Promoting best practices in ocean forecasting through an Operational Readiness Level

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 categories - production, 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.

Authors

Institutions

Publication Details

Journal
Frontiers in Marine Science
Published
2024-11-18
DOI
https://doi.org/10.3389/fmars.2024.1443284
Citations
10
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
3.47

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Promoting best practices in ocean forecasting through an Operational Readiness Level

Katja Fennel, Fearghal O’Donncha, Pierre‐Yves Le Traon, Marco Mancini et al.
10 citations
Frontiers in Marine Science
Geochemistry and Geologic Mapping
3.47
article

Promoting best practices in ocean forecasting through an Operational Readiness Level

Katja Fennel, Fearghal O’Donncha, Pierre‐Yves Le Traon, Marco Mancini, Gianpaolo Coro, P. Bahurel, SP Corney, A. Schiller, Karina von Schuckmann, Sivareddy Sanikommu, Stefania Angela Ciliberti, Marie Drévillon, Fraser Davidson, Yann Drillet, Jay Pearlman, Andrew Porter, Laurent Bertino, Jean‐Michel Lellouche, Fangli Qiao, Pascal Matte, Marina Tonani, Kamyar Azizzadenesheli, John Siddorn, Arthur Capet, Gary B. Brassington, Fabrice Ardhuin, Emanuela Clementi, M. Cirano, Eric P. Chassignet, T. Eric McConnell, Jose Maria Garcia Valdecasas, Gianpiero Cossarini, O. Le Galloudec, Jan-Bart Calewaert, M. G. Sotillo, Felipe Hernández, Loreta Cornacchia, Kirsten Wilmer-Becker, Simone Libralato, Aihong Zhong, Cécile Thomas-Courcoux, M. J. Bell, John Wilkin, Andrew M. Moore, Yoshiyuki Miyazawa, David Ford, SK Behera, Renaud Dussurget, Antonio Novellino, Xi Huang, R. Aznar, Joanna V. Staneva, Simon A. Josey, Pramod Thupaki, Sudheer Joseph, Matthew Martin, Giles Fearon, Stefano Ciavatta, Ibrahim Hoteit, Heather Regan, P. Heimbach, Romane Zufic, Ling Wan, Jennifer Veitch, Ghada El Serafy, A. Arnaud, Angelique Melet, Enrique Álvarez Fanjul, Patrick Hogan, J. Robert-Jones
article en
10 citations

Abstract

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 categories - production, 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.

Frontiers in Marine ScienceVol. 11
Rutgers, The State University of New Jersey (US), Florida State University (US), Universidade Federal do Rio de Janeiro (BR), Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (IT), Bureau of Meteorology (AU), Commonwealth Scientific and Industrial Research Organisation (AU), Met Office (GB), UNESCO (FR), University of Tasmania (AU), Dalhousie University (CA), National Oceanic and Atmospheric Administration (US), Japan Agency for Marine-Earth Science and Technology (JP), Fisheries and Oceans Canada (CA), Université Libre de Bruxelles (BE), Indian National Centre for Ocean Information Services (IN), Environment and Climate Change Canada (CA), Ifremer (FR), University of Cape Town (ZA), Science and Technology Facilities Council (GB), University of California, Santa Cruz (US), National Oceanography Centre (GB), Ministry of Natural Resources (CN), Institute of Natural Sciences (BE), Daresbury Laboratory (GB), Stennis Space Center (US), Institute of Electrical and Electronics Engineers (US), Tula Foundation (CA), Intergovernmental Oceanographic Commission of UNESCO (FR), Deltares (NL), CMCC Foundation - Euro-Mediterranean Center on Climate Change (IT), Laboratoire d’Études en Géophysique et Océanographie Spatiales (FR), First Institute of Oceanography (CN), Mercator Ocean (France) (FR), Nvidia (United States) (US), Institut d'écologie et des sciences de l'environnement de Paris (FR), Helmholtz-Zentrum Hereon (DE), South African Environmental Observation Network (ZA), IBM Research - Ireland (IE), National Marine Environmental Forecasting Center (CN), Exeter Hospital (GB), National Institute of Oceanography and Applied Geophysics (IT), Institut de Recherche pour le Développement (FR), Hakai Institute, Nansen Environmental and Remote Sensing Center (NO), King Abdullah University of Science and Technology (SA), The University of Texas at Austin (US)
Sight Research UK, Natural Environment Research Council
Openalex Percentile: Top 6%
Geochemistry and Geologic Mapping
3.47
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.