The Extreme Precipitation Index (EPI): A Coupled Dynamic–Thermodynamic Metric to Diagnose Midlatitude Floods Associated with Flow Reversal
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The extreme precipitation index (EPI) is a coupled dynamic–thermodynamic metric that can diagnose extreme precipitation events associated with flow reversal in the mid- to upper troposphere (e.g., Rex and omega blocks, cutoff cyclones, Rossby wave breaks). Recent billion dollar (U.S. dollars) floods across the Northern Hemisphere midlatitudes were associated with flow reversal, as long-duration ascent (dynamics) occurred in the presence of anomalously warm and moist air (thermodynamics). The EPI can detect this potent combination of ingredients and offers advantages over model precipitation forecasts because it relies on mass fields instead of parameterizations. The EPI’s dynamics component incorporates modified versions of two accepted blocking criteria, designed to detect flow reversal during the relatively short duration of extreme precipitation events. The thermodynamic component utilizes standardized anomalies of equivalent potential temperature. Proof-of-concept is demonstrated using four high-impact floods: the 2013 Alberta Flood, Canada’s second costliest natural disaster on record; the 2016 western Europe Flood, which caused the worst flooding in France in a century; the 2000 southern Alpine event responsible for major flooding in Switzerland; and the catastrophic August 2016 Louisiana Flood. EPI frequency maxima are located across the North Atlantic and North Pacific mid- and high latitudes, including near the climatological subtropical jet stream, while secondary maxima are located near the Rockies and Alps. EPI accuracy is briefly assessed using pattern correlation and qualitative associations with an extreme precipitation event climatology. Results show that the EPI may provide potential benefits to flood forecasters, particularly in the 3–10-day range.
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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 | 0.001 | 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 it