Description Usage Arguments Value Methods References See Also
An implementation of a parameter estimation algorithm that uses the ensemble Kalman filter (Evensen, G. (1994)) to perform the filtering step in the parameterperturbed iterated filtering scheme of Ionides et al. (2015) following the pseudocode in Asfaw, et al. (2020).
1 2 3 4 5 6 7 8 9 10 11 
data 
an object of class 
Nenkf 
number of iterations of perturbed EnKF. 
rw.sd 
specification of the magnitude of the randomwalk perturbations that will be applied to some or all model parameters.
Parameters that are to be estimated should have positive perturbations specified here.
The specification is given using the ifelse(time==time[1],s,0). Likewise, ifelse(time==time[lag],s,0). See below for some examples. The perturbations that are applied are normally distributed with the specified s.d. If parameter transformations have been supplied, then the perturbations are applied on the transformed (estimation) scale. 
cooling.type 
specifications for the cooling schedule,
i.e., the manner and rate with which the intensity of the parameter perturbations is reduced with successive filtering iterations.

cooling.fraction.50 
specifications for the cooling schedule,
i.e., the manner and rate with which the intensity of the parameter perturbations is reduced with successive filtering iterations.

Np 
The number of particles used within each replicate for the adapted simulations. 
... 
If a 
verbose 
logical; if 
Upon successful completion, ienkf
returns an object of class
‘ienkfd_spatPomp’. This object contains the convergence record of the iterative algorithm with
respect to the likelihood and the parameters of the model (which can be accessed using the traces
attribute) as well as a final parameter estimate, which can be accessed using the coef()
.
The following methods are available for such an object:
coef
gives the Monte Carlo estimate of the maximum likelihood.
Evensen, G. (1994) Sequential data assimilation with a nonlinear quasigeostrophic 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 SpringerVerlag.
Anderson, J. L. (2001) An Ensemble Adjustment Kalman Filter for Data Assimilation Monthly Weather Review 129:2884–2903
Other particle filter methods:
abfir()
,
abf()
,
bpfilter()
,
enkf()
,
girf()
,
igirf()
,
iubf()
Other spatPomp parameter estimation methods:
igirf()
,
iubf()
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