Ocean FAIR Data Services

For investigating ocean activities and comprehending the role of the oceans in global climate change, it is essential to gather high-quality ocean data. However, existing ocean observation data have deficiencies such as inconsistent spatial and temporal distribution, severe fragmentation, and restricted observation depth layers. Data assimilation is computationally intensive, and other conventional data fusion techniques offer poor fusion precision. This research proposes a novel multi-source ocean data fusion network (ODF-Net) based on deep learning as a solution for these issues. The ODF-Net comprises a number of one-dimensional residual blocks that can rapidly fuse conventional observations, satellite observations, and three-dimensional model output and reanalysis data. The model utilizes vertical ocean profile data as target constraints, integrating physics-based prior knowledge to improve the precision of the fusion. The network structure contains channel and spatial attention mechanisms that guide the network model’s attention to the most crucial features, hence enhancing model performance and interpretability. Comparing multiple global sea temperature datasets reveals that the ODF-Net achieves the highest accuracy and correlation with observations. To evaluate the feasibility of the proposed method, a global monthly three-dimensional sea temperature dataset with a spatial resolution of 0.25°×0.25° is produced by fusing ocean data from multiple sources from 1994 to 2017. The rationality tests on the fusion dataset show that ODF-Net is reliable for integrating ocean data from various sources.

Ocean observations are acquired from a great variety of platforms at different spatial and temporal scales. Additionally, a great number of physical, chemical, geological and biological variables need to be measured to characterise and understand ocean dynamics and the ocean ecosystem. Developing robust infrastructures that facilitate the search and access to diverse oceanographic data becomes essential to be able to use ocean observations for increasing our knowledge of ocean dynamics and of the ocean ecosystem and for producing information, services and products to support the great diversity of marine stakeholders.In this communication, we will present recent developments of data infrastructures and data services at the the Spanish Institute of Oceanography (IEO-CSIC) in the framework of different national and international funded projects (DEMON, Oceans+, iFADO, DATAMARE...). IEO was founded in 1914 and focuses its activity on oceanography and marine research for different marine stakeholders and to provide scientific advice to the government to support the implementation of marine related policies. IEO is part of the National Oceanographic Data Centres (NODC) network, which adhere to FAIR principles (Findable, Accessible, Interoperable, and Reusable) to ensure standardized data management and global exchange of ocean data. In this contribution we will focus on our recent developments of standard catalogues using open-source tools like GeoNetwork and we will show how this approach has enabled IEO to organise and present a vast catalogue of oceanographic data and of data services that disseminate marine data sets following FAIR principles. Proper hierarchization ensure that users (individuals or data hubs) can navigate the IEO catalogue effectively, taking advantage of the search capabilities to access data for research and applications. Different examples of access to multidisciplinary data sets obtained with different ocean observing platforms will be provided. In addition, we will show how the ocean data stored at IEO can be catalogued to support the implementation of the EU Marine Strategy Framework Directive (MSFD).Finally, we will present the integrated marine data platform being developed by the EU funded Galician Marine Sciences Program by different academic and research actors in Galicia (CESGA, CSIC, MeteoGalicia, INTECMAR, CETMAR and universities of Santiago and Coruña) with the objective of demonstrating the improvement in the management of marine data using supercomputing technologies (High Performance Computing, HPC). We will show practical examples of how access and processing of multidisciplinary marine data sets can be performed in the infrastructure in a powerful, standardized, fast and simple way.

Referring to GEOSS Sensor Web Workshop and OGC-SWE standardisation, this paper presents the actual sensor network configuration for ocean observing system and the design&implementation of Ocean Sensor Web(OceanSW), which can be implemented to describe, organize, store and manage the ocean sensor data. The OceanSW system is represented in XML, which is easy to apply. And gives the details of Ocean Sensor Observation Service. On this basis, Ocean Sensor Web Prototype System based on Web Service is put forward, which aims to store and manage the ocean sensor observation data effectively.

