Comprehensive Analysis of Bias in TEMPO NO <sub>2</sub> Column Densities Through Pandora Observations
Bibliographic record
Abstract
Abstract Tropospheric Emissions: Monitoring of Pollution (TEMPO) is the first geostationary satellite instrument to monitor air pollutants across North America. This study uses Pandora observations to analyze the bias in TEMPO Level‐3 total column density of NO 2 (TOTNO 2 ) from August 2023 to December 2024. TEMPO achieves high accuracy at 5% cloud‐filtering threshold: correlation coefficient (R) of 0.86, index of agreement (IOA) of 0.91, mean absolute bias (MAB) of 1.423 × 10 15 molecules/cm 2 , and a percentage MAB (MABP) of 23.1%, corresponding to a 12% underestimation. Accuracy decreases when pixels with greater cloud‐cover are included. Solar zenith angle (SZA) of 10–20° yields the highest accuracy (R: 0.87, MABP: 22.7%), whereas SZAs of 70–80° yield the lowest (R: 0.71, MABP: 35.2%). Consequently, early‐morning or near‐sunset observations are less reliable than midday. This discrepancy could stem from inaccurate simulation of diurnal variations in the boundary‐layer height in the a‐priori, and from larger uncertainties in radiative transfer at high SZAs. TEMPO overestimates TOTNO 2 at low NO 2 levels and underestimates at high levels, with maximum biases of +16% (low) and −31% (high), respectively. Station‐to‐station performance varies considerably, with R ranging from 0.29 to 0.84 and MABP from 14.9% to 49.3%. Stations situated at higher altitudes relative to the ground show reduced agreement with TEMPO, as Pandora cannot detect NO 2 below the instrument's altitude, whereas TEMPO retrieves the full column. Validation of TEMPO TOTNO 2 at TROPOMI overpass time indicates that TEMPO's performance relative to Pandora (IOA: 0.93, MABP: 22.3%) closely matches that of TROPOMI (IOA: 0.92, MABP: 20.1%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".