Surface reflectivity from the Ozone Monitoring Instrument using the Moderate Resolution Imaging Spectroradiometer to eliminate clouds: Effects of snow on ultraviolet and visible trace gas retrievals
Bibliographic record
Abstract
Satellite retrievals of tropospheric composition from measurements of solar backscatter require accurate information about surface reflectivity. We use clear‐sky data from the Ozone Monitoring Instrument (OMI) to determine global surface reflectivity under both snow‐covered and snow‐free conditions at 354 nm. Clear‐sky scenes are determined using cloud and aerosol data from the Moderate Resolution Imaging Spectroradiometer/Aqua satellite instrument that flies 12 min ahead of OMI/Aura. The result is a database of OMI‐observed Lambertian equivalent reflectivity (LER) that does not rely on statistical methods to eliminate cloud and aerosol contamination. We apply this database to evaluate previous climatologies of surface reflectivity. Except for regions of seasonal snow cover, agreement is best with a climatology from OMI, which selects the surface reflectivity from a histogram of observed LER (mean difference, 0.0002; standard deviation, 0.011). Three other climatologies of surface reflectivity from Total Ozone Mapping Spectrometer, Global Ozone Monitoring Experiment, and OMI, based on minimum observed LER, are less consistent with our cloud‐ and aerosol‐filtered data set (mean difference, −0.008, 0.012, and −0.002; standard deviation, 0.022, 0.026, and 0.033). Snow increases the sensitivity of solar backscatter measurements at ultraviolet and visible wavelengths to trace gases in the lower troposphere. However, all four existing LER climatologies poorly represent seasonal snow. Surface reflectivity over snow‐covered lands depends strongly on the vegetation type covering the surface. The monthly variation of snow‐covered reflectivity varies by less than 0.1 in fall and winter. Applying our snow‐covered surface reflectivity database to OMI NO2 retrievals could change the retrieved NO2 column by 20%–50% over large regions with seasonal snow cover.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".