Many regions including Europe, the USA, Australia and Canada are making significant progress in facilitating the discovery, access and long-term stewardship of marine data through the development, implementation and operation of national, regional or international distributed ocean observing and data management infrastructures such as SeaDataNet, EMODnet, IOOS, R2R, and IMOS. The Ocean Data Interoperability Platform (ODIP) project, supported by the EU Framework Programme 7 (FP7), National Science Foundation (USA) and Australian government, was initiated 1st October 2012 and continues as ODIP II for another 3 years with EU HORIZON 2020 funding. ODIP includes many of the major organizations engaged in ocean data management in Europe, US, Australia and Canada. ODIP is also supported by the IOC-IODE, closely linking this activity with its Ocean Data Portal (ODP) and Ocean Data Standards Best Practices (ODSBP) projects.

In response to a request by the United Nations Intergovernmental Oceanographic Commission (IOC) for member states to provide data management services for oceanographic data collected during the First Garp Global Experiment, (FGGE), also known as the Global Weather Experiment, the Environmental Data and Information Service of NOAA (EDIS) will serve as a Responsible National Oceanographic Data Center for the FGGE Operational Year (RNODC‐FOY. In this capacity, EDIS will (1) develop a global inventory of all ocean data collected during FOY and (2) create a global ocean climate data base of selected FOY physical oceanographic parameters. All data will be exchanged through normal international exchange channels.The global ocean data inventory will contain directory information regarding all known oceanographic data of any kind recorded during FOY (December 1, 1978 through November 30, 1979). The inventory will be available both in published form and online to computer terminal users internationally via either Tymnet or Telenet. The global ocean climate data base will include physical oceanographic data for this same period acquired by the RNODC‐FOY. Bathythermograph data, Nansen/Nisken bottle data, salinity‐temperature‐depth data, and current meter data, as well as phosphate, silicate, oxygen, and sea level data will be formated and quality controlled.

In order to realize comprehensive observation and research on the ocean, large-scale data integration needs to be carried out on the existing heterogeneous ocean observing systems. However, the content and format of ocean observation data sources are extremely complicated, which causes problems such as large amount of calculation, long time for matching and high error rate in the process of ocean observation element conversion. By using edge computing and improved KNN algorithm, we propose a new automatic matching method for ocean observation elements based on edge computing. According to the characteristics and advantages of the local, edge and cloud, the different processes of matching observation elements are executed. In order to solve the problem of large amount of computation and occupying much space, KNN algorithm is improved by grouping fast search (G-KNN) and weighted Euclidean distance (ω-KNN). The prototype system was established by using the actual ocean observation data set to verify the method. The results show that this method can improve the execution efficiency by 2.67 times and the accuracy rate of 96%, which can further improve the efficiency while ensuring the accuracy of ocean observation element matching.

We introduce data management concepts, including what we mean by data and its management, sources of data, interoperability, and data geometry. We then discuss various components of a data management system. Finally, we summarize some existing ocean and coastal data management efforts. We make specific recommendations throughout the paper. We are generally optimistic that ocean and coastal data management is an interesting and solvable challenge that will provide great benefit to society.

