Stochastic generation of subgrid‐scale cloudy columns for large‐scale models
Bibliographic record
Abstract
Abstract To use the Monte Carlo Independent Column Approximation method for computing domain‐average radiative fluxes in large‐scale atmospheric models (LSAMs), a method is needed for generating cloudy subcolumns within LSAM columns. Here, a stochastic cloud generator is introduced to produce the subcolumns. The generator creates a cloud field on a column‐by‐column basis using information about layer cloud fraction, vertical overlap of cloud fraction and cloud condensate for adjacent layers, and density functions describing horizontal variations in cloud water content. The performance of the generator is assessed using a single day's worth of data from an LSAM simulation that employed a low‐resolution two‐dimensional cloud‐resolving model (CRM) within each LSAM column (a total of ∼59 000 cloudy domains). Statistical characteristics of generated cloud fields are compared against original CRM data, and radiative‐transfer biases associated with the generator are evaluated. When the generator is initialized to the greatest extent possible with information obtained from the CRM fields, overall biases are small. For example, global‐mean total cloud fraction exhibits a bias of −0.004, as compared with −0.024 for maximum‐random overlap (MRO) and 0.047 for random overlap. Biases in radiative fluxes and heating rates are in general ¼ to ½ those for MRO with horizontally homogeneous clouds. Copyright © 2004 Royal Meteorological Society
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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 | 0.002 |
| 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) | 0.001 | 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".