Sensitivity Testing of Extratropical Transitions Using Potential Vorticity Inversions to Modify Initial Conditions: Hurricane Earl Case Study
Bibliographic record
Abstract
This study uses the Mesoscale Compressible Community model to simulate the extratropical transition and reintensification of Hurricane Earl (1998) for the purposes of testing sensitivity to modification of the model's initial conditions. Though relatively strong “classical” cyclogenetic forcings were present in this case, operational forecasts seriously underpredicted the severity of the reintensification. Employing a piecewise potential vorticity (PV) inversion, the authors remove localized PV anomaly (PV′) maxima from the initial conditions and rebalance the fields for input to the model. Several PV′ structures in an upstream trough, and the PV′ associated with the hurricane, are removed individually and the model is rerun. Comparison of the resulting output with that of the control integration allows for a quantification of the impact of each PV anomaly on the regeneration of Earl. It is found that the existence of an upstream trough is of primary importance to the storm's reintensification, while the presence of the low-level circulation associated with the decaying hurricane plays only a secondary role.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".