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Storm-scale interactions with rough terrain are complex. Terrain has been theorized to impact the strength of low-level mesocyclones. Surface roughness and modifications of the surrounding environment also may impact tornadogenesis or tornado intensity. The Mountainburg, Arkansas EF2 tornado on 13 April 2018 traveled along a path with minor variations in intensity and elevation throughout most of the nearly 19-km (11.8 mi) damage path as the storm moved along a river valley. A detailed damage survey showed that the tornado then made an abrupt ascent of more than 200 m (656 ft) in the last 2 km (1.2 mi) before dissipating. By examining model soundings and conducting a detailed terrain analysis, this study examines what role terrain may have had in channeling the momentum surge and enhancing the low-level vorticity to influence tornadogenesis. Other storm-scale factors are investigated to determine their potential impact on the demise of the tornado. The differential reflectivity column is studied to determine if the updraft was weakening. The relative position of the tornado and mesocyclone also are examined as the tornado ascended the terrain and dissipated to determine whether the change in elevation impacted the overall strength of the storm and to evaluate whether the storm was undergoing a traditional occlusion cycle. Finally, a large-eddy simulation model is used to explore physical changes in a tornado encountering terrain similar to the Mountainburg, Arkansas, tornado near its demise.

Techniques to mitigate analysis errors arising from the nonsimultaneity of data collections typically use advection-correction procedures based on the hypothesis (frozen turbulence) that the analyzed field can be represented as a pattern of unchanging form in horizontal translation. It is more difficult to advection correct the radial velocity than the reflectivity because even if the vector velocity field satisfies this hypothesis, its radial component does not—but that component does satisfy a second-derivative condition. We treat the advection correction of the radial velocity (υ r ) as a variational problem in which errors in that second-derivative condition are minimized subject to smoothness constraints on spatially variable pattern-translation components (U, V). The Euler–Lagrange equations are derived, and an iterative trajectory-based solution is developed in which U, V, and υ r are analyzed together. The analysis code is first verified using analytical data, and then tested using Atmospheric Imaging Radar (AIR) data from a band of heavy rainfall on 4 September 2018 near El Reno, Oklahoma, and a decaying tornado on 27 May 2015 near Canadian, Texas. In both cases, the analyzed υ r field has smaller root-mean-square errors and larger correlation coefficients than in analyses based on persistence, linear time interpolation, or advection correction using constant U and V. As some experimentation is needed to obtain appropriate parameter values, the procedure is more suitable for non-real-time applications than use in an operational setting. In particular, the degree of spatial variability in U and V, and the associated errors in the analyzed υ r field are strongly dependent on a smoothness parameter.

As the existing NEXRAD network nears the end of its life cycle, intense study and planning are underway to design a viable replacement system. Ideally, such a system would offer improved temporal resolution compared to NEXRAD, without a loss in data quality. In this study, scan speedup techniques—such as beam multiplexing (BMX) and radar imaging—are tested to assess their viability in obtaining high-quality rapid updates for a simulated long-range weather radar. The results of this study—which uses a Weather Research and Forecasting (WRF) Model–simulated supercell case—show that BMX generally improves data quality for a given scan time or can provide a speedup factor of 1.69–2.85 compared to NEXRAD while maintaining the same level of data quality. Additionally, radar imaging is shown to improve data quality and/or decrease scan time when selectively used; however, deleterious effects are observed when imaging is used in regions with sharp reflectivity gradients parallel to the beam spoiling direction. Consideration must be given to the subsequent loss of sensitivity and beam broadening. Finally, imaging is shown to have an effect on the radar-derived mesocyclone strength (ΔV) of a simulated supercell. Because BMX and radar imaging are most easily achieved with an all-digital phased array radar (PAR), these results make a strong argument for the use of all-digital PAR for high-resolution weather observations. It is believed that the results from this study can inform decisions about possible scanning strategies and design of a NEXRAD replacement system for high-resolution weather radar data.

The hurricane boundary layer (HBL) has been observed in great detail through aircraft investigations of tropical cyclones over the open ocean, but the coastal transition of the HBL has been less frequently observed. During the landfall of Hurricane Irene (2011), research and operational aircraft over water sampled the open-ocean HBL simultaneously with ground-based research and operational Doppler radars onshore. The location of the radars afforded 13 h of dual-Doppler analysis over the coastal region. Thus, the HBL from the coastal waterways, through the coastal transition, and onshore was observed in great detail for the first time. Three regimes of HBL structure were found. The outer bands were characterized by temporal perturbations of the HBL structure with attendant low-level wind maxima in the vicinity of rainbands. The inner core, in contrast, did not produce such perturbations, but did see a reduction of the height of the maximum wind and a more jet-like HBL wind profile. In the eyewall, a tangential wind maximum was observed within the HBL over water as in past studies and above the HBL onshore. However, the transition of the tangential wind maximum through the coastal transition showed that the maximum continued to reside in the HBL through 5 km inland, which has not been observed previously. It is shown that the adjustment of the HBL to the coastal surface roughness discontinuity does not immediately mix out the residual high-momentum jet aloft. Thus, communities closest to the coast are likely to experience the strongest winds onshore prior to the complete adjustment of the HBL.

