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.
Accurately describing nearshore wind fields from coastline winds is crucial for forecasting ocean waves, currents, and selecting wind farm locations. Deploying and maintaining buoys for coastal wind measurements, particularly in shallow waters, poses challenges. Empirical relationships are established to estimate over‐water wind based on fixed wind towers on land. Initially developed for offshore winds in fetch‐limited wave generation studies, these relationships often involve complex non‐linear look‐up curves. However, such studies lacked extensive observations and complete arrays for comprehensively addressing cross‐shore dependence. Four field campaigns with observations from several cross‐shore arrays using new, shallow‐water, spar‐like buoys improve the description of . The aim is to develop a new, simplified model utilizing commonly available inputs. Given the complexity of nearshore wind fields, sophisticated approaches devoid of a priori assumptions on fit relationships, such as machine learning models, are necessary and allow for exploring dependence on environmental parameters. Here, an examination of nearshore wind patterns and associated mechanisms is provided. Machine learning shows that offshore, onshore, and alongshore winds slow about 10 km from the coast and vary based on coastal wind speed and air‐ocean temperature differences.
There is a linear correlation between wind and temperature measures over water and near the coast, with a decorrelation scale of 10 km Nearshore surface winds slow in fair‐weather for offshore, onshore, and alongshore flows, with patterns shaped by variable co‐dependence Wind gustiness, a significant element of nearshore winds, shows a cross‐shore dependence, resembling open ocean normalization but larger