Winter Lightning to the Lee of Lake Ontario: The Lake-Effect Electrification (LEE) Field Campaign
Bibliographic record
Abstract
Abstract The National Science Foundation–sponsored Lake-Effect Electrification (LEE) field campaign intensive observation periods occurred between November and early February 2022–23 across the eastern Lake Ontario region. Project LEE documented, for the first time, the total lightning and electrical charge structures of lake-effect storms and the associated storm environment using a lightning mapping array (LMA), a mobile dual-polarization X-band radar, and balloon-based soundings that measured vertical profiles of temperature, humidity, wind, electric field, and hydrometeor types. LEE also observed abundant wind turbine-initiated lightning, which is climatologically more likely during the winter. The frequent occurrence of intense lake-effect storms and the proximity of a wind farm with nearly 300 turbines each more than 100 m tall to the lee of Lake Ontario provided an ideal laboratory for this study. The field project involved many undergraduate (>20) and graduate students. Some foreseen and unforeseen challenges included clearing the LMA solar panels of snow and continuous operation in low-sunlight conditions, large sonde balloons prematurely popping due to extremely cold conditions, sonde line breaking, recovering probes in deep snow in heavily forested areas, vehicles getting stuck in the snowpack, and an abnormally dry season for parts of the LEE domain. In spite of these difficulties, a dataset was collected in multiple lake-effect snowstorms (11 observation periods) and one extratropical cyclone snowstorm that clarifies the electrical structure of these systems. A key finding was the existence of a near-surface substantial positive charge layer (1 nC m −3 ) near the shoreline during lake-effect thunderstorms. Significance Statement Winter lightning is an exciting phenomenon and important to study as man-made objects (e.g., wind turbines) can be more likely to be struck this time of year. We involved over 20 undergraduate students in collecting total lightning, mobile radar, and sounding (e.g., electric field) data in several lake-effect storms. These storms have been known to have substantial electrification and occur many times each winter in a small, consistent region east of Lake Ontario. We analyzed 246 lightning flashes from the main data collection period, and over a third of them were associated with towers. The results from this study will have impacts on our general understanding of electrification in winter and summer storms.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".