Sensitivity of Real-Data Simulations of the 3 May 1999 Oklahoma City Tornadic Supercell and Associated Tornadoes to Multimoment Microphysics. Part II: Analysis of Buoyancy and Dynamic Pressure Forces in Simulated Tornado-Like Vortices
Bibliographic record
Abstract
Abstract Vortex stretching by intense upward accelerations is a critical process for tornadogenesis and maintenance. Two high-resolution (250-m grid spacing) real-data simulations of the 3 May 1999 Oklahoma City, Oklahoma, supercell and associated tornadoes, using single- and triple-moment microphysics parameterization schemes, respectively, are examined. Microphysical, thermodynamic, and dynamic impacts on the vertical accelerations near and within simulated tornado-like vortices (TLVs) are analyzed. Systematic differences in behavior of the TLVS between the two experiments are found; the TLV in the triple-moment simulation is substantially more intense and longer lived than in the single-moment case. The triple-moment scheme in this case produces less rain and hail mass in the low levels and drop size distributions of rain shifted toward larger drops, relative to the single-moment scheme, leading to less latent cooling and warmer outflow. Trajectory analyses reveal that more parcels entering the TLV in the triple-moment simulation have a history of dynamically induced descent, whereas buoyantly driven descent is more prevalent in the single-moment experiment. It is found that the intensity and longevity of the TLV are tied to weaker negative or neutral thermal buoyancy in the air flowing into the TLV in the triple-moment case, consistent with previous observational and modeling studies. Finally, the contribution to buoyancy from pressure perturbations is found to be of prime importance within the TLV, where strong negative pressure perturbations lead to substantial positive buoyancy. This contribution compensates for the slight negative thermal buoyancy and negative dynamic pressure gradient acceleration in the triple-moment case.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".