Analysis of Characteristics of Tropical Cyclones Severely Affecting the Coast of Zhejiang Province in China Using ERA5 Data
Bibliographic record
Abstract
Tropical cyclones (TCs) are primarily responsible for producing hazardous sea states, coastal flooding and erosion, and subsequent damages to coastal infrastructure. Using data from automatic weather stations, buoys, TC best track attributes, and ERA5 datasets, we examine the top 20 TCs with significant precipitation affecting the Zhejiang coast. Composite analyses are performed in a TC–centred reference frame. The findings indicate that the selected TCs typically reach intensities of severe typhoons or super typhoons. The ERA5 data provide more precise TC locations in their maintenance stage. In comparison to the best TC tracking data, the ERA5 data tend to underestimate the maximum wind speeds around TC eyewalls by a factor of 1.84, with the largest bias reaching about 25 m s−1 in the maintenance stage. However, the ERA5 reanalysis performs better in the weakening and development stages than in the maintenance stage at the five buoys of Zhejiang Province. The mean sea level pressure fields exhibit a generally symmetric distribution within 4° of latitude/longitude from the TC centres, whereas the mean wind fields display an asymmetric pattern, with higher values on the right–hand side and maximum wind zones located approximately 0.5° to 1.5° away from the centres. Areas of rough to very rough seas on the right–hand side are nearly twice as extensive as those on the left. Furthermore, as the distance from the TC centre increases, the percentage of wind seas contributing to significant wave height decreases, along with a reduction in wave period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".