Zonal sections
Jens Daniel Müller
02 November, 2021
Last updated: 2021-11-02
Checks: 7 0
Knit directory: emlr_obs_analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 57cfc36 | jens-daniel-mueller | 2021-11-01 | Build site. |
| html | 4331a22 | jens-daniel-mueller | 2021-10-29 | Build site. |
| html | ae5ae64 | jens-daniel-mueller | 2021-10-26 | Build site. |
| html | 581baa0 | jens-daniel-mueller | 2021-10-07 | Build site. |
| html | a7af62f | jens-daniel-mueller | 2021-10-06 | Build site. |
| Rmd | 277e227 | jens-daniel-mueller | 2021-10-06 | add no N/P analysis |
1 Read files
ensemble_members <-
read_rds("data/ensemble_members_no_n.rds")
MLR_basins_in <-
read_rds("data/MLR_basins_in_no_n.rds")# identify required version IDs
Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = "v_19")
Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = "v_29")
# Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
# pattern = "v_30")
Version_IDs <- c(Version_IDs_1, Version_IDs_2)
# Version_IDs <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)
rm(Version_IDs_1, Version_IDs_2)
# rm(Version_IDs_1, Version_IDs_2, Version_IDs_3)
# Version_IDs <- Version_IDs[1:length(Version_IDs)-1]
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
print(i_Version_IDs)
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_zonal <-
read_csv(paste(path_version_data,
"dcant_zonal.csv",
sep = ""))
dcant_zonal_mod_truth <-
read_csv(paste(path_version_data,
"dcant_zonal_mod_truth.csv",
sep = ""))
dcant_zonal_bias <-
read_csv(paste(path_version_data,
"dcant_zonal_bias.csv",
sep = ""))
dcant_zonal <- bind_rows(dcant_zonal,
dcant_zonal_mod_truth)
dcant_zonal <- dcant_zonal %>%
mutate(Version_ID = i_Version_IDs)
dcant_zonal_bias <- dcant_zonal_bias %>%
mutate(Version_ID = i_Version_IDs)
params_local <-
read_rds(paste(path_version_data,
"params_local.rds",
sep = ""))
params_local <- bind_cols(
Version_ID = i_Version_IDs,
MLR_basins = params_local$MLR_basins,
tref1 = params_local$tref1,
tref2 = params_local$tref2,
gap_filling = params_local$gap_filling,
rarefication = params_local$rarefication,
rarefication_threshold = params_local$rarefication_threshold,
MLR_predictors = str_c(params_local$MLR_predictors, collapse = "+"),
vif_max = params_local$vif_max
)
tref <- read_csv(paste(path_version_data,
"tref.csv",
sep = ""))
params_local <- params_local %>%
mutate(median_year_1 = sort(tref$median_year)[1],
median_year_2 = sort(tref$median_year)[2],
duration = median_year_2 - median_year_1,
period = paste(median_year_1, "-", median_year_2))
if (exists("dcant_zonal_all")) {
dcant_zonal_all <- bind_rows(dcant_zonal_all, dcant_zonal)
}
if (!exists("dcant_zonal_all")) {
dcant_zonal_all <- dcant_zonal
}
if (exists("dcant_zonal_bias_all")) {
dcant_zonal_bias_all <- bind_rows(dcant_zonal_bias_all, dcant_zonal_bias)
}
if (!exists("dcant_zonal_bias_all")) {
dcant_zonal_bias_all <- dcant_zonal_bias
}
if (exists("params_local_all")) {
params_local_all <- bind_rows(params_local_all, params_local)
}
if (!exists("params_local_all")) {
params_local_all <- params_local
}
}[1] "v_1901"
[1] "v_1902"
[1] "v_1903"
[1] "v_1904"
[1] "v_1905"
[1] "v_1906"
[1] "v_2901"
[1] "v_2902"
[1] "v_2903"
[1] "v_2904"
[1] "v_2905"
[1] "v_2906"rm(dcant_zonal, dcant_zonal_bias, dcant_zonal_mod_truth,
