kalman: Ensemble Kalman filters

Description

The ensemble Kalman filter and ensemble adjustment Kalman filter.

Usage

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

Value

An object of class ‘kalmand_pomp’.

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.
...: additional arguments are passed to pomp. This allows one to set, unset, or modify basic model components within a call to this function.
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.
verbose: logical; if TRUE, diagnostic messages will be printed to the console.

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

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. tools:::Rd_expr_doi("10.1029/94JC00572").

J.L. Anderson. An ensemble adjustment Kalman filter for data assimilation. Monthly Weather Review 129, 2884--2903, 2001. tools:::Rd_expr_doi("10.1175/1520-0493(2001)129<2884:aeakff>2.0.CO;2").

G. Evensen. Data assimilation: the ensemble Kalman filter. Springer-Verlag, 2009. tools:::Rd_expr_doi("10.1007/978-3-642-03711-5").