{"id":1071,"date":"2004-09-06T06:12:30","date_gmt":"2004-09-06T10:12:30","guid":{"rendered":"http:\/\/mseas.net16.net\/?p=1071"},"modified":"2021-08-16T21:12:20","modified_gmt":"2021-08-17T01:12:20","slug":"adaptive-coupled-physical-and-biogeochemical-ocean-predictions-a-conceptual-basis-2","status":"publish","type":"post","link":"https:\/\/mseas.mit.edu\/?p=1071","title":{"rendered":"Adaptive Coupled Physical and Biogeochemical Ocean Predictions: A Conceptual Basis"},"content":{"rendered":"Physical and biogeochemical ocean dynamics can be intermittent\r\nand highly variable, and involve interactions on multiple scales.\r\nIn general, the oceanic fields, processes and interactions that matter thus\r\nvary in time and space. For efficient forecasting, the structures and parameters\r\nof models must evolve and respond dynamically to new data injected\r\ninto the executing prediction system. The conceptual basis of this\r\nadaptive modeling and corresponding computational scheme is the subject\r\nof this presentation. Specifically, we discuss the process of adaptive\r\nmodeling for coupled physical and biogeochemical ocean models. The\r\nadaptivity is introduced within an interdisciplinary prediction system.\r\nModel-data misfits and data assimilation schemes are used to provide\r\nfeedback from measurements to applications and modify the runtime behavior\r\nof the prediction system. Illustrative examples in Massachusetts\r\nBay and Monterey Bay are presented to highlight ongoing progress.","protected":false},"excerpt":{"rendered":"<p>Physical and biogeochemical ocean dynamics can be intermittent and highly variable, and involve interactions on multiple scales. In general, the oceanic fields, processes and interactions that matter thus vary in time and space. For efficient forecasting, the structures and parameters of models must evolve and respond dynamically to new data injected into the executing prediction [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[182,37,34,36,39,5,61,62,58],"tags":[],"class_list":["post-1071","post","type-post","status-publish","format-standard","hentry","category-learning-and-data-assimilation","category-applications-to-ocean-dynamics","category-data-assimilation","category-scientific-ml-deep-learning-bayesian-adaptive-modeling","category-biogeochemical-physical-interactions","category-publications","category-papers-in-refereed-journals-scientific-ml","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\/1071","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=1071"}],"version-history":[{"count":2,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/posts\/1071\/revisions"}],"predecessor-version":[{"id":1142,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/posts\/1071\/revisions\/1142"}],"wp:attachment":[{"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1071"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1071"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1071"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}