@article{fdi:010092262,
  title = {{P}romoting best practices in ocean forecasting through an {O}perational {R}eadiness {L}evel},
  author = {{F}anjul, {E}. {A}. and {C}iliberti, {S}. and {P}earlman, {J}. and {W}ilmer-{B}ecker, {K}. and {B}ahurel, {P}. and {A}rdhuin, {F}. and {A}rnaud, {A}. and {A}zizzadenesheli, {K}. and {A}znar, {R}. and {B}ell, {M}. and {B}ertino, {L}. and {B}ehera, {S}. and {B}rassington, {G}. and {C}alewaert, {J}. {B}. and {C}apet, {A}. and {C}hassignet, {E}. and {C}iavatta, {S}. and {C}irano, {M}. and {C}lementi, {E}. and {C}ornacchia, {L}. and {C}ossarini, {G}. and {C}oro, {G}. and {C}orney, {S}. and {D}avidson, {F}. and {D}revillon, {M}. and {D}rillet, {Y}. and {D}ussurget, {R}. and {E}l {S}erafy, {G}. and {F}earon, {G}. and {F}ennel, {K}. and {F}ord, {D}. and {L}e {G}alloudec, {O}. and {H}uang, {X}. and {L}ellouche, {J}. {M}. and {H}eimbach, {P}. and {H}ernandez, {F}abrice and {H}ogan, {P}. and {H}oteit, {I}. and {J}oseph, {S}. and {J}osey, {S}. and {L}e {T}raon, {P}. {Y}. and {L}ibralato, {S}. and {M}ancini, {M}. and {M}artin, {M}. and {M}atte, {P}. and {M}c{C}onnell, {T}. and {M}elet, {A}. and {M}iyazawa, {Y}. and {M}oore, {A}. {M}. and {N}ovellino, {A}. and {O}'{D}onncha, {F}. and {P}orter, {A}. and {Q}iao, {F}. and {R}egan, {H}. and {R}obert-{J}ones, {J}. and {S}anikommu, {S}. and {S}chiller, {A}. and {S}iddorn, {J}. and {S}otillo, {M}. {G}. and {S}taneva, {J}. and {T}homas-{C}ourcoux, {C}. and {T}hupaki, {P}. and {T}onani, {M}. and {V}aldecasas, {J}. {M}. {G}. and {V}eitch, {J}. and von {S}chuckmann, {K}. and {W}an, {L}. and {W}ilkin, {J}. and {Z}hong, {A}. and {Z}ufic, {R}.},
  editor = {},
  language = {{ENG}},
  abstract = {{P}redicting 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. {A}chieving 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. {T}he vision of {O}cean{P}rediction {D}ecade {C}ollaborative {C}enter, endorsed by the {UN} {D}ecade of {O}cean {S}cience for {S}ustainable {D}evelopment 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. {T}o measure the capacity of ocean forecasting systems, the {O}cean{P}rediction {D}ecade {C}ollaborative {C}enter proposes a novel approach based on the definition of an {O}perational {R}eadiness {L}evel ({ORL}). {T}his approach is designed to guide and promote the adoption of best practices by qualifying and quantifying the overall operational status. {C}onsidering three identified operational categories - production, validation, and data dissemination - the proposed {ORL} is computed through a cumulative scoring system. {T}his method is determined by fulfilling specific criteria, starting from a given base level and progressively advancing to higher levels. {T}he goal of {ORL} and the computed scores per operational category is to support ocean forecasters in using and producing ocean data, information, and knowledge. {T}his 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. {T}his 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 ; interoperability ; digital twins},
  booktitle = {},
  journal = {{F}rontiers in {M}arine {S}cience},
  volume = {11},
  numero = {},
  pages = {1443284 [18 ]},
  year = {2024},
  DOI = {10.3389/fmars.2024.1443284},
  URL = {https://www.documentation.ird.fr/hor/fdi:010092262},
}