Publications Since 2010
Bodine, D., M. Kumjian, R. Palmer, P. Heinselman, A. Ryzhkov, 2013: Tornado Damage Estimation Using Polarimetric Radar. Weather and Forecasting, 28, 139-158.
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.
Newman, J., V. Lakshmanan, P. Heinselman, M. Richman, T. Smith, 2013: Range-Correcting Azimuthal Shear in Doppler Radar Data. Weather and Forecasting, 28, 194-211, doi:10.1175/WAF-D-11-00154.1.
The current tornado detection algorithm (TDA) used by the National Weather Service produces a large number of false detections, primarily because it calculates azimuthal shear in a manner that is adversely impacted by noisy velocity data and range-degraded velocity signatures. Coincident with the advent of new radar-derived products and ongoing research involving new weather radar systems, the National Severe Storms Laboratory is developing an improved TDA. A primary component of this algorithm is the local, linear least squares derivatives (LLSD) azimuthal shear field. The LLSD method incorporates rotational derivatives of the velocity field and is affected less strongly by noisy velocity data in comparison with traditional “peak to peak” azimuthal shear calculations. LLSD shear is generally less range dependent than peak-to-peak shear, although some range dependency is unavoidable. The relationship between range and the LLSD shear values of simulated circulations was examined to develop a range correction for LLSD shear. A linear regression and artificial neural networks (ANNs) were investigated as range-correction models. Both methods were used to produce fits for the simulated shear data, although the ANN excelled as it could capture the nonlinear nature of the data. The range-correction methods were applied to real radar data from tornadic and nontornadic events to measure the capacity of the corrected shear to discriminate between tornadic and nontornadic circulations. The findings presented herein suggest that both methods increased shear values during tornadic periods by nearly an order of magnitude, facilitating differentiation between tornadic and nontornadic scans in tornadic events.
Elmore, K. L., P. L. Heinselman, D. J. Stensrud, 2012: Using WSR-88D Data and Insolation Estimates to Determine Convective Boundary Layer Depth. Journal of Atmospheric and Oceanic Technology, 29, 581-588, doi:http://dx.doi.org/10.1175/JTECH-D-11-00043.1.
Prior work shows that Weather Surveillance Radar-1988 Doppler (WSR-88D) clear-air reflectivity can be used to determine convective boundary layer (CBL) depth. Based on that work, two simple linear regressions are developed that provide CBL depth. One requires only clear-air radar reflectivity from a single 4.5° elevation scan, whereas the other additionally requires the total, clear-sky insolation at the radar site, derived from the radar location and local time. Because only the most recent radar scan is used, the CBL depth can, in principle, be computed for every scan. The “true” CBL depth used to develop the models is based on human interpretation of the 915-MHz profiler data. The regressions presented in this work are developed using 17 summer days near Norman, Oklahoma, that have been previously investigated. The resulting equations and algorithms are applied to a testing dataset consisting of 7 days not previously analyzed. Though the regression using insolation estimates performs best, errors from both models are on the order of the expected error of the profiler-estimated CBL depth values. Of the two regressions, the one that uses insolation yields CBL depth estimates with an RMSE of 208 m, while the regression with only clear-air radar reflectivity yields CBL depth estimates with an RMSE of 330 m.
Available online at http://journals.ametsoc.org/doi/pdf/10.1175/JTECH-D-11-00043.1.
Heinselman, P. L., D. S. LaDue, H. Lazrus, 2012: Exploring Impacts of Rapid-Scan Radar Data on NWS Warning Decisions. Weather and Forecasting, 27, 1031-1044, doi:http://dx.doi.org/10.1175/WAF-D-11-00145.1.
