A New Stochastic Model for the Boundary Layer Clouds and Stratocumulus Phase Transition Regimes: Open Cells, Closed Cells, and Convective Rolls
Bibliographic record
Abstract
Abstract Adequate forecasting, nowcasting, and parameterization of fog and low clouds is still challenging despite being the focus of intensive research for a long time. Stratocumulus clouds have the ability to self‐organize into a variety of topological structures, including closed and open convection cells, convection rolls, and scattered cumulus. A lot is known about the large‐scale conditions in which shallow clouds and fog develop and decay. However, because of the various complex interactions with the environment, transitions between these various cloud regimes are hard to capture in numerical models. Recent work viewed these cloud regimes as the equilibrium states of phase transition in a stochastic model. Here we build on this idea to propose a new stochastic model based on the lattice particles‐Ising model of statistical mechanics, bringing in important improvements by allowing, for example, multiple equilibria and for direct feedback onto the large‐scale dynamics. Idealized numerical simulations demonstrate that the new model reproduces qualitatively the observed regimes of stratocumulus when the external forcing is varied. The new model forms a metastable dynamical system where transitions between extreme regimes occur dynamically, that is, within the same numerical simulation, for a large range of fixed parameter values, and sometimes lead to the co‐occurrence of mixed states with pockets of closed cells and open cells intercepted by regions of scattered cloudiness, resembling the emergence of convection rolls in nature. This is believed to be a step forward in improving the parameterization of shallow clouds in climate models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".