Southern Caribbean Hurricane Case Study: Observations and WRF Simulation
Bibliographic record
Abstract
The meteorological conditions of three hurricanes passing through the southern Caribbean are analyzed using Hurdat, atmosphere - ocean reanalysis, satellite rainfall and SST fields, and buoy observations. The cases are Ivan 2004, Emily 2005 and Dean 2007, each traced to an African easterly wave moving zonally beneath an atmospheric ridge over the North Atlantic. SST were > 29 o C west of the Antilles Islands due to +40 W/m 2 surplus net heat flux and weak upper ocean currents. The analysis results suggested that the warm sea temperatures enhanced moisture fluxes, rain rates and convective heating, which affected these hurricanes significantly. WRF model simulations at 3 km resolution are evaluated for ability to forecast intensification up to 48 h lead time. In Emily (2005), the simulation results showed that upper winds disrupt the outflow. As the result, the model recovers the vortex too slowly. Overall, the WRF forecast decrease in central pressure was close to observed in Ivan (2004), but less than half of the observed in Dean (2007). Although the WRF model simulations gave evidence of initialization and intensification errors in comparison with NHC observations, the WRF 3 km resolution domain is essential for improving hurricane wind simulations. Nevertheless, significant improvements still depended on the initialization and data assimilation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".