FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2034997740> ?p ?o ?g. }

• W2034997740 endingPage "20331" @default.
• W2034997740 startingPage "20331" @default.
• W2034997740 abstract "A necessary ingredient of an ensemble Kalman filter (EnKF) is covariance inflation, used to control filter divergence and compensate for model error. There is an on-going search for inflation tunings that can be learned adaptively. Early in the development of Kalman filtering, Mehra (1970, 1972) enabled adaptivity in the context of linear dynamics with white noise model errors by showing how to estimate the model error and observation covariances. We propose an adaptive scheme, based on lifting Mehra's idea to the non-linear case, that recovers the model error and observation noise covariances in simple cases, and in more complicated cases, results in a natural additive inflation that improves state estimation. It can be incorporated into non-linear filters such as the extended Kalman filter (EKF), the EnKF and their localised versions. We test the adaptive EnKF on a 40-dimensional Lorenz96 model and show the significant improvements in state estimation that are possible. We also discuss the extent to which such an adaptive filter can compensate for model error, and demonstrate the use of localisation to reduce ensemble sizes for large problems." @default.
• W2034997740 created "2016-06-24" @default.
• W2034997740 creator A5022515123 @default.
• W2034997740 creator A5029217710 @default.
• W2034997740 date "2013-12-01" @default.
• W2034997740 modified "2023-10-16" @default.
• W2034997740 title "Adaptive ensemble Kalman filtering of non-linear systems" @default.
• W2034997740 cites W1987308763 @default.
• W2034997740 cites W1995262681 @default.
• W2034997740 cites W2009104157 @default.
• W2034997740 cites W2009701174 @default.
• W2034997740 cites W2021315116 @default.
• W2034997740 cites W2032182598 @default.
• W2034997740 cites W2049013344 @default.
• W2034997740 cites W2073142925 @default.
• W2034997740 cites W2076163510 @default.
• W2034997740 cites W2092220788 @default.
• W2034997740 cites W2095663925 @default.
• W2034997740 cites W2105934661 @default.
• W2034997740 cites W2119220952 @default.
• W2034997740 cites W2121990344 @default.
• W2034997740 cites W2123487311 @default.
• W2034997740 cites W2123940107 @default.
• W2034997740 cites W2132600099 @default.
• W2034997740 cites W2143030612 @default.
• W2034997740 cites W2146803308 @default.
• W2034997740 cites W2156064084 @default.
• W2034997740 cites W2157098139 @default.
• W2034997740 cites W2160444846 @default.
• W2034997740 cites W2161358768 @default.
• W2034997740 cites W2166317254 @default.
• W2034997740 cites W2168102396 @default.
• W2034997740 cites W2174784159 @default.
• W2034997740 cites W2178837886 @default.
• W2034997740 cites W2179584279 @default.
• W2034997740 cites W2794413559 @default.
• W2034997740 cites W4231204432 @default.
• W2034997740 cites W4239340087 @default.
• W2034997740 doi "https://doi.org/10.3402/tellusa.v65i0.20331" @default.
• W2034997740 hasPublicationYear "2013" @default.
• W2034997740 type Work @default.
• W2034997740 sameAs 2034997740 @default.
• W2034997740 citedByCount "56" @default.
• W2034997740 countsByYear W20349977402013 @default.
• W2034997740 countsByYear W20349977402014 @default.
• W2034997740 countsByYear W20349977402015 @default.
• W2034997740 countsByYear W20349977402016 @default.
• W2034997740 countsByYear W20349977402017 @default.
• W2034997740 countsByYear W20349977402018 @default.
• W2034997740 countsByYear W20349977402019 @default.
• W2034997740 countsByYear W20349977402020 @default.
• W2034997740 countsByYear W20349977402021 @default.
• W2034997740 countsByYear W20349977402022 @default.
• W2034997740 countsByYear W20349977402023 @default.
• W2034997740 crossrefType "journal-article" @default.
• W2034997740 hasAuthorship W2034997740A5022515123 @default.
• W2034997740 hasAuthorship W2034997740A5029217710 @default.
• W2034997740 hasBestOaLocation W20349977401 @default.
• W2034997740 hasConcept C102248274 @default.
• W2034997740 hasConcept C105795698 @default.
• W2034997740 hasConcept C106131492 @default.
• W2034997740 hasConcept C112633086 @default.
• W2034997740 hasConcept C11413529 @default.
• W2034997740 hasConcept C115961682 @default.
• W2034997740 hasConcept C119857082 @default.
• W2034997740 hasConcept C138885662 @default.
• W2034997740 hasConcept C151730666 @default.
• W2034997740 hasConcept C154945302 @default.
• W2034997740 hasConcept C157286648 @default.
• W2034997740 hasConcept C163175372 @default.
• W2034997740 hasConcept C178650346 @default.
• W2034997740 hasConcept C206833254 @default.
• W2034997740 hasConcept C207390915 @default.
• W2034997740 hasConcept C2775924081 @default.
• W2034997740 hasConcept C2779343474 @default.
• W2034997740 hasConcept C31972630 @default.
• W2034997740 hasConcept C33923547 @default.
• W2034997740 hasConcept C41008148 @default.
• W2034997740 hasConcept C41895202 @default.
• W2034997740 hasConcept C47446073 @default.
• W2034997740 hasConcept C76155785 @default.
• W2034997740 hasConcept C79334102 @default.
• W2034997740 hasConcept C8639503 @default.
• W2034997740 hasConcept C86803240 @default.
• W2034997740 hasConcept C99498987 @default.
• W2034997740 hasConceptScore W2034997740C102248274 @default.
• W2034997740 hasConceptScore W2034997740C105795698 @default.
• W2034997740 hasConceptScore W2034997740C106131492 @default.
• W2034997740 hasConceptScore W2034997740C112633086 @default.
• W2034997740 hasConceptScore W2034997740C11413529 @default.
• W2034997740 hasConceptScore W2034997740C115961682 @default.
• W2034997740 hasConceptScore W2034997740C119857082 @default.
• W2034997740 hasConceptScore W2034997740C138885662 @default.
• W2034997740 hasConceptScore W2034997740C151730666 @default.
• W2034997740 hasConceptScore W2034997740C154945302 @default.
• W2034997740 hasConceptScore W2034997740C157286648 @default.
• W2034997740 hasConceptScore W2034997740C163175372 @default.
• W2034997740 hasConceptScore W2034997740C178650346 @default.

• next