Statistical-based model development with surrogate rainfall fields and its use to assess typhoon rain hazard for coastal region of mainland China
Bibliographic record
Abstract
Tropical cyclones (TCs) could cause intense rainfall along the coastal region of mainland China. The modeling of rainfall intensity could be carried out using snapshots of the rainfall intensity field obtained from satellite data such as those from the Tropical Rainfall Measuring Mission (TRMM) satellite data. In the present study, both the precipitation radar TRMM (PR-TRMM) data and the Microwave Imager (TMI) TRMM (TMI-TRMM) data that affect onshore sites in mainland China are considered to develop rainfall intensity models. To augment the database for the model development, surrogates of the snapshots are generated using an efficient iterative simulation algorithm and applying the discrete orthogonal S-transform. The simulated surrogates for the seed snapshots follow the same marginal distribution and power spectral density distribution. Empirical models of the rainfall intensity are developed using the surrogates so to increase the sample size. For the development, the TC rainfall intensity field is represented as a superposition of the axisymmetric, asymmetric, and topographic components. This consideration is guided by the parametric hurricane rain model (PHRaM) available in the literature. It is shown that the rainfall intensity inferred from the snapshots from PR-TRMM is greater than that from the snapshots from TMI-TRMM. The developed models are used in a simulation framework to map TC rain hazard (in terms of the return period value of the annual maximum accumulated rain per day).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".