A simulated vortex within a large-eddy simulation is subjected to various surface terrain, implemented through the immersed boundary method, to analyze the effects of complex topography on vortex behavior. Thirty simulations, including a control with zero-height terrain, are grouped into four categories—2D sinusoidal hills, 3D hills, valleys, and ridges—with slight modifications within each category. A medium-swirl-ratio vortex is translated over shallow terrain, which is modest in size relative to the vortex core diameter and with no explicitly defined surface roughness. While domain size restricts results to the very near-field effects of terrain, vortex–terrain interaction yields notable results. Terrain influences act to increase the variability of the near-surface vortex, including a notable leftward (rightward) deflection, acceleration (deceleration), and an expansion (a contraction) of the vortex as it ascends (descends) the terrain owing to changes in the corner flow swirl ratio. Additionally, 10-m track analyses show stronger horizontal wind speeds are found 1) on upslope terrain, resulting from transient subvortices that are more intense compared to the control simulation, and 2) in between adjacent hills simultaneous with strong pressure perturbations that descend from aloft. Composite statistics confirm that the region in between adjacent hills has the strongest horizontal wind speeds, while upward motions are more intense during ascent. Overall, valley (ridge) simulations have the largest horizontal (vertically upward) wind speeds. Last, horizontal and vertical wind speeds are shown to be affected by other terrain properties such as slope steepness and two-dimensionality of the terrain.

Phased-array radar (PAR) technology offers the flexibility of sampling the storm and clear-air regions with different update times. As such, the radial velocity from clear-air regions, typically with a lower signal-to-noise ratio, can be measured more accurately. In this work, observing system simulation experiments are conducted to explore the potential value of assimilating clear-air radial velocity observations to improve numerical prediction of supercell thunderstorms. Synthetic PAR observations of a splitting supercell are assimilated at different life cycle stages using an ensemble Kalman filter. Results show that assimilating environmental clear-air radial velocity can reduce wind errors in the near-storm environment and within the precipitation region. Improvements in the forecast are seen at different stages, especially for the forecast after 30 min. After assimilating clear-air radial velocity observations, the probabilities of updraft helicity and precipitation within the corresponding swaths of the truth simulation increase up to 30%–40%. Additional diagnostics suggest that the more accurate track forecast, stronger vertical motion, and better-maintained supercell can be attributed to the better analysis and prediction of the mean environmental winds and linear and nonlinear dynamic forces. Consequently, assimilating clear-air radial velocity produces accurate storm structure (rotating updrafts), updraft size, and storm track, and improves the surface accumulated precipitation forecast. The performance of forecasts with a higher frequency of assimilating clear-air radial velocity does not show systematic improvement. These results highlight the potential of assimilating clear-air radial velocity observations to improve numerical weather prediction forecasts of supercell thunderstorms.

A detailed damage survey is combined with high-resolution mobile, rapid-scanning X-band polarimetric radar data collected on the Shawnee, Oklahoma, tornado of 19 May 2013. The focus of this study is the radar data collected during a period when the tornado was producing damage rated EF3. Vertical profiles of mobile radar data, centered on the tornado, revealed that the radar reflectivity was approximately uniform with height and increased in magnitude as more debris was lofted. There was a large decrease in both the cross-correlation coefficient (ρhv) and differential radar reflectivity (ZDR) immediately after the tornado exited the damaged area rated EF3. Low ρhv and ZDR occurred near the surface where debris loading was the greatest. The 10th percentile of ρhv decreased markedly after large amounts of debris were lofted after the tornado leveled a number of structures. Subsequently, ρhv quickly recovered to higher values. This recovery suggests that the largest debris had been centrifuged or fallen out whereas light debris remained or continued to be lofted. Range–height profiles of the dual-Doppler analyses that were azimuthally averaged around the tornado revealed a zone of maximum radial convergence at a smaller radius relative to the leading edge of lofted debris. Low-level inflow into the tornado encountering a positive bias in the tornado-relative radial velocities could explain the existence of the zone. The vertical structure of the convergence zone was shown for the first time.

