Lake-Effect Thunderstorms in the Lower Great Lakes
Bibliographic record
Abstract
Abstract Cloud-to-ground (CG) lightning, radar, and radiosonde data were examined to determine how frequently lake-effect storms (rain/snow) with lightning occurred over and near the lower Great Lakes region (Lakes Erie and Ontario) from September 1995 through March 2007. On average, lake-effect lightning occurred on 7.9 days and with 5.8 storm events during a particular cool season (September–March). The CG lightning with these storms had little inland extent and was usually limited to a few flashes per storm. Some storms had considerably more, with the most intense storm (based on National Lightning Detection Network observations) producing 1551 CG flashes over a 4-day period. Thundersnow events were examined in more detail because of the rarity of this phenomenon across the United States. Most lake-effect thundersnow events (75%) occurred in November and December. An analysis of model sounding data using the Buffalo Toolkit for Lake Effect Snow (BUFKIT) software package in which lower boundary conditions can be modified by lake surfaces showed that thundersnow events had an 82% increase in the mean height of the −10°C level when compared with nonelectrified lake-effect snowstorms (1.2 vs 0.7 km AGL), had higher lake-induced equilibrium levels (EL; above 3.6 km AGL) and convective available potential energy (CAPE; >500 J kg−1), had low wind shear environments, and were intense, single-band storms. A nomogram of the altitude of the −10°C isotherm and EL proved to be useful in predicting lake-effect thundersnowstorms.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".