Ocean Surface Wind Speed Estimation From GNSS-R Data Using Physics-Informed Attention-Aided Convolutional Neural Network
Bibliographic record
Abstract
Accurate global ocean surface wind speed estimation is crucial for weather forecasting and maritime transportation. Traditional retrieval methods based on Global Navigation Satellite System Reflectometry (GNSS-R) data and deep learning techniques often struggle to capture the complex, nonlinear relationships between GNSS-R observables and wind speed. This challenge is further exacerbated by imbalanced data distributions, which lead to significant underestimation under high wind conditions (15 - 30 m/s, here). To mitigate these issues, this study proposes a novel Physics-Informed Attention-Aided Convolutional Neural Network (PA-CNN). The proposed model incorporates an attention mechanism into the CNN architecture to adaptively focus on the most informative features. In addition, geophysical principles related to GNSS-R signal scattering are integrated with data-driven learning, improving both the interpretability and generalization capability of the network. Moreover, a spatial-temporal smoothing post-processing step is designed to enhance consistency in wind speed retrieval. Extensive experiments using Cyclone Global Navigation Satellite System (CYGNSS) datasets demonstrate that PA-CNN with smoothing outperforms existing deep learning approaches, achieving an overall root mean square difference (RMSD) of 1.38 m/s compared with ERA5 reanalysis data and 1.56 m/s against buoy measurements, highlighting the potential of combining physics-informed modeling with advanced deep-learning techniques for improved ocean surface wind speed retrieval.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".