Impact of subgrid‐scale vertical turbulent mixing on eyewall asymmetric structures and mesovortices of hurricanes
Bibliographic record
Abstract
Abstract In this study, the multiple nested state‐of‐the‐art Weather Research and Forecasting (WRF) model is used to investigate the impact of the subgrid‐scale (SGS) vertical turbulent mixing parametrization on hurricane eyewall asymmetric structures and the formation of eyewall mesovortices. Hurricane Isabel (2003) was simulated by a series of numerical experiments with different SGS vertical turbulent mixing parametrizations including the Yonsei University, Mellor–Yamada–Janjic, Mellor–Yamada–Nakanishi–Niino 2.5 level and Mellor–Yamada–Nakanishi–Niino 3 level schemes. The simulations show that the vertical turbulent mixing scheme not only substantially affects the SGS vertical transport of heat and moisture but also has an important bearing on the storm axisymmetric structure, eyewall mesovortices and other resolved asymmetric features in the vicinity of the hurricane eyewall. The analyses show that the vertical turbulent mixing processes provide a mechanism to affect the barotropic instability for generation of eyewall mesovortices through changing the vortex basic state potential vorticity (PV) field and generating eyewall disturbances with different frequencies. Our numerical experiments show for given external conditions that the magnitude and vertical distribution of the eddy exchange coefficients are the key factors that regulate the characteristics of eyewall disturbances. Such a modulation of eyewall structure by the eddy exchange coefficients is realized through the complicated interaction among SGS vertical turbulent mixing, mesoscale structures, diabatic heating and barotropic instability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".