Prepare chla data and vertically align to same depths as (pH) climatology

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Last updated: 2023-12-06

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Knit directory: bgc_argo_r_argodata/

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Task

This markdown file reads previously created pH climatology file and uses that as the definition of the depth levels that the Argo chlorophyll a data should be aligned to. Previously created BGC data (bgc_data.rds) and metadata (bgc_metadata.rds) are loaded from the BGC preprocessed folder.

Base data qc flags are checked to ensure that the float position, pressure measurements and chlorophyll a measurements are good. Pressure is used to derive the depth of each measurement. The chlorophyll a profile is checked to ensure that significant gaps (specified by the opt_gap_limit, opt_gap_min_depth and opt_gap_max_depth) do not exist. Profiles are assigned a profile_range field that identifies the depth 1 = 614 m, 2 = 1225 m and 3 = 1600 m.

The float chlorophyll a profiles are then aligned using the spline function to match the depth levels of the climatology resulting in data frame bgc_data_chla_interpolated_clean.

Set directories

location of pre-prepared data

Set options

Define options that are used to determine profiles that we will us in the ongoing analysis

# Options

# opt_profile_depth_range
# The profile must have at least one chla reading at a depth <= opt_profile_depth_range[1, ]
# The profile must have at least one chla reading at a depth >= opt_profile_depth_range[2, ].
# In addition if the profile depth does not exceed the min(opt_profile_depth_range[2, ]) (i.e. 600) it will be removed.
profile_range <- c(1, 2, 3)
min_depth <- c(10, 10, 10)
max_depth <- c(614, 1225, 1600)
opt_profile_depth_range <- data.frame(profile_range, min_depth, max_depth)

# opt_gap...
# The profile should not have a gap greater that opt_gap_limit within the range defined by opt_gap_min_depth and opt_gap_max_depth
opt_gap_limit <- c(28, 55, 110)
opt_gap_min_depth <- c(0, 400, 1000)
opt_gap_max_depth <- c(400, 1000, 1600)

# opt_measure_label, opt_xlim and opt_xbreaks are associated formatting
opt_measure_label <- expression("chlorophyll a ( mg m"^"-3"~")")
opt_xlim <- c(-0.5, 2.0)
opt_xbreaks <- c(-0.5, 0, 0.5, 1.0, 1.5, 2.0)

# opt_n_prof_sel
# The selection criteria that is used against n_prof, here set to 1
# Description of n_prof usage is provided at https://argo.ucsd.edu/data/data-faq/version-3-profile-files/ the next two lines are from that page.
#     The main Argo CTD profile is stored in N_PROF=1. All other parameters (including biogeochemical parameters) that are measured 
#     with the same vertical sampling scheme and at the same location and time as the main Argo CTD profile are also stored in N_PROF=1.
opt_n_prof_sel = 1

read climatology

read pH climatology, values are provided at set depths

# climatology values (pH_clim_va) available for lat, lon, month and depth
pH_clim_va <- read_rds(file = paste0(path_argo_preprocessed, "/pH_clim_va.rds"))

# What is the max depth we are interested in
opt_profile_max_depth <- max(opt_profile_depth_range$max_depth)

# existing depth levels that we will align to
target_depth_levels <-   pH_clim_va %>%
  filter(depth <= opt_profile_max_depth) %>%
  rename(target_depth = depth) %>%
  distinct(target_depth)

rm(pH_clim_va)
gc()

          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1276527 68.2    2476861 132.3  2476861 132.3
Vcells 2192510 16.8   91951884 701.6 92803038 708.1

read chla data

read chla profile and carry out basic checks, good data.

# base data and associated metadata
bgc_data <- read_rds(file = paste0(path_argo_preprocessed, '/bgc_data.rds'))
bgc_metadata <- read_rds(file = paste0(path_argo_preprocessed, '/bgc_metadata.rds'))

# Select relevant field from metadata ready to join to bgc_data
bgc_metadata_select <- bgc_metadata %>%
  filter(position_qc == 1) %>%
  select(file_id,
         date,
         lat,
         lon) %>%
  mutate(year = year(date),
         month = month(date),
         .after = date)

