Last updated: 2021-02-20

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library(tidyverse)
library(seacarb)
library(broom)
library(lubridate)
library(tibbletime)
library(patchwork)

1 Scope of this script

Determine response time \(\tau\) from flush periods of the pCO₂ sensor
Analyse determined \(\tau\)
Apply response time correction to pCO₂ data
- First only to profiles for quality assessment
- Best parameterization than applied to all data

2 List of relevant parameters

The following aspects were tested and adjusted to improve the performance of the response time correction.

Response time determination

Fit interval length
tau residual threshold
Mean vs T-dependent tau

Response time correction

smoothing

Quality assessment of response time correction

Difference between up and downcast; comparison to reference value
Depth interval width for offset calculation
Max depth for Up-down-difference
NA criterion for included Down-Up-difference

3 Response time (\(\tau\)) determination

3.1 HydroC sensor settings

The sensor was first run with a low power pump (1W). Later - and for most parts of the expedition - with a stronger (8W) pump. Pumps were switched between recordings (data file: SD_datafile_20180718_170417CO2-0618-001.txt):

2018-07-17;13:08:34
2018-07-17;13:08:35

Logging frequency for all measurement modes (Measure, Zero, Flush) was increased in two steps, It was:

10 sec for all recordings including SD_datafile_20180714_073641CO2-0618-001.txt

2 sec after change in SD_datafile_20180717_121052CO2-0618-001.txt at:

2018-07-14;07:52:02
2018-07-14;07:52:12
2018-07-14;07:52:14

1 sec after change in SD_datafile_20180718_170417CO2-0618-001 at:

2018-07-17;12:27:25
2018-07-17;12:27:27
2018-07-17;12:27:28

3.2 Model fitting

Response times were determined by fitting a nonlinear least-squares model with the nls function as described here by Douglas Watson.

Flush period length: variable
Flush period restricted to equilibration phase, avoiding initial gas mixing effects occurring at the start of each Flush period
only completed Flush periods (duration > 500 sec) included

# read merged data file
tm <-
  read_csv(
    here::here(
      "data/intermediate/_merged_data_files/merging_interpolation",
      "tm.csv"
    ),
    col_types = cols(
      ID = col_character(),
      pCO2_analog = col_double(),
      pCO2_corr = col_double(),
      Zero = col_factor(),
      Flush = col_factor(),
      Zero_counter = col_integer(),
      deployment = col_integer(),
      duration = col_double(),
      mixing = col_character(),
      lat = col_double(),
      lon = col_double()
    )
  )

# select relevant columns
tm <- tm %>%
  select(date_time,
         ID,
         dep,
         tem,
         Flush,
         pCO2_corr,
         Zero_counter,
         duration,
         mixing)

# subset flush data after completed mixing phase
tm_flush <- tm %>%
  filter(Flush == 1, mixing == "equilibration")

# calculate flush duration
tm_flush <- tm_flush %>%
  group_by(Zero_counter) %>%
  mutate(duration = duration - min(duration)) %>%
  ungroup()

rm(tm)

3.2.1 Example plot

An example plot for a nls model fitted to pCO₂ observations during a Flush phase is shown below.

# select example flush period
i_Zero_counter <- 51

# set example duration
tm_flush_counter <- tm_flush %>%
  filter(Zero_counter == i_Zero_counter,
         duration <= 300)

# fit RT model
fit <- tm_flush_counter %>%
  nls(pCO2_corr ~ SSasymp(duration, yf, y0, log_alpha), data = .)

# extract relevant model parameters
tau <- as.numeric(exp(-tidy(fit)[3, 2]))
pCO2_corr_end <- as.numeric(tidy(fit)[1, 2])
pCO2_corr_start <- as.numeric(tidy(fit)[2, 2])
pCO2_corr_delta = pCO2_corr_end - pCO2_corr_start
resid_abs_mean <- mean(abs(resid(fit)))

# plot RT fit
augment(fit) %>%
  ggplot(aes(duration, pCO2_corr)) +
  geom_vline(xintercept = tau) +
  geom_hline(yintercept = pCO2_corr_start + 0.63 * (pCO2_corr_delta)) +
  geom_point(shape = 21) +
  geom_line(aes(y = .fitted), col = "red") +
  labs(y = expression(italic(p)*CO[2] ~ (µatm)), x = "time (sec)")

Example response time determination by non-linear least squares fit to the pCO~2~ recovery signal after zeroing. The vertical line indicates the determined response time tau. The horizontal line indicates 63% of the difference between start and final fitted pCO~2~.

Example response time determination by non-linear least squares fit to the pCO₂ recovery signal after zeroing. The vertical line indicates the determined response time tau. The horizontal line indicates 63% of the difference between start and final fitted pCO₂.

ggsave(
  here::here(
    "output/Plots/Figures_publication/appendix",
    "Fig_A1.pdf"
  ),
  width = 83,
  height = 50,
  dpi = 300,
  units = "mm"
)

ggsave(
  here::here(
    "output/Plots/Figures_publication/appendix",
    "Fig_A1.png"
  ),
  width = 83,
  height = 50,
  dpi = 300,
  units = "mm"
)

rm(
  tm_flush_counter,
  fit,
  i_Zero_counter,
  tau,
  pCO2_corr_delta,
  pCO2_corr_end,
  pCO2_corr_start,
  resid_abs_mean
)

3.2.2 Flush intervals

Due to speculations about the dependence of determined response times (\(\tau\)) on the chosen duration of the fit interval, the response time \(\tau\) was determined for all zeroings and for folllowing duration limits:

150, 200, 250, 300, 350, 400, 450, 500 secs

3.2.3 Fitting algorithm

The code chunk below, fits the response to all Flush periods and duration limits, and creates a pdf with a plot for each individual fit.

pdf(
  file = here::here(
    "output/Plots/response_time",
    "tau_determination_pCO2_corr_flushperiods_nls.pdf"
  ),
  onefile = TRUE,
  width = 7,
  height = 4
)

for (i in unique(tm_flush$Zero_counter)) {
  for (max_duration in parameters$duration_intervals) {
    tm_flush_counter <- tm_flush %>%
      filter(Zero_counter == i, duration <= max_duration)
    
    fit <-
      try(tm_flush_counter %>%
            nls(pCO2_corr ~ SSasymp(duration, yf, y0, log_alpha), data = .),
          TRUE)
    
    if (class(fit) == "nls") {
      tau <- as.numeric(exp(-tidy(fit)[3, 2]))
      pCO2_corr_end <- as.numeric(tidy(fit)[1, 2])
      pCO2_corr_start <- as.numeric(tidy(fit)[2, 2])
      pCO2_corr_delta = pCO2_corr_end - pCO2_corr_start
      resid_abs_mean <- mean(abs(resid(fit)) / pCO2_corr_end) * 100
      
      temp <- as_tibble(
        bind_cols(
          Zero_counter = i,
          duration = max_duration,
          date_time = mean(tm_flush_counter$date_time),
          dep = mean(tm_flush_counter$dep),
          tem = mean(tm_flush_counter$tem),
          pCO2_corr = pCO2_corr_end,
          tau = tau,
          resid = resid_abs_mean
        )
      )
      
      if (exists("tau_values")) {
        tau_values <- bind_rows(tau_values, temp)
      }
      else {
        tau_values <- temp
      }
      
      if (resid_abs_mean > parameters$pCO2_resid_lim) {
        warn <- "orange"
      }
      else {
        warn <- "black"
      }
      
      print(
        augment(fit) %>%
          ggplot(aes(duration, pCO2_corr)) +
          geom_point(col = warn) +
          geom_line(aes(y = .fitted)) +
          geom_vline(xintercept = tau) +
          geom_hline(yintercept = pCO2_corr_start + 0.63 * (pCO2_corr_delta)) +
          labs(
            y = expression(italic(p)*CO[2] ~ (µatm)),
            x = "Duration of Flush period (s)",
            title = paste(
              "Zero_counter: ",
              i,
              "Tau: ",
              round(tau, 1),
              "Mean absolute residual (%): ",
              round(resid_abs_mean, 2)
            )
          ) +
          xlim(0, 600)
      )
      
