Weather Surveillance Radar-1988 Doppler Research Articles

Evaluation of Wildfire Plume Injection Heights Estimated from Operational Weather Radar Observations Using Airborne Lidar Retrievals

AbstractThe vertical distribution of wildfire smoke aerosols is important in determining its environmental impacts but existing observations of smoke heights generally do not possess the temporal resolution required to fully resolve the diurnal behavior of wildfire smoke injection. We use Weather Surveillance Radar‐1988 Doppler (WSR‐88D) dual polarization data to estimate injection heights of Biomass Burning Debris (BBD) generated by fires. We detect BBD as a surrogate for smoke aerosols, which are often collocated with BBD near the fire but are not within the size range detectable by these radars. Injection heights of BBD are derived for 2–10 August 2019, using WSR‐88D reflectivity (Z ≥ 10 dBZ) and dual polarization correlation coefficients (0.2 < C.C < 0.9) to study the Williams Flats fire. Results show the expected diurnal cycles with maximum injection heights present during the late afternoon period when the fire's intensity and convective mixing are maximized. WSR‐88D and airborne lidar injection height comparisons reveal that this method is sensitive to outliers and generally overpredicts maximum heights by 40%, though mean and median heights are better captured (<20% mean error). WSR‐88D heights between the 75th and 90th percentile seem to accurately represent the maximum heights, with the exception of heights estimated during the occurrence of a pyro‐cumulonimbus. Location specific mapping of WSR‐88D and lidar injection heights reveal that they diverge further away from the fire as expected due to BBD settling. Most importantly, WSR‐88D‐derived injection height estimates provide near continuous smoke height information, allowing for the study of diurnal variability of smoke injections.

Simulated WSR-88D Observations of the Streamwise Vorticity Current

The streamwise vorticity current (SVC) has been shown in recent research to have a role in the tornadogenesis process in some supercells. Although field experiments have succeeded in observing the feature using mobile radars, limited knowledge exists on how/if SVCs can be observed using operational radars. To explore this possibility, simple radar emulation software was used to create simulated Weather Surveillance Radar 1988 Doppler (WSR-88D) radar images of an SVC in a high-resolution numerical simulation of a supercell thunderstorm. This proof-of-concept approach shows that SVCs likely can be observed in an operational setting under idealized circumstances. Two analysis times from the simulation are used to compare the signatures of both a weak and a strong SVC. This study suggests that results are highly contingent on radar viewing angle, and quality observations of the SVC may only be possible when a storm is within 45 km of the radar site. In Plan Position Indicator (PPI) scans, SVCs are found to be associated with inflow winds that penetrate more rearward with height downstream of the mesocyclone. Range Height Indicator (RHI) reconstructions show a reflectivity billow that resembles a density current head containing a Kelvin-Helmholtz (KH) billow, as well as a radial velocity couplet co-located with the SVC. A vertical shear product is introduced to visualize low-level vertical wind shear in the vicinity of the SVC to facilitate identification of the feature without interrogation of multiple elevation angles of radial velocity data.

Polarimetric Radar Signatures in Various Lightning Activities During Seoul (Korea) Flood on August 8, 2022

On August 8 and 9, 2022, a record-breaking rain rate of 142 mm h−1, with an accumulated rainfall of more than 500 mm, was observed in the Seoul metropolitan area, Republic of Korea. This study focuses on analyzing the concentration of lightning in southern Seoul, which occurred solely on August 8. It is worth noting that the daily rainfall of August 8 was approximately twice that of August 9 (381 mm on August 8 vs. 198 mm on August 9). The RKSG (located in Yongin, 40 km south of Seoul) Weather Surveillance Radar-1988 Doppler was used to explore the characteristics of cloud microphysics associated with lightning activity. Four major heavy rain periods on August 8 were grouped into three categories of lightning rate (e.g., intense, moderate, and none), and their polarimetric signatures were compared. Significant differences in the vertical distribution of graupel were found within the temperature range of 0 °C and − 20 °C, as indicated by radar reflectivity (ZH) > 40 dBZ and differential reflectivity (ZDR) < 0.5 dB. Although graupel was detected in all three categories at the relatively warm temperatures of 0 °C to − 10 °C, its presence extended into colder regions exclusively in the intense category. This observation preceded the appearance of lightning by approximately 6 min. At heights with temperature ≤ − 20 °C, a high concentration of vertically aligned ice crystals was observed in lightning-prone regions, leading to a decrease in differential phase (ΦDP). In summary, this study provides valuable insights into the microphysical characteristics of thunderstorms and their relationship to lightning activity in the Seoul metropolitan area.