Rapid-scan weather radars, such as the S-band phased array radar at the National Weather Radar Testbed in Norman, Oklahoma, improve precision in the depiction of severe storm processes. To explore potential impacts of such data on forecaster warning decision making, 12 National Weather Service forecasters participated in a preliminary study with two control conditions: 1) when radar scan time was similar to volume coverage pattern 12 (4.5 min) and 2) when radar scan time was faster (43 s). Under these control conditions, forecasters were paired and worked a tropical tornadic supercell case. Their decision processes were observed and audio was recorded, interactions with data displays were video recorded, and the products were archived. A debriefing was conducted with each of the six teams independently and jointly, to ascertain the forecaster decision-making process. Analysis of these data revealed that teams examining the same data sometimes came to different conclusions about whether and when to warn. Six factors contributing toward these differences were identified: 1) experience, 2) conceptual models, 3) confidence, 4) tolerance of possibly missing a tornado occurrence, 5) perceived threats, and 6) software issues. The three 43-s teams issued six warnings: three verified, two did not verify, and one event was missed. Warning lead times were the following: tornado, 18.6 and 11.5 min, and severe, 6 min. The three tornado warnings issued by the three 4.5-min teams verified, though warning lead times were shorter: 4.6 and 0 min (two teams). In this case, use of rapid-scan data showed the potential to extend warning lead time and improve forecasters’ confidence, compared to standard operations.
Newman, J. F., P. L. Heinselman, 2012: Evolution of a Quasi-Linear Convective System Sampled by Phased Array Radar. Monthly Weather Review, 140, 3467-3486, doi:http://dx.doi.org/10.1175/MWR-D-12-00003.1.
On 2 April 2010, a quasi-linear convective system (QLCS) moved eastward through Oklahoma during the early morning hours. Wind damage in Rush Springs, Oklahoma, approached (enhanced Fujita) EF1-scale intensity and was likely associated with a mesovortex along the leading edge of the QLCS. The evolution of the QLCS as it produced its first bow echo was captured by the National Weather Radar Testbed Phased Array Radar (NWRT PAR) in Norman, Oklahoma. The NWRT PAR is an S-band radar with an electronically steered beam, allowing for rapid volumetric updates (~1 min) and user-defined scanning strategies. The rapid temporal updates and dense vertical sampling of the PAR created a detailed depiction of the damaging wind mechanisms associated with the QLCS. Key features sampled by the PAR include microbursts, an intensifying midlevel jet, and rotation associated with the mesovortex. In this work, PAR data are analyzed and compared to data from nearby operational radars, highlighting the advantages of using high-temporal-resolution data to monitor storm evolution.
The PAR sampled the events preceding the Rush Springs circulation in great detail. Based on PAR data, the midlevel jet in the QLCS strengthened as it approached Rush Springs, creating an area of strong midlevel convergence where it impinged on the system-relative front-to-rear flow. As this convergence extended to the lower levels of the storm, a preexisting azimuthal shear maximum increased in magnitude and vertical extent, and EF1-scale damage occurred in Rush Springs. The depiction of these events in the PAR data demonstrates the complex and rapidly changing nature of QLCSs.
Bodine, D., D. Michaud, R. D. Palmer, P. L. Heinselman, J. Brotzge, N. Gasperoni, B. L. Cheong, M. Xue, J. Gao, 2011: Understanding radar refractivity: Sources of uncertainty. Journal of Applied Meteorology and Climatology, 50, 2543-2560, doi:http://dx.doi.org/10.1175/2011JAMC2648.1.
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.
Emersic, C., P. L. Heinselman, D. R. MacGorman, E. Bruning, 2011: Lightning activity in a hail-producing storm observed with phased-array radar. Monthly Weather Review, 139, 1809-1825, doi:10.1175/2010MWR3574.1.
This study examined lightning activity relative to the rapidly evolving kinematics of a hail-producing storm on 15 August 2006. Data were provided by the National Weather Radar Testbed Phased-Array Radar, the Oklahoma Lightning Mapping Array, and the National Lightning Detection Network.
