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4-D-Var or ensemble Kalman filter?
Eufenia Kalnay
, Hong Li
, Takemasa Miyoshi
,
Shu Chih Yang
, Joaquim Ballabrera-Poy
Center for GPS Science and Application Research
Department of Atmospheric Sciences
Research output
:
Contribution to journal
›
Article
›
peer-review
230
Scopus citations
Overview
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Keyphrases
Ensemble Kalman Filter (EnKF)
100%
Model Errors
28%
Operational Model
14%
Relative Advantage
14%
Lorenz Model
14%
Background Error Covariance
14%
Data Assimilation Method
14%
Low Error
14%
Window Size
14%
Channel Model
14%
Order of Accuracy
14%
Accuracy Analysis
14%
Non-Gaussian
14%
Real Observations
14%
System Reach
14%
Primitive Equations
14%
Weak Constraint
14%
Quasi-geostrophic
14%
Variance Inflation
14%
Length Error
14%
Observation Coverage
14%
Operational Implementation
14%
SPEEDY
14%
Physics
Gaussian Distribution
100%
Covariance
100%
Data Assimilation
100%
Primitive Equation
100%
Mathematics
Kalman Filtering
100%
Variance
14%
Error Covariance
14%
Gaussian Distribution
14%
Channel Model
14%
Earth and Planetary Sciences
Kalman Filter
100%
Data Assimilation
14%
Covariance
14%
Primitive Equation
14%