| dc.description.abstract | The purpose of this study is to quantify the spatial variability of wind, wave, and surface current features at the subgrid scale. In this study, 76 miniature wave buoys were deployed in the southern part of the East China Sea for one month starting from September 22, 2021, to observe the surface current velocity, significant wave height, mean period, and surface wind speed and other meteorological parameters in the area.
In this study, we discuss the ratio of the Deterministic process to the mutual error of the instruments and the random characteristics of nature in the observations of meteorological factors. We first compare the differences between the experimental data and the satellite remote sensing data and numerical model results. The temporal and spatial variability of the meteorological factors from different data sources are consistent, but the spatial variability of the measured data is higher.
Further statistical analysis of the surface wind and wave parameters reveals that the variability characteristics increase with the spatial scale, and the relationship between the coefficient of variation (C.O.V.) and the spatial scale is consistent with previous studies. The spatial variability of the numerical model data at the subgrid scale is found to be different from the actual observations, and the spatial variability of each meteorological factor, the ratio between the actual observations and the numerical model is up to 6-17.9, and this ratio decreases with the increase of the spatial scale. The spatial variability of the spatial variability is parameterized by using a half Normal distribution for the spatial variability without considering the instrumentation error and a Weibull distribution for the spatial variability with considering the instrumentation error.
For the surface currents, two Lagrangian analysis methods are used to analyze the surface currents: one is to calculate the horizontal dispersion coefficient in the study area using the buoy array position; the other is to calculate the corresponding Finite Size Lyapunov Exponent (FSLE). In this paper, we compare the finite-size Lyapunov exponent with the spatial scale of the ocean dispersion conditions, and the results show that the dispersion distance (δ) in the southern part of the East China Sea during the study period follows the slope of δ-2/3 and the dispersion coefficient follows the slope of δ4/3, both of which exhibit Richardson′s Law. The diffusion characteristics of the turbulent flow according to Richardson′s Law are similar with described by Corrado et al. (2017) for the North Pacific.
The results of spatial variability parameterization of meteorological parameters can provide the basis for data validation and quality control of numerical models and remote sensing at subgrid scale. | en_US |