The relationship between polar mesospheric clouds and their background atmosphere as observed by Odin-SMR and Odin-OSIRIS
Bibliographic record
Abstract
Abstract. In this study the properties of polar mesospheric clouds (PMCs) and the background atmosphere in which they exist are studied using measurements from two instruments, OSIRIS and SMR, on board the Odin satellite. The data comes from a set of tomographic measurements conducted by the satellite during 2010 and 2011. The expected ice mass density and cloud frequency for conditions of thermodynamic equilibrium, calculated using the temperature and water vapour as measured by SMR, are compared to the ice mass density and cloud frequency as measured by OSIRIS. We find that assuming thermodynamic equilibrium reproduces the seasonal, latitudinal and vertical variations in ice mass density and cloud frequency, but with a high bias of a factor of 2 in ice mass density. To investigate this bias, we use a simple ice particle growth model to estimate the time it would take for the observed clouds to sublimate completely and the time it takes for these clouds to reform. We find a difference in the median sublimation time (1.8 h) and the reformation time (3.2 h) at peak cloud altitudes (82–84 km). This difference implies that temperature variations on these timescales have a tendency to reduce the ice content of the clouds, possibly explaining the high bias of the equilibrium model. Finally, we detect and are, for the first time, able to positively identify cloud features with horizontal scales of 100 to 300 km extending far below the region of supersaturation ( > 2 km). Using the growth model, we conclude these features cannot be explained by sedimentation alone and suggest that these events may be an indication of strong vertical transport.
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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 | 0.001 |
| 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".