View source: R/update_block_LEnKPF.R
Assimilate observations sequentially in blocks. Smooth discontinuities by conditional resampling using the empirical covariance structure. The method does not work without a taper for P. The specific update of one block is in one_block_update REM: for historical reasons the indices referred to as u and v in the paper are named in the code as x and v.
1 2 3 4 | block_LEnKPF(xb, y, H, R, l, block_size = l/2,
get_partition = ring_partition, ndim = nrow(xb), taper = 1,
gam.fix = NA, d = length(y), K = ncol(xb), q = nrow(xb),
unif = runif(1), ...)
|
xb |
the background ensemble |
y |
the observations |
H |
the observation linear operator |
R |
the observations error covariance |
l |
the localization radius (not used directly) |
block_size |
size of domain considered to include observations in a block |
get_partition |
function to partition the observations in blocks, depending on geometry (typically ring_partition or sweq_partition) |
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 |
... |
additional parameters passed to adaptive_gamma, typically e.0 and e.1 |
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