This study utilizes data collected by the University of Oklahoma Advanced Radar Research Center’s Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME) C-band radar as well as the federal KTLX and KOUN WSR-88D S-band radars to study a supercell that simultaneously produced a long-track EF-4 tornado and an EF-2 landspout tornado (EF indicates the enhanced Fujita scale) near Norman, Oklahoma, on 10 May 2010. Contrasting polarimetric characteristics of two tornadoes over similar land cover but with different intensities are documented. Also, the storm-scale sedimentation of debris within the supercell is investigated, which includes observations of rotation and elongation of a tornadic debris signature with height. A dual-wavelength comparison of debris at S and C bands is performed. These analyses indicate that lofted debris within the tornado was larger than debris located outside the damage path of the tornado and that debris size outside the tornado increased with height, likely as the result of centrifuging. Profiles of polarimetric variables were observed to become more vertically homogeneous with time.

On 27 May 2015, the Atmospheric Imaging Radar (AIR) collected high-temporal resolution radar observations of an EF-2 tornado near Canadian, Texas. The AIR is a mobile, X-band, imaging radar that uses digital beamforming to collect simultaneous RHI scans while steering mechanically in azimuth to obtain rapid-update weather data. During this deployment, 20°-by-80° (elevation × azimuth) sector volumes were collected every 5.5 s at ranges as close as 6 km. The AIR captured the late-mature and decaying stages of the tornado. Early in the deployment, the tornado had a radius of maximum winds (RMW) of 500 m and exhibited maximum Doppler velocities near 65 m s−1. This study documents the rapid changes associated with the dissipation stages of the tornado. A 10-s resolution time–height investigation of vortex tilt and differential velocity is presented and illustrates an instance of upward vortex intensification as well as downward tornado decay. Changes in tornado intensity over periods of less than 30 s coincided with rapid changes in tornado diameter. At least two small-scale vortices were observed being shed from the tornado during a brief weakening period. A persistent layer of vortex tilt was observed near the level of free convection, which separated two layers with contrasting modes of tornado decay. Finally, the vertical cross correlation of vortex intensity reveals that apart from the brief instances of upward vortex intensification and downward decay, tornado intensity was highly correlated throughout the observation period.

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.

Polarimetric weather radars are capable of detecting tornadic debris signatures (TDSs), which result from debris being lofted to the level of the radar beam and can be modulated by centrifuging and debris fallout. TDSs have been used in promising applications, such as enhanced tornado detection, improved warning and assessment of a potential tornado threat, and estimating tornado damage potential and intensity. Regions with negative differential reflectivity have been found in TDS observations but a physical explanation is yet to be determined. Some hypotheses suggest a common alignment of debris or non-Rayleigh scattering to be the cause. However, because it is inherently difficult and extremely dangerous to verify this, a simulated environment can aid in this context to reveal information that would otherwise be impossible to retrieve in practice. Under the simulation environment, the true construct of the debris is known, wherefrom the bulk distributions of position and orientation data can be extracted for statistical analysis. The primary focus of this work is to investigate the cause of nonzero mean values of in TDSs with simulated data from SimRadar, which is a polarimetric radar time series simulator developed for tornadic debris studies. The 6-degrees-of-freedom (DOF) model shows that for both small and large platelike debris, the debris face tends to have some common degree of alignment normal to the wind direction, which may be a plausible cause for the occurrence of negative in real polarimetric radar observations. Potential explanations for other hypotheses regarding tornado and debris dynamics are also briefly discussed.

This study examines organizational changes and periods of rapid forward propagation in an MCS on 6 July 2015 in South Dakota. The MCS case was the focus of a Plains Elevated Convection at Night (PECAN) IOP. Data from the Sioux Falls WSR-88D and a high-resolution WRF simulation are analyzed to examine two periods of rapid forward propagation (or surges) and organizational changes. During the first surge (surge A), the northern portion of the convective line propagates eastward faster than the southern portion, and the northern portion of the leading line transitions from a single convective core to a multicellular structure as it merges with convection initiation. Radar reflectivity factor Z and graupel concentrations decrease above the melting layer, while at lower altitudes Z increases. The MCS cold pool also intensifies and deepens beneath an expanded region of high rainwater content and subsaturated air. Throughout surge A, a mesoscale circulation with strong rear-to-front near-surface flow and front-to-rear midlevel flow is also evident. By the end of surge A, the leading edge of the MCS cold pool is beneath developing convection initiation ahead of the original convective line while the original convective updraft weakened and moved rearward. This MCS evolution is similar to discrete propagation events discussed in past studies, except with new convection developing along an intersecting convective band. During surge B, the MCS transitions from a multicellular structure to a single, intense updraft. Smaller microphysical and thermodynamic changes are observed within the MCS during surge B compared to surge A, and the mesoscale circulation continues to develop.

