波浪滑翔器海洋观测数据质量控制研究

Quality Control of Ocean Observation Data Based on Wave Glider

观测数据的准确性和可信度是波浪滑翔器数据质量控制的核心,有效的数据质量控制方法是波浪滑翔器观测数据推广应用必不可少的技术手段。为提升波浪滑翔器观测数据质量,文中以气温和气压数据为例,提出一种新型海洋观测数据质量控制方法,该方法包括数据检验和数据修正环节:数据检验通过范围检验和尖峰检验对波浪滑翔器观测数据进行异常值剔除;数据修正采用反向传播(BP)神经网络算法对检验后的观测数据进行数据修正,提升观测数据整体准确性。利用“黑珍珠”波浪滑翔器集成的AIRMAR-BP200和GILL-GMX600气象传感器进行比对试验并获取大量数据样本,将该样本数据用于BP神经网络模型训练。同时,为验证所提出的数据质量控制方法的有效性,对“黑珍珠”波浪滑翔器海上试验获取的观测数据进行数据质量控制和分析,结果表明文中提出的数据质量控制方法可有效提高观测数据的准确性。

The accuracy and reliability of observation data form the core of data quality control for wave gliders.An effectivedata quality control method is essential to promote the popularization and application of wave glider observation data.Toimprove the data quality of a wave glider,a new marine observation data quality control method with data inspection and datacorrection algorithms was developed,considering air temperature and pressure data as examples.Data inspection includesrange and peak inspection,and abnormal values of the observation data are eliminated.A backpropagation(BP)neural networkalgorithm was adopted to correct the inspected observation data and improve the overall accuracy.In the early stage,sea trialswere conducted with the“Black Pearl”wave glider-integrated AIRMAR-BP200 and GILL-GMX600 meteorological sensors,and a large number of data samples were obtained for BP neural network model training.Meanwhile,to verify theeffectiveness of the proposed data quality control method,a sea trial was conducted,and the observation data obtained fromthe“Black Pearl”wave glider were analyzed.The experimental results show that the proposed data quality control methodcan effectively improve the accuracy of the observation data.