The influence of WSR-88D intra-volume scanning strategies on thunderstorm observations and warnings in the dual-polarization radar era: 2011-2020

The Weather Surveillance Radar - 1988 Doppler (WSR-88D) network has undergone several improvements in the last decade with the upgrade to dual-polarization capabilities and the ability for forecasters to re-scan the lowest levels of the atmosphere more frequently through the use of Supplemental Adaptive Intra-volume Scanning (SAILS). SAILS reduces the revisit period for scanning the lowest 1 km of the atmosphere but comes at the cost of a longer delay between scans at higher altitudes. This study quantifies how often radar Volume Coverage Patterns (VCPs) and all available SAILS options are used during the issuance of 148,882 severe thunderstorm and 18,263 tornado warnings, and near 10,474 tornado, 58,934 hail, and 127,575 wind reports in the dual-polarization radar era. A large majority of warnings and storm reports were measured with a VCP providing denser low-level sampling coverage. More frequent low-level updates were employed near tornado warnings and reports compared to severe thunderstorm warnings and hail or wind hazards. Warnings issued near a radar providing three extra low-level scans (SAILSx3) were more likely to be verified by a hazard with a positive lead time than warnings with fewer low-level scans. However, extra low-level scans were more frequently used in environments supporting organized convection as shown using watches issued by the Storm Prediction Center. Recently, the number of mid-level radar elevation scans is declining per hour, which can adversely affect the tracking of convective polarimetric signatures, like ZDR columns, which were found above the 0.5° elevation angle in over 99% of cases examined.

Business Behavior in the Face of Severe Weather: Studying the Effects of Deterministic and Probabilistic Warning Systems

Tornadoes are nature’s most violent storm and annually cause billions in damage along with the threat of fatalities and injuries. To improve tornado warnings, the National Weather Service is considering a change from a deterministic to a probabilistic paradigm. While studies have been conducted on how individual behavior may change with the new While studies have been conducted on how individual behavior may change with the new businesses. This project is a response to the Weather Research and Forecasting Innovation Act of 2017, H.R. 353, which calls for the use of social and behavioral science to study and improve storm warning systems. The goal is to discuss business response to probabilistic tornado warnings through descriptive and regression-based statistics using a survey administered to businesses in North Texas. Prior to release, the survey was vetted by a focus group comprised of businesses in Grayson County, TX who assisted in the creation of a behavior ranking scale. The scale ranked behaviors from low to high effort. Responses allowed for determining if the business reacted to the warning in a passive or active manner. Returned surveys came from large and small businesses in North Texas and represent a wide variety of industries. Regression analysis explores which variables have the greatest influence on businesses’ behavior and show that beyond increases in probability from the probabilistic warnings, trust in the warning provides the most significant change to behavior.

Tornado Warning Guidance and Graphics: Implications of the Inclusion of Protective Action Information on Perceptions and Efficacy

Effective warning messages should tell people what they should do, how they should do it, and how to maximize their health and safety. Guidance essentially delivers two types of information: 1) information that instructs people about the actions to take in response to a threat, and 2) information about how and why these recommended protective actions will reduce harm. However, recent research reported that while automated tornado warnings, sent by the National Weather Service Storm Prediction Center via the account @NWStornado on Twitter, included useful information about the location of the threat, the potential impacts, and populations at risk, it failed to provide content that would contribute to successful protective actions. In this experimental study we investigate how the inclusion and presentation of protective action guidance affects participant perceptions of a tornado warning message and their perceived ability to act upon the information (i.e., self- and response-efficacy). We find that the inclusion of protective action guidance results an increase in the participants’ understanding of the message, their ability to decide what to do, and their perceived self- and response-efficacy. Knowing how to take action to protect oneself, and believing the actions will make oneself safe, are key motivators to taking action when faced with a significant threat. Future warning research should draw from other persuasive messaging and health behavior theories, and should include self- and response- efficacy as important causal factors. It should also look across additional hazards to determine if these outcomes differ by the length of forewarning and hazard type.

