Random subset
Jens Daniel Müller
05 January, 2021
Last updated: 2021-01-05
Checks: 7 0
Knit directory: emlr_mod_preprocessing/
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|---|---|---|---|---|
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path_GLODAP_preprocessing <-
paste(path_root, "/observations/preprocessing/", sep = "")
path_cmorized <-
"/nfs/kryo/work/loher/CESM_output/RECCAP2/cmorized_Dec2020/split_monthly/"
path_preprocessing <-
paste(path_root, "/model/preprocessing/", sep = "")# 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)1 Read GLODAPv2_2020 preprocessed files
GLODAP <-
read_csv(paste(path_GLODAP_preprocessing,
"GLODAPv2.2020_preprocessed.csv",
sep = ""))
GLODAP <- GLODAP %>%
mutate(month = month(date))2 Randomly subset model data
Here we randomly subset cmorized (1x1) model with variable forcing, according to the total number of GLODAP observations for the whole period from a previously cleaned file. The number for the annual subset remains the same for each year, which could be expressed by the total number of observations divided by number of years.
Besides, Model results are given in [mol m-3], whereas GLODAP data are in [µmol kg-1]. This refers to the variables:
- DIC
- ALK
- O2
- NO3
- PO4
- SiO3
- AOU (calculated)
For comparison, model results were converted from [mol m-3] to [µmol kg-1]
# read in a random model
model <-
read_ncdf(paste(path_cmorized, "dissic_CESM-ETHZ_A_1_gr_1982.nc", sep = "")) %>%
as_tibble() %>%
drop_na()
# convert longitudes and mutate month
model <- model %>%
mutate(lon = if_else(lon < 20, lon + 360, lon)) %>%
mutate(month = month(time_mon))
# only consider model grids within basinmask
model <- inner_join(model, basinmask)
# model grid with depth
model_grid_depth <- model %>%
select(month, lat, lon, depth)
rm(model)
# set name of model to be subsetted
model_ID <- "A"
# for loop across years
years <- c("1982":"2019")
# set equal number of random model sampling will be made for each year
n = floor(nrow(GLODAP) / length(years))
for (i_year in years) {
# i_year <- years[1]
# random sample n = GLODAP / years from model grid depth
model_resample_grid_depth <- sample_n(model_grid_depth, n) %>%
arrange(lat, lon, depth, month) %>%
mutate(year = i_year)
# for loop across variables
variables <-
c("so", "thetao", "dissic", "talk", "o2", "no3", "po4", "si")
for (i_variable in variables) {
# i_variable <- variables[2]
# read list of all files
file <-
list.files(
path = path_cmorized,
pattern = paste(
"^",
i_variable,
"_CESM-ETHZ_",
model_ID,
"_1_gr_",
i_year,
".nc",
sep = ""
)
)
print(file)
# read in data
variable_data <-
read_ncdf(paste(path_cmorized,
file,
sep = ""))
# convert to tibble
variable_data_tibble <- variable_data %>%
as_tibble()
# remove open link to nc file
rm(variable_data)
# remove na values
variable_data_tibble <-
variable_data_tibble %>%
filter(!is.na(!!sym(i_variable)))
# convert longitudes
variable_data_tibble <- variable_data_tibble %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
# only consider model grids within basinmask
variable_data_tibble <- inner_join(variable_data_tibble, basinmask) %>%
select(-basin_AIP)
# mutate variables
variable_data_tibble <- variable_data_tibble %>%
mutate(month = month(time_mon),
!!sym(i_variable) := as.numeric(!!sym(i_variable))) %>%
select(-time_mon)
# random sample for each model variable in specific year
model_resample_grid_depth <-
left_join(model_resample_grid_depth, variable_data_tibble)
}
# add random sample model subset for each year together
if (exists("model_resample")) {
model_resample <-
bind_rows(model_resample, model_resample_grid_depth)
}
if (!exists("model_resample")) {
model_resample <- model_resample_grid_depth
}
}
# calculate model temperature
model_resample <- model_resample %>%
mutate(temp = gsw_pt_from_t(
SA = so,
t = thetao,
p = 10.1325,
p_ref = depth
))
# unit transfer from mol/m3 to µmol/kg
model_resample <- model_resample %>%
mutate(
rho = gsw_pot_rho_t_exact(
SA = so,
t = temp,
p = depth,
p_ref = 10.1325
),
dissic = dissic * (1e+6 / rho),
talk = talk * (1e+6 / rho),
o2 = o2 * (1e+6 / rho),
no3 = no3 * (1e+6 / rho),
po4 = po4 * (1e+6 / rho),
si = si * (1e+6 / rho)
)
# calculate AOU
model_resample <- model_resample %>%
mutate(
oxygen_sat_m3 = gas_satconc(
S = so,
t = temp,
