Typhoon Wind Hazard Estimation and Mapping for Coastal Region in Mainland China
Bibliographic record
Abstract
Eight or nine tropical cyclones (TC) per year make landfall over mainland China and cause large typhoon wind speeds and economic loss. This study estimates the return period value of the annual maximum typhoon wind speed, vT, for a set of grid points in the coastal region of mainland China. vT can be used to characterize the typhoon’s wind hazard and to assign the wind load in design codes. The estimation uses a typhoon wind hazard model consisting of the TC track and wind field models. For the estimation, the development of track model for a circular subregion centered at each of the grid points is carried out by using the best-track dataset from China Meteorological Administration, and a well-accepted wind field model for simulating the TC is adopted. The spatial trends of the parameters controlling the track model are investigated, and the time histories of the wind speeds estimated by using the adopted wind field model are compared with those observed from two historical typhoon events. The estimated vT values at the grid points are used to develop the typhoon wind hazard contour maps. A comparison of the contour maps to those recommended in the Chinese design code is given. The comparison provides a forward step towards the rational assessment of the wind pressure implemented in Chinese design codes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".