Separation and spatial variations of typhoon and non‐typhoon rainfall at different timescales in typical region of southeast China
Bibliographic record
Abstract
Abstract Rainfall in East Asia is affected by two rain‐bearing systems: tropical cyclones and monsoon‐related frontal systems. Distinguishing typhoon rainfall (TR) and non‐typhoon rainfall (non‐TR) helps to understand the evolution process of regional rainfall at different timescales. Taking Fujian Province in the southeast coast of China as an example, based on the fixed box approach of separating TR, the method of determining the size of fixed box is explored. TR and non‐TR are separated in Fujian Province, and the spatial variations of TR and non‐TR at different timescales (annual, monthly, day of annual‐maximum‐rain) are analysed. The results showed that (1) according to the relationship between the sizes of fixed box and the rate of change of TR, the size of fixed box could be reasonably determined; thus, the expended size of fixed box suitable for separating TR in Fujian was 3.5°, namely the range of 20°–31.8°N and 112.3°–124.2°E. (2) The spatial variations of TR at different timescales in Fujian were similar: TR decreased from the coast to the inland, and the northeast coast of Fujian was the high‐value region. Due to the difference of water vapour sources, non‐TR in March–June increased from the coast to the inland, but the high value of non‐TR in July–September was distributed in the eastern coast. (3) The contribution rates of average TR to the total rainfall of the year, July–September and 1 day were 12.8%, 34.6% and 35.7%, respectively. In eastern coast, TR in July–September accounted for 1/3–1/2 of total rainfall, and the rainfall in a day was mainly affected by TR; while in western inland, rainfall was mainly non‐TR and the influence of TR was less than 1/4.
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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".