Cloud Microphysics in Global Cloud Resolving Models
Bibliographic record
Abstract
Global cloud resolving models (GCRMs) are a new type of general circulation model that explicitly calculates the growth of cloud systems with fine spatial resolutions and more than 10 GCRMs have been developed at present. This work reviews cloud microphysics schemes used in GCRMs with introductions to the recent progress and researches with GCRMs. Especially, research progress using a pioneer of GCRMs, Nonhydrostatic ICosahedral Atmospheric Model (NICAM), is focused. Since GCRMs deal with climatology and meteorology, it is a challenging issue to establish cloud microphysics schemes for GCRMs. A brief history of the development of cloud microphysics schemes and cloud-radiation coupling in NICAM is described. In addition, current progress in analytical techniques using satellite simulators is described. The combined use of multi-optical sensors enables us to constrain uncertain processes in cloud microphysics without artificial tuning. As a result, cloud microphysics schemes used in the NICAM naturally represent cloud systems, and hence, the radiative budget is well balanced with little optimization. Finally, a new satellite and a ground validation campaign are introduced for future work.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".