A Deep Learning–Based Velocity Dealiasing Algorithm Derived from the WSR-88D Open Radar Product Generator

Abstract Radial velocity estimates provided by Doppler weather radar are critical measurements used by operational forecasters for the detection and monitoring of life-impacting storms. The sampling methods used to produce these measurements are inherently susceptible to aliasing, which produces ambiguous velocity values in regions with high winds and needs to be corrected using a velocity dealiasing algorithm (VDA). In the United States, the Weather Surveillance Radar-1988 Doppler (WSR-88D) Open Radar Product Generator (ORPG) is a processing environment that provides a world-class VDA; however, this algorithm is complex and can be difficult to port to other radar systems outside the WSR-88D network. In this work, a deep neural network (DNN) is used to emulate the two-dimensional WSR-88D ORPG dealiasing algorithm. It is shown that a DNN, specifically a customized U-Net, is highly effective for building VDAs that are accurate, fast, and portable to multiple radar types. To train the DNN model, a large dataset is generated containing aligned samples of folded and dealiased velocity pairs. This dataset contains samples collected from WSR-88D Level-II and Level-III archives and uses the ORPG dealiasing algorithm output as a source of truth. Using this dataset, a U-Net is trained to produce the number of folds at each point of a velocity image. Several performance metrics are presented using WSR-88D data. The algorithm is also applied to other non-WSR-88D radar systems to demonstrate portability to other hardware/software interfaces. A discussion of the broad applicability of this method is presented, including how other Level-III algorithms may benefit from this approach. Significance Statement Accurate and timely estimates of wind within storms are critically important for a number of applications, including severe storm nowcasting, maritime operational planning, aviation forecasting, and public safety coordination. Velocity aliasing is a common artifact that requires data quality control. While velocity dealiasing algorithms (VDAs) have been developed for decades, they remain a computationally complex and challenging problem. This paper presents an application of deep neural networks (DNNs) to increase the computational efficiency and portability of VDAs. A DNN is trained to emulate an operational algorithm, and performance is quantified over a large dataset. This work gives a convincing example of the benefits that deep learning can provide for radar algorithms, and future work highlighting these opportunities is discussed.

Dual-Pol VPR Corrections for Improved Operational Radar QPE in MRMS

Abstract The radar bright band is caused by melting ice crystals, and results in inflated reflectivity observations. If uncorrected, the bright band can result in large errors in radar-derived quantitative precipitation estimation (QPE). In the operational Multi-Radar Multi-Sensor (MRMS) system up to version 12.1, the effects of the bright band are corrected through the use of a reflectivity-only, tilt-based apparent vertical profile of reflectivity (tilt-VPR). This study utilizes dual-polarization (dual-pol) radar observations to improve the tilt-VPR methodology. To accomplish this, a brightband area delineation was developed within the MRMS framework and the brightband top and bottom heights were identified for individual tilts of radar data. This information was used to develop a radially dependent dual-pol VPR (dpVPR) model that can better correct reflectivity in situations of nonisotropic bright bands and low brightband events. This algorithm has been tested on 14 varying brightband events across the CONUS and compared with the tilt-VPR and the National Weather Service Weather Surveillance Radar-1988 Doppler Level-3 Digital Precipitation Rate (DPR) products. The radially dependent dpVPR correction provided a more accurate detection of brightband areas and a more effective reduction in QPE errors within and above the bright band than the tilt-VPR and DPR QPEs, especially for precipitation events with low melting layers or with strong variability of vertical motions. The brightband delineation and dpVPR methodology are also evaluated in the real-time MRMS testbed for their robustness and computational efficiency and has been transitioned into operations in 2022.

