DP within greater Z _H in the DGL. The high-Z _DR regions are likely dominated by growth of a mixture of highly oblate dendrites and/or hexagonal plates, or prolate needles. Regions of high K _DP 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.

Abstract

Specific differential propagation phase (K _DP) is examined for estimating convective rainfall in Colorado and Kansas. Estimates are made at S band with K _DP alone and in combination with radar reflectivity (Z _H). Results are compared to gauge observations by computing bias factors, defined as the sum of gauge-measured rainfalls divided by the sum of radar estimates at gauges reporting rainfall, and the correlation coefficient between the gauge and radar-estimated amounts. Rainfall accumulations computed from positive-only values of K _DP (provided Z _H ≥ 25 dBZ) yield bias factors that vary from 0.76 to 2.42 for 3 storms in Colorado and from 0.78 to 1.46 for 10 storms in Kansas. Correlation coefficients between gauge-observed and radar-estimated rainfalls are 0.76 to 0.95. When negative K _DP’s are included as negative rainfall rates, bias factors range from 0.81 to 3.00 in Colorado and from 0.84 to 2.31 in Kansas. In most storms, the correlation between gauge and radar rainfalls decreases slightly.

In an experiment with the K _DP/Z _H combination, rainfall rates are computed from K _DP when K _DP is ≥0.4° km⁻¹ and from Z _H for K _DP < 0.4° km⁻¹ and Z _H ≥ 25 dBZ. Neglect of the negative K _DP’s and substitution of the always positive Z _H rainfall rates result in a tendency to overestimate rainfall. Bias factors are 0.63–1.46 for Colorado storms and 0.68–0.97 for Kansas storms, and correlation coefficients between gauge and radar amounts are 0.80–0.95. In yet another test with the K _DP/Z _H pair, rainfall estimates are computed from K _DP when Z _H ≥ 40 dBZ and from Z _H when 25 ⩽ Z _H < 40 dBZ. For this experiment, bias factors range from 0.90 to 1.91 in Colorado and from 0.88 to 1.46 in Kansas. Correlation coefficients are 0.80–0.96.

Since bias factors and correlation coefficients between estimated rainfalls and gauge observations for K _DP are similar to those for radar reflectivity, there was no obvious benefit with K _DP rainfalls for a well-calibrated radar. Large underestimates with K _DP in two storms were attributed to rainfalls dominated by small drops. In one storm, the problem was aggravated by widespread negative K _DP’s thought related to vertical gradients of precipitation. An advantage of K _DP-derived rainfall estimates confirmed here is an insensitivity to anomalous propagation.

Abstract

Characteristics of the magnitude and phase of correlation coefficients between horizontally and vertically polarized returns from ground clutter echoes are quantified by analyzing histograms obtained with an 11-cm wavelength weather surveillance radar in Norman, Oklahoma. The radar receives simultaneously horizontal and vertical (SHV) electric fields and can transmit either horizontal fields or both vertical and horizontal fields. The differences between correlations obtained in this SHV mode and correlations measured in alternate H, V mode are reviewed; a histogram of differential phase obtained in Florida using alternate H, V mode is also presented. Data indicate that the backscatter differential phase of clutter has a broad histogram that completely overlaps the narrow histogram of precipitation echoes. This is important as it implies that a potent discriminator for separating clutter from meteorological echoes is the texture of the differential phase. Values of the copolar cross-correlation coefficient from clutter overlap completely those from precipitation, and effective discrimination is possible only if averages in range are taken. It is demonstrated that the total differential phase (system and backscatter) depends on the polarimetric measurement technique and the type of scatterers. In special circumstances, such as calibrating or monitoring the radar, clutter signal can be beneficial. Specifically, system differential phase can be estimated from histograms of ground clutter, receiver differential phase can be estimated from precipitation returns, and from these two, the differential phase of transmitted waves is easily computed.

Abstract

This study is the third part of a paper series investigating the polarimetric radar properties of melting hail and application of those properties for operational polarimetric hail detection and determination of its size. The results of theoretical simulations in Part I were used to develop a hail size discrimination algorithm (HSDA) described in Part II. The HSDA uses radar reflectivity Z, differential reflectivity Z _DR, and cross-correlation coefficient ρ_hv along with melting-level height within a fuzzy-logic scheme to distinguish among three hail size classes: small hail (with diameter D < 2.5 cm), large hail (2.5 < D < 5.0 cm), and giant hail (D > 5.0 cm). The HSDA validation is performed using radar data collected by numerous WSR-88D sites and more than 3000 surface hail reports obtained from the Severe Hazards Analysis and Verification Experiment (SHAVE). The original HSDA version was modified in the process of validation, and the modified algorithm demonstrates probability of detection of 0.594, false-alarm ratio of 0.136, and resulting critical success index (CSI) equal to 0.543. The HSDA outperformed the current operational single-polarization hail detection algorithm, which only provides a single hail size estimate per storm and is characterized by CSI equal to 0.324. It is shown that HSDA is particularly sensitive to the quality of Z _DR measurements, which might be affected by possible radar miscalibration and anomalously high differential attenuation.