Quality control of marine big data—a case study of real-time observation station data in Qingdao

Offshore waters provide resources for human beings, while on the other hand, threaten them because of marine disasters. Ocean stations are part of offshore observation networks, and the quality of their data is of great significance for exploiting and protecting the ocean. We used hourly mean wave height, temperature, and pressure real-time observation data taken in the Xiaomaidao station (in Qingdao, China) from June 1, 2017, to May 31, 2018, to explore the data quality using eight quality control methods, and to discriminate the most effective method for Xiaomaidao station. After using the eight quality control methods, the percentages of the mean wave height, temperature, and pressure data that passed the tests were 89.6%, 88.3%, and 98.6%, respectively. With the marine disaster (wave alarm report) data, the values failed in the test mainly due to the influence of aging observation equipment and missing data transmissions. The mean wave height is often affected by dynamic marine disasters, so the continuity test method is not effective. The correlation test with other related parameters would be more useful for the mean wave height.