Mixture frequency analysis for tropical cyclone and non‐tropical cyclone extreme precipitation in the coastal areas: A case of Fujian in China
Bibliographic record
Abstract
Abstract The precipitation is classified into tropical cyclone (TC)‐induced precipitation and others, that is, non‐TC precipitation in the Asian continent and the western North Pacific. Traditional frequency analysis for the precipitation, which is a mixture series of annual maximum precipitation generated by TC and non‐TC, does not consider the difference in the generation mechanism with the assumption of independently and identically distributed. To reveal the effect of different generation mechanisms on the frequency analysis of the extreme precipitation, a multiplicative mixture frequency analysis (MMFA) model is adopted to study the interaction of non‐TC maximum precipitation (NTCMP) and TC maximum precipitation (TCMP) to the total precipitation frequency. Applying the NTCMP and TCMP data in Fujian Province located in southeast China, the results indicated that (a) the inconsistencies between NTCMP and TCMP are common, and different inconsistent patterns existed in space; (b) Different characteristics of inconsistency between the two types of precipitation had distinct effect on the estimation results of MMFA model, especially for the high return levels; and (c) The estimation results of MMFA model were demonstrated to be more secure and reliable. Therefore, the MMFA model is a suitable approach to solve the problem of mixture precipitation frequency caused by two generation mechanisms of non‐TC and TC.
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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".