Springtime aerosol load as observed from ground-based and airborne lidars over Northern Norway
Bibliographic record
Abstract
Abstract. To investigate the origin of springtime aerosols in the Arctic region we performed ground-based and airborne 355 nm-Raman lidar observations in the North of Norway (Hammerfest). Two lidars were embedded (i) on an ultra-light aircraft for vertical (nadir) or horizontal line-of-sight measurements, (ii) in an air-conditioned van on the ground for vertical (zenith) measurements. This field experiment was designed as part of the Pollution in the ARCtic System (PARCS) project of the French Arctic initiative, and took place from 13 to 26 May, 2016. The consistency between lidar measurements is verified by comparing nadir, horizontal line-of-sight, and ground-based Raman lidar profiles. Dispersion of the order of 0.01 km-1 is obtained between lidar-derived aerosol extinction coefficients at 355 nm. The aerosol load measured in the three first kilometers of the troposphere remains low throughout the campaign, with aerosol optical thickness (AOT) ≾ 0.1 at 355 nm (~ 0.05 at 550 nm). The main contributors to the evolution of the aerosol load at low altitude prove to be one of the flares of the nearby Melkoya gas processing facility, the oceanic source and the transport of aerosols from industrial sites in Russia. Moreover, ground-based lidar measurements allowed the possibility to identify three cases of long-range aerosol transport (between 3 and 8 km above the mean sea level). Using back trajectories computed with the Lagrangian model FLEXPART-WRF, these aerosol plumes are shown to be the result of the strong forest fires that occurred in the area of Fort McMurray, in Canada. They can, at most double the AOT value over the Arctic area, with an anomaly of 0.1 on the AOT at 355 nm.
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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 | 0.000 | 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) | 0.001 | 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".