Merging and interpolation of observations
Jens Daniel Müller
01 October, 2020
Last updated: 2020-10-01
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Knit directory: BloomSail/
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library(tidyverse)
library(lubridate)
library(zoo)1 CTD (ts) + HydroC CO2 data (th)
1.1 Merging summarized data sets
# Load Sensor and HydroC data ---------------------------------------------
ts <- read_csv(here::here("data/intermediate/_summarized_data_files",
"ts.csv"),
col_types = list("pCO2_analog" = col_double()))
th <- read_csv(here::here("data/intermediate/_summarized_data_files",
"th.csv"))
# Time offset correction ----------------------------------------------
# Time offset was determined by comparing zeroing reads from Sensor and th
# in the plots produced in the section Time stamp synchronicity below
# before applying this correction
ts <- ts %>%
mutate(day = yday(date_time),
date_time = if_else(day >= 206 & day <= 220,
date_time - 80, date_time - 10)) %>%
select(-day)
# Merge Sensor and HydroC data --------------------------------------------
ts_th <- full_join(ts, th) %>%
arrange(date_time)
rm(th, ts)1.2 Interpolation to common time stamp
CTD and auxillary recordings (15 sec measurment interval) are interpolated to HydroC time stamps (first 10 sec, than 1 sec measurement interval) when gaps between observations are not larger than 20. Thereafter, HydroC readings not falling in regular transects/profilings are removed, by removing rows with NA depth values. Furthermore, CTD readings without corresponding HydroC reading are removed, except during periods when HydroC was not operating.
# Interpolate Sensor data to HydroC time stamp
ts_th <- ts_th %>%
mutate(
dep_maxgap = na.approx(dep, na.rm = FALSE, maxgap = 20),
dep = approxfun(date_time, dep)(date_time),
sal = approxfun(date_time, sal)(date_time),
tem = approxfun(date_time, tem)(date_time),
pCO2_analog = approxfun(date_time, pCO2_analog)(date_time)
) %>%
filter(!is.na(dep_maxgap)) %>% #remove HC readings not falling in regular transects/profiling
select(-dep_maxgap) %>%
fill(ID, type, station) %>%
filter(!is.na(deployment),!is.na(pCO2_analog)) # removes CTD readings without corresponding HydroC reading
# filter(!is.na(deployment) | is.na(pCO2_analog)) # removes CTD readings without corresponding HydroC reading, except during periods when HydroC was not operating
# Time stamp synchronicity
ts_th_Zero <- ts_th %>%
filter(Zero == 1 | Flush == 1 & duration < 120)
pdf(
file = here::here(
"output/Plots/merging_interpolation",
"Zero_time_synchronization.pdf"
),
onefile = TRUE,
width = 5,
height = 5
)
for (i_deployment in unique(ts_th$deployment)) {
#i_deployment <- unique(ts_th_Zero$deployment)[1]
ts_th_Zero_deployment <- ts_th_Zero %>%
filter(deployment == i_deployment)
for (i_Zero_counter in unique(ts_th_Zero_deployment$Zero_counter)) {
#i_Zero_counter <- unique(ts_th_Zero_deployment$Zero_counter)[1]
print(
ts_th_Zero_deployment %>%
filter(Zero_counter == i_Zero_counter) %>%
ggplot() +
geom_point(aes(date_time, pCO2_corr, col = "HydroC")) +
geom_point(aes(date_time, pCO2_analog, col = "analog")) +
labs(
title = paste("Depl: ", i_deployment,
" | Zero_counter: ", i_Zero_counter)
)
)
}
}
dev.off()png
2 rm(ts_th_Zero,
ts_th_Zero_deployment,
i_deployment,
i_Zero_counter)1.3 Time series pCO2
1.3.1 Read cleaned processed data
HydroC pCO2 data were provided by KM Contros after applying a drift correction to the raw data, which was based on pre- and post-deployment calibration results.