Extended Polarimetric Observations of Chaff Using the WSR-88D Weather Radar Network

Military chaff is a metallic, fibrous radar countermeasure that is released by aircraft and rockets for diversion and masking of targets. It is often released across the United States for training purposes, and, due to its resonant cut lengths, is often observed on the S-band Weather Surveillance Radar – 1988 Doppler (WSR-88D) network. Efforts to identify and characterize chaff and other non-meteorological targets algorithmically require a statistical understanding of the targets. Previous studies of chaff characteristics have provided important information that has proven to be useful for algorithmic development. However, recent changes to the WSR-88D processing suite have allowed for a vastly extended range of differential reflectivity, a prime topic of previous studies on chaff using weather radar. Motivated by these changes, a new dataset of 2.8 million range gates of chaff from 267 cases across the United States is analyzed. With a better spatiotemporal representation of cases compared to previous studies, new analyses of height dependence, as well as changes in statistics by volume coverage pattern are examined, along with an investigation of the new “full” range of differential reflectivity. A discussion of how these findings are being used in WSR-88D algorithm development is presented, specifically with a focus on machine learning and separation of different target types.

A Conditional Generative Adversarial Network for Weather Radar Beam Blockage Correction

Missing or low-quality data regions usually happen to weather radars. One of the most common situations is beam blockage or partial beam blockage. Therefore, correction of weather radar observations that are partially or fully blocked is an indispensable step in radar data quality control and subsequent quantitative applications, especially in complex terrain environments such as the western United States. In this article, we propose a deep learning framework based on generative adversarial networks (GANs) for restoring partial beam blockage regions in polarimetric radar observations using local and global contextual information. Due to the diverse precipitation types, blockage conditions, and ground information in different areas, two radars deployed in two different regions characterized by different precipitation types are used to demonstrate the proposed methodology. Both are S-band operational Weather Surveillance Radar – 1988 Doppler (WSR-88D): KFWS located in Fort Worth, northern Texas, and KDAX located in Davis, northern California. For training the GAN model, this article simulates the partial beam blockage situations by manually cropping observation sectors of both KDAX and KFWS radar data. The trained models were tested using independent precipitation events in Texas and California to demonstrate the model effectiveness in inpainting “missing" data. In addition, this paper cross-tested the data with different precipitation features to examine the generalization capacity of the beam blockage correction models. The beam blockage correction performance is also compared with a traditional linear interpolation approach. The results show that for both domains the continuity of precipitation observations is greatly improved after applying the deep learning-based inpainting approach. For the KFWS test data, some visible discrepancies exist between the results from models trained based on convective and stratiform precipitation events in Texas and California, respectively, yet both models outperform the traditional interpolation method. For the KDAX test data, both the model trained using the KFWS data from convective precipitation events in Texas and the model trained using KDAX data from stratiform precipitation events in California render a similar performance. Although ground truth is not available for the real blocked radar data, the repaired observations demonstrated a great potential for improved quantitative applications.

Snowband Characteristics Associated With Lake‐Effect Misovortices During the OWLeS Project

