Publications Since 2013
Wilson, K. A., P. L. Heinselman, C. M. Kuster, D. M. Kingfield, Z. Kang, 2017: Forecaster Performance and Workload: Does Radar Update Time Matter?. Weather and Forecasting, 32, 253-274, doi:10.1175/WAF-D-16-0157.1.
Impacts of radar update time on forecasters’ warning decision processes were analyzed in the 2015 Phased
Array Radar Innovative Sensing Experiment. Thirty National Weather Service forecasters worked nine archived
phased-array radar (PAR) cases in simulated real time. These cases presented nonsevere, severe hail
and/or wind, and tornadic events. Forecasters worked each type of event with approximately 5-min (quarter
speed), 2-min (half speed), and 1-min (full speed) PAR updates. Warning performance was analyzed with
respect to lead time and verification. Combining all cases, forecasters’ median warning lead times when using
full-, half-, and quarter-speed PAR updates were 17, 14.5, and 13.6 min, respectively. The use of faster PAR
updates also resulted in higher probability of detection and lower false alarm ratio scores. Radar update speed
did not impact warning duration or size. Analysis of forecaster performance on a case-by-case basis showed
that the impact of PAR update speed varied depending on the situation. This impact was most noticeable
during the tornadic cases, where radar update speed positively impacted tornado warning lead time during
two supercell events, but not for a short-lived tornado occurring within a bowing line segment. Forecasters’
improved ability to correctly discriminate the severe weather threat during a nontornadic supercell event with
faster PAR updates was also demonstrated. Forecasters provided subjective assessments of their cognitive
workload in all nine cases. On average, forecasters were not cognitively overloaded, but some participants
did experience higher levels of cognitive workload at times. A qualitative explanation of these particular
instances is provided.
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.
Kuster, C. M., P. L. Heinselman, T. J. Schuur, 2016: Rapid-Update Radar Observations of Downbursts Occurring within an Intense Multicell Thunderstorm on 14 June 2011. Weather and Forecasting, 31, 827-851, doi:http://dx.doi.org/10.1175/WAF-D-15-0081.1.
On 14 June 2011, an intense multicell thunderstorm produced one nonsevere and three severe downbursts
within 35 km of the rapid-update, S-band phased array radar (PAR) at the National Weather Radar Testbed
in Norman, Oklahoma, and the nearby polarimetric research Weather Surveillance Radar 1988-Doppler
(KOUN). Data collected from these radars provided the opportunity to conduct a quantitative analysis of
downburst precursor signature evolution depicted by 1-min PAR data and the associated evolution of dif-
ferential reflectivity ZDR depicted by 5-min KOUN data. Precursors analyzed included descent of the re-
flectivity core, evolution of the magnitude and size of midlevel convergence (i.e., number of bins), and
descending ‘‘troughs’’ of ZDR. The four downbursts exhibited midlevel convergence that rapidly increased to
peak magnitude as the reflectivity core (65-dBZ isosurface) bottom and top descended. The ZDR troughs seen
in the 5-min KOUN data appeared to descend along with the core bottom. Midlevel convergence size in-
creased to a peak value and decreased as the reflectivity core descended in the three severe downbursts. In
contrast, midlevel convergence size exhibited little change in the nonsevere downburst. The time scale of
trends seen in the PAR data was 11 min or less and happened several minutes prior to each downburst’s
maximum intensity. These results point to the importance of 1-min volumetric data in effectively resolving the
evolution of downburst precursors, which could be beneficial to forecast operations
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.
Wilson, K. A., P. L. Heinselman, Z. Kang, 2016: Exploring Applications of Eye Tracking in Operational Meteorology Research. Bulletin of the American Meteorological Society, 97, 2019-2025, doi:10.1175/BAMS-D-15-00148.1.
Eye-tracking technology can observe where and how someone’s eye gaze is directed, and therefore provides information about one’s attention and related cognitive processes in real time. The use of eye-tracking methods is evident in a variety of research domains, and has been used on few occasions within the meteorology community. With the goals of Weather Ready Nation in mind, eye-tracking applications in meteorology have so far supported the need to address how people interpret meteorological information through televised forecasts and graphics. However, eye-tracking has not yet been applied to learning about forecaster behavior and decision processes. In this article, we consider what current methods are being used to study forecasters and why we believe eye-tracking is a method that should be incorporated into our efforts. We share our first data collection of an NWS forecaster’s eye gaze data, and explore the types of information that this data provides about the forecaster’s cognitive processes. We also discuss how eye-tracking methods could be applied to other aspects of operational meteorology research in the future and provide motivation for further exploration on this topic.
Available online at http://journals.ametsoc.org/doi/pdf/10.1175/BAMS-D-15-00148.1.
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.
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.