The ocean is rapidly changing through warming, sea-level rise, ocean acidification, declining oxygen concentrations, and altered circulation patterns, which are reshaping marine ecosystems worldwide. These changes necessitate a complete and holistic view of affected marine ecosystems. Long-term ocean monitoring programs are crucial for understanding these changes and guiding sustainable management. The California Cooperative Oceanic Fisheries Investigations (CalCOFI)*1, initiated in 1949, is one of the world's longest-running and most comprehensive marine ecosystem observing programs. CalCOFI collects extensive ocean data across all Essential Ocean Variables in the Southern California Current four times a year (Gallo et al. 2022). This encompasses over 36 physical and chemical parameters, 2,500 biological parameters, and 50,000 environmental DNA sequences, measured simultaneously (Satterthwaite et al. 2025). These data have illuminated long-term ecosystem patterns, supported marine management, and enhanced our understanding of how climate variability and change impact the California Current. Given its long history and collaborative nature, CalCOFI's data are fragmented across more than 30 disparate datasets, managed by different entities, served in various locations, with inconsistent naming and limited integration (Fig. 1), which hinders a holistic ecosystem understanding. Thus, CalCOFI exemplifies similar challenges and opportunities of mobilizing and integrating ocean data on a global scale. Our vision has been to modernize and build an integrated data system for CalCOFI that automates dataset ingestion, integration, and serving for timely use in research and management (Fig. 1). We are developing workflows to publish these datasets into portals promoting Findable, Accessible, Interoperable, and Reusable (FAIR; Wilkinson et al. 2016) data principles. Our initial efforts focused on two core CalCOFI datasets: Conductivity, Temperature, and Depth (CTD) bottle data and larval fish data to develop a workflow for easier access, integration, and use (Fig. 2). Data providers upload processed datasets into a shared Google Drive in simple formats (e.g., .csv), with globally unique identifiers and standardized naming conventions (Suppl. material 1). Data are automatically detected and integrated into a PostgreSQL database (Fig. 2). Newly added data trigger a system rebuild and validation, checking primary keys, spatial geometry, taxonomy, and metadata. Managers are notified of errors; otherwise, the system updates production databases, records a new version, and publishes a Github release with source files and a DOI via Zenodo. Original source data files are retained for past version reconstruction. While PostgreSQL maintains relationships between data, we also produce an analytical database in DuckDB for a single, portable database with fast querying, even on remote servers (Fig. 2). CalCOFI data and observations are accessible through multiple repositories and platforms, tools, interfaces, and portals based on user needs and funder requirements (Fig. 2; Suppl. material 1). For example, some tools support flexible analysis and visualization (e.g., NOAA Environmental Research Division's Data Access Program), others provide long-term archiving with standardized metadata (e.g., Environmental Data Initiative). We are also publishing biological observations in DarwinCore (Wieczorek et al. 2012) format with Extended Measurement or Fact and uploading them to the Ocean Biodiversity Information System (OBIS) so that CalCOFI data can be integrated with global biodiversity datasets worldwide. CalCOFI program information is on the Global Ocean Observing System Biology & Ecosystem Portal, and dataset records are structured for indexing by portals like Ocean Data Information System (ODIS) and Google Dataset Search. To facilitate data access and visualization, we also developed the CalCOFI Integrated Data Viewer*2 (Fig. 3). This web app allows users to explore decades of CalCOFI oceanographic and larval fish data across space, time, and depth. We plan to integrate additional CalCOFI datasets and collaborate with other data providers to build a more complete ecosystem-level view of the California Current. Our ultimate goal is to enhance CalCOFI data findability, integration, and usability for scientific and management communities, enabling a better understanding of ocean changes and ensuring a sustainable future for marine ecosystems and human communities.

Due to the rapid development of ocean observation technology, the volume of ocean data is increasing exponentially. To better utilize these data, more and more computational methods have been applied to exploring, sharing, and processing such data. Although great efforts have been made, there is an urgent need for a single platform containing a set of visualization components for every kind of ocean data. This paper presents a distributed, scalable and service-oriented visualization platform. The development of the platform is based on a common data model, by which ocean data with different spatial and temporal distribution can be organized and accessed in a uniform fashion, which sets a solid foundation for designing and implementing all kinds of visualization components and facilitates collaborative oceanographic research. The platform is run in China Ocean Observation System (COOS), and enables easy and convenient development of all kinds of visualization components for professionals and the general public and greatly advances ocean data sharing and scientific research collaboration.