    }
    
    
    else {
      temp <- as_tibble(
        bind_cols(
          Zero_counter = i,
          duration = max_duration,
          date_time = mean(tm_flush_counter$date_time),
          dep = mean(tm_flush_counter$dep),
          tem = mean(tm_flush_counter$tem),
          pCO2_corr = pCO2_corr_end,
          tau = NaN,
          resid = NaN
        )
      )
      
      if (exists("tau_values")) {
        tau_values <- bind_rows(tau_values, temp)
      }
      else {
        tau_values <- temp
      }
      
      print(
        tm_flush_counter %>%
          ggplot(aes(duration, pCO2_corr)) +
          geom_point(col = "red") +
          labs(
            y = expression(italic(p)*CO[2] ~ (µatm)),
            x = "Duration of Flush period (s)",
            title = paste("Zero_counter: ", i,
                          "nls model failed")
          ) +
          xlim(0, 600)
      )
      
    }
  }
}

dev.off()

rm(
  tm_flush_counter,
  fit,
  i,
  tau,
  pCO2_corr_delta,
  pCO2_corr_end,
  pCO2_corr_start,
  temp,
  max_duration,
  resid_abs_mean,
  warn
)

tau_values %>%
  write_csv(
    here::here(
      "data/intermediate/_merged_data_files/response_time",
      "tau_values.csv"
    )
  )

rm(tm_flush)

A pdf with plots of all individual response time fits can be accessed here.

In this pdf, response time fits that exceed the residual criterion (Mean absolute residual >1% of final pCO₂) are printed in orange. Data from flush periods without succesful fit are printed red.

3.2.4 Residual threshold

A mean absolute residual threshold of >1% of final pCO₂ was applied to all determined response times.

# define periods of different pumps used
max_Zero_counter <-
  max(unique(tau_values[tau_values$date_time < parameters$pump_switch, ]$Zero_counter))

tau_values <- tau_values %>%
  mutate(pump_power = if_else(Zero_counter <= max_Zero_counter, "1W", "8W"))


# subset determined tau values by residual threshold
tau_resid <- tau_values %>%
  group_by(Zero_counter) %>%
  mutate(resid_max = max(resid, na.rm = TRUE)) %>%
  filter(resid_max < parameters$pCO2_resid_lim) %>%
  select(-resid_max) %>%
  ungroup()

tau_resid_out <- tau_values %>%
  group_by(Zero_counter) %>%
  mutate(resid_max = max(resid, na.rm = TRUE)) %>%
  filter(resid_max > parameters$pCO2_resid_lim) %>%
  select(-resid_max) %>%
  ungroup()

# Flush periods where model failure occurred
tau_values %>%
  filter(is.na(resid)) %>%
  group_by(Zero_counter) %>%
  summarise(n()) %>%
  ungroup()

# Flush periods removed due to residual criterion
tau_resid_out %>%
  group_by(Zero_counter) %>%
  summarise(n()) %>%
  ungroup()

The first determined tau value, which is twice as high as the mean of all others for an unkown reason, was removed.

# mean tau of first RT determination
tau_resid %>%
  filter(Zero_counter == 2) %>%
  summarise(tau = mean(tau))

# mean tau of all RT determinations before pump switch, except first
tau_resid %>%
  filter(Zero_counter != 2, Zero_counter <= 20) %>%
  summarise(tau = mean(tau))

# remove first tau value which is twice as high as the mean of all others
tau_resid <- tau_resid %>%
  filter(Zero_counter != 2)

3.3 Analysis

3.3.1 General considerations

Estimated \(\tau\) values were only taken into account when stable environmental pCO₂ levels were present. Absence of stable environmental pCO₂ was assumed when the mean absolute fit residual was above 1 % of the final equilibrium pCO₂. If one model fit (irrespective the chosen fit interval length) of a particular flush period did not match that criterion, the flush period was ignored entirely. Usually, fits with higher duration did not meet this criterion. For some unexplained reason the first \(\tau\) determination resulted in values about twice as high as all other flush periods and was therefore removed as an outlier.

Metrics to characterize the fitting procedure include the number of:

n_Zero_counters <- tau_values %>%
  group_by(Zero_counter) %>%
  n_groups()

n_duration_intervals <-
  length(parameters$duration_intervals)

n_tau_max <- n_Zero_counters * length(parameters$duration_intervals)
n_tau_total <- nrow(tau_values %>% filter(!is.na(resid)))
n_tau_resid <- nrow(tau_resid)

Flush periods: 76
Duration intervals: 8
Exercised response time fits: 608
Succesful response times determinations: 576 (94.7)%
\(\tau\)’s after removing groups of fits with high absolute fit residual: 416 (68.4 %)

It should be noted that all failed model fits occured in flush periods where the residual criterion was not meet by at least one other fit (i.e. fitting only failed under unstable conditions).

tau_values %>%
  ggplot(aes(resid)) +
  geom_histogram() +
  facet_wrap( ~ duration, labeller = label_both) +
  geom_vline(xintercept = parameters$pCO2_resid_lim) +
  labs(x = expression(Mean ~ absolute ~ residuals ~ ("%" ~ of ~ equilibrium ~
                                                       italic(p)*CO[2])))

Histogram of residuals from fit displayed for the investigate durations of the fit interval. Vertical line represents the chosen threshold.

3.3.2 Fit interval length

No clear dependence of \(\tau\) on the length of the flushing period was found.

tau_resid %>%
  group_by(Zero_counter) %>%
  mutate(d_tau = tau - mean(tau)) %>%
  ggplot(aes(duration, d_tau)) +
  geom_hline(yintercept = 0) +
  geom_smooth() +
  geom_point() +
  facet_wrap( ~ Zero_counter, ncol = 4, labeller = label_both) +
  labs(x = "Duration (sec)", y = "Deviation from mean tau (sec)")

Determined tau values as a function of the fit interval duration, displayed individually for each flush period.

tau_resid %>%
  group_by(Zero_counter) %>%
  mutate(d_tau = tau - mean(tau)) %>%
  ggplot(aes(duration, d_tau, group = duration)) +
  geom_violin() +
  geom_point() +
  labs(x = "Duration (sec)", y = "Deviation from mean tau (sec)") +
  facet_wrap( ~ pump_power)

Determined tau values as a function of the fit interval duration, pooled for all flush period.

duration_min_tau_sd <- tau_resid %>%
  group_by(Zero_counter) %>%
  mutate(d_tau = tau - mean(tau)) %>%
  ungroup() %>%
  group_by(duration) %>%
  summarise(d_tau_sd = sd(d_tau, na.rm = TRUE)) %>%
  ungroup() %>%
  slice(which.min(d_tau_sd)) %>%
  select(duration) %>%
  pull()

The lowest standard deviation of \(\tau\) values was found for a duration of:

300 sec

\(\tau\) values determined with this duration were filtered for further analysis.

tau_resid <- tau_resid %>%
  filter(duration == duration_min_tau_sd) %>%
  select(-duration)

rm(duration_min_tau_sd)

3.3.3 Time series of \(\tau\)

No obvious change of \(\tau\) over time was detected, but a dependence on the pump used.

ggplot() +
  geom_smooth(
    data = tau_resid %>% filter(dep < 10),
    aes(date_time, tau, linetype = pump_power),
    method = "lm",
    se = FALSE,
    col = "red"
  ) +
  geom_point(data = tau_resid,
             aes(date_time, tau, col = dep, shape = pump_power)) +
  scale_color_viridis_c(name = "Depth (m)") +
  labs(y = "Tau (sec)", x = "Date") +
  ylim(0, NA)

Tau for all Zeroings with color representing water depth. Red lines represent linear regression trends for tau determined in surface waters (<10m).

3.3.4 Temperature dependence

A temperature dependence of determined response times \(\tau\) was found, with similar slopes but different intercepts for both pumps used.

tau_resid %>%
  ggplot(aes(tem, tau, col = dep)) +
  geom_smooth(method = "lm") +
  geom_point() +
  scale_color_viridis_c(name = "Depth (m)") +
  labs(y = "Tau (sec)", x = "Temperature (deg C)") +
  facet_wrap( ~ pump_power, labeller = label_both)

Tau as a function of temperature for all zeroings determined with low power (left) and strong (right) pump. Color represents the water depth.

