Spatial statistics of marine boundary layer clouds
Bibliographic record
Abstract
An analysis is presented of the structure functions and scalar spectra for 25 satellite‐derived marine stratocumulus cloud optical depth fields. The scenes, which cover a horizontal domain of 58 × 58 km at a resolution of 28.5 m, are partitioned into two ensembles on the basis of cloud fraction. For the fully cloudy scenes, although there is wide scene‐to‐scene variability, both the average isotropic scalar spectrum and the average isotropic second‐order structure function exhibit power law behavior over approximately two decades, with scale‐invariant exponents equal to those expected for inertial‐subrange passive tracer fluctuations. Higher‐order structure functions show anomalous scaling that closely matches that observed for wind tunnel temperature fluctuations and for other fully cloudy observations. The partly cloudy scenes, while scaling, show different behavior. The average isotropic second‐order structure function and average isotropic scalar spectrum have scale‐invariant exponents that are significantly smaller than those of the fully cloudy scenes, and the analysis of the higher‐order structure functions indicates that the field has much more intermittent fluctuations than the fully cloudy scenes. Fits to random cascade models for the fully cloudy scenes show that the increment statistics are consistent with an underlying log normal distribution. For the partly cloudy scenes a divergence of higher‐order moments is predicted, indicating that the field fluctuations are necessarily derived from fat‐tailed distributions and that there will be significant realization dependence of the measured statistics. In addition, the presence of long‐range correlations in all the data predicts that single‐point histograms of the field values will have significant scene‐to‐scene variability, or equivalently, the use of spatial averages in the approximation of the parameters of the single‐point probability density function of the field will result in random fluctuations of the estimated parameters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".