Simulated and observed horizontal inhomogeneities of opticalthickness of Arctic stratus
Bibliographic record
Abstract
Abstract. Two-dimensional (2D) horizontal fields of cloud optical thickness derived from airborne measurements of solar spectral radiance during the Vertical Distribution of Ice in Arctic Clouds (VERDI) campaign (carried out in Inuvik, Canada in April/May 2012) are compared with semi–idealized Large Eddy Simulations (LES) of Arctic stratus performed with the COnsortium for Small-Scale MOdeling (COSMO) atmospheric model. The input for the LES is obtained from collocated airborne dropsonde observations. Four consecutive days of a persistent Arctic stratus observed above the sea–ice free Beaufort Sea are selected for the comparison. Macrophysical cloud properties such as cloud top altitude and vertical extent are well captured by COSMO. Cloud horizontal inhomogeneity quantified by the standard deviation and one-dimensional (1D) inhomogeneity parameters show that COSMO produces only half of the measured horizontal cloud inhomogeneities, while the directional structure of the cloud inhomogeneity is well represented by the model. Differences between the individual cases are mainly associated with the wind shear near cloud top and the vertical structure of the atmospheric boundary layer. A sensitivity study changing the wind velocity in COSMO by a vertically constant scaling factor shows that the directional cloud inhomogeneity structures strongly depend on the mean wind speed. A threshold wind velocity is identified, which determines when the cloud inhomogeneity stops increasing with increasing wind velocity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".