Tornadoes are capable of lofting large pieces of debris that present irregular shapes, near-random orientations, and a wide range of dielectric constants to polarimetric radars. The unique polarimetric signature associated with lofted debris is called the tornadic debris signature (TDS). While ties between TDS characteristics and tornado- and storm-scale kinematic processes have been speculated upon or investigated using photogrammetry and single-Doppler analyses, little work has been done to document the three-dimensional wind field associated with the TDS. Data collected by the Oklahoma City, Oklahoma (KTLX), and Norman, Oklahoma (KOUN), WSR-88D S-band radars as well as the University of Oklahoma’s (OU) Advanced Radar Research Center’s Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME) C-band radar are used to construct single- and dual-Doppler analyses of a tornadic supercell that produced an EF4 tornado near the towns of Moore and Choctaw, Oklahoma, on 10 May 2010. This study documents the spatial distribution of polarimetric radar variables and how each variable relates to kinematic fields such as vertical velocity and vertical vorticity. Special consideration is given to polarimetric signatures associated with subvortices within the tornado. An observation of negative differential reflectivity ([Formula: see text]) at the periphery of tornado subvortices is presented and discussed. Finally, dual-Doppler wind retrievals are compared to single-Doppler axisymmetric wind fields to illustrate the merits of each method.

In an effort to study and characterize scattering mechanisms of debris particles in tornadoes, a numerical polarimetric radar emulator was developed. This paper is primarily motivated by attempts to explain radar observations near tornadoes. One such observation is the regions of negative differential reflectivity, which have been found near tornadoes but they are yet to be explained physically. There are hypotheses that suggest common debris alignment and/or dominant scattering from objects with high radar-cross-section (RCS) values that cause negative $Z_{DR}$ , but they are extremely challenging to verify due to the inherent danger near the vicinity of tornadoes. It is, however, possible to numerically construct the scenes through representative simulations to verify the plausible causes. This serves as our primary motivation to develop the radar emulator. The novel aspects of this paper are the realistic trajectory derivation, which is based on a physical air-drag model, and the representative diversity of RCS contributions from each debris object, developed through realistic polarimetric RCS modeling and anechoic chamber measurements.

Mobile radar platforms designed for observation of severe local storms have consistently pushed the boundaries of spatial and temporal resolution in order to allow for detailed analysis of storm structure and evolution. Digital beamforming, or radar imaging, is a technique that is similar in nature to a photograwphic camera, where data samples from different spaces at the same range are collected simultaneously. This allows for rapid volumetric update rates compared to radars that scan with a single narrow beam. The Atmospheric Imaging Radar (AIR) is a mobile X-band (3.14-cm wavelength) imaging weather radar that transmits a vertical, 20° fan beam and uses a 36-element receive array to form instantaneous range–height indicators (RHIs) with a native beamwidth of 1° × 1°. Rotation in azimuth allows for 20° × 90° volumetric updates in under 6 s, while advanced pulse compression techniques achieve 37.5-m range resolution. The AIR has been operational since 2012 and has collected data on tornadoes and supercells at ranges as close as 6 km, resulting in high spatial and temporal resolution observations of severe local storms. The use of atmospheric imaging is exploited to detail rapidly evolving phenomena that are difficult to observe with traditional scanning weather radars.

To obtain accurate radar-measured wind measurements in tornadoes, differences between air and Doppler velocities must be corrected. These differences can cause large errors in radar estimates of maximum tangential wind speeds, and large errors in single-Doppler retrievals of radial and vertical velocities. Since larger scatterers (e.g., debris) exhibit larger differences from air velocities compared to small scatterers (e.g., raindrops), the dominant scatterer type affecting radar measurements is examined. In this study, radar variables are simulated for common weather radar frequencies using debris and raindrop trajectories computed with a large-eddy simulation model and two electromagnetic scattering models. These simulations include a large range of raindrop and wood board sizes and concentrations, and reveal the significant frequency dependence of the equivalent reflectivity factor and Doppler velocity. At S band, dominant scatterers are wood boards, except when wood board concentrations are very low. In contrast, raindrops are the dominant scatterers at Ka and W bands even when large concentrations of wood boards are present, except for low raindrop concentrations. Dual-wavelength velocity differences exhibit high correlation with air and Doppler velocity differences for most cases, which may enable direct measurements of scatterer-induced Doppler velocity bias in tornadoes. Moreover, dual-wavelength ratios are shown to exhibit strong correlations with dominant scatterer size, except when Rayleigh scatterers are dominant. Finally, vertical velocity retrievals are shown to exhibit lower errors at high frequencies, and large errors remain at centimeter wavelengths even after debris centrifuging corrections are applied in cases with high debris concentration.

