Improving the anticipation of potential tornado intensity across different time scales

Sessa, Michael F

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https://hdl.handle.net/2142/125561 Copy

Description

Title

Improving the anticipation of potential tornado intensity across different time scales

Author(s)

Sessa, Michael F

Issue Date

2024-07-07

Director of Research (if dissertation) or Advisor (if thesis)

Trapp, Jeff

Doctoral Committee Chair(s)

Trapp, Jeff

Committee Member(s)

• Nesbitt, Steve
• Klees, Alicia
• Kosiba, Karen

Department of Study

Atmospheric Sciences

Discipline

Atmospheric Sciences

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• atmospheric sciences
• severe thunderstorms
• tornadoes
• mesocyclones
• radar meteorology
• numerical models

Abstract

Previous studies of tornadoes have focused on understanding and predicting tornadogenesis or diagnosing the intensity of an ongoing tornado. Given that the majority of damage and fatalities are caused by strong to violent tornadoes, there is a need for robust operational tools that focus on anticipating tornado intensity rather than simply on tornadogenesis or ongoing tornadoes. Thus motivated, this study will improve the understanding of and provide new tools for the anticipation of tornado intensity before tornadoes form from the tornado watch time scale to the pre-tornadic stages of ongoing thunderstorms. The first objective of this research uses an observational pre-tornadic radar and near-storm environmental dataset to confirm and further explore relationships with tornado intensity. Analyses of Doppler radar data and environmental parameters are used to propose an alternative framework for tornado intensity prediction during pre-tornadic stages of ongoing storms, conditional on tornadogenesis. A robust linear relationship (R² = 0.69) is found between pretornadic mesocyclone width and the EF rating of the subsequent tornado. Relationships between environmental parameters and tornado intensity depend in part on how the tornado-intensity categories are distributed [i.e., nonsignificant (EF0–1) versus significant (EF2–5), or weak (EF0–1) versus strong (EF2–3) versus violent (EF4–5)]. Low-level shear parameters discriminate the environments of significant tornadoes from nonsignificant tornadoes, but not the environments of violent tornadoes from strong tornadoes. The converse is true for thermodynamic parameters. The need for real-time, automated quantification of mesocyclone width in addition to intensity and other attributes for operational implementation of this framework for the purposes of impact-based warnings is described. The information gained from this pre-tornadic analysis would allow an operational forecaster to be aware of and communicate information about potential tornado intensity to the public before a tornado forms to better protect life and property. The second objective of this research uses nine classification machine-learning algorithms to examine their skill in making short-fused, storm-based predictions of significant or non-significant tornado-damage intensity, conditioned upon tornadogenesis, using pretornadic mesocyclone characteristics and the near-storm environment. Radar predictors are from the, approximately, 30 minutes before tornadogenesis, while environmental predictors are from the model-analysis hour nearest but before the time of tornadogenesis. The most-skilled classifiers were logistic regression, random forests, and gradient boosting as measured by each model’s cross-validated accuracy (≈89%), precision (≈93%), and recall (≈73%), and other binary-classification metrics. Learning curves indicated adequate training of models, and calibration curves revealed the reliability of predicted probabilities, with random forests being the most reliable. Also, permutation tests demonstrated the statistical significance of the cross-validated model accuracy. An exploration of feature importance revealed the predictors of radar-derived pre-tornadic mesocyclone width and differential velocity were the most important over convective mode and distance from the radar, followed by environmental vertical wind shear and composite parameters. Specifically, wider and stronger pre-tornadic mesocyclones in environments characterized by larger values of vertical wind shear and composite parameters increase the likelihood of significant tornadoes. The model results could build forecaster confidence in the anticipation of tornado-damage intensity and aid forecasters in making informed impact-based warning tag decisions by quickly summarizing data relevant to potential tornado-damage rating before a tornado forms to better protect life and property. Important future work includes the addition of other radar-based predictors and the development of a more diverse and realistic sample of tornadic events. The third objective of this research uses High-Resolution Rapid Refresh (HRRR) model forecasts of storm-scale diagnostics such as updraft helicity (UH) and vertical vorticity as well as other environmental parameters such as the significant tornado parameter (STP) prior to convection initiation to examine their skill in predicting whether a severe weather event or hour will be associated with no tornadoes, non-significant tornadoes (EF0-1), or significant tornadoes (EF2+). Each event and the associated HRRR forecasts are sampled within Storm Prediction Center tornado outlook regions using different thresholds of storm-scale diagnostics in combination with the large-scale environment. The results are highlighted by a statistically significant separation between several characteristics of 0–2 km and 2–5 km UH swaths associated with significant tornado events (EF2–5) from those associated with nontornadic and nonsignificant tornado events (EF0–1). An hourly analysis revealed that both 0-2 km and 2-5 km UH intensity and other storm-scale parameters like 0–1- and 0–2-km vertical vorticity could be used to also anticipate significant tornado hours as well. A comparison of UH swath characteristics and tornado path length and width revealed moderate linear relationships between event cumulative UH swath length and event cumulative tornado path length (R² ~ 0.5). Relationships between the UH metrics themselves, other storm-scale parameters and environmental parameters are discussed as well including the relatively strong linear relationships between the intensity of 2-5 km and 0-2 km UH (R² = 0.69) and their event-total swath numbers (R² = 0.71). Ultimately, this analysis reveals the operational usefulness of 3-km HRRR forecasts of storm-scale diagnostics, combined with the background environment, for the anticipation and communication of potential significant tornado events during the tornado watch time scale.

Graduation Semester

2024-08

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/125561

Copyright and License Information

Copyright 2024 Michael Sessa

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