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pomp (version 1.10)

Ensemble Kalman filters: Ensemble Kalman filters

Description

The ensemble Kalman filter and ensemble adjustment Kalman filter.

Usage

"enkf"(object, params, Np, h, R, verbose = getOption("verbose"), ...)
"eakf"(object, params, Np, C, R, verbose = getOption("verbose"), ...)
"logLik"(object, ...)
"cond.logLik"(object, ...)
"pred.mean"(object, pars, ...)
"filter.mean"(object, pars, ...)

Arguments

object
An object of class pomp or inheriting class pomp.
params
optional named numeric vector containing the parameters at which the filtering should be performed. By default, params = coef(object).
Np
the number of particles to use.
verbose
logical; if TRUE, progress information is reported.
h
function returning the expected value of the observation given the state.
C
matrix converting state vector into expected value of the observation.
R
matrix; variance of the measurement noise.
pars
Names of variables.
...
additional arguments (currently ignored).

Value

An object of class kalmand.pomp. This class inherits from class pomp.

Methods

References

Evensen, G. (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics Journal of Geophysical Research: Oceans 99:10143--10162

Evensen, G. (2009) Data assimilation: the ensemble Kalman filter Springer-Verlag.

Anderson, J. L. (2001) An Ensemble Adjustment Kalman Filter for Data Assimilation Monthly Weather Review 129:2884--2903

See Also

pomp, pfilter, and the tutorials on the package website.