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Petar Bukovčić
,
Alexander V. Ryzhkov
, and
Jacob T. Carlin

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, σ e ( K DP , Z ) = γ K DP α Z β . 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.

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Jacob T. Carlin
,
Heather D. Reeves
, and
Alexander V. Ryzhkov

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.

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Erica M. Griffin
,
Terry J. Schuur
, and
Alexander V. Ryzhkov

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 Z H = Z HmaxZ Hrain).

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Erica M. Griffin
,
Terry J. Schuur
, and
Alexander V. Ryzhkov

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.

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Edward A. Brandes
,
Alexander V. Ryzhkov
, and
Dus̆an S. Zrnić

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.

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Dusan S. Zrnić
,
Valery M. Melnikov
, and
Alexander V. Ryzhkov

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.

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Kiel L. Ortega
,
John M. Krause
, and
Alexander V. Ryzhkov

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 ρhv 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.

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Heather Dawn Reeves
,
Alexander V. Ryzhkov
, and
J. Krause

Abstract

A new approach for distinguishing precipitation types at the surface, the spectral bin classifier (SBC), is presented. This algorithm diagnoses six categories of precipitation: rain (RA), snow (SN), a rain–snow mix (RASN), freezing rain (FZRA), ice pellets (PL), and a freezing rain–ice pellet mix (FZRAPL). It works by calculating the liquid-water fraction f w for a spectrum of falling hydrometeors given a prescribed temperature T and relative humidity profile. Demonstrations of the SBC output show that it provides reasonable estimates of f w of various-sized hydrometeors for the different categories of precipitation. The SBC also faithfully represents the horizontal distribution of precipitation type inasmuch as the model analyses and surface observations are consistent with each other. When applied to a collection of observed soundings associated with RA, SN, FZRA, and PL, the classifier has probabilities of detection (PODs) that range from 62.4% to 98.3%. The PODs do decrease when the effects of model uncertainty are accounted for. This decrease is modest for RA, SN, and PL but is large for FZRA as a result of the fact that this form of precipitation is very sensitive to small changes in the thermal profile. The effects of the choice of the degree of riming above the melting layer, the drop size distribution, and the assumed temperature at which ice nucleates are also examined. Recommendations on how to mitigate all forms of uncertainty are discussed. These include the use of dual-polarized radar observations, incorporating output from the microphysical parameterization scheme, and the use of ensemble model forecasts.

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Yadong Wang
,
Jian Zhang
,
Alexander V. Ryzhkov
, and
Lin Tang

Abstract

To obtain accurate radar quantitative precipitation estimation (QPE) for extreme rainfall events such as land-falling typhoon systems in complex terrain, a new method was developed for C-band polarimetric radars. The new methodology includes a correction method based on vertical profiles of the specific differential propagation phase (VPSDP) for low-level blockage and an optimal relation between rainfall rate ( ) and the specific differential phase ( ). In the VPSDP-based correction approach, a screening process is applied to fields, where missing or unreliable data from lower tilts caused by severe beam blockage are replaced with data from upper and unblocked tilts. The data from upper tilts are adjusted to account for variations in the vertical profile of . The corrected field is then used for rain-rate estimations. To acquire an accurate QPE result, a new relation for C-band polarimetric radars was derived through simulations using drop size distribution (DSD) and drop shape relation (DSR) observations from typhoon systems in Taiwan. The VPSDP-based correction method with the new relation was evaluated using the typhoon cases of Morakot (2009) and Fanapi (2010).

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Silke Trömel
,
Michael Ziegert
,
Alexander V. Ryzhkov
,
Christian Chwala
, and
Clemens Simmer

Abstract

The variability in raindrop size distributions and attenuation effects are the two major sources of uncertainty in radar-based quantitative precipitation estimation (QPE) even when dual-polarization radars are used. New methods are introduced to exploit the measurements by commercial microwave radio links to reduce the uncertainties in both attenuation correction and rainfall estimation. The ratio α of specific attenuation A and specific differential phase K DP is the key parameter used in attenuation correction schemes and the recently introduced R(A) algorithm. It is demonstrated that the factor α can be optimized using microwave links at Ku band oriented along radar radials with an accuracy of about 20%–30%. The microwave links with arbitrary orientation can be utilized to optimize the intercepts in the R(K DP) and R(A) relations with an accuracy of about 25%. The performance of the suggested methods is tested using the polarimetric C-band radar operated by the German Weather Service on Mount Hohenpeissenberg in southern Germany and two radially oriented Ku-band microwave links from Ericsson GmbH.

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