Intensity Identification of Typhoon Haikui (1211) During the Landing Stage
Bibliographic record
Abstract
ABSTRACT: In daily typhoon operation, identifying the intensity of typhoons is always a contentious problem, which can be attributed to the absence of direct observational data when typhoons are present on the ocean. When typhoons move to the offshore region, where many automatic weather stations (AWSs) are present, utilizing automatic observations in non-standard conditions is a good way of identifying the intensity or wind of a typhoon. Before identification, AWS data should be conversed or revised based on statistical experiences from a multilayer wind tower.In this study, the intensity of Haikui (1211) at the landing stage (from 08071200 UTC to 08071920 UTC) is revised carefully. Calculating the wind conversion coefficient between different heights from a 300m multilayer tower observation, the wind data caught by two offshore AWSs were converted to the standard wind of 10 meters and used to identify the intensity of the landing Haikui. The maximum surface wind of Haikui in the landing period was about 45 m/s to 48 m/s and then reduced to 40 m/s to 42 m/s approximately just before landing.On the basis of the discussion in this study, the AWS data in a non-standard environment can be utilized to determine the surface wind at 10 m height by arithmetic conversion. This implies that we should pay more attention and patient to the wind data observed in offshore island AWSs during typhoon identification. Keywords: typhoon intensity identification, automatic weather station data, surface wind conversion
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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 | 0.000 |
| 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) | 0.001 | 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".