Alexander V. Ryzhkov

Utilization of specific differential phase K_DP combined with specific attenuation A for rainfall estimation introduced a decade ago was a real game changer in radar quantitative precipitation estimation (QPE) at S band. The R(A)/R(K_DP) QPE algorithm (named Q3DP) operationally implemented on the Multi-Radar Multi-Sensor (MRMS) system platform in the United States demonstrated well-documented improvement in rainfall estimation for the nationwide network of the WSR-88D radars, particularly for heavy rain associated with flash floods. Further modifications of R(A) and R(K_DP) methodologies comprising the Q3DP algorithm are suggested in this study. A new scheme for estimating a key parameter of R(A) technique, the ratio α = A/K_DP, is suggested that automatically takes into account its spatial variability across the radar field of view as well as inherent dependence of A on temperature. A novel approach for the use of K_DP to quantify heavy rain is introduced which implies optimization of a multiplier in the power-law R(K_DP) relation based on the combination of K_DP and radar reflectivity Z. The improvement in rainfall estimation resulting from utilization of a new R(A)/R(K_DP) methodology is illustrated for a few heavy rainfall events. Significance Statement: The importance of accurate radar rainfall estimation is hard to overestimate. In this study, further modifications of the advanced polarimetric radar QPE methodologies are suggested to improve their performance, particularly in heavy or extreme rain. In addition to the QPE benefits, better estimates of specific attenuation will provide important advantages for radar signal attenuation correction and microphysical retrievals in rain.

Wet downbursts are commonly associated with heavy rain. Because the precipitation flux is proportional to the summed product of the mass and downward velocity of precipitating hydrometeors, the rain rate within downbursts can be significantly amplified due to the increased fall velocities. All existing radar methodologies for rainfall estimation assume that the fall velocity of raindrops is equal to their terminal velocity in still air. This results in a strong underestimation of precipitation in the presence of downbursts or microbursts.Hail and graupel play an important role in generating downbursts via precipitation loading and negative buoyancy caused by melting of ice hydrometeors. These effects are quantified in the framework of our 1D cloud model with spectral bin microphysics that explicitly treats melting, sublimation, and evaporation for various size distributions of ice particles aloft and vertical thermodynamic profiles. The cloud model is coupled with an advanced polarimetric radar forward operator and generates vertical profiles of radar variables such as radar reflectivity Z, specific differential phase KDP, and specific attenuation A used in modern radar QPE methods.KDP is the primary radar variable used for rain rate (R) estimation when rain is mixed with hail. However, the parameters of the power-law R(KDP) relation may vary depending on the predominant hail size. For example, storms producing a large amount of small hail (SPLASH storms) in high concentration are frequently characterized by anomalously high values of KDP. On the other hand, the effects of diabatic cooling that determine the strength of the downdraft (along with precipitation loading) are stronger for SPLASH storms.The major points of this study will be illustrated by the results of model simulations and polarimetric radar observations of hail-bearing storms.

A novel polarimetric radar algorithm for melting-layer (ML) detection and determination of its height has been developed and tested for a large number of cold-season weather events. The algorithm uses radial profiles of the cross-correlation coefficient (ρ_hv or CC) at the lowest elevation angles (H_t and H_b, at distances up to 150 km from the radar. Integrating the CC information from all available antenna elevations makes it possible to produce accurate maps of H_t and H_b over large areas of radar coverage as opposed to the previous ML detection methods including the existing algorithm implemented on the U.S. network of the WSR-88Ds. The initial version of the algorithm has been implemented in C++ and tested for a multitude of cold-season weather events characterized by a low ML with different degrees of spatial nonuniformity including cases with sharp frontal boundaries and rain–snow transitions. The new ML detection algorithm (MLDA) exhibits robust performance, demonstrating spatial and temporal continuity, and showing general consistency of the ML designations matching those obtained from the regional model and the quasi-vertical profiles (QVP) methodology output.