This analysis is the first to compare the electrical characteristics of a hail-producing storm with reflectivity and radial velocity structure at temporal resolutions of <1 min. Total flash rates increased to ~220 / min as the storm’s updraft first intensified, leveled off during its first mature stage, and then decreased for 2–3 min despite the simultaneous development of another updraft surge. This reduction in flash rate occurred as wet hail formed in the new updraft and was likely related to the wet growth; wet growth is not conducive to hydrometeor charging and probably contributed to the formation of a “lightning hole” without a mesocyclone. Total flash rates subsequently increased to ~450 / min as storm volume and inferred graupel volume increased, and then decreased as the storm dissipated. Vertical charge structure in the storm initially formed a positive tripole (midlevel negative charge between upper and lower positive charges). Charge structure in the second updraft surge consisted of negative charge above deep midlevel positive charge, a reversal consistent with the effect of large liquid water contents on hydrometeor charge polarity in laboratory experiments. Prior to the second updraft surge, the storm produced two cloud-to-ground flashes, both lowering the usual negative charge to ground. Shortly before hail likely reached ground, the storm produced four cloud-to-ground flashes, all lowering positive charge. Episodes of high singlet VHF sources were observed at ~13–15 km during the initial formation and later intensification of the storm’s updraft.
Heinselman, P. L., S. M. Torres, 2011: High-temporal-resolution capabilities of the National Weather Radar Testbed Phased-Array Radar. Journal of Applied Meteorology and Climatology, 50, 579-593.
Since 2007 the advancement of the National Weather Radar Testbed Phased-Array Radar (NWRT PAR) hardware and software capabilities has been supporting the implementation of high-temporal-resolution (1 min) sampling. To achieve the increase in computational power and data archiving needs required for high-temporal-resolution sampling, the signal processor was upgraded to a scalable, Linux-based cluster with a distributed computing architecture. The development of electronic adaptive scanning, which can reduce update times by focusing data collection on significant weather, became possible through functionality added to the radar control interface and real-time controller. Signal processing techniques were implemented to address data quality issues, such as artifact removal and range-and-velocity ambiguity mitigation, absent from the NWRT PAR at its installation. The hardware and software advancements described above have made possible the development of conventional and electronic scanning capabilities that achieve high-temporal-resolution sampling. Those scanning capabilities are sector- and elevation-prioritized scanning, beam multiplexing, and electronic adaptive scanning. Each of these capabilities and related sampling trade-offs are explained and demonstrated through short case studies.
Heinselman, P., D. LaDue, D. Kingfield, R. Hoffman, B. MacAloney II, 2013: Simulated NWS Tornado Warning Decisions Using Rapid-scan Radar Data. Extended Abstracts, 29th Conference on Environmental Information Processing, Austin, TX, USA, Amer. Meteor. Soc., 8.3.
Analysis of a tropical tornadic supercell by NWS forecasters during the 2010 Phased Array Radar Innovative Sensing Experiment (PARISE) suggested that the use of rapid-scan radar data can result in longer warning lead times compared to use of traditional 4.5-min data. To increase the sample size and learn more about forecasters’ conceptual models and warning decisions, twelve NWS forecasters participated in the 2012 PARISE, which ran for six weeks during June – August 2012. Two forecasters participated each week. The experiment's goal was to test whether rapid, adaptive sampling with the phased array radar at the National Weather Radar Testbed increases NWS forecasters’ ability to effectively cope with tough tornado warning cases.
During the experiment each forecaster worked four cases ranging from 18 – 52 min in length. Their activity was videotaped. For each case, the forecasters’ goal was to decide whether a tornado warning was warranted. Before each case the forecasters received a pre-recorded weather briefing that provided situational awareness of the environment. The forecasters then worked the case in displaced real time using AWIPS II. RecordMyDesktop software recorded forecaster interaction with the radar data. After each case the forecasters reviewed the video and worked with researchers to build a timeline of their actions, what they saw and interpreted in the data, and their decision process. They also completed confidence and mental workload rankings. Finally, forecasters drew pictures and described the conceptual model they developed during each case. Findings from the experiment will be presented.