AbstractVortices of diameters 100 m to 10 km have been observed during lake‐effect snowstorms. Lines of misovortices (diameters = 40–4,000 m) have recently been documented forming over Lake Ontario during long lake‐axis‐parallel (LLAP)‐type lake‐effect storms. Using National Weather Service Weather Surveillance Radar—1988 Doppler (WSR‐88D) and Doppler on Wheels radar data from the Ontario Winter Lake‐effect Systems (OWLeS) project, lines of misovortices (also referred to as “misovortex lines” in this study) were investigated for intensive observation period (IOP)7 (7 January 2014) and IOP9 (9 January 2014). Results revealed that the misovortex lines formed on the southern or northern side of the west‐to‐east‐oriented LLAP band, whichever was closest to a low‐level boundary nearest the corresponding south or north shoreline (northern part in IOP7, southern part in IOP9). Examination of these two IOPs in the context of a total of 23 OWLeS IOPs, 11 of which had predominantly LLAP band morphology, showed that, in 4 of the 11 LLAP IOPs, horizontal shear zones/reflectivity bands formed in preferred regions over Lake Ontario and nearby land areas where low‐level boundaries (e.g., land breeze fronts) have been noted to develop near shoreline irregularities. Horizontal shear zones were predominant in approximately half of the OWLeS LLAP IOPs and were associated with storms having a well‐organized, solid banded reflectivity structure. Misovortices occurred when horizontal shear zones were prevalent, supporting the idea that horizontal shearing instability was important to their formation.

Impact of WSR-88D Intra-Volume Low-Level Scans on Severe Weather Warning Performance

Abstract The statistical relationship between supplemental adaptive intra-volume low-level scan (SAILS) usage on the Weather Surveillance Radar-1988 Doppler and National Weather Service severe storm warning performance during 2014–20 is analyzed. Results show statistically significant improvement in severe thunderstorm (SVR), flash flood (FF), and tornado (TOR) warning performance associated with SAILS-on versus SAILS-off. Within the three possible SAILS modes of one (SAILSx1), two (SAILSx2), and three (SAILSx3) additional base scans per volume, for SVR, SAILSx2 and SAILSx3 are associated with better warning performance compared to SAILSx1; for FF and TOR, SAILSx3 is associated with better warning performance relative to SAILSx1 and SAILSx2. Two severe storm cases (one that spawned a tornado, one that did not) are presented where SAILS usage helped forecasters make the correct TOR warning decision, lending real-life credence to the statistical results. Furthermore, a statistical analysis of automated volume scan evaluation and termination effects, parsed by SAILS usage and mode, yield a statistically significant association between volume scan update rate and SVR warning lead time.

Damage Analysis and Close-Range Radar Observations of the 13 April 2019 Greenwood Springs, Mississippi, Tornado during VORTEX-SE Meso18-19

Abstract A tornado outbreak occurred across the Southeast United States on 13–14 April 2019, during the Verification of the Origins of Rotation in Tornadoes Experiment–Southeast (VORTEX-SE) Meso18-19 experiment. Among the most noteworthy events was a pair of large tornadoes in Monroe County, Mississippi, near the Columbus Air Force Base (GWX) Weather Surveillance Radar–1988 Doppler (WSR-88D). The second tornado, near the Greenwood Springs community, formed within the “no data” region near the radar and passed about 900 m to its east, rapidly strengthening into an intense tornado. This tornado produced forest devastation and electrical infrastructure damage up to at least EF4 intensity. The maximum radial velocity from GWX was 81.5 m s−1 (182 mph) in a resolution volume centered at 56 m (183 ft) above radar level. This paper presents a damage survey of the Greenwood Springs tornado and compares this assessment to the GWX data. A displacement of the maximum forest damage from the maximum radial velocity, despite the radar beam sampling &lt;100 m ARL, is documented, as well as other likely effects of debris loading by the tornado on the observed radar signatures. The radar observations are placed into context with past mobile radar studies to illustrate the unique nature of this dataset. The relationship between radar data and damage observations, the implications for tornado structure in rough terrain and land cover, and the use of forest damage and radar data in tornado intensity estimation are discussed. Significance Statement This study showcases radar and damage observations of an intense tornado in a forested region of Mississippi. The formation of the tornado within 1 km of a WSR-88D allowed for near-surface radar observations to be collected as significant tree destruction was occurring. Doppler velocities below 60 m above radar level (ARL), near tree canopy top, exceeded 80 m s−1. Tree damage patterns were complicated while the tornado was near maximum intensity. The most severe tree damage was notably displaced from the highest radar-observed velocities, despite the radar sampling as low as 45 m ARL. These findings highlight challenges in utilizing radar data to estimate tornado intensity and structure, particularly in a region of relatively high surface and terrain roughness.

