SearcharxivSearch

arXiv · 2607.08483

New features of the sea-surface slope distribution revealed by IASI observations: Directional properties, influence of the wave height, large wave tilts

Abstract

By analysis of the reflected solar contribution to mid-infrared radiance spectra collected by the IASI instrument, we investigate the influences of both the wind and significant wave height on the wave-slope probability distribution function (PDF). We show that, at equal wind speed, smaller wave heights enhance the directional (upwind-downwind and upwind-crosswind) asymmetries of the probabilities. We also point out that the sea-surface mean square slopes slightly decrease as the wave height increases (by about $5\%$ per meter for winds slower than 6 m/s), an original result for which we propose possible causes (short-scale damping by long waves and/or effect of compounding statistical processes). We also investigate the so far practically unstudied case of steep slopes and find large deviations from the quasi Gaussian behavior of the central part of the PDF, with a quite universal exponential decay of the probabilities with increasing tilt. We here observe larger probabilities in the downwind direction, but similar values in the up- and cross-wind ones. The physical mechanisms responsible for these trends are discussed. Finally, we propose refined parametrizations of the mean square slope versus wind-speed, which departs from the Cox and Munk linear relationships at moderate wind speed (5-8 m/s).

Explore related subjects

Keep this discovery

BibTeXRIS

Charles-Antoine Guérin, Virginie Capelle, Jean-Michel Hartmann. 2026-07-09. New features of the sea-surface slope distribution revealed by IASI observations: Directional properties, influence of the wave height, large wave tilts. https://arxiv.org/abs/2607.08483

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

KEEP EXPLORING

Related papers

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

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