Quality Control of Ocean Observation Data Based on Wave Glider

Abstract

The accuracy and reliability of observation data form the core of data quality control for wave gliders. An effective data quality control method is essential to promote the popularization and application of wave glider observation data. To improve the data quality of a wave glider, a new marine observation data quality control method with data inspection and data correction algorithms was developed, considering air temperature and pressure data as examples. Data inspection includes range and peak inspection, and abnormal values of the observation data are eliminated. A backpropagation(BP) neural network algorithm was adopted to correct the inspected observation data and improve the overall accuracy. In the early stage, sea trials were 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 the effectiveness of the proposed data quality control method, a sea trial was conducted, and the observation data obtained from the “Black Pearl” wave glider were analyzed. The experimental results show that the proposed data quality control method can effectively improve the accuracy of the observation data.

Keywords

• wave glider
• data quality control
• bp neural network
• ocean observation