Geostationary Satellites Total Ozone Observations: First Results on Ground‐Based Networks Validation Efforts for TEMPO and GEMS
Bibliographic record
Abstract
The Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument, launched in April 2023, is North America's first geostationary air pollution monitoring satellite mission. Together with Asia's Geostationary Environment Monitoring Spectrometer (GEMS) launched in 2020 and Europe's upcoming Sentinel-4, TEMPO contributes to nearly global coverage provided by geostationary satellite constellation. TEMPO and GEMS offer hourly, high-resolution data of ozone surpassing the once-daily observations of instruments like the TROPOspheric Monitoring Instrument (TROPOMI) in temporal resolution. This study presents TEMPO's total ozone data, demonstrating TEMPO's ability to observe sudden changes in ozone and UV index. Furthermore, TEMPO and GEMS measurements are validated using ground-based monitoring networks (Brewer, Dobson, and Pandora). Results show good agreement but also highlight latitude-dependent discrepancies between the satellite and ground-based data sets (-2% to 2% for TEMPO, -1% to -3% for GEMS). Findings are further validated using TROPOMI data and reanalysis models.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".