MLD
Jens Daniel Müller
18 March, 2020
Last updated: 2020-03-18
Checks: 7 0
Knit directory: BloomSail/
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library(tidyverse)
library(seacarb)
library(oce)
library(marelac)1 Sensor data
1m gridded, downcast profiles were used.
df <-
read_csv(here::here("Data/_merged_data_files", "BloomSail_CTD_HydroC_CT.csv"))2 Mixed layer depth
df <- df %>%
mutate(rho = swSigma(salinity = sal, temperature = tem, pressure = dep/10))
# seacarb = rho(S = sal, T = tem, P = pres),
# marelac = sw_dens(S = sal, t = tem, p = pres + 1, method = "Gibbs")# density criterion
df <- expand_grid(df, rho_lim = c(0.1,0.2,0.5))
df <- df %>%
group_by(ID, date_time_ID, station, rho_lim) %>%
mutate(d_rho = rho - min(rho)) %>%
mutate(layer = if_else(d_rho > rho_lim, "deep", "surface")) %>%
ungroup()
MLD <- df %>%
group_by(ID, date_time_ID, station, rho_lim) %>%
filter(d_rho > rho_lim) %>%
summarise(MLD = min(dep)) %>%
ungroup()
df <- full_join(df, MLD)df %>%
filter(dep < 30) %>%
arrange(dep) %>%
ggplot(aes(rho, dep))+
geom_hline(aes(yintercept = MLD))+
geom_path()+
geom_point(aes(col=layer))+
scale_y_reverse()+
scale_color_brewer(palette = "Set1", direction = -1)+
facet_grid(ID~station)MLD_sum <- MLD %>%
select(-station) %>%
group_by(ID, date_time_ID, rho_lim)%>%
summarise_all(list(~mean(.),~sd(.),~min(.), ~max(.)))
MLD_sum %>%
ggplot()+
geom_ribbon(aes(date_time_ID, ymin = min, ymax = max), alpha = 0.5)+
geom_path(aes(date_time_ID, mean))+
geom_errorbar(aes(date_time_ID, ymin = mean-sd, ymax = mean+sd))+
geom_point(aes(date_time_ID, mean))+
labs(x="Mean transect date", y="Mixed layer depth [m]")+
ylim(0,30)+
facet_wrap(~rho_lim)
Rho, S, and T profiles were plotted individually pdf here.
df_long <- df %>%
pivot_longer(cols = c("sal", "tem", "rho"), names_to = "parameter", values_to = "value")
pdf(file=here::here("output/Plots/MLD",
"profiles_MLD_individual.pdf"), onefile = TRUE, width = 9, height = 5)
for(i_ID in unique(df_long$ID)){
for(i_station in unique(df_long$station)){
#i_ID <- unique(df_long$ID)[1]
#i_station <- unique(df_long$station)[1]
if (nrow(df_long %>% filter(ID == i_ID, station == i_station)) > 0){
print(
df_long %>%
arrange(date_time) %>%
filter(ID == i_ID,
station == i_station,
dep<20) %>%
ggplot(aes(value,dep))+
geom_hline(aes(yintercept = MLD))+
geom_path()+
scale_y_reverse()+
scale_color_brewer(palette = "Set1")+
labs(y="Depth [m]", title = str_c(i_ID," | ",i_station))+
facet_wrap(~parameter, scales = "free_x")+
theme_bw()
)
}
}
}
dev.off()
rm(i_ID, i_station)3 NCP penetration depth
CT_ts <-
read_csv(here::here("Data/_merged_data_files", "BloomSail_CTD_HydroC_CT_cumulative_timeseries.csv"))
CT_profiles <-
read_csv(here::here("Data/_merged_data_files", "BloomSail_CTD_HydroC_CT_cumulative_profiles.csv"))
CT_conc_surf <-
CT_profiles %>%
filter(dep < 3,
parameter == "CT",
sign == "neg") %>%
group_by(ID, date_time_ID) %>%
summarise(dCT = mean(diff_value))
CT_conc_surf %>%
ggplot(aes(date_time_ID, dCT))+
geom_path()
NCP_integ <- CT_ts %>%
filter(sign == "neg") %>%
group_by(ID, date_time_ID) %>%
summarise(dNCPi = sum(dCT))
NCP_integ %>%
ggplot(aes(date_time_ID, dNCPi))+
geom_path()
CT <- full_join(CT_conc_surf, NCP_integ)
CT <- CT %>%
mutate(dep_NCP = -(dNCPi/dCT)*1e3)
CT_long <- CT %>%
pivot_longer(3:5, names_to = "parameter", values_to = "values")
CT_long %>%
ggplot(aes(date_time_ID, values))+
geom_path()+
geom_point()+
facet_grid(parameter~., scales = "free_y")
sessionInfo()R version 3.5.0 (2018-04-23)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
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] marelac_2.1.9 shape_1.4.4 seacarb_3.2.12 oce_1.2-0
[5] gsw_1.0-5 testthat_2.3.1 forcats_0.4.0 stringr_1.4.0
[9] dplyr_0.8.3 purrr_0.3.3 readr_1.3.1 tidyr_1.0.0
[13] tibble_2.1.3 ggplot2_3.3.0 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 xfun_0.10 haven_2.2.0 lattice_0.20-35
[5] colorspace_1.4-1 vctrs_0.2.0 generics_0.0.2 htmltools_0.4.0
[9] yaml_2.2.0 rlang_0.4.5 later_1.0.0 pillar_1.4.2
[13] withr_2.1.2 glue_1.3.1 DBI_1.0.0 dbplyr_1.4.2
[17] modelr_0.1.5 readxl_1.3.1 lifecycle_0.1.0 munsell_0.5.0
[21] gtable_0.3.0 workflowr_1.6.0 cellranger_1.1.0 rvest_0.3.5
[25] evaluate_0.14 labeling_0.3 knitr_1.26 httpuv_1.5.2
[29] broom_0.5.3 Rcpp_1.0.2 promises_1.1.0 backports_1.1.5
[33] scales_1.0.0 jsonlite_1.6 fs_1.3.1 hms_0.5.2
[37] digest_0.6.22 stringi_1.4.3 grid_3.5.0 rprojroot_1.3-2
[41] here_0.1 cli_1.1.0 tools_3.5.0 magrittr_1.5
[45] crayon_1.3.4 pkgconfig_2.0.3 zeallot_0.1.0 xml2_1.2.2
[49] reprex_0.3.0 lubridate_1.7.4 rstudioapi_0.10 assertthat_0.2.1
[53] rmarkdown_2.0 httr_1.4.1 R6_2.4.0 nlme_3.1-137
[57] git2r_0.26.1 compiler_3.5.0