Spatial and temporal variability of column‐integrated aerosol optical properties in the southern Arabian Gulf and United Arab Emirates in summer
Bibliographic record
Abstract
A mesoscale network of 14 AERONET Sun photometers was established in the UAE and adjacent Arabian Gulf from August through September 2004 as a component of the United Arab Emirates Unified Aerosol Experiment (UAE2). These measurements allowed for spatial, temporal and spectral characterization of the complex aerosol mixtures present in this environment where coarse mode desert dust aerosols often mix with fine mode pollution aerosols largely produced by the petroleum industry. Aerosol loading was relatively high with 2‐month averages of aerosol optical depth (AOD) at 500 nm (τa500) ranging from 0.40 to 0.53. A higher fine mode fraction of AOD was observed over Arabian Gulf island sites with Angstrom exponent at 440–870 nm (α440–870) of 0.77 as compared to an average of 0.64 over coastal sites and 0.50–0.57 at inland desert sites. During pollution events with α440–870 > 1 the retrieved fine mode radius was larger over an island site than a desert site probably because of hygroscopic growth over the humid marine environment. For these same pollution cases, single scattering albedo (ωo) at all wavelengths was ∼0.03 higher (less absorption) over the marine environment than over the desert, also consistent with aerosol humidification growth. At an inland desert location, the ωo at 440 nm remained relatively constant as Angstrom exponent varied since the fine mode pollution and coarse mode dust were both strong absorbers at short wavelengths. However, at longer wavelengths (675–1020 nm) the dust was much less absorbing than the pollution resulting in dynamic ωo as a function of α440–870.
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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 | 0.000 |
| 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 | 0.001 | 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".