A performance analysis of the North American Lightning Detection Network using CN Tower lightning data
Bibliographic record
Abstract
The North American Lightning Detection Network (NALDN) is a commercial lightning detection network operated by Vaisala Inc., and is composed of the U.S. National Lightning Detection Network (NLDN) and the Environment Canada owned Canadian Lightning Detection Network (CLDN). The CN Tower is one of the best sites in the world to observe the lightning phenomenon and provides an excellent opportunity to evaluate the performance of the NALDN in the Toronto area. Using CN Tower lightning data acquired during 2005, the performance characteristics of the NALDN were thoroughly evaluated, including the flash detection efficiency (DE), stroke DE, absolute location error, peak current estimation and location accuracy model (50%, 90% and 99% error ellipses) error. Although a similar test was performed using rocket-triggered lightning in Florida at Camp Blanding, this test evaluated a completely different region of the NALDN. Moreover, rocket-triggered lightning artificially initiates a lightning discharge, whereas lightning events to the CN Tower occur naturally and are similar to discharges that occur to tall structures or objects at high altitude or mountainous areas. Excluding two flashes understood to be composed of M-components, the NALDN detected 7 out of 7 flashes recorded at the CN Tower, resulting in a 100% flash DE. Furthermore, the NALDN detected 22 out of 39 strokes recorded at the CN Tower, resulting in a stroke DE of 56%. Relative to the CN Tower, the NALDN was found to have a median absolute location error of 0.356 km and a mean error of 0.390 km for the 22 strokes it detected. It was also demonstrated that the NALDN stroke location error seems to have a large bias towards the north of the CN Tower and a slight bias towards the east, with 19 of the 22 strokes predicted north-east of the CN Tower. The 50%, 90% and 99% error ellipses provided by the NALDN were also evaluated. It was found that 73% (16 out of the 22) detected strokes were enclosed by the 50% error ellipse, 91% (20 out of the 22) detected strokes were enclosed by the 90% error ellipse and 95% (21 out of the 22) detected strokes were enclosed by the 99% error ellipse. The minimum value for the 50% error ellipse axes is set at 0.4 km by Vaisala, and 21 out of the 22 detected strokes had a semi-major axis length of 0.4 km, suggesting that the median location error for CN Tower strokes is 0.4 or less. The 0.356 km median location error obtained for the 22 detected strokes appears to support this.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".