Past numerical simulation studies found that debris loading from sand-sized particles may substantially affect tornado dynamics, causing reductions in near-surface wind speeds up to 50%. To further examine debris loading effects, simulations are performed using a large-eddy simulation model with a two-way drag force coupling between air and sand. Simulations encompass a large range of surface debris fluxes that cause negligible to substantial impact on tornado dynamics for a high-swirl tornado vortex simulation. Simulations are considered for a specific case with a single vortex flow type (swirl ratio, intensity, and translation velocity) and a fixed set of debris and aerodynamic parameters. Thus, it is stressed that these findings apply to the specific flow and debris parameters herein and would likely vary for different flows or debris parameters. For this specific case, initial surface debris fluxes are varied over a factor of 16 384, and debris cloud mass varies by only 42% of this range because a negative feedback reduces near-surface horizontal velocities. Debris loading effects on the axisymmetric mean flow are evident when maximum debris loading exceeds 0.1 kg kg−1, but instantaneous maximum wind speed and TKE exhibit small changes at smaller debris loadings (greater than 0.01 kg kg−1). Initially, wind speeds are reduced in a shallow, near-surface layer, but the magnitude and depth of these changes increases with higher debris loading. At high debris loading, near-surface horizontal wind speeds are reduced by 30%–60% in the lowest 10 m AGL. In moderate and high debris loading scenarios, the number and intensity of subvortices also decrease close to the surface.

Ground-based radar technologies have made a tremendous impact by revealing the three-dimensional structure of winds and precipitation in tornadic storms and tornadoes. In this chapter, a historical perspective on ground-based radar technology is presented, including the exciting technological evolution from early weather radars that observed the first hook echoes to modern-day polarimetric and phased array radar technology capable of scanning storms in less than 10 s. Early radar studies revealed the importance of Doppler radar capabilities, and led to implementation of a nationwide operational Doppler radar network that substantially improved tornado warning lead-time and accuracy. Scientists have used ground-based radars extensively in field studies of tornadoes and tornadic storms, including mobile radar systems that allow scientists to get very close to storms to obtain very high resolution data. These field studies include large-scale field experiments such as the first and second Verification of the Origins of Rotation in Tornadoes EXperiments (VORTEX), which deployed a network of mobile radars with a suite of other instruments. From the combined efforts of scientists and engineers to develop ground-based radars and conduct field experiments, new scientific discoveries have transformed our understanding of tornadoes and tornadic storms. By combining data from multiple radars, scientists have developed an understanding of the three-dimensional flows with supercells and tornadoes, and documented how changes in the storm-scale flow impact tornadoes and their formation. Rapid-scan radar data have shown that tornadoes can form or dissipate in less than 1 min, and that the tornado’s intensity and three-dimensional structure can drastically change in 10 s or less. Using dual-polarization radars, new capabilities have emerged to detect tornado debris remotely and infer tornado intensity and storm-scale processes. Exciting mobile radars and operational radar prototypes are under development that will provide both rapid-scanning and dual-polarization capabilities. These new radar systems will have a critical role in helping scientists discover the missing pieces in our understanding of tornadogenesis and tornado dynamics, and will continue to improve tornado warning performance to better protect society.

Statistical properties of tornado debris signatures (TDSs) are investigated using S- and C-band polarimetric radar data with comparisons to damage surveys and satellite imagery. Close proximity of the radars to the 10 May 2010 Moore–Oklahoma City, Oklahoma, tornado that was rated as a 4 on the enhanced Fujita scale (EF4) provides a large number of resolution volumes, and good temporal and spatial matching for dual-wavelength comparisons. These comparisons reveal that S-band TDSs exhibit a higher radar reflectivity factor (ZHH) and copolar cross-correlation coefficient (ρhv) than do C-band TDSs. Higher S-bandρhvmay result from a smaller ratio of non-Rayleigh scatterers to total scatterers due to the smaller electrical sizes of debris and, consequently, reduced resonance effects. A negativeZDRsignature is observed at 350 m AGL at both the S and C bands as the tornado passes over a vegetated area near a large body of water. Another interesting signature is a positive (negative) shift in propagation differential phase (ΦDP) at S band (C band), which could result from increased phase folding at C band. With increasing height above 350 m AGL, the S- and C-bandZHHdecreases andρhvincreases, indicating a decrease in debris size. To investigate relationships between polarimetric variables and tornado wind fields, range profiles of radial and tangential wind speeds are obtained using two radars. Velocity profiles reveal radial divergence within vortex core flow through 700 m AGL collocated with the TDS. Formation of a weak-echo hole and higherρhvin the vortex center aloft suggests debris centrifuging, outward motion of scatterers due to radial divergence (i.e., two-cell vortex flow), or both.

