Statistical analysis of an LES shallow cumulus cloud ensemble using a cloud tracking algorithm
Bibliographic record
Abstract
Abstract. A technique for the tracking of individual clouds in a Large Eddy Simulation (LES) is presented. We use this technique on an LES of a shallow cumulus cloud field based upon the Barbados Oceanographic and Meteorological Experiment (BOMEX) to calculate statistics of cloud height, lifetime, and other physical properties for individual clouds in the model. We also examine the question of nature versus nurture in shallow cumulus clouds: do properties at cloud base determine the upper-level properties of the clouds (nature), or are cloud properties determined by the environmental conditions they encounter (nurture). We find that clouds which ascend through an environment that has been pre-moistened by previous cloud activity are no more likely to reach the inversion than clouds that ascend through a drier environment. Cloud base thermodynamic properties are uncorrelated with upper-level cloud properties, while mean fractional entrainment and detrainment rates display moderate correlations with cloud properties up to the inversion. Conversely, cloud base area correlates well with upper-level cloud area and maximum cloud height. We conclude that cloud thermodynamic properties are primarily influenced by entrainment and detrainment processes, cloud area and height are primarily influenced by cloud base area, and thus nature and nurture both play roles in the dynamics of BOMEX shallow cumulus 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 | 0.000 | 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 | 0.000 | 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) | 0.003 | 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".