Automatic Matching Method for Ocean Observation Elements Based on Edge Computing

Over the past two years, the UNH Coastal Observing Center and the Gulf of Maine Ocean Observing System (GoMOOS) have co-sponsored two week-long teacher workshops that offer curriculum content and hands-on experience in using ocean observation data as a successful teaching tool in the classroom. Teachers ranging from grammar school to high school levels worked to create practical lessons that incorporate ocean observing data into their current classroom settings and are using it today. This paper will explain how the UNH/GOMOOS summer Educator Institutes have been run, what teachers have been learning in these workshops, and how they planned and started using ocean observing data in their classrooms.

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.

The Caribbean Coastal Ocean Observing System (CariCOOS) is the observing arm of the Caribbean Regional Association for Integrated Coastal Ocean Observing (CaRA). This effort, funded by the NOAA Integrated Ocean Observing System (IOOS) office, is one of eleven coastal observing systems and regional associations which along with federal agencies constitute the national coastal component of the US Integrated Ocean Observing System. CariCOOS observational assets provide a real time network to monitor wind, waves and currents and consists of four ocean data buoys and thirteen hurricane-hardened land based weather stations, distributed along the coastal areas of Puerto Rico and the US Virgin Islands. In this paper we present a detailed description of our observational infrastructure and provide analysis of wind and wave observations during the passage of Hurricane Irene over Puerto Rico and the US Virgin Islands during August 21-22, 2011.

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Tanhua, T., Pouliquen, S., Hausman, J., O'Brien, K., Bricher, P., de Bruin, T., Buck, J. J. H., Burger, E. F., Carval, T., Casey, K. S., Diggs, S., Giorgetti, A., Glaves, H., Harscoat, V., Kinkade, D., Muelbert, J. H., Novellino, A., Pfeil, B., Pulsifer, P. L., Van de Putte, A., Robinson, E., Schaap, D., Smirnov, A., Smith, N., Snowden, D., Spears, T., Stall, S., Tacoma, M., Thijsse, P., Tronstad, S., Vandenberghe, T., Wengren, M., Wyborn, L., & Zhao, Z. Ocean FAIR data services. Frontiers in Marine Science, 6, (2019): 440, doi:10.3389/fmars.2019.00440.

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.

In this study, the accuracy of ocean analysis data, which are produced from the Korea Meteorological Administration (KMA) Nucleus for European Modelling of the Ocean/Variational Data Assimilation (NEMO/NEMOVAR, hereafter NEMO) system and the HYbrid Coordinate Ocean Model/Navy Coupled Ocean Data Assimilation (HYCOM/NCODA, hereafter HYCOM) system, was evaluated using various oceanic observation data from March 2015 to February 2016. The evaluation was made for oceanic thermal environments in the tropical Pacific, the western North Pacific, and the Korean peninsula. NEMO generally outperformed HYCOM in the three regions. Particularly, in the tropical Pacific, the RMSEs (Root Mean Square Errors) of NEMO for both the sea surface temperature and vertical water temperature profile were about 50% smaller than those of HYCOM. In the western North Pacific, in which the observational data were not used for data assimilation, the RMSE of NEMO profiles up to 1000 m (0.49℃) was much lower than that of HYCOM (0.73℃). Around the Korean peninsula, the difference in RMSE between the two models was small (NEMO, 0.61oC; HYCOM, 0.72oC), in which their errors show relatively big in the winter and small in the summer. The differences reported here in the accuracy between NEMO and HYCOM for the thermal environments may be attributed to horizontal and vertical resolutions of the models, vertical coordinate and mixing scheme, data quality control system, data used for data assimilation, and atmosphere forcing. The present results can be used as a basic data to evaluate the accuracy of NEMO, before it becomes the operational model of the KMA providing real-time ocean analysis and prediction data.

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.

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.

