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- Author or Editor: Christopher D. Karstens x
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Abstract
This study provides a quantitative climatological analysis of the fundamental geospatial components of storm-based warnings and offers insight into how the National Weather Service (NWS) uses the current storm-based warning system under the established directives and policies. From October 2007 through May 2016, the NWS issued over 500 000 storm-based warnings and severe weather statements (SVSs), primarily concentrated east of the Rocky Mountains. A geospatial analysis of these warning counts by county warning area (CWA) shows local maxima in the lower Mississippi valley, southern plains, central plains, and the southern Appalachians. Regional uniformity exists in the patterns of average speed and direction provided by the time/motion/location tags, while the mean duration and polygon area varies significantly by CWA and region. These observed consistencies and inconsistencies may be indicative of how local weather forecast office (WFO) policy and end-user needs factor into the warning issuance and update process. This research concludes with a comparison of storm-based warnings to NWS policy and an analysis of CWAs with the greatest number of warnings issued during a single convective day.
Abstract
This study provides a quantitative climatological analysis of the fundamental geospatial components of storm-based warnings and offers insight into how the National Weather Service (NWS) uses the current storm-based warning system under the established directives and policies. From October 2007 through May 2016, the NWS issued over 500 000 storm-based warnings and severe weather statements (SVSs), primarily concentrated east of the Rocky Mountains. A geospatial analysis of these warning counts by county warning area (CWA) shows local maxima in the lower Mississippi valley, southern plains, central plains, and the southern Appalachians. Regional uniformity exists in the patterns of average speed and direction provided by the time/motion/location tags, while the mean duration and polygon area varies significantly by CWA and region. These observed consistencies and inconsistencies may be indicative of how local weather forecast office (WFO) policy and end-user needs factor into the warning issuance and update process. This research concludes with a comparison of storm-based warnings to NWS policy and an analysis of CWAs with the greatest number of warnings issued during a single convective day.
Abstract
Mobile mesonet sampling in the hook echo/rear-flank downdraft (RFD) region of a tornadic supercell near Bowdle, South Dakota, provided the opportunity to examine RFD thermodynamic and kinematic attributes and evolution. Focused analysis of the fifth low-level mesocyclone cycle that produced two significant tornadoes including a violent tornado, revealed four RFD internal surge (RFDIS) events. RFDISs appeared to influence tornado development, intensity, and demise by altering the thermodynamic and kinematic character of the RFD region bounding the pretornadic and tornadic circulations. Significant tornadoes developed and matured when the RFD, modulated by internal surges, was kinematically strong, only weakly negatively buoyant, and very potentially buoyant. In contrast, the demise of the Bowdle tornado was concurrent with a much cooler RFDIS that replaced more buoyant and far more potentially buoyant RFD air near the tornado. This surge also likely contributed to a displacement of the tornado from the storm updraft. Development of the first tornado and rapid intensification of the Bowdle tornado occurred when an RFDIS boundary convergence zone interacted with the pretornadic and tornadic circulations, respectively. In the latter case, a strong vertical vortex sheet along an RFDIS boundary appeared to be a near-surface cyclonic vorticity source for the tornado. A downdraft closely bounding the right flank of the developing first tornado and intensifying Bowdle tornado provided some of the inflow to these circulations. For the Bowdle tornado, parcels were also streaming toward the tornado from its immediate east and northeast. A cyclonic–anticyclonic vortex couplet was observed during a portion of each significant tornado cycle.
