Evaluation of Mean State in NCEP Climate Forecast System (Version 2) Simulation Using a Stochastic Multicloud Model Calibrated With DYNAMO RADAR Data
Bibliographic record
Abstract
Abstract Stochastic parameterizations are continuously providing promising simulations of unresolved atmospheric processes for global climate models (GCMs). One of the stochastic multi‐cloud model (SMCM) features is to mimic the life cycle of the three most common cloud types (congestus, deep, and stratiform) in tropical convective systems. To better represent organized convection in the Climate Forecast System version 2 (CFSv2), the SMCM parameterization is adopted in CFSv2 (SMCM‐CTRL) in lieu of the pre‐existing revised simplified Arakawa–Schubert (RSAS) cumulus scheme and has shown essential improvements in different large‐scale features of tropical convection. But the sensitivity of the SMCM parameterization from the observations is yet to be ascertained. Radar data during the Dynamics of the Madden‐Julian Oscillation (DYNAMO) field campaign is used to tune the SMCM in the present manuscript. The DYNAMO radar observations have been used to calibrate the SMCM using a Bayesian inference procedure to generate key time scale parameters for the transition probabilities of the underlying Markov chains of the SMCM as implemented in CFS (hereafter SMCM‐DYNAMO). SMCM‐DYNAMO improves many aspects of the mean state climate compared to RSAS, and SMCM‐CTRL. Significant improvement is noted in the rainfall probability distribution function over the global tropics. The global distribution of different types of clouds, particularly low‐level clouds, is also improved. The convective and large‐scale rainfall simulations are investigated in detail.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| 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".