Improved retrieval of nitrogen dioxide (NO <sub>2</sub> ) column densities by means of MKIV Brewer spectrophotometers
Bibliographic record
Abstract
Abstract. A new algorithm to retrieve nitrogen dioxide (NO2) column densities using MKIV Brewer spectrophotometers is described. The method includes several improvements, such as a more recent spectroscopic dataset, the reduction of the measurement noise and interferences by other atmospheric species and instrumental settings, and a better determination of the air mass enhancement factors. The technique was tested during an ad-hoc calibration campaign at the high-altitude site of Izaña (Tenerife, Spain) and provided results compatible to those obtained from a spectrometer associated to the Network for the Detection of Atmospheric Composition Change (NDACC), with deviations of less than 0.02 DU. To determine the extraterrestrial constant, an easily implementable generalisation of the standard Langley technique was developed which takes into account the daytime linear drift of nitrogen dioxide due to the photochemistry. Estimates obtained from different observation geometries, by collecting the light from either the sun or the zenith sky, were found to be comparable within the measurement uncertainty. The latter was thoroughly determined by using a Monte Carlo technique. Finally, a method to retrieve additional products such as the degree of linear polarisation of the zenith sky and the oxygen dimer optical depth is presented. The new algorithm is backward-compatible, thus allowing for the reprocessing of historical datasets.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".