Response time correction of Contros HydroC pCO2 data
Jens Daniel Müller
08 November, 2019
Last updated: 2019-11-08
Checks: 7 0
Knit directory: BloomSail/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 6cb1935 | jens-daniel-mueller | 2019-11-08 | response_time updated |
| html | 33e3659 | jens-daniel-mueller | 2019-10-22 | Build site. |
| Rmd | efcafd1 | jens-daniel-mueller | 2019-10-22 | Added data base, merging, and RT determination |
| html | 1595fe9 | jens-daniel-mueller | 2019-10-21 | Build site. |
| html | a059c41 | jens-daniel-mueller | 2019-10-21 | Build site. |
| Rmd | eff54ce | jens-daniel-mueller | 2019-10-21 | Added CTD read-in |
| html | 32ec4f7 | jens-daniel-mueller | 2019-10-21 | Build site. |
| Rmd | b2d2bbb | jens-daniel-mueller | 2019-10-21 | Structured data base and response time Rmd |
| html | bafa88f | jens-daniel-mueller | 2019-10-21 | Build site. |
| Rmd | 53ad162 | jens-daniel-mueller | 2019-10-21 | Structured data base and response time Rmd |
| html | 076a36b | jens-daniel-mueller | 2019-10-21 | Build site. |
| Rmd | 3e8a32e | jens-daniel-mueller | 2019-10-21 | Structured data base and response time Rmd |
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| Rmd | 53ae361 | jens-daniel-mueller | 2019-10-21 | Added data base and response time Rmd |
library(tidyverse)
library(seacarb)
library(data.table)
library(broom)
library(lubridate)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<-data.table(df)
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(pCO[2]~(µatm)), y=expression(Delta~pCO[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.
| Version | Author | Date |
|---|---|---|
| 33e3659 | jens-daniel-mueller | 2019-10-22 |
rm(df, Tem, pCO2)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 set to:
10 sec for all recordings including SD_datafile_20180714_073641CO2-0618-001.txt
Increase to 2 sec 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
Increase to 2 sec 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
Response time determination
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 occuring at the start of each Flush period
- only completet Flush periods (duration > 500 sec) included
# Read and prepare data
df <- read_csv(here::here("data/_merged_data_files",
"BloomSail_CTD_HydroC_Contros_clean.csv"),
col_types = cols(ID = col_character(),
pCO2_analog = col_double(),
pCO2 = col_double(),
Zero = col_factor(),
Flush = col_factor(),
Zero_ID = col_integer(),
duration = col_double(),
mixing = col_character()))
df <- df %>%
select(date_time, ID, dep, tem, Flush, pCO2, Zero_ID, duration, mixing)
df <- df %>%
filter(Flush == 1, mixing == "equilibration")
df <- df %>%
group_by(Zero_ID) %>%
mutate(duration = duration- min(duration),
max_duration = max(duration)) %>%
ungroup() %>%
filter(max_duration >= 500) %>%
select(-max_duration)An example plot for a nls model fitted to pCO2 observations during a Flush phase is shown below.
# Plot example Flush period with exponential fit ----------------------
i <- 95
df_ID <- df %>%
filter(Zero_ID == i, duration <= 300)
fit <-
df_ID %>%
nls(pCO2 ~ SSasymp(duration, yf, y0, log_alpha), data = .)
tau <- as.numeric(exp(-tidy(fit)[3,2]))
pCO2_end <- as.numeric(tidy(fit)[1,2])
pCO2_start <- as.numeric(tidy(fit)[2,2])
dpCO2 = pCO2_end - pCO2_start
mean_abs_resid <- mean(abs(resid(fit)))
augment(fit) %>%
ggplot(aes(duration, pCO2))+
geom_point()+
geom_line(aes(y = .fitted))+
geom_vline(xintercept = tau)+
geom_hline(yintercept = pCO2_start + 0.63 *(dpCO2))+
labs(y=expression(pCO[2]~(µatm)), x="Duration of Flush period (s)")
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.