# Read Contros corrected data file, based on cleaned recordings
th_new_withAW <-
read_csv2(
here::here(
"data/input/TinaV/Sensor/HydroC-pCO2/corrected_Contros",
"parameter&pCO2s(method 43)_new_withAW.txt"
),
col_names = c(
"date_time",
"Zero",
"Flush",
"p_NDIR",
"p_in",
"T_control",
"T_gas",
"%rH_gas",
"Signal_raw",
"Signal_ref",
"T_sensor",
"pCO2_corr",
"Runtime",
"nr.ave"
)
) %>%
mutate(
date_time = dmy_hms(date_time),
Flush = as.factor(as.character(Flush)),
Zero = as.factor(as.character(Zero))
)
th_new_withAW <- th_new_withAW %>%
slice(seq(1, n(), 10))
# Read Contros corrected data file, based on cleaned recordings without water vapor correction
th_new_withoutAW_all <-
read_csv2(
here::here(
"data/input/TinaV/Sensor/HydroC-pCO2/corrected_Contros",
"parameter&pCO2s(method 43)_new_withoutAW.txt"
),
col_names = c(
"date_time",
"Zero",
"Flush",
"p_NDIR",
"p_in",
"T_control",
"T_gas",
"%rH_gas",
"Signal_raw",
"Signal_ref",
"T_sensor",
"pCO2_corr",
"Runtime",
"nr.ave"
)
) %>%
mutate(
date_time = dmy_hms(date_time),
Flush = as.factor(as.character(Flush)),
Zero = as.factor(as.character(Zero))
)
th_new_withoutAW <- th_new_withoutAW_all %>%
slice(seq(1, n(), 10))
th_pre_cleaning <-
read_csv(here::here(
"data/intermediate/_summarized_data_files",
"th_pre_cleaning.csv"
))
th_pre_cleaning <- th_pre_cleaning %>%
slice(seq(1, n(), 10))
ts_th_sub <- ts_th %>%
slice(seq(1, n(), 10))1.3.2 Comparison of preliminary pCO2 data
1.3.2.1 Analog vs internal
ggplot() +
geom_path(data = th_pre_cleaning, aes(date_time, pCO2_corr, col = "pre cleaning")) +
geom_path(data = ts_th_sub, aes(date_time, pCO2_corr, col = "HydroC, drift corrected")) +
geom_path(data = ts_th_sub, aes(date_time, pCO2_analog, col = "analog CTD")) +
scale_color_brewer(palette = "Set1", name = "pCO2 record") +
coord_cartesian(ylim = c(0, 600)) +
labs(y = expression(pCO[2] ~ (µatm)), x = "") +
facet_wrap( ~ deployment, scales = "free_x", ncol = 1)
pCO2 record after interpolation to HydroC timestamp (analog output from HydroC and drift corrected data provided by Contos). ID refers to the starting date of each cruise. Please note that pCO2_analog measurement range is technically restricted to 100-500 µatm. Zeroing periods are included.
1.3.2.2 Raw vs clean
th_comparison <- full_join(
ts_th_sub %>% select(date_time, deployment, pCO2_corr),
th_new_withAW %>% select(date_time, pCO2_corr) %>% rename(pCO2_withAW = pCO2_corr)
)
th_comparison <- full_join(
th_comparison,
th_new_withoutAW %>% select(date_time, pCO2_corr) %>% rename(pCO2_withoutAW = pCO2_corr)
)
th_comparison %>%
ggplot() +
geom_path(data = th_pre_cleaning, aes(date_time, pCO2_corr, col = "pre cleaning")) +
geom_path(aes(date_time, pCO2_corr, col = "HydroC, drift corrected")) +
geom_path(aes(date_time, pCO2_withAW, col = "withAW")) +
geom_path(aes(date_time, pCO2_withoutAW, col = "withoutAW")) +
scale_color_brewer(palette = "Set1", name = "pCO2 record") +
coord_cartesian(ylim = c(0, 600)) +
labs(y = expression(pCO[2] ~ (µatm)), x = "") +
facet_wrap( ~ deployment, scales = "free_x", ncol = 1)
1.3.2.3 Water vapor correction
th_comparison %>%
ggplot() +
