Tropical Cyclone Multi‐Level Wind‐Speed Structure Reconstruction From Sparse Dropsonde Data Via Adversarial Learning
Bibliographic record
Abstract
Abstract Tropical cyclones (TCs) pose significant global hazards due to their intense winds, heavy rainfall, and associated storm surges. While in situ observations from aircraft are crucial for understanding TC structures, these measurements are often spatially sparse, limiting the characterization of the wind speed structure. In this study, we introduce a deep learning (DL) framework based on generative adversarial networks to reconstruct multi‐level wind‐speed (including that at the 10 m surface layer) from sparse dropsonde inputs within seconds. Our simulation experiments incorporate realistic flight parameters and account for the horizontal drift of dropsondes. We present a case study that reconstructs TC structures using a combination of observations from Hurricane Hunter missions. This approach demonstrates the potential of DL for reconstructing the multi‐level wind‐speed structure of TCs from sparse data. The current framework focuses on reconstructing wind speed, showing significant promise for surface wind estimation and operational storm surge modeling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".