{"id":943,"date":"2003-09-06T05:08:49","date_gmt":"2003-09-06T09:08:49","guid":{"rendered":"http:\/\/mseas.net16.net\/?p=943"},"modified":"2021-08-16T21:17:08","modified_gmt":"2021-08-17T01:17:08","slug":"the-use-of-data-assimilation-in-coupled-hydrodynamic-ecological-and-bio-geo-chemical-models-of-the-ocean","status":"publish","type":"post","link":"https:\/\/mseas.mit.edu\/?p=943","title":{"rendered":"The use of data assimilation in coupled hydrodynamic, ecological and bio-geo-chemical models of the ocean"},"content":{"rendered":"The International Lie`ge Colloquium on Ocean\r\nDynamics is organized annually. The topic differs\r\nfrom year to year in an attempt to address, as much\r\nas possible, recent problems and incentive new subjects\r\nin oceanography.\r\nAssembling a group of active and eminent scientists\r\nfrom various countries and often different disciplines,\r\nthe Colloquia provide a forum for discussion\r\nand foster a mutually beneficial exchange of information\r\nopening on to a survey of recent discoveries,\r\nessential mechanisms, impelling question marks and\r\nvaluable recommendations for future research.\r\nThe objective of the 2001 Colloquium was to\r\nevaluate the progress of data assimilation methods in\r\nmarine science and, in particular, in coupled hydrodynamic,\r\necological and bio-geo-chemical models of\r\nthe ocean.\r\nThe past decades have seen important advances\r\nin the understanding and modelling of key processes\r\nof the ocean circulation and bio-geo-chemical\r\ncycles. The increasing capabilities of data and\r\nmodels, and their combination, are allowing the\r\nstudy of multidisciplinary interactions that occur\r\ndynamically, in multiple ways, on multiscales and\r\nwith feedbacks.\r\nThe capacity of dynamical models to simulate interdisciplinary\r\nocean processes over specific space-\r\ntime windows and thus forecast their evolution over\r\npredictable time scales is also conditioned upon the\r\navailability of relevant observations to: initialise and\r\ncontinually update the physical and bio-geo-chemical\r\nsectors of the ocean state; provide relevant atmospheric\r\nand boundary forcing; calibrate the parameterizations\r\nof sub-grid scale processes, growth rates and\r\nreaction rates; construct interdisciplinary and multiscale\r\ncorrelation and feature models; identify and\r\nestimate the main sources of errors in the models;\r\ncontrol or correct for mis-represented or neglected\r\nprocesses.\r\nThe access to multivariate data sets requires the\r\nimplementation, exploitation and management of dedicated\r\nocean observing and prediction systems. However,\r\nthe available data are often limited and, for\r\ninstance, seldom in a form to be directly compatible\r\nor directly inserted into the numerical models. To relate\r\nthe data to the ocean state on all scales and regions that\r\nmatter, evolving three-dimensional and multivariate\r\n(measurement) models are becoming important.\r\nEqually significant is the reduction of observational\r\nrequirements by design of sampling strategies via\r\nObservation System Simulation Experiments and\r\nadaptive sampling.\r\nData assimilation is a quantitative approach to\r\nextract adequate information content from the data\r\nand to improve the consistency between data sets and\r\nmodel estimates. It is also a methodology to dynamically\r\ninterpolate between data scattered in space and\r\ntime, allowing comprehensive interpretation of multivariate\r\nobservations.\r\nIn general, the goals of data assimilation are to:\r\ncontrol the growth of predictability errors; correct\r\ndynamical deficiencies; estimate model parameters,\r\nincluding the forcings, initial and boundary conditions;\r\ncharacterise key processes by analysis of four-\r\n0924-7963\/03\/$ &#8211; see front matter D 2003 Elsevier Science B.V. All rights reserved.\r\ndoi:10.1016\/S0924-7963(03)00027-7\r\nwww.elsevier.com\/locate\/jmarsys\r\nThe use of data assimilation in coupled hydrodynamic, ecological and\r\nbio-geo-chemical models of the ocean\r\nJournal of Marine Systems 40-41 (2003) 1-3\r\ndimensional fields and their statistics (balances of\r\nterms, etc.); carry out advanced sensitivity studies\r\nand Observation System Simulation Experiments,\r\nand conduct efficient operations, management and\r\nmonitoring.\r\nThe theoretical framework of data assimilation\r\nfor marine sciences is now relatively well established,\r\nrouted in control theory, estimation theory or inverse\r\ntechniques, from variational to sequential approaches.\r\nOngoing research efforts of special importance for\r\ninterdisciplinary applications include the: stochastic\r\nrepresentation of processes and determination of\r\nmodel and data errors; treatment of (open) boundary\r\nconditions and strong nonlinearities; space-time,\r\nmultivariate extrapolation of limited and noisy data\r\nand determination of measurement models; demonstration\r\nthat bio-geo-chemical models are valid\r\nenough and of adequate structures for their deficiencies\r\nto be controlled by data assimilation; and finally,\r\nability to provide accurate estimates of fields, parameters,\r\nvariabilities and errors, with large and complex\r\ndynamical models and data sets.\r\nOperationally, major engineering and computational\r\nchallenges for the coming years include the:\r\ndevelopment of theoretically sound methods into\r\nuseful, practical and reliable techniques at affordable\r\ncosts; implementation of scalable, seamless and automated\r\nsystems linking observing systems, numerical\r\nmodels and assimilation schemes; adequate mix of\r\nintegrated and distributed (Web-based) networks; construction\r\nof user-friendly architectures and establishment\r\nof standards for the description of data and\r\nsoftware (metadata) for efficient communication, dissemination\r\nand management.