Analysis of cant estimates
Jens Daniel Müller
13 April, 2021
Last updated: 2021-04-13
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Knit directory: emlr_obs_v_XXX/
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1 Data sources
Cant estimates from this sensitivity case:
- Mean and SD per grid cell (lat, lon, depth)
- Zonal mean and SD (basin, lat, depth)
- Inventories (lat, lon)
cant_3d <-
read_csv(paste(path_version_data,
"cant_3d.csv",
sep = ""))
cant_3d_model_truth <-
read_csv(paste(path_version_data,
"cant_3d_model_truth.csv",
sep = ""))
cant_3d <- bind_rows(cant_3d,
cant_3d_model_truth %>% mutate(data_source = "model_truth",
gamma_slab = NA))
cant_zonal <-
read_csv(paste(path_version_data,
"cant_zonal.csv",
sep = ""))
cant_zonal_model_truth <-
read_csv(paste(path_version_data,
"cant_zonal_model_truth.csv",
sep = ""))
cant_zonal <- bind_rows(
cant_zonal,
cant_zonal_model_truth %>% mutate(data_source = "model_truth",
gamma_slab = NA)
)2 Cant - positive
In a first series of plots we explore the distribution of Cant, taking only positive estimates into account (positive here refers to the mean cant estimate across MLR model predictions available for each grid cell). Negative values were set to zero before calculating mean sections and inventories.
cant_zonal_bias <- cant_zonal %>%
filter(data_source %in% c("mod", "model_truth")) %>%
select(lat, depth, basin_AIP, data_source, cant_pos_mean) %>%
pivot_wider(names_from = data_source,
values_from = cant_pos_mean) %>%
mutate(cant_pos_bias = mod - model_truth)
# cant_zonal %>%
# group_split(data_source, basin_AIP) %>%
# map(
# i_basin_AIP = unique(.x$basin_AIP),
# ~ p_section_zonal(
# df = .x,
# var = "cant_pos_mean",
# subtitle_text = paste("data_source:", unique(.x$data_source),
# "basin:", unique(.x$basin_AIP))
# ))
# i_basin_AIP <- unique(cant_zonal$basin_AIP)[2]
# i_data_source <- unique(cant_zonal$data_source)[1]
for (i_basin_AIP in unique(cant_zonal$basin_AIP)) {
for (i_data_source in unique(cant_zonal$data_source)) {
print(
p_section_zonal(
df = cant_zonal %>%
filter(basin_AIP == i_basin_AIP,
data_source == i_data_source),
var = "cant_pos_mean",
subtitle_text =
paste("Basin:", i_basin_AIP, "| data_source:", i_data_source))
)
}
print(
p_section_zonal(
df = cant_zonal_bias %>%
filter(basin_AIP == i_basin_AIP),
var = "cant_pos_bias",
col="divergent",
breaks = params_global$breaks_cant_offset,
plot_slabs = "n",
subtitle_text =
paste("Basin:", i_basin_AIP, "| data_source: mod - model_truth")
)
)
}
| Version | Author | Date |
|---|---|---|
| eff1ad7 | jens-daniel-mueller | 2021-04-12 |
| Version | Author | Date |
|---|---|---|
| eff1ad7 | jens-daniel-mueller | 2021-04-12 |
| Version | Author | Date |
|---|---|---|
| eff1ad7 | jens-daniel-mueller | 2021-04-12 |
rm(cant_zonal_bias)2.1 Global sections
for (i_data_source in unique(cant_3d$data_source)) {
print(
p_section_global(
df = cant_3d %>% filter(data_source == i_data_source),
var = "cant_pos",
subtitle_text = paste("data_source:", i_data_source)
)
)
}
| Version | Author | Date |
|---|---|---|
| 062160b | jens-daniel-mueller | 2021-04-12 |
3 Cant - all
In a second series of plots we explore the distribution of all Cant, taking positive and negative estimates into account.
cant_zonal_bias <- cant_zonal %>%
filter(data_source %in% c("mod", "model_truth")) %>%
select(lat, depth, basin_AIP, data_source, cant_mean) %>%
pivot_wider(names_from = data_source,
values_from = cant_mean) %>%
mutate(cant_bias = mod - model_truth)
# i_data_source <- unique(cant_zonal$data_source)[1]
# i_basin_AIP <- unique(cant_zonal$basin_AIP)[1]
for (i_basin_AIP in unique(cant_zonal$basin_AIP)) {
for (i_data_source in unique(cant_zonal$data_source)) {
print(
p_section_zonal(
df = cant_zonal %>%
filter(basin_AIP == i_basin_AIP,
data_source == i_data_source),
var = "cant_mean",
gamma = "gamma_mean",
breaks = params_global$breaks_cant,
col = "divergent",
subtitle_text =
paste("Basin:", i_basin_AIP, "| data_source:", i_data_source))
)
}
print(
p_section_zonal(
df = cant_zonal_bias %>%
filter(basin_AIP == i_basin_AIP),
var = "cant_bias",
col="divergent",
breaks = params_global$breaks_cant_offset,
plot_slabs = "n",
subtitle_text =
paste("Basin:", i_basin_AIP, "| data_source: mod - model_truth")
)
)
}
| Version | Author | Date |
|---|---|---|
| 145d676 | jens-daniel-mueller | 2021-04-12 |
| Version | Author | Date |
|---|---|---|
| 145d676 | jens-daniel-mueller | 2021-04-12 |
| Version | Author | Date |
|---|---|---|
| 145d676 | jens-daniel-mueller | 2021-04-12 |
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 metR_0.9.0 scico_1.2.0 patchwork_1.1.1
[9] collapse_1.5.0 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.5
[13] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[17] 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_0.13
[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] plyr_1.8.6 grid_4.0.3 blob_1.2.1
[46] parallel_4.0.3 promises_1.1.1 crayon_1.3.4
[49] lattice_0.20-41 haven_2.3.1 hms_0.5.3
[52] seacarb_3.2.14 knitr_1.30 pillar_1.4.7
[55] reprex_0.3.0 glue_1.4.2 evaluate_0.14
[58] RcppArmadillo_0.10.1.2.0 data.table_1.13.2 modelr_0.1.8
[61] vctrs_0.3.5 httpuv_1.5.4 testthat_2.3.2
[64] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[67] xfun_0.18 broom_0.7.5 RcppEigen_0.3.3.7.0
[70] later_1.1.0.1 viridisLite_0.3.0 memoise_1.1.0
[73] ellipsis_0.3.1 here_0.1