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You searched for +publisher:"University of Oklahoma" +contributor:("Heinselman, Pamela"). Showing records 1 – 3 of 3 total matches.

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University of Oklahoma

1. Wilson, Katie. Forecaster Warning Decision Making with Rapidly-Updating Radar Data.

Degree: PhD, 2017, University of Oklahoma

Phased-array radar is being considered as a potential future replacement technology for the current operational Weather Surveillance Radar 1988 Doppler system. One of the most notable differences in these weather radar systems is the temporal resolution. With phased-array radar collecting volumetric updates 4–6 times more frequently, the operational impacts of rapidly-updating radar data on forecasters’ warning decision processes must be assessed. The Phased Array Radar Innovative Sensing Experiment (PARISE) was therefore designed to examine forecasters’ warning performance and related warning decision processes during use of ~1-min radar updates in simulated real-time warning operation scenarios. While the 2010, 2012, and 2013 PARISE studies reported encouraging findings for forecasters’ use of these data, each of these studies were limited in terms of sample size and the chosen methods. Additionally, important research questions that had not yet been explored remained unanswered. To address these limitations and investigate new research questions, thirty National Weather Service forecasters were invited to the NOAA Hazardous Weather Testbed to participate in the 2015 PARISE. Participating forecasters completed three components of this study: 1) the traditional experiment, 2) an eye-tracking experiment, and 3) a focus group. The first component was designed to build on previous work by assessing and comparing forecasters’ warning performance and related cognitive workload when using 1-min, 2-min, and 5-min phased-array radar updates during simulated warning operations. This traditional experiment was comprised of nine weather events that varied in terms of weather threat. Next, forecasters’ eye movement data were observed as they each worked a single weather event with either 1-min or 5-min phased-array radar updates. This work was motivated by an eye-tracking pilot study, in which a forecaster’s eye movement data was found to correspond meaningfully to their retrospective recall data that described their warning decision process. The 2015 PARISE eye-tracking experiment allowed for an objective analysis of how forecasters interacted with a radar display and warning interface for a single weather event, and more specifically, supported an investigation of whether radar update speed impacts how forecasters distribute their attention. Lastly, six focus groups were conducted to enable forecasters to share their experiences on their use of rapidly-updating phased-array radar data during the experiment. The findings from the focus groups provide motivation for the integration of rapidly-updating radar data into the forecast office and highlight some important considerations for successful use of these data during warning operations. The work presented in this dissertation was approved by the University of Oklahoma’s Office of Human Research Participant Protection Institutional Review Board under projects5226 and #5580. Advisors/Committee Members: Heinselman, Pamela (advisor), Parsons, David (advisor), Palmer, Robert (committee member), Chilson, Philip (committee member), Kang, Ziho (committee member).

Subjects/Keywords: Decision making; Weather forecasting; Human factors; Meteorology

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APA (6th Edition):

Wilson, K. (2017). Forecaster Warning Decision Making with Rapidly-Updating Radar Data. (Doctoral Dissertation). University of Oklahoma. Retrieved from http://hdl.handle.net/11244/52382

Chicago Manual of Style (16th Edition):

Wilson, Katie. “Forecaster Warning Decision Making with Rapidly-Updating Radar Data.” 2017. Doctoral Dissertation, University of Oklahoma. Accessed January 23, 2021. http://hdl.handle.net/11244/52382.

MLA Handbook (7th Edition):

Wilson, Katie. “Forecaster Warning Decision Making with Rapidly-Updating Radar Data.” 2017. Web. 23 Jan 2021.

Vancouver:

Wilson K. Forecaster Warning Decision Making with Rapidly-Updating Radar Data. [Internet] [Doctoral dissertation]. University of Oklahoma; 2017. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/11244/52382.

Council of Science Editors:

Wilson K. Forecaster Warning Decision Making with Rapidly-Updating Radar Data. [Doctoral Dissertation]. University of Oklahoma; 2017. Available from: http://hdl.handle.net/11244/52382


University of Oklahoma

2. Bodine, David. Polarimetric Radar Observations and Numerical Simulations of Tornadic Debris.

Degree: PhD, 2014, University of Oklahoma

Tornadic debris are critical aspects of tornado studies because airborne debris pose significant threats to life and property, and debris often dominate backscattered radar signals, causing biased Doppler velocity measurements. Polarimetric radar offers new research opportunities because debris produce a unique polarimetric radar signature called the tornadic debris signature (TDS). In this study, new applications of TDSs are examined using Transmission (T) matrix calculations, polarimetric radar observations, and numerical simulations. To illuminate electromagnetic scattering characteristics of different debris types, T-matrix calculations are presented. While most TDS studies have focused on tornado detection, this study conducts a detailed analysis of 14 TDS cases to determine relationships between TDS parameters and EF-rating. As tornado EF-rating increases, 90th percentile radar reflectivity factor, TDS height, and TDS volume increase, and 10th percentile co-polar cross-correlation coefficient and differential reflectivity decrease. While the TDS parameter analysis focuses on a single radar frequency, debris scattering characteristics vary depending on radar frequency, and thus multiple frequency polarimetric radar observations may provide new information about debris. In a statistical analysis of dual-wavelength TDSs, higher radar reflectivity factor and lower co-polar cross-correlation coefficient are observed at S band compared to C band, and negative differential reflectivity is sometimes observed simultaneously at both frequencies. Multiple frequency radar observations have additional utility in determining debris concentrations to assess debris loading impacts. To simulate polarimetric radar signatures, tornado vortices are simulated in a Large-Eddy Simulation (LES) model with a drag force coupling parameterization based on debris trajectories, enabling momentum exchange between air and debris. As debris loading increases, simulations reveal decreasing near-surface radial, tangential and vertical velocities in the lowest grid cell. Further increases in debris loading cause greater reductions in near-surface velocities and reduced tornado core tangential and vertical velocities. Using T-matrix calculations and LES model runs, equivalent radar reflectivity factor and two-way attenuation rates are calculated to determine if equivalent radar reflectivity factor or attenuation provide useful upper-bounds on debris loading. These simulations reveal that if sufficient amounts of debris loading are present to affect tornado dynamics, significant attenuation will occur at W band, in many cases fully attenuating the transmitted radar signal. Advisors/Committee Members: Palmer, Robert (advisor), Biggerstaff, Michael (committee member), Bluestein, Howard (committee member), Heinselman, Pamela (committee member), Yeary, Mark (committee member).

