GLODAPv2_2016_Mappedclimatologies
Jens Daniel Müller
23 July, 2020
Last updated: 2020-07-23
Checks: 7 0
Knit directory: Cant_eMLR/
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|---|---|---|---|---|
| html | c1289a2 | jens-daniel-mueller | 2020-07-23 | Build site. |
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| Rmd | 09b3ca8 | jens-daniel-mueller | 2020-07-22 | eding plain text, restructuring some code |
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| Rmd | d2ed0f8 | jens-daniel-mueller | 2020-07-21 | harmonied lat lon labeling |
| html | a268ace | jens-daniel-mueller | 2020-07-21 | Build site. |
| Rmd | 48de3ae | jens-daniel-mueller | 2020-07-21 | added pacific section |
| html | ca73a98 | jens-daniel-mueller | 2020-07-21 | Build site. |
| Rmd | e6c4220 | jens-daniel-mueller | 2020-07-21 | added section plots atlantic ocean |
| html | a842785 | jens-daniel-mueller | 2020-07-21 | Build site. |
| Rmd | 16cb19e | jens-daniel-mueller | 2020-07-21 | added section plots atlantic ocean |
| html | 55d905d | jens-daniel-mueller | 2020-07-21 | Build site. |
| Rmd | 87cc2f4 | jens-daniel-mueller | 2020-07-21 | added section plots atlantic ocean |
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| Rmd | f881ecf | jens-daniel-mueller | 2020-07-21 | added section plots atlantic ocean |
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| Rmd | f9548b0 | jens-daniel-mueller | 2020-07-21 | added section plots atlantik |
| html | 21eb4f3 | jens-daniel-mueller | 2020-07-21 | Build site. |
| Rmd | e3d90cd | jens-daniel-mueller | 2020-07-21 | added section plots atlantik |
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| Rmd | 81f9f3f | jens-daniel-mueller | 2020-07-17 | Plots for all Glodap mapped variables |
| html | 30e2e22 | jens-daniel-mueller | 2020-07-14 | Build site. |
| Rmd | a48c8f0 | jens-daniel-mueller | 2020-07-14 | Read plot netcdf woth tidync |
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| Rmd | c18812a | jens-daniel-mueller | 2020-07-14 | Read plot netcdf woth tidync |
| html | 2979fdf | jens-daniel-mueller | 2020-07-14 | Build site. |
| Rmd | 6823c5d | jens-daniel-mueller | 2020-07-14 | Read plot netcdf woth tidync |
| html | 56c3ed9 | jens-daniel-mueller | 2020-07-14 | Build site. |
| Rmd | aed89af | jens-daniel-mueller | 2020-07-14 | added Rmd for MappedClimatologies |
library(tidyverse)
library(lubridate)
library(tidync)
library(stars)
library(metR)
library(patchwork)1 Read ncdf, write csv, plot
- Data source: glodap.info
file_list <- list.files(path= "data/GLODAPv2_2016b_Mappedclimatologies", pattern = "*.nc")
print("files used:")[1] "files used:"print(file_list)[1] "GLODAPv2.2016b.Cant.nc" "GLODAPv2.2016b.NO3.nc"
[3] "GLODAPv2.2016b.oxygen.nc" "GLODAPv2.2016b.PO4.nc"
[5] "GLODAPv2.2016b.salinity.nc" "GLODAPv2.2016b.silicate.nc"
[7] "GLODAPv2.2016b.TAlk.nc" "GLODAPv2.2016b.TCO2.nc"
[9] "GLODAPv2.2016b.temperature.nc"#file <- file_list[1]
for (file in file_list) {
clim_stars <- read_stars(here::here("data/GLODAPv2_2016b_Mappedclimatologies",
file))
parameter <- str_split(file, pattern = "6b.", simplify = TRUE)[2]
parameter <- str_split(parameter, pattern = ".nc", simplify = TRUE)[1]
map_clim_stars <- clim_stars %>% select(all_of(parameter))
map_clim_stars %>%
as_tibble() %>%
rename(lat = y, lon = x, depth = depth_surface) %>%
mutate(lon = if_else(lon>180, lon - 360, lon)) %>%
write_csv(here::here("data/GLODAPv2_2016b_MappedClimatologies/_summarized_files",
paste(parameter,".csv", sep = "")))
map_clim_stars_horizons <- map_clim_stars %>%
filter(depth_surface %in% c(0,100,500,2000))
print(
ggplot()+
geom_stars(data = map_clim_stars_horizons)+
geom_vline(xintercept = c(190.5,335.5), col="white")+
scale_fill_viridis_b(n.breaks = 8)+
labs(title = file)+
facet_wrap(~depth_surface, ncol = 2)+
coord_quickmap(expand = 0)
)
Atl_section <- map_clim_stars %>%
as_tibble() %>%
rename(lat = y, lon = x, depth = depth_surface, parameter = 4) %>%
filter(lon == 335.5)
Atl_section_bathy <- Atl_section %>%
filter(is.na(parameter)) %>%
group_by(lat) %>%
summarise(bottom_depth = min(depth)) %>%
ungroup()
p_Atl_section <-
ggplot()+
