Statistical approaches to error identification for plane‐parallel retrievals of optical and microphysical properties of three‐dimensional clouds: Bayesian inference
Bibliographic record
Abstract
This paper addresses the effects of three‐dimensional (3‐D) radiative transfer on the retrieval of optical depth for inhomogeneous stratiform liquid water clouds from passive satellite imagery. A nonparametric Bayesian classifier is developed to identify locations in a scene where plane‐parallel retrievals fail to meet the requirements of a criterion that dictates a specified level of accuracy. Receiver operating characteristics are introduced that provide useful metrics that assess the quality of the error identification procedure as functions of illumination‐viewing geometry. By fixing droplet effective radii, distributions of errors for retrieved optical depth are estimated at a scale of 120 m. These estimates suggest the best performance that can be expected for optical depth retrievals when 3‐D radiative transfer cannot be ignored. The developments in this paper were made possible through the use of Monte Carlo radiative transfer simulations on stratiform clouds that were generated by a cloud system‐resolving model. Plane‐parallel retrievals employ the CloudSat optical depth retrieval algorithm.
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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 | 0.016 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".