Radar quantitative precipitation estimation (QPE) is one of the primary tasks of weather radars. The QPE quality was substantially improved after polarimetric upgrade of the radars. This study provides an overview of existing polarimetric methodologies for rain and snow estimation and their operational implementation. The variability of drop size distributions (DSDs) is a primary factor affecting the quality of rainfall estimation and its impact on the performance of various radar rainfall relations at S, C, and X microwave frequency bands is one of the focuses of this review. The radar rainfall estimation algorithms based on the use of specific attenuation A and specific differential phase K_DP are the most efficient. Their brief description is presented and possible ways for their further optimization are discussed. Polarimetric techniques for the vertical profile of reflectivity (VPR) correction at longer distances from the radar are also summarized. Radar quantification of snow is particularly challenging and it is demonstrated that polarimetric methods for snow measurements show good promise. Finally, the article presents a summary of the latest operational radar QPE products available in the US by integration of the information from the WSR-88D radars via the Multi-Radar Multi-Sensor (MRMS) platform.

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.

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 Z_DR, correlation coefficient ρ_hυ, and differential phase Φ_DP 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.

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

The potential utilization of specific attenuation A for rainfall estimation, mitigation of partial beam blockage, and radar networking is investigated. The R(A) relation is less susceptible to the variability of drop size distributions than traditional rainfall algorithms based on radar reflectivity Z, differential reflectivity ZDR, and specific differential phase KDP in a wide range of rain intensity. Specific attenuation is estimated from the radial profile of the measured Z and the total span of the differential phase using the ZPHI method. Since the estimated A is immune to reflectivity biases caused by radar miscalibration, attenuation, partial beam blockage, and wet radomes, rain retrieval from R(A) is also immune to the listed factors. The R(A) method was tested at X band using data collected by closely located radars in Germany and at S band for polarimetrically upgraded Weather Surveillance Radar-1988 Doppler (WSR-88D) radars in the United States. It is demonstrated that the two adjacent X-band radars—one of which is miscalibrated and another which is affected by partial beam blockage—produce almost indistinguishable fields of rain rate. It is also shown that the R(A) method yields robust estimates of rain rates and rain totals at S band, where specific attenuation is vanishingly small. The X- and S-band estimates of rainfall obtained from R(A) are in good agreement with gauges.

The results of theoretical modeling in Part I are utilized to develop practical recommendations for developing the algorithms for hail detection and determination of its size as well as attenuation correction and rainfall estimation in the presence of hail. A new algorithm for discrimination between small hail (with maximal size of less than 2.5 cm), large hail (with diameters between 2.5 and 5.0 cm), and giant hail with size exceeding 5.0 cm is proposed and implemented for applications with the S-band dual-polarization Weather Surveillance Radar-1988 Doppler (WSR-88D) systems. The fuzzy-logic algorithm is based on the combined use of radar reflectivity Z, differential reflectivity Z_DR, and cross-correlation coefficient ρ_hv. The parameters of the membership functions depend on the height of the radar resolution volume with respect to the freezing level, exploiting the size-dependent melting characteristics of hailstones. The attenuation effects in melting hail are quantified in this study, and a novel technique for polarimetric attenuation correction in the presence of hail is suggested. The use of a rainfall estimator that is based on specific differential phase K_DP is justified on the basis of the results of theoretical simulations and comparison of actual radar retrievals at S band with gauge measurements for storms containing large hail with diameters exceeding 2.5 cm.

This study investigates the use of tornadic debris signature (TDS) parameters to estimate tornado damage severity using Norman, Oklahoma (KOUN), polarimetric radar data (polarimetric version of the Weather Surveillance Radar-1988 Doppler radar). Several TDS parameters are examined, including parameters based on the 10th or 90th percentiles of polarimetric variables (lowest tilt TDS parameters) and TDS parameters based on the TDS volumetric coverage (spatial TDS parameters). Two highly detailed National Weather Service (NWS) damage surveys are compared to TDS parameters. The TDS parameters tend to be correlated with the enhanced Fujita scale (EF) rating. The 90th percentile reflectivity, TDS height, and TDS volume increase during tornado intensification and decrease during tornado dissipation. For 14 tornado cases, the maximum or minimum TDS parameter values are compared to the tornado’s EF rating. For tornadoes with a higher EF rating, higher maximum values of the 90th percentile ZHH, TDS height, and volume, as well as lower minimum values of 10th percentile ρHV and ZDR, are observed. Maxima in spatial TDS parameters are observed after periods of severe, widespread tornado damage for violent tornadoes. This paper discusses how forecasters could use TDS parameters to obtain near-real-time information about tornado damage severity and spatial extent.