Bulletin of Chinese Academy of Sciences (Chinese Version)


ocean big data; research actuality; future direction; key technologies

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With major improvements in ocean observations and modeling, as well as in data science development, current oceanography has gone through three critical transformations:theory-driven, technology-driven, and data-dominant. Oceanography big data are indispensable in humankind's journey to better learn the seas. The importance of big data in society development and national core competence is increasingly recognized by all countries. This paper addresses the overall development of big data acquiring, analysis, and application. Big data now can be obtained from platforms based in the space, the air, the land, and the ocean. However, breakthroughs are needed in such conditions as deep seas, extreme environments, and where high resolution is required. Compared to other nations, big data storage, management, and mining in China are relatively preliminary. On the other hand, we are leading in data visualization. Many areas are seeking for the application of ocean big data and paying attention to the latest discoveries. Still, connections between the applications and the upstream data acquiring and analyses are sometimes lost. Ocean big data have such features as being spatial-temporal coupled and geographically related when it comes to the fields of marine food safety, ocean pollution and mankind health, marine accidents, marine biodiversity, blue economy, etc. It is a general trend to apply ocean big data in the newly established fields. First, being spatial-temporal coupled is one of the most important features for air-land-sea data sets, which are also known as multi-dimensional data. As the observational techniques developing increasingly fast, it is getting more critical to get as high resolution data as possible in every dimension. Therefore, it is necessary to start analyzing the data sets from both temporal and spatial aspects, while this could pose a serious challenge to the ocean big data deep analyses since multiple factors should be considered in time and space. Another character of air-land-sea data is that they are geographically related. Different from being randomly distributed like the big data in other areas, ocean big data are all influenced by adjacent pixels. Since pixels sitting close to each other typically has linear or non-linear relationships with each other, the models on different temporal-spatial scales are presented in different ways. As a result, it is still very challenging in terms of ocean big data obtaining, analysis and application. Therefore, it becomes a critical issue which needs to be addressed by closely combining oceanography and data field in order to deal with the scientific or technical or engineering or even humanity challenges. This study proposes the possible research topics and key techniques in ocean big data field in 5 to 10 years:(1) investigating the potential areas and methods of the fusion of ocean science and big data field; (2) exploring the possible observation plans in support of ocean big data development; (3) determining the oceanography and information development trends which can fit the most features of big data; (4) studying the techniques and platforms of ocean big data sharing; and (5) exploring the development and technical requirements of possible new areas of ocean big data applications. In the future, step by step, we hope to keep investigating the methods of obtaining ocean big data with high resolution in extreme conditions such as deep sea trenches, and to find new analyses theories and techniques to make possible breakthroughs in terms of the multi-dimensional analyses of ocean big data. We will also try to realize the establishment of a new platform where ocean big data can be freely shared. Another goal is to satisfy the different needs in areas like marine food safety, marine pollution and society health, marine accidents, biodiversity, etc. and to ensure the new ocean big data based industries being smoothly established. This paper will be a significant contribution in understanding the oceans and improving our marine forecast abilities.

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Bulletin of Chinese Academy of Sciences


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