Often in CTD profiling, the goal is to isolate only the downcast, discarding measurements made in
the air, in an equilibration phase in which the device is held below the water surface, and then the
upcast phase that follows the downcast. This is handled reasonably well by ctdTrim
with
method="downcast"
, although it is almost always best to use plotScan()
to
investigate the data, and then use the method="index"
or method="scan"
method based on
visual inspection of the data.
ctdTrim(
x,
method,
removeDepthInversions = FALSE,
parameters = NULL,
indices = FALSE,
debug = getOption("oceDebug")
)
Either a ctd object or a logical vector of length matching
the data. In the first case, which is the default, the elements of the data
slot will have been trimmed, along with some elements of the metadata
slot
(e.g. metadata4flags
and, if present and of length matching
data$pressure
, both metadata$longitude
and metadata$latitude
). The
second case, achieved by setting indices=FALSE
, may be helpful for advanced
users who wish to do things like construct data flags to be inserted into the
object.
a ctd object.
A string (or a vector of two strings) specifying the trimming method, or a function to
be used to determine data indices to keep. If method
is not provided, "downcast"
is
assumed. See “Details”.
Logical value indicating whether to remove any levels at which depth is
less than, or equal to, a depth above. (This is needed if the object is to be assembled into a
section, unless ctdDecimate()
will be used, which will remove the inversions.
A list whose elements depend on the method; see “Details”.
Logical value indicating what to return. If indices=FALSE
(the default),
then the return value is a subsetted ctd object. If indices=TRUE
,
then the return value is a logical vector that could be used to subset the data
with subset,ctd-method()
or to set data-quality flags.
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.
The subsetting of longitude
and latitude
in the metadata
slot was
introduced on 2022-12-13, for use with ctd objects created using
as.ctd()
on rsk objects created by using read.rsk()
on Ruskin
files that hold data from RBR CTD instruments linked with phone/tablet
devices equipped with GPS sensors.
Dan Kelley and Clark Richards
ctdTrim
begins by examining the pressure differences between subsequent samples. If
these are all of the same value, then the input ctd
object is returned, unaltered.
This handles the case of pressure-binned data. However, if the pressure difference
varies, a variety of approaches are taken to trimming the dataset.
If method[1]
is "downcast"
then an attempt is made to keep only data for
which the CTD is descending. This is done in stages, with variants based on method[2]
, if
supplied.
The pressure data are despiked with a smooth() filter with method "3R". This removes wild spikes that arise from poor instrument connections, etc.
Step 2. If no parameters
are given, then any data with negative pressures
are deleted. If there is a parameter named pmin
, then that pressure (in decibars)
is used instead as the lower limit. This is a commonly-used setup, e.g.
ctdTrim(ctd, parameters=list(pmin=1))
removes the top decibar (roughly 1m) from
the data. Specifying pmin
is a simple way to remove near-surface
data, such as a shallow equilibration phase, and if specified will cause ctdTrim
to skip step 4 below.
The maximum pressure is determined, and data acquired subsequent to that point are deleted. This removes the upcast and any subsequent data.
If the pmin
parameter is not specified, an attempt is made to remove an initial
equilibrium phase by a regression of pressure on scan number. There are three
variants to this, depending on the value of the second method
element.
If method
is "A"
(or not given), the procedure is to
call nls()
to fit a piecewise linear model of pressure as a function of scan,
in which pressure is constant for scan less than a critical value, and then
linearly varying for with scan. This is meant to handle the common situation
in which the CTD is held at roughly constant depth (typically
a metre or so) to equilibrate, before it is lowered through the water column.
If method
is "B"
, the procedure is similar, except that the pressure
in the surface region is taken to be zero (this does not make
much sense, but it might help in some cases). Note that, prior to early 2016,
method "B"
was called method "C"
; the old "B"
method was judged useless
and so it was removed.
If method="upcast"
, a sort of reverse of "downcast"
is used. This
was added in late April 2017 and has not been well tested yet.
If method="sbe"
, a method similar to that described
in the SBE Data Processing manual is used to remove the "soak"
period at the beginning of a cast (see Section 6 under subsection
"Loop Edit"). The method is based on the soak procedure whereby
the instrument sits at a fixed depth for a period of time, after
which it is raised toward the surface before beginning the actual
downcast. This enables equilibration of the sensors while still
permitting reasonably good near-surface data. Parameters for the
method can be passed using the parameters
argument, which
include minSoak
(the minimum depth for the soak) and
maxSoak
the maximum depth of the soak. The method finds
the minimum pressure prior to the maxSoak
value being
passed, each of which occurring after the scan in which the
minSoak
value was reached. For the method to work, the
pre-cast pressure minimum must be less than the minSoak
value. The default values of minSoak
and maxSoak
are 1 and 20 dbar, respectively.
If method="index"
or "scan"
, then each column of data is subsetted according to the
value of parameters
. If the latter is a logical vector of length matching data column
length, then it is used directly for subsetting. If parameters
is a numerical vector with
two elements, then the index or scan values that lie between parameters[1]
and parameters[2]
(inclusive) are used for subsetting. The
two-element method is probably the most useful, with the values being determined by visual
inspection of the results of plotScan()
. While this may take a minute or two, the
analyst should bear in mind that a deep-water CTD profile might take 6 hours, corresponding to
ship-time costs exceeding a week of salary.
If method="range"
then data are selected based on the value of the column named
parameters$item
. This may be by range or by critical value. By range: select values
between parameters$from
(the lower limit) and parameters$to
(the upper limit) By
critical value: select if the named column exceeds the value. For example, ctd2 <-
ctdTrim(ctd, "range", parameters=list(item="scan", from=5))
starts at scan number 5 and
continues to the end, while
ctdTrim(ctd,"range", parameters=list(item="scan", from=5, to=100))
also starts at scan 5,
but extends only to scan 100.
If method
is a function, then it must return a vector of logical()
values, computed based on two arguments: data
(a
list()
), and parameters
as supplied to ctdTrim
. Both
inferWaterDepth
and removeInversions
are ignored in the function case. See
“Examples”.
The Seabird CTD instrument is described at
http://www.seabird.com/products/spec_sheets/19plusdata.htm
.
Seasoft V2: SBE Data Processing, SeaBird Scientific, 05/26/2016
Other things related to ctd data:
CTD_BCD2014666_008_1_DN.ODF.gz
,
[[,ctd-method
,
[[<-,ctd-method
,
as.ctd()
,
cnvName2oceName()
,
ctd-class
,
ctd.cnv.gz
,
ctdDecimate()
,
ctdFindProfilesRBR()
,
ctdFindProfiles()
,
ctdRaw
,
ctdRepair()
,
ctd_aml.csv.gz
,
ctd
,
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.aml()
,
read.ctd.itp()
,
read.ctd.odf()
,
read.ctd.odv()
,
read.ctd.sbe()
,
read.ctd.ssda()
,
read.ctd.woce.other()
,
read.ctd.woce()
,
read.ctd()
,
setFlags,ctd-method
,
subset,ctd-method
,
summary,ctd-method
,
woceNames2oceNames()
,
woceUnit2oceUnit()
,
write.ctd()
if (FALSE) {
library(oce)
data(ctdRaw)
# Example 1: focus on downcast
plot(ctdTrim(ctdRaw))
# Example 2: user-supplied function.
trimByIndex<-function(data, parameters) {
parameters[1] < data$scan & data$scan < parameters[2]
}
trimmed <- ctdTrim(ctdRaw, trimByIndex, parameters=c(130, 380))
plot(trimmed)
}
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