Abstract
Mobile mesonet sampling in the hook echo/rear-flank downdraft (RFD) region of a tornadic supercell near Bowdle, South Dakota, provided the opportunity to examine RFD thermodynamic and kinematic attributes and evolution. Focused analysis of the fifth low-level mesocyclone cycle that produced two significant tornadoes including a violent tornado, revealed four RFD internal surge (RFDIS) events. RFDISs appeared to influence tornado development, intensity, and demise by altering the thermodynamic and kinematic character of the RFD region bounding the pretornadic and tornadic circulations. Significant tornadoes developed and matured when the RFD, modulated by internal surges, was kinematically strong, only weakly negatively buoyant, and very potentially buoyant. In contrast, the demise of the Bowdle tornado was concurrent with a much cooler RFDIS that replaced more buoyant and far more potentially buoyant RFD air near the tornado. This surge also likely contributed to a displacement of the tornado from the storm updraft. Development of the first tornado and rapid intensification of the Bowdle tornado occurred when an RFDIS boundary convergence zone interacted with the pretornadic and tornadic circulations, respectively. In the latter case, a strong vertical vortex sheet along an RFDIS boundary appeared to be a near-surface cyclonic vorticity source for the tornado. A downdraft closely bounding the right flank of the developing first tornado and intensifying Bowdle tornado provided some of the inflow to these circulations. For the Bowdle tornado, parcels were also streaming toward the tornado from its immediate east and northeast. A cyclonic–anticyclonic vortex couplet was observed during a portion of each significant tornado cycle.
Abstract
Extracting explicit severe weather forecast guidance from convection-allowing ensembles (CAEs) is challenging since CAEs cannot directly simulate individual severe weather hazards. Currently, CAE-based severe weather probabilities must be inferred from one or more storm-related variables, which may require extensive calibration and/or contain limited information. Machine learning (ML) offers a way to obtain severe weather forecast probabilities from CAEs by relating CAE forecast variables to observed severe weather reports. This paper develops and verifies a random forest (RF)-based ML method for creating day 1 (1200–1200 UTC) severe weather hazard probabilities and categorical outlooks based on 0000 UTC Storm-Scale Ensemble of Opportunity (SSEO) forecast data and observed Storm Prediction Center (SPC) storm reports. RF forecast probabilities are compared against severe weather forecasts from calibrated SSEO 2–5-km updraft helicity (UH) forecasts and SPC convective outlooks issued at 0600 UTC. Continuous RF probabilities routinely have the highest Brier skill scores (BSSs), regardless of whether the forecasts are evaluated over the full domain or regional/seasonal subsets. Even when RF probabilities are truncated at the probability levels issued by the SPC, the RF forecasts often have BSSs better than or comparable to corresponding UH and SPC forecasts. Relative to the UH and SPC forecasts, the RF approach performs best for severe wind and hail prediction during the spring and summer (i.e., March–August). Overall, it is concluded that the RF method presented here provides skillful, reliable CAE-derived severe weather probabilities that may be useful to severe weather forecasters and decision-makers.
Abstract
Extracting explicit severe weather forecast guidance from convection-allowing ensembles (CAEs) is challenging since CAEs cannot directly simulate individual severe weather hazards. Currently, CAE-based severe weather probabilities must be inferred from one or more storm-related variables, which may require extensive calibration and/or contain limited information. Machine learning (ML) offers a way to obtain severe weather forecast probabilities from CAEs by relating CAE forecast variables to observed severe weather reports. This paper develops and verifies a random forest (RF)-based ML method for creating day 1 (1200–1200 UTC) severe weather hazard probabilities and categorical outlooks based on 0000 UTC Storm-Scale Ensemble of Opportunity (SSEO) forecast data and observed Storm Prediction Center (SPC) storm reports. RF forecast probabilities are compared against severe weather forecasts from calibrated SSEO 2–5-km updraft helicity (UH) forecasts and SPC convective outlooks issued at 0600 UTC. Continuous RF probabilities routinely have the highest Brier skill scores (BSSs), regardless of whether the forecasts are evaluated over the full domain or regional/seasonal subsets. Even when RF probabilities are truncated at the probability levels issued by the SPC, the RF forecasts often have BSSs better than or comparable to corresponding UH and SPC forecasts. Relative to the UH and SPC forecasts, the RF approach performs best for severe wind and hail prediction during the spring and summer (i.e., March–August). Overall, it is concluded that the RF method presented here provides skillful, reliable CAE-derived severe weather probabilities that may be useful to severe weather forecasters and decision-makers.
