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Knit directory: emlr_obs_preprocessing/
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Rmd | b948168 | jens-daniel-mueller | 2021-05-31 | ingest GLODAPv2_2021 beta data |
path_glodapv2_2021 <- "/nfs/kryo/work/updata/glodapv2_2021/"
path_preprocessing <- paste(path_root, "/observations/preprocessing/", sep = "")
Main data source for this project is a pre-release version of GLODAPv2.2021_Merged_Master_File.csv
received from Nico Lange in June 2021.
GLODAP <-
read_csv(
paste(
path_glodapv2_2021,
"GLODAPv2.2021_Merged_Master_File_beta_20210624.csv",
sep = ""
),
na = "-9999",
col_types = cols(.default = col_double())
)
# select relevant columns
GLODAP <- GLODAP %>%
rename_with(~str_remove(., 'G2'))
# select relevant columns
GLODAP <- GLODAP %>%
select(cruise:talkqc)
# create date column
GLODAP <- GLODAP %>%
mutate(date = ymd(paste(year, month, day))) %>%
relocate(date)
# harmonize column names
GLODAP <- GLODAP %>%
rename(sal = salinity,
temp = temperature)
# harmonize coordinates
GLODAP <- GLODAP %>%
rename(lon = longitude,
lat = latitude) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
# remove irrelevant columns
GLODAP <- GLODAP %>%
select(-c(region,
month:minute,
maxsampdepth, bottle, sigma0:sigma4,
nitrite:nitritef))
The vast majority of rows is removed due to missing tco2
observations.
GLODAP <- GLODAP %>%
filter(!is.na(tco2))
For merging with other data sets, all observations were grouped into latitude intervals of:
GLODAP <- m_grid_horizontal(GLODAP)
# use only three basin to assign general basin mask
# ie this is not specific to the MLR fitting
basinmask <- basinmask %>%
filter(MLR_basins == "2") %>%
select(lat, lon, basin_AIP)
GLODAP <- inner_join(GLODAP, basinmask)
GLODAP_obs_grid <- GLODAP %>%
count(lat, lon)
GLODAP <- GLODAP %>%
mutate(row_number = row_number()) %>%
relocate(row_number)
GLODAP %>%
write_csv(paste(path_preprocessing,
"GLODAPv2.2021_preprocessed.csv",
sep = ""))
For the following plots, the cleaned data set was re-opened and observations were gridded spatially to intervals of:
GLODAP <- m_grid_horizontal_coarse(GLODAP)
GLODAP_histogram_lat <- GLODAP %>%
group_by(lat_grid) %>%
tally() %>%
ungroup()
GLODAP_histogram_lat %>%
ggplot(aes(lat_grid, n)) +
geom_col() +
coord_flip() +
theme(legend.title = element_blank())
rm(GLODAP_histogram_lat)
GLODAP_histogram_year <- GLODAP %>%
group_by(year) %>%
tally() %>%
ungroup()
GLODAP_histogram_year %>%
ggplot() +
geom_col(aes(year, n)) +
theme(
axis.title.x = element_blank()
)
rm(GLODAP_histogram_year)
GLODAP_hovmoeller_year <- GLODAP %>%
group_by(year, lat_grid) %>%
tally() %>%
ungroup()
GLODAP_hovmoeller_year %>%
ggplot(aes(year, lat_grid, fill = log10(n))) +
geom_tile() +
geom_vline(xintercept = c(1999.5, 2012.5)) +
scale_fill_viridis_c(option = "magma", direction = -1) +
theme(legend.position = "top",
axis.title.x = element_blank())
rm(GLODAP_hovmoeller_year)
map +
geom_raster(data = GLODAP_obs_grid,
aes(lon, lat, fill = log10(n))) +
scale_fill_viridis_c(option = "magma",
direction = -1)
GLODAP_obs_grid_all_vars <- GLODAP %>%
select(year, lat, lon, cruise, sal, temp, oxygen,
phosphate, nitrate, silicate, tco2, talk) %>%
pivot_longer(cols = sal:talk,
names_to = "parameter",
values_to = "value") %>%
mutate(presence = if_else(is.na(value), "missing", "available")) %>%
count(year, lat, lon, parameter, presence)
GLODAP_obs_grid_all_vars_wide <- GLODAP_obs_grid_all_vars %>%
pivot_wider(names_from = "presence",
values_from = n,
values_fill = 0) %>%
mutate(ratio_available = available/(available+missing))
all_plots <- GLODAP_obs_grid_all_vars_wide %>%
# mutate(cruise = as.factor(cruise)) %>%
group_split(year) %>%
# tail(3) %>%
map(
~ map +
geom_tile(
data = .x,
aes(
x = lon,
y = lat,
width = 1,
height = 1,
fill = ratio_available
)
) +
scale_fill_scico(palette = "berlin",
limits = c(0,1)) +
labs(title = unique(.x$year)) +
facet_wrap(~ parameter)
)
pdf(file = paste0(path_preprocessing, "GLODAPv2.2021_preprocessed_coverage_maps.pdf"),