geom_contour_fill(data = Atl_section,
aes(lat, depth, z=parameter),
na.fill = TRUE)+
geom_ribbon(data = Atl_section_bathy,
aes(x = lat, ymin = bottom_depth, ymax = 5500),
fill = "grey80",
col="black")+
labs(title = "Atlantic ocean N-S section")+
scale_y_reverse()+
scale_fill_viridis_b(name = parameter,
n.breaks = 8)+
coord_cartesian(expand = 0)
Pac_section <- map_clim_stars %>%
as_tibble() %>%
rename(lat = y, lon = x, depth = depth_surface, parameter = 4) %>%
filter(lon == 190.5)
Pac_section_bathy <- Pac_section %>%
filter(is.na(parameter)) %>%
group_by(lat) %>%
summarise(bottom_depth = min(depth)) %>%
ungroup()
p_Pac_section <-
ggplot()+
geom_contour_fill(data = Pac_section,
aes(lat, depth, z=parameter),
na.fill = TRUE)+
geom_ribbon(data = Pac_section_bathy,
aes(x = lat, ymin = bottom_depth, ymax = 5500),
fill = "grey80",
col="black")+
labs(title = "Pacific ocean N-S section")+
scale_y_reverse()+
scale_fill_viridis_b(name = parameter,
n.breaks = 8)+
coord_cartesian(expand = 0)
print(
p_Atl_section / p_Pac_section
)
# clim_tidync <- tidync(here::here("data/GLODAPv2_2016b_Mappedclimatologies",
# file))
# print(clim_tidync)
#
# Atl_sec_clim <- clim_tidync %>% hyper_filter(lon = lon == 335.5)
# Atl_sec_clim_tibble <- Atl_sec_clim %>% hyper_tibble()
#
# Atl_sec_clim_tibble %>%
# ggplot(aes(lat, depth_surface, fill=Cant))+
# geom_point()+
# scale_y_reverse()+
# scale_fill_viridis_b()
}Cant, Cant_error, Input_mean, Input_std, Input_N, Cant_relerr, 
NO3, NO3_error, Input_mean, Input_std, Input_N, NO3_relerr, 
oxygen, oxygen_error, Input_mean, Input_std, Input_N, oxygen_relerr, 
PO4, PO4_error, Input_mean, Input_std, Input_N, PO4_relerr, 
salinity, salinity_error, Input_mean, Input_std, Input_N, salinity_relerr, 
silicate, silicate_error, Input_mean, Input_std, Input_N, silicate_relerr, 
TAlk, TAlk_error, Input_mean, Input_std, Input_N, TAlk_relerr, 
TCO2, TCO2_error, Input_mean, Input_std, Input_N, TCO2_relerr, 
temperature, temperature_error, Input_mean, Input_std, Input_N, temperature_relerr, 
2 Plot Cant section
Cant <- read_csv(here::here("data/GLODAPv2_2016b_MappedClimatologies/_summarized_files",
"Cant.csv"))
Cant_Atl_section <- Cant %>%
filter(x == 335.5)
Cant_Atl_section_bathy <- Cant_Atl_section %>%
filter(is.na(Cant)) %>%
group_by(y) %>%
summarise(depth_surface = min(depth_surface)) %>%
ungroup()
ggplot()+
geom_contour_fill(data = Cant_Atl_section,
aes(y, depth_surface, z=Cant),
na.fill = TRUE)+
geom_ribbon(data = Cant_Atl_section_bathy,
aes(x = y, ymin = depth_surface, ymax = 5500),
fill = "grey30",
col="black")+
scale_y_reverse()+
scale_fill_viridis_b(breaks = seq(0,100,10))+
coord_cartesian(expand = 0)3 Open tasks
- none
4 Questions
- none
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: i386-w64-mingw32/i386 (32-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] patchwork_1.0.1 metR_0.7.0 stars_0.4-3 sf_0.9-4
[5] abind_1.4-5 tidync_0.2.4 lubridate_1.7.9 forcats_0.5.0
[9] stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
[13] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 whisker_0.4 knitr_1.29 xml2_1.3.2
[5] magrittr_1.5 units_0.6-7 hms_0.5.3 rvest_0.3.5
[9] tidyselect_1.1.0 lattice_0.20-41 viridisLite_0.3.0 here_0.1
[13] colorspace_1.4-1 R6_2.4.1 rlang_0.4.7 fansi_0.4.1
[17] parallel_3.6.3 broom_0.7.0 xfun_0.15 e1071_1.7-3
[21] ncmeta_0.2.5 cubelyr_1.0.0 dbplyr_1.4.4 modelr_0.1.8
[25] withr_2.2.0 git2r_0.27.1 ellipsis_0.3.1 htmltools_0.5.0
[29] class_7.3-17 assertthat_0.2.1 rprojroot_1.3-2 digest_0.6.25
[33] lifecycle_0.2.0 RNetCDF_2.3-1 haven_2.3.1 rmarkdown_2.3
[37] sp_1.4-2 compiler_3.6.3 cellranger_1.1.0 pillar_1.4.6
[41] scales_1.1.1 backports_1.1.5 generics_0.0.2 classInt_0.4-3
[45] jsonlite_1.7.0 httpuv_1.5.4 pkgconfig_2.0.3 rstudioapi_0.11
[49] munsell_0.5.0 plyr_1.8.6 blob_1.2.1 httr_1.4.1
[53] tools_3.6.3 grid_3.6.3 data.table_1.12.8 checkmate_2.0.0
[57] gtable_0.3.0 KernSmooth_2.23-16 DBI_1.1.0 cli_2.0.2
[61] readxl_1.3.1 yaml_2.2.1 lwgeom_0.2-5 crayon_1.3.4
[65] farver_2.0.3 later_1.1.0.1 promises_1.1.1 fs_1.4.2
[69] vctrs_0.3.1 ncdf4_1.17 memoise_1.1.0 glue_1.4.1
[73] evaluate_0.14 labeling_0.3 reprex_0.3.0 stringi_1.4.6