Odin stratospheric proxy NO <sub>y</sub> measurements and climatology
Bibliographic record
Abstract
Abstract. Five years of OSIRIS (Optical Spectrograph and InfraRed Imager System) NO2 and SMR (Sub-Millimetre Radiometer) HNO3 observations from the Odin satellite, combined with data from a photochemical box model, have been used to construct a stratospheric proxy NOy data set including the gases: NO, NO2, HNO3, 2×N2O5 and CIONO2. This Odin NOy climatology is based on all daytime measurements and contains monthly mean and standard deviation, expressed as mixing ratio or number density, as function of latitude or equivalent latitude (5° bins) on 17 vertical layers (altitude, pressure or potential temperature) between 14 and 46 km. Comparisons with coincident NOy profiles from the Atmospheric Chemistry Experiment–Fourier Transform Spectrometer (ACE-FTS) instrument were used to evaluate several methods to combine Odin observations with model data. This comparison indicates that the most appropriate merging technique uses OSIRIS measurements of NO2, scaled with model NO/NO2 ratios, to estimate NO. The sum of 2×N2O5 and CIONO2 is estimated from uncertainty-based weighted averages of scaled observations of SMR HNO3 and OSIRIS NO2. Comparisons with ACE-FTS suggest the precision (random error) and accuracy (systematic error) of Odin NOy profiles are about 15% and 20%, respectively. Further comparisons between Odin and the Canadian Middle Atmosphere Model (CMAM) show agreement to within 20% and 2 ppb throughout most of the stratosphere except in the polar vortices. A particularly large disagreement within the Antarctic vortex in the upper stratosphere during spring indicates too strong descent of air in CMAM. The combination of good temporal and spatial coverage, a relatively long data record, and good accuracy and precision make this a valuable NOy product for various atmospheric studies and model assessments.
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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) | 0.001 | 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".