Witt, A., T. M. Smith, P. L. Heinselman, S. T. Irwin, K. L. Manross, 2013: Wet Microburst Events Observed with Phased Array Radar. Extended Abstracts, 36th Conference on Radar Meteorology, Breckenridge, CO, USA, Amer. Meteor. Soc., 148.
Phased array radar (PAR) offers an advantage over conventional parabolic-antenna based radar systems in its ability to quickly scan the atmosphere with an electronically steered beam. This gives users of PAR data the opportunity to better observe rapidly evolving hazardous weather. This study focuses on one such phenomenon, the wet microburst, a potential safety hazard to avaition. From PAR data collected at the National Weather Radar Testbed located in Norman, Oklahoma during 2007-2012, 27 microburst-producing storms and 22 storms not producing a microburst were identified and analyzed to determine the potential improvements that PAR offers compared to the current operational WSR-88D system. Based on prior research of microburst precursors, the trends of several parameters measuring vertical reflectivity and convergence characteristics in the storms were selected for analysis. The ability of these selected parameters to discriminate between storms that produce microburst events and those that do not will be presented, along with probability-of-detection and lead times between the identification of these parameters and the initial time of hazardous outflow (divergence) near the surface.
Available online at https://ams.confex.com/ams/36Radar/webprogram/Paper228603.html.
Elliott, M. S., D. R. MacGorman, T. J. Schuur, P. L. Heinselman, 2012: An analysis of overshooting top lightning mapping array signatures in supercell thunderstorms. Extended Abstracts, 4th International Lightning Meteorology Conference, Broomfield, CO, USA, Vaisala, 1-12.
During the evening of 24 May 2011, four tornadic supercells traversed central Oklahoma’s dense network of meteorological observing stations. This study examines three-dimensional data from the Oklahoma Lightning Mapping Array (OK-LMA) and radar observations from the National Weather Service’s KOUN polarimetric WSR-88D radar and the National Weather Radar Testbed Phased-Array Radar (NWRT PAR). These high-quality observations are used to analyze unusual VHF sources clustered in and near the overshooting tops (OT) of storms relative to the storms’ evolving kinematics and microphysics
All four of the supercell storms on 24 May 2011 had extensive time periods when secondary maxima in VHF source density were clearly discernible in the OT of the storms (which we will call an OT LMA signature). The storm that produced a violent EF-5 tornado near El Reno and Piedmont is the primary focus of this study, because it passed almost directly over the LMA network and was also positioned well for polarimetric and phased-array radar observations. For more than two hours, this storm had a quasi-steady OT LMA signature. Several upward pulses in VHF source density rose at roughly 20 m s-1 from within the storm to a maximum altitude well above the level of neutral buoyancy (LNB). Later upward pulses eventually reached an altitude of 18 km MSL, 5 km above the LNB. The high-temporal-resolution sampling (< 1 min for a sector volume scan) of the phased array radar reveals that the upward surges in VHF densities correlated well with upward surges in reflectivity.
The discharges in the OT usually can be described as a continual emission of VHF radiation sources occurring at a rate on the order of a few per second for a period of several minutes to hours. This small VHF source rate contrasts with rates at lower altitudes, which tend to be much larger and can be as large as thousands of sources per second. Yet clusters of VHF sources typically form a secondary maximum of source density within or near the OT, and individual sources appear to be separate from, and not linked to, flashes lower in the storm.
Occasionally a small flash is initiated and propagates vertically completely within the OT. These small flashes usually consist of fewer than 100 VHF sources and have spatial and temporal dimensions less than 1 km x 1 km x 1 km and 100 ms, respectively. However, most points in the OT do not cluster in this way, but are more isolated in time and exhibit no systematic progression of location from an initiation region.
