Analysis of dcant estimates
Jens Daniel Müller
23 July, 2021
Last updated: 2021-07-23
Checks: 7 0
Knit directory: emlr_obs_v_XXX/
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| Rmd | b599874 | jens-daniel-mueller | 2021-07-23 | calculate slab dcant SD |
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1 Version ID
The results displayed on this site correspond to the Version_ID: v_XXX
2 Data sources
dcant estimates from this sensitivity case:
- Mean and SD per grid cell (lat, lon, depth)
- Zonal mean and SD (basin, lat, depth)
- Inventories (lat, lon)
dcant_3d <-
read_csv(paste(path_version_data,
"dcant_3d.csv",
sep = ""))
dcant_3d_mod_truth <-
read_csv(paste(path_version_data,
"dcant_3d_mod_truth.csv",
sep = ""))
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 <- bind_rows(dcant_zonal,
dcant_zonal_mod_truth)
dcant_inv <-
read_csv(paste(path_version_data,
"dcant_inv.csv",
sep = ""))
dcant_slab <-
read_csv(paste0(path_version_data,
"dcant_slab.csv"))3 Zonal sections
3.1 SD across models
Standard deviation across dcant from all MLR models was calculate for each grid cell (XYZ). The zonal mean of this standard deviation should reflect the uncertainty associated to the predictor selection within each slab and era.
dcant_zonal %>%
group_by(basin_AIP, data_source) %>%
group_split() %>%
head(1) %>%
map(
~ p_section_zonal(
df = .x,
var = "dcant_sd_mean",
gamma = "gamma_mean",
legend_title = "sd",
breaks = c(seq(0, 3, 0.3), Inf),
title_text = "Zonal mean section of SD across models",
subtitle_text =
paste(
"Basin:",
unique(.x$basin_AIP),
"| data_source:",
unique(.x$data_source)
)
)
)[[1]]
3.2 SD across basins
Standard deviation of mean dcant values was calculate across all longitudes. This standard deviation should reflect the zonal variability of dcant within the basin and era.
dcant_zonal %>%
group_by(basin_AIP, data_source) %>%
group_split() %>%
head(1) %>%
map(
~ p_section_zonal(
df = .x,
var = "dcant_sd",
gamma = "gamma_mean",
legend_title = "sd",
breaks = c(seq(0, 3, 0.3),Inf),
title_text = "Zonal mean section of dcant SD",
subtitle_text =
paste(
"Basin:",
unique(.x$basin_AIP),
"| data_source:",
unique(.x$data_source)
)
)
)[[1]]
4 Correlation
dcant_min <- -20
dcant_max <- 20
dcant_sd_max <- 104.0.1 dcant vs model SD
4.0.1.1 Era vs basin
dcant_3d %>%
ggplot(aes(dcant, dcant_sd)) +
geom_bin2d(binwidth = 1) +
geom_vline(xintercept = 0,
col = "red") +
scale_fill_viridis_c(trans = "log10",
name = "log10(n)") +
facet_grid(basin_AIP ~ data_source) +
coord_cartesian(ylim = c(0, dcant_sd_max),
xlim = c(dcant_min, dcant_max))
4.0.1.2 Basin vs gamma
dcant_3d %>%
group_split(basin_AIP) %>%
# head(1) %>%
map(
~ ggplot(data = .x, aes(dcant, dcant_sd)) +
geom_bin2d(binwidth = 1) +
geom_vline(xintercept = 0,
col = "red") +
scale_fill_viridis_c(trans = "log10",
name = "log10(n)") +
coord_cartesian(
ylim = c(0, dcant_sd_max),
xlim = c(dcant_min, dcant_max)
) +
facet_grid(gamma_slab ~ data_source) +
labs(title = paste("Basin:", unique(.x$basin_AIP)))
)[[1]]
[[2]]
[[3]]
4.0.2 dcant vs regional SD
4.0.2.1 Era vs basin
dcant_zonal %>%
ggplot(aes(dcant_mean, dcant_sd)) +
geom_bin2d() +
geom_vline(xintercept = 0,
col = "red") +
scale_fill_viridis_c(trans = "log10",
name = "log10(n)") +
coord_cartesian(ylim = c(0, dcant_sd_max),
xlim = c(dcant_min, dcant_max)) +
facet_grid(basin_AIP ~ data_source)
4.0.2.2 Era vs basin
dcant_zonal %>%
group_split(basin_AIP) %>%
# head(1) %>%
map(
~ ggplot(data = .x, aes(dcant_mean, dcant_sd)) +
geom_bin2d() +
geom_vline(xintercept = 0) +
scale_fill_viridis_c(trans = "log10") +
facet_grid(gamma_slab ~ data_source) +
coord_cartesian(
ylim = c(0, dcant_sd_max),
xlim = c(dcant_min, dcant_max)
) +
labs(title = paste("Basin:", unique(.x$basin_AIP)))
)[[1]]
[[2]]
[[3]]
4.0.3 dcant bias vs model SD
