Interpolate a CTD profile to specified pressure values. This is used
by sectionGrid()
, but is also useful for dealing with individual
CTD/bottle profiles.
ctdDecimate(
x,
p = 1,
method = "boxcar",
rule = 1,
e = 1.5,
na.rm = FALSE,
debug = getOption("oceDebug")
)
A ctd object, with pressures that are as set by
the "p"
parameter and all other properties modified appropriately.
a ctd object.
pressure increment, or vector of pressures. In the first case,
pressures from 0dbar to the rounded maximum pressure are used, incrementing by
p
dbars. If a vector of pressures is given, interpolation is done to
these pressures.
the method to be used for calculating decimated values. This may be a string specifying the method, or a function. In the string case, the possibilities are as follows.
"boxcar"
(based on a local average)
"approx"
(based on linear
interpolation between neighboring points, using approx()
with the rule
argument specified here)
"approxML"
as "approx"
,
except that a mixed layer is assumed to apply above the top data value; this
is done by setting the yleft
argument to approx()
, and
by calling that function with rule=c(2, 1))
"lm"
(based on local
regression, with e
setting the size of the local region);
"rr"
for the Reiniger and Ross method, carried out with oce.approx()
;
"unesco"
(for the UNESCO method, carried out with oce.approx()
.
On the other hand, if method
is a function, then it must take
two arguments, named data
and parameters
. The first is set to x@data
by
ctdTrim()
. The second is passed directly to the user's function (see
Example 2). The return value from the function must be a logical vector of
the same length as the pressure
data, with TRUE values meaning to keep the
corresponding entries of the data
slot.
an integer that is passed to approx()
, in the
case where method
is "approx"
. Note that the default value
for rule
is 1, which will inhibit extrapolation beyond the observed
pressure range. This is a change from the behaviour previous to May 8, 2017,
when a rule
of 2 was used (without stating so as an argument).
is an expansion coefficient used to calculate the local neighbourhoods
for the "boxcar"
and "lm"
methods. If e=1
, then the
neighbourhood for the i-th pressure extends from the (i-1
)-th pressure to
the (i+1
)-th pressure. At the endpoints it is assumed that the outside
bin is of the same pressure range as the first inside bin. For other values of
e
, the neighbourhood is expanded linearly in each direction. If the
"lm"
method produces warnings about "prediction from a rank-deficient
fit", a larger value of "e"
should be used.
logical value indicating whether to remove NA values
before decimating. This value is ignored unless method
is
boxcar
in which case it is passed to binMean1D()
which does the
averaging. This parameter was added in February 2024, and the
behaviour of ctdDecimate()
prior that date was equivalent
to na.rm=FALSE
, so that is the default value, even though
it is expected that many uses will find using TRUE is more
convenient. See https://github.com/dankelley/oce/issues/2192
for more discussion.
an integer specifying whether debugging information is
to be printed during the processing. This is a general parameter that
is used by many oce
functions. Generally, setting debug=0
turns off the printing, while higher values suggest that more information
be printed. If one function calls another, it usually reduces the value of
debug
first, so that a user can often obtain deeper debugging
by specifying higher debug
values.
Data-quality flags contained within the original object are ignored by this
function, and the returned value contains no such flags. This is because such
flags represent an assessment of the original data, not of quantities derived
from those data. This function produces a warning to this effect. The
recommended practice is to use handleFlags()
or some other means to
deal with flags before calling the present function.
Dan Kelley
The "approx"
and "approxML"
methods may be best for bottle data,
in which the usual task is
to interpolate from a coarse sampling grid to a finer one. The distinction
is that "approxML"
assumes a mixed-layer above the top sample value. For CTD data, the
"boxcar"
method may be the preferred choice, because the task is normally
to sub-sample, and some degree of smoothing is usually desired. (The
"lm"
method can be quite slow, and its results may be quite similar to those of the
boxcar method.)
For widely-spaced data, a sort of numerical cabeling effect can result when density is computed based on interpolated salinity and temperature. See reference 2 for a discussion of this issue and possible solutions.
R.F. Reiniger and C.K. Ross, 1968. A method of interpolation with application to oceanographic data. Deep Sea Research, 15, 185-193.
Oguma, Sachiko, Toru Suzuki, Yutaka Nagata, Hidetoshi Watanabe, Hatsuyo Yamaguchi, and Kimio Hanawa. “Interpolation Scheme for Standard Depth Data Applicable for Areas with a Complex Hydrographical Structure.” Journal of Atmospheric and Oceanic Technology 21, no. 4 (April 1, 2004): 704-15.
The documentation for ctd explains the structure of CTD objects, and also outlines the other functions dealing with them.
Other things related to ctd data:
CTD_BCD2014666_008_1_DN.ODF.gz
,
[[,ctd-method
,
[[<-,ctd-method
,
as.ctd()
,
cnvName2oceName()
,
ctd
,
ctd-class
,
ctd.cnv.gz
,
ctdFindProfiles()
,
ctdFindProfilesRBR()
,
ctdRaw
,
ctdRepair()
,
ctdTrim()
,
ctd_aml.csv.gz
,
d200321-001.ctd.gz
,
d201211_0011.cnv.gz
,
handleFlags,ctd-method
,
initialize,ctd-method
,
initializeFlagScheme,ctd-method
,
oceNames2whpNames()
,
oceUnits2whpUnits()
,
plot,ctd-method
,
plotProfile()
,
plotScan()
,
plotTS()
,
read.ctd()
,
read.ctd.aml()
,
read.ctd.itp()
,
read.ctd.odf()
,
read.ctd.odv()
,
read.ctd.saiv()
,
read.ctd.sbe()
,
read.ctd.ssda()
,
read.ctd.woce()
,
read.ctd.woce.other()
,
setFlags,ctd-method
,
subset,ctd-method
,
summary,ctd-method
,
woceNames2oceNames()
,
woceUnit2oceUnit()
,
write.ctd()
library(oce)
data(ctd)
plotProfile(ctd, "salinity", ylim = c(10, 0))
p <- seq(0, 45, 1)
ctd2 <- ctdDecimate(ctd, p = p)
lines(ctd2[["salinity"]], ctd2[["pressure"]], col = "blue")
p <- seq(0, 45, 1)
ctd3 <- ctdDecimate(ctd, p = p, method = function(x, y, xout) {
predict(smooth.spline(x, y, df = 30), xout)$y
})
lines(ctd3[["salinity"]], ctd3[["pressure"]], col = "red")
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