geom_path(data = th_pre_cleaning %>% slice_sample(prop = 0.1),
aes(date_time, 0, col = "pre runtime")) +
geom_path(aes(date_time, pCO2_corr - pCO2_withAW, col = "orig - with AW")) +
scale_color_brewer(palette = "Set1", name = "pCO2 record") +
labs(y = expression(pCO[2] ~ (µatm)), x = "") +
facet_wrap( ~ deployment, scales = "free_x", ncol = 1)
th_comparison %>%
ggplot() +
geom_path(data = th_pre_cleaning, aes(date_time, 0, col = "pre runtime")) +
geom_path(aes(date_time, pCO2_withoutAW - pCO2_withAW, col = "without - with AW")) +
scale_color_brewer(palette = "Set1", name = "pCO2 record") +
labs(y = expression(pCO[2] ~ (µatm)), x = "") +
facet_wrap( ~ deployment, scales = "free_x", ncol = 1)
rm(ts_th_sub,
th_pre_cleaning,
th_new_withAW,
th_new_withoutAW,
th_comparison)1.3.3 replace pCO2 data
th_new_withoutAW_all <- th_new_withoutAW_all %>%
select(date_time, pCO2_corr)
ts_th <- ts_th %>%
select(-pCO2_corr)
ts_th <- full_join(ts_th, th_new_withoutAW_all)
rm(th_new_withoutAW_all)1.4 Write merged file
ts_th %>%
write_csv(here::here("data/intermediate/_merged_data_files/merging_interpolation", "ts_th.csv"))1.4.1 Offset analog vs post-processed pCO2
ts_th %>%
ggplot() +
geom_path(aes(date_time, pCO2_corr - pCO2_analog)) +
ylim(-30, 0) +
labs(y = expression(pCO[2] ~ (µats_th)), x = "") +
facet_wrap( ~ ID, scales = "free_x", ncol = 1)
pCO2 difference betweeb HydroC and drift corrected data provided by Contos. Please note that pCO2 range is restricted to +/- 50 µatm.
2 Merges sensor (ts_th) + track (tt) data
tt <- read_csv(here::here("data/intermediate/_summarized_data_files",
"tt.csv"))
tm <- full_join(ts_th, tt) %>%
arrange(date_time)
# interpolate tt data and than remove columns that originate from tt time stamp
tm <- tm %>%
mutate(lat = approxfun(date_time, lat)(date_time),
lon = approxfun(date_time, lon)(date_time)) %>%
filter(!is.na(dep))
tm %>% write_csv(here::here("data/intermediate/_merged_data_files/merging_interpolation",
"tm.csv"))
rm(tm, ts_th, tt)
sessionInfo()R version 4.0.2 (2020-06-22)
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] zoo_1.8-8 lubridate_1.7.9 forcats_0.5.0 stringr_1.4.0
[5] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0
[9] tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 here_0.1 lattice_0.20-41 assertthat_0.2.1
[5] rprojroot_1.3-2 digest_0.6.25 R6_2.4.1 cellranger_1.1.0
[9] backports_1.1.8 reprex_0.3.0 evaluate_0.14 highr_0.8
[13] httr_1.4.2 pillar_1.4.6 rlang_0.4.7 readxl_1.3.1
[17] rstudioapi_0.11 whisker_0.4 blob_1.2.1 rmarkdown_2.3
[21] labeling_0.3 munsell_0.5.0 broom_0.7.0 compiler_4.0.2
[25] httpuv_1.5.4 modelr_0.1.8 xfun_0.16 pkgconfig_2.0.3
[29] htmltools_0.5.0 tidyselect_1.1.0 fansi_0.4.1 crayon_1.3.4
[33] dbplyr_1.4.4 withr_2.2.0 later_1.1.0.1 grid_4.0.2
[37] jsonlite_1.7.0 gtable_0.3.0 lifecycle_0.2.0 DBI_1.1.0
[41] git2r_0.27.1 magrittr_1.5 scales_1.1.1 cli_2.0.2
[45] stringi_1.4.6 farver_2.0.3 fs_1.4.2 promises_1.1.1
[49] xml2_1.3.2 ellipsis_0.3.1 generics_0.0.2 vctrs_0.3.2
[53] RColorBrewer_1.1-2 tools_4.0.2 glue_1.4.1 hms_0.5.3
[57] yaml_2.2.1 colorspace_1.4-1 rvest_0.3.6 knitr_1.30
[61] haven_2.3.1