For the response times determined near the surface (<10m, restricted temperature range), no clear temperature dependence of \(\tau\) was detected.

tau_resid %>%
  filter(dep < 10) %>%
  ggplot(aes(tem, tau, col = dep)) +
  geom_smooth(method = "lm") +
  geom_point() +
  scale_color_viridis_c(name = "Depth (m)") +
  labs(y = "Tau (sec)", x = "Temperature (deg C)") +
  facet_wrap( ~ pump_power, labeller = label_both)

Surface tau (<10m) as a function of temperature for all zeroings determined with low power (left) and strong (right) pump. Color represents the water depth.

3.3.5 Final \(\tau\) values

The mean \(\tau\) values are:

# calculate mean tau for each pump
tau_mean <- tau_resid %>% 
  group_by(pump_power) %>% 
  summarise(tau = mean(tau, na.rm = TRUE))

tau_mean

# A tibble: 2 x 2
  pump_power   tau
  <chr>      <dbl>
1 1W          76.7
2 8W          56.5

The linear response of \(\tau\) on water temperature was fitted as:

# fit linear regression of tau for each pump
tau_fit <- tau_resid %>%
  nest_by(pump_power) %>%
  mutate(fit = list(lm(tau ~ tem, data = data))) %>%
  summarise(tidy(fit)) %>%
  select(pump_power, term, estimate) %>%
  spread(term, estimate)

tau_fit

# A tibble: 2 x 3
# Groups:   pump_power [2]
  pump_power `(Intercept)`    tem
  <chr>              <dbl>  <dbl>
1 1W                  94.5 -1.12 
2 8W                  70.4 -0.648

tau_fit %>% write_csv(here::here(
  "data/intermediate/_merged_data_files/response_time",
  "tau_fit.csv"
))

# clean workspace
rm(list = setdiff(ls(), c(
  "tau_resid", "tau_fit", "parameters"
)))

Only the T-dependent \(\tau\) estimate will be applied to correct the recorded pCO₂ profiles.

4 Response time correction

4.1 Data preparation

Following tasks were performed to prepare data for the response time correction:

Select only profiles
Assign deployment periods with 1W- and 8W- pump

tm <- read_csv(here::here("data/intermediate/_merged_data_files/merging_interpolation",
                          "tm.csv"),
               col_types = cols(ID = col_character(),
                                pCO2_analog = col_double(),
                                pCO2_corr = col_double(),
                                Zero = col_factor(),
                                Flush = col_factor(),
                                Zero_counter = col_integer(),
                                deployment = col_integer(),
                                duration = col_double(),
                                mixing = col_character(),
                                lat = col_double(),
                                lon = col_double()))

# select relevant columns
tm <- tm %>%
  select(date_time, ID, type, station, dep, sal, tem,
         Zero, Flush, pCO2_corr, deployment, Zero_counter)

# filter profiles
tm <- tm %>% 
  filter(type == "P")

# assign pump types
tm <- tm %>%
  group_by(ID, station) %>% 
  mutate(duration = as.numeric(date_time - min(date_time)),
         pump_power = if_else(date_time < parameters$pump_switch, "1W", "8W")) %>%
  arrange(date_time)

Include manually derived meta-information about the profiling status. Those meta data were assigned through visual inspection of each profiles (depth vs time) and distinguish phases of the casts, in particular the continuous up and downcast operation.

# Load profile meta data 
meta <- read_csv(here::here("data/input/TinaV/Sensor",
                            "Sensor_meta.csv"),
                 col_types = cols(ID = col_character()))


# Merge profiles and meta information 
tm <- full_join(tm, meta)
rm(meta)


# assign profiling phases according to meta data

tm <- tm %>% 
  mutate(phase = "standby",
         phase = if_else(duration >= start &
                           duration < down & 
                           !is.na(down) &
                           !is.na(start),
                         "down", phase),
         phase = if_else(duration >= down  & 
                           duration < lift & 
                           !is.na(lift) & 
                           !is.na(down ),   
                         "low",  phase),
         phase = if_else(duration >= lift  & 
                           duration < up   & 
                           !is.na(up  ) & 
                           !is.na(lift  ),  
                         "mid",  phase),
         phase = if_else(duration >= up    & 
                           duration < end  & 
                           !is.na(end ) & 
                           !is.na(up   ),   
                         "up",   phase))

tm <- tm %>% 
  select(-c(start, down, lift, up, end, comment))

# discard zero and flush periods
tm <- tm %>% 
  filter(Zero == 0, Flush == 0)

Subset reference pCO₂ recordings at the end of equilibration periods executed at constant depth

tm_pCO2_equi <- tm %>% 
  filter(phase %in% c("mid")) %>% 
  group_by(ID, station) %>% 
  top_n(5, row_number()) %>% 
  summarise(date_time = mean(date_time),
            duration = mean(duration),
            pCO2_corr = mean(pCO2_corr, na.rm = TRUE),
            dep = mean(dep, na.rm = TRUE)) %>% 
  ungroup()


# tm_pCO2_equi %>% 
#  write_csv(here::here("data/intermediate/_merged_data_files/response_time",
#                       "tm_pCO2_equi.csv"))

cast_dep <- tm %>%
  pivot_longer(c(dep, pCO2_corr), names_to = "parameter", values_to = "value")

cast_dep_equi <- tm_pCO2_equi %>%
  pivot_longer(c(dep, pCO2_corr), names_to = "parameter", values_to = "value")

max_duration <- round(max(cast_dep$duration) / 1000, 0) * 1000

cast_dep_equi_sub <- cast_dep_equi %>%
  filter(ID == parameters$example_ID,
         station == parameters$example_station)


cast_dep %>%
  filter(ID == parameters$example_ID,
         station == parameters$example_station) %>%
  ggplot(aes(duration, value, col = phase)) +
  geom_point(size = 0.5) +
  geom_point(data = cast_dep_equi_sub, aes(duration, value), col = "black") +
  scale_y_reverse() +
  scale_x_continuous(breaks = seq(0, 6000, 500)) +
  labs(title = str_c(
    "Date: ",
    parameters$example_ID,
    " | Station: ",
    parameters$example_station
  )) +
  facet_grid(parameter ~ ., scales = "free_y")

Example timeseries of profiling depth and pCO~2~. Colors represent manually assigned profiling phases. The black points represent reference data collected at the end of the mid equilibration period.

Example timeseries of profiling depth and pCO₂. Colors represent manually assigned profiling phases. The black points represent reference data collected at the end of the mid equilibration period.

rm(cast_dep, cast_dep_equi, cast_dep_equi_sub, max_duration)

cast_dep <- tm %>% 
  pivot_longer(c(dep, pCO2_corr), names_to = "parameter", values_to = "value")

cast_dep_equi <- tm_pCO2_equi %>% 
  pivot_longer(c(dep, pCO2_corr), names_to = "parameter", values_to = "value")

max_duration <- round(max(cast_dep$duration)/1000,0)*1000

pdf(file=here::here("output/Plots/response_time",
                    "time_series_depth_pCO2_corr_by_profile.pdf"),
    onefile = TRUE, width = 7, height = 4)

for(i_ID in unique(cast_dep$ID)){
  for(i_station in unique(cast_dep$station)){

    if (nrow(cast_dep %>% filter(ID == i_ID, station == i_station)) > 0){
      
        cast_dep_equi_sub <- cast_dep_equi %>%
        filter(ID == i_ID,
               station == i_station)

      print(
      
        cast_dep %>%
        filter(ID == i_ID,
               station == i_station) %>%
        ggplot(aes(duration, value, col=phase))+
        geom_point(size=0.5)+
        geom_point(data = cast_dep_equi_sub, aes(duration, value), col="black")+
        scale_y_reverse()+
        scale_x_continuous(breaks = seq(0,6000,500))+
        labs(title = str_c("Date: ",i_ID," | Station: ",i_station))+
        facet_grid(parameter~., scales = "free_y")+
        theme_bw()
      
      )

    }

  }
}

dev.off()

rm(cast_dep, cast_dep_equi, cast_dep_equi_sub, i_station, i_ID, max_duration)

A pdf with all timeseries plots of profiling depth and pCO₂ can be accessed here.

