Wind vector retrieval from miniaturized wave-enabled sea-surface drifters

arXiv:2606.14520 · physics.ao-ph · Submitted 2026-06-12 · Read on arXiv

physics.ao-ph

Submitted: 2026-06-12

Updated: 2026-10-29

Comments: 26 pages. Revised version (2nd revision) submitted to Journal of Atmospheric and Oceanic Technology

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Calibration — 19× in this paper · explained in 9 episodes
• Accuracy — 16× in this paper · explained in 1 episode
• Coherence — 12× in this paper · explained in 2 episodes
• Regularization — 8× in this paper · explained in 3 episodes
• Symbolic Regression — 6× in this paper · explained in 6 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Feature Extraction — 2× in this paper · explained in 3 episodes
• Feature Selection — 2× in this paper · explained in 4 episodes
• Absorb — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Data Assimilation — 1× in this paper · explained in 2 episodes
• Evaluation Protocol — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 14 episodes
• Global Positioning System — 1× in this paper
• Hysteresis — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Iridium Short Burst Data — 1× in this paper
• National Data Buoy Center — 1× in this paper
• Parameterization — 1× in this paper · explained in 2 episodes
• Parsimony — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 6 episodes
• Surface Water and Ocean Topography — 1× in this paper
• Surface Wave Instrument Float with Tracking — 1× in this paper
• Verification — 1× in this paper · explained in 2 episodes
• signal-to-noise ratios — 1× in this paper · explained in 2 episodes

Sources

• Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

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