Analysis of Forest-Based Tornadoes Using Treefall Patterns
Bibliographic record
Abstract
Abstract Many tornadoes occur in rural forested regions of the world where traditional damage indicators are often unavailable to determine, or may underestimate, a tornado’s true intensity. Future versions of the enhanced Fujita (EF) scale are expected to include guidelines for treefall pattern matching to determine tornado intensity. This study explores the reliability and uncertainty of using tornado treefall patterns as a method for assessing the maximum 3-s gust speed and swirl ratio of tornadoes in forested regions. Through mathematical analysis, treefall patterns are determined to depend primarily on the ratio of the tornado’s radial, tangential, and translational speeds, as well as the critical tree failure speed. Moreover, four typical types of tornado treefall patterns are differentiated based on vortex–tree failure locations relative to the tornado’s radius of maximum wind speed. As a result, when estimates for the critical tree failure and tornado translation speeds are known, treefall patterns can be consistently linked to an estimate of a tornado’s maximum wind speed and swirl ratio. The damage paths of four tornadoes, including the previously analyzed Alonsa, Manitoba, EF4 tornado, are analyzed using custom software implementing a Monte Carlo simulation for treefall pattern matching to estimate the maximum 3-s gust speed and swirl ratio, as well as the associated uncertainty. Of the three newly analyzed events, two are estimated to have higher EF-scale ratings than those suggested by the current Canadian EF scale. Significance Statement Current methods in the Canadian enhanced Fujita (EF) scale are often unable to rate tornadoes above EF2 when considering only tree damage. However, previous studies have shown that treefall pattern matching has the potential to provide higher ratings. Building on previous works, this study explores the reliability and uncertainty in using treefall pattern matching for estimating a tornado’s maximum 3-s gust speed and swirl ratio. Through the use of a Monte Carlo simulation approach, improved estimates of the range of tornado wind speeds are obtained.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".