An updated VIIRS dark target aerosol product for continuity with MODIS: assessing regional aerosol trends
Bibliographic record
Abstract
Aerosol optical depth (AOD) is a crucial data record to understand aerosols and their direct and indirect effects on air quality and climate forcing. The Dark Target aerosol retrieval product includes AOD and other properties derived from multispectral satellite imagers, available for MODIS on Terra (from 2000), MODIS on Aqua (from 2002), and VIIRS on Suomi-NPP (from 2012). Although Terra now has over 25 years of observations, the record must continue onto VIIRS beyond the end of the MODIS mission to meet requirements as a Global Climate Observing System (GCOS) climate data record. We present the recent update to version 2.0 of the VIIRS product, which now includes NOAA-20 VIIRS (from 2017) and algorithm improvements. The combined MODIS-VIIRS dataset is examined for consistency and to ascertain aerosol trends. Overall, the VIIRS products show consistency with the MODIS products. To assess regional trends, two time intervals are studied: a 22-year record that compares Terra and Aqua, and a more recent 12-year record (the VIIRS era) that compares three sensors. According to linear regressions of monthly average AOD for each global 1°×1° grid cell, AOD has decreased by between 0.003 and 0.01 per year over parts of China, the United States, Brazil, and much of Europe, while increasing on the same scale over India and parts of Canada, while more modestly but significantly increasing over the southern oceans. For seven regions with significant AOD trends, this study examines the seasonal dependence, relationship to aerosol size parameters, and whether the sign or magnitude of these trends have changed. With high agreement among sensors, we are confident that the Dark Target AOD record can extend into the 2030s and beyond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 teacher head, 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".