Investigating the Potential of Using Radar Echo Reflectivity to Nowcast Cloud-to-Ground Lightning Initiation over Southern Ontario
Bibliographic record
Abstract
Abstract The potential for using radar echo reflectivity to forecast cloud-to-ground (CG) lightning initiation in the 0–1-h time frame was investigated in southern Ontario, Canada. The main purpose of this investigation was to determine a reflectivity threshold at an isothermal altitude and a threshold for echo tops that best predict CG lightning initiation. The study examined lightning, radar, and upper-air sounding data for only airmass-type convection during the summer of 2008. The best predictor of the onset of CG lightning was found to be a 40-dBZ reflectivity level detected at an altitude with an environmental temperature of −10°C, with an average lead time of 17 min. Echo tops reaching or exceeding 7 km were a necessary condition prior to or at the time of the first CG lightning occurrence. Also, certain differences were observed depending on the polarity of the initial lightning flashes. Positive lightning flashes, when compared to negative ones, tended to deliver stronger electric currents and to be farther away from the locations of highest reflectivity on maximum reflectivity (MAXR) radar products. Lead times were observed to be shorter for positive lightning, which might suggest that positive-lightning-producing storm clouds became strongly electrified faster than their negative counterparts. Findings indicate the potential to develop a lightning nowcast algorithm suitable for Canadian forecast operational use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".