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ABSTRACT
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.
ABSTRACT
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.
Abstract
Since 2007 the advancement of the National Weather Radar Testbed Phased-Array Radar (NWRT PAR) hardware and software capabilities has been supporting the implementation of high-temporal-resolution (∼1 min) sampling. To achieve the increase in computational power and data archiving needs required for high-temporal-resolution sampling, the signal processor was upgraded to a scalable, Linux-based cluster with a distributed computing architecture. The development of electronic adaptive scanning, which can reduce update times by focusing data collection on significant weather, became possible through functionality added to the radar control interface and real-time controller. Signal processing techniques were implemented to address data quality issues, such as artifact removal and range-and-velocity ambiguity mitigation, absent from the NWRT PAR at its installation. The hardware and software advancements described above have made possible the development of conventional and electronic scanning capabilities that achieve high-temporal-resolution sampling. Those scanning capabilities are sector- and elevation-prioritized scanning, beam multiplexing, and electronic adaptive scanning. Each of these capabilities and related sampling trade-offs are explained and demonstrated through short case studies.
Abstract
Since 2007 the advancement of the National Weather Radar Testbed Phased-Array Radar (NWRT PAR) hardware and software capabilities has been supporting the implementation of high-temporal-resolution (∼1 min) sampling. To achieve the increase in computational power and data archiving needs required for high-temporal-resolution sampling, the signal processor was upgraded to a scalable, Linux-based cluster with a distributed computing architecture. The development of electronic adaptive scanning, which can reduce update times by focusing data collection on significant weather, became possible through functionality added to the radar control interface and real-time controller. Signal processing techniques were implemented to address data quality issues, such as artifact removal and range-and-velocity ambiguity mitigation, absent from the NWRT PAR at its installation. The hardware and software advancements described above have made possible the development of conventional and electronic scanning capabilities that achieve high-temporal-resolution sampling. Those scanning capabilities are sector- and elevation-prioritized scanning, beam multiplexing, and electronic adaptive scanning. Each of these capabilities and related sampling trade-offs are explained and demonstrated through short case studies.
Abstract
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.
Abstract
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.
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. Composites constructed from the NCEP–NCAR 40-Year Reanalysis Project data show that regime occurrence is related to the north–south location of the 500-hPa geopotential height ridge axis of the Bermuda high and the east–west location of the 500-hPa monsoon boundary, a boundary between dry air to the west and moist air to the east. Consequently, precipitable water from the 1200 UTC Phoenix soundings is the best parameter for discriminating the six regimes.
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. Composites constructed from the NCEP–NCAR 40-Year Reanalysis Project data show that regime occurrence is related to the north–south location of the 500-hPa geopotential height ridge axis of the Bermuda high and the east–west location of the 500-hPa monsoon boundary, a boundary between dry air to the west and moist air to the east. Consequently, precipitable water from the 1200 UTC Phoenix soundings is the best parameter for discriminating the six regimes.
Abstract
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.
Abstract
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.
Abstract
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, 12 forecasters participated in the 2013 PARISE over a 6-week period during the summer of 2013. Participants were assigned to either a control [5-min phased-array radar (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. It was 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 was 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.
Abstract
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, 12 forecasters participated in the 2013 PARISE over a 6-week period during the summer of 2013. Participants were assigned to either a control [5-min phased-array radar (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. It was 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 was 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.
Abstract
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.
Abstract
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.
Abstract
On 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 mesocyclone occlusion, in which the midlevel mesocyclone split and a portion is shed rearward with respect to storm motion. During the merger process, the midlevel mesocyclone and updraft structure in the El Reno storm became relatively disorganized. New updraft pulses that 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 rapidly reintensified, as access to buoyant inflow sector air was restored.
Abstract
On 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 mesocyclone occlusion, in which the midlevel mesocyclone split and a portion is shed rearward with respect to storm motion. During the merger process, the midlevel mesocyclone and updraft structure in the El Reno storm became relatively disorganized. New updraft pulses that 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 rapidly reintensified, as access to buoyant inflow sector air was restored.
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: NOAA's 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 effectively communicate weather information to viewers. The stories told by our participants vividly illustrated the advancing nature of weather detection with radar, and why there are still issues with weather radar and radar-derived information. Analysis of the stories, which ranged from accounts of severe weather to winter weather, revealed four underlying radar needs: 1) clean, accurate data without intervention, 2) higher spatial-and temporal-resolution data than that provided by the WSR-88D, 3) consistent and low-altitude information, and 4) more accurate information on precipitation type, size, intensity, and distribution.
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: NOAA's 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 effectively communicate weather information to viewers. The stories told by our participants vividly illustrated the advancing nature of weather detection with radar, and why there are still issues with weather radar and radar-derived information. Analysis of the stories, which ranged from accounts of severe weather to winter weather, revealed four underlying radar needs: 1) clean, accurate data without intervention, 2) higher spatial-and temporal-resolution data than that provided by the WSR-88D, 3) consistent and low-altitude information, and 4) more accurate information on precipitation type, size, intensity, and distribution.
Abstract
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 these data provide 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.
Abstract
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 these data provide 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.
