Evaluation of a numerically efficient aerosol activation scheme by using worldwide cloud data from multiple field campaigns in continental and marine regions
Bibliographic record
Abstract
This research summarizes a numerically efficient aerosol activation scheme and evaluates it by using stratus and stratocumulus cloud data sampled during multiple aircraft campaigns in China, Canada, Brazil, and Chile. The scheme employs a Quasi-steady state approximation of the cloud Droplet Growth Equation (QDGE) to efficiently simulate aerosol activation and vertical profiles of supersaturation and cloud droplet number concentration (CDNC) near the cloud base. We evaluated the scheme by specifying observed environmental thermodynamic variables and aerosol properties from 36 cloud cases as input and comparing the simulated CDNC and other simulated variables with cloud microphysical observations. The relative error (RE) of the mean simulated CDNC ranges from 15.27 % for Chile to 23.97 % for China, with an average of 19.69 %, indicating that the scheme successfully reproduces observed variations in CDNC over a wide range of different meteorological conditions and aerosol regimes. Subsequently, we carried out an error analysis by calculating the Maximum Information Coefficient (MIC) values between RE and individual input variables and sorted them by aerosol properties, pollution degree, environmental humidity, and dynamic condition according to their importance. Based on this analysis we find that the magnitude of the RE is sensitive to the specification of aerosol chemical composition and updraft velocity in the simulation, which can partly explain differences between simulated and observed CDNC in some of the regions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".