getIndex()
Until this point, we have demonstrated how the user can become
familiar with embedded indices. As previously described, however, actual
work always starts with downloading a full index of float data. As shown
by Figure 1, the getIndex() command is used to get an index
of available Argo float profiles, either by downloading information from
a data repository or by reusing an index (stored as an .rda file) that
was prepared by a recent call to the function.
The getIndex() command works by specifying the server,
with first trying the Ifremer server and then the USGODAE server if that
does not work. The next step is to specify the file name. The table
below can be obtained using ?getIndex(). As shown, the user
has the ability to write the specific file name from the server, or to
simply use the embedded nicknames within the package:
"core", "bgc" or "bgcargo", or
"synthetic". The following table summarizes the contents of
the various files indicated by the filename argument.
ar_greylist.txt |
- |
Suspicious/malfunctioning floats |
ar_index_global_meta.txt.gz |
- |
Metadata files |
ar_index_global_prof.txt.gz |
"core" |
Argo data |
ar_index_global_tech.txt.gz |
- |
Technical files |
ar_index_global_traj.txt.gz |
- |
Trajectory files |
argo_bio-profile_index.txt.gz |
"bgc" or "bgcargo" |
Biogeochemical data (without S or T) |
argo_bio-traj_index.txt.gz |
- |
Biogeochemical trajectory files |
argo_synthetic-profile_index.txt.gz |
"synthetic" |
Synthetic data, successor to "merge" |
Additionally, the destdir argument has a default of
~/data/argo, where it should be noted that ~
is a short cut for C:\Users\. See ?getIndex()
for further description about the additional arguments for this
command.
To get the index from the Ifremer server, the following code is
used:
subset()
As shown by Figure 1, the next step when working with the
argoFloats package is to use the subset()
function to focus on a subset of profiles. The argoFloats
package provides tools to sift through profiles based on ID, time,
geography, variable, institution, ocean, dataMode, cycle, direction,
profile, dataStateIndicator, section, etc.
For geographic subsetting, the user has the ability to subset by
circle, rectangle, polygon, or
section.
To subset for specific groups of Argo profiling floats off the coast
of Bahamas, the following code is used:
# Subsetting by circle
aiCircle <- subset(ai, circle=list(longitude=-77.5, latitude=27.5, radius=50))
# Subsetting by polygon
lonPoly <- c(-76.5, -76.0, -75.5)
latPoly <- c(25.5, 26.5, 25.5)
aiPoly <- subset(ai, polygon=list(longitude=lonPoly, latitude=latPoly))
# Plotting the subsets together
CP <- merge(aiCircle, aiPoly)
plot(CP, bathymetry=FALSE) # also, try using bathymetry=TRUE
Exercise 1: Use the subset by rectangle function to
add a rectangle subset onto Figure 3 and subset all of the data to only
include samples between 2012 and 2020.
As of March, 2021, a subset by section was developed to
create a section of Argo data, similarly to what is done with CTD data.
A subset by section combined with subset by
time can provide insightful information of Argo vs CTD
sampling efforts. An example of this is highlighted below, which
compares a section of Argo data from the Mediterranean outflow region
across to North America to the line A03 CTD section data collected in
1993-09-11.
Exercise 2: Use the information from Figure 4 to
compare Argo and CTD section sampling methods.
library(oce)
library(argoFloats)
oldpar <- par(no.readonly=TRUE)
par(mfrow=c(2,1))
data(section, package="oce")
#getIndex()
ai <- getIndex()
#subset by section
lonlim <- c(-70, -64,-10)
latlim <-c(40,35,35)
index1 <- subset(ai, section=list(longitude=lonlim, latitude=latlim, width=100))
#subset by time
from <- as.POSIXct("2020-09-23", tz="UTC")
to <- as.POSIXct("2020-10-25", tz="UTC")
index2 <- subset(index1, time=list(from=from, to=to))
plot(index2, bathymetry=FALSE, asp=1/cos(mean(range(unlist(index2[["latitude"]]), na.rm=TRUE))*pi/180), mgp=getOption("oceMgp")
)
points(lonlim, latlim, pch=21, col="black", bg="red", type="o")
plot(section, which="map", col="tan")
par(oldpar)
readProfiles()
The readProfiles() command works with either a list of
local NetCDF files, or a argoFloats object type
profiles, as created by getProfiles().
The command can be useful for analyzing individual profiles, for
example:
index1 <- subset(index, 1:2) # To subset for profiles
profiles <- getProfiles(index1)
argos <- readProfiles(profiles)
argosClean <- applyQC(argos)
plot(argosClean, which="profile", type="p")
Exercise 3: Using the profile in the previous
example, plot a TS diagram.
