Retrieving mesospheric water vapour from observations of volume scattering radiances
Bibliographic record
Abstract
Abstract. This study examines the possibility for a theoretical approach in the estimation of water vapour mixing ratios in the vicinity of polar mesospheric clouds (PMC) using satellite observations of Volume Scattering Radiances (VSR) obtained at the wavelength of 553 nm. The PMC scattering properties perturb the underlying molecular Rayleigh scattered solar radiance of the background atmosphere. As a result, the presence of PMC leads to an enhancement in the observed VSR at the altitude of the layer; the PMC VSRs are superimposed on the exponentially decreasing with height Rayleigh VSR, of the PMC-free atmosphere. The ratio between the observed and the Rayleigh VSR of the background atmosphere is used to simulate the environment in which the cloud layer is formed. In addition, a microphysical model of ice particle formation is employed to predict the PMC VSRs. The initial water vapour profile is perturbed until the modelled VSRs match the observed, at which point the corresponding temperature and water vapour profiles can be considered as a first approximation of those describing the atmosphere at the time of the observations. The role of temperature and water vapour in the cloud formation is examined by a number of sensitivity tests suggesting that the water vapour plays a dominant role in the cloud formation in agreement with experimental results. The estimated water vapour profiles are compared with independent observations to examine the model capability in the context of this study. The results obtained are in a good agreement at the peak of the PMC layer although the radiance rapidly decreases with height below the peak. This simplified scenario indicates that the technique employed can give a first approximation estimate of the water vapour mixing ratio, giving rise to the VSR observed in the presence of PMC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 0.001 |
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 teacher head, 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".