{"corpus_id":122248060,"paper_sha":"1a84b30dc93be36da5d56559b8eea5418c417c4c","doi":"10.1016/S0377-0265(97)00014-6","arxiv_id":null,"pmid":null,"pmcid":null,"mag_id":2036661651,"dblp_id":null,"acl_id":null,"title":"Adaptive filtering: application to satellite data assimilation in oceanography","year":1998,"publication_date":"1998-04-01","venue":"Oceanographic Literature Review","journal":{"name":"Oceanographic Literature Review","pages":null,"volume":null},"journal_issn":null,"journal_title":null,"publication_types":["JournalArticle"],"pubmed_pub_types":null,"s2_fields_of_study":["Physics","Computer Science","Environmental Science"],"reference_count":24,"citation_count":28,"influential_citation_count":1,"is_open_access":false,"arxiv_categories":null,"arxiv_license":null,"arxiv_journal_ref":null,"mesh_headings":null,"chemicals":null,"comments_corrections":null,"source_flags":1,"s2_open_access_pdf_url":null,"s2_open_access_landing_url":null,"s2_open_access_license":null,"s2_open_access_status":null,"pmc_open_access_pdf_url":null,"pmc_open_access_landing_url":null,"pmc_open_access_license":null,"pmc_open_access_status":null,"unpaywall_open_access_pdf_url":null,"unpaywall_open_access_landing_url":null,"unpaywall_open_access_license":null,"unpaywall_open_access_status":null,"abstract":"Abstract A new approach to assimilation of observations is proposed, which extends previous work on adaptive Kalman filtering. In the latter, the gain matrix of the filter was progressively determined without a priori explicit specification of the covariance matrices of the model or observation noise, so as to minimize the norm of the innovation vector. The new step taken here is to introduce a (relatively) low dimension parameterization of the gain matrix, thereby substantially decreasing the numerical cost of the filter. The reduced-order adaptive filter (ROAF) thus defined is tested on a simple diffusive-reactive equation, and implemented on the four-layer adiabatic Miami isopycnical coordinate ocean model (MICOM). In the latter case, the filter is used to assimilate synthetic observations of surface height. Both sets of experiments clearly show the efficiency of the proposed approach, and its superiority, in terms of the quality of the results, on Newtonian relaxation. In the case of the diffusive-reactive equation, the reduced-order adaptive filter is also superior to, and more economical than, a reduced non-adaptative Kalman filter.","claims":[{"public_id":"cl_d4e093dc63e3a8cf30f7acea8c19cfac","status":"active","text":"Introducing a low-dimensional parameterization of the gain matrix substantially decreases the numerical cost of the adaptive filter.","confidence":0.98,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_d4e093dc63e3a8cf30f7acea8c19cfac"},{"public_id":"cl_713833cab859e429133cf21b294cb42e","status":"active","text":"On the diffusive-reactive equation, the reduced-order adaptive filter is both superior to and more economical than a reduced non-adaptive Kalman filter.","confidence":0.95,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_713833cab859e429133cf21b294cb42e"},{"public_id":"cl_f909a86d7a70568995b2f9e319c3390e","status":"active","text":"The reduced-order adaptive filter performs efficiently on both a simple diffusive-reactive equation and the four-layer adiabatic Miami isopycnical coordinate ocean model.","confidence":0.93,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_f909a86d7a70568995b2f9e319c3390e"},{"public_id":"cl_fdf72883acff374b01d3afddfb727006","status":"active","text":"Using the reduced-order adaptive filter to assimilate synthetic surface-height observations in the ocean model yields better results than Newtonian relaxation.","confidence":0.96,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_fdf72883acff374b01d3afddfb727006"}],"concepts":[{"public_id":"co_1fc3f499fd60a33a70d746d51e995443","status":"active","name":"reduced non-adaptative Kalman filter","description":"A reduced-order Kalman filtering method without adaptive gain adjustment.","types":["baseline method"],"aliases":["reduced non-adaptive Kalman filter"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_1fc3f499fd60a33a70d746d51e995443"},{"public_id":"co_2e48575acfa73bdae72599f8170144d5","status":"active","name":"covariance matrices of the model or observation noise","description":"Matrices representing uncertainty in the model dynamics and observation errors.","types":["statistical quantity"],"aliases":["model noise covariance matrices","observation noise covariance matrices"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_2e48575acfa73bdae72599f8170144d5"},{"public_id":"co_42893285753bc2512e9178b40e4dcce1","status":"active","name":"gain