Testing Mixed-Phase Cloud Water Vapor Parameterizations with SHEBA/FIRE–ACE Observations
Bibliographic record
Abstract
The parameterization of in-cloud water vapor pressure below 0C is examined using in situ aircraft observations from Canadian National Research Council (NRC) Convair-580 flights during the Surface Heat Budget of the Arctic Ocean (SHEBA)/First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment-Arctic Cloud Experiment (FIRE-ACE) campaign. The accuracy of in-cloud water vapor measurements is evaluated against the saturated water vapor pressure in liquid water clouds as derived from measured temperatures, which have a mean bias of about 1%. This study reveals that the parameterization used in the ECMWF cloud scheme, which employs a temperature-weighted average of the values with respect to ice and liquid water underestimates the saturated water vapor by 9% when applied to all in-cloud data from the campaign. It is found that a parameterization that relates the weighting to the cloud liquid and ice water contents agrees well with the observations. This study also reveals that it is incorrect to assume that water vapor is in equilibrium with liquid water in mixed-phase clouds.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".