Publications Since 2012
Bowden, K. A., P. L. Heinselman, 2016: A Qualitative Analysis of NWS Forecasters’ use of Phased Array Radar Data during Severe Hail and Wind Events. Weather and Forecasting, 31, 43-55, doi:10.1175/WAF-D-15-0089.1.
The 2013 Phased Array Radar Innovative Sensing Experiment (PARISE) investigated the impacts of higher-temporal resolution radar data on National Weather Service forecasters’ warning decision processes during severe hail and wind events. In total, twelve forecasters participated in the 2013 PARISE over a six week period during the summer of 2013. Participants were assigned to either a control (5-min PAR updates) or experimental (1-min PAR updates) group, and worked two cases in simulated real time. This paper focuses on the qualitative retrospective reports of participants’ warning decision processes that were collected using the recent case walk-through method. Timelines of participants’ warning decision process were created for both cases, which were then thematically coded according to a situational awareness framework. Coded themes included: perception, comprehension, and projection. We found that the experimental group perceived significantly more information during both cases than the control group (case 1 p=0.045 and case 2 p=0.041), which may have improved the quality of their comprehensions and projections. Analysis of timelines reveals that 1-min PAR updates were important to the experimental group’s more timely and accurate warning decisions. Not only did the 1-min PAR updates enable experimental participants to perceive precursor signatures earlier than control participants, but through monitoring trends in radar data, the experimental group were able to better detect storm motion, more accurately identify expected weather threats from severe thunderstorms, more easily observe strengthening and diminishing trends in storms, and make more correct tornado-related warning decisions.
Available online at http://journals.ametsoc.org/doi/pdf/10.1175/WAF-D-15-0089.1.
Tanamachi, R. L., P. L. Heinselman, 2016: Rapid-scan, polarimetric observations of central Oklahoma severe storms on 31 May 2013. Weather and Forecasting, 31, 19-42, doi:http://dx.doi.org/10.1175/WAF-D-15-0111.1.
On 31 May 2013, a polarimetric WSR-88D located in Norman, Oklahoma (KOUN), was used to collect sectorized volumetric observations in a tornadic supercell. Because only a fraction of the full azimuthal volume was observed, rapid volume update times of ~1–2 min were achieved. In addition, the number of pulses used in each radial was larger than is conventional, increasing the statistical robustness of the calculated polarimetric variables. These rapid observations serve as a proxy for those of a future dual-polarized phased-array radar. Through comparison with contemporaneous observations from two nearby dual-polarized WSR-88Ds [Twin Lakes, Oklahoma (KTLX), and near University of Oklahoma Westheimer Airport in Norman (KCRI)], a number of instances in which the rapidly scanned KOUN radar detected or better resolved (in a temporal sense) features of severe convective storms are highlighted. In particular, the polarimetric signatures of merging updrafts, a rapidly descending giant hail core, an anticyclonic tornado, and a dissipating storm cell are examined. These observations provided insights into the rapid evolution of severe convective storms that could not be made (or would have been made with much lower confidence) with current, operational WSR-88D scanning strategies. Possible implications of these rapid updates for the warning decision process are discussed.
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.
Kuster, C. M., P. L. Heinselman, M. Austin, 2015: 31 May 2013 El Reno Tornadoes: Advantages of Rapid-Scan Phased-Array Radar Data from a Warning Forecaster’s Perspective. Weather and Forecasting, 30, 933-956.
On 31 May 2013, a supercell produced a tornado rated as 3 on the enhanced Fujita scale (EF3) near El Reno, Oklahoma, which was sampled by the S-band phased-array radar (PAR) at the National Weather Radar Testbed in Norman, Oklahoma. Collaboration with the forecaster who issued tornado warnings for the El Reno supercell during real-time operations focused the analysis on critical radar signatures frequently assessed during warning operations. The wealth of real-world experience provided by the forecaster, along with the quantitative analysis, highlighted differences between rapid-scan PAR data and the Weather Surveillance Radar-1988 Doppler located near Oklahoma City, Oklahoma (KTLX), within the context of forecast challenges faced on 31 May 2013. The comparison revealed that the 70-s PAR data proved most advantageous to the forecaster’s situational awareness in instances of rapid storm organization, sudden mesocyclone intensification, and abrupt, short-term changes in tornado motion. Situations where PAR data were most advantageous in the depiction of storm-scale processes included 1) rapid variations in mesocyclone intensity and associated changes in inflow magnitude; 2) imminent radar-indicated development of the short-lived (EF0) Calumet, Oklahoma, and long-lived (EF3) El Reno tornadoes; and 3) precise location and motion of the tornado circulation. As a result, it is surmised that rapid-scan volumetric radar data in cases like this would augment a forecaster’s ability to observe rapidly evolving storm features and deliver timely, life-saving information to the general public.
Available online at http://journals.ametsoc.org/doi/abs/10.1175/WAF-D-14-00142.1.
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.
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.
Wood, V. T., P. L. Heinselman, R. A. Brown, D. L. Priegnitz, D. W. Burgess, 2014: An analysis of terminal Doppler weather and phased-array radar velocity and reflectivity signatures of the 20 May 2013, Moore, Oklahoma tornado. Extended Abstracts, 27th Conference on Severe Local Storms, Madison, WI, USA, AMS, P116.
On 20 May 2013, a single supercell thunderstorm in central Oklahoma produced a tornado that touched down west of Moore, rapidly intensifying and attaining EF4 intensity within 3 minutes and eventually EF5 intensity. The deadly tornado stayed on the ground for about 40 minutes over a 23-km path, tearing through a heavily populated section of Moore, killing 24 people and injuring scores of others.
This preliminary study describes the evolution of the tornado using the Oklahoma City Terminal Doppler Weather Radar located south of Moore and the Phased Array Radar located in Norman. The objective of the study is to analyze and compare the detailed high-resolution Doppler velocity and reflectivity signatures in and around the tornado as viewed simultaneously from two different radars.
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.