# we drive alignment from pressure and chla data
# conditions 
# n_prof == 1
# pres_adjusted_qc %in% c(1, 8) - pressure data marked as good
# chla_adjusted_qc %in% c(1, 8) - chla data marked as good
# !is.na(pres_adjusted) - pressure value must be present
# !is.na(chla_adjusted) - chla value must be present
bgc_data_chla <- bgc_data %>%
  filter(
    pres_adjusted_qc %in% c(1, 8) &
      chla_adjusted_qc %in% c(1, 8) &
      n_prof == opt_n_prof_sel &
      !is.na(pres_adjusted) &
      !is.na(chla_adjusted)
  ) %>%
  select(file_id,
         pres_adjusted,
         chla_adjusted)

# join with metadata information and calculate depth field
bgc_data_chla <- inner_join(bgc_metadata_select %>% select(file_id, lat),
                          bgc_data_chla) %>%
  mutate(depth = gsw_z_from_p(pres_adjusted, latitude = lat) * -1.0,
         .before = pres_adjusted) %>%
  select(-c(lat, pres_adjusted))

# ensure we have a depth, and chla_adjusted for all rows in bgc_data_chla
bgc_data_chla <- bgc_data_chla %>%
                  filter(!is.na(depth) & !is.na(chla_adjusted))

# clean up working tables
rm(bgc_data, bgc_metadata)
gc()

           used  (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells  1313111  70.2    2476861   132.3    2476861   132.3
Vcells 60100401 458.6 3356059698 25604.8 3478895921 26541.9

Profile limits

Apply the rules that are determined by options set in set_options. Profile must cover a set range and not contain gaps.

# Determine profile min and max depths
bgc_profile_limits <- bgc_data_chla %>%
  group_by(file_id) %>%
  summarise(
    min_depth = min(depth),
    max_depth = max(depth),
  ) %>%
  ungroup()

# The profile much match at least one of the range criteria
force_min <- min(opt_profile_depth_range$min_depth)
force_max <- min(opt_profile_depth_range$max_depth)

# Apply profile min and max restrictions
bgc_apply_limits <- bgc_profile_limits %>%
  filter(
    min_depth <= force_min &
    max_depth >= force_max
    )

# Ensure working data set only contains profiles that have confrormed to the range test
bgc_data_chla <- right_join(bgc_data_chla,
                          bgc_apply_limits %>% select(file_id))


# Add profile type field and set all to 1.  
# All profile that meet the minimum requirements are profile_range = 1
bgc_data_chla <- bgc_data_chla %>%
  mutate(profile_range = 1)

for (i in 2:nrow(opt_profile_depth_range)) {

  #i = 3
  range_min <- opt_profile_depth_range[i,'min_depth']
  range_max <- opt_profile_depth_range[i,'max_depth']

  # Apply profile min and max restrictions
  bgc_apply_limits <- bgc_profile_limits %>%
    filter(min_depth <= range_min &
             max_depth >= range_max) %>%
    select(file_id) %>%
    mutate (profile_range = i)

  # Update profile range to i for these profiles
  # bgc_data_temp <- full_join(bgc_data_temp, bgc_apply_limits) %>%
  #                         filter(!is.na(min_depth))
  bgc_data_chla <-
    bgc_data_chla %>% rows_update(bgc_apply_limits, by = "file_id")
  
}

# Find the gaps within the profiles
profile_gaps <- full_join(bgc_data_chla,
                          opt_profile_depth_range) %>%
  filter(depth >= min_depth & 
           depth <= max_depth) %>% 
  select(file_id,
         depth) %>%
  arrange(file_id, depth) %>%
  group_by(file_id) %>%
  mutate(gap = depth - lag(depth, default = 0)) %>%
  ungroup()

# Ensure we do not have gaps in the profile that invalidate it 
for (i_gap in opt_gap_limit) {

  # The limits to be applied in that pass of for loop
  # i_gap <- opt_gap_limit[3]
  i_gap_min = opt_gap_min_depth[which(opt_gap_limit == i_gap)]
  i_gap_max = opt_gap_max_depth[which(opt_gap_limit == i_gap)]
  
  # Which gaps are greater than i_gap
  profile_gaps_remove <- profile_gaps %>%
    filter(gap > i_gap) %>%
    filter(depth >= i_gap_min & depth <= i_gap_max) %>%
    distinct(file_id) %>% 
    pull()
  
  # Remonve these profiles from working data set
  bgc_data_chla <- bgc_data_chla %>% 
    filter(!file_id %in% profile_gaps_remove)