4.1.1 Profiling speed

We plotted a histogram of mean downcast profiling speed (m/s) per profile and calculated the mean profiling speed.

downcast <- tm %>%
  filter(phase == "down")

downcast_speed <- downcast %>%
  group_by(ID, station) %>%
  summarise(
    duration = as.numeric(max(date_time) - min(date_time)),
    distance = max(dep) - min(dep),
    speed = distance / duration
  ) %>%
  ungroup()

downcast_speed %>% 
  ggplot(aes(speed)) +
  geom_histogram()

downcast_speed %>% 
  summarise(mean(speed),
            sd(speed))

# A tibble: 1 x 2
  `mean(speed)` `sd(speed)`
          <dbl>       <dbl>
1          1.95       0.448

rm(downcast, downcast_speed)

4.2 Apply correction

The executed response time correction featured the following aspects:

Correction according to Bittig et al. (2018, supplement)
RT: T-dependent response times applied
tau_factor: Factors of 0.8, 1, 1.2, 1.4, 1.6 were applied to determined tau values
Post-smoothing: 30 sec running mean (eg across 15 observations at 2 sec measurement frequency)

# Define function for response time correction after Bittig, 2018

RT_corr <- function(c1, c0, dt, tau) {
  (1 / (2 * ((1 + (
    2 * tau / dt
  )) ^ (-1)))) * (c1 - (1 - (2 * ((
    1 + (2 * tau / dt)
  ) ^ (
    -1
  )))) * c0)
}


# Assign T-dependent response time (tau) values

tau_fit <- tau_fit %>%
  rename(tau_intercept = `(Intercept)`, tau_slope = tem)

tm <- full_join(tm, tau_fit)

tm <- tm %>%
  mutate(tau = tau_intercept + tau_slope * tem) %>%
  select(-tau_intercept,-tau_slope)


# Prepare data set for RT correction

tm <- tm %>%
  arrange(date_time) %>%
  mutate(dt = as.numeric(as.character(date_time - lag(date_time))))


# determine measurement frequency of sensor

freq <- tm %>%
  filter(dt < 13) %>%
  group_by(ID) %>%
  summarise(dt_mean = round(mean(dt, na.rm = TRUE), 0))

tm <- full_join(tm, freq)

# apply tau factors

tm <- expand_grid(tm, tau_factor = parameters$tau_factors)

tm <- tm %>%
  mutate(tau_test = tau * tau_factor)


# Apply RT correction to profiling data

for (i_ID in unique(tm$ID)) {

  freq_sub <- freq %>% filter(ID == i_ID) %>% pull(dt_mean)
  
  # window width for smoothing
  window <- parameters$smoothing_duration / freq_sub
  # function for rolling mean
  rolling_mean   <-
    rollify( ~ mean(.x, na.rm = TRUE), window = window)
  
  # data subset for each cruise day, and RT correction per station
  tm_sub <- tm %>%
    filter(ID == i_ID) %>%
    group_by(station, tau_factor) %>%
    mutate(
      pCO2_RT = RT_corr(pCO2_corr, lag(pCO2_corr), dt, tau_test),
      pCO2_RT = if_else(pCO2_RT %in% c(Inf,-Inf), NaN, pCO2_RT),
      window = window,
      pCO2 = rolling_mean(pCO2_RT)
    ) %>%
    ungroup()
  
  # time shift RT corrected data
  shift <- as.integer(as.character(window / 2))
  
  tm_sub <- tm_sub %>%
    group_by(station, tau_factor) %>%
    mutate(pCO2 = lead(pCO2, shift)) %>%
    ungroup()
  
  # append to new data frame
  if (exists("tm_RT")) {
    tm_RT <- bind_rows(tm_RT, tm_sub)
  }
  else{
    tm_RT <- tm_sub
  }
  
  rm(tm_sub, freq_sub, rolling_mean, shift, window)
  
}

tm <- tm_RT

rm(i_ID, freq, RT_corr, tau_fit, tau_resid, tm_RT)

5 Diagnosis

In the following, the success of the response time correction is assessed through the

visual inspection of RT corrected profiles,

as well as the offset between the downcast and:

Upcast
pCO₂ reference value (recorded after equilibration period during upcast)

The offset comparison requires to discretize the continous depth recording. Depth intervals of 1m were chosen.

First, we analyze all profiles individually. Later we’ll merge the information across profiles and come up with a single metric to quantify the quality of the response time correction

5.1 Individual profiles

equi_cast <- tm_pCO2_equi %>%
  filter(ID == parameters$example_ID,
         station == parameters$example_station)

tm %>%
  filter(
    ID == parameters$example_ID,
    station == parameters$example_station,
    phase %in% c("up", "down")
  ) %>%
  ggplot() +
  geom_path(aes(pCO2_corr, dep, linetype = phase, col = "raw")) +
  geom_path(aes(pCO2, dep, linetype = phase, col = "corrected")) +
  geom_point(data = equi_cast, aes(pCO2_corr, dep)) +
  scale_y_reverse() +
  coord_cartesian(ylim = c(35, 0),
                  xlim = c(70, 270)) +
  scale_color_brewer(palette = "Set1", name = "") +
  labs(title = str_c(
    "Date: ",
    parameters$example_ID,
    " | Station: ",
    parameters$example_station
  )) +
  facet_grid(tau_factor ~ ., labeller = label_both)

Example plot of response time corrected and raw pCO~2~ profiles. Panels highlight the effect of constant vs T-dependent tau estimates (columns) and the optimization by applying a constant factor (rows).

Example plot of response time corrected and raw pCO₂ profiles. Panels highlight the effect of constant vs T-dependent tau estimates (columns) and the optimization by applying a constant factor (rows).

rm(equi_cast)

A pdf with all timeseries plots of profiling depth and pCO₂ can be accessed here

pdf(
  file = here::here("output/Plots/response_time",
                    "profiles_pCO2.pdf"),
  onefile = TRUE,
  width = 7,
  height = 11
)

for (i_ID in unique(tm$ID)) {
  for (i_station in unique(tm$station)) {
    if (nrow(tm %>% filter(ID == i_ID, station == i_station)) > 0) {
      equi_cast <- tm_pCO2_equi %>%
        filter(ID == i_ID,
               station == i_station)
      
      print(
        tm %>%
          filter(ID == i_ID,
                 station == i_station,
                 phase %in% c("up", "down")) %>%
          ggplot() +
          geom_path(aes(
            pCO2_corr, dep, linetype = phase, col = "raw"
          )) +
          geom_path(aes(
            pCO2, dep, linetype = phase, col = "corrected"
          )) +
          geom_point(data = equi_cast, aes(pCO2_corr, dep)) +
          scale_y_reverse() +
          coord_cartesian(ylim = c(35, 0),
                          xlim = c(70, 270)) +
          scale_color_brewer(palette = "Set1", name = "") +
          labs(title = str_c(
            "Date: ", i_ID, " | Station: ", i_station
          )) +
          theme_bw() +
          facet_grid(tau_factor ~ ., labeller = label_both)
        
      )
      
      
    }
    
  }
}

dev.off()

rm(equi_cast, i_ID, i_station)

5.2 Down- vs upcast

# pCO2 offset up - down cast
tm_grid <- tm %>%
  filter(phase %in% c("down", "up")) %>%
  mutate(dep_grid = as.numeric(as.character(cut(
    dep, seq(0, 40, 1), seq(0.5, 39.5, 1)
  ))),
  tau_factor = as.factor(tau_factor)) %>%
  select(ID, station, tau_factor, p_type, dep_grid, phase, pCO2_corr, pCO2) %>%
  group_by(ID, station, tau_factor, p_type, dep_grid, phase) %>%
  summarise_all("mean", na.rm = TRUE) %>%
  ungroup() %>%
  pivot_longer(cols = c(pCO2_corr, pCO2), names_to = "correction") %>%
  pivot_wider(names_from = phase, values_from = value) %>%
  mutate(
    pCO2_delta = up - down,
    pCO2_up_down_average = (down + up) / 2,
    pCO2_delta_rel = 100 * pCO2_delta / pCO2_up_down_average
  )

tm_pCO2_equi_grid <- tm_pCO2_equi %>%
  mutate(dep_grid = as.numeric(as.character(cut(
    dep, seq(0, 40, 1), seq(0.5, 39.5, 1)
  )))) %>%
  select(ID, station, dep_grid, pCO2_equi = pCO2_corr)

tm_pCO2_equi_grid_sub <- tm_pCO2_equi_grid %>%
  filter(ID == parameters$example_ID,
         station == parameters$example_station)


tm_grid %>%
  filter(ID == parameters$example_ID,
         station == parameters$example_station) %>%
  arrange(dep_grid) %>%
  ggplot() +
  geom_path(aes(down, dep_grid, col = correction, linetype = "down")) +
  geom_path(aes(up, dep_grid, col = correction, linetype = "up")) +
  geom_point(data = tm_pCO2_equi_grid_sub, aes(pCO2_equi, dep_grid)) +
  scale_y_reverse() +
  coord_cartesian(ylim = c(35, 0),
                  xlim = c(70, 270)) +
  scale_linetype(name = "cast") +
  scale_color_brewer(palette = "Set1", direction = -1) +
  labs(
    y = "Depth [m]",
    x = expression(italic(p) * CO[2] ~ (µatm)),
    title = str_c(
      "Date: ",
      parameters$example_ID,
      " | Station: ",
      parameters$example_ID
    )
  ) +
  facet_grid(tau_factor ~ ., labeller = label_both)

Example plot of discretized, response time corrected and raw pCO~2~ profiles. Panels highlight the effect of constant vs T-dependent tau estimates (columns) and the optimization by applying a constant factor (rows). The black point indicates the reference pCO~2~ value.