width = 10,
height = 5)
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dev.off()
png
2
source("/net/kryo/work/uptools/co2_calculation/CANYON-B/CANYONB.R")
GLODAP_Can_B <- GLODAP %>%
mutate(lon = if_else(lon > 180, lon - 360, lon)) %>%
arrange(year) %>%
select(row_number, year, date, lat, lon, depth, basin_AIP,
temp, sal, oxygen,
talk, tco2, nitrate, phosphate, silicate)
# filter rows with essential variables for Canyon-B
GLODAP_Can_B <- GLODAP_Can_B %>%
filter(across(c(lat, lon, depth,
temp, sal, oxygen), ~ !is.na(.x)))
GLODAP_Can_B <- GLODAP_Can_B %>%
mutate(as_tibble(
CANYONB(
date = paste0(as.character(date), " 12:00"),
lat = lat,
lon = lon,
pres = depth,
temp = temp,
psal = sal,
doxy = oxygen,
param = c("AT", "CT", "NO3", "PO4", "SiOH4")
)
))
GLODAP_Can_B <- GLODAP_Can_B %>%
select(-ends_with(c("_cim", "_cin", "_cii")))
GLODAP_Can_B <- GLODAP_Can_B %>%
rename(
"talk_CANYONB" = "AT",
"tco2_CANYONB" = "CT",
"nitrate_CANYONB" = "NO3",
"phosphate_CANYONB" = "PO4",
"silicate_CANYONB" = "SiOH4"
)
variables <- c("talk", "tco2", "nitrate", "phosphate", "silicate")
for (i_variable in variables) {
# i_variable <- variables[1]
# calculate equal axis limits and binwidth
axis_lims <- GLODAP_Can_B %>%
drop_na() %>%
summarise(max_value = max(c(max(
!!sym(i_variable)
),
max(!!sym(
paste0(i_variable, "_CANYONB")
)))),
min_value = min(c(min(
!!sym(i_variable)
),
min(!!sym(
paste0(i_variable, "_CANYONB")
)))))
binwidth_value <- (axis_lims$max_value - axis_lims$min_value) / 60
axis_lims <- c(axis_lims$min_value, axis_lims$max_value)
print(
ggplot(GLODAP_Can_B, aes(
x = !!sym(i_variable),
y = !!sym(paste0(i_variable, "_CANYONB"))
)) +
geom_bin2d(binwidth = binwidth_value) +
scale_fill_viridis_c(trans = "log10") +
geom_abline(slope = 1, col = 'red') +
coord_equal(xlim = axis_lims,
ylim = axis_lims) +
facet_wrap( ~ basin_AIP) +
labs(title = "All years")
)
for (i_year in unique(GLODAP_Can_B$year)) {
# i_year <- 2017
print(
ggplot(
GLODAP_Can_B %>% filter(year == i_year),
aes(x = !!sym(i_variable),
y = !!sym(paste0(
i_variable, "_CANYONB"
)))
) +
geom_bin2d(binwidth = binwidth_value) +
scale_fill_viridis_c(trans = "log10") +
geom_abline(slope = 1, col = 'red') +
coord_equal(xlim = axis_lims,
ylim = axis_lims) +
facet_wrap( ~ basin_AIP) +
labs(title = paste("Year:", i_year))
)
}
}
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GLODAP_Can_B %>%
select(row_number,
talk_CANYONB, tco2_CANYONB,
nitrate_CANYONB, phosphate_CANYONB, silicate_CANYONB) %>%
write_csv(paste(path_preprocessing,
"GLODAPv2.2021_Canyon-B.csv",
sep = ""))
sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2
Matrix products: default
BLAS: /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] mgcv_1.8-33 nlme_3.1-149 lubridate_1.7.9 ggforce_0.3.3
[5] metR_0.9.0 scico_1.2.0 patchwork_1.1.1 collapse_1.5.0
[9] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.5 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 splines_4.0.3 viridisLite_0.3.0
[4] jsonlite_1.7.1 modelr_0.1.8 assertthat_0.2.1
[7] blob_1.2.1 cellranger_1.1.0 yaml_2.2.1
[10] pillar_1.4.7 backports_1.1.10 lattice_0.20-41
[13] glue_1.4.2 RcppEigen_0.3.3.7.0 digest_0.6.27
[16] promises_1.1.1 polyclip_1.10-0 checkmate_2.0.0
[19] rvest_0.3.6 colorspace_1.4-1 htmltools_0.5.0
[22] httpuv_1.5.4 Matrix_1.2-18 pkgconfig_2.0.3
[25] broom_0.7.5 haven_2.3.1 scales_1.1.1
[28] tweenr_1.0.2 whisker_0.4 later_1.1.0.1
[31] git2r_0.27.1 generics_0.0.2 farver_2.0.3
[34] ellipsis_0.3.1 withr_2.3.0 cli_2.1.0
[37] magrittr_1.5 crayon_1.3.4 readxl_1.3.1
[40] evaluate_0.14 fs_1.5.0 fansi_0.4.1
[43] MASS_7.3-53 xml2_1.3.2 RcppArmadillo_0.10.1.2.0
[46] tools_4.0.3 data.table_1.13.2 hms_0.5.3
[49] lifecycle_1.0.0 munsell_0.5.0 reprex_0.3.0
[52] compiler_4.0.3 rlang_0.4.10 grid_4.0.3
[55] rstudioapi_0.11 labeling_0.4.2 rmarkdown_2.5
[58] gtable_0.3.0 DBI_1.1.0 R6_2.5.0
[61] knitr_1.30 rprojroot_2.0.2 stringi_1.5.3
[64] parallel_4.0.3 Rcpp_1.0.5 vctrs_0.3.5
[67] dbplyr_1.4.4 tidyselect_1.1.0 xfun_0.18