UV index climatology over the United States and Canada from ground‐based and satellite estimates
Bibliographic record
Abstract
Long‐term monthly mean UV index values for Canada and the United States were calculated using information from two sources: from noon erythemal UV estimated from Total Ozone Mapping Spectrometer (TOMS) total ozone and reflectivity data and from UV index values derived from observations of global solar radiation, total ozone, dew point, and snow cover. The results are presented as monthly maps of mean noon UV index values. Mean UV index values in summer range from 1.5 in the Arctic to 11.5 over southern Texas. Both climatologies were validated against spectral UV irradiance measurements made by Brewer spectrophotometers. With snow on the ground the TOMS‐based data underestimate UV by up to 60% with respect to Brewer measurements and UV derived from global solar radiation and other parameters. In summer, TOMS UV index climatology values are from 10 to 30% higher than those derived from global solar radiation and other parameters. The difference is probably related to aerosol absorption and pollution effects in the lower troposphere that are not currently detected from space. For 21 of 28 midlatitude Brewer sites, long‐term mean summer UV measured values and UV derived from global solar radiation and other parameters agree to within +5 to −7%. The remaining seven sites are located in “clean” environments where TOMS estimates agree with Brewer measurements while UV derived from global solar radiation and other parameters is 10–13% lower. Brewer data also demonstrate that clean and “typical” sites can be as little as 70–120 km apart.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.003 | 0.001 |
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".