Mobile weather radars often utilize rapid-scan strategies when collecting observations of severe weather. Various techniques have been used to improve volume update times, including the use of agile and multibeam radars. Imaging radars, similar in some respects to phased arrays, steer the radar beam in software, thus requiring no physical motion. In contrast to phased arrays, imaging radars gather data for an entire volume simultaneously within the field of view (FOV) of the radar, which is defined by a broad transmit beam. As a result, imaging radars provide update rates significantly exceeding those of existing mobile radars, including phased arrays. The Advanced Radar Research Center (ARRC) at the University of Oklahoma (OU) is engaged in the design, construction, and testing of a mobile imaging weather radar system called the atmospheric imaging radar (AIR). Initial tests performed with the AIR demonstrate the benefits and versatility of utilizing beamforming techniques to achieve high spatial and temporal resolution. Specifically, point target analysis was performed using several digital beamforming techniques. Adaptive algorithms allow for improved resolution and clutter rejection when compared to traditional techniques. Additional experiments were conducted during two severe weather events in Oklahoma. Several digital beamforming methods were tested and analyzed, producing unique, simultaneous multibeam measurements using the AIR.

Because they are most sensitive to atmospheric moisture content, radar refractivity observations can provide high-resolution information about the highly variable low-level moisture field. In this study, simulated radar refractivity-related phase-change data were created using a radar simulator from realistic highresolution model simulation data for a dryline case. These data were analyzed using the 2DVAR system developed specifically for the phase-change data. Two sets of experiments with the simulated observations were performed, one assuming a uniform target spacing of 250 m and one assuming nonuniform spacing between 250 m to 4 km. Several sources of observation error were considered, and their impacts were examined. They included errors due to ground target position uncertainty, typical random errors associated with radar measurements, and gross error due to phase wrapping. Without any additional information, the 2DVAR system was incapable of dealing with phase-wrapped data directly. When there was no phase wrapping in the data, the 2DVAR produced excellent analyses, even in the presence of both position uncertainty and random radar measurement errors. When a separate pre-processing step was applied to unwrap the phase-wrapped data, quality moisture analyses were again obtained, although the analyses were smoother due to the reduced effective resolution of the observations by interpolation and smoothing involved in the unwrapping procedure. The unwrapping procedure was effective even when significant differences existed between the analyzed state and the state at a reference time. The results affirm the promise of using radar refractivity phase-change measurements for near-surface moisture analysis.

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.

This study presents a 2-yr-long comparison of Weather Surveillance Radar-1988 Doppler (WSR-88D) refractivity retrievals with Oklahoma Mesonetwork (“Mesonet”) and sounding measurements and discusses some challenges to implementing radar refractivity operationally. Temporal and spatial analyses of radar refractivity exhibit high correlation with Mesonet data; however, periods of large refractivity differences between the radar and Mesonet are observed. Several sources of refractivity differences are examined to determine the cause of large refractivity differences. One source for nonklystron radars includes magnetron frequency drift, which can introduce errors up to 10 N-units if the frequency drift is not corrected. Different reference maps made at different times can “shift” refractivity values. A semiautomated method for producing reference maps is presented, including trade-offs for making reference maps under different conditions. Refractivity from six Mesonet stations within the clutter domain of the Oklahoma City, Oklahoma, WSR-88D (KTLX) is compared with radar refractivity retrievals. The analysis revealed that the six Mesonet stations exhibited a prominent diurnal trend in differences between radar and Mesonet refractivity measurements. The diurnal range of the refractivity differences sometimes exceeded 20 or 30 N-units in the warm season, which translated to a potential dewpoint temperature difference of several degrees Celsius. A seasonal analysis revealed that large refractivity differences primarily occurred during the warm season when refractivity is most sensitive to moisture. Ultimately, the main factor in determining the magnitude of the differences between the two refractivity platforms is the vertical gradient of refractivity because of the difference in observation height between the radar and a surface station.

