Multiplicative Non-Gaussian Model Error Estimation in Data Assimilation

Multiplicative Non-Gaussian Model Error Estimation in Data Assimilation

Model uncertainty quantification is an essential component of effective data assimilation. Model errors associated with sub-grid scale processes are often represented through stochastic parameterizations of the unresolved process. Many existing Stochastic Parameterization schemes are only applicable...

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Bibliographic Details
Main Authors: Pathiraja, S. (Author), van Leeuwen, P.J (Author)
Format: Article
Language:English
Published: John Wiley and Sons Inc 2022
Subjects:
data assimilation
Data assimilation
Data driven
data-driven
Errors
Gaussian distribution
Gaussian noise (electronic)
Lorenz 96
methodology
Model errors
model uncertainty
Modeling uncertainties
non-Gaussian
Non-Gaussian
probability
Stochastic models
Stochastic systems
Stochastics
sub-grid scale
Subgrid scale
Sub-grid scale process
training
Uncertainty analysis
uncertainty quantification
Uncertainty quantifications
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