An Analysis of 2016-2018 Tornadoes and National Weather Service Tornado Warnings across the contiguous United States

Previous work has considered tornado occurrence with respect to radar data, both WSR-88D and mobile research radars, and a few studies have examined techniques to potentially improve tornado warning performance. To date, though, there has been little work focusing on systematic, large-sample evaluation of National Weather Service (NWS) tornado warnings with respect to radar-observable quantities and the near-storm environment. In this work, three full years (2016–2018) of NWS tornado warnings across the contiguous United States were examined, in conjunction with supporting data in the few minutes preceding warning issuance, or tornado formation in the case of missed events. The investigation herein examines WSR-88D and Storm Prediction Center (SPC) mesoanalysis data associated with these tornado warnings with comparisons made to the current Warning Decision Training Division (WDTD) guidance.Combining low-level rotational velocity and the significant tornado parameter (STP), as used in prior work, shows promise as a means to estimate tornado warning performance, as well as relative changes in performance as criteria thresholds vary. For example, low-level rotational velocity peaking in excess of 30 kt (15 m s−1), in a near-storm environment which is not prohibitive for tornadoes (STP > 0), results in an increased probability of detection and reduced false alarms compared to observed NWS tornado warning metrics. Tornado warning false alarms can also be reduced through limiting warnings with weak (<30 kt), broad (>1nm) circulations in a poor (STP=0) environment, careful elimination of velocity data artifacts like sidelobe contamination, and through greater scrutiny of human-based tornado reports in otherwise questionable scenarios.

The Influence of Weather Watch Type on the Quality of Tornado Warnings and its Implications for Future Forecasting Systems

While many studies have looked at the quality of forecast products, few have attempted to understand the relationship between them. We begin to consider whether or not such an influence exists by analyzing storm-based tornado warning product metrics with respect to whether they occurred within a severe weather watch and, if so, what type of watch they occurred within.The probability of detection, false alarm ratio, and lead time all show a general improvement with increasing watch severity. In fact, the probability of detection increased more as a function of watch-type severity than the change in probability of detection during the time period of analysis. False alarm ratio decreased as watch type increased in severity, but with a much smaller magnitude than the difference in probability of detection. Lead time also improved with an increase in watch-type severity. Warnings outside of any watch had a mean lead time of 5.5 minutes, while those inside of a particularly dangerous situation tornado watch had a mean lead time of 15.1 minutes. These results indicate that the existence and type of severe weather watch may have an influence on the quality of tornado warnings. However, it is impossible to separate the influence of weather watches from possible differences in warning strategy or differences in environmental characteristics that make it more or less challenging to warn for tornadoes. Future studies should attempt to disentangle these numerous influences to assess how much influence intermediate products have on downstream products.

An analysis of tornado warning reception and response across time: leveraging respondent's confidence and a nocturnal tornado climatology

Nocturnal tornadoes are challenging to forecast and even more challenging to communicate. Numerous studies have evaluated the forecasting challenges, but fewer have investigated when and where these events pose the greatest communication challenges. This study seeks to evaluate variation in confidence among US residents in receiving and responding to tornado warnings by hour-of-day. Survey experiment data comes from the Severe Weather and Society Survey, an annual survey of US adults. Results indicate that respondents are less confident about receiving warnings overnight, specifically in the early morning hours (12 AM to 4 AM local time). We then use the survey results to inform an analysis of hourly tornado climatology data. We evaluate where nocturnal tornadoes are most likely to occur during the time frame when residents are least confident in their ability to receive tornado warnings. Results show that the Southeast experiences the highest number of nocturnal tornadoes during the time period of lowest confidence, as well as the largest proportion of tornadoes in that time frame. Finally, we estimate and assess two multiple linear regression models to identify individual characteristics that may influence a respondent’s confidence in receiving a tornado between 12 AM and 4 AM. These results indicate that age, race, weather awareness, weather sources, and the proportion of nocturnal tornadoes in the local area relate to warning reception confidence. The results of this study should help inform policymakers and practitioners about the populations at greatest risk for challenges associated with nocturnal tornadoes. Discussion focuses on developing more effective communication strategies, particularly for diverse and vulnerable populations.

A Variational Method for Filling in Missing Data in Doppler Velocity Fields

Single-Doppler radar data are often missing in important regions of a severe storm due to low return power, low signal-to-noise ratio, ground clutter associated with normal and anomalous propagation, and missing radials associated with partial or total beam blockage. Missing data impact the ability of WSR-88D algorithms to detect severe weather. To aid the algorithms, we develop a variational technique that fills in Doppler velocity data voids smoothly by minimizing Doppler velocity gradients while not modifying good data. This method provides estimates of the analysed variable in data voids without creating extrema.Actual single-Doppler radar data of four tornadoes are used to demonstrate the variational algorithm. In two cases, data are missing in the original data, and in the other two, data are voided artificially. The filled-in data match the voided data well in smoothly varying Doppler velocity fields. Near singularities such as tornadic vortex signatures, the match is poor as anticipated. The algorithm does not create any velocity peaks in the former data voids, thus preventing false triggering of tornado warnings. Doppler circulation is used herein as a far-field tornado detection and advance-warning parameter. In almost all cases, the measured circulation is quite insensitive to the data that have been voided and then filled. The tornado threat is still apparent.