: The feasibility of global ocean weather prediction was just emerging as the Global Ocean Data Assimilation Experiment (GODAE) began in 1997. Ocean weather includes phenomena such as meandering currents and fronts, the surface mixed layer and sea surface temperature (SST), waves, upwelling of cold water, all influencing ocean variables such as temperature (T), salinity (S), currents, and sea surface height (SSH). Adequate real-time data input, computing power, numerical ocean models, data assimilation capabilities, atmospheric forcing, and bathymetric/boundary constraints are essential to make such prediction possible. The ocean models dynamically interpolate data in conjunction with data assimilation, convert atmospheric forcing into oceanic responses, and forecast the ocean weather. The results are substantially influenced by ocean model simulation skill and it is advantageous to use an ocean model that is eddy-resolving, not just eddy-permitting. Because the most abundant ocean observations are satellite surface data, and subsurface data are very sparse, downward projection of surface data is a key challenge in ocean data assimilation. The need for accurate prediction of ocean features that are inadequately observed places a major burden on the ocean model, data assimilation, and atmospheric forcing. The sensitivity of ocean phenomena to atmospheric forcing and the time scale for response affect the oceanic data requirements and prediction system design. Outside of surface boundary layers and shallow regions, forecast skill is about one month globally and over many subregions, and is only modestly reduced by using climatological forcing. In addition, global ocean prediction systems must demonstrate the ability to provide initial and boundary conditions to nested regional and coastal models that enhance their predictive skill.

<p>Integration of data management systems is a persistent problem in European projects that span multiple agencies. Months, if not years of projects are often expended on the integration of disparate database structures, data types, methodologies and outputs. Moreover, this work is usually confined to a single effort, meaning it is needlessly repeated on subsequent projects. The legacy effect of removing these barriers could therefore yield monetary and time savings for all involved, far beyond a single cross-jurisdictional project. </p><p>The European Union’s INTERREG VA Programme has funded the COMPASS project to better manage marine protected areas (MPA) in peripheral areas. Involving five organisations, spread across two nations, the project has developed a cross-border network for marine monitoring. Three of those organisations are UK-based and bound for Brexit (the Agri-Food and Biosciences Institute, Marine Scotland Science and the Scottish Association of Marine Science). With that network under construction, significant efforts have been placed on harmonizing data management processes and procedures between the partners. </p><p>A data management quality management framework (DM-QMF) was introduced to guide this harmonization and ensure adequate quality controls would be enforced. As lead partner on data management, the Irish Marine Institute (MI) initially shared guidelines for infrastructure, architecture and metadata. The implementation of those requirements were then left to the other four partners, with the MI acting as facilitator. This led to the following being generated for each process in the project:</p><p>Data management plan: Information on how and what data were to be generated as well as where it would be stored. </p><p>Flow diagrams: Diagrammatic overview of the flow of data through the project. </p><p>Standard Operating Procedures: Detailed explanatory documents on the precise workings of a process.</p><p>Data management processes were allowed to evolve naturally out of a need to adhere to this set standard. Organisations were able to work within their operational limitations, without being required to alter their existing procedures, but encouraged to learn from each other. Very quickly it was found that there were similarities in processes, where previously it was thought there were significant differences. This process of sharing data management information has created mutually benefiting synergies and enabled the convergence of procedures within the separate organisations. </p><p>The downstream data management synergies that COMPASS has produced have already taken effect. Sister INTERREG VA projects, SeaMonitor and MarPAMM, have felt the benefits. The same data management systems cultivated as part of the COMPASS project are being reused, while the groundwork in creating strong cross boundary channels of communication and cooperation are saving significant amounts of time in project coordination.</p><p>Through data management, personal and institutional relationships have been strengthened, both of which should persist beyond the project terminus in 2021, well into a post-Brexit Europe. The COMPASS project has been an exemplar of how close collaboration can persist and thrive in a changing political environment, in spite of the ongoing uncertainty surrounding Brexit.</p>

