R: Ensemble Kalman filters

kalman {pomp}

R Documentation

Ensemble Kalman filters

Description

The ensemble Kalman filter and ensemble adjustment Kalman filter.

Usage

## S4 method for signature 'data.frame'
enkf(
  data,
  Np,
  params,
  rinit,
  rprocess,
  emeasure,
  vmeasure,
  ...,
  verbose = getOption("verbose", FALSE)
)

## S4 method for signature 'pomp'
enkf(data, Np, ..., verbose = getOption("verbose", FALSE))

## S4 method for signature 'kalmand_pomp'
enkf(data, Np, ..., verbose = getOption("verbose", FALSE))

## S4 method for signature 'data.frame'
eakf(
  data,
  Np,
  params,
  rinit,
  rprocess,
  emeasure,
  vmeasure,
  ...,
  verbose = getOption("verbose", FALSE)
)

## S4 method for signature 'pomp'
eakf(data, Np, ..., verbose = getOption("verbose", FALSE))

Arguments

`data`	either a data frame holding the time series data, or an object of class ‘pomp’, i.e., the output of another pomp calculation. Internally, `data` will be coerced to an array with storage-mode `double`.
`Np`	integer; the number of particles to use, i.e., the size of the ensemble.
`params`	optional; named numeric vector of parameters. This will be coerced internally to storage mode `double`.
`rinit`	simulator of the initial-state distribution. This can be furnished either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. Setting `rinit=NULL` sets the initial-state simulator to its default. For more information, see rinit specification.
`rprocess`	simulator of the latent state process, specified using one of the rprocess plugins. Setting `rprocess=NULL` removes the latent-state simulator. For more information, see rprocess specification for the documentation on these plugins.
`emeasure`	the expectation of the measured variables, conditional on the latent state. This can be specified as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. Setting `emeasure=NULL` removes the emeasure component. For more information, see emeasure specification.
`vmeasure`	the covariance of the measured variables, conditional on the latent state. This can be specified as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. Setting `vmeasure=NULL` removes the vmeasure component. For more information, see vmeasure specification.
`...`	additional arguments are passed to `pomp`.
`verbose`	logical; if `TRUE`, diagnostic messages will be printed to the console.

Value

An object of class ‘kalmand_pomp’.

Note for Windows users

Some Windows users report problems when using C snippets in parallel computations. These appear to arise when the temporary files created during the C snippet compilation process are not handled properly by the operating system. To circumvent this problem, use the cdir and cfile options to cause the C snippets to be written to a file of your choice, thus avoiding the use of temporary files altogether.

Author(s)

Aaron A. King

References

G. Evensen. 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, 1994.

J.L. Anderson. An ensemble adjustment Kalman filter for data assimilation. Monthly Weather Review 129, 2884–2903, 2001.

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