GLODAPv2_2020
Jens Daniel Müller
30 November, 2020
Last updated: 2020-11-30
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Knit directory: emlr_obs_preprocessing/
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| File | Version | Author | Date | Message |
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| Rmd | cd676e8 | jens-daniel-mueller | 2020-11-30 | created global parameterization file params_global.rds |
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| Rmd | 92e10aa | Jens Müller | 2020-11-27 | Initial commit |
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path_glodapv2_2020 <- "/nfs/kryo/work/updata/glodapv2_2020/"
path_preprocessing <- "/nfs/kryo/work/updata/emlr_cant/observations/preprocessing/"path_functions <- "/nfs/kryo/work/updata/emlr_cant/utilities/functions/"
path_files <- "/nfs/kryo/work/updata/emlr_cant/utilities/files/"1 Libraries
Loading libraries specific to the the analysis performed in this section.
library(lubridate)2 Read files
Main data source for this project is GLODAPv2.2020_Merged_Master_File.csv downloaded from glodap.info in June 2020.
GLODAP <-
read_csv(
paste(
path_glodapv2_2020,
"GLODAPv2.2020_Merged_Master_File.csv",
sep = ""
),
na = "-9999",
col_types = cols(.default = col_double())
)# 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,
tem = 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(month:minute,
maxsampdepth, bottle, sigma0:sigma4,
nitrite:nitritef))3 Data preparation
3.1 Subset tco2 data
The vast majority of rows is removed due to missing tco2 observations.
GLODAP <- GLODAP %>%
filter(!is.na(tco2))3.2 Horizontal gridding
For merging with other data sets, all observations were grouped into latitude intervals of:
- 1° x 1°
GLODAP <- m_grid_horizontal(GLODAP)3.3 Basin mask
The basin mask from the World Ocean Atlas was used. For details consult the data base subsection for WOA18 data.
Please note that some GLODAP observations were made outside the WOA18 basin mask (i.e. in marginal seas) and will be removed for further analysis.
# use only data inside basinmask
GLODAP <- inner_join(GLODAP, basinmask)3.4 Create clean observations grid
GLODAP_obs_grid <- GLODAP %>%
count(lat, lon)3.5 Write summary file
GLODAP %>% write_csv(paste(path_preprocessing,
"GLODAPv2.2020_clean.csv",
sep = ""))4 Overview plots
4.1 Assign coarse spatial grid
For the following plots, the cleaned data set was re-opened and observations were gridded spatially to intervals of:
- 5° x 5°
GLODAP <- m_grid_horizontal_coarse(GLODAP)4.2 Histogram Zonal coverage
GLODAP_histogram_lat <- GLODAP %>%
group_by(lat_grid, basin) %>%
tally() %>%
ungroup()
GLODAP_histogram_lat %>%
ggplot(aes(lat_grid, n)) +
geom_col() +
coord_flip() +
facet_wrap(~ basin) +
theme(legend.title = element_blank())
rm(GLODAP_histogram_lat)4.3 Histogram temporal coverage
GLODAP_histogram_year <- GLODAP %>%
group_by(year, basin) %>%
tally() %>%
ungroup()
GLODAP_histogram_year %>%
ggplot() +
geom_col(aes(year, n)) +
facet_wrap(~ basin, ncol = 1) +
theme(
axis.title.x = element_blank()
)
rm(GLODAP_histogram_year)4.4 Zonal temporal coverage (Hovmoeller)
GLODAP_hovmoeller_year <- GLODAP %>%
group_by(year, lat_grid, basin) %>%
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) +
facet_wrap( ~ basin, ncol = 1) +
theme(legend.position = "top",
axis.title.x = element_blank())
rm(GLODAP_hovmoeller_year)
5 Exploratory data analysis
source(paste(path_functions,
"eda.R",
sep = ""))
eda(GLODAP, "GLODAPv2_2020_preprocessed")
rm(eda)The output of an automated Exploratory Data Analysis (EDA) performed with the package DataExplorer can be accessed here:
Link to EDA report of GLODAPv2_2020 raw data
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.1
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] lubridate_1.7.9 metR_0.9.0 scico_1.2.0 patchwork_1.1.0
[5] collapse_1.4.2 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2
[9] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[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 lattice_0.20-41 assertthat_0.2.1
[4] rprojroot_1.3-2 digest_0.6.27 R6_2.5.0
[7] cellranger_1.1.0 backports_1.1.10 reprex_0.3.0
[10] evaluate_0.14 httr_1.4.2 pillar_1.4.6
[13] rlang_0.4.8 readxl_1.3.1 data.table_1.13.2
[16] rstudioapi_0.11 whisker_0.4 blob_1.2.1
[19] Matrix_1.2-18 checkmate_2.0.0 rmarkdown_2.5
[22] labeling_0.4.2 RcppEigen_0.3.3.7.0 munsell_0.5.0
[25] broom_0.7.2 compiler_4.0.3 httpuv_1.5.4
[28] modelr_0.1.8 xfun_0.18 pkgconfig_2.0.3
[31] htmltools_0.5.0 tidyselect_1.1.0 viridisLite_0.3.0
[34] fansi_0.4.1 crayon_1.3.4 dbplyr_1.4.4
[37] withr_2.3.0 later_1.1.0.1 grid_4.0.3
[40] jsonlite_1.7.1 gtable_0.3.0 lifecycle_0.2.0
[43] DBI_1.1.0 git2r_0.27.1 magrittr_1.5
[46] scales_1.1.1 cli_2.1.0 stringi_1.5.3
[49] farver_2.0.3 fs_1.5.0 promises_1.1.1
[52] RcppArmadillo_0.10.1.0.0 xml2_1.3.2 ellipsis_0.3.1
[55] generics_0.0.2 vctrs_0.3.4 tools_4.0.3
[58] glue_1.4.2 hms_0.5.3 parallel_4.0.3
[61] yaml_2.2.1 colorspace_1.4-1 rvest_0.3.6
[64] knitr_1.30 haven_2.3.1