It appears that the source points in the OT are not extensions of flashes lower in the storm. They may be caused by small regions of enhanced electric field produced as upper screening-layer charge is folded by turbulent eddies closer to the internal charge lofted into the OT by intense updrafts.
Heinselman, P., S. Torres, D. Russell, R. Adams, 2012: ADAPTS Performance: Can We Further Reduce Update Time?. Preprints, 28th Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, New Orleans, LA, USA, Amer. Meteor. Soc., CD-ROM, 6B.4.
In spring 2009, the NOAA National Severe Storms Laboratory implemented a basic, electronic adaptive scanning algorithm, ADAPTS (Adaptive Digital Signal Processing Algorithm for PAR Timely Scans), on the National Weather Radar Testbed Phased-array Radar (NWRT PAR). The primary goal of ADAPTS is to improve scan time by focusing sampling on significant weather targets. For a given scanning strategy, beam positions are turned “on” or “off” based on three criteria. These criteria and their associated thresholds were chosen to ensure that: 1) lower elevation angles are always scanned, 2) at higher elevation angles only significant weather returns are sampled, and 3) “on” beam-positions account for storm growth, decay, and advection. Though limited preliminary testing suggested that these criteria accomplished these goals, a more thorough analysis of their impact on weather sampling and update time is needed.
In this study, ADAPTS's performance is examined by assessing the weather sampling and time savings resulting from implementation of the three criteria. Based on this assessment, the three criteria are refined and sensitivity tests on threshold values run to determine impacts on ADAPTS performance. Performance improvements resulting from the sensitivity studies are illustrated for a few weather events, and remaining limitations are discussed.
Priegnitz, D., S. Torres, P. Heinselman, 2012: An Adaptive Pedestal Control Algorithm for the National Weather Radar Testbed Phased Array Radar. Extended Abstracts, 28th Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, New Orleans, LA, USA, Amer. Meteor. Soc., CD-ROM, P1.7.
The National Weather Radar Testbed (NWRT) Phased Array Radar (PAR), located in Norman Oklahoma, consists of a single antenna array capable of electronically scanning a 90 degree azimuthal sector at any given moment. The antenna is mounted on a pedestal which can be commanded to move in any azimuthal direction allowing researchers to follow areas of interesting weather. Until now, when tracking a weather feature, an operator had to decide when and where to move the pedestal in order to keep the feature in the field of view, which imposed a significant operational burden. This paper describes an adaptive algorithm that uses reflectivity data to track an operator-defined weather feature and automatically adjusts the pedestal position to optimally keep it in the field of view.
Torres, S., P. Heinselman, R. Adams, C. Curtis, E. Forren, I. Ivic, D. Priegnitz, J. Thompson, D. Warde, 2012: ADAPTS Implementation: Can we exploit phased-array radar's electronic beam steering capabilities to reduce update times?. Extended Abstracts, 28th Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, New Orleans, LA, USA, Amer. Meteor. Soc., 6B.3.
It is well understood that high-temporal resolution data has the potential to improve the understanding, detection, and warning of hazardous weather phenomena. In fact, in a 2008 survey about scanning strategy improvements conducted by the US National Weather Service, 62% of forecasters indicated the need for faster updates. One of the strongest advantages of using phased-array radars for weather observations is their potential to produce data with very high temporal resolution. Naturally, this has been a major research and development thrust on the National Severe Storms Lab’s (NSSL) National Weather Radar Testbed Phased-Array Radar (NWRT PAR).
One way to get faster updates without loss in data quality is by adaptively focusing observations to the regions of interest. This is the purpose of the Adaptive DSP Algorithm for Timely Scans (ADAPTS), which was first demonstrated in 2009. ADAPTS works by activating or deactivating individual beam positions within a scanning strategy based on elevation, significance, and neighborhood criteria. Preliminary evaluations of ADAPTS showed significant time savings, but also helped identify areas for further improvement. This paper describes the initial implementation of ADAPTS, its recent evolution, and outlines a plan for future enhancements towards obtaining the best weather observations in the shortest amount of time.
