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Synthesis of ocean observations using data assimilation for operational, real-time and reanalysis systems: A more complete picture of the state of the ocean

Marine observations map from JCOMMOPS
TitleSynthesis of ocean observations using data assimilation for operational, real-time and reanalysis systems: A more complete picture of the state of the ocean
Publication TypeJournal Article
Year of Publication2019
AuthorsMoore A.M, Martini M.J, Akella S., Arango H.G, Balmaseda M., Bertino L., Ciavatta S., Cornuelle B., Cummings J., Frolov S., Lermusiaux P., Oddo P., Oke P.R, Storto A., Teruzzi A., Vidard A., Weaver A.T, Assimilation GOceanView
JournalFrontiers in Marine Science
Date Published2019/03
Type of ArticleReview
Accession NumberWOS:000462673500001
Keywordsbias; Calculus of variations; Data assimilation; Ensembles; Environmental Sciences & Ecology; error; filter; hybrid; impact; implementation; Kalman filters; Marine & Freshwater Biology; model; modeling; scheme

Ocean data assimilation is increasingly recognized as crucial for the accuracy of real-time ocean prediction systems and historical re-analyses. The current status of ocean data assimilation in support of the operational demands of analysis, forecasting and reanalysis is reviewed, focusing on methods currently adopted in operational and real-time prediction systems. Significant challenges associated with the most commonly employed approaches are identified and discussed. Overarching issues faced by ocean data assimilation are also addressed, and important future directions in response to scientific advances, evolving and forthcoming ocean observing systems and the needs of stakeholders and downstream applications are discussed.


There are many exciting new directions and future opportunities for discovery in ocean DA [data assimilation]. Perhaps the most immediate development borrowed from NWP [numerical weather prediction] is the merger of ensemble and variational methods that draws on the strengths of both approaches. Specifically, the static estimate of P used in Var and the flow-dependent estimate of P from an ensemble are combined to form a “hybrid” P that is employed in a DA system (e.g., Lorenc et al., 2015). In this way, the dynamical interpolation properties of the adjoint and the flow-dependent covariance information from the ensemble are simultaneously exploited. Experience in NWP suggests that the performance of hybrid approaches can improve the performance of an analysis-forecast system (Lorenc and Jardak, 2018), and efforts are underway to develop similar procedures for global (e.g., Penny et al., 2015; Frolov et al., 2016; Storto et al., 2018) and regional (e.g., Oddo et al., 2016) ocean prediction systems.

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