Modeling aerosols and their interactions with shallow cumuli during the 2007 CHAPS field study
Bibliographic record
Abstract
Abstract The Weather Research and Forecasting model coupled with chemistry, using 2 km horizontal grid spacing, is used to simulate two important relationships between aerosols and clouds in the vicinity of Oklahoma City during the June 2007 Cumulus Humilis Aerosol Processing Study (CHAPS). First, the model reproduces the trends of higher nitrate volume fractions in cloud droplet residuals compared to interstitial nonactivated aerosols, as measured using an aerosol mass spectrometer. Comparing simulations with cloud chemistry turned on and off, we show that nitric acid vapor uptake by cloud droplets explains the higher nitrate content of cloud droplet residuals. Second, the model reasonably represents the observations of the first aerosol indirect effect where pollutants in the vicinity of Oklahoma City increase cloud droplet number concentrations and decrease the droplet effective radius. In addition, as documented using an offline optical code, simulated aerosol optical properties depend on several compensating effects including aerosol water content, size‐resolved chemical composition, and refractory index of various particle chemical species. All of our four sensitivity test cases clearly show an increase in simulated absorption and a decrease in single scattering albedo within the Oklahoma City plume relative to conditions outside the plume. While previous studies have often focused on cloud–aerosol interactions in stratiform and deep convective clouds, this study highlights the ability of regional‐scale models to represent some of the important aspects of cloud–aerosol interactions associated with fields of short‐lived shallow cumuli.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".