Airborne observation of aerosol optical depth during ARCTAS: vertical profiles, inter-comparison, fine-mode fraction and horizontal variability
Bibliographic record
Abstract
Abstract. We describe aerosol optical depth (AOD) measured during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) experiment, conducted in North America in April and June–July 2008, focusing on vertical profiles, inter-comparison with correlative observations, fine-mode fraction and horizontal variability. The AOD spectra spanning 354–2139 nm measured with the 14-channel Ames Airborne Tracking Sunphotometer (AATS-14) are generally less wavelength-dependent below 2 km (499-nm Angstrom exponent 1.4 ± 0.3) than in 2–4 km (1.6–1.8) for Alaska in April 2008. Together with concurrent aerosol mass spectrometry and black carbon incandescence measurements, this corroborates the hypothesis that Arctic haze in these layers originates mainly from anthropogenic emission and biomass burning, respectively. The spectra are within 3%+0.02 of the vertical integral of local visible-light scattering and absorption for two thirds of the 55 vertical profiles examined. The horizontal structure of smoke plumes in central Canada in June and July 2008 explains most outliers. The differences in mid-visible Angstrom exponent are <0.10 for 63% of the profiles with 499-nm AOD>0.1. The retrieved fine-mode fraction of AOD is mostly between 0.7 and 1.0, and its root mean square difference from column-integral submicron fraction (measured with nephelometers, absorption photometers and an impactor) is 0.12. These AOD measurements from the NASA P-3 aircraft, after compensation for below-aircraft light attenuation by vertical extrapolation, mostly fall within 0.02 of AERONET ground-based measurements for five overpass events. Evidently, the fresh local emission in Canada in June and July makes the horizontal distribution of AOD highly heterogeneous (standard deviation ~19% of the mean over 20 km) and random (autocorrelation r=0.37 across 20 km), in contrast to long-range transport to Alaska in April (std~2%, r=0.95). The variability observed over 6 km is noticeably smaller (std~9%, r=0.71). The decrease represents the reduction in collocation error that remote sensing can potentially achieve by improving resolution for ARCTAS Canada and similar environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".