Vortex "Bogusing" using advanced microwave sounding unit data, applied to hurricane floyd
Bibliographic record
Abstract
A case study of hurricane Floyd (1999) is performed using the Penn State/NCAR MM5 model. Hurricane Floyd was the third most costly hurricane to have hit the United States. To predict accurately the track and evolution of the hurricane, a vortex bogusing technique has been devised. A more realistic initial vortex was specified and introduced into the large-scale analysis for model initialization. The technique used follows closely that described by Zhu et al. (2002) where Advanced Microwave Sounding Unit (AMSU) data are employed to retrieve the temperature of the hurricane vortex. An algorithm is then applied to compute the sea level pressure, geopotential heights, winds and moisture content. Three experiments initialized with three different data sets were performed, using respectively the original Canadian Meteorological Centre (CMC) analysis, the bogus-vortex modified CMC analysis with the original CMC sea surface temperature (SST) field, and a bogus-vortex modified CMC analysis with a spatially-constant SST of 28°C. (Abstract shortened by UMI.)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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; both teacher heads agree on what is shown here.
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".