A tornado outbreak occurred in central Oklahoma on 10 May 2010, including two tornadoes with enhanced Fujita scale ratings of 4 (EF-4). Tragically, three deaths were reported along with significant property damage. Several strong and violent tornadoes occurred near Norman, Oklahoma, which is a major hub for severe storms research and is arguably one of the best observed regions of the country with multiple Doppler radars operated by both the federal government and the University of Oklahoma (OU). One of the most recent additions to the radars in Norman is the high-resolution OU Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME). As the name implies, the radar is used as a platform for research and education in both science and engineering studies using polarimetric radar. To facilitate usage of the system by students and faculty, OU-PRIME was constructed adjacent to the National Weather Center building on the OU research campus. On 10 May 2010, several tornadoes formed near the campus while OU researchers were operating OU-PRIME in a sector scanning mode, providing polarimetric radar data with unprecedented resolution and quality. In this article, the environmental conditions leading to the 10 May 2010 outbreak will be described, an overview of OU-PRIME will be provided, and several examples of the data and possible applications will be summarized. These examples will highlight supercell polarimetric signatures during and after tornadogenesis, and they will describe how the polarimetric signatures are related to observations of reflectivity and velocity.

A case study illustrating the impact of moisture variability on convection initiation in a synoptically active environment without strong moisture gradients is presented. The preconvective environment on 30 April 2007 nearly satisfied the three conditions for convection initiation: moisture, instability, and a low-level lifting mechanism. However, a sounding analysis showed that a low-level inversion layer and high LFC would prevent convection initiation because the convective updraft velocities required to overcome the convective inhibition (CIN) were much higher than updraft velocities typically observed in convergence zones. Radar refractivity retrievals from the Twin Lakes, Oklahoma (KTLX), Weather Surveillance Radar-1988 Doppler (WSR-88D) showed a moisture pool contributing up to a 2°C increase in dewpoint temperature where the initial storm-scale convergence was observed. The analysis of the storm-relative wind field revealed that the developing storm ingested the higher moisture associated with the moisture pool. Sounding analyses showed that the moisture pool reduced or nearly eliminated CIN, lowered the LFC by about 500 m, and increased CAPE by 2.5 times. Thus, these small-scale moisture changes increased the likelihood of convection initiation within the moisture pool by creating a more favorable thermodynamic environment. The results suggest that refractivity data could improve convection initiation forecasts by assessing moisture variability at finer scales than the current observation network.

The 2007 and 2008 spring refractivity experiments at KTLX investigated the potential utility of high-resolution, near-surface refractivity measurements to operational forecasting. During these experiments, forecasters at the Norman, Oklahoma, National Weather Service Forecast Office (NWSFO) assessed refractivity and scan-to-scan refractivity change fields retrieved from the Weather Surveillance Radar-1988 Doppler weather radar near Oklahoma City—Twin Lakes, Oklahoma (KTLX). Both quantitative and qualitative analysis methods were used to analyze the 41 responses from seven forecasters to a questionnaire designed to measure the impact of refractivity fields on forecast operations. The analysis revealed that forecasts benefited from the refractivity fields on 25% of the days included in the evaluation. In each of these cases, the refractivity fields provided complementary information that somewhat enhanced the forecasters’ capability to analyze the near-surface environment and boosted their confidence in moisture trends. A case in point was the ability to track a retreating dryline after its location was obscured by a weak reflectivity bloom caused by biological scatterers. Forecasters unanimously agreed, however, that the impact of this complementary information on their forecasts was too insignificant to justify its addition as an operational dataset. The applicability of these findings to other NWSFOs may be limited to locations with similar weather situations and access to surface data networks like the Oklahoma Mesonet.

Temperature and wind data from a rural micronet and nearby site of the Oklahoma Mesonet are analyzed to study the frequency, strength, and formation processes of cold-pool events in a region with gentle terrain. Spatial analyses were performed for a 2-yr-long temperature record from 26 temperature/humidity surface stations, deployed across a 120 m × 320 m micronet located in a region of gently sloped terrain with maximum elevation changes of ∼25 m. Cold pools frequently formed at the base of a gentle slope in a small depression of only ∼6-m depth that is also sheltered by trees. The strength of each cold-pool event was classified according to a cold-pool index based on average nocturnal temperature perturbations within the cold-pool region. Wind data collected with sonic anemometers on a 15-m-tall tower at the micronet for a period of three months (spring 2005) suggest that flow sheltering by vegetation plays an important role in the cold-pool formation. The wind data also show signatures of katabatic flow for about 50% of the strong cold-pool events. However, a heat budget analysis for these nights suggested that the katabatic flows were associated with warm-air advection along the slope and that if katabatic jets had penetrated the cold pool, they would have produced substantial warming in the region of the cold pool. Since such warming was not observed, it is concluded that the katabatic jets did not actually penetrate the cold pool but likely flowed over it. An analysis of Richardson numbers demonstrates that cold-pool formation frequently occurs under strongly stable conditions that tend to suppress vertical turbulent mixing in the surface layer. Observations that significant temperature changes can occur even with elevation changes on the order of 6 m have important implications in agriculture as well as in data assimilation.