Abstract
Real-time prediction of storm longevity is a critical challenge for National Weather Service (NWS) forecasters. These predictions can guide forecasters when they issue warnings and implicitly inform them about the potential severity of a storm. This paper presents a machine-learning (ML) system that was used for real-time prediction of storm longevity in the Probabilistic Hazard Information (PHI) tool, making it a Research-to-Operations (R2O) project. Currently, PHI provides forecasters with real-time storm variables and severity predictions from the ProbSevere system, but these predictions do not include storm longevity. We specifically designed our system to be tested in PHI during the 2016 and 2017 Hazardous Weather Testbed (HWT) experiments, which are a quasi-operational naturalistic environment. We considered three ML methods that have proven in prior work to be strong predictors for many weather prediction tasks: elastic nets, random forests, and gradient-boosted regression trees. We present experiments comparing the three ML methods with different types of input data, discuss trade-offs between forecast quality and requirements for real-time deployment, and present both subjective (human-based) and objective evaluation of real-time deployment in the HWT. Results demonstrate that the ML system has lower error than human forecasters, which suggests that it could be used to guide future storm-based warnings, enabling forecasters to focus on other aspects of the warning system.
Abstract
Real-time prediction of storm longevity is a critical challenge for National Weather Service (NWS) forecasters. These predictions can guide forecasters when they issue warnings and implicitly inform them about the potential severity of a storm. This paper presents a machine-learning (ML) system that was used for real-time prediction of storm longevity in the Probabilistic Hazard Information (PHI) tool, making it a Research-to-Operations (R2O) project. Currently, PHI provides forecasters with real-time storm variables and severity predictions from the ProbSevere system, but these predictions do not include storm longevity. We specifically designed our system to be tested in PHI during the 2016 and 2017 Hazardous Weather Testbed (HWT) experiments, which are a quasi-operational naturalistic environment. We considered three ML methods that have proven in prior work to be strong predictors for many weather prediction tasks: elastic nets, random forests, and gradient-boosted regression trees. We present experiments comparing the three ML methods with different types of input data, discuss trade-offs between forecast quality and requirements for real-time deployment, and present both subjective (human-based) and objective evaluation of real-time deployment in the HWT. Results demonstrate that the ML system has lower error than human forecasters, which suggests that it could be used to guide future storm-based warnings, enabling forecasters to focus on other aspects of the warning system.
Abstract
In this study, aerial imagery of tornado damage is used to digitize the falling direction of trees (i.e., tree fall) along the 22 May 2011 Joplin, Missouri, and 27 April 2011 Tuscaloosa–Birmingham, Alabama, tornado tracks. Normalized mean patterns of observed tree fall from each tornado’s peak-intensity period are subjectively compared with results from analytical vortex simulations of idealized tornado-induced tree fall to characterize mean properties of the near-surface flow as depicted by the model. A computationally efficient method of simulating tree fall is applied that uses a Gumbel distribution of critical tree-falling wind speeds on the basis of the enhanced Fujita scale. Results from these simulations suggest that both tornadoes had strong radial near-surface winds. A few distinct tree-fall patterns are identified at various locations along the Tuscaloosa–Birmingham tornado track. Concentrated bands of intense tree fall, collocated with and aligned parallel to the axis of underlying valley channels, extend well beyond the primary damage path. These damage patterns are hypothesized to be the result of flow acceleration caused by channeling within valleys. Another distinct pattern of tree fall, likely not linked to the underlying topography, may have been associated with a rear-flank downdraft (RFD) internal surge during the tornado’s intensification stage. Here, the wind field was strong enough to produce tornado-strength damage well beyond the visible funnel cloud. This made it difficult to distinguish between tornado- and RFD-related damage and thus illustrates an ambiguity in ascertaining tornado-damage-path width in some locations.