P = 1.013253,
species = "O2"
),
oxygen_sat_kg = oxygen_sat_m3 * (1e+3 / rho),
aou = oxygen_sat_kg - o2
) %>%
select(-oxygen_sat_kg,-oxygen_sat_m3)
# write file for random model sampling
model_resample %>%
write_csv(paste(path_preprocessing,
"GLODAPv2.2020_preprocessed_model_runA_random_subset.csv",
sep = ""))3 Control plots
3.1 Spatial distribution
# read in random model sampling file
model_resample <-
read_csv(paste(path_preprocessing,
"GLODAPv2.2020_preprocessed_model_runA_random_subset.csv",
sep = ""))
# plot random sampling cmorized grids in each year
years <- c("1982", "1990", "2000", "2010", "2019")
for (i_year in years) {
# i_year <- years[1]
model_resample_year <- model_resample %>%
filter(year == i_year)
print(
map +
geom_bin2d(data = model_resample_year,
aes(lon, lat),
binwidth = 1) +
scale_fill_viridis_c(direction = -1) +
coord_quickmap(expand = 0) +
labs(
title = paste("Random Model Sampling of year", i_year),
subtitle = paste("Nr of observations", nrow(model_resample_year)),
x = "Longitude",
y = "Latitude"
)
)
}
3.2 Depth distribution
# Calculate and plot depth distribution of model subset
model_resample_depth <- model_resample %>%
count(depth, year)
model_resample_depth_average <- model_resample_depth %>%
group_by(depth) %>%
summarise(n = mean(n))
model_resample_depth_average %>%
arrange(depth) %>%
ggplot(aes(n, depth)) +
geom_point(data = model_resample_depth,
aes(n, depth, col = "All years")) +
geom_point(aes(n, depth, col = "Average")) +
geom_path(aes(n, depth, col = "Average")) +
scale_color_brewer(palette = "Set1",
name = "",
direction = -1) +
scale_y_reverse() +
coord_cartesian(xlim = c(0,500),
ylim = c(6000,0)) +
labs(title = "Depth distribution of random model subset")
# Calculate and plot depth distribution of GLODAP data
# Depths gridded to model depth levels for comparison
GLODAP_depth <- GLODAP %>%
mutate(depth = as.numeric(as.character(cut(depth,
c(0,unique(model_resample_depth_average$depth)),
labels = unique(model_resample_depth_average$depth))))) %>%
count(depth, year)
GLODAP_depth_average <- GLODAP_depth %>%
group_by(depth) %>%
summarise(n = mean(n))
GLODAP_depth_average %>%
arrange(depth) %>%
ggplot(aes(n, depth)) +
geom_point(data = GLODAP_depth,
aes(n, depth, col = "All years")) +
geom_point(aes(n, depth, col = "Average")) +
geom_path(aes(n, depth, col = "Average")) +
scale_color_brewer(palette = "Set1",
name = "",
direction = -1) +
scale_y_reverse() +
coord_cartesian(xlim = c(0,500),
ylim = c(6000,0)) +
labs(title = "Depth distribution of GLODAP observations")
| Version | Author | Date |
|---|---|---|
| 6e83d29 | jens-daniel-mueller | 2020-12-22 |
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2
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] marelac_2.1.10 shape_1.4.5 gsw_1.0-5 testthat_3.0.1
[5] rqdatatable_1.2.9 rquery_1.4.6 wrapr_2.0.4 lubridate_1.7.9
[9] stars_0.4-3 sf_0.9-6 abind_1.4-5 metR_0.9.0
[13] scico_1.2.0 patchwork_1.1.1 collapse_1.4.2 forcats_0.5.0
[17] stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
[21] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0
[25] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.0 RColorBrewer_1.1-2 httr_1.4.2
[4] rprojroot_2.0.2 tools_4.0.3 backports_1.2.1
[7] R6_2.5.0 KernSmooth_2.23-17 DBI_1.1.0
[10] colorspace_2.0-0 withr_2.3.0 tidyselect_1.1.0
[13] compiler_4.0.3 git2r_0.27.1 cli_2.2.0
[16] rvest_0.3.6 xml2_1.3.2 labeling_0.4.2
[19] scales_1.1.1 checkmate_2.0.0 classInt_0.4-3
[22] digest_0.6.27 oce_1.2-0 rmarkdown_2.5
[25] pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
[28] rlang_0.4.9 readxl_1.3.1 rstudioapi_0.13
[31] farver_2.0.3 generics_0.1.0 jsonlite_1.7.2
[34] magrittr_2.0.1 Matrix_1.2-18 Rcpp_1.0.5
[37] munsell_0.5.0 fansi_0.4.1 lifecycle_0.2.0
[40] stringi_1.5.3 whisker_0.4 yaml_2.2.1
[43] grid_4.0.3 blob_1.2.1 parallel_4.0.3
[46] promises_1.1.1 crayon_1.3.4 lattice_0.20-41
[49] haven_2.3.1 seacarb_3.2.14 hms_0.5.3
[52] knitr_1.30 pillar_1.4.7 reprex_0.3.0
[55] glue_1.4.2 evaluate_0.14 RcppArmadillo_0.10.1.2.0
[58] data.table_1.13.4 modelr_0.1.8 vctrs_0.3.6
[61] httpuv_1.5.4 cellranger_1.1.0 gtable_0.3.0
[64] assertthat_0.2.1 xfun_0.19 lwgeom_0.2-5
[67] broom_0.7.3 RcppEigen_0.3.3.9.1 e1071_1.7-4
[70] later_1.1.0.1 viridisLite_0.3.0 class_7.3-17
[73] units_0.6-7 ellipsis_0.3.1