ctdDecimate {oce}R Documentation

Decimate a CTD profile

Description

Interpolate a CTD profile to specified pressure values.

Usage

ctdDecimate(x, p=1, method="boxcar", e=1.5, debug=getOption("oceDebug"))

Arguments

x

a ctd object, e.g. as read by read.ctd.

p

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.

method

the method to be used for calculating decimated values. This may be a function or a string naming a built-in method. The built-in methods are "boxcar" (based on a local average), "approx" (based on linear interpolation between neighboring points), "lm" (based on local regression, with e setting the size of the local region), "rr" (for the Reineger and Ross method, carried out with oce.approx) and "unesco" (for the UNESCO method, carried out with. oce.approx. If method is a function, then it must take three arguments, the first being pressure, the second being an arbitrary variable in another column of the data, and the third being a vector of target pressures at which the calculation is carried out, and the return value must be a vector. See “Examples”.

e

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.

debug

a Boolean, set to TRUE to debug the reading process.

Details

The "approx" method is best for bottle data, in which the usual task is to interpolate from a coarse sampling grid to a finer one. For CTD data, the "boxcar" method is the more common choice, because the task is normally to sub-sample, and some degree of smoothing is usually desired. (The "lm" method is quite slow, and the results are similar to those of the boxcar method.)

NB. A sort of numerical cabeling effect can result from this procedure, but it can be avoided as follows

xd <- ctdDecimate(x)
xd[["sigmaTheta"]] <- swSigmaTheta(xd[["salinity"]],xd[["temperature"]],xd[["pressure"]])

Value

An object of class "ctd", with pressures that are as set by the "p" parameter and all other properties modified appropriately.

Author(s)

Dan Kelley

References

R.F. Reiniger and C.K. Ross, 1968. A method of interpolation with application to oceanographic data. Deep Sea Research, 15, 185-193.

See Also

The documentation for ctd-class explains the structure of CTD objects, and also outlines the other functions dealing with them.

Examples

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), p)$y)
lines(ctd3[["salinity"]], ctd3[["pressure"]], col="red")

[Package oce version 0.9-18 Index]