Mapping
Jens Daniel Müller
28 July, 2020
Last updated: 2020-07-28
Checks: 7 0
Knit directory: Cant_eMLR/
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| Rmd | c63f537 | jens-daniel-mueller | 2020-07-28 | included model coeffcients in mapping |
| html | 4eebe14 | jens-daniel-mueller | 2020-07-24 | Build site. |
| Rmd | 12f9ef2 | jens-daniel-mueller | 2020-07-24 | started neutral density calculation |
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| html | 64978a1 | jens-daniel-mueller | 2020-07-24 | Build site. |
| Rmd | 7cbc7ec | jens-daniel-mueller | 2020-07-24 | first publish |
library(tidyverse)
library(lubridate)
library(oce)1 Required data
Required are:
- GLODAPv2_2016b_MappedClimatologies
- World Ocean Atlas 2018
- eMLR model coeffcients
variables <- c("NO3", "oxygen", "PO4", "silicate")
for (i_variable in variables) {
#i_variable <- variables[2]
print(i_variable)
temp <- read_csv(
here::here("data/GLODAPv2_2016b_MappedClimatologies/_summarized_files",
paste(i_variable,".csv", sep = "")))
if (exists("GLODAP_predictors")) {
GLODAP_predictors <- full_join(GLODAP_predictors, temp)
}
if (!exists("GLODAP_predictors")) {
GLODAP_predictors <- temp
}
}[1] "NO3"
[1] "oxygen"
[1] "PO4"
[1] "silicate"rm(temp, i_variable, variables)
GLODAP_depths <- unique(GLODAP_predictors$depth)WOA18_predictors <-
read_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
"WOA18_predictors.csv"))
WOA18_predictors <- WOA18_predictors %>%
rename(salinity = s_an, temperature = t_an)
WOA18_depths <- unique(WOA18_predictors$depth)all_lm <- read_csv(here::here("data/eMLR",
"all_lm.csv"))2 Join predictor climatologies
CAVEAT: Coverage of GLODAP climatologies differs slightly for parameters (some are NA in some regions)
predictors <- full_join(GLODAP_predictors, WOA18_predictors)
rm(GLODAP_predictors, WOA18_predictors)
predictors <- predictors %>%
group_by(lat, lon) %>%
mutate(n_NO3 = sum(!is.na(NO3)),
n_oxygen = sum(!is.na(oxygen)),
n_PO4 = sum(!is.na(PO4)),
n_silicate = sum(!is.na(silicate)),
n_salinity = sum(!is.na(salinity)),
n_temperature = sum(!is.na(temperature))) %>%
ungroup()
predictors <- predictors %>%
filter(n_NO3 > 1,
n_oxygen > 1,
n_PO4 > 1,
n_silicate > 1,
n_salinity > 1,
n_temperature > 1) %>%
select(-c(n_NO3 , n_oxygen , n_PO4 , n_silicate , n_salinity , n_temperature))
predictors %>%
ggplot(aes(lon, lat)) +
geom_bin2d(binwidth = c(1,1)) +
scale_fill_viridis_c(direction = -1) +
coord_quickmap(expand = 0) +
theme(legend.position = "bottom")
predictors %>%
filter(depth == 0) %>%
ggplot(aes(lon, lat, fill = PO4)) +
geom_raster() +
scale_fill_viridis_c() +
coord_quickmap(expand = 0) +
theme(legend.position = "bottom") +
labs(title = "Surface values")
predictors %>%
filter(depth == 0) %>%
ggplot(aes(lon, lat, fill = temperature)) +
geom_raster() +
scale_fill_viridis_c() +
coord_quickmap(expand = 0) +
theme(legend.position = "bottom") +
labs(title = "Surface values")
2.1 Vertically interpolate WOA18
predictors <- predictors %>%
group_by(lat, lon) %>%
arrange(depth) %>%
mutate(temperature = approxfun(depth, temperature, rule = 2)(depth),
salinity = approxfun(depth, salinity, rule = 2)(depth)) %>%
ungroup()
predictors <- predictors %>%
filter(depth %in% GLODAP_depths)2.1.1 Predictor profiles
N_Atl <- predictors %>%
filter(lat == 40.5, lon == -20.5)
N_Atl <- N_Atl %>%
pivot_longer(NO3:temperature, names_to = "parameter", values_to = "value")
N_Atl %>%
ggplot(aes(value, depth)) +
geom_path() +
geom_point() +
scale_y_reverse() +
facet_wrap(~parameter, scales = "free_x")
3 Neutral density slabs
GLODAP <- read_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
"GLODAP_MLR_fitting_ready.csv"))
cruises_meridional <- c("1041")
GLODAP_cruise <- GLODAP %>%
filter(cruise %in% cruises_meridional)
GLODAP_cruise <- GLODAP_cruise %>%
mutate(gamma_calc = swRho(salinity = salinity,
temperature = temperature,
pressure = depth,
longitude = lon,
latitude = lat,
eos = "gsw"))
GLODAP_cruise <- GLODAP_cruise %>%
mutate(gamma_calc = gamma_calc - 1000,
delta_gamma = gamma - gamma_calc)
lat_section <-
GLODAP_cruise %>%
ggplot(aes(lat, depth)) +
scale_y_reverse() +
scale_color_viridis_c() +
theme(legend.position = "bottom")
lat_section +
geom_point(aes(col = gamma))
lat_section +
geom_point(aes(col = gamma_calc))
lat_section +
geom_point(aes(col = delta_gamma))
4 Prepare model coefficients
all_lm <- all_lm %>%
select(term, estimate, basin, era, gamma_slab, model)
all_lm_wide <- all_lm %>%
pivot_wider(values_from = estimate, names_from=term)
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] oce_1.2-0 gsw_1.0-5 testthat_2.3.2 lubridate_1.7.9
[5] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4
[9] readr_1.3.1 tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2
[13] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 here_0.1 assertthat_0.2.1 rprojroot_1.3-2
[5] digest_0.6.25 R6_2.4.1 cellranger_1.1.0 backports_1.1.8
[9] reprex_0.3.0 evaluate_0.14 httr_1.4.2 pillar_1.4.6
[13] rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11 whisker_0.4
[17] blob_1.2.1 rmarkdown_2.3 labeling_0.3 munsell_0.5.0
[21] broom_0.7.0 compiler_4.0.2 httpuv_1.5.4 modelr_0.1.8
[25] xfun_0.16 pkgconfig_2.0.3 htmltools_0.5.0 tidyselect_1.1.0
[29] fansi_0.4.1 viridisLite_0.3.0 crayon_1.3.4 dbplyr_1.4.4
[33] withr_2.2.0 later_1.1.0.1 grid_4.0.2 jsonlite_1.7.0
[37] gtable_0.3.0 lifecycle_0.2.0 DBI_1.1.0 git2r_0.27.1
[41] magrittr_1.5 scales_1.1.1 cli_2.0.2 stringi_1.4.6
[45] farver_2.0.3 fs_1.4.2 promises_1.1.1 xml2_1.3.2
[49] ellipsis_0.3.1 generics_0.0.2 vctrs_0.3.2 tools_4.0.2
[53] glue_1.4.1 hms_0.5.3 yaml_2.2.1 colorspace_1.4-1
[57] rvest_0.3.6 knitr_1.29 haven_2.3.1