Deterministic and constrained stochastic models of thunderstorm winds
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 statistical sense indistinguishable from a measured wind. The constrain in the stochastic model is the value of the reported dameging wind gust. The models are validated against measured thunderstorm wind records and used to reconstruct thunderstorm wind time series from the damage data. • Deterministic and stochastic models developed using information about damaging gust. • First constrained stochastic model of turbulent thunderstorm winds. • Stochastic model validated against high-frequency anemometer measurements. • Models used to reconstruct time histories of winds that caused damage to structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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 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".