}

# clean up working tables
rm(bgc_profile_limits, profile_gaps, profile_gaps_remove, bgc_apply_limits)
gc()

           used  (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells  1328165  71.0    2476861   132.3    2476861   132.3
Vcells 38808593 296.1 2684847759 20483.8 3478895921 26541.9

Vertical align chla

We have a set of chla profiles that match our criteria we now need to align that data set to match the depth that are in target_depth_range, this will match the range of climatology values in ucsd_clim

# create unique combinations of file_id and profile ranges
profile_range_file_id <- 
  bgc_data_chla %>% 
  distinct(file_id, profile_range)


# select variable of interest and prepare target_depth field
bgc_data_chla_clean <- bgc_data_chla %>%
  select(-profile_range) %>%
  mutate(target_depth = depth, .after = depth)

rm(bgc_data_chla)
gc()

           used  (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells  1327923  71.0    2476861   132.3    2476861   132.3
Vcells 28673960 218.8 2147878208 16387.1 3478895921 26541.9

# create all possible combinations of location, month and depth levels for interpolation
target_depth_grid <-
  expand_grid(
    target_depth_levels,
    profile_range_file_id
  )

# Constrain target_depth_grid to profile depth range
target_depth_grid <-
  left_join(target_depth_grid, opt_profile_depth_range) %>%
  filter(target_depth <= max_depth)

target_depth_grid <- target_depth_grid %>%
  select(target_depth,
         file_id)

# extend chla depth vectors with target depths
bgc_data_chla_extended <-
  full_join(bgc_data_chla_clean, target_depth_grid) %>%
  arrange(file_id, target_depth)

rm(bgc_data_chla_clean)
gc()

           used  (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells  1330239  71.1    2476861   132.3    2476861   132.3
Vcells 43724484 333.6 1718302567 13109.7 3478895921 26541.9

# predict spline interpolation on adjusted depth grid for chla location and month
bgc_data_chla_interpolated <-
  bgc_data_chla_extended %>%
  group_by(file_id) %>%
  mutate(chla_spline = spline(target_depth, chla_adjusted,
                                method = "natural",
                                xout = target_depth)$y) %>%
  ungroup()

rm(bgc_data_chla_extended)
gc()

           used  (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells  1332014  71.2    2476861   132.3    2476861   132.3
Vcells 54877682 418.7 1374642054 10487.7 3478895921 26541.9

# subset interpolated values on target depth range
bgc_data_chla_interpolated_clean <- 
  inner_join(target_depth_levels, bgc_data_chla_interpolated)

rm(bgc_data_chla_interpolated)
gc()

          used (Mb) gc trigger   (Mb)   max used    (Mb)
Ncells 1332058 71.2    2476861  132.3    2476861   132.3
Vcells 9092286 69.4 1099713644 8390.2 3478895921 26541.9

# select columns and rename to initial names
bgc_data_chla_interpolated_clean <-
  bgc_data_chla_interpolated_clean %>%
  select(file_id,
         depth = target_depth,
         chla = chla_spline)

# merge with profile range
bgc_data_chla_interpolated_clean <-
  full_join(bgc_data_chla_interpolated_clean,
            profile_range_file_id)

# merge with meta data
bgc_data_chla_interpolated_clean <-
  left_join(bgc_data_chla_interpolated_clean,
            bgc_metadata_select)

Write files

Write the interpolated chla profiles that map onto depth levels.

# Write files
bgc_data_chla_interpolated_clean %>%
  write_rds(file = paste0(path_argo_preprocessed, "/chla_bgc_va.rds"))

# Rename so that names match if just reading existing files
chla_bgc_va <- bgc_data_chla_interpolated_clean

rm(bgc_data_chla_interpolated_clean)
gc()

           used (Mb) gc trigger   (Mb)   max used    (Mb)
Ncells  1331859 71.2    2476861  132.3    2476861   132.3
Vcells 12987725 99.1  879770916 6712.2 3478895921 26541.9

read files

Read files that were previously created ready for analysis

# read files
chla_bgc_va <- read_rds(file = paste0(path_argo_preprocessed, "/chla_bgc_va.rds"))

Analysis

chla mean profile

max_depth_1 <- opt_profile_depth_range[1, "max_depth"]
max_depth_2 <- opt_profile_depth_range[2, "max_depth"]
max_depth_3 <- opt_profile_depth_range[3, "max_depth"]

