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, |

`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 |

`rinit` |
simulator of the initial-state distribution.
This can be furnished either as a C snippet, an |

`rprocess` |
simulator of the latent state process, specified using one of the rprocess plugins.
Setting |

`emeasure` |
the expectation of the measured variables, conditional on the latent state.
This can be specified as a C snippet, an |

`vmeasure` |
the covariance of the measured variables, conditional on the latent state.
This can be specified as a C snippet, an |

`...` |
additional arguments are passed to |

`verbose` |
logical; if |

### 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. doi:10.1029/94JC00572.

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

G. Evensen. *Data assimilation: the ensemble Kalman filter*. Springer-Verlag, 2009. doi:10.1007/978-3-642-03711-5.

### See Also

More on sequential Monte Carlo methods:
`bsmc2()`

,
`cond_logLik()`

,
`eff_sample_size()`

,
`filter_mean()`

,
`filter_traj()`

,
`mif2()`

,
`pfilter()`

,
`pmcmc()`

,
`pred_mean()`

,
`pred_var()`

,
`saved_states()`

,
`wpfilter()`

More on pomp elementary algorithms:
`elementary_algorithms`

,
`pfilter()`

,
`pomp-package`

,
`probe()`

,
`simulate()`

,
`spect()`

,
`trajectory()`

,
`wpfilter()`

*pomp*version 5.11.0.0 Index]