WSR-88D Sidelobe Contamination: From a Conceptual Model to Diagnostic Strategies for Improving NWS Warning Performance

Abstract Providing timely warnings for severe and potentially tornadic convection is a critical component of the NWS mission, and owing to the associated large reflectivity gradients, sidelobe contamination is possible. This paper focuses on elevation sidelobe contamination appearing in the low-level inflow region of supercells. A qualitative conceptual model of the Weather Surveillance Radar-1988 Doppler (WSR-88D) antenna pattern interacting with supercells is introduced, along with Doppler power spectrum representations of the potential mix of returned power from the main lobe and the sidelobes. These tools inform the multiple ways elevation sidelobe contamination appears in the low levels, primarily below 3 km (10 kft) of radar data. The most common manifestation is somewhat noisy data similar to particulates or biota in clear air. Trained NWS forecasters are accustomed to mentally filtering out noisy clear-air returns as less important. Elevation sidelobe contamination can be mixed with the three-body scatter spike (TBSS) artifact, though the TBSS remains the more salient feature. The most consequential form is the apparent circulation, and when it is incorrectly interpreted as valid, contributes to the false alarm ratio (FAR) for NWS tornado warnings. Quantitative results on the effect of elevation sidelobe contamination on FAR are presented. Diagnostic techniques are emphasized, and with familiarization, can be used in real-time warning operations to identify the apparent circulation as either valid or an imposter. Identification of these contaminated velocity signatures offers a unique opportunity to reduce the NWS tornado warning FAR without also reducing the probability of detection (POD). Significance Statement The WSR-88D weather radars provide overall high-quality data for users. However, with some severe thunderstorms, an artifact called elevation sidelobe contamination can produce what looks like a rotation signature, but it may not be real. These ambiguous velocity signatures can contribute to tornado warnings based on rotation signatures that are false circulations. This paper specifically focuses on elevation sidelobe contamination due to its impact on tornado warning decisions. Diagnostic techniques, including several examples, are presented here to aid the reader in correctly identifying elevation sidelobe contamination and why it may occur. Correct identification of an apparent circulation as an imposter due to contamination is a unique opportunity to improve NWS tornado warning performance by reducing warning false alarms.

A Polarimetric Radar Operator and Application for Convective Storm Simulation

In this study, a polarimetric radar forward model operator was developed for the Weather Research and Forecasting (WRF) model that was based on a scattering algorithm using the T-matrix methodology. Three microphysics schemes—Thompson, Morrison 2-moment, and Milbrandt-Yau 2-moment—were supported in the operator. This radar forward operator used the microphysics, thermodynamic, and wind fields from WRF model forecasts to compute horizontal reflectivity, radial velocity, and polarimetric variables including differential reflectivity (ZDR) and specific differential phase (KDP) for S-band radar. A case study with severe convective storms was used to examine the accuracy of the radar operator. Output from the radar operator was compared to real radar observations from the Weather Surveillance Radar–1988 Doppler (WSR-88D) radar. The results showed that the radar forward operator generated realistic polarimetric signatures. The distribution of polarimetric variables agreed well with the hydrometer properties produced by different microphysics schemes. Similar to the observed polarimetric signatures, radar operator output showed ZDR and KDP columns from low-to-mid troposphere, reflecting the large amount of rain within strong updrafts. The Thompson scheme produced a better simulation for the hail storm with a ZDR hole to indicate the existence of graupel in the low troposphere.