Firm Behavior in the Face of Severe Weather: Economic Analysis between Probabilistic and Deterministic Warnings

Tornadoes cause billions of dollars in damage and over 100 fatalities on average annually. Yet, an indirect cost to these storms is found in lost sales and/or lost productivity from responding to over 2000 warnings per year. This project responds to the Weather Research and Forecasting Innovation Act of 2017, H.R. 353, which calls for the use of social and behavioral science to study and improve storm warning systems. Our goal is to provide an analysis of cost avoidance that could accrue from a change to the warning paradigm, particularly to include probabilistic hazard information at storm scales. A survey of nearly 500 firms was conducted in and near the Dallas–Fort Worth metropolitan area asking questions about experience with tornadoes, sources of information for severe weather, expected cost of responding to tornado warnings, and how the firm would respond to either deterministic or probabilistic warnings. We find a dramatic change from deterministic warnings compared to the proposed probabilistic and that a probabilistic information system produces annual cost avoidance in a range of $2.3–$7.6 billion (U.S. dollars) compared to the current deterministic warning paradigm.

Improving Tornado Warning Communication for Deaf and Hard of Hearing Audiences

Although specialized personal and residential Deaf warning technologies exist, receipt and comprehension of tornado warning information from local television is often delayed or misunderstood because of closed-captioning deficiencies. In order to suggest improvements for the communication of tornado warnings to Deaf and Hard of Hearing (D/HoH) audiences, interviews and a focus group were conducted within the active tornado counties of Alabama. D/HoH individuals generally use more information sources than the hearing population to better understand their risk. Protective action decision-making by our sample was characterized by more hesitation, uncertainty, and indecision than in the hearing population. The most common suggestion for improving tornado-warning communication was to have an American Sign Language (ASL) interpreter shown on screen with a local television meteorologist during a tornado warning. A split-screen television product with an ASL interpreter in a remote studio was prototyped showing that this type of live broadcast is possible for local tornado-warning coverage. Several screen formats were evaluated by a focus group with the conclusion that the ASL interpreter should be on the left side of the screen without obscuring any part of the weather broadcast. The split-screen product with an ASL interpreter resulted in full access to all broadcast information, the ability to make immediate safety decisions, and was welcomed with excitement by the focus-group participants. This modification, along with the education and preparedness efforts of the National Weather Service, help remedy the information gaps and comprehension delays of this underserved population.

Random Forest Model to Assess Predictor Importance and Nowcast Severe Storms using High-Resolution Radar–GOES Satellite–Lightning Observations

Few studies have assessed combined satellite, lightning, and radar databases to diagnose severe storm potential. The research goal here is to evaluate next-generation, 60-second update frequency geostationary satellite and lightning information with ground-based radar to isolate which variables, when used in concert, provide skillful discriminatory information for identifying severe (hail ≥2.5 cm in diameter, winds ≥25 m s–1, tornadoes) versus non-severe storms. The focus of this study is predicting severe thunderstorm and tornado warnings. A total of 2,004 storms in 2014–2015 were objectively tracked with 49 potential predictor fields related to May, daytime Great Plains convective storms. All storms occurred when 1-min Geostationary Operational Environmental Satellite (GOES)–14 “super rapid scan” data were available. The study used three importance methods to assess predictor importance related to severe warnings, and random forests to provide a model and skill evaluation measuring the ability to predict severe storms. Three predictor importance methods show that GOES mesoscale atmospheric motion vector derived cloud-top divergence and above anvil cirrus plume presence provide the most satellite-based discriminatory power for diagnosing severe warnings. Other important fields include Earth Networks Total Lightning flash density, GOES estimated cloud-top vorticity, and overshooting-top presence. Severe warning predictions are significantly improved at the 95% confidence level when a few important satellite and lightning fields are combined with radar fields, versus when only radar data are used in the random forests model. This study provides a basis for including satellite and lightning fields within machine-learning models to help forecast severe weather.