Insights from modeling and observational evaluation of a precipitating continental cumulus event observed during the MC3E field campaign
Bibliographic record
Abstract
Abstract A case of shallow cumulus and precipitating cumulus congestus sampled at the Atmospheric Radiation Measurement Program Southern Great Plains supersite during the Midlatitude Continental Convective Clouds Experiment is analyzed using a multisensor observational approach and numerical simulation. Observations from a new radar suite surrounding the facility are used to characterize the evolving statistical behavior of the precipitating cloud system. This is accomplished using distributions of different measures of cloud geometry and precipitation properties. Large‐eddy simulation (LES) with size‐resolved (bin) microphysics is employed to determine the forcings most important in producing the salient aspects of the cloud system captured in the radar observations. Our emphasis is on assessing the importance of time‐varying versus steady state large‐scale forcing on the model's ability to reproduce the evolutionary behavior of the cloud system. Additional consideration is given to how the characteristic spatial scale and homogeneity of the forcing imposed on the simulation influences the evolution of cloud system properties. Results indicate that several new scanning radar estimates such as distributions of cloud top are useful to differentiate the value of time‐varying (or at least temporally well‐matched) forcing on LES solution fidelity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".