A Multidisciplinary Method to Support the Evolution of NWS Weather Radar Technology

Abstract The highly successful fleet of Weather Surveillance Radar-1988 Doppler (WSR-88D) radars is approaching its end of service, and research efforts are under way to inform a decision toward a possible WSR-88D replacement. A methodology to link radar design characteristics to impacts on how radar data are used to diagnose hazardous weather was developed through a unique partnership between radar-engineering innovations in radar simulations and National Weather Service (NWS) radar data interpretation expertise. Deep commitment to two-way learning across disciplines resulted in a methodology that is both efficient and highly relevant to the NWS hazardous weather warning program. The methodology presented in this paper is a model for revealing complex trade-offs between weather radar characteristics and their resultant impact on NWS data interpretation for threat identification. This qualitative methodology is presented in the context of a broader proof-of-concept study from which it was developed. Adapted for further research, it can support the crucial role of deriving quantitative radar design criteria that balance the trade-offs among radar capabilities, cost, and impact to users. That is, the proposed methodology supports the evaluation of candidates for a potential WSR-88D replacement and any necessary major system upgrades in the interim. Significance Statement Defining the requirements for an operational weather radar system is ideally achieved with clearly identified trade-offs among cost, radar design characteristics, and impacts on user data interpretation. This work is an advancement of the historic evolution of weather radar development to support the U.S. National Weather Service (NWS) mission, based on collaboration among researchers, radar engineers, and NWS forecasters. The methodology presented here is an adaptable tool for revealing these essential, but complex trade-offs, providing a roadmap for further studies toward the next-generation NWS weather radar fleet.

The Influence of WSR-88D Intra-Volume Scanning Strategies on Thunderstorm Observations and Warnings in the Dual-Polarization Radar Era: 2011–20

Abstract The Weather Surveillance Radar-1988 Doppler (WSR-88D) network has undergone several improvements in the last decade with the upgrade to dual-polarization capabilities and the ability for forecasters to rescan the lowest levels of the atmosphere more frequently through the use of Supplemental Adaptive Intra-volume Scanning (SAILS). SAILS reduces the revisit period for scanning the lowest 1 km of the atmosphere but comes at the cost of a longer delay between scans at higher altitudes. This study quantifies how often radar volume coverage patterns (VCPs) and all available SAILS options are used during the issuance of 148 882 severe thunderstorm and 18 263 tornado warnings, and near 10 474 tornado, 58 934 hail, and 127 575 wind reports in the dual-polarization radar era. A large majority of warnings and storm reports were measured with a VCP providing denser low-level sampling coverage. More frequent low-level updates were employed near tornado warnings and reports compared to severe thunderstorm warnings and hail or wind hazards. Warnings issued near a radar providing three extra low-level scans (SAILSx3) were more likely to be verified by a hazard with a positive lead time than warnings with fewer low-level scans. However, extra low-level scans were more frequently used in environments supporting organized convection as shown using watches issued by the Storm Prediction Center. In recent years, the number of midlevel radar elevation scans is declining per hour, which can adversely affect the tracking of convective polarimetric signatures, like ZDR columns, which were found above the lowest elevation angle in over 99% of cases examined.

Integration of the Motion-Compensated Steering and Distributed Beams’ Techniques for Polarimetric Rotating Phased Array Radar

The rotating phased array radar (RPAR) has the potential to improve the capabilities of the current U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) operational network and can be more affordable than other candidate phased array radar (PAR) architectures that have been evaluated to replace the WSR-88D. Considering the demanding functional requirements for the future U.S. weather surveillance radar, it is expected that several advanced RPAR scanning techniques will need to be applied simultaneously to achieve them. In this letter, we present the integration of two such RPAR scanning techniques: motion-compensated steering (MCS) and distributed beams (DBs). MCS exploits beam agility to mitigate beam smearing, while DB exploits digital beamforming to reduce the scan time or the standard deviation (SD) of estimates. The integration of these techniques is demonstrated with the National Severe Storms Laboratory's (NSSL) dual-polarization advanced technology demonstrator (ATD) radar system. Results show that these techniques can be used simultaneously to enhance azimuthal resolution and reduce the SD of estimates without impacting data quality if certain obtainable tradeoff considerations are incorporated in the radar design process.

