Publications Since 2011
Bowden, K. A., P. L. Heinselman, D. M. Kingfield, R. P. Thomas, 2015: Impacts of phased-array radar data on forecaster performance during severe hail and wind events. Weather and Forecasting, 30, 389-404, doi:10.1175/WAF-D-14-00101.1.
The ongoing Phased Array Radar Innovative Sensing Experiment (PARISE) investigates the impacts of higher-temporal-resolution radar data on the warning decision process of NWS forecasters. Twelve NWS forecasters participated in the 2013 PARISE and were assigned to either a control (5-min updates) or an experimental (1-min updates) group. Participants worked two case studies in simulated real time. The first case presented a marginally severe hail event, and the second case presented a severe hail and wind event. While working each event, participants made decisions regarding the detection, identification, and reidentification of severe weather. These three levels compose what has now been termed the compound warning decision process. Decisions were verified with respect to the three levels of the compound warning decision process and the experimental group obtained a lower mean false alarm ratio than the control group throughout both cases. The experimental group also obtained a higher mean probability of detection than the control group throughout the first case and at the detection level in the second case. Statistical significance (p value = 0.0252) was established for the difference in median lead times obtained by the experimental (21.5 min) and control (17.3 min) groups. A confidence-based assessment was used to categorize decisions into four types: doubtful, uninformed, misinformed, and mastery. Although mastery (i.e., confident and correct) decisions formed the largest category in both groups, the experimental group had a larger proportion of mastery decisions, possibly because of their enhanced ability to observe and track individual storm characteristics through the use of 1-min updates.
Heinselman, P. L., D. LaDue, D. M. Kingfield, R. Hoffman, 2015: Tornado Warning Decisions Using Phased-Array Radar Data. Weather and Forecasting, 30, 1, 57-78, doi:10.1175/WAF-D-14-00042.1.
The 2012 Phased Array Radar Innovative Sensing Experiment identified how rapidly scanned full-volumetric data captured known mesoscale processes and impacted tornado-warning lead time. Twelve forecasters from nine National Weather Service forecast offices used this rapid-scan phased-array radar (PAR) data to issue tornado warnings on two low-end tornadic and two nontornadic supercell cases. Verification of the tornadic cases revealed that forecasters’ use of PAR data provided a median tornado-warning lead time (TLT) of 20 min. This 20-min TLT exceeded by 6.5 and 9 min, respectively, participants’ forecast office and regions’ median spring season, low-end TLTs (2008–13). Furthermore, polygon-based probability of detection ranged from 0.75 to 1.0 and probability of false alarm for all four cases ranged from 0.0 to 0.5. Similar performance was observed regardless of prior warning experience. Use of a cognitive task analysis method called the recent case walk-through showed that this performance was due to forecasters’ use of rapid volumetric updates. Warning decisions were based upon the intensity, persistence, and important changes in features aloft that are precursors to tornadogenesis. Precursors that triggered forecasters’ decisions to warn occurred within one or two typical Weather Surveillance Radar-1988 Doppler (WSR-88D) scans, indicating PAR’s temporal sampling better matches the time scale at which these precursors evolve.
Tanamachi, R. L., P. L. Heinselman, L. J. Wicker, 2015: Impacts of a storm merger on the 24 May 2011 El Reno, Oklahoma tornadic supercell. Weather and Forecasting, 30, 501-524, doi:10.1175/WAF-D-14-00164.1.
On 24 May 2011, a tornadic supercell (the El Reno, Oklahoma storm) produced an EF-3 and EF-5 tornadoes in series during an Oklahoma severe weather outbreak. The transition (“handoff”) between the two tornadoes occurred as the El Reno storm merged with a weaker, ancillary storm. To examine the impacts of the merger on the dynamics of these storms, a series of three-dimensional cloud-scale analyses are created by assimilating 1-min volumetric observations from the National Weather Radar Testbed’s Phased Array Radar into a numerical cloud model using the Local Ensemble Transform Kalman Filter technique. We objectively identify the El Reno storm, its updrafts, and vortices in the analyzed fields, and examine the changes in these objects before, during, and after the merger.
It is found that the merger did not cause the tornado handoff, which preceded the updraft merger by about five min. Instead, the handoff likely resulted from midlevel mesocyclone occlusion, in which the midlevel mesocyclone split and a portion shed rearward with respect to storm motion. During the merger process, the midlevel mesocylone and updraft structure in the El Reno storm became relatively disorganized. New updraft pulses formed above colliding outflow boundaries between the two storms tilted environmental vorticity from low levels to generate an additional midlevel vortex that later merged with the El Reno storm’s midlevel mesocyclone. Once the ~10-min merger process was complete, the El Reno storm and its mesocyclone reintensified rapidly, as access to buoyant inflow sector air was restored.
Available online at http://journals.ametsoc.org/doi/abs/10.1175/WAF-D-14-00164.1.
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.
Priegnitz, D. L., P. Heinselman, R. Brown, 2014: Adaptive Storm-based Scanning at the National Weather Radar Testbed Phased Array Radar. Extended Abstracts, The Eighth European Conference on Radar in Meteorology and Hydrology, Garmisch-Partenkirchen, Germany, DWD DLR, TEC.P09.
Volume Coverage Patterns (VCPs) used by operational weather radars contain fixed sets of elevation angles for scanning the vertical structure of weather. Typically, these VCPs oversample in elevation at lower elevations and undersample in elevation at higher elevations, leaving gaps in the vertical coverage of storms near the radar. Both oversampling at lower elevations and undersampling at higher elevations are maximized when storms are located near the radar.
A new automated VCP algorithm is described that creates VCPs with vertical coverage tailored to a storm's range. Incorporated into the adaptive storm scheduling function at the National Weather Radar Testbed (NWRT) Phased Array Radar (PAR), this algorithm is applied to storm clusters chosen for focused scanning by the user. PAR data collected with the VCP algorithm and operational VCPs are used to compare impacts of these sampling methods on the resolved vertical storm structure.
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.