Stochastic parameterization identification using ensemble Kalman filtering combined with maximum likelihood methods

For modelling geophysical systems, large-scale processes are described through a set of coarse-grained dynamical equations while small-scale processes are represented via parameterizations. This work proposes a method for identifying the best possible stochastic parameterization from noisy data. Sta...

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Vydané v:	Tellus. Series A, Dynamic meteorology and oceanography Ročník 70; číslo 1; s. 1 - 17
Hlavní autori:	Pulido, Manuel, Tandeo, Pierre, Bocquet, Marc, Carrassi, Alberto, Lucini, Magdalena
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Stockholm Taylor & Francis 01.01.2018 Stockholm University Press
Predmet:	Bayesian analysis Data collection Dynamics Earth Sciences Engineering Sciences expectation-maximization algorithm Kalman filters Machine learning Markov chains Meteorology model error estimation Modelling Ocean, Atmosphere parameter estimation Parameterization Parameters Probability theory Sciences of the Universe Signal and Image processing Statistical methods stochastic parameterization Teaching methods
ISSN:	1600-0870, 0280-6495, 1600-0870
On-line prístup:	Získať plný text
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