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

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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Bibliographic Details
Main Authors: Manuel Pulido, Pierre Tandeo, Marc Bocquet, Alberto Carrassi, Magdalena Lucini
Format: Article
Language:English
Published: Stockholm University Press 2018-01-01
Series:Tellus: Series A, Dynamic Meteorology and Oceanography
Subjects:
parameter estimation
model error estimation
stochastic parameterization
expectation–maximization algorithm
Online Access:http://dx.doi.org/10.1080/16000870.2018.1442099

Internet

http://dx.doi.org/10.1080/16000870.2018.1442099

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