Abstract
In this study, aerial imagery of tornado damage is used to digitize the falling direction of trees (i.e., tree fall) along the 22 May 2011 Joplin, Missouri, and 27 April 2011 Tuscaloosa–Birmingham, Alabama, tornado tracks. Normalized mean patterns of observed tree fall from each tornado’s peak-intensity period are subjectively compared with results from analytical vortex simulations of idealized tornado-induced tree fall to characterize mean properties of the near-surface flow as depicted by the model. A computationally efficient method of simulating tree fall is applied that uses a Gumbel distribution of critical tree-falling wind speeds on the basis of the enhanced Fujita scale. Results from these simulations suggest that both tornadoes had strong radial near-surface winds. A few distinct tree-fall patterns are identified at various locations along the Tuscaloosa–Birmingham tornado track. Concentrated bands of intense tree fall, collocated with and aligned parallel to the axis of underlying valley channels, extend well beyond the primary damage path. These damage patterns are hypothesized to be the result of flow acceleration caused by channeling within valleys. Another distinct pattern of tree fall, likely not linked to the underlying topography, may have been associated with a rear-flank downdraft (RFD) internal surge during the tornado’s intensification stage. Here, the wind field was strong enough to produce tornado-strength damage well beyond the visible funnel cloud. This made it difficult to distinguish between tornado- and RFD-related damage and thus illustrates an ambiguity in ascertaining tornado-damage-path width in some locations.
Abstract
Since the spring of 2002, tornadoes were sampled on nine occasions using Hardened In-Situ Tornado Pressure Recorder probes, video probes, and mobile mesonet instrumentation. This study describes pressure and, in some cases, velocity data obtained from these intercepts. In seven of these events, the intercepted tornadoes were within the radar-indicated or visually identified location of the supercell low-level mesocyclone. In the remaining two cases, the intercepted tornadoes occurred outside of this region and were located along either the rear-flank downdraft gust front or an internal rear-flank downdraft surge boundary.
The pressure traces, sometimes augmented with videography, suggest that vortex structures ranged from single-cell to two-cell, quite similar to the swirl-ratio-dependent continuum of vortex structures shown in laboratory and numerical simulations. Although near-ground tornado observations are quite rare, the number of contemporary tornado measurements now available permits a comparative range of observed pressure deficits for a wide variety of tornado sizes and intensities to be presented.
Abstract
Since the spring of 2002, tornadoes were sampled on nine occasions using Hardened In-Situ Tornado Pressure Recorder probes, video probes, and mobile mesonet instrumentation. This study describes pressure and, in some cases, velocity data obtained from these intercepts. In seven of these events, the intercepted tornadoes were within the radar-indicated or visually identified location of the supercell low-level mesocyclone. In the remaining two cases, the intercepted tornadoes occurred outside of this region and were located along either the rear-flank downdraft gust front or an internal rear-flank downdraft surge boundary.
The pressure traces, sometimes augmented with videography, suggest that vortex structures ranged from single-cell to two-cell, quite similar to the swirl-ratio-dependent continuum of vortex structures shown in laboratory and numerical simulations. Although near-ground tornado observations are quite rare, the number of contemporary tornado measurements now available permits a comparative range of observed pressure deficits for a wide variety of tornado sizes and intensities to be presented.
Abstract
The Storm Prediction Center (SPC) has developed a database of damage-surveyed tornadoes in the contiguous United States (2009–17) that relates environmental and radar-derived storm attributes to damage ratings that change during a tornado life cycle. Damage indicators (DIs), and the associated wind speed estimates from tornado damage surveys compiled in the Damage Assessment Toolkit (DAT) dataset, were linked to the nearest manual calculations of 0.5° tilt angle maximum rotational velocity V rot from single-site WSR-88D data. For each radar scan, the maximum wind speed from the highest-rated DI, V rot, and the significant tornado parameter (STP) from the SPC hourly objective mesoscale analysis archive were recorded and analyzed. Results from examining V rot and STP data indicate an increasing conditional probability for higher-rated DIs (i.e., EF-scale wind speed estimate) as both STP and V rot increase. This work suggests that tornadic wind speed exceedance probabilities can be estimated in real time, on a scan-by-scan basis, via V rot and STP for ongoing tornadoes.
