Composite Analysis-Based Machine Learning for Prediction of Tropical Cyclone-Induced Sea Surface Height Anomaly
Bibliographic record
Abstract
Sea surface height anomaly (SSHA) induced by tropical cyclones (TCs) is closely associated with oscillations and is a crucial proxy for thermocline structure and ocean heat content in the upper ocean. The prediction of TC-induced SSHA, however, has been rarely investigated. This study presents a new composite analysis-based random forest (RF) approach to predict daily TC-induced SSHA. The proposed method utilizes TC's characteristics and pre-storm upper oceanic parameters as input features to predict TC-induced SSHA up to 30 days after TC passage. Simulation results suggest that the proposed method is skillful at inferring both the amplitude and temporal evolution of SSHA induced by TCs of different intensity groups. Using a TC-centered 5°×5° box, the proposed method achieves highly accurate prediction of TC-induced SSHA over the Western North Pacific with root mean square error of 0.024m, outperforming alternative machine learning methods and the numerical model. Moreover, the proposed method also demonstrated good prediction performance in different geographical regions, i.e., the South China Sea and the Western North Pacific subtropical ocean. The study provides insight into the application of machine learning in improving the prediction of SSHA influenced by extreme weather conditions. Accurate prediction of TC-induced SSHA allows for better preparedness and response, reducing the impact of extreme events (e.g., storm surge) on people and property.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".