Example plot of discretized, response time corrected and raw pCO₂ profiles. Panels highlight the effect of constant vs T-dependent tau estimates (columns) and the optimization by applying a constant factor (rows). The black point indicates the reference pCO₂ value.

rm(tm_pCO2_equi_grid_sub)

A pdf with all discretized pCO₂ profiles can be assessed here

pdf(
  file = here::here("output/Plots/response_time",
                    "profiles_pCO2_grid.pdf"),
  onefile = TRUE,
  width = 7,
  height = 11
)

for (i_ID in unique(tm_grid$ID)) {
  for (i_station in unique(tm_grid$station)) {
    if (nrow(tm_grid %>% filter(ID == i_ID, station == i_station)) > 0) {
      tm_pCO2_equi_grid_sub <- tm_pCO2_equi_grid %>%
        filter(ID == i_ID,
               station == i_station)
      
      print(
        tm_grid %>%
          filter(ID == i_ID,
                 station == i_station) %>%
          arrange(dep_grid) %>%
          ggplot() +
          geom_path(aes(
            down, dep_grid, col = correction, linetype = "down"
          )) +
          geom_path(aes(
            up, dep_grid, col = correction, linetype = "up"
          )) +
          geom_point(data = tm_pCO2_equi_grid_sub, aes(pCO2_equi, dep_grid)) +
          scale_y_reverse() +
          coord_cartesian(ylim = c(35, 0),
                          xlim = c(70, 270)) +
          scale_linetype(name = "cast") +
          scale_color_brewer(palette = "Set1", direction = -1) +
          labs(
            y = "Depth [m]",
            title = str_c("Date: ", i_ID, " | Station: ", i_station)
          ) +
          theme_bw() +
          facet_grid(tau_factor ~ ., labeller = label_both)
        
      )
      
      rm(tm_pCO2_equi_grid_sub)
    }
    
  }
}

dev.off()

tm_grid %>%
  filter(
    ID == parameters$example_ID,
    station == parameters$example_station,
    correction == "pCO2"
  ) %>%
  arrange(dep_grid) %>%
  ggplot(aes(pCO2_delta, dep_grid, col = as.factor(tau_factor))) +
  geom_path() +
  geom_point() +
  scale_y_reverse(breaks = seq(0, 40, 2)) +
  scale_color_discrete(name = "tau factor") +
  labs(
    x = expression(Delta ~ italic(p) * CO[2] ~ (µatm)),
    y = "Depth [m]",
    title = str_c(
      "Date: ",
      parameters$example_ID,
      " | Station: ",
      parameters$example_station
    )
  ) +
  geom_vline(xintercept = 0) +
  geom_vline(xintercept = c(-10, 10), col = "red")

Example plot of absolute pCO~2~ offset profiles. Panels highlight the effect of constant vs T-dependent tau estimates. Colour indicates the optimization by applying a constant factor to tau. Vertical red lines mark an arbitray 10 µatm pCO~2~ threshold.

Example plot of absolute pCO₂ offset profiles. Panels highlight the effect of constant vs T-dependent tau estimates. Colour indicates the optimization by applying a constant factor to tau. Vertical red lines mark an arbitray 10 µatm pCO₂ threshold.

pdf(
  file = here::here(
    "output/Plots/response_time",
    "profiles_pCO2_delta_grid.pdf"
  ),
  onefile = TRUE,
  width = 7,
  height = 7
)

for (i_ID in unique(tm_grid$ID)) {
  for (i_station in unique(tm_grid$station)) {
    if (nrow(tm_grid %>% filter(ID == i_ID, station == i_station)) > 0) {
      print(
        tm_grid %>%
          filter(ID == i_ID,
                 station == i_station,
                 correction == "pCO2") %>%
          arrange(dep_grid) %>%
          ggplot(aes(
            pCO2_delta, dep_grid, col = as.factor(tau_factor)
          )) +
          geom_path() +
          geom_point() +
          scale_y_reverse(breaks = seq(0, 40, 2)) +
          scale_color_discrete(name = "tau factor") +
          labs(
            x = "delta pCO2 [µatm]",
            y = "Depth [m]",
            title = str_c("Date: ", i_ID, " | Station: ", i_station)
          ) +
          geom_vline(xintercept = 0) +
          geom_vline(xintercept = c(-10, 10), col = "red") +
          theme_bw()
        
      )
      
    }
    
  }
}

dev.off()

A pdf with all absolute pCO₂ offset profiles can be assessed here.

tm_grid %>%
  filter(
    ID == parameters$example_ID,
    station == parameters$example_station,
    correction == "pCO2"
  ) %>%
  arrange(dep_grid) %>%
  ggplot(aes(pCO2_delta_rel, dep_grid, col = as.factor(tau_factor))) +
  geom_path() +
  geom_point() +
  scale_y_reverse(breaks = seq(0, 40, 2)) +
  scale_color_discrete(name = "tau factor") +
  labs(
    x = expression(Delta ~ italic(p) * CO[2] ~ ("%" ~ of ~ absolute ~ value)),
    y = "Depth [m]",
    title = str_c(
      "Date: ",
      parameters$example_ID,
      " | Station: ",
      parameters$example_station
    )
  ) +
  geom_vline(xintercept = 0) +
  geom_vline(xintercept = c(-10, 10), col = "red")

Example plot of relative offset pCO~2~ profiles. Panels highlight the effect of constant vs T-dependent tau estimates. Colour indicates the optimization by applying a constant factor to tau. Vertical red lines mark an arbitray 10% threshold.

Example plot of relative offset pCO₂ profiles. Panels highlight the effect of constant vs T-dependent tau estimates. Colour indicates the optimization by applying a constant factor to tau. Vertical red lines mark an arbitray 10% threshold.

A pdf with all relative pCO₂ offset profiles can be assessed here.

pdf(
  file = here::here(
    "output/Plots/response_time",
    "profiles_pCO2_delta_rel_grid.pdf"
  ),
  onefile = TRUE,
  width = 7,
  height = 7
)

for (i_ID in unique(tm_grid$ID)) {
  for (i_station in unique(tm_grid$station)) {
    if (nrow(tm_grid %>% filter(ID == i_ID, station == i_station)) > 0) {
      print(
        tm_grid %>%
          filter(ID == i_ID,
                 station == i_station,
                 correction == "pCO2") %>%
          arrange(dep_grid) %>%
          ggplot(aes(
            pCO2_delta_rel, dep_grid, col = as.factor(tau_factor)
          )) +
          geom_path() +
          geom_point() +
          scale_y_reverse(breaks = seq(0, 40, 2)) +
          scale_color_discrete(name = "tau factor") +
          labs(
            x = "delta pCO2 [% of absolute value]",
            y = "Depth [m]",
            title = str_c("Date: ", i_ID, " | Station: ", i_station)
          ) +
          geom_vline(xintercept = 0) +
          geom_vline(xintercept = c(-10, 10), col = "red") +
          theme_bw()
        
      )
      
    }
    
  }
}

dev.off()

5.3 Downcast vs reference value

Referenced values were obtained occasionally through interuption of the profiling measurements to achieve sensor equilibration. Readings under equilibrated conditions were extracted and compared to the corresponding continous cast value.

tm_equi_delta <- full_join(tm_grid, tm_pCO2_equi_grid) %>% 
  filter(!is.na(pCO2_equi)) %>% 
  mutate(pCO2_delta_equi = down - pCO2_equi,
         pCO2_delta_equi_rel = 100 * pCO2_delta_equi / pCO2_equi)

tm_equi_delta %>%
  filter(tau_factor == parameters$tau_factor_used, correction == "pCO2") %>%
  ggplot(aes(pCO2_equi, pCO2_delta_equi)) +
  geom_hline(yintercept = 0) +
  geom_point() +
  labs(x = expression(Reference ~ italic(p)*CO[2] ~ (µatm)),
       y = expression(Delta ~ italic(p)*CO[2] ~ from ~ reference ~ (µatm)))

Offset between pCO~2~ downcast and upcast reference value as a function of absolute pCO~2~.

