World Ocean Atlas 2018
Jens Daniel Müller
24 July, 2020
Last updated: 2020-07-24
Checks: 7 0
Knit directory: Cant_eMLR/
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library(tidyverse)
library(lubridate)
library(tidync)
library(stars)1 Data source
- Data source: World Ocean Atlas 2018
2 Basin mask
2.1 Read data
The surface mask (0m) with 1x1° resolution from the file basinmask_01.msk was used.
basinmask_01 <- read_csv(here::here("data/World_Ocean_Atlas_2018",
"basinmask_01.msk"),
skip = 1)
basinmask_01 <- basinmask_01 %>%
select(Latitude:Basin_0m) %>%
mutate(Basin_0m = as.factor(Basin_0m)) %>%
rename(lat = Latitude, lon = Longitude)2.2 Basin labels
According to WOA FAQ website, number codes in the mask file refer to Ocean basins as follows:
- 1: Atlantic Ocean (with 10 - Southern Ocean, between 63°W and 20°E)
- 2: Pacific Ocean (with 10 - Southern Ocean, between 147°E and 63°W)
- 3: Indian Ocean (with 10 - Southern Ocean, between 20°E and 147°E)
From this, the Atlantik and the Indo-Pacific were labeled.
basinmask_01 <- basinmask_01 %>%
filter(Basin_0m %in% c("1", "2", "3", "10")) %>%
mutate(basin = if_else(Basin_0m == "10" & lon >= -63 & lon < 20,
"Atlantic", "Indo-Pacific"),
basin = if_else(Basin_0m == "1",
"Atlantic", basin)) %>%
select(-Basin_0m)2.3 Plot world map
mapWorld <- borders("world", colour="gray60", fill="gray60")
basinmask_01 %>%
ggplot(aes(lon, lat, fill=basin))+
mapWorld+
geom_raster()+
scale_fill_brewer(palette = "Dark2")+
coord_quickmap(expand = 0)
rm(mapWorld)2.4 Write file
basinmask_01 %>%
write_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
"basin_mask_WOA18.csv"))
rm(basinmask_01)3 Read/plot ncdf files
File naming conventions: PREF_DDDD_VTTFFGG.EXT, where:
- PREF: prefix
- DDDD: decade
- V: variable
- TT: time period
- FF: field type
- GG: grid (5deg- 5°, 01- 1°, 04 - 1/4°)
- EXT: file extention
Note: ‘.dat’ - ASCII; ‘.csv’ - comma separated value; ‘.dbf’, ‘.shp’, ‘.shx’ - ArcGIS shape files; ‘.nc’ - netCDF files
Short description of the statistical fields in WOA
- Objectively analyzed climatologies are the objectively interpolated mean fields for oceanographic variables at standard - depth levels for the World Ocean.
- The statistical mean is the average of all unflagged interpolated values at each standard depth level for each variable - in each 1° square which contains at least one measurement for the given oceanographic variable.
- The number of observations of each variable in each 1° square of the World Ocean at each standard depth level.
- The standard deviation about the statistical mean of each variable in each 1° square at each standard depth level.
- The standard error of the mean of each variable in each 1° square at each standard depth level.
- The seasonal or monthly climatology minus the annual climatology at each 1° square at each standard depth.
- The statistical mean minus the climatological mean at each 1° square at each standard depth. This value is used as an estimate of interpolation and smoothing error.
- The number of 1° squares within the smallest radius of influence around each 1° square which contain a statistical mean.
WOA_tem <- tidync(here::here("data/World_Ocean_Atlas_2018",
"woa18_decav_t00_01.nc"))
print(WOA_tem)
Data Source (1): woa18_decav_t00_01.nc ...
Grids (10) <dimension family> : <associated variables>
[1] D2,D1,D3,D4 : t_an, t_mn, t_dd, t_sd, t_se, t_oa, t_gp **ACTIVE GRID** ( 6609600 values per variable)
[2] D0,D1 : lat_bnds
[3] D0,D2 : lon_bnds
[4] D0,D3 : depth_bnds
[5] D0,D4 : climatology_bounds
[6] D1 : lat
[7] D2 : lon
[8] D3 : depth
[9] D4 : time
[10] S : crs
Dimensions 5 (4 active):
dim name length min max start count dmin dmax unlim coord_dim
<chr> <chr> <dbl> <dbl> <dbl> <int> <int> <dbl> <dbl> <lgl> <lgl>
1 D1 lat 180 -89.5 89.5 1 180 -89.5 89.5 FALSE TRUE
2 D2 lon 360 -180. 180. 1 360 -180. 180. FALSE TRUE
3 D3 depth 102 0 5500 1 102 0 5500 FALSE TRUE
4 D4 time 1 4326 4326 1 1 4326 4326 FALSE TRUE
Inactive dimensions:
dim name length min max unlim coord_dim
<chr> <chr> <dbl> <dbl> <dbl> <lgl> <lgl>
1 D0 nbounds 2 1 2 FALSE FALSE WOA_tem_tibble <- WOA_tem %>% hyper_tibble()
WOA_tem_tibble <- WOA_tem_tibble %>%
select(t_an, lon, lat, depth) %>%
drop_na()
WOA_tem_tibble %>%
filter(depth == 0) %>%
ggplot(aes(lon, lat, fill=t_an))+
geom_raster()+
coord_quickmap()+
scale_fill_viridis_c()
WOA_tem_tibble %>%
filter(lon == -20.5) %>%
ggplot(aes(lat, depth, z=t_an))+
geom_contour_filled()+
scale_y_reverse()+
coord_cartesian(expand = 0)
WOA_sal <- tidync(here::here("data/World_Ocean_Atlas_2018",
"woa18_decav_s00_01.nc"))
print(WOA_sal)
Data Source (1): woa18_decav_s00_01.nc ...