tref)
dcant_zonal_all <- full_join(dcant_zonal_all,
params_local_all)
dcant_zonal_bias_all <- full_join(dcant_zonal_bias_all,
params_local_all)dcant_zonal_all <- dcant_zonal_all %>%
filter(MLR_basins %in% MLR_basins_in)
dcant_zonal_bias_all <- dcant_zonal_bias_all %>%
filter(MLR_basins %in% MLR_basins_in)2 Individual cases
2.1 Absoulte values
dcant_zonal_all %>%
filter(data_source %in% c("mod", "obs")) %>%
group_by(basin_AIP, data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "dcant",
plot_slabs = "n",
subtitle_text = paste(
"data_source: ",
unique(.x$data_source),
"| basin:",
unique(.x$basin_AIP)
)
) +
facet_grid(MLR_basins ~ period)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
p_dcant_Indian_1994_2004 <-
dcant_zonal_all %>%
filter(data_source %in% c("obs"),
period == "1994 - 2004",
basin_AIP == "Indian") %>%
p_section_zonal_continous_depth(var = "dcant",
plot_slabs = "n",
subtitle_text = "Indian Ocean") +
facet_grid(MLR_basins ~ period)
ggsave(plot = p_dcant_Indian_1994_2004,
path = "output/publication",
filename = "zonal_indian_1994_2004.png",
height = 8,
width = 5)
p_dcant_Indian_2004_2014 <-
dcant_zonal_all %>%
filter(data_source %in% c("obs"),
period == "2004 - 2014",
basin_AIP == "Pacific") %>%
p_section_zonal_continous_depth(var = "dcant",
plot_slabs = "n",
subtitle_text = "Pacific Ocean") +
facet_grid(MLR_basins ~ period)
ggsave(plot = p_dcant_Indian_2004_2014,
path = "output/publication",
filename = "zonal_Pacific_2004_2014.png",
height = 8,
width = 5)2.2 Biases
dcant_zonal_bias_all %>%
group_by(basin_AIP) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "dcant_bias",
col = "divergent",
plot_slabs = "n",
subtitle_text = paste("basin:",
unique(.x$basin_AIP)
)
) +
facet_grid(MLR_basins ~ period)
)[[1]]
[[2]]
[[3]]
2.3 Bias correlation
dcant_zonal_bias_all_corr <- dcant_zonal_bias_all %>%
select(lat, depth, basin_AIP, dcant_bias, MLR_basins, period) %>%
pivot_wider(names_from = period,
values_from = dcant_bias,
names_prefix = "dcant_bias ")
dcant_zonal_bias_all_corr %>%
ggplot(aes(`dcant_bias 1994 - 2004`, `dcant_bias 2004 - 2014`)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 0) +
geom_bin2d() +
facet_grid(basin_AIP ~ MLR_basins) +
scale_fill_viridis_c()
| Version | Author | Date |
|---|---|---|
| a7af62f | jens-daniel-mueller | 2021-10-06 |
3 Ensemble
dcant_zonal_ensemble <- dcant_zonal_all %>%
filter(data_source %in% c("mod", "obs"),
MLR_basins %in% ensemble_members) %>%
group_by(lat, depth, basin_AIP, data_source, period) %>%
summarise(dcant_ensemble_mean = mean(dcant),
dcant_sd = sd(dcant),
dcant_range = max(dcant)- min(dcant)) %>%
ungroup()3.1 Mean
dcant_zonal_ensemble %>%
group_by(basin_AIP) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "dcant_ensemble_mean",
plot_slabs = "n",
subtitle_text = paste("basin:",
unique(.x$basin_AIP))
) +
facet_grid(data_source ~ period)
)[[1]]
[[2]]
[[3]]
3.2 Mean bias
dcant_zonal_ensemble_bias <- full_join(
dcant_zonal_ensemble %>%
filter(data_source == "mod") %>%
select(lat, depth, basin_AIP, period, dcant_ensemble_mean, dcant_sd),
dcant_zonal_all %>%
filter(data_source == "mod_truth",
MLR_basins == "2") %>%
select(lat, depth, basin_AIP, period, dcant_mod_truth = dcant)
)
dcant_zonal_ensemble_bias <- dcant_zonal_ensemble_bias %>%
