Finnmaid and GETM
Jens Daniel Müller und Lara Sophie Burchardt
23 March, 2020
Last updated: 2020-03-23
Checks: 6 1
Knit directory: BloomSail/
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library(tidyverse)
library(ncdf4)
library(vroom)
library(lubridate)
library(here)
library(seacarb)
library(oce)
library(patchwork)# route
select_route <- "E"
# latitude limits
low_lat <- 57.3
high_lat <- 57.5
#depth range to subset GETM 3d files
d1_shallow <- 0
d1_deep <- 80
# date limits
start_date <- "2018-06-01"
end_date <- "2018-08-31"1 GETM Data preparation
1.1 Salinity and temperature profiles
Mean salinity and temperature profiles within the BloomSail area were extracted from daily GETM transects beneath the Finnmaid track.
nc <- nc_open(here::here("data/GETM", "Finnmaid.E.3d.2018.nc"))
lat <- ncvar_get(nc, "latc")
time_units <- nc$dim$time$units %>% #we read the time unit from the netcdf file to calibrate the time
substr(start = 15, stop = 33) %>% #calculation, we take the relevant information from the string
ymd_hms() # and transform it to the right format
t <- time_units + ncvar_get(nc, "time") # read time vector
rm(time_units)
d <- ncvar_get(nc, "zax") # read depths vector
for (var_3d in c("salt", "temp")) {
array <- ncvar_get(nc, var_3d) # store the data in a 3-dimensional array
#dim(array) # should be 3d with dimensions: 544 coordinates, 51 depths, and number of days of month
fillvalue <- ncatt_get(nc, var_3d, "_FillValue")
# Working with the data
array[array == fillvalue$value] <- NA
for (i in seq(1,length(t),1)){
# i <- 3
array_slice <- array[, , i] # slices data from one day
array_slice_df <- as.data.frame(t(array_slice))
array_slice_df <- as_tibble(array_slice_df)
gt_3d_part <- array_slice_df %>%
set_names(as.character(lat)) %>%
mutate(dep = -d) %>%
gather("lat", "value", 1:length(lat)) %>%
mutate(lat = as.numeric(lat)) %>%
filter(lat > low_lat, lat < high_lat,
dep >= d1_shallow, dep <= d1_deep) %>%
#summarise_all("mean") %>%
mutate(var = var_3d,
date_time=t[i]) %>%
dplyr::select(date_time, dep, value, var) #%>%
#filter(date_time >= start_date, date_time <= end_date)
if (exists("gt_3d")) {
gt_3d <- bind_rows(gt_3d, gt_3d_part)
} else {gt_3d <- gt_3d_part}
rm(array_slice, array_slice_df, gt_3d_part)
}
rm(array, fillvalue)
}
nc_close(nc)
rm(nc)
gt_3d_jun_aug <- gt_3d %>%
filter(date_time >= start_date & date_time <= end_date) %>%
group_by(dep,var,date_time ) %>%
summarise_all(list(value=~mean(.,na.rm=TRUE))) %>%
ungroup()
gt_3d_jun_aug %>%
vroom_write((here::here("data/_summarized_data_files", file = "gt_3d_jun_aug_2018.csv")))
rm(gt_3d, gt_3d_jun_aug, d1_deep, d1_shallow, i, lat, d, t, var_3d)1.2 Mixed layer depth
Regional mean mixed layer depth estimates based on sewater density and windspeed within the BloomSail area were extracted from 3h GETM surface data along the Finnmaid track.
nc_2d <- nc_open(here("data/GETM", "Finnmaid.E.2d.2018.nc"))
#print(nc_2d)
lat <- ncvar_get(nc_2d, "latc")
time_units <- nc_2d$dim$time$units %>% #we read the time unit from the netcdf file to calibrate the time
substr(start = 15, stop = 33) %>% #calculation, we take the relevant information from the string
ymd_hms() # and transform it to the right format
t <- time_units + ncvar_get(nc_2d, "time") # read time vector
rm(time_units)
for (var in c("mld_rho", "u10", "v10")) {
#var <- "mld_rho"
array <- ncvar_get(nc_2d, var) # store the data in a 3-dimensional array
fillvalue <- ncatt_get(nc_2d, var, "_FillValue")
array[array == fillvalue$value] <- NA
array <- as.data.frame(t(array), xy=TRUE)
array <- as_tibble(array)
gt_2d_part <- array %>%
set_names(as.character(lat)) %>%
mutate(date_time = t) %>%
filter(date_time >= start_date & date_time <= end_date) %>%
gather("lat", "value", 1:length(lat)) %>%
mutate(lat = as.numeric(lat)) %>%
filter(lat > low_lat, lat<high_lat) %>%
select(-lat) %>%
group_by(date_time) %>%
summarise_all(list(value=~mean(.,na.rm=TRUE))) %>%
ungroup() %>%
mutate(var = var)
if (exists("gt_2d")) {
gt_2d <- bind_rows(gt_2d, gt_2d_part)
} else {gt_2d <- gt_2d_part}
