The gapctd package includes modules (filters and operations) for processing CTD data that are based on modules in SBE Data Processing (SBEDP) software. The gapctd modules replicate the output of SBEDP modules. However, the gapctd modules do not always include the full range of processing options that are available in SBEDEP modules because the gapctd modules are intended for limited use cases.
Modules accept and return ctd objects and are designed to be
interchangable (i.e., the order in which they are used can be swapped).
This document describes the modules and demonstrates their use for
processing CTD data.
Read-in the example CTD deployment file (.cnv) as a ctd object using
the read.oce function from the oce package. The deployment file
contains five variable columns that are populated in the oce object. A
sixth field, salinity (simply called ‘salinity’), is derived when the
data are read-in using read.oce.
| CTD File (.cnv) Variable | ctd object Variable |
Description |
|---|---|---|
| timeS | timeS | Time, Elapsed [seconds] |
| tv290C | temperature | Temperature [ITS-90, deg C] |
| prdM | pressure | Pressure, Strain Gauge [db] |
| c0S/m | conductivity | Conductivity [S/m] |
| flag | flag | Data quality flag |
| - | salinity | Practical Salinity [PSS-78] |
The file includes data from the entire deployment but only the downcast
will be used for this demonstration. The oce::ctdTrim function is used
to automatically detect and remove scan data that were not in the
downcast.
library(gapctd)
ctd_data <- oce::read.oce(file = system.file("extdata/example/2021_06_24_0001_raw.cnv", package = "gapctd"))
dc <- oce::ctdTrim(ctd_data, method = "sbe")
# Assign correct time zone for CTD data (survey time)
dc@metadata$startTime <- lubridate::force_tz(dc@metadata$startTime,
tz = "America/Anchorage")
# Generate standard oce plot
plot(dc)
The median_filter() module is based on the median filter option in the
SBEDP Window Filter (Wfilter) module. It calculates the median for
selected channels within a specified scan window. Below, the median
window filter is applied to temperature and conductivity channels for
five (5) scan windows (corresponding with 1.25 seconds for the SBE19plus
V2) and salinity is recalculated.
dc_1 <- gapctd::median_filter(dc,
variables = c("temperature", "conductivity"),
window = c(5,5))
dc_1@data$salinity <- oce::swSCTp(dc_1) # Calculate salinity
par(mfrow = c(1,2))
plot(dc, which = 1, type = 'l')
text(x = 31.56, y = 35, labels = "(1) Raw Downcast")
plot(dc_1, which = 1, type = 'l')
text(x = 31.56, y = 35, labels = "(2) Median Filter\nT+C")
The lowpass_filter() module is based on the SBEDP Filter module.
Below, the filter is applied to temperature, conductivity, and pressure
channels with time constants of 0.5 s, 0.5 s, and 1 s, respectively.
Salinity is then recalculated.
dc_2 <- gapctd::lowpass_filter(dc_1,
variables = c("temperature", "conductivity", "pressure"),
time_constant = c(0.5, 0.5, 1),
freq_n = 0.25) # scan interval
dc_2@data$salinity <- oce::swSCTp(dc_2) # Calculate salinity
The align_var() module is based on the SBEDP Align (AlignCTD) module.
The module shifts channel data in time and interpolates between
measurements when alignment parameters are not factors of the scan
interval. In the example below, temperature is advanced by 0.5 seconds
and salinity is recalculated. Plots showing a close-up of the section
between 10 and 12 dbar.
dc_3 <- gapctd::align_var(dc_2,
variables = "temperature",
offset = -0.5)
dc_3@data$salinity <- oce::swSCTp(dc_3) # Calculate salinity
The conductivity_correction() module is based on the SBEDP
Conductivity Cell Thermal Mass Correction (CellTM) module. The module
implements a discrete time filter on conductivity based on the sample
interval (freq_n), the initial conductivity error (alpha_C), and a time
decay constant (beta_C). The function uses the sample interval in the
data to estimate the sample interval if it is not provided.
Below, the conductivity correction is applied using the typical parameters recommended by the CTD manufacturer and salinity is recalculated.
dc_4 <- gapctd::conductivity_correction(dc_3,
alpha_C = 0.04,
beta_C = 1/8,
freq_n = 0.25)
dc_4@data$salinity <- oce::swSCTp(dc_4)
The slowdown() module flags (but does not remove) scans where the CTD
slowed below a user-specified speed threshold. To account for variable
profiling speeds in trawl-mounted CTDs, the function also excludes the
surface and bottom from flagging. This module is similar SBEDP module
‘loop edit.’
