Aura Ozone Monitoring Instrument (OMI) Collection 4 Formaldehyde Product
Bibliographic record
Abstract
This study presents the Ozone Monitoring Instrument (OMI) Collection 4 formaldehyde (HCHO) retrieval developed with the Smithsonian Astrophysical Observatory’s (SAO) Making Earth System Data Records for Use in Research Environments (MEaSUREs) algorithm. The retrieval algorithm updates and makes improvements to the NASA operational OMI HCHO (OMI Collection 3 HCHO) algorithm, and has been transitioned to use OMI Collection 4 Level-1B radiances. This paper describes the updated retrieval algorithm and compares Collection 3 and Collection 4 data products. The OMI Collection 4 HCHO exhibits remarkably improved stability over time in comparison to the OMI Collection 3 HCHO product, with better precision and the elimination of artificial trends present in the Collection 3 during the later years of the mission. We validate the OMI Collection 4 HCHO data product using Fourier-Transform Infrared (FTIR) ground-based HCHO measurements. The climatological monthly averaged OMI Collection 4 HCHO vertical column densities (VCDs) agree well with the FTIR VCDs, with a correlation coefficient of 0.83, root-mean-square error (RMSE) of 2.98 × 10 15 molecules cm -2 , regression slope of 0.79, and intercept of 8.21 × 10 14 molecules cm -2 . Additionally, we compare the monthly averaged OMI Collection 4 HCHO VCDs to OMPS Suomi NPP, OMPS NOAA-20, and TROPOMI HCHO VCDs in overlapping years for twelve geographic regions. This comparison demonstrates high correlation coefficients of 0.98 (OMPS Suomi NPP), 0.97 (OMPS NOAA-20), and 0.90 (TROPOMI).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".