# Profiles to 600m
chla_overall_mean_1 <- chla_bgc_va %>% 
  filter(profile_range %in% c(1, 2, 3) & depth <= max_depth_1) %>%
  group_by(depth) %>% 
  summarise(count_measures = n(),
            chla_mean = mean(chla, na.rm = TRUE),
            chla_sd = sd(chla, na.rm = TRUE))

chla_year_mean_1 <- chla_bgc_va %>% 
  filter(profile_range %in% c(1, 2, 3) & depth <= max_depth_1) %>%
  group_by(year, depth) %>% 
  summarise(count_measures = n(),
            chla_mean = mean(chla, na.rm = TRUE),
            chla_sd = sd(chla, na.rm = TRUE))

# Profiles to 1200m
chla_overall_mean_2 <- chla_bgc_va %>% 
  filter(profile_range %in% c(2, 3) & depth <= max_depth_2) %>%
  group_by(depth) %>% 
  summarise(count_measures = n(),
            chla_mean = mean(chla, na.rm = TRUE),
            chla_sd = sd(chla, na.rm = TRUE))

chla_year_mean_2 <- chla_bgc_va %>% 
  filter(profile_range %in% c(2, 3) & depth <= max_depth_2) %>%
  group_by(year, depth) %>% 
  summarise(count_measures = n(),
            chla_mean = mean(chla, na.rm = TRUE),
            chla_sd = sd(chla, na.rm = TRUE))

# Profiles to 1500m
chla_overall_mean_3 <- chla_bgc_va %>% 
  filter(profile_range %in% c(3) & depth <= max_depth_3) %>%
  group_by(depth) %>% 
  summarise(count_measures = n(),
            chla_mean = mean(chla, na.rm = TRUE),
            chla_sd = sd(chla, na.rm = TRUE))

chla_year_mean_3 <- chla_bgc_va %>% 
  filter(profile_range %in% c(3) & depth <= max_depth_3) %>%
  group_by(year, depth) %>% 
  summarise(count_measures = n(),
            chla_mean = mean(chla, na.rm = TRUE),
            chla_sd = sd(chla, na.rm = TRUE))

# All years
chla_overall_mean_1 %>% 
  ggplot()+
  geom_path(aes(x = chla_mean,
                y = depth))+
  geom_ribbon(aes(
    xmax = chla_mean + chla_sd,
    xmin = chla_mean - chla_sd,
    y = depth
  ),
  alpha = 0.2) +
  scale_y_reverse()+
  coord_cartesian(xlim = opt_xlim)+
  scale_x_continuous(breaks = opt_xbreaks)+
  labs(
    title = paste0('Overall mean chlorophyll a to ', max_depth_1, 'm'), 
    x=opt_measure_label, 
    y='depth (m)'
  )

Past versions of plot_overall_mean_chla_profiles-1.png

Version	Author	Date
80c16c2	ds2n19	2023-11-15

chla_overall_mean_2 %>% 
  ggplot()+
  geom_path(aes(x = chla_mean,
                y = depth))+
  geom_ribbon(aes(
    xmax = chla_mean + chla_sd,
    xmin = chla_mean - chla_sd,
    y = depth
  ),
  alpha = 0.2) +
  scale_y_reverse()+
  coord_cartesian(xlim = opt_xlim)+
  scale_x_continuous(breaks = opt_xbreaks)+
  labs(
    title = paste0('Overall mean chlorophyll a to ', max_depth_2, 'm'), 
    x=opt_measure_label, 
    y='depth (m)'
  )

Past versions of plot_overall_mean_chla_profiles-2.png

Version	Author	Date
80c16c2	ds2n19	2023-11-15

chla_overall_mean_3 %>% 
  ggplot()+
  geom_path(aes(x = chla_mean,
                y = depth))+
  geom_ribbon(aes(
    xmax = chla_mean + chla_sd,
    xmin = chla_mean - chla_sd,
    y = depth
  ),
  alpha = 0.2) +
  scale_y_reverse()+
  coord_cartesian(xlim = opt_xlim)+
  scale_x_continuous(breaks = opt_xbreaks)+
  labs(
    title = paste0('Overall mean chlorophyll a to ', max_depth_3, 'm'), 
    x=opt_measure_label, 
    y='depth (m)'
  )

Past versions of plot_overall_mean_chla_profiles-3.png

Version	Author	Date
80c16c2	ds2n19	2023-11-15

# by years
chla_year_mean_1 %>% 
  ggplot()+
  geom_path(aes(x = chla_mean,
                y = depth))+
  geom_ribbon(aes(