Abstract
The Storm Prediction Center (SPC) has developed a database of damage-surveyed tornadoes in the contiguous United States (2009–17) that relates environmental and radar-derived storm attributes to damage ratings that change during a tornado life cycle. Damage indicators (DIs), and the associated wind speed estimates from tornado damage surveys compiled in the Damage Assessment Toolkit (DAT) dataset, were linked to the nearest manual calculations of 0.5° tilt angle maximum rotational velocity V rot from single-site WSR-88D data. For each radar scan, the maximum wind speed from the highest-rated DI, V rot, and the significant tornado parameter (STP) from the SPC hourly objective mesoscale analysis archive were recorded and analyzed. Results from examining V rot and STP data indicate an increasing conditional probability for higher-rated DIs (i.e., EF-scale wind speed estimate) as both STP and V rot increase. This work suggests that tornadic wind speed exceedance probabilities can be estimated in real time, on a scan-by-scan basis, via V rot and STP for ongoing tornadoes.
Abstract
A sample of damage-surveyed tornadoes in the contiguous United States (2009–17), containing specific wind speed estimates from damage indicators (DIs) within the Damage Assessment Toolkit dataset, were linked to radar-observed circulations using the nearest WSR-88D data in Part I of this work. The maximum wind speed associated with the highest-rated DI for each radar scan, corresponding 0.5° tilt angle rotational velocity V rot, significant tornado parameter (STP), and National Weather Service (NWS) convective impact-based warning (IBW) type, are analyzed herein for the sample of cases in Part I and an independent case sample from parts of 2019–20. As V rot and STP both increase, peak DI-estimated wind speeds and IBW warning type also tend to increase. Different combinations of V rot, STP, and population density—related to ranges of peak DI wind speed—exhibited a strong ability to discriminate across the tornado damage intensity spectrum. Furthermore, longer duration of high V rot (i.e., ≥70 kt) in significant tornado environments (i.e., STP ≥ 6) corresponds to increasing chances that DIs will reveal the occurrence of an intense tornado (i.e., EF3+). These findings were corroborated via the independent sample from parts of 2019–20, and can be applied in a real-time operational setting to assist in determining a potential range of wind speeds. This work provides evidence-based support for creating an objective and consistent, real-time framework for assessing and differentiating tornadoes across the tornado intensity spectrum.
Abstract
A sample of damage-surveyed tornadoes in the contiguous United States (2009–17), containing specific wind speed estimates from damage indicators (DIs) within the Damage Assessment Toolkit dataset, were linked to radar-observed circulations using the nearest WSR-88D data in Part I of this work. The maximum wind speed associated with the highest-rated DI for each radar scan, corresponding 0.5° tilt angle rotational velocity V rot, significant tornado parameter (STP), and National Weather Service (NWS) convective impact-based warning (IBW) type, are analyzed herein for the sample of cases in Part I and an independent case sample from parts of 2019–20. As V rot and STP both increase, peak DI-estimated wind speeds and IBW warning type also tend to increase. Different combinations of V rot, STP, and population density—related to ranges of peak DI wind speed—exhibited a strong ability to discriminate across the tornado damage intensity spectrum. Furthermore, longer duration of high V rot (i.e., ≥70 kt) in significant tornado environments (i.e., STP ≥ 6) corresponds to increasing chances that DIs will reveal the occurrence of an intense tornado (i.e., EF3+). These findings were corroborated via the independent sample from parts of 2019–20, and can be applied in a real-time operational setting to assist in determining a potential range of wind speeds. This work provides evidence-based support for creating an objective and consistent, real-time framework for assessing and differentiating tornadoes across the tornado intensity spectrum.