The mission of the Earth System Grid Federation (ESGF) is to provide the worldwide climate-research community with access to the data, information, model codes, analysis tools, and intercomparison capabilities required to make sense of enormous climate data sets. Its specific goals are to (1) provide an easy-to-use and secure web-based data access environment for data sets; (2) add value to individual data sets by presenting them in the context of other data sets and tools for comparative analysis; (3) address the specific requirements of participating organizations with respect to bandwidth, access restrictions, and replication; (4) ensure that the data are readily accessible through the analysis and visualization tools used by the climate research community; and (5) transfer infrastructure advances to other domain areas. For the ESGF, the U.S. Department of Energy's (DOE's) Earth System Grid Center for Enabling Technologies (ESG-CET) team has led international development and delivered a production environment for managing and accessing ultra-scale climate data. This production environment includes multiple national and international climate projects (such as the Community Earth System Model and the Coupled Model Intercomparison Project), ocean model data (such as the Parallel Ocean Program), observation data (Atmospheric Radiation Measurement Best Estimate, Carbon Dioxide Information and Analysis Center, Atmospheric Infrared Sounder, etc.), and analysis and visualization tools, all serving a diverse user community. These data holdings and services are distributed across multiple ESG-CET sites (such as ANL, LANL, LBNL/NERSC, LLNL/PCMDI, NCAR, and ORNL) and at unfunded partner sites, such as the Australian National University National Computational Infrastructure, the British Atmospheric Data Centre, the National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory, the Max Planck Institute for Meteorology, the German Climate Computing Centre, the National Aeronautics and Space Administration Jet Propulsion Laboratory, and the National Oceanic and Atmospheric Administration. The ESGF software is distinguished from other collaborative knowledge systems in the climate community by its widespread adoption, federation capabilities, and broad developer base. It is the leading source for present climate data holdings, including the most important and largest data sets in the global-climate community, and - assuming its development continues - we expect it to be the leading source for future climate data holdings as well. Recently, ESG-CET extended its services beyond data-file access and delivery to include more detailed information products (scientific graphics, animations, etc.), secure binary data-access services (based upon the OPeNDAP protocol), and server-side analysis. The latter capabilities allow users to request data subsets transformed through commonly used analysis and intercomparison procedures. As we transition from development activities to production and operations, the ESG-CET team is tasked with making data available to all users seeking to understand, process, extract value from, visualize, and/or communicate it to others. This ongoing effort, though daunting in scope and complexity, will greatly magnify the value of numerical climate model outputs and climate observations for future national and international climate-assessment reports. The ESG-CET team also faces substantial technical challenges due to the rapidly increasing scale of climate simulation and observational data, which will grow, for example, from less than 50 terabytes for the last Intergovernmental Panel on Climate Change (IPCC) assessment to multiple Petabytes for the next IPCC assessment. In a world of exponential technological change and rapidly growing sophistication in climate data analysis, an infrastructure such as ESGF must constantly evolve if it is to remain relevant and useful. Regretfully, we submit our final report at the end of project funding. To continue to serve the climate-science community, we are currently seeking additional funding. Such funding would allow us to maintain and enhance ESGF production and operation of this vital endeavor of cataloging, serving, and analyzing ultra-scale climate science data. At this time, the entire ESG-CET team would like to take this opportunity to sincerely thank our funding agencies in the DOE Scientific Discovery through Advanced Computing (SciDAC) program and the Office of Biological and Environmental Research (OBER) - as well as our national and international collaborators, stakeholders, and partners - for allowing us to work with you and serve the community these past several years.

