A New Metric to Diagnose Precipitation Distribution in Transitioning Tropical Cyclones
Bibliographic record
Abstract
A new coupled dynamic and thermodynamic metric is developed based on the Eady Moist Baroclinic Growth Rate (EMBGR), to discriminate between left-of-track (LOT) and right-of-track (ROT) precipitation distributions in transitioning tropical cyclones (TCs). LOT events pose a major flood risk even when a TC tracks along a coastline or just offshore, as flash flooding can occur hundreds of kilometers inland from the cyclone center. The EMBGR can improve human-produced quantitative precipitation forecasts (QPF) because it is dependent on relatively well-forecast large-scale mass fields. The ability of the EMBGR to identify precipitation distribution is first explored in a case study of TC Matthew (2016), using reanalysis and numerical model forecasts. Subsequently, a composite analysis of 36 years (1979–2014) of United States landfalling TCs using reanalysis data shows that the EMBGR is an effective discriminator between LOT and ROT distributions. The utility of the EMBGR is quantified using a pattern correlation analysis for both TC Matthew and the composites. Finally, a conceptual schematic is developed for LOT cases so that forecasters can most effectively utilize the EMBGR to improve human QPF skill during transitioning TCs.
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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) | 0.003 | 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".