The Lake Thunderbird Micronet is a dense network of environmental sensors and a meteorological tower situated on ~10 acres of rural land in central Oklahoma. The Micronet was established in the spring of 2002 as part of a grassroots effort by a team of faculty and researchers at the University of Oklahoma to provide unique training and research opportunities for undergraduate and graduate students in meteorology and related environmental sciences. The history and design of the Micronet and use of the Micronet in undergraduate and graduate student training and research are described. Examples of interesting phenomena sampled at the Micronet are also presented.

In an effort to investigate scattering characteristics of debris particles in tornadoes, a numerical polarimetric radar emulator was developed. Regions of negative differential reflectivity have commonly been observed but are still yet to be explained physically. There are hypotheses that suggest common debris alignment and/or dominant scattering from objects with high radar-cross-section (RCS) values, but they are extremely challenging to verify due to the danger within tornadoes. It is, however, possible to numerically construct the scenes through representative simulation to verify the plausible causes. This serves as our primary motivation to develop the radar emulator. The novel aspects of this work are the realistic trajectory derivation, which is based on physical air-drag model, and the representative diversity of RCS contributions from each debris particle, developed through a realistic polarimetric RCS modeling and anechoic chamber measurements.

The Atmospheric Imaging Radar (AIR) is a mobile X-band imaging weather radar system designed and built by the Advanced Radar Research Center (ARRC) at the University of Oklahoma. By utilizing a vertical 20° fan beam on transmit, as well as 36 receiving elements, digital beamforming techniques can be used in order to form RHI scans with simultaneous beams. Azimuthal pedestal rotation allows for sector scans in quickly evolving weather situations, resulting in full volumetric updates on a storm of interest in as little as 6 s. This combination of spatial and temporal resolution allows for the detection and analysis of critical storm structure elements and evolutionary characteristics that occur on extremely short timescales. The AIR has been deployed in convective field campaigns each spring in the Southern Plains since 2012 and has collected data on tornadoes, supercells, and squall lines at ranges as close as 3 km, resulting in very high spatial and temporal observations of severe convective storms. The progression of system configurations across field experiments is discussed, and preliminary data are presented. Features that were previously unobservable with typical scanning weather radars, as well as context regarding the implications of imaging radar for severe weather observations are discussed. Future analysis opportunities and hardware implementations are outlined.

A polarimetric radar time-series emulator that produces both meteorological and non-meteorological radar echoes was developed. There are two main constituents in the radar emulator: a set of quasi-randomly populated tracers to emulate meteorological echoes and another set of point scatterers to emulate individual tornadic debris objects. Based on previous work to emulate meteorological radar echoes, the concept was expanded to the new framework. Two key innovations of the framework are the physically consistent trajectories of individual debris objects and realistic polarimetric radar cross section modeling of the debris targets.

Rapid updates are a highly desired feature in the field of mobile weather radars. Various techniques have been utilized to improve volume update times, including the use of agile and multi-beam radars. Imaging radars, similar in some respects to phased arrays, steer the radar beam in software, thus requiring no physical motion. In contrast to phased arrays, imaging radars gather data for an entire volume simultaneously within the field-of-view (FOV) of the radar, which is defined by the broad transmit beam. As a result, imaging radars provide update rates exceeding those of existing mobile radars, including phased arrays. The Atmospheric Radar Research Center (ARRC) at the University of Oklahoma (OU) is currently engaged in evaluating a mobile X-band imaging radar system by through observations of severe weather events.

Rapid updates are a highly desired feature in the field of mobile weather radars. Various techniques have been used to improve volume update times, including the use of agile and multi-beam radars. Imaging radars, similar in some respects to phased arrays, steer the radar beam in software, thus requiring no physical motion. In contrast to phased arrays, imaging radars gather data for an entire volume simultaneously within the field-of-view (FOV) of the radar, which is defined by the broad transmit beam. As a result, imaging radars provide update rates exceeding those of existing mobile radars, including phased arrays. The Atmospheric Radar Research Center (ARRC) at the University of Oklahoma (OU) is currently engaged in the design and fabrication of the world's first mobile imaging weather radar.