mutate(dcant_bias = dcant_ensemble_mean - dcant_mod_truth)
dcant_zonal_ensemble_bias %>%
group_by(basin_AIP) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "dcant_bias",
col = "divergent",
plot_slabs = "n",
subtitle_text = paste("basin:",
unique(.x$basin_AIP)
)
) +
facet_grid(. ~ period)
)[[1]]
[[2]]
[[3]]
3.3 Standard deviation
dcant_zonal_ensemble %>%
group_by(basin_AIP) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "dcant_sd",
breaks = c(seq(0,4,0.4), Inf),
plot_slabs = "n",
subtitle_text = paste("basin:",
unique(.x$basin_AIP))
) +
facet_grid(data_source ~ period)
)3.4 SD vs bias
dcant_zonal_ensemble_bias %>%
ggplot(aes(dcant_bias, dcant_sd)) +
geom_bin2d() +
scale_fill_viridis_c() +
facet_grid(basin_AIP ~ period)
dcant_zonal_ensemble_bias %>%
select(dcant_ensemble_mean, dcant_bias, period) %>%
pivot_longer(dcant_ensemble_mean:dcant_bias,
names_to = "estimate",
values_to = "value") %>%
ggplot(aes(value, col=estimate, linetype = period)) +
scale_color_brewer(palette = "Set1") +
geom_density()
dcant_zonal_ensemble %>%
ggplot(aes(dcant_sd)) +
geom_histogram() +
facet_grid(data_source ~ period) +
coord_cartesian(ylim = c(0,50))4 Cases vs ensemble
4.1 Offset from mean
dcant_zonal_all <- full_join(dcant_zonal_all %>% select(-dcant_sd),
dcant_zonal_ensemble)
dcant_zonal_all <- dcant_zonal_all %>%
mutate(dcant_offset = dcant - dcant_ensemble_mean)
legend_title <- expression(atop(Delta * C[ant,offset],
(mu * mol ~ kg ^ {
-1
})))
dcant_zonal_all %>%
filter(data_source %in% c("mod", "obs")) %>%
group_by(basin_AIP, data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "dcant_offset",
col = "divergent",
plot_slabs = "n",
subtitle_text = paste("basin:",
unique(.x$basin_AIP)
)
) +
facet_grid(MLR_basins ~ period)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
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] ggforce_0.3.3 metR_0.9.0 scico_1.2.0 patchwork_1.1.1
[5] collapse_1.5.0 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.5
[9] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.3
[13] ggplot2_3.3.5 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.2 sass_0.4.0 viridisLite_0.3.0
[4] jsonlite_1.7.1 modelr_0.1.8 bslib_0.2.5.1
[7] assertthat_0.2.1 highr_0.8 blob_1.2.1
[10] cellranger_1.1.0 yaml_2.2.1 pillar_1.6.2
[13] backports_1.1.10 lattice_0.20-41 glue_1.4.2
[16] RcppEigen_0.3.3.7.0 digest_0.6.27 promises_1.1.1
[19] polyclip_1.10-0 checkmate_2.0.0 rvest_0.3.6
[22] colorspace_2.0-2 htmltools_0.5.1.1 httpuv_1.5.4
[25] Matrix_1.2-18 pkgconfig_2.0.3 broom_0.7.9
[28] haven_2.3.1 scales_1.1.1 tweenr_1.0.2
[31] whisker_0.4 later_1.2.0 git2r_0.27.1
[34] farver_2.0.3 generics_0.1.0 ellipsis_0.3.2
[37] withr_2.3.0 cli_3.0.1 magrittr_1.5
[40] crayon_1.3.4 readxl_1.3.1 evaluate_0.14
[43] fs_1.5.0 fansi_0.4.1 MASS_7.3-53
[46] xml2_1.3.2 RcppArmadillo_0.10.1.2.0 tools_4.0.3
[49] data.table_1.14.0 hms_0.5.3 lifecycle_1.0.0
[52] munsell_0.5.0 reprex_0.3.0 isoband_0.2.2
[55] compiler_4.0.3 jquerylib_0.1.4 rlang_0.4.11
[58] grid_4.0.3 rstudioapi_0.13 labeling_0.4.2
[61] rmarkdown_2.10 gtable_0.3.0 DBI_1.1.0
[64] R6_2.5.0 lubridate_1.7.9 knitr_1.33
[67] utf8_1.1.4 rprojroot_2.0.2 stringi_1.5.3
[70] parallel_4.0.3 Rcpp_1.0.5 vctrs_0.3.8
[73] dbplyr_1.4.4 tidyselect_1.1.0 xfun_0.25