Subjects/Keywords: Atmospheric Sciences.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bodine, D. (2014). Polarimetric Radar Observations and Numerical Simulations of Tornadic Debris. (Doctoral Dissertation). University of Oklahoma. Retrieved from http://hdl.handle.net/11244/10424

Chicago Manual of Style (16th Edition):

Bodine, David. “Polarimetric Radar Observations and Numerical Simulations of Tornadic Debris.” 2014. Doctoral Dissertation, University of Oklahoma. Accessed January 23, 2021. http://hdl.handle.net/11244/10424.

MLA Handbook (7th Edition):

Bodine, David. “Polarimetric Radar Observations and Numerical Simulations of Tornadic Debris.” 2014. Web. 23 Jan 2021.

Vancouver:

Bodine D. Polarimetric Radar Observations and Numerical Simulations of Tornadic Debris. [Internet] [Doctoral dissertation]. University of Oklahoma; 2014. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/11244/10424.

Council of Science Editors:

Bodine D. Polarimetric Radar Observations and Numerical Simulations of Tornadic Debris. [Doctoral Dissertation]. University of Oklahoma; 2014. Available from: http://hdl.handle.net/11244/10424


University of Oklahoma

3. Argyle, Elizabeth Mintmire. Supporting Situation Awareness and Decision Making in Weather Forecasting.

Degree: PhD, 2016, University of Oklahoma

Weather forecasting is full of uncertainty, and as in domains such as air traffic control or medical decision making, decision support systems can affect a forecaster’s ability to make accurate and timely judgments. Well-designed decision aids can help forecasters build situation awareness (SA), a construct regarded as a component of decision making. SA involves the ability to perceive elements within a system, comprehend their significance, and project their meaning into the future in order to make a decision. However, how SA is affected by uncertainty within a system has received little attention. This tension between managing uncertainty, situation assessment, and the impact that technology has on the two, is the focus of this dissertation. To address this tension, this dissertation is centered on the evaluation of a set of coupled models that integrate rainfall observations and hydrologic simulations, coined “the FLASH system” (Flooded Locations and Simulated Hydrographs project). Prediction of flash flooding is unique from forecasting other weather-related threats due to its multi-disciplinary nature. In the United States, some weather forecasters have limited hydrologic forecasting experience. Unlike FLASH, current flash flood forecasting tools are based upon rainfall rates, and with the recent expansion into coupled rainfall and hydrologic models, forecasters have to learn quickly how to incorporate these new data sources into their work. New models may help forecasters to increase their prediction skill, but no matter how far the technology advances, forecasters must be able to accept and integrate the new tools into their work in order to gain any benefit. A focus on human factors principles in the design stage can help to ensure that by the time the product is transitioned into operational use, the decision support system addresses users’ needs while minimizing task time, workload, and attention constraints. This dissertation discusses three qualitative and quantitative studies designed to explore the relationship between flash flood forecasting, decision aid design, and SA. The first study assessed the effects of visual data aggregation methods on perception and comprehension of a flash flood threat. Next, a mixed methods approach described how forecasters acquire SA and mitigate situational uncertainty during real-time forecasting operations. Lastly, the third study used eye tracking assessment to identify the effects of an automated forecasting decision support tool on SA and information scanning behavior. Findings revealed that uncertainty management in forecasting involves individual, team, and organizational processes. We make several recommendations for future decision support systems to promote SA and performance in the weather forecasting domain. Advisors/Committee Members: Shehab, Randa L. (advisor), Kang, Ziho (advisor), Gourley, Jonathan J. (committee member), Gronlund, Scott (committee member), Heinselman, Pamela (committee member), Trafalis, Theodore (committee member).

Subjects/Keywords: human factors; Situation Awareness; decision making; weather forecasting

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Argyle, E. M. (2016). Supporting Situation Awareness and Decision Making in Weather Forecasting. (Doctoral Dissertation). University of Oklahoma. Retrieved from http://hdl.handle.net/11244/34617

Chicago Manual of Style (16th Edition):

Argyle, Elizabeth Mintmire. “Supporting Situation Awareness and Decision Making in Weather Forecasting.” 2016. Doctoral Dissertation, University of Oklahoma. Accessed January 23, 2021. http://hdl.handle.net/11244/34617.

MLA Handbook (7th Edition):

Argyle, Elizabeth Mintmire. “Supporting Situation Awareness and Decision Making in Weather Forecasting.” 2016. Web. 23 Jan 2021.

Vancouver:

Argyle EM. Supporting Situation Awareness and Decision Making in Weather Forecasting. [Internet] [Doctoral dissertation]. University of Oklahoma; 2016. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/11244/34617.

Council of Science Editors:

Argyle EM. Supporting Situation Awareness and Decision Making in Weather Forecasting. [Doctoral Dissertation]. University of Oklahoma; 2016. Available from: http://hdl.handle.net/11244/34617

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