Fair‐Weather Nearshore Surface Winds: Observational Insights and Gaussian Process Regression Analysis

Abstract The fair‐weather (wind speeds 10 m/s), surface ( 4 m), nearshore wind field modification is examined with multiple cross‐shore arrays spanning from the coastline to 40 km offshore, deployed within four‐month‐long field experiments, measuring winds and air‐water temperatures. The over‐water to coastline winds ratio, , was previously explored for offshore winds with limited observations and minimally for onshore and alongshore winds. Array observations provide a complete picture of the nearshore wind field for all wind orientations. The over‐water wind and temperature are linearly related to coastline values near the coastline, with decreasing with distance from shore, , with a decorrelation scale of 10 km. Mean as a function of differs per wind orientation, consistent with prior work. An model is developed from exponential Gaussian Process Regression (GPR), which accurately predicts the wind field with 20% data set holdback to elucidate the cross‐shore patterns and variable co‐dependence. The modeled Partial Dependence Plots provide dependency as a function of on coastline winds, and temperature differences without preconceptions. A consistent nearshore wind slowing occurs that varies in amplitude and distance, and changes with variable co‐dependence for wind orientation. The onshore wind slowing is counterintuitive, though consistent with sophisticated numerical models. Wind gustiness exhibits dependence, with linear normalization akin to the open ocean but larger. The observations and GPR highlight nearshore winds' cross‐shore extent and complexity, which are important for atmospheric and oceanic studies.