Heinselman, P. L., S. M. Torres, D. LaDue, H. Lazrus, 2011: 2010 Phased-array radar innovative sensing experiment. Extended Abstracts, 27th Interactive Information Processing Systems, Seattle, WA, USA, Amer. Meteor. Soc., 12B.4.
The National Weather Radar Testbed phased-array radar (NWRT PAR) has unique electronic scanning capabilities for weather surveillance. A key objective of the 2010 Phased-Array Radar Innovative Sensing Experiment (PARISE) is the demonstration and testing of the radar's capability to produce efficient and effective rapid sampling of severe storms. Rapid sampling is achieved through the implementation of electronic adaptive scanning, range oversampling, and other techniques, over a 90-degree sector. At the same time, an enhanced depiction of storm structure is attained through dense vertical sampling (22 tilts) and 50% azimuthal oversampling. The high-quality, rapid update data collected in spring 2010 provides the opportunity to improve understanding of storm processes in bowing line segments, hail storms, and fast moving tornadic storms.
The social science-component of the 2010 PARISE engaged 12 forecasters from three regions of the National Weather Service in the analysis of NWRT PAR data and took place during the last three weeks of April 2010. The primary objective of this user-focused experiment is to build an understanding of potential operational impacts of higher-temporal resolution data on the warning decision process and warning lead time. To accomplish this objective, on Tuesday and Wednesday of each week the four participants received training on the NWRT PAR instrumentation and gained experience analyzing the data and issuing warnings using three playback events. The three events included a microburst, quasi-linear convective system, and isolated supercell. On Thursdays forecasters participated in day-long experiment for which NWRT PAR data for two cases were used with two different update times for each case: one with the full-temporal resolution data, and the other with WSR-88D-like temporal resolution data. The two events included a low-topped supercell that formed in a tropical environment and a supercell that formed in more of a traditional Southern Plains environment. Following each event, each team discussed their warning decision making process with their facilitators, and then met with the other team to compare and contrast their warning decision experiences. These debriefings produced a very rich dataset that illustrates possible impacts of higher-temporal resolution data on the warning decision making process and how NWRT PAR data may be eventually be introduced to the field.
Available online at http://ams.confex.com/ams/91Annual/webprogram/Paper184192.html.
Newman, J. F., V. Lakshmanan, P. L. Heinselman, T. M. Smith, 2011: Range correction for radar-derived azimuthal shear: applications to a tornado detection algorithm. Extended Abstracts, 27th Conference on Interactive Information Processing Systems (IIPS), Seattle, WA, USA, AMS, 8B.4.
The current Tornado Detection Algorithm (TDA) used with the Weather Surveillance Radar- 1988 Doppler network utilizes an input velocity field that is often noisy and subject to de-aliasing errors. The current TDA also relies on azimuthal shear calculations, which are affected by noisy velocity data and can degrade significantly with range. Because of these and other data accuracy issues, the current TDA is prone to producing false detections and inaccurate circulation tracks.
Coincident with the advent of new radar-derived products and ongoing research involving new weather radar systems (e.g., Phased Array Radar), the National Severe Storms Laboratory is developing an improved TDA. A primary component of this algorithm will be the local, linear least squares derivatives (LLSD) azimuthal shear field. The LLSD method uses rotational derivatives of the velocity field and is less affected by noisy velocity data in comparison to the more traditional “peak-to-peak” azimuthal shear calculations.
Initial detections will be made on a field of maximum low-level LLSD shear and diagnosed for potentially tornadic characteristics. Although LLSD shear is less range-dependent than peak-to-peak shear, some range dependency is unavoidable. A preliminary study of 31 tornadoes indicated that the threshold LLSD shear value needed to detect tornadoes was moderately dependent on range from the radar. A regression analysis was completed to determine the relationship between range and shear values such that range-corrected shear values could be estimated.