Offset between pCO₂ downcast and upcast reference value as a function of absolute pCO₂.

tm_equi_delta %>%
  ggplot(aes(as.factor(tau_factor), pCO2_delta_equi, fill = correction)) +
  geom_hline(yintercept = 0) +
  geom_violin() +
  scale_fill_brewer(palette = "Set1")

Offset between pCO~2~ downcast and upcast reference value. Panels highlight the effect of constant vs T-dependent tau estimates. Colour distinguish raw and corrected offsets.

Offset between pCO₂ downcast and upcast reference value. Panels highlight the effect of constant vs T-dependent tau estimates. Colour distinguish raw and corrected offsets.

5.4 Summary metrics

In order to decide, which conditions resulted in the best response correction the mean absoulte and relative pCO₂ offset across all profiles was calculated for:

the offset between downcast and reference value
the offset between downcast and upcast
T-dependent tau
applied tau factors

tm_grid_stat <- tm_grid %>% 
  filter(correction == "pCO2") %>% 
  group_by(ID, station) %>% 
  summarise(dep_max = max(dep_grid),
            pCO2_max = max(down)) %>% 
  ungroup()

tm_grid_stat %>% 
  ggplot(aes(dep_max, pCO2_max)) +
  geom_point()

tm_grid <- full_join(tm_grid, tm_grid_stat)

meta <- read_csv(here::here("data/input/TinaV/Sensor",
                            "Sensor_meta.csv"),
                 col_types = cols(ID = col_character()))

tm_grid_stat <- full_join(meta, tm_grid_stat)

rm(tm_grid_stat, meta)

Summary metric are restricted to profiles that did not exceed:

maximum sampling depth of 30 m
pCO2 of 300 µatm
Nr of missing depth grid levels of 2

# apply restrictions
tm_grid_shallow <- tm_grid %>% 
  filter(dep_max <= parameters$RT_stats_dep_max,
         pCO2_max <= parameters$RT_stats_pCO2_max,
         dep_grid <= parameters$RT_stats_dep) %>% 
  group_by(ID, station, tau_factor, correction) %>% 
  mutate(nr_na = sum(is.na(pCO2_delta))) %>% 
  ungroup() %>% 
  filter(nr_na < parameters$max_gap)

# calculate summary metrics up vs downcast
tm_grid_shallow_sum <- tm_grid_shallow %>% 
  mutate(pCO2_delta_abs = abs(pCO2_delta),
         pCO2_delta_rel_abs = abs(pCO2_delta_rel)) %>% 
  group_by(tau_factor, dep_grid, correction) %>% 
  summarise(mean         = mean(pCO2_delta, na.rm = TRUE),
            sd           = sd(pCO2_delta, na.rm = TRUE),
            mean_abs     = mean(pCO2_delta_abs, na.rm = TRUE),
            mean_rel     = mean(pCO2_delta_rel, na.rm = TRUE),
            sd_rel       = sd(pCO2_delta_rel, na.rm = TRUE),
            mean_rel_abs = mean(pCO2_delta_rel_abs, na.rm = TRUE)) %>% 
  ungroup() %>% 
  pivot_longer(cols = sd:mean_rel_abs,
               names_to = "estimate", values_to = "pCO2_delta")

# calculate summary metrics equilibrium comparison
tm_equi_delta_sum <- tm_equi_delta %>%
  mutate(pCO2_delta_equi_abs = abs(pCO2_delta_equi),
         pCO2_delta_equi_rel_abs = abs(pCO2_delta_equi_rel)) %>%
  group_by(correction, tau_factor) %>%
  summarise(mean         = mean(pCO2_delta_equi, na.rm = TRUE),
            mean_abs     = mean(pCO2_delta_equi_abs, na.rm = TRUE),
            mean_rel = mean(pCO2_delta_equi_rel, na.rm = TRUE),
            mean_rel_abs = mean(pCO2_delta_equi_rel_abs, na.rm = TRUE)) %>%
  ungroup() %>%
  pivot_longer(cols = mean:mean_rel_abs,
               names_to = "estimate", values_to = "dpCO2")

tm_grid_shallow_sum %>%
  filter(correction == "pCO2",
         estimate %in% c("mean_abs", "mean_rel_abs", "sd", "sd_rel")) %>%
  ggplot() +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 20) +
  geom_vline(xintercept = c(10), col = "red") +
  geom_path(aes(pCO2_delta, dep_grid, col = as.factor(tau_factor))) +
  scale_y_reverse() +
  scale_color_discrete(name = "Tau factor") +
  labs(x = expression(Delta ~ italic(p)*CO[2] ~ (µatm)), y = "Depth intervals (1m)") +
  facet_grid(estimate ~ .)

Offset between up- and downcast. Panel columns: Constant and T-dependent tau. Panel rows from top to bottom: Mean of absolute offset, mean of relative absolute offset, standard deviation of offset, standard deviation of relative offset.

Mean offset values calculated for the upper 5 and 20 m of the water column:

tm_grid_shallow_sum %>%
  filter(correction == "pCO2",
         estimate %in% c("mean_abs"),
         tau_factor == parameters$tau_factor_used,
         dep_grid < 5) %>% 
  summarise(dep_lim = 5,
            mean(pCO2_delta))

# A tibble: 1 x 2
  dep_lim `mean(pCO2_delta)`
    <dbl>              <dbl>
1       5               2.38

tm_grid_shallow_sum %>%
  filter(correction == "pCO2",
         estimate %in% c("mean_abs"),
         tau_factor == parameters$tau_factor_used,
         dep_grid < 20) %>% 
  summarise(dep_lim = 20,
            mean(pCO2_delta))

# A tibble: 1 x 2
  dep_lim `mean(pCO2_delta)`
    <dbl>              <dbl>
1      20               7.16

Likewise, we analyse the offset from the pCO₂ reference value:

tm_equi_delta_sum %>%
  filter(estimate %in% c("mean_abs", "mean_rel_abs")) %>%
  ggplot(aes(tau_factor, dpCO2, linetype = correction, shape = correction)) +
  geom_point() +
  geom_hline(yintercept = 0) +
  labs(x = "Tau factor", y = expression(Mean ~ Delta ~ italic(p)*CO[2])) +
  facet_wrap( ~ estimate)

Mean pCO~2~ offset from reference values as a function of the factor applied to tau. The lines between discrete tau factors result from the same analysis performed with high resolution of the tau factor. Left Panel: Mean absolute offset (µatm). Right panel: Mean relative offset (% of absolute value).

Mean pCO₂ offset from reference values as a function of the factor applied to tau. The lines between discrete tau factors result from the same analysis performed with high resolution of the tau factor. Left Panel: Mean absolute offset (µatm). Right panel: Mean relative offset (% of absolute value).

