Skip to main content

Table 8 Comparison of the sequential (UKF) and variational (4D-Var) approaches to data assimilation

From: Data Assimilation Methods for Neuronal State and Parameter Estimation

 

UKF

4D-Var

Implementation choices

initial covariance (\(P_{xx}\))

model uncertainty (\(Q^{-1}\))

sigma points (λ)

type of optimizer/optimizer settings

process covariance matrix (Q)

state and parameter bounds

Data requirements

Pro: can handle a large amount of data

Pro: may find a good solution with a small amount of data

Con: may not find a good solution with a small amount of data

Con: cannot handle a large amount of data

Run time

Minutes

Days, hours, or minutes depending on choice of optimizer and settings

Scalability to larger models

Harder to choose Q

Search dimension is (N + 1)L + D

EnKF may use a smaller number of ensemble members

Sparse Hessian can be exploited during optimization