Cloud fraction parameterization as a function of mean cloud water content and its variance using in‐situ observations
Bibliographic record
Abstract
The main objective of the present work is to use in‐situ data to parameterize cloud fraction (Cs) as a function of both cloud condensed water content and its variability over scales of 10 km and 100 km. In‐situ data were obtained during the Alliance Icing Research Study (AIRS) field project and represent mid‐latitude winter stratiform clouds. The main observations used in the analysis were condensed total water content (qc) and liquid water content. It is shown that Cs, defined as the length of cloudy segments divided by the clear and cloudy segments along a flight leg, can change up to 5%–25% if the averaging scale is changed from 10 km to 100 km. A new parameterization of Cs versus the ratio of qc to its variance is suggested. The sub‐grid scale variability is usually not known directly from the models and it needs to be obtained by prognostic or diagnostic equations. It is found that cloud sub‐grid variability, represented by the standard deviation (σ) of qc, increases with increasing qc for both 10 and 100 km scales, and σqc for 10 km scales is larger by about 50% as compared to 100 km scales for a given qc value. These conclusions suggest that both averaging scales and sub‐grid scale variability should be considered for cloud cover parameterizations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".