Evaluation of Marine Boundary layer cloud in the NCEP Climate Forecast System (Version 2) via Stochastic Multicloud Model
Bibliographic record
Abstract
Marine boundary layer (MBL) cloud is one of the major sources of uncertainty in the climate models and they have been identified in the Intergovernmental Panel on Climate Change’s (IPCC’s) fourth assessment as a primary source of uncertainty in determining the sensitivity of climate models. Further simulating it realistically is a huge challenge. To better represent organized convection in the Climate Forecast System version 2 (CFSv2), a stochastic multicloud model (SMCM) parameterization is adopted and it has showed promising improvement in different features of tropical convection. But the simulation of marine boundary cloud in CFSv2 SMCM (EXP1) is yet to be ascertained. We have calibrated the model by using radar observations and followed Markov-chain process to generate key parameters like transition probability, required for EXP1. This paper describes climate simulations of the EXP1 and 25 year run is made and last 20years are analysed. It replaces pre-existing convection scheme (CTL) and shows improvement in many aspects of climate compared to CTL. In addition, global distribution of MBL cloud is also improved and it is also with better agreement with observational analysis, which is inaccurate in CTL. Further, the transition from stratocumulus to trade cumulus is well simulated in EXP1. These results are also supported also by quantitative analyses like Root Mean Square Error (RMSE) etc. The improvement seen in EXP1 can be largely attributed to the general improved in the representation of shallow and cumulus clouds compared to CTL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".