Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression
Bibliographic record
Abstract
Abstract. Average properties of the cloud field, such as cloud size distribution and cloud fraction, have previously been observed to show periodic, oscillatory changes. Identifying this behaviour, however, remains difficult due to the intrinsic variability of the boundary-layer cloud distribution. We use the Gaussian Process (GP) regression to identify this oscillatory behaviour in the statistical distributions of individual cloud properties. Individual cloud samples are retrieved from high-resolution LES model results, and the distribution of cloud sizes is modelled as a power-law distribution. We construct the time-series for the slope of the cloud size distribution b, a slope that is consistent with satellite observations of marine boundary-layer clouds, by observing the changes in the slope of the modelled cloud size distribution. Then, we build a GP model based on prior assumptions about the cloud field following observational studies: a boundary-layer cloud field goes through a phase of relatively strong convection where large clouds dominate, followed by a phase of relatively weak convection where precipitation causes formation of cold pools and suppression of convective growth. The GP model successfully identifies oscillatory motions from the noisy time-series, with a period of 95 ± 3.2 minutes. Furthermore, we examine the time-series of cloud fraction fc and average vertical mass flux M, whose periods were 93 ± 2.5 and 93 ± 3.7 minutes, respectively. The oscillations reveal the role of precipitation in governing convective activities through recharge-discharge cycles.
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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 | 0.001 | 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 | 0.001 | 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".