Abstract
High-impact weather events, such as severe thunderstorms, tornadoes, and hurricanes, cause significant disruptions to infrastructure, property loss, and even fatalities. High-impact events can also positively impact society, such as the impact on savings through renewable energy. Prediction of these events has improved substantially with greater observational capabilities, increased computing power, and better model physics, but there is still significant room for improvement. Artificial intelligence (AI) and data science technologies, specifically machine learning and data mining, bridge the gap between numerical model prediction and real-time guidance by improving accuracy. AI techniques also extract otherwise unavailable information from forecast models by fusing model output with observations to provide additional decision support for forecasters and users. In this work, we demonstrate that applying AI techniques along with a physical understanding of the environment can significantly improve the prediction skill for multiple types of high-impact weather. The AI approach is also a contribution to the growing field of computational sustainability. The authors specifically discuss the prediction of storm duration, severe wind, severe hail, precipitation classification, forecasting for renewable energy, and aviation turbulence. They also discuss how AI techniques can process “big data,” provide insights into high-impact weather phenomena, and improve our understanding of high-impact weather.
Abstract
High-impact weather events, such as severe thunderstorms, tornadoes, and hurricanes, cause significant disruptions to infrastructure, property loss, and even fatalities. High-impact events can also positively impact society, such as the impact on savings through renewable energy. Prediction of these events has improved substantially with greater observational capabilities, increased computing power, and better model physics, but there is still significant room for improvement. Artificial intelligence (AI) and data science technologies, specifically machine learning and data mining, bridge the gap between numerical model prediction and real-time guidance by improving accuracy. AI techniques also extract otherwise unavailable information from forecast models by fusing model output with observations to provide additional decision support for forecasters and users. In this work, we demonstrate that applying AI techniques along with a physical understanding of the environment can significantly improve the prediction skill for multiple types of high-impact weather. The AI approach is also a contribution to the growing field of computational sustainability. The authors specifically discuss the prediction of storm duration, severe wind, severe hail, precipitation classification, forecasting for renewable energy, and aviation turbulence. They also discuss how AI techniques can process “big data,” provide insights into high-impact weather phenomena, and improve our understanding of high-impact weather.
Abstract
During spring 2016 the Probabilistic Hazard Information (PHI) prototype experiment was run in the National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) as part of the Forecasting a Continuum of Environmental Threats (FACETS) program. Nine National Weather Service forecasters were trained to use the web-based PHI prototype tool to produce dynamic PHI for severe weather threats. Archived and real-time weather scenarios were used to test this new paradigm of issuing probabilistic information, rather than deterministic information. The forecasters’ mental workload was evaluated after each scenario using the NASA-Task Load Index (TLX) questionnaire. This study summarizes the analysis results of mental workload experienced by forecasters while using the PHI prototype. Six subdimensions of mental workload: mental demand, physical demand, temporal demand, performance, effort, and frustration were analyzed to derive top contributing factors to workload. Average mental workload was 46.6 (out of 100, standard deviation: 19, range 70.8). Top contributing factors to workload included using automated guidance, PHI object quantity, multiple displays, and formulating probabilities in the new paradigm. Automated guidance provided support to forecasters in maintaining situational awareness and managing increased quantities of threats. The results of this study provided understanding of forecasters’ mental workload and task strategies and developed insights to improve usability of the PHI prototype tool.
Abstract
During spring 2016 the Probabilistic Hazard Information (PHI) prototype experiment was run in the National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) as part of the Forecasting a Continuum of Environmental Threats (FACETS) program. Nine National Weather Service forecasters were trained to use the web-based PHI prototype tool to produce dynamic PHI for severe weather threats. Archived and real-time weather scenarios were used to test this new paradigm of issuing probabilistic information, rather than deterministic information. The forecasters’ mental workload was evaluated after each scenario using the NASA-Task Load Index (TLX) questionnaire. This study summarizes the analysis results of mental workload experienced by forecasters while using the PHI prototype. Six subdimensions of mental workload: mental demand, physical demand, temporal demand, performance, effort, and frustration were analyzed to derive top contributing factors to workload. Average mental workload was 46.6 (out of 100, standard deviation: 19, range 70.8). Top contributing factors to workload included using automated guidance, PHI object quantity, multiple displays, and formulating probabilities in the new paradigm. Automated guidance provided support to forecasters in maintaining situational awareness and managing increased quantities of threats. The results of this study provided understanding of forecasters’ mental workload and task strategies and developed insights to improve usability of the PHI prototype tool.