dcant_3d <- bind_rows(dcant_3d,
dcant_3d_mod_truth)
dcant_3d_bias <- dcant_3d %>%
filter(data_source %in% c("mod", "mod_truth")) %>%
select(lat, lon, depth, data_source, basin_AIP, gamma_slab, dcant) %>%
pivot_wider(names_from = data_source,
values_from = dcant) %>%
mutate(dcant_bias = mod - mod_truth)
dcant_3d_bias_sd <- dcant_3d %>%
filter(data_source %in% c("mod")) %>%
select(lat, lon, depth, dcant, dcant_sd)
dcant_3d_bias <- full_join(dcant_3d_bias, dcant_3d_bias_sd)
dcant_3d_bias %>%
ggplot(aes(dcant_bias, dcant_sd)) +
scale_fill_viridis_c(trans = "log10") +
geom_bin2d(binwidth = 1) +
coord_cartesian(ylim = c(0, dcant_sd_max),
xlim = c(dcant_min, dcant_max)) +
geom_vline(xintercept = 0,
col = "red")
dcant_3d_bias %>%
ggplot(aes(dcant_bias, dcant_sd)) +
scale_fill_viridis_c(trans = "log10") +
geom_bin2d(binwidth = 1) +
coord_cartesian(ylim = c(0, dcant_sd_max),
xlim = c(dcant_min, dcant_max)) +
geom_vline(xintercept = 0,
col = "red") +
facet_grid(gamma_slab ~ basin_AIP)
4.0.4 dcant bias vs dcant
dcant_3d_bias %>%
ggplot(aes(dcant_bias, dcant)) +
scale_fill_viridis_c(trans = "log10") +
geom_bin2d(binwidth = 1) +
coord_cartesian(ylim = c(dcant_min, dcant_max),
xlim = c(dcant_min, dcant_max)) +
geom_vline(xintercept = 0,
col = "red")
dcant_3d_bias %>%
ggplot(aes(dcant_bias)) +
geom_histogram(binwidth = 1) +
coord_cartesian(xlim = c(dcant_min, dcant_max)) +
geom_vline(xintercept = 0,
col = "red")
dcant_3d_bias %>%
ggplot(aes(dcant)) +
geom_histogram(binwidth = 1) +
coord_cartesian(xlim = c(dcant_min, dcant_max)) +
geom_vline(xintercept = 0,
col = "red")
dcant_3d_bias %>%
ggplot(aes(dcant_bias, dcant)) +
scale_fill_viridis_c(trans = "log10") +
geom_bin2d(binwidth = 1) +
coord_cartesian(ylim = c(dcant_min, dcant_max),
xlim = c(dcant_min, dcant_max)) +
geom_vline(xintercept = 0,
col = "red") +
facet_grid(gamma_slab ~ basin_AIP)
dcant_3d_bias %>%
ggplot(aes(dcant_bias)) +
geom_histogram(binwidth = 1) +
coord_cartesian(xlim = c(dcant_min, dcant_max)) +
geom_vline(xintercept = 0,
col = "red") +
facet_grid(gamma_slab ~ basin_AIP)
dcant_3d_bias %>%
ggplot(aes(dcant)) +
geom_histogram(binwidth = 1) +
coord_cartesian(xlim = c(dcant_min, dcant_max)) +
geom_vline(xintercept = 0,
col = "red") +
facet_grid(gamma_slab ~ basin_AIP)
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] gt_0.2.2 kableExtra_1.3.1 marelac_2.1.10 shape_1.4.5
[5] scales_1.1.1 ggforce_0.3.3 metR_0.9.0 scico_1.2.0
[9] patchwork_1.1.1 collapse_1.5.0 forcats_0.5.0 stringr_1.4.0
[13] dplyr_1.0.5 purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
[17] tibble_3.0.4 ggplot2_3.3.3 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.0 lubridate_1.7.9 gsw_1.0-5
[4] webshot_0.5.2 httr_1.4.2 rprojroot_2.0.2
[7] tools_4.0.3 backports_1.1.10 R6_2.5.0
[10] DBI_1.1.0 colorspace_1.4-1 withr_2.3.0
[13] tidyselect_1.1.0 compiler_4.0.3 git2r_0.27.1
[16] cli_2.1.0 rvest_0.3.6 xml2_1.3.2
[19] isoband_0.2.2 labeling_0.4.2 checkmate_2.0.0
[22] digest_0.6.27 rmarkdown_2.5 oce_1.2-0
[25] pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
[28] rlang_0.4.10 readxl_1.3.1 rstudioapi_0.11
[31] farver_2.0.3 generics_0.0.2 jsonlite_1.7.1
[34] magrittr_1.5 Matrix_1.2-18 Rcpp_1.0.5
[37] munsell_0.5.0 fansi_0.4.1 lifecycle_1.0.0
[40] stringi_1.5.3 whisker_0.4 yaml_2.2.1
[43] MASS_7.3-53 plyr_1.8.6 grid_4.0.3
[46] blob_1.2.1 parallel_4.0.3 promises_1.1.1
[49] crayon_1.3.4 lattice_0.20-41 haven_2.3.1
[52] hms_0.5.3 seacarb_3.2.14 knitr_1.30
[55] pillar_1.4.7 reprex_0.3.0 glue_1.4.2
[58] evaluate_0.14 RcppArmadillo_0.10.1.2.0 data.table_1.13.2
[61] modelr_0.1.8 vctrs_0.3.5 tweenr_1.0.2
[64] httpuv_1.5.4 testthat_2.3.2 cellranger_1.1.0
[67] gtable_0.3.0 polyclip_1.10-0 assertthat_0.2.1
[70] xfun_0.18 broom_0.7.5 RcppEigen_0.3.3.7.0
[73] later_1.1.0.1 viridisLite_0.3.0 memoise_1.1.0
[76] ellipsis_0.3.1 here_0.1