dc_5 <- gapctd::slowdown(dc_4,
min_speed = 0.1,
window = 5,
cast_direction = "downcast",
exclude_bottom = 2)
Note that slowdown() flags slowdowns and reversals but does not remove
them. As such, the plots above are identical even though slowdowns and
reversals were flagged in the data:
dc_4@data$flag[409:415]## [1] 0 0 0 0 0 0 0
dc_5@data$flag[409:415]## [1] -9 -9 -9 -9 -9 -9 -9
The derive_eos() module uses functions from the oce package to
calculate the following variables from scan data:
| Variable | Description | Method | oce function |
|---|---|---|---|
| depth | Depth [m] | EOS-80 | swDepth() |
| salinity | Practical salinity [PSS-78] | EOS-80 | swSCTp(eos=“unesco”) |
| density | In-situ density [kg m^-3] | EOS-80 | swRho() |
| absolute_salinity | Absolute salinity | TEOS-10 | swSCTp(eos=“gsw”) |
| N2 | Squared buoyancy frequency [s^-2] | swN2() |
dc_6 <- gapctd::derive_eos(dc_5)
head(as.data.frame(dc_6@data))## timeS temperature pressure conductivity flag salinity scan C_corr velocity
## 1 155.25 4.4349 0.123 2.998519 -9 31.5361 622 0e+00 0.000
## 2 155.50 4.4349 0.122 2.998515 -9 31.5360 623 0e+00 0.000
## 3 155.75 4.4349 0.121 2.998505 -9 31.5359 624 0e+00 -0.005
## 4 156.00 4.4348 0.119 2.998488 -9 31.5358 625 0e+00 -0.004
## 5 156.25 4.4346 0.118 2.998464 -9 31.5357 626 -1e-06 -0.003
## 6 156.50 4.4344 0.118 2.998436 -9 31.5356 627 -2e-06 0.002
## depth absolute_salinity sound_speed density N2
## 1 0.122 31.5361 1463.99 1024.989 0.0001150
## 2 0.121 31.5360 1463.99 1024.989 0.0000333
## 3 0.120 31.5359 1463.99 1024.989 -0.0000398
## 4 0.118 31.5358 1463.99 1024.989 -0.0001602
## 5 0.117 31.5357 1463.99 1024.989 -0.0002074
## 6 0.117 31.5356 1463.98 1024.989 -0.0002074
The bin_average() module is based on the SBDEP Bin Average (BinAvg)
module. The module calculates means of variables by depth or pressure
bin, with bad scan (flag < 0) data excluded. Binning can be performed
by pressure or depth bins and surface data can be excluded based on a
user-specified depth or pressure threshold (default = 0.5). In the
example below, means are calculated for 1 m depth bins and data from <
0.5 m depth are excluded.
dc_7 <- gapctd::bin_average(dc_6, by = "depth", bin_width = 1)
The whole processing workflow can be run using pipe operators (native
|> or magrittr %>%).
dc_8 <- dc |>
gapctd::median_filter(variables = c("temperature", "conductivity"),
window = c(5,5)) |>
gapctd::lowpass_filter(variables = c("temperature", "conductivity", "pressure"),
time_constant = c(0.5, 0.5, 1),
freq_n = 0.25) |>
gapctd::align_var(variables = "temperature",
offset = -0.5) |>
gapctd::conductivity_correction(alpha_C = 0.04,
beta_C = 1/8) |>
gapctd::slowdown(min_speed = 0.1,
window = 5,
cast_direction = "downcast") |>
gapctd::derive_eos() |>
gapctd::bin_average(by = "depth", bin_width = 1)
The modules above can be used with any ctd object data from any CTD.
However, some modules are specifically been developed for processing
GAP’s CTD data, and their use is demonstrated below.
| Module | Purpose |
|---|---|
append_haul_data() |
Adds haul metadata from RACEBASE to ctd objects based on CTD time stamps and event time stamps in RACEBASE. |
assign_metadata_fields() |
Assigns metadata fields to downcast or upcast fields based on user-specified cast_direction |
section_oce() |
Extracts a section (downcast, bottom, or upcast) from a ctd object based on scan time stamps and event time stamps in metadata fields. |
# Haul data from the same vessel/cruise/CTD as the data file
ex_haul <- readRDS(file = system.file("extdata/example/HAUL_DATA_162_202101_202102.rds",
package = "gapctd"))
dc@metadata$startTime <- lubridate::force_tz(dc@metadata$startTime,
tz = "America/Anchorage")
dc_9 <- dc |>
gapctd:::append_haul_data(haul_df = ex_haul) |>
gapctd:::assign_metadata_fields(cast_direction = "downcast") |>
gapctd:::section_oce(by = "datetime",
cast_direction = "downcast") |>
gapctd::median_filter(variables = c("temperature", "conductivity"),
window = c(5,5)) |>
gapctd::lowpass_filter(variables = c("temperature", "conductivity", "pressure"),
time_constant = c(0.5, 0.5, 1),
freq_n = 0.25) |>
gapctd::align_var(variables = "temperature",
offset = -0.5) |>
gapctd::conductivity_correction(alpha_C = 0.04,
beta_C = 1/8) |>
gapctd::slowdown(min_speed = 0.1, window = 5, cast_direction = "downcast") |>
gapctd::derive_eos() |>
gapctd::bin_average(by = "depth", bin_width = 1)
plot(dc_9)
The modules for working with GAP CTD data appended location data to the metadata object so now the location of the cast can be shown in the standard oce plots.