Grids (10) <dimension family> : <associated variables>
[1] D2,D1,D3,D4 : s_an, s_mn, s_dd, s_sd, s_se, s_oa, s_gp **ACTIVE GRID** ( 6609600 values per variable)
[2] D0,D1 : lat_bnds
[3] D0,D2 : lon_bnds
[4] D0,D3 : depth_bnds
[5] D0,D4 : climatology_bounds
[6] D1 : lat
[7] D2 : lon
[8] D3 : depth
[9] D4 : time
[10] S : crs
Dimensions 5 (4 active):
dim name length min max start count dmin dmax unlim coord_dim
<chr> <chr> <dbl> <dbl> <dbl> <int> <int> <dbl> <dbl> <lgl> <lgl>
1 D1 lat 180 -89.5 89.5 1 180 -89.5 89.5 FALSE TRUE
2 D2 lon 360 -180. 180. 1 360 -180. 180. FALSE TRUE
3 D3 depth 102 0 5500 1 102 0 5500 FALSE TRUE
4 D4 time 1 4326 4326 1 1 4326 4326 FALSE TRUE
Inactive dimensions:
dim name length min max unlim coord_dim
<chr> <chr> <dbl> <dbl> <dbl> <lgl> <lgl>
1 D0 nbounds 2 1 2 FALSE FALSE WOA_sal_tibble <- WOA_sal %>% hyper_tibble()
WOA_sal_tibble <- WOA_sal_tibble %>%
select(s_an, lon, lat, depth) %>%
drop_na()
WOA_sal_tibble %>%
filter(depth == 0) %>%
ggplot(aes(lon, lat, fill=s_an))+
geom_raster()+
coord_quickmap()+
scale_fill_viridis_c()
WOA_sal_tibble %>%
filter(lon == -20.5) %>%
ggplot(aes(lat, depth, z=s_an))+
geom_contour_filled()+
scale_y_reverse()+
coord_cartesian(expand = 0)
3.1 Write files
WOA18_predictors <- full_join(WOA_sal_tibble, WOA_tem_tibble)
WOA18_predictors %>%
write_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
"WOA18_predictors.csv"))
rm(WOA18_predictors, WOA_sal, WOA_sal_tibble, WOA_tem, WOA_tem_tibble)4 Open tasks
- none
5 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] stars_0.4-3 sf_0.9-4 abind_1.4-5 tidync_0.2.4
[5] lubridate_1.7.9 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0
[9] purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 tibble_3.0.3
[13] ggplot2_3.3.2 tidyverse_1.3.0 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 maps_3.3.0 units_0.6-7 hms_0.5.3
[9] rvest_0.3.5 tidyselect_1.1.0 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 dbplyr_1.4.4 modelr_0.1.8 withr_2.2.0
[25] git2r_0.27.1 ellipsis_0.3.1 htmltools_0.5.0 class_7.3-17
[29] assertthat_0.2.1 rprojroot_1.3-2 digest_0.6.25 lifecycle_0.2.0
[33] RNetCDF_2.3-1 haven_2.3.1 rmarkdown_2.3 compiler_3.6.3
[37] cellranger_1.1.0 pillar_1.4.6 scales_1.1.1 backports_1.1.5
[41] generics_0.0.2 classInt_0.4-3 jsonlite_1.7.0 httpuv_1.5.4
[45] pkgconfig_2.0.3 rstudioapi_0.11 munsell_0.5.0 blob_1.2.1
[49] httr_1.4.1 tools_3.6.3 grid_3.6.3 gtable_0.3.0
[53] utf8_1.1.4 KernSmooth_2.23-16 DBI_1.1.0 cli_2.0.2
[57] readxl_1.3.1 yaml_2.2.1 lwgeom_0.2-5 crayon_1.3.4
[61] farver_2.0.3 RColorBrewer_1.1-2 later_1.1.0.1 promises_1.1.1
[65] fs_1.4.2 vctrs_0.3.1 isoband_0.2.2 ncdf4_1.17
[69] glue_1.4.1 evaluate_0.14 labeling_0.3 reprex_0.3.0
[73] stringi_1.4.6