Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2501.16926

An oceanic basin oscillation-driving mechanism for tides

Yongfeng Yang
View original source ↗Read source document ↗Export metadata ↓

Abstract

Tides represent the daily alternations of high and low waters along coastlines and in oceans, and the current theory (termed the gravitational forcing mechanism) explains them as a manifestation of the response of ocean water to the Moon's (Sun's) gravitational force. However, although the purely hydrodynamic models representing the current theory have been widely tested over global ocean,their tidal elevation accuracies are generally low. This implies an uncertainty as to whether the gravitational forcing mechanism is the best explanation for tides. In this study, we present a new theory (termed the oceanic basin oscillation-driving mechanism), in which tides are explained as a manifestation of oscillating ocean basin that is intricately linked to the elongated spinning solid Earth due to the Moon (Sun). Based on this new theory, we develop an algebraic tide model and test it using 11-year observations from 33 bottom pressure stations over the Pacific Ocean, the average Root Mean Square (RMS) deviation of tidal elevation predicted by this model against observation is 7.54 cm. Using a ratio of M2 elevation RMS of ocean tide model EOT11a and its total tidal elevation RMS as a reference, we estimate the total tidal elevation RMS of six purely hydrodynamic models (i.e.,Hallberg Isopycnal Model, OSU Tidal Inversion software-GN ,STORMTIDE model, OSU Tidal Inversion Software-ERB,STM-1B, and HYbrid Coordinate Ocean Model to be 59.93, 51.64, 57.05, 38.56, 86.92, and 53.56 cm, respectively.

Explore related subjects

physics.ao-phphysics.flu-dynphysics.geo-phphysics.pop-phphysics.soc-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yongfeng Yang. 2025-01-28. An oceanic basin oscillation-driving mechanism for tides. https://doi.org/10.1063/5.0250036

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↗