Predictors in the regression equation include circulation diameter and calculated LLSD shear. The circulation diameter was estimated by calculating the distance between minimum and maximum velocity values at a constant range. This value is assumed to represent the diameter of a mesocyclone-scale circulation, with the understanding that small-scale circulations will not be resolvable at far ranges. The resulting regression equation was applied to range-degraded shear values from tornadic circulations in the initial test set. Range-corrected shear values were compared to actual tornado intensities, as determined by damage surveys, to assess their validity.
Available online at http://ams.confex.com/ams/91Annual/webprogram/Paper184514.html.
Newman, J. F., V. Lakshmanan, P. L. Heinselman, T. M. Smith, 2011: Effects of radar range and azimuthal resolution on tornadic shear signatures: applications to a tornado detection algorithm. Extended Abstracts, 25th Conference on Severe Local Storms, Denver, CO, USA, AMS, P5.4.
The current Tornado Detection Algorithm (TDA) used with the Weather Surveillance Radar-1988 Doppler (WSR-88D) network utilizes an input velocity field that is often noisy and subject to de-aliasing errors. The current TDA also depends on azimuthal shear calculations, which are affected by noisy velocity data and can degrade significantly with range. Because of these and other data accuracy issues, the current TDA is prone to producing false detections and inaccurate circulation tracks.
Coincident with the advent of new radar-derived products and ongoing research involving new weather radar systems (e.g., Phased Array Radar; PAR), the National Severe Storms Laboratory is developing an improved TDA. A primary component of this algorithm will be the local, linear least squares derivatives (LLSD) azimuthal shear field. The LLSD method uses rotational derivatives of the velocity field and is less affected by noisy velocity data in comparison to the more traditional “peak-to-peak” azimuthal shear calculations.
Initial detections will be made on a field of maximum low-level LLSD shear and diagnosed for potentially tornadic characteristics. Although LLSD shear is less range-dependent than peak-to-peak shear, some range dependency is unavoidable. A preliminary study of 31 tornadoes indicated that the threshold LLSD shear value needed to detect tornadoes was moderately dependent on range from the radar. A regression analysis was completed to determine the relationship between range and shear values so that range-corrected shear values could be estimated.
In addition to range, azimuthal sampling is an important consideration in tornado detection. Of particular interest for this work is the azimuthal resolution of the National Weather Radar Testbed PAR in Norman, Oklahoma. The beamwidth of the PAR increases smoothly with increasing angle from boresight, ranging from 1.5° at boresight to 2.1° at an angle of 45° from boresight. Although overlapped sampling is applied to the PAR to increase the azimuthal resolution, the PAR does not currently reach the super-resolution capabilities in use with the WSR-88D network. A two -dimensional Rankine vortex model was used to demonstrate the effects of azimuthal resolution and range on peak-to-peak and LLSD shear calculations. Simulated Rankine vortices were sampled with azimuthal resolution mimicking that of the PAR and a typical WSR-88D radar and results were compared.
Available online at http://ams.confex.com/ams/25SLS/techprogram/paper_175382.htm.
Newman, J. F., P. L. Heinselman, 2011: Evolution of a quasi-linear convective system observed by phased-array radar. Extended Abstracts, 27th Conference on Interactive Information Processing Systems, Seattle, WA, USA, Amer. Meteor. Soc., 13B.5.
On 2 April 2010, a quasi-linear convective system (QLCS) formed in southwestern Oklahoma and northern Texas and moved eastward through central Oklahoma during the early morning hours. Storm formation was initially limited to the Oklahoma panhandle and southern Kansas, where an advancing cold front merged with a retreating dry line in an uncapped environment. An upper-level trough approached from the west overnight, supporting large-scale ascent and a strengthening southwesterly low-level jet. Soundings in central and northern Oklahoma on the evening prior to the event indicated a strongly capped environment with a deep elevated mixed layer. The arrival of the upper-level trough during the early morning hours of 2 April 2010 provided the ascent necessary to overcome convective inhibition and promote storm formation.