matrix","description":"The matrix of filter gains that determines how observations are weighted in the update step.","types":["method component"],"aliases":["filter gain matrix"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_42893285753bc2512e9178b40e4dcce1"},{"public_id":"co_5a9a7f7458221e2b825daf8f520c5ba1","status":"active","name":"diffusive-reactive equation","description":"A simple test equation combining diffusion and reaction dynamics.","types":["test equation"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_5a9a7f7458221e2b825daf8f520c5ba1"},{"public_id":"co_6201cb7b3d17add2d347a984370c5745","status":"active","name":"low dimension parameterization","description":"A reduced representation of a matrix or parameter set using relatively few degrees of freedom.","types":["representation method"],"aliases":["low-dimensional parameterization"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_6201cb7b3d17add2d347a984370c5745"},{"public_id":"co_68f391716bdbd9546e0c455f1860a34b","status":"active","name":"synthetic observations of surface height","description":"Artificially generated measurements of sea-surface height used for assimilation experiments.","types":["observation data"],"aliases":["synthetic surface height observations"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_68f391716bdbd9546e0c455f1860a34b"},{"public_id":"co_6c4c395cfbe6003413f936cbb88cac7e","status":"active","name":"adaptive Kalman filtering","description":"A Kalman filtering approach in which the filter gain is adjusted adaptively rather than fixed from fully specified noise covariances.","types":["method"],"aliases":["adaptive Kalman filter"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_6c4c395cfbe6003413f936cbb88cac7e"},{"public_id":"co_95e3269b9fdc9c5b8a18bd37fb442392","status":"active","name":"Newtonian relaxation","description":"A relaxation-based data assimilation approach used as a comparison method.","types":["baseline"],"aliases":["nudging"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_95e3269b9fdc9c5b8a18bd37fb442392"},{"public_id":"co_d55eadb20d32f364a6eee50915fbdeac","status":"active","name":"Miami isopycnical coordinate ocean model","description":"A four-layer adiabatic ocean circulation model formulated in isopycnal coordinates.","types":["ocean model"],"aliases":["MICOM"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_d55eadb20d32f364a6eee50915fbdeac"},{"public_id":"co_edaa398dc2af0b1b5edcc11356e57557","status":"active","name":"innovation vector","description":"The mismatch between observations and model forecasts used to drive filter updates.","types":["signal"],"aliases":["innovation"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_edaa398dc2af0b1b5edcc11356e57557"},{"public_id":"co_f35b0c8688575b25e3b0cfa84ed851d8","status":"active","name":"reduced-order adaptive filter","description":"An adaptive filtering method defined by a low-dimensional parameterization of the gain matrix to lower computational cost.","types":["method"],"aliases":["ROAF"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_f35b0c8688575b25e3b0cfa84ed851d8"}],"external_ids":{"DOI":"10.1016/S0377-0265(97)00014-6","ArXiv":null,"PubMed":null,"PubMedCentral":null,"MAG":2036661651,"DBLP":null,"ACL":null},"open_access":{"is_open_access":false,"pdf_url":null,"landing_url":"https://sah.borca.ai/papers/122248060","source":null,"pdf_url_source":null,"license":null,"reason":"pdf_url_not_indexed"},"reference_availability":{"status":"available","references_indexed":true,"full_text_available":false,"full_text_source":null,"count_basis":"semantic_scholar_metadata","extraction_status":"not_applicable","reason":null},"source":{"provider":"episteme2","base_corpus":"semantic_scholar_dump","freshness_mode":"unknown","basis":["semantic_scholar_metadata","postgres_metadata"],"limits":["paper metadata is based on indexed upstream scholarly datasets","claims and concepts are available only for extracted papers","absence of claims or concepts means no extracted graph data is available in this response"],"status":"available","degraded":false,"degraded_reasons":[],"diagnostics":{"status":"available","degraded":false,"degraded_reasons":[],"metadata_status":"available","graph_status":"available","abstract_status":"available"},"source_flags":1},"paper_id":637826,"paper_uid":"777c232b-4dee-49d3-9f47-a5667f705fcb","canonical_identity":{"paper_id":637826,"paper_uid":"777c232b-4dee-49d3-9f47-a5667f705fcb","identity_status":"available","lookup_basis":"semantic_scholar_external_id","compatibility_path":"corpus_id"},"url":"https://sah.borca.ai/papers/122248060"}