Dynamics and Predictability of the Rapid Intensification of Super Typhoon Usagi (2013)
Bibliographic record
Abstract
Abstract This study explores the dynamics and predictability of the rapid intensification (RI) of Super Typhoon Usagi (2013) through a 60‐member convection‐permitting ensemble using the Weather Research and Forecasting (WRF) model and an ensemble Kalman filter (EnKF) data assimilation method. The surface maximum wind speed of Usagi, which was an intense category 4 western North Pacific tropical cyclone (TC), increased by 33 m s −1 over a 24‐hr period. The RI process was captured by the WRF simulation initialized with the global analysis but with a unique forecast challenge of early prediction. We improved the intensity forecasts by assimilating satellite‐derived atmospheric motion vectors into the WRF‐EnKF, which primarily reduced the strength of both the primary and secondary circulations in the TC vortex. Nevertheless, our ensemble forecasts initialized with the EnKF analysis ensemble predicted a significant spread in the intensity with considerable differences in the RI onset timing among individual members. Our analyses show that variation in the RI timing is most sensitive to differences in the initial TC vortex intensity and inner‐core moisture. Ensemble members with similar initial intensities but greater tropospheric moisture content exhibited earlier vortex axisymmetrization and consequently earlier RI. Further sensitivity experiments showed that variations in the inner‐core moisture content have an immediate impact on the structure and strength of inner‐core convection. These variations in inner‐core convection gradually caused differences in intensity between the TC vortices. In this study, we highlight the importance of accurate estimates of the inner‐core moisture content in the modeling and forecasting of TC intensity.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".