Comment on egusphere-2023-821
Bibliographic record
Abstract
Abstract. The convective lifecycle, from initiation to maturity and dissipation, is driven by a combination of kinematic, thermodynamic, microphysical, and radiative processes that are strongly coupled and variable in time and space. Radars have been traditionally used to provide the convective clouds characteristics. Here, we analyzed climatological convective cell radar characteristics to obtain and assess the diurnal cycle of shallow, modest deep, and vigorous deep convective cells that formed in the Greater Houston area, using the National Weather Service radar from Houston, Texas and a multi-cell identification and tracking algorithm. The examined dataset spans over four years (2018–2021) and for the warm season months (June to September). The analysis showed the clear diurnal cycles in cell initiation (CI), cell evolution parameters (e.g., maximum reflectivity, cloud top height, and the height of maximum reflectivity), consistent with the sea breeze circulation. The cell evolution is well represented by relationships between 1) the maximum radar reflectivity and its height, 2) the cloud top and the maximum vertically-integrated liquid, 3) the maximum reflectivity and columnar average reflectivity, and 4) cloud top ascent rate and cell lifetime. The relationships presented herein help to identify the cell lifecycle stages such as early shallow convection, vigorous vertical development, anvil development, and convective core dissipation. We also analyzed the near-storm environment to address any differences in the environmental conditions present at the time of CI and how they may differ between convective type (shallow, modest deep, and vigorous deep cells).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.444 | 0.311 |
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".