GLODAPv2_2020
Jens Daniel Müller
18 December, 2020
Last updated: 2020-12-18
Checks: 7 0
Knit directory: emlr_obs_preprocessing/
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path_glodapv2_2020 <- "/nfs/kryo/work/updata/glodapv2_2020/"
path_preprocessing <- paste(path_root, "/observations/preprocessing/", sep = "")1 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,
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(month:minute,
maxsampdepth, bottle, sigma0:sigma4,
nitrite:nitritef))2 Data preparation
2.1 Subset tco2 data
The vast majority of rows is removed due to missing tco2 observations.
GLODAP <- GLODAP %>%
filter(!is.na(tco2))2.2 Horizontal gridding
For merging with other data sets, all observations were grouped into latitude intervals of:
- 1° x 1°
GLODAP <- m_grid_horizontal(GLODAP)2.3 Apply basin mask
# 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)2.4 Create clean observations grid
GLODAP_obs_grid <- GLODAP %>%
count(lat, lon)2.5 Write file
GLODAP %>% write_csv(paste(path_preprocessing,
"GLODAPv2.2020_preprocessed.csv",
sep = ""))3 Overview plots
3.1 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)3.2 Histogram Zonal coverage
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)3.3 Histogram temporal coverage
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)3.4 Zonal temporal coverage (Hovmoeller)
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)3.5 Coverage map
map +
geom_raster(data = GLODAP_obs_grid,
aes(lon, lat, fill = log10(n))) +
scale_fill_viridis_c(option = "magma",
direction = -1)
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_2.0.2 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_1.4.7
[13] rlang_0.4.9 readxl_1.3.1 data.table_1.13.2
[16] rstudioapi_0.13 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_2.0.1
[46] scales_1.1.1 cli_2.2.0 stringi_1.5.3
[49] farver_2.0.3 fs_1.5.0 promises_1.1.1
[52] RcppArmadillo_0.10.1.2.0 xml2_1.3.2 ellipsis_0.3.1
[55] generics_0.0.2 vctrs_0.3.5 tools_4.0.3
[58] glue_1.4.2 hms_0.5.3 parallel_4.0.3
[61] yaml_2.2.1 colorspace_2.0-0 rvest_0.3.6
[64] knitr_1.30 haven_2.3.1