rm(df_ID, fit, i, tau, dpCO2, pCO2_end, pCO2_start)duration_intervals <- seq(150,500,50)In the following we determine the response time tau for all zeroings and for total durations of:
150, 200, 250, 300, 350, 400, 450, 500 secs
# Plot all individual Flush periods with exponential fit ----------------------
pdf(file=here::here("docs/figure",
"RT_determination.pdf"), onefile = TRUE, width = 7, height = 4)
for (i in unique(df$Zero_ID)) {
for (max_duration in duration_intervals) {
df_ID <- df %>%
filter(Zero_ID == i, duration <= max_duration)
fit <-
try(
df_ID %>%
nls(pCO2 ~ SSasymp(duration, yf, y0, log_alpha), data = .),
TRUE)
if (class(fit) == "nls"){
tau <- as.numeric(exp(-tidy(fit)[3,2]))
pCO2_end <- as.numeric(tidy(fit)[1,2])
pCO2_start <- as.numeric(tidy(fit)[2,2])
dpCO2 = pCO2_end - pCO2_start
mean_abs_resid <- mean(abs(resid(fit))/pCO2_end)*100
temp <- as_tibble(bind_cols(Zero_ID = i, duration = max_duration, tau = tau, resid = mean_abs_resid))
if (exists("tau_df")){tau_df <- bind_rows(tau_df, temp)}
else {tau_df <- temp}
print(
augment(fit) %>%
ggplot(aes(duration, pCO2))+
geom_point()+
geom_line(aes(y = .fitted))+
geom_vline(xintercept = tau)+
geom_hline(yintercept = pCO2_start + 0.63 *(dpCO2))+
labs(y=expression(pCO[2]~(µatm)), x="Duration of Flush period (s)",
title = paste("Zero_ID: ", i, "Mean absolute residual (%): ", round(mean_abs_resid*100, 2)))+
xlim(0,600)
)
}
}
}
dev.off()png
2 rm(df_ID, fit, i, tau, dpCO2, pCO2_end, pCO2_start, temp, max_duration, mean_abs_resid)
# Plot individual Flush periods with linearized response variable --------
# for (i in unique(df$Zero_ID)) {
#
# #i <- 50
# df_ID <- df %>%
# filter(Zero_ID == i,
# mixing == "equilibration")
#
# mean_pCO2 <- df_ID %>%
# slice((n()-4) : n()) %>%
# summarise(mean_pCO2 = mean(pCO2))
#
# df_ID <- full_join(df_ID, mean_pCO2) %>%
# mutate(dpCO2 = max(pCO2) - pCO2,
# ln_dpCO2 = log(dpCO2))
#
#
# df_ID %>%
# ggplot(aes(duration_equi, ln_dpCO2))+
# geom_point()+
# geom_smooth(method = "lm")+
# theme_bw()
#
# # augment(fit) %>%
# # ggplot(aes(duration_equi, pCO2))+
# # geom_point()+
# # geom_line(aes(y = .fitted))+
# # geom_vline(xintercept = tau)
#
# ggsave(here::here("/Plots/TinaV/Sensor/HydroC_diagnostics/Response_time_fits",
# paste(i,"_Zero_ID_HydroC_RT_linear.jpg", sep="")),
# width = 10, height = 4)
# }A pdf with plots of all individual response time fits can be accessed here
tau_high <- 120
tau_low <- 20
resid_limit <- 1
tau_df_sub <- tau_df %>%
filter(resid < resid_limit, tau > tau_low, tau < tau_high)
tau_total <- nrow(tau_df)
tau_sub <- nrow(tau_df_sub)Outcome
Response times were determined sucessfully determined by nls in a total number of 417 cases.
Restriction of the determined tau values to those falling between 20 and 120 seconds and corresponding to a fit with a mean absolute residual below 1 % of the final equilibrium pCO2, results in 365 remaining tau values (87.5 %).
tau_df_sub %>%
ggplot(aes(resid))+
geom_histogram()+
facet_wrap(~duration)
tau_df_sub %>%
ggplot(aes(Zero_ID, tau, col=duration))+
geom_point()+
scale_color_viridis_c()
tau_df_sub %>%
group_by(Zero_ID) %>%
mutate(d_tau = tau - mean(tau)) %>%
ggplot(aes(duration, d_tau))+
geom_violin(aes(group=duration))+
geom_point()
tau_df_sub %>%
group_by(Zero_ID) %>%
mutate(d_tau = tau - mean(tau)) %>%
ggplot(aes(duration, d_tau))+
geom_smooth()+
geom_point()+
facet_wrap(~Zero_ID)
tau_df_sub %>%
group_by(duration) %>%
mutate(n_tau = n()) %>%
ggplot(aes(duration, n_tau))+
geom_point()
This approach
Contros in-house
Comparison
Pre-smoothing
Response time correction
Post-smoothing
Response time optimization
Open tasks / questions
- Compare Contros and own response time estimates
- Compare differnt response time correction methods (Bittig vs. Fiedler, Miloshevich, Fietzek)
- Test impact of duration for response time estimation on final mean response time
- Test impact of selection criterion for “good” response time estimates on final mean response time
- Check results from field response time experiment (high zeroing frequency)
- Why does nls model failure increase with higher fit duration
sessionInfo()R version 3.5.0 (2018-04-23)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17763)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.7.4 broom_0.5.2 data.table_1.12.6
[4] seacarb_3.2.12 oce_1.1-1 gsw_1.0-5
[7] testthat_2.2.1 forcats_0.4.0 stringr_1.4.0
[10] dplyr_0.8.3 purrr_0.3.3 readr_1.3.1
[13] tidyr_1.0.0 tibble_2.1.3 ggplot2_3.2.1
[16] tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 xfun_0.10 haven_2.1.1
[4] lattice_0.20-35 colorspace_1.4-1 vctrs_0.2.0
[7] generics_0.0.2 viridisLite_0.3.0 htmltools_0.4.0
[10] yaml_2.2.0 rlang_0.4.1 pillar_1.4.2
[13] glue_1.3.1 withr_2.1.2 modelr_0.1.5
[16] readxl_1.3.1 lifecycle_0.1.0 munsell_0.5.0
[19] gtable_0.3.0 workflowr_1.4.0 cellranger_1.1.0
[22] rvest_0.3.4 evaluate_0.14 labeling_0.3
[25] knitr_1.25 highr_0.8 Rcpp_1.0.2
[28] scales_1.0.0 backports_1.1.5 jsonlite_1.6
[31] fs_1.3.1 hms_0.5.1 digest_0.6.22
[34] stringi_1.4.3 grid_3.5.0 rprojroot_1.3-2
[37] here_0.1 cli_1.1.0 tools_3.5.0
[40] magrittr_1.5 lazyeval_0.2.2 crayon_1.3.4
[43] whisker_0.4 pkgconfig_2.0.3 zeallot_0.1.0
[46] xml2_1.2.2 assertthat_0.2.1 rmarkdown_1.16
[49] httr_1.4.1 rstudioapi_0.10 R6_2.4.0
[52] nlme_3.1-137 git2r_0.26.1 compiler_3.5.0