A Diagnostic Metric for Predicting Tropical Cyclone and Mid-Latitude Floods
Bibliographic record
Abstract
This study details a dynamic and thermodynamic metric (i.e., Extreme Flood Index [EFI]) designed to diagnose the frequency and intensity of extreme precipitation events associated with stagnant mid-latitude flow patterns (i.e., Rex blocks). As the global climate warms, rapid Arctic warming may be helping to slow the mid-latitude westerly jet stream, resulting in increased mid-latitude flow stagnation. The combination of long-duration ascent associated with easterly winds and warm moist air increases the severity of extreme precipitation events; as such, the EFI is specifically designed to detect this potent combination of ingredients. In 2013, a Rex block stalled a low-pressure system over Alberta which caused the worst Canadian flood disaster ever seen. To that end, the recent billion-dollar flood catastrophe produced by Tropical Cyclone (TC) Harvey was also associated with a Rex Block (dynamics) in the presence of warm, moist air (thermodynamics). Despite dynamic differences between TC-related and mid-latitude floods, the EFI is successfully able to detect both. The dynamics component of the EFI is derived from two atmospheric blocking criteria, used operationally by the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Oceanic and Atmospheric Administration (NOAA), respectively, and adapted here for the shorter duration of extreme precipitation events. The EFI’s thermodynamic component utilizes standardized anomalies of equivalent potential temperature. Finally, the ability of the EFI to diagnose and predict high-impact flood events using reanalysis data and operational numerical weather prediction models is explored.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".