Marginally severe hail was reported with the earlier storms in southern Kansas, but the most severe damage resulted from the QLCS in southwestern Oklahoma. After the QLCS formed in southwestern Oklahoma, it moved eastward into a corridor of moderately high instability, with mixed-layer CAPE values exceeding 1000 J kg-1. Strong unidirectional low-level wind shear was supportive of organized bow echo structures and low-level mesovortices. Wind damage in Rush Springs, Oklahoma approached EF1-scale intensity and was likely associated with one of the mesovortices that formed along the leading edge of the QLCS.
The evolution of the QLCS was captured by the National Weather Radar Testbed Phased Array Radar (NWRT PAR) in Norman, Oklahoma. The NWRT PAR is an S-band radar with an electronically steered beam, allowing for rapid volumetric updates (~1 min) and user-defined scanning strategies. The rapid temporal updates and dense vertical sampling of the PAR created a detailed depiction of the evolution and damaging wind mechanisms associated with the QLCS. Features in the PAR data include microbursts, multicellular storm evolution, an intensifying rear-inflow jet, and a bowing segment and rotation associated with the Rush Springs damage. PAR data are analyzed and compared to data from the nearby S-band WSR-88D radar in Twin Lakes, Oklahoma and C-band Terminal Doppler Weather Radar in Oklahoma City, Oklahoma.
Available online at http://ams.confex.com/ams/91Annual/webprogram/Paper184493.html.
Smith, A. J., P. B. Chilson, P. L. Heinselman, 2011: Using the NWRT PAR to evaluate temporal sampling during two rapidly evolving tornado events. Preprints, 27th Interactive Information Processing Systems, Seattle, WA, USA, Amer. Meteor. Soc., 13B.4.
During severe weather events, a tornado may develop on the order of minutes or seconds. Operational radars such as the WSR-88D are capable of detecting tornadic vortex signatures (TVSs), but the WSR-88Ds are limited to using predefined volume coverage patterns with an update interval of 4.5 min or longer. Such update times are insufficient to track the rapid evolution of TVSs that persist for only a few minutes. Additionally, more frequent volumetric updates are needed to detect and monitor the rapid evolution of radar-based signatures that may indicate tornadic development.
This study uses data from the National Weather Radar Testbed Phased-Array Radar (NWRT PAR) to evaluate the impact of rapid sampling during two short-lived tornado events. In each event, volumetric updates were obtained with a maximum update time of 60 s; this scanning method provided frequent updates on the evolution of the observed circulations. On 19 August 2007, 45-s updates depicted the life cycle of a circulation associated with a tornado that formed between 0144 and 0147 UTC. Two minutes prior to tornado development, strong gate-to-gate shear of 40—50 m s-1 was found over a depth of 2 km, and this shear persisted through a 10-min period including the tornado lifetime. A second circulation was sampled on 07 May 2008, when a mesoscale convective vortex (MCV) developed in the vicinity of a surface cyclone. A tornado developed at the western edge of the MCV and remained on the ground through the period 2221—2226 UTC. Strong gate-to-gate shear in excess of 30 m s-1 was detected at 1.5 km AGL as early as 2217 UTC, providing indications that a strong circulation developed several minutes before the tornado reached the ground.
To examine the impact of sampling intervals on the evolution of these circulations, the original NWRT PAR data from both events are modified to produce temporal updates that are comparable with WSR-88D scanning strategies. Changes in gate-to-gate shear within the TVS are measured to compare the depiction and evolution of the tornadic vortex signatures. In addition, the positions of the TVSs are compared to evaluate the improvement that rapid sampling provides when tracking the location of a possible tornado.
Available online at http://ams.confex.com/ams/91Annual/webprogram/Paper182379.html.