A method for characterizing the spatial organization of convection in deep convective systems’ cloud shield
Bibliographic record
Abstract
Deep convective systems (DCSs) play a major role in the radiative budget and the water cycle over the tropics, as they are responsible for a significant part of the tropical precipitation and represents the major contributors to extreme rain rates. The spatial arrangement of deep convection within the convective system’s cloud shield exerts a strong influence on the morphology of the systems shield yet difficult to quantify objectively.A new method is introduced that aims to evaluate this spatial arrangement of convective areas in the cloud shield. The method is based on 2D autocorrelation metrics and a stochastic approach to generate randomly organized scenes. A bootstrap technique permits to compare each scene with respect to these stochastic distributions. The technique is applied on a large satellite-based dataset and a non-supervised classification of spatial arrangement is performed. The classification reveals well separated classes corresponding to well identified organization of convection. The method is further applied onto idealized km scale simulations and is shown to hold also for the model. A comparison of the results of our approach with existing metrics will also be shown at the conference to highlight the added value of the present effort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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 | 0.001 |
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".