Radar-Based Assessment of the Detectability of Giant Hail

&#x0D; The occurrence of giant hail, defined as hail ≥102 mm (4.00 in) in diameter, is a relatively rare phenomenon, accounting for less than 1% of all hail reports in the United States. Despite the infrequent nature of these events, hail of this magnitude has the potential to cause extreme damage to property and a substantial threat to exposed life. The short-term prediction of these events has been challenging. For giant hail since 2005, only 7% of convective warnings and severe-weather statements issued by the National Weather Service (NWS) accurately predicted a maximum hail size ≥102 mm prior to the report, with an average underestimated size error of 55.6 mm (2.19 in).&#x0D; The objectives of this study are to determine the detectability of giant hail in convective storms and to improve advanced recognition of these events during NWS warning operations. A total of 568 giant-hail reports, gathered over a 15-y period from 1 January 1995 through 31 December 2009 throughout the contiguous United States, served as the primary database for the research. Weather Surveillance Radar-1988 Doppler (WSR-88D) data and North American Regional Reanalysis (NARR) environmental data were collected for each case. Several radar signatures were examined to assess their utility in discriminating storms most favorable for giant hail. It was found that 99% of the storms were supercells with well-organized structure. Giant-hail producing storms were characterized by median values of rotational velocities of 24 m s-1 (47 kt), storm-top divergence magnitudes of 72 m s-1 (140 kt), and 50-dBZ and 60-dBZ echo heights of 13 100 m (43 000 ft) and 10 600 m (34 800 ft) respectively. Vertically integrated liquid water (VIL)-based products, maximum reflectivity within the storm, and reflectivity within the preferred hail-growth zone showed little to no skill in discriminating between giant hail and smaller hail sizes.&#x0D;

Observed Bulk Hook Echo Drop Size Distribution Evolution in Supercell Tornadogenesis and Tornadogenesis Failure

AbstractThe time preceding supercell tornadogenesis and tornadogenesis “failure” has been studied extensively to identify differing attributes related to tornado production or lack thereof. Studies from the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX) found that air in the rear-flank downdraft (RFD) regions of non- and weakly tornadic supercells had different near-surface thermodynamic characteristics than that in strongly tornadic supercells. Subsequently, it was proposed that microphysical processes are likely to have an impact on the resulting thermodynamics of the near-surface RFD region. One way to view proxies to microphysical features, namely drop size distributions (DSDs), is through use of polarimetric radar data. Studies from the second VORTEX used data from dual-polarization radars to provide evidence of different DSDs in the hook echoes of tornadic and non-tornadic supercells. However, radar-based studies during these projects were limited to a small number of cases preventing result generalizations. This study compiles 68 tornadic and 62 non-tornadic supercells using Weather Surveillance Radar–1988 Doppler (WSR-88D) data to analyze changes in polarimetric radar variables leading up to, and at, tornadogenesis and tornadogenesis failure. Case types generally did not show notable hook echo differences in variables between sets, but did show spatial hook echo quadrant DSD differences. Consistent with past studies, differential radar reflectivity factor (ZDR) generally decreased leading up to tornadogenesis and tornadogenesis failure; in both sets, estimated total number concentration increased during the same times. Relationships between DSDs and the near-storm environment, and implications of results for nowcasting tornadogenesis, also are discussed.

Multi-Radar Analysis of the 20 May 2013 Moore, Oklahoma Supercell through Tornadogenesis and Intensification

