Reconstructing Thunderstorm Wind Speed Time Histories Using Forensic Damage Analysis
Bibliographic record
Abstract
Strong winds are one of the major causes of structural damage inflicted by thunderstorms. Specialized agencies and research groups regularly provide post-disaster damage assessment reports after a severe thunderstorm has passed over an area. If the observed structural damage was caused by severe winds, an assessment of the characteristic wind gust is provided based on the wind engineering analysis of the damage. Given that high frequency anemometer measurements of wind speed are practically never available at or close to the observed damage, this study investigates the following question: What would an anemometer measure during the storm if it was installed at the location of the observed damage? This paper presents two models for thunderstorm wind time series reconstruction based on the observed damage. One model is a simple analytical equation that provides a closed-form solution to relate the estimated wind gust that caused damage to the mean wind speed and instantaneous peak during the storm. Another model is uses constrained stochastic simulations that are based on power spectral density of turbulent winds to reproduce a thunderstorm wind record that is in a statistical sense indistinguishable from a measured wind. The constrain in the stochastic model is the value of the reported damaging wind gust. The models are validated against measured thunderstorm wind records and used to reconstruct thunderstorm wind time series from the damage data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 source (direct Gemma or distilled Codex), 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".