Common Ingredients and Orographic Rain Index (ORI) for Heavy Precipitation Associated with Tropical Cyclones Passing Over the Appalachian Mountains
Bibliographic record
Abstract
Relative contributions of common ingredients to heavy orographic rainfall associated with the passage of Hurricanes Hugo (1989) and Isabel (2003) over the Appalachian Mountains are examined using a numerical weather prediction model. It is found that the key ingredients for producing local heavy orographic rainfall were: high precipitation efficiency, strong low-level flow, strong orographically forced upward motion associated with strong low-level flow over relatively gentle upslope, concave geometry providing local areas of convergence, high moist flow upstream, a relatively large convective system associated with both tropical cyclones (TCs), and relatively slower movement. In addition, neither conditional instability nor potential (convective) instability is found to play essential roles in producing strong upward motion leading to heavy orographic TC rain. A modified Orographic Rain Index (ORI) is proposed as a predictor for heavy orographic TC precipitation, which includes the upstream incoming horizontal wind speed normal to the local orography, the steepness of the mountain, the relative humidity, the TC moving speed, and the horizontal scale of the TC. It is found that the ORI estimated in regions of local maximum rainfall by using fine-resolution numerically simulated results correlate well with rainfall rates for both hurricanes, indicating that it may serve as a predictor for heavy orographic TC rainfall.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".