Calibration Experiments of CFOSAT Wavelength in the Southern South China Sea by Artificial Neural Networks

Bo Li,Wuyang Chen,Yupeng Liu,Shilin Tang,Junmin Li,Ping Shi,Junliang Liu

doi:10.3390/rs14030773

Abstract

The wave data measured by CFOSAT (China France Oceanography Satellite) have been validated mainly based on numerical model outputs and altimetry products on a global scale. It is still necessary to further calibrate the data for specific regions, e.g., the southern South China Sea. This study analyses the practicability of calibrating the dominant wavelength by using artificial neural networks and mean impact value analysis based on two sets of buoy data with a 2-year observation period and contemporaneous ERA5 reanalysis data. The artificial neural network modeling experiments are repeated 1000 times randomly by Monte Carlo methods to avoid sampling uncertainty. Both experimental results based on the random sampling method and chronological sampling method are performed. Independent buoy observations are used to validate the calibration model. The results show that although there are obvious differences between the CFOSAT wavelength data and the field observations, the parameters observed by the satellite itself can effectively calibrate the data. In addition to the wavelength, nadir significant wave height, nadir wind speed, and the distance between the calibration point and satellite observation point are the most important parameters for the calibration. Accurate data from other sources, such as ERA5, would be helpful to further improve the calibration results. The variable contributing the most to the calibration effect is the mean wave period, which virtually provides relatively accurate wavelength information for the calibration network. These results verify the possibility of synchronous self-calibration for the CFOSAT wavelength data and provide a reference for the further calibration of the satellite products in other regions.

Highlights

IntroductionThe South China Sea (SCS) (Figure 1), the second-largest marginal sea in the Northwest
To avoid the uncertainty caused by insufficient data samples, the input parameters of each stage were randomly sampled 1000 times by the Monte Carlo method, and the calibration experiment was repeated
The critical parameters were used as input parameters to establish model NN2, and the relative mean impact value (MIV) and PMIV were calculated again

Summary

Introduction

The South China Sea (SCS) (Figure 1), the second-largest marginal sea in the Northwest. Pacific, is one of the busiest shipping routes in the world and connects the Pacific Ocean and the Indian Ocean. Wave parameters are of great significance to marine engineering, marine transportation, fishing, renewable wave energy harvesting, typhoon disaster prevention, and military activities in the SCS. Many satellites have been widely applied in ocean wave observations [1]. Wavelength (or wave period) parameters can be retrieved from altimetry wave height and wind speed parameters by empirical models with artificial neural network (NN), e.g., [2,3].

Methods

Results

Discussion

Conclusion