Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2606.14520

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

Alexey S. Mironov
View original source ↗Read source document ↗Export metadata ↓

Abstract

The open ocean lacks systematic in situ wind observations, and satellite scatterometer calibration depends on collocated surface measurements largely absent away from coastlines. Compact wave-sensing drifters already retrieve the open-ocean wind vector, but at modest accuracy -- about 1-2 m/s in speed and with unreliable direction at low wind. We show that a compact freely drifting GNSS/IMU drifter (MELODI) improves both components at a small fraction of the cost of moored platforms. Wind speed is read from the full shape of the measured wave acceleration spectrum rather than a single equilibrium-range level. A supervised model (Wind Inversion using Tikhonov Regularization, WITR -- a regularized regression with a gated residual correction), trained against scatterometer winds and evaluated by leave-one-buoy-out cross-validation (ASCAT MetOp-B/C, HY-2B/C; ERA5 used during feature development), reaches an RMSE of 0.90 m/s and defines the empirical skill ceiling of the feature set; it serves both directly as a wind retrieval and as a teacher model for distillation. Symbolic regression then distills this teacher into compact, interpretable closed-form laws suited to onboard implementation -- a spectrum-only law (RMSE about 1.0 m/s) and a motion-enhanced reduced drag law (0.93-0.94 m/s) -- that reproduce the teacher's skill within the present validation uncertainty. Wind direction is recovered independently from IMU-derived directional wave moments in the wind-sea band, with a mean absolute error of 9.4 degrees against scatterometer winds. These accuracies are scatterometer-consistent (comparable to published scatterometer-buoy differences) and close to a factor-of-two improvement over the traditional single-band Toba inversion; winds are retrieved plausibly up to 18 m/s, with higher winds flagged as lower confidence.

Explore related subjects

physics.ao-ph

Keep this discovery

BibTeXRIS

Alexey S. Mironov. 2026-06-12. Wind vector retrieval from miniaturized wave-enabled sea-surface drifters. https://arxiv.org/abs/2606.14520

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph↗
arXiv2026

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph↗