Investigation of the Mountain Thunderstorm Minima
Bibliographic record
Abstract
Conventional meteorological wisdom states that mountains provide a favorable environment for convection initiation.There exists an expansive body of knowledge on mountains being regions of preferred convective initiation and enhancement via orographic lift, thermal circulations, and other mechanisms.However, our analysis shows that some mountains, such as the Appalachians and Adirondacks, are characterized by significant convection minima; they either hamper cell maintenance or the formation of new cells.There is limited previous research into mountains having a negative effect on convective storms.This project investigates processes in which mountains disrupt pre-existing storms or inhibit convective initiation.Using lightning, radar, and ERA5 reanalysis data, we characterize the convection minimum and its attributes such as its timing and the thermodynamic atmospheric properties associated with it.To further investigate processes contributing to the convective minimum, we conducted highresolution simulations using the WRF model.Processes explored span a variety of scales from synoptic frontal delays to interactions between storm cold pool and complex terrain.The leading hypothesis for the convection minimum is that our mountains of study are minima of both convective inhibition and convective available potential energy.They are a favorable location for convection initiation and an unfavorable location for storm maintenance leading to weaker storms forming efficiently in poor convective environments.
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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 | 0.000 |
| 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 | 0.001 | 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".