Model Error Representation Using the Stochastically Perturbed Hybrid Physical–Dynamical Tendencies in Ensemble Data Assimilation System

Model Error Representation Using the Stochastically Perturbed Hybrid Physical–Dynamical Tendencies in Ensemble Data Assimilation System

Ensemble data assimilation systems generally suffer from underestimated background error covariance that leads to a filter divergence problem—the analysis diverges from the natural state by ignoring the observation influence due to the diminished estimation of model uncertainty. To alleviate this pr...

Full description

Bibliographic Details
Main Authors: Sujeong Lim, Myung-Seo Koo, In-Hyuk Kwon, Seon Ki Park
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Applied Sciences
Subjects:
model error
ensemble data assimilation
stochastic perturbation
stochastic perturbed hybrid tendencies scheme
dynamical tendency
physical tendency
Online Access:https://www.mdpi.com/2076-3417/10/24/9010

Internet

https://www.mdpi.com/2076-3417/10/24/9010

Similar Items