Ocean data determining the state of the ocean are required for various purposes. The data are needed to understand the ocean and its interrelation, for instance, with the climate as well as to provide information and benefits for numerous sectors, such as for the coastal protection or pollution control. In order to meet the multiple needs for ocean information, a global and sustained system for ocean observation activities is necessary. Steps towards such a global system have been taken within the last decades, especially by setting up the Global Ocean Observing System (GOOS). However, the existing international structures and systems have been lacking governmental support and are held to be insufficient. In this context, the questions are raised whether states are already required by international law to observe and monitor the ocean and to cooperate in doing so and whether states are even obliged to contribute to international ocean observing programmes. Although to some extent, states’ obligations can be found in international conventions, such as in the UN Convention on the Law of the Sea (UNCLOS), they cannot meet all the various needs for ocean data. Specific and substantive obligations in international law meeting the multiple needs for ocean data are, however, required. If such obligations were included in the UNCLOS, issues relating to Marine Scientific Research would have to be kept in mind. Another option of creating specific commitments could be seen in a new agreement relating to the GOOS.

Effective ocean and coastal data management are needed to manage marine ecosystem health. Past ocean and coastal data management systems were often very specific to a particular application and region, but this focused approach often lacks real-time data and sharing/interoperating capability. The challenge for the ocean observing community is to devise standards and practices that enable integration of data from sensors across devices, manufacturers, users, and domains to enable new types of applications and services that facilitate much more comprehensive understanding and analyses of marine ecosystem. A given kind of sensor may be deployed on various platforms such as floats, gliders or moorings, and thus must be integrated with different operation, and data management systems. Simplifying the integration process in existing or newly established observing systems would benefit system operators and is important for the broader application of diverse sensors. This paper describes a geospatial “sensor web” architecture developed by the “NeXOS” project for ocean and coastal data management, based on the concepts of spatial data infrastructure and the Sensor Web Enablement framework of the Open Geospatial Consortium. This approach reduces the effort to propagate data from deployed sensors to users. To support the realization of the proposed Next generation Ocean Sensors (NeXOS) architecture, hardware and software specifications for a Smart Electronic Interface for Sensors and Instruments (SEISI) are described. SEISI specifies small lower-power electronics, minimal operating system, and standards-basedresearch software to enable web-based sharing, discovery, exchange, and processing of sensor observations as well as operation of sensor devices. An experimental scenario is presented in which sensor data from a low-power glider with low-bandwidth intermittent satellite communications is integrated into the geospatial sensor web using the NeXOS architecture.

El Nino–Southern Oscillation (ENSO) is one of the most influential fluctuations of the coupled atmosphere-ocean climate system in the Seasonal-to-Interannual (SI) time scale for global and local communities. Ocean data assimilation systems are generally adopted for monitoring ENSO because the variation of the heat content in the ocean interior of the equatorial Pacific is considered a good precursor of El Ninos, and essential for understanding the ENSO process. They are also utilized for the initialization of Coupled Atmosphere-OceanGeneral Circulation Models (CGCMs), along with atmosphere data assimilation systems in the SI forecasting systems of various operational centers. In this paper, we discuss the capacity of current operational ocean data assimilation systems adopted for ENSO monitoring and SI forecasting based on studies using the system of the Japan Meteorological Agency (JMA). It then demonstrates the benefits of assimilating ocean observation data through those systems in SI forecasts using the JMA seasonal and ENSO forecasting system. It also introduces the recent effort of JMA/Meteorological Research Institute (MRI) to resolve “coupled shock, which is one of the most crucial issues concerning the initialization with uncoupled ocean and atmosphere data assimilation systems in SI forecasting. This chapter is organized as follows. Section 2 summarizes the process of developing ocean data assimilation systems for up-to-date ENSOmonitoring and SI forecasting. In particular, it describes efforts to improve the Temperature-Salinity (T-S) balance in the assimilation results over the past decade. We then introduce the ocean data assimilation system used in the JMA seasonal and ENSO forecasting system as a state-of-the-art system in Section 3. Section 4 demonstrates the importance of assimilating ocean observation data through the ocean data assimilation system for ENSO and seasonal forecasting. Section 5 introduces the recent effort to resolve the coupled shock at JMA/MRI. This chapter is summarized in Section 6. Assimilating Ocean Observation Data for ENSO Monitoring and Forecasting