In recent years, network attacks have occurred frequently, and Trojan horses, worms, and ransomware have emerged endlessly on the Internet. This poses a serious threat to cyber security and even national security. Therefore, a new three-branch decision soft increment K-nearest neighbor algorithm is proposed, representing the class cluster as an interval set. The interval set’s upper, boundary, and lower bound correspond to the positive, boundary, and negative domains generated by the three-branch decision. An initial K-nearest neighbor algorithm based on representative points is proposed. The new datasets are pre-clustered in the same way to eliminate the influence of data processing order on the final clustering results. The results showed that the improved K-nearest algorithm could accurately classify Normal, Dos, and Probing, and the classification accuracy was above 85%. The accuracy of U2R was lower, with an average of 57.32%. The proposed method had the highest classification accuracy on the dataset, and the detection rate of all types of attacks reached more than 98%. This proposal is of great significance for the protection of network security.

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.

ABSTRACTIn the last 10 years, new monitoring and modelling technologies have emerged allowing real-time observation and forecasting of the coastal ocean at regional and local scales. These technologies are at the core of multi-platform integrated observing and forecasting systems, such as the Balearic Islands Coastal Ocean Observing and Forecasting System (SOCIB). New capabilities to characterise the state of the ocean and its variability at finer spatial and temporal scales are emerging, supporting science and products for society.SOCIB has a well-defined mission to deliver ocean observing for the benefit of science and society. From initiation in 2010, SOCIB has provided high-quality, free and open data. In order to increase our utility, we developed a Products and Services Strategy rooted in business best practice. Ten sectors – groups of users with common data needs – were identified, for which SOCIB has information and knowledge of high value. Dedicated products were developed in cooperation with the end-users: beach lifeguards/managers and sustainable marine resources managers. We illustrate an integrative approach, combining business concepts with collaborative software development methodologies and ocean observing science, to turn ocean observations and forecasts into products and services, with benefits for society in the sustainable blue economy era.

Due to the harsh conditions of the ocean observation environment and the influence of many uncertain factors, data anomalies often appear in the ocean observation system. Therefore, how to detect and diagnosis data anomalies and take corresponding measures has become an urgent problem to be solved. In this paper, a novel method based on deep learning is presented for detecting and diagnosing the data anomalies of ocean observation system, which combines convolution neural network (CNN) and bidirectional long short-term memory recurrent neural network (BiLSTM) with attention mechanism (CBLA for short). By constructing a prediction model based on the CBLA, the short-term trend of ocean observation data is predicted, and the error threshold is set to realize the detection of data anomalies. In addition, through the discussion of different types of data anomalies, the causes of data anomalies are analyzed, and the diagnosis of data anomalies is actively explored. The experimental results show the accuracy of the prediction model and the effectiveness of the overall data anomalies detection and diagnosis algorithm.

Within the past 20 years, the international Argo program has acquired more than 2 million temperature and salinity profiles throughout the global ocean. It has become the most efficient means of obtaining ocean observations from upper–intermediate layers. The profile data provided by the Argo Global Data Assembly Centres (GDACs) are submitted by 11 data centres in 9 countries after undergoing quality control. However, because the quality of the submitted data lacks uniformity, users must conduct post-quality-control processing prior to using the data. For this purpose, the China Argo Real-Time Data Centre (CARDC) has developed a system to achieve both rapid global Argo data reception and post-quality-control processing of all temperature and salinity profiles. Already in operation, the system allows daily transfer of global post-quality-controlled Argo data to various operating divisions. The system synchronizes with the GDAC server once each day, extracts all updated Argo profiles, and then automatically reads the data and performs a post-quality-control process comprising 15 quality control tests. The automatic post-quality-control system can detect errors in observation time and satellite fix, as well as identify temperature and salinity spikes, frozen profiles, density inversions and salinity drift/offset. The data quality following the post-quality-control processing has been shown improved effectively. In the future, it is expected that CARDC will update the Argo global ocean observational data set once a quarter.