\r\nIn addition to addressing the above items, the 33rd\r\nLie`ge Colloquium has offered the opportunity to:\r\n&#8211; review the status and current progress of data\r\nassimilation methodologies utilised in the physical,\r\nacoustical, optical and bio-geo-chemical\r\nscientific communities;\r\n&#8211; demonstrate the potentials of data assimilation\r\nsystems developed for coupled physical\/ecosystem\r\nmodels, from scientific to management inquiries;\r\n&#8211; examine the impact of data assimilation and\r\ninverse modelling in improving model parameterisations;\r\n&#8211; discuss the observability and controllability properties\r\nof, and identify the missing gaps in current\r\nobserving and prediction systems; and\r\n exchange the results of and the learnings from preoperational\r\nmarine exercises.\r\nThe presentations given during the Colloquium\r\nlead to discussions on a series of topics organized\r\nwithin the following sections: (1) Interdisciplinary\r\nresearch progress and issues: data, models, data\r\nassimilation criteria. (2) Observations for interdisciplinary\r\ndata assimilation. (3) Advanced fields estimation\r\nfor interdisciplinary systems. (4) Estimation of\r\ninterdisciplinary parameters and model structures. (5)\r\nAssimilation methodologies for physical and interdisciplinary\r\nsystems. (6) Toward operational interdisciplinary\r\noceanography and data assimilation. A subset\r\nof these presentations is reported in the present\r\nSpecial Issue.\r\nAs was pointed out during the Colloquium, coupled\r\nbiological-physical data assimilation is in its infancy\r\nand much can be accomplished now by the immediate\r\napplication of existing methods. Data assimilation\r\nintimately links dynamical models and observations,\r\nand it can play a critical role in the important area of\r\nfundamental biological oceanographic dynamical\r\nmodel development and validation over a hierarchy\r\nof complexities. Since coupled assimilation for coupled\r\nprocesses is challenging and can be complicated, care\r\nmust be exercised in understanding, modeling and\r\ncontrolling errors and in performing sensitivity analyses\r\nto establish the robustness of results. Compatible\r\ninterdisciplinary data sets are essential and data assimilation\r\nshould iteratively define data impact and data\r\nrequirements.\r\nBased on the results presented during the Colloquium,\r\ndata assimilation is expected to enable future\r\nmarine technologies and naval operations otherwise\r\nimpossible or not feasible. Interdisciplinary predictability\r\nresearch, multiscale in both space and time, is\r\nrequired. State and parameter estimation via data\r\nassimilation is central to the successful establishment\r\nof advanced interdisciplinary ocean observing and\r\nprediction systems which, functioning in real time,\r\nwill contribute to novel and efficient capabilities to\r\nmanage, and to operate in our oceans.\r\nThe Scientific Committee and the participants to\r\nthe 33rd Lie`ge Colloquium wish to express their\r\n2 Preface\r\ngratitude to the Ministe`re de l&#8217;Enseignement Supe&#8217;rieur\r\net de la Recherche Scientifique de la Communaute\r\n&#8211; Francaise de Belgique, the Fonds National de\r\nla Recherche Scientifique de Belgique (F.N.R.S.,\r\nBelgium), the Ministe`re de l&#8217;Emploi et de la Formation\r\ndu Gouvernement Wallon, the University of\r\nLie`ge, the Commission of European Union, the\r\nScientific Committee on Oceanographic Research\r\n(SCOR), the International Oceanographic Commission\r\nof the UNESCO, the US Office of Naval\r\nResearch, the National Science Foundation (NSF,\r\nUSA) and the International Association for the\r\nPhysical Sciences of the Ocean (IAPSO) for their\r\nmost valuable support.","protected":false},"excerpt":{"rendered":"<p>The International Lie`ge Colloquium on Ocean Dynamics is organized annually. The topic differs from year to year in an attempt to address, as much as possible, recent problems and incentive new subjects in oceanography. Assembling a group of active and eminent scientists from various countries and often different disciplines, the Colloquia provide a forum for [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[182,37,34,38,39,5,59,62,58],"tags":[],"class_list":["post-943","post","type-post","status-publish","format-standard","hentry","category-learning-and-data-assimilation","category-applications-to-ocean-dynamics","category-data-assimilation","category-physical-oceanography","category-biogeochemical-physical-interactions","category-publications","category-papers-in-refereed-journals-physical-oceanography","category-papers-in-refereed-journals-data-assimilation","category-papers-in-refereed-journals-biogeochemical-physical-interactions"],"_links":{"self":[{"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/posts\/943","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=943"}],"version-history":[{"count":5,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/posts\/943\/revisions"}],"predecessor-version":[{"id":5732,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/posts\/943\/revisions\/5732"}],"wp:attachment":[{"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=943"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=943"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=943"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}