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David R. Harrison
and
Christopher D. Karstens

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.

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Bruce D. Lee
,
Catherine A. Finley
, and
Christopher D. Karstens

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.

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Amy McGovern
,
Christopher D. Karstens
,
Travis Smith
, and
Ryan Lagerquist

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.

Open access
Christopher D. Karstens
,
William A. Gallus Jr.
,
Bruce D. Lee
, and
Catherine A. Finley

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.

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Christopher D. Karstens
,
Timothy M. Samaras
,
Bruce D. Lee
,
William A. Gallus Jr.
, and
Catherine A. Finley

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.

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Amy McGovern
,
Kimberly L. Elmore
,
David John Gagne II
,
Sue Ellen Haupt
,
Christopher D. Karstens
,
Ryan Lagerquist
,
Travis Smith
, and
John K. Williams

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.

Open access
Bryan T. Smith
,
Richard L. Thompson
,
Douglas A. Speheger
,
Andrew R. Dean
,
Christopher D. Karstens
, and
Alexandra K. Anderson-Frey

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.

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Bryan T. Smith
,
Richard L. Thompson
,
Douglas A. Speheger
,
Andrew R. Dean
,
Christopher D. Karstens
, and
Alexandra K. Anderson-Frey

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.

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Christopher D. Karstens
,
Greg Stumpf
,
Chen Ling
,
Lesheng Hua
,
Darrel Kingfield
,
Travis M. Smith
,
James Correia Jr.
,
Kristin Calhoun
,
Kiel Ortega
,
Chris Melick
, and
Lans P. Rothfusz

Abstract

A proposed new method for hazard identification and prediction was evaluated with forecasters in the National Oceanic and Atmospheric Administration Hazardous Weather Testbed during 2014. This method combines hazard-following objects with forecaster-issued trends of exceedance probabilities to produce probabilistic hazard information, as opposed to the static, deterministic polygon and attendant text product methodology presently employed by the National Weather Service to issue severe thunderstorm and tornado warnings. Three components of the test bed activities are discussed: usage of the new tools, verification of storm-based warnings and probabilistic forecasts from a control–test experiment, and subjective feedback on the proposed paradigm change. Forecasters were able to quickly adapt to the new tools and concepts and ultimately produced probabilistic hazard information in a timely manner. The probabilistic forecasts from two severe hail events tested in a control–test experiment were more skillful than storm-based warnings and were found to have reliability in the low-probability spectrum. False alarm area decreased while the traditional verification metrics degraded with increasing probability thresholds. The latter finding is attributable to a limitation in applying the current verification methodology to probabilistic forecasts. Relaxation of on-the-fence decisions exposed a need to provide information for hazard areas below the decision-point thresholds of current warnings. Automated guidance information was helpful in combating potential workload issues, and forecasters raised a need for improved guidance and training to inform consistent and reliable forecasts.

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Christopher D. Karstens
,
James Correia Jr.
,
Daphne S. LaDue
,
Jonathan Wolfe
,
Tiffany C. Meyer
,
David R. Harrison
,
John L. Cintineo
,
Kristin M. Calhoun
,
Travis M. Smith
,
Alan E. Gerard
, and
Lans P. Rothfusz

Abstract

Providing advance warning for impending severe convective weather events (i.e., tornadoes, hail, wind) fundamentally requires an ability to predict and/or detect these hazards and subsequently communicate their potential threat in real time. The National Weather Service (NWS) provides advance warning for severe convective weather through the issuance of tornado and severe thunderstorm warnings, a system that has remained relatively unchanged for approximately the past 65 years. Forecasting a Continuum of Environmental Threats (FACETs) proposes a reinvention of this system, transitioning from a deterministic product-centric paradigm to one based on probabilistic hazard information (PHI) for hazardous weather events. Four years of iterative development and rapid prototyping in the National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) with NWS forecasters and partners has yielded insights into this new paradigm by discovering efficient ways to generate, inform, and utilize a continuous flow of information through the development of a human–machine mix. Forecasters conditionally used automated object-based guidance within four levels of automation to issue deterministic products containing PHI. Forecasters accomplished this task in a timely manner while focusing on communication and conveying forecast confidence, elements considered necessary by emergency managers. Observed annual increases in the usage of first-guess probabilistic guidance by forecasters were related to improvements made to the prototyped software, guidance, and techniques. However, increasing usage of automation requires improvements in guidance, data integration, and data visualization to garner trust more effectively. Additional opportunities exist to address limitations in procedures for motion derivation and geospatial mapping of subjective probability.

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