Seasonal, Monthly, and Weekly Distributions of NLDN and GLD360 Cloud-to-Ground Lightning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Annual maps of cloud-to-ground lightning flash density have been produced since the deployment of the National Lightning Detection Network (NLDN). However, a comprehensive national summary of seasonal, monthly, and weekly lightning across the contiguous United States has not been developed. Cloud-to-ground lightning is not uniformly distributed in time, space, or frequency. Knowledge of these variations is useful for understanding meteorological processes responsible for lightning occurrence, planning outdoor events, anticipating impacts of lightning on power reliability, and relating to severe weather. To address this gap in documentation of lightning occurrence, the variability on seasonal, monthly, and weekly scales is first addressed with NLDN flash data from 2005 to 2014 for the 48 states and adjacent regions. Flash density and the percentage of each season’s portion of the annual total are compiled. In spring, thunderstorms occur most often over southeastern states. Lightning spreads north and west until by June, most areas have lightning. New England, the northern Rockies, most of Canada, and the Florida Peninsula have a small percentage of lightning outside of summer. Arizona and portions of adjacent states have the highest incidence in July and August. Flash densities reduce in September in most regions. This seasonal, monthly, and weekly overview complements a recent study of diurnal variations of flashes to document when and where lightning occurs over the United States. NLDN seasonal maps indicate a summer lightning dominance in the northern and western United States that extends into Canada using data compiled from GLD360 network observations. GLD360 also extends NLDN seasonal maps and percentages into Mexico, the Caribbean, and offshore regions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it