A multi-radar analysis of the 20 May 2013 Moore, Oklahoma, U.S. supercell is presented using three Weather Surveillance Radars 1988 Doppler (WSR-88Ds) and PX-1000, a rapid-scan, polarimetric, X-band radar, with a focus on the period between 1930 and 2008 UTC, encompassing supercell maturation through rapid tornado intensification. Owing to the 20-s temporal resolution of PX-1000, a detailed radar analysis of the hook echo is performed on (1) the microphysical characteristics through a hydrometeor classification algorithm (HCA)—inter-compared between X- and S-band for performance evaluation—including a hail and debris class and (2) kinematic properties of the low-level mesocyclone (LLM) assessed through ΔVr analyses. Four transient intensifications in ΔVr prior to tornadogenesis are documented and found to be associated with two prevalent internal rear-flank downdraft (RFD) momentum surges, the latter surge coincident with tornadogenesis. The momentum surges are marked by a rapidly advancing reflectivity (ZH) gradient traversing around the LLM, descending reflectivity cores (DRCs), a drop in differential reflectivity (ZDR) due to the advection of smaller drops into the hook echo, a decrease in correlation coefficient (ρhv), and the detection of debris from the HCA. Additionally, volumetric analyses of ZDR and specific differential phase (KDP) signatures show general diffusivity of the ZDR arc even after tornadogenesis in contrast with explosive deepening of the KDP foot downshear of the updraft. Similarly, while the vertical extent of the ZDR and KDP columns decrease leading up to tornadogenesis, the phasing of these signatures are offset after tornadogenesis, with the ZDR column deepening the lagging of KDP.

An Automated Python Algorithm to Quantify ZDR Arc and KDP–ZDR Separation Signatures in Supercells

AbstractSupercell thunderstorms often have pronounced signatures of hydrometeor size sorting within their forward-flank regions, including an arc-shaped region of high differential reflectivity (ZDR) along the inflow edge of the forward flank known as the ZDR arc and a clear horizontal separation between this area of high ZDR values and an area of enhanced KDP values deeper into the storm core. Recent work has indicated that ZDR arc and KDP–ZDR separation signatures in supercell storms may be related to environmental storm-relative helicity and low-level shear. Thus, characteristics of these signatures may be helpful to indicate whether a given storm is likely to produce a tornado. Although ZDR arc and KDP–ZDR separation signatures are typically easy to qualitatively identify in dual-polarization radar fields, quantifying their characteristics can be time-consuming and makes research into these signatures and their potential operational applications challenging. To address this problem, this paper introduces an automated Python algorithm to objectively identify and track these signatures in Weather Surveillance Radar-1988 Doppler (WSR-88D) data and quantify their characteristics. This paper will discuss the development of the algorithm, demonstrate its performance through comparisons with manually generated time series of ZDR arc and KDP–ZDR separation signature characteristics, and briefly explore potential uses of this algorithm in research and operations.

Distributed Beams: Concept of Operations for Polarimetric Rotating Phased Array Radar

Important requirements for a future generation of weather surveillance radars include improvements in data quality and more rapid update of volumetric data. Phased array radar (PAR) is a candidate technology capable of providing the required functionality. The rotating PAR (RPAR) is a potential architecture that could improve the capabilities of the current parabolic-reflector-based US Weather Surveillance Radar—1988 Doppler (WSR-88D) operational network and is more affordable than other candidate PAR architectures. However, RPAR concept of operations that support observational needs has to be developed. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Distributed Beams</i> (DB) technique introduced in this article provides a way to either reduce the scan times or to reduce the variance of radar-variable estimates by azimuthally spoiling the transmit beam while receiving multiple digital beams as the radar rotates in azimuth. Specifically, the rotation speed of the pedestal is derived from the duration of the coherent processing interval (CPI) to produce the desired spatial sampling. This results in beams from subsequent CPIs in approximately the same directions, which increases the number of available data samples for processing. The increased number of available samples can be coherently processed to reduce the variance of estimates. Alternatively, by reducing the number of samples per CPI and increasing the RPAR’s rotation rate, the scan time can be reduced without increasing the variance of estimates. Results presented demonstrate both applications of the DB technique for dual-polarization observations. Given that this technique makes use of spoiled transmit beams, its benefits come at the expense of degraded angular resolution (beamwidth and sidelobe levels), and reduced sensitivity compared with the use of pencil beams. The technique could be implemented as part of an RPAR concept of operations to meet requirements for the future weather surveillance network if certain tradeoffs are accounted for in the radar design process.