The final, formatted version of the article will be published soon.
METHODS article
Front. Mar. Sci.
Sec. Ocean Observation
Volume 11 - 2024 |
doi: 10.3389/fmars.2024.1443284
Promoting Best Practices in Ocean Forecasting through an Operational Readiness Level
Provisionally accepted- 1 Mercator Ocean International, Toulouse, France
- 2 Nologin Oceanic Weather Systems, Madrid, Spain
- 3 IEEE France Section, Paris, France
- 4 Met Office Hadley Centre (MOHC), Exeter, England, United Kingdom
- 5 Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Nantes, Pays de la Loire, France
- 6 Nvidia (United States), Santa Clara, California, United States
- 7 Nansen Environmental and Remote Sensing Center (NERSC), Bergen, Hordaland, Norway
- 8 Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Kanagawa, Japan
- 9 Bureau of Meteorology, Melbourne, Australia
- 10 European Marine Observation and Data Network (EMODnet), Brussels, Belgium
- 11 Royal Belgian Institute of Natural Sciences, Brussels, Belgium
- 12 Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, United States
- 13 Department of Meteorology, Institute of Geosciences, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- 14 CMCC Foundation - Euro-Mediterranean Center on Climate Change, Bologna, Italy
- 15 Data Science and Water Quality, Deltares (Netherlands), Delft, Netherlands
- 16 Dipartimento di Oceanografia, Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Trieste, Friuli-Venezia Giulia, Italy
- 17 InfraScience Lab, Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo", Dipartimento di Ingegneria, ICT e Tecnologie per l'Energia e i Trasporti, National Research Council (CNR), Roma, Lazio, Italy
- 18 Institute for Marine and Antarctic Studies, Oceans and Cryosphere, University of Tasmania, Hobart, Tasmania, Australia
- 19 North Atlantic Fisheries Center, Oceanography Department, Fisheries and Oceans Canada, St. John's, Canada
- 20 Department of Oceanography, University of Cape Town, Cape Town, South Africa
- 21 Department of Oceanography, Dalhousie University, Halifax, Canada
- 22 Bureau of Meteorology (Australia), Melbourne, Victoria, Australia
- 23 Oden Institute for Computational Engineering and Sciences, University of texas at Austin, Austin, United States
- 24 Institut de Recherche Pour le Développement (IRD), Marseille, Provence-Alpes-Côte d'Azur, France
- 25 Stennis Space Center, National Centers for Environmental Information, National Oceanic and Atmospheric Administration (NOAA), Asheville, North Carolina, United States
- 26 Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- 27 Indian National Centre for Ocean Information Services (INCOIS), Telangana, India
- 28 Marine Systems Modelling, National Oceanography Center, Southampton, United Kingdom
- 29 Opennebula Systems, Madrid, Spain
- 30 CMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, Italy
- 31 Meteorological Research Division, Environment and Climate Change Canada, Québec, Canada
- 32 Decade Coordinating Office - Ocean Observing, Intergovernmental Oceanographic Commission of UNESCO (IOC-UNESCO), Paris, France
- 33 Physical & Biological Sciences Division, Ocean Sciences Department Institute of Marine Sciences, Institute of Marine Sciences, University of California, Santa Cruz, Santa Cruz, California, United States
- 34 ETT – People and Technology, Genoa, Italy
- 35 IBM Research (Ireland), Dublin, Ireland
- 36 Science and Technology Facilities Council, The Hartree Centre, STFC Daresbury Laboratory, Warrington, United Kingdom
- 37 First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China
- 38 Nansen Environmental and Remote Sensing Center, Bergen, Hordaland, Norway
- 39 CSIRO Environment, Tasmania, Australia
- 40 Data, Science and Technology, National Oceanography Center, Southampton, United Kingdom
- 41 Institute of Coastal Systems - Analysis and Modeling, Helmholtz Center Hereon, Helmholtz Association of German Research Centres (HZ), Geesthacht, Hamburg, Germany
- 42 Hakai Institute, Heriot Bay, British Columbia, Canada
- 43 Egagasini Node, South African Environmental Observation Network (SAEON), Pretoria, South Africa
- 44 National Marine Environmental Forecasting Center, Beijing, China
- 45 Department of Marine and Coastal Sciences, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, United States
Predicting the ocean state in a reliable and interoperable way, while ensuring high-quality products, requires forecasting systems that synergistically combine science-based methodologies with advanced technologies for timely, user-oriented solutions. Achieving this objective necessitates the adoption of best practices when implementing ocean forecasting services, resulting in the proper design of system components and the capacity to evolve through different levels of complexity. The vision of OceanPrediction Decade Collaborative Center, endorsed by the UN Decade of Ocean Science for Sustainable Development 2021-2030, is to support this challenge by developing a "predicted ocean based on a shared and coordinated global effort" and by working within a collaborative framework that encompasses worldwide expertise in ocean science and technology. To measure the capacity of ocean forecasting systems, the OceanPrediction Decade Collaborative Center proposes a novel approach based on the definition of an Operational Readiness Level (ORL). This approach is designed to guide and promote the adoption of best practices by qualifying and quantifying the overall operational status. Considering three identified operational categoriesproduction, validation, and data dissemination -the proposed ORL is computed through a cumulative scoring system. This method is determined by fulfilling specific criteria, starting from a given base level and progressively advancing to higher levels. The goal of ORL and the computed scores per operational category is to support ocean forecasters in using and producing ocean data, information, and knowledge. This is achieved through systems that attain progressively higher levels of readiness, accessibility, and interoperability by adopting best practices that will be linked to the future design of standards and tools. This paper discusses examples of the application of this methodology, concluding on the advantages of its adoption as a reference tool to encourage and endorse services in joining common frameworks.
Keywords: Operational Oceanography, Ocean predictions, ocean observations, best practices, Standards, data sharing
Received: 03 Jun 2024; Accepted: 17 Oct 2024.
Copyright: © 2024 Alvarez Fanjul, Ciliberti, Pearlman, Wilmer-Becker, Bahurel, Ardhuin, Arnaud, Azizzadenesheli, Aznar, Bell, Bertino, Behera, Brassington, Calewaert, Capet, Chassignet, Ciavatta, Cirano, Clementi, Cornacchia, Cossarini, Coro, Corney, Davidson, Drevillon, Drillet, Dussurget, El Serafy, Fearon, Fennel, Ford, Le Galloudec, Huang, Lellouche, Heimbach, Hernandez, Hogan, Hoteit, Joseph, Josey, Le Traon, Libralato, Mancini, Martin, Matte, McConnell, Melet, Miyazawa, Moore, Novellino, O Donncha, Porter, Qiao, Regan, Robert-Jones, Sanikommu, Schiller, Siddorn, Sotillo, Staneva, Thomas-Courcoux, Thupaki, Tonani, Valdecasas, Veitch, Von Schuckmann, Wan, Wilkin, Zhong and Zufic. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Enrique Alvarez Fanjul, Mercator Ocean International, Toulouse, France
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.