The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series
Bibliographic record
Abstract
The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series This repository contains global lightning stroke density and stroke power calculated from georeferenced stroke count data from the World Wide Lightning Location Network WWLLN. The real-time raw stroke count data were reprocessed by WWLLN to remove artifacts and improve geolocation, which resulted in the "AE" georeferenced and timestamped stroke count data. These data were then gridded at 0.5 degree 5 arc-minute and hourly resolution, converted into density, and corrected for detection efficiency using the WWLLN global gridded detection efficiency maps. Mean, median, and standard deviation of stroke power are also provided at 30-minute resolution. The corrected hourly rasters were then aggregated into daily and monthly totals and into a multi-year monthly mean climatology. The data cover the period 2010-2022 and will be updated in the coming years. For a complete description of the data see: Kaplan, J. O., & Lau, K. H.-K. (2021). The WGLC global gridded lightning climatology and time series. Earth System Science Data, 13(7), 3219-3237. doi:10.5194/essd-13-3219-2021 Kaplan, J. O., & Lau, K. H.-K. (2022). World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series, 2022 update. Earth System Science Data, 14(12), 5665-5670. doi:10.5194/essd-14-5665-2022 The data are stored in a NetCDF (version 4) files and have the following attributes: Spatial extent: Entire Earth Spatial reference system (SRS): Unprojected (geographic, WGS84) Spatial resolution: half-degree and 5 arc-minute Temporal extent: 2010-2022 Temporal resolution: daily and monthly1,2 Variables included in this release Lightning density (strokes km-2 day-1) Lightning mean, median, and standard deviation of stroke power (MW, 30 arc-minute version only) For further details, see https://github.com/ARVE-Research/WGLC 1*4748 elements in the time dimension for daily data; 156 for monthly data; 12 for the climatology. 2*Daily fields currently available at 30-minute resolution only. The WWLLN Global Lightning Climatology and timeseries (WGLC) © 2023 by Jed O. Kaplan is licensed under CC BY-SA 4.0
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.016 |
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".