A Simple Parameterization Coupling the Convective Daytime Boundary Layer and Fair-Weather Cumuli
Bibliographic record
Abstract
Abstract A new parameterization for boundary layer cumulus clouds, called the cumulus potential (CuP) scheme, is introduced. This scheme uses joint probability density functions (JPDFs) of virtual potential temperature (θυ) and water-vapor mixing ratio (r), as well as the mean vertical profiles of θυ, to predict the amount and size distribution of boundary layer cloud cover. This model considers the diversity of air parcels over a heterogeneous surface, and recognizes that some parcels rise above their lifting condensation level to become cumulus, while other parcels might rise as noncloud updrafts. This model has several unique features: 1) cloud cover is determined from the boundary layer JPDF of θυ versus r, 2) clear and cloudy thermals are allowed to coexist at the same altitude, and 3) a range of cloud-base heights, cloud-top heights, and cloud thicknesses are predicted within any one cloud field, as observed. Using data from Boundary Layer Experiment 1996 and a model intercomparsion study using large eddy simulation (LES) based on Barbados Oceanographic and Meteorological Experiment (BOMEX), it is shown that the CuP model does a good job predicting cloud-base height and cloud-top height. The model also shows promise in predicting cloud cover, and is found to give better cloud-cover estimates than three other cumulus parameterizations: one based on relative humidity, a statistical scheme based on the saturation deficit, and a slab model.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".