Evaluation of modeled cloud properties against aircraft observations for air quality applications
Bibliographic record
Abstract
Cloud microphysical properties are critical for simulating cloud processing of gases and aerosols in air quality models. In this study, cloud liquid water contents (LWC) predicted from a meteorological model at two horizontal resolutions (15 and 2.5 km) are evaluated against aircraft observations during the 2004 International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) campaign. A point‐by‐point comparison along flight tracks shows good model‐observation correlation for temperature and humidity but poor correlation for LWC due to the mismatch in timing and positioning of the clouds between model simulations and observations. Thus a statistical approach is used to compare properties of modeled and observed clouds over the flight domain. The model captures the observed vertical variation of LWC for the towering cumulus (TCu) cases and reproduces the observed variation of LWC from flight to flight independent of cloud types. The model is able to distinguish the difference in the mean and standard deviation of LWC between stratocumulus (SCu) and TCu. However, the “in‐cloud” LWC values were generally overpredicted by the model at both resolutions. For SCu, the overprediction is 99% and 45% for the 15‐ and 2.5‐km resolution simulations, respectively, while the overprediction for TCu is slightly smaller at 74% for the 15‐km resolution and 24% for the 2.5‐km resolution model simulations. The SCu observations were scaled up to enable comparisons at the model‐grid scales for these flights. This comparison also shows overpredictions of LWC by the model, although the overprediction is smaller for the model at 15‐km resolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".