Local and Regional Diurnal Variability of Aerosol Properties Retrieved by DSCOVR/EPIC UV Algorithm
Bibliographic record
Abstract
Abstract The hour‐to‐hour variability of 388 nm aerosol optical depth (AOD) and single scattering albedo (SSA) derived from near UV observations by the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory has been evaluated at multiple locations around the world. AOD retrievals by the EPIC near UV algorithm (EPICAERUV) have been compared to ground based AOD measurements at 16 Aerosol Robotic Network (AERONET) stations representative of the most commonly observed aerosol types over geographic regions in three continents. Obtained results show that, in general, the EPICAERUV algorithm reproduces closely the hour‐to‐hour AOD variability reported by AERONET ground‐truth observations. Although most sites in the analysis show high correlation between the AOD hourly measurements by the ground‐based and space‐borne measuring techniques. Best algorithm performance is observed in the presence of carbonaceous and desert dust aerosols. The diurnal cycle of the retrieved SSA product was also analyzed. Although, a direct comparison of hourly EPICAERUV retrievals to equivalent ground‐based observations was not possible, the satellite result shows that diurnal SSA variability as large as 0.05 can be observed mostly associated with carbonaceous aerosols. EPICAERUV observed diurnal cycle of retrieved AOD on a regional basis was examined for the unusually active seasons of aerosol production of Saharan desert dust aerosols in 2020, and during the 2023 Canadian wildfires. Results presented in this study confirm the EPIC near UV aerosol product is well suited for observing diurnal variability of aerosols and, therefore, it is an important resource for climate and air quality studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".