rm(array, fillvalue, gt_2d_part)
}
nc_close(nc_2d)
rm(nc_2d)
gt_2d <- gt_2d %>%
pivot_wider(values_from = value, names_from = var) %>%
mutate(U_10 = (sqrt(u10^2 + v10^2))) %>%
select(-c(u10, v10))
gt_2d %>%
vroom_write((here::here("data/_summarized_data_files", file = "gt_2d_jun_aug_2018.csv")))
rm(t, var, gt_2d, lat)2 Hovmoeller Plots
gt_3d_jun_aug <-
read_tsv((here::here("data/_summarized_data_files", file = "gt_3d_jun_aug_2018.csv")))
gt_2d_jun_aug <-
read_tsv((here::here("data/_summarized_data_files", file = "gt_2d_jun_aug_2018.csv")))
gt_3d_jun_aug <- gt_3d_jun_aug %>%
pivot_wider(values_from = value, names_from = var) %>%
mutate(rho = swSigma(salinity = salt, temperature = temp, pressure = dep/10))
gt_2d_jun_aug_daily <- gt_2d_jun_aug %>%
mutate(day = yday(date_time)) %>%
group_by(day) %>%
summarise_all(list(~mean(.,na.rm=TRUE))) %>%
ungroup() %>%
select(-day)
# gt_temp_salt_ngs_3d_jun_aug <- gt_temp_salt_ngs_3d_jun_aug %>%
# mutate(date = ymd(date_time),
# year = year(date_time))
p_sal <- gt_3d_jun_aug %>%
filter(dep <= 30) %>%
ggplot()+
geom_raster(aes(date_time, dep, fill=salt))+
scale_fill_viridis_c(name="Salinity ", direction = -1)+
scale_y_reverse()+
coord_cartesian(expand = 0)+
labs(y="Depth [m]")+
theme_bw()+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank())+
geom_line(data= gt_2d_jun_aug_daily,
aes(x = date_time, y = mld_rho), color = "white")+
scale_color_discrete(name = "Legend", labels = c("MLD Rho"))
p_tem <- gt_3d_jun_aug %>%
filter(dep <= 30) %>%
ggplot()+
geom_raster(aes(date_time, dep, fill=temp))+
scale_fill_viridis_c(name="Temperature (°C)", option = "B")+
scale_y_reverse()+
coord_cartesian(expand = 0)+
labs(y="Depth [m]", x="")+
theme_bw()+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank())+
geom_line(data= gt_2d_jun_aug_daily,
aes(x = date_time, y = mld_rho), color = "white")+
scale_color_discrete(name = "Legend", labels = c("MLD Rho"))
p_rho <- gt_3d_jun_aug %>%
filter(dep <= 30) %>%
ggplot()+
geom_raster(aes(date_time, dep, fill=rho))+
scale_fill_viridis_c(name="d Rho (kg/m^3)", direction = -1)+
scale_y_reverse()+
coord_cartesian(expand = 0)+
labs(y="Depth [m]", x="")+
theme_bw()+
theme(axis.title.x = element_blank())+
geom_line(data= gt_2d_jun_aug_daily,
aes(x = date_time, y = mld_rho), color = "white")+
scale_color_discrete(name = "Legend", labels = c("MLD Rho"))
p_sal / p_tem / p_rho
3 Windspeeds
gt_2d_jun_aug %>%
ggplot()+
geom_line(aes(x= date_time, y = U_10, col="3-hourly"))+
geom_line(data = gt_2d_jun_aug_daily,
aes(x= date_time, y = U_10, col="Daily mean"))+
labs(y="U (m/s)", x = "Date")+
scale_color_brewer(palette = "Set1", name="", direction = -1)+
theme_bw()
4 Finnmaid data
Finnmaid data, including reconstructed data during LICOS operation failure.
#df <-
# read_csv(here::here("Data/_summarized_data_files",
# "Finnmaid.csv"))
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] patchwork_1.0.0 seacarb_3.2.12 oce_1.2-0 gsw_1.0-5
[5] testthat_2.3.1 here_0.1 lubridate_1.7.4 vroom_1.2.0
[9] ncdf4_1.17 forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3
[13] purrr_0.3.3 readr_1.3.1 tidyr_1.0.0 tibble_2.1.3
[17] ggplot2_3.3.0 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.2 lattice_0.20-35 assertthat_0.2.1 zeallot_0.1.0
[5] rprojroot_1.3-2 digest_0.6.22 R6_2.4.0 cellranger_1.1.0
[9] backports_1.1.5 reprex_0.3.0 evaluate_0.14 httr_1.4.1
[13] pillar_1.4.2 rlang_0.4.5 readxl_1.3.1 rstudioapi_0.10
[17] rmarkdown_2.0 labeling_0.3 bit_1.1-14 munsell_0.5.0
[21] broom_0.5.3 compiler_3.5.0 httpuv_1.5.2 modelr_0.1.5
[25] xfun_0.10 pkgconfig_2.0.3 htmltools_0.4.0 tidyselect_0.2.5
[29] workflowr_1.6.0 viridisLite_0.3.0 crayon_1.3.4 dbplyr_1.4.2
[33] withr_2.1.2 later_1.0.0 grid_3.5.0 nlme_3.1-137
[37] jsonlite_1.6 gtable_0.3.0 lifecycle_0.1.0 DBI_1.0.0
[41] git2r_0.26.1 magrittr_1.5 scales_1.0.0 cli_1.1.0
[45] stringi_1.4.3 fs_1.3.1 promises_1.1.0 xml2_1.2.2
[49] generics_0.0.2 vctrs_0.2.0 RColorBrewer_1.1-2 tools_3.5.0
[53] bit64_0.9-7 glue_1.3.1 hms_0.5.2 yaml_2.2.0
[57] colorspace_1.4-1 rvest_0.3.5 knitr_1.26 haven_2.2.0