naive_LEnKPF: naive LEnKPF analysis

In robertsy/assimilr: Local Ensemble Kalman Particle Filters

Description

Apply an independent EnKPF analysis at each location using only neighboring information.

Special cases: setting gam.fix=1: LEnKF setting gam.fix=0: LPF

Usage

naive_LEnKPF(xb, y, H, R, l, get_neighbours = ring_neighbours,
  ndim = nrow(xb), taper = 1, gam.fix = NA, d = length(y),
  K = ncol(xb), q = nrow(xb), unif = runif(1), R2 = msqrt(R),
  eps1 = R2 %*% matrix(rnorm(d * K), d, K), eps2 = R2 %*% matrix(rnorm(d
  * K), d, K), ...)

Arguments

xb	the background ensemble
y	the observations
H	the observation linear operator
R	the observations error covariance
l	the localization radius
get_neighbours	the function to get neighboring points, depending on geometry (typically ring_neighbours or sweq_neighbours)
taper	the tapering correlation matrix applied to P
gam.fix	fixed gamma value
d	dimension of observation, ensemble and state space
K	dimension of observation, ensemble and state space
q	dimension of observation, ensemble and state space
unif	used for balanced sampling
R2	matrix square-root of R
eps1	random perturbations N(0,R)
eps2	random perturbations N(0,R)
...	additional parameters passed to adaptive_gamma, typically e.0 and e.1

robertsy/assimilr documentation built on May 27, 2019, 10:33 a.m.