Comparison of Aircraft Observations with Mixed-Phase Cloud Simulations
Bibliographic record
Abstract
In order to provide guidance for the further improvement of a mixed-phase cloud scheme being developed for use in an NWP model, comparisons of dynamical, thermodynamical, and microphysical variables between in situ aircraft data and model data were made. A total of 21 flights (∼88 h of data) from the First and Third Canadian Freezing Drizzle Experiments were selected and simulated. The basis of the evaluation of the model performance is a point-by-point comparison of each pertinent variable along the real and “virtual” aircraft trajectories. The virtual aircraft trajectory is constructed by choosing, for every observed data point, the closest available model data point in terms of time, pressure level, and latitude–longitude position. Observed and model data were used to calculate simple descriptive statistics to evaluate the ability of the forecast system to predict the presence of clouds, their phase, and water content. Even though a point-by-point comparison of the aircraft and model data is a very severe test given the errors in the initial conditions and the disparity in temporal and spatial resolution, the results were encouraging for about half the flights simulated. It was found that, in general, the model predicts ice clouds better than water clouds. The model generally overpredicts (underpredicts) both the presence and the quantities of ice water content (supercooled liquid water content). Furthermore, where mixed-phase clouds are present in the model, the ice phase represents a large fraction of the total water content, contrary to the observations. These conclusions suggest that the parameterization of the ice particle size distribution is an important aspect of the mixed-phase cloud scheme that should be optimized.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".