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Dive into the research topics where Pamela L. Heinselman is active.

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Featured researches published by Pamela L. Heinselman.


Weather and Forecasting | 2008

Rapid Sampling of Severe Storms by the National Weather Radar Testbed Phased Array Radar

Pamela L. Heinselman; David Priegnitz; Kevin L. Manross; Travis M. Smith; Richard Adams

Abstract A key advantage of the National Weather Radar Testbed Phased Array Radar (PAR) is the capability to adaptively scan storms at higher temporal resolution than is possible with the Weather Surveillance Radar-1988 Doppler (WSR-88D): 1 min or less versus 4.1 min, respectively. High temporal resolution volumetric radar data are a necessity for rapid identification and confirmation of weather phenomena that can develop within minutes. The purpose of this paper is to demonstrate the PAR’s ability to collect rapid-scan volumetric data that provide more detailed depictions of quickly evolving storm structures than the WSR-88D. Scientific advantages of higher temporal resolution PAR data are examined for three convective storms that occurred during the spring and summer of 2006, including a reintensifying supercell, a microburst, and a hailstorm. The analysis of the reintensifying supercell (58-s updates) illustrates the capability to diagnose the detailed evolution of developing and/or intensifying areas ...


Weather and Forecasting | 2006

Validation of Polarimetric Hail Detection

Pamela L. Heinselman; Alexander V. Ryzhkov

This study describes, illustrates, and validates hail detection by a simplified version of the National Severe Storms Laboratory’s fuzzy logic polarimetric hydrometeor classification algorithm (HCA). The HCA uses four radar variables: reflectivity, differential reflectivity, cross-correlation coefficient, and “reflectivity texture” to classify echoes as rain mixed with hail, ground clutter–anomalous propagation, biological scatterers (insects, birds, and bats), big drops, light rain, moderate rain, and heavy rain. Diagnostic capabilities of HCA, such as detection of hail, are illustrated for a variety of storm environments using polarimetric radar data collected mostly during the Joint Polarimetric Experiment (JPOLE; 28 April–13 June 2003). Hail classification with the HCA is validated using 47 rain and hail reports collected by storm-intercept teams during JPOLE. For comparison purposes, probability of hail output from the Next-Generation Weather Radar Hail Detection Algorithm (HDA) is validated using the same ground truth. The anticipated polarimetric upgrade of the Weather Surveillance Radar-1988 Doppler network drives this direct comparison of performance. For the four examined cases, contingency table statistics show that the HCA outperforms the HDA. The superior performance of the HCA results primary from the algorithm’s lack of false alarms compared to the HDA. Statistical significance testing via bootstrapping indicates that differences in the probability of detection and critical success index between the algorithms are statistically significant at the 95% confidence level, whereas differences in the false alarm rate and Heidke skill score are statistically significant at the 90% confidence level.


Weather and Forecasting | 2013

Tornado Damage Estimation Using Polarimetric Radar

David J. Bodine; Matthew R. Kumjian; Robert D. Palmer; Pamela L. Heinselman; Alexander V. Ryzhkov

AbstractThis 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, ...


Weather and Forecasting | 2012

Exploring Impacts of Rapid-Scan Radar Data on NWS Warning Decisions

Pamela L. Heinselman; Daphne LaDue; Heather Lazrus

AbstractRapid-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...


Weather and Forecasting | 2015

Impacts of Phased-Array Radar Data on Forecaster Performance during Severe Hail and Wind Events

Katie A. Bowden; Pamela L. Heinselman; Darrel M. Kingfield; Rick P. Thomas

AbstractThe 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 probabi...


Weather and Forecasting | 2013

Range-Correcting Azimuthal Shear in Doppler Radar Data

Jennifer F. Newman; Valliappa Lakshmanan; Pamela L. Heinselman; Michael B. Richman; Travis M. Smith

AbstractThe 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 ...


Weather and Forecasting | 2015

Tornado Warning Decisions Using Phased-Array Radar Data

Pamela L. Heinselman; Daphne LaDue; Darrel M. Kingfield; Robert R. Hoffman

AbstractThe 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 d...


Bulletin of the American Meteorological Society | 2010

Strengths and Limitations of Current Radar Systems for Two Stakeholder Groups in the Southern Plains

Daphne LaDue; Pamela L. Heinselman; Jennifer F. Newman

Advancements in radar technology since the deployment of the Weather Surveillance Radar-1988 Doppler (WSR-88D) network have prompted consideration of radar replacement technologies. In order for the outcomes of advanced radar research and development to be the most beneficial to users, an understanding of user needs must be established early in the process and considered throughout. As an important early step in addressing this need, this study explored the strengths and limitations of current radar systems for nine participants from two key stakeholder groups: NOAAs NWS and broadcast meteorologists. Critical incident interviews revealed the role of each stakeholder group and attained stories that exemplified radar strengths and limitations in their respective roles. NWS forecasters emphasized using radar as an essential tool to assess the current weather situation and communicate hazards to key stakeholder groups. TV broadcasters emphasized adding meaning and value to NWS information and using radar to ...


Weather and Forecasting | 2015

Impacts of a Storm Merger on the 24 May 2011 El Reno, Oklahoma, Tornadic Supercell

Robin L. Tanamachi; Pamela L. Heinselman; Louis J. Wicker

AbstractOn 24 May 2011, a tornadic supercell (the El Reno, Oklahoma, storm) produced tornadoes rated as category 3 and 5 events on the enhanced Fujita scale (EF3 and EF5, respectively) during a 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. The El Reno storm, its updrafts, and vortices in the analyzed fields are objectively identified, and the changes in these objects before, during, and after the merger are examined. It is found that the merger did not cause the tornado handoff, which preceded the updraft merger by about 5 min. Instead, the handoff likely resulted from midlevel mesoc...


Weather and Forecasting | 2006

Intraseasonal variability of summer storms over central Arizona during 1997 and 1999

Pamela L. Heinselman; David M. Schultz

Abstract Although previous climatologies over central Arizona show a summer diurnal precipitation cycle, on any given day precipitation may differ dramatically from this climatology. The purpose of this study is to investigate the intraseasonal variability of diurnal storm development over Arizona and explore the relationship to the synoptic-scale flow and Phoenix soundings during the 1997 and 1999 North American monsoons. Radar reflectivity mosaics constructed from Phoenix and Flagstaff Weather Surveillance Radar-1988 Doppler reflectivity data reveal six repeated storm development patterns or regimes. The diurnal evolution of each regime is illustrated by computing frequency maps of 25 dBZ and greater reflectivity during 3-h periods. These regimes are named to reflect their regional and temporal characteristics: dry regime, eastern mountain regime, central-eastern mountain regime, central-eastern mountain and Sonoran-isolated regime, central-eastern mountain and Sonoran regime, and nondiurnal regime. Com...

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Ziho Kang

University of Oklahoma

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John Y. N. Cho

Massachusetts Institute of Technology

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Katie A. Bowden

National Oceanic and Atmospheric Administration

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Louis J. Wicker

National Oceanic and Atmospheric Administration

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