5.5 Summary plots

i_tau_factor <- "1"

cast_dep <- tm %>%
  filter(tau_factor == i_tau_factor) %>%
  pivot_longer(c(dep, pCO2_corr, pCO2),
               names_to = "parameter",
               values_to = "value")

cast_dep_equi <- tm_pCO2_equi %>%
  pivot_longer(c(dep, pCO2_corr), names_to = "parameter", values_to = "value")

tm_sub <- tm %>%
  filter(tau_factor == i_tau_factor)

tm_grid_sub <- tm_grid %>%
  filter(tau_factor == i_tau_factor)

max_duration <- round(max(cast_dep$duration) / 1000, 0) * 1000

pdf(
  file = here::here("output/Plots/response_time",
                    "all_plots.pdf"),
  onefile = TRUE,
  width = 7,
  height = 10
)

for (i_ID in unique(tm$ID)) {
  for (i_station in unique(tm$station)) {
    # i_ID <- unique(cast_dep$ID)[1]
    # i_station <- unique(cast_dep$station)[1]
    i_ID
    i_station
    
    if (nrow(cast_dep %>% filter(ID == i_ID, station == i_station)) > 0) {
      if (nrow(tm %>% filter(ID == i_ID, station == i_station)) > 0) {
        if (nrow(tm_grid %>% filter(ID == i_ID, station == i_station)) > 0) {
          cast_dep_equi_sub <- cast_dep_equi %>%
            filter(ID == i_ID,
                   station == i_station)
          
          p_time_series <- cast_dep %>%
            filter(ID == i_ID,
                   station == i_station) %>%
            ggplot(aes(duration, value, col = phase)) +
            geom_point(size = 0.5) +
            geom_point(data = cast_dep_equi_sub,
                       aes(duration, value),
                       col = "black") +
            scale_y_reverse() +
            scale_x_continuous(breaks = seq(0, 6000, 500)) +
            labs(title = str_c("ID: ", i_ID, " | Station: ", i_station)) +
            facet_grid(parameter ~ ., scales = "free_y") +
            theme_bw()
          
          p_profile <- tm_sub %>%
            filter(ID == i_ID,
                   station == i_station,
                   phase %in% c("up", "down")) %>%
            ggplot() +
            geom_path(aes(pCO2_corr, dep, linetype = phase, col = "raw")) +
            geom_path(aes(pCO2, dep, linetype = phase, col = "corrected")) +
            scale_y_reverse() +
            coord_cartesian(ylim = c(25, 0), xlim = c(70, 250)) +
            scale_color_brewer(palette = "Set1",
                               name = "",
                               guide = FALSE) +
            scale_linetype(guide = FALSE) +
            labs(y = "Depth [m]",
                 x = "pCO2",
                 title = "full res")
          
          
          tm_pCO2_equi_grid_sub <- tm_pCO2_equi_grid %>%
            filter(ID == i_ID,
                   station == i_station)
          
          p_profile_grid <- tm_grid_sub %>%
            filter(ID == i_ID,
                   station == i_station) %>%
            arrange(dep_grid) %>%
            ggplot() +
            geom_path(aes(down, dep_grid, col = correction, linetype = "down")) +
            geom_path(aes(up, dep_grid, col = correction, linetype = "up")) +
            geom_point(data = tm_pCO2_equi_grid_sub, aes(pCO2_equi, dep_grid)) +
            scale_y_reverse() +
            coord_cartesian(ylim = c(25, 0), xlim = c(70, 250)) +
            scale_linetype(name = "cast") +
            scale_color_brewer(palette = "Set1", direction = -1) +
            labs(x = "pCO2",
                 title = "1m grid") +
            theme(axis.title.y = element_blank(),
                  axis.text.y = element_blank())
          
          
          p_delta_abs <- tm_grid %>%
            filter(ID == i_ID,
                   station == i_station,
                   correction == "pCO2") %>%
            arrange(dep_grid) %>%
            ggplot(aes(pCO2_delta, dep_grid, col = as.factor(tau_factor))) +
            geom_path() +
            geom_point() +
            scale_y_reverse(breaks = seq(0, 40, 2)) +
            scale_color_discrete(name = "tau factor", guide = FALSE) +
            labs(x = "delta pCO2 [µatm]", y = "Depth [m]") +
            geom_vline(xintercept = 0) +
            geom_vline(xintercept = c(-10, 10), col = "red")
          
          p_delta_rel <- tm_grid %>%
            filter(ID == i_ID,
                   station == i_station,
                   correction == "pCO2") %>%
            arrange(dep_grid) %>%
            ggplot(aes(pCO2_delta_rel, dep_grid, col = as.factor(tau_factor))) +
            geom_path() +
            geom_point() +
            scale_y_reverse(breaks = seq(0, 40, 2)) +
            scale_color_discrete(name = "tau factor") +
            labs(x = "delta pCO2 [% of absolute value]", y = "Depth [m]") +
            geom_vline(xintercept = 0) +
            geom_vline(xintercept = c(-10, 10), col = "red") +
            theme(axis.title.y = element_blank(),
                  axis.text.y = element_blank())
          
          print(p_time_series / (p_profile |
                                   p_profile_grid) / (p_delta_abs | p_delta_rel))
          rm(p_time_series,
             p_profile,
             p_profile_grid,
             p_delta_abs,
             p_delta_rel)
          
        }
      }
    }
    
    
    
  }
}

dev.off()

rm(
  cast_dep,
  cast_dep_equi,
  cast_dep_equi_sub,
  i_station,
  i_ID,
  max_duration,
  tm_pCO2_equi_grid_sub
)

6 Conclusion

Taking the temperature dependence of tau into account resulted in a slightly better agreement between up- and downcast, as well as downcast and reference value (Results not included above anymore)
For most quality metrics we find improved agreement for slightly positive tau factor ranging from 1.04 - 1.24
Still, the improvement by using a tau factor other then 1 is much lower than the standard deviation of the offset. Therefore, we assume that the improvement is not significant and choose to avoid the application of a tau factor for further analysis

7 Correct entire data set

Finally, the response time correction was applied to the full data set (not only profile data) based on the optimum parameterization determined above.

# Response time correction approach after Bittig et al, 2018
RT_corr <- function(c1, c0, dt, tau) {
  (1 / (2 * ((1 + (
    2 * tau / dt
  )) ^ (-1)))) * (c1 - (1 - (2 * ((
    1 + (2 * tau / dt)
  ) ^ (
    -1
  )))) * c0)
}



tm <-
  read_csv(
    here::here(
      "data/intermediate/_merged_data_files/merging_interpolation",
      "tm.csv"
    ),
    col_types = cols(
      ID = col_character(),
      pCO2_analog = col_double(),
      pCO2_corr = col_double(),
      Zero = col_factor(),
      Flush = col_factor(),
      Zero_counter = col_integer(),
      deployment = col_integer(),
      duration = col_double(),
      mixing = col_character(),
      lat = col_double(),
      lon = col_double()
    )
  )

tm <- tm %>%
  group_by(ID, station) %>%
  mutate(
    duration = as.numeric(date_time - min(date_time)),
    pump_power = if_else(date_time < ymd_hms("2018-07-17;13:08:34"), "1W", "8W")
  ) %>%
  arrange(date_time)


tau_fit <-
  read_csv(here::here(
    "data/intermediate/_merged_data_files/response_time",
    "tau_fit.csv"
  ))



# Assign T-dependent response time (tau) values
tau_fit <- tau_fit %>%
  rename(tau_intercept = `(Intercept)`, tau_slope = tem)

tm <- full_join(tm, tau_fit)

tm <- tm %>%
  mutate(tau = tau_intercept + tau_slope * tem) %>%
  select(-tau_intercept, -tau_slope)


# Prepare data set for RT correction

tm <- tm %>%
  group_by(ID, station) %>%
  arrange(date_time) %>%
  mutate(dt = as.numeric(as.character(date_time - lag(date_time)))) %>%
  ungroup()

# determine measurement frequency

freq <- tm %>%
  filter(dt < 13) %>%
  group_by(ID) %>%
  summarise(dt_mean = round(mean(dt, na.rm = TRUE), 0))

tm <- full_join(tm, freq)

# apply tau factor used
tm <- expand_grid(tm, tau_factor = parameters$tau_factor_used)

tm <- tm %>%
  mutate(tau_test = tau * tau_factor)


# Apply RT correction to entire data set

for (i_ID in unique(tm$ID)) {
  #i_ID <- "180716"
  
  freq_sub <- freq %>% filter(ID == i_ID) %>% pull(dt_mean)
  
  window <- 30 / freq_sub
  rolling_mean   <-
    rollify(~ mean(.x, na.rm = TRUE), window = window)
  
  tm_sub <- tm %>%
    filter(ID == i_ID) %>%
    group_by(station) %>%
    mutate(
      pCO2_RT = RT_corr(pCO2_corr, lag(pCO2_corr), dt, tau_test),
      pCO2_RT = if_else(pCO2_RT %in% c(Inf, -Inf), NaN, pCO2_RT),
      window = window,
      pCO2 = rolling_mean(pCO2_RT)
    ) %>%
    ungroup()
  
  # time shift RT corrected data
  shift <- as.integer(as.character(window / 2))
  
  tm_sub <- tm_sub %>%
    group_by(station) %>%
    mutate(pCO2 = lead(pCO2, shift)) %>%
    ungroup()
  
  
  if (exists("tm_corr")) {
    tm_corr <- bind_rows(tm_corr, tm_sub)
  }
  else{
    tm_corr <- tm_sub
  }
  
  rm(tm_sub, freq_sub, rolling_mean, shift, window)
  
}


rm(RT_corr, i_ID, freq)

tm_corr <- tm_corr %>%
  select(-c(tau, tau_factor, tau_test, window))

rm(tm)

7.1 Histogram

Below, histograms of RT corrected pCO₂ are shown, for selected ranges of pCO₂.

tm_corr %>% 
  ggplot(aes(pCO2)) +
  geom_histogram()

tm_corr %>% 
  filter(pCO2 < 0) %>% 
  ggplot(aes(pCO2)) +
  geom_histogram()

tm_corr %>% 
  filter(pCO2 < 200,
         pCO2 > 0) %>% 
  ggplot(aes(pCO2)) +
  geom_histogram()

8 Write summary file

Response time corrected pCO₂ data with absolute values >50 are written to file for further analysis. Negative pCO₂ readings are considered artefacts of the response time correction.

tm_corr %>%
  filter(pCO2 > 50) %>% 
  write_csv(
    here::here(
      "data/intermediate/_merged_data_files/response_time",
      "tm_RT_all.csv"
    )
  )

9 Sensitivity considerations

A change in DIC of 1 µmol kg^-1 corresponds to a change in pCO₂ of around 1 µatm, in the Central Baltic Sea at a pCO₂ of around 100 µatm (summertime conditions).

df <- data.frame(cbind((c(1720)),
                       (c(7))))

Tem <- seq(5, 25, 5)
pCO2 <- seq(50, 500, 20)

df <- merge(df, Tem)
names(df) <- c("AT", "S", "Tem")

df <- merge(df, pCO2)
names(df) <- c("AT", "S", "Tem", "pCO2")

df$AT <- df$AT * 1e-6

df$DIC <-
  carb(
    flag = 24,
    var1 = df$pCO2,
    var2 = df$AT,
    S = df$S,
    T = df$Tem,
    k1k2 = "m10",
    kf = "dg",
    pHscale = "T"
  )[, 16]
df$pCO2.corr <-
  carb(
    flag = 15,
    var1 = df$AT,
    var2 = df$DIC,
    S = df$S,
    T = df$Tem,
    k1k2 = "m10",
    kf = "dg",
    pHscale = "T"
  )[, 9]

df$pCO2.2 <- df$pCO2.corr + 25
df$DIC.2 <-
  carb(
    flag = 24,
    var1 = df$pCO2.2,
    var2 = df$AT,
    S = df$S,
    T = df$Tem,
    k1k2 = "m10",
    kf = "dg",
    pHscale = "T"
  )[, 16]


df$ratio <- (df$pCO2.2 - df$pCO2.corr) / (df$DIC.2 * 1e6 - df$DIC * 1e6)

df %>%
  ggplot(aes(pCO2, ratio, col = as.factor(Tem))) +
  geom_line() +
  scale_color_viridis_d(option = "C", name = "Tem [°C]") +
  labs(x = expression(italic(p)*CO[2] ~ (µatm)),
       y = expression(Delta ~ italic(p)*CO[2] ~ "/" ~ Delta ~ DIC ~ (µatm ~ µmol ^ {
         -1
       } ~ kg))) +
  scale_y_continuous(limits = c(0, 8), breaks = seq(0, 10, 1))

pCO₂ sensitivity to changes in DIC.

rm(df, Tem, pCO2)

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_Germany.1252  LC_CTYPE=English_Germany.1252   
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C                    
[5] LC_TIME=English_Germany.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] patchwork_1.1.1   tibbletime_0.1.6  lubridate_1.7.9.2 broom_0.7.3      
 [5] seacarb_3.2.14    oce_1.2-0         gsw_1.0-5         testthat_3.0.1   
 [9] forcats_0.5.0     stringr_1.4.0     dplyr_1.0.2       purrr_0.3.4      
[13] readr_1.4.0       tidyr_1.1.2       tibble_3.0.4      ggplot2_3.3.3    
[17] tidyverse_1.3.0   workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] httr_1.4.2         viridisLite_0.3.0  jsonlite_1.7.2     splines_4.0.3     
 [5] here_1.0.1         modelr_0.1.8       assertthat_0.2.1   highr_0.8         
 [9] cellranger_1.1.0   yaml_2.2.1         pillar_1.4.7       backports_1.2.1   
[13] lattice_0.20-41    glue_1.4.2         digest_0.6.27      RColorBrewer_1.1-2
[17] promises_1.1.1     rvest_0.3.6        colorspace_2.0-0   htmltools_0.5.0   
[21] httpuv_1.5.4       Matrix_1.2-18      pkgconfig_2.0.3    haven_2.3.1       
[25] scales_1.1.1       whisker_0.4        later_1.1.0.1      git2r_0.27.1      
[29] mgcv_1.8-33        generics_0.1.0     farver_2.0.3       ellipsis_0.3.1    
[33] withr_2.3.0        cli_2.2.0          magrittr_2.0.1     crayon_1.3.4      
[37] readxl_1.3.1       evaluate_0.14      ps_1.5.0           fs_1.5.0          
[41] fansi_0.4.1        nlme_3.1-149       xml2_1.3.2         tools_4.0.3       
[45] hms_0.5.3          lifecycle_0.2.0    munsell_0.5.0      reprex_0.3.0      
[49] compiler_4.0.3     rlang_0.4.10       grid_4.0.3         rstudioapi_0.13   
[53] labeling_0.4.2     rmarkdown_2.6      gtable_0.3.0       DBI_1.1.0         
[57] R6_2.5.0           knitr_1.30         utf8_1.1.4         rprojroot_2.0.2   
[61] stringi_1.5.3      Rcpp_1.0.5         vctrs_0.3.6        dbplyr_2.0.0      
[65] tidyselect_1.1.0   xfun_0.19

Response time correction of Contros HydroC pCO₂ data

Jens Daniel Müller

20 February, 2021

1 Scope of this script

2 List of relevant parameters

3 Response time (\(\tau\)) determination

3.1 HydroC sensor settings

3.2 Model fitting

3.2.1 Example plot

3.2.2 Flush intervals

3.2.3 Fitting algorithm

3.2.4 Residual threshold

3.3 Analysis

3.3.1 General considerations

3.3.2 Fit interval length

3.3.3 Time series of \(\tau\)

3.3.4 Temperature dependence

3.3.5 Final \(\tau\) values

4 Response time correction

4.1 Data preparation

4.1.1 Profiling speed

4.2 Apply correction

5 Diagnosis

5.1 Individual profiles

5.2 Down- vs upcast

5.3 Downcast vs reference value

5.4 Summary metrics

5.5 Summary plots

6 Conclusion

7 Correct entire data set

7.1 Histogram

8 Write summary file

9 Sensitivity considerations

Response time correction of Contros HydroC pCO2 data

Jens Daniel Müller

20 February, 2021

1 Scope of this script

2 List of relevant parameters

3 Response time (\(\tau\)) determination

3.1 HydroC sensor settings

3.2 Model fitting

3.2.1 Example plot

3.2.2 Flush intervals

3.2.3 Fitting algorithm

3.2.4 Residual threshold

3.3 Analysis

3.3.1 General considerations

3.3.2 Fit interval length

3.3.3 Time series of \(\tau\)

3.3.4 Temperature dependence

3.3.5 Final \(\tau\) values

4 Response time correction

4.1 Data preparation

4.1.1 Profiling speed

4.2 Apply correction

5 Diagnosis

5.1 Individual profiles

5.2 Down- vs upcast

5.3 Downcast vs reference value

5.4 Summary metrics

5.5 Summary plots

6 Conclusion

7 Correct entire data set

7.1 Histogram

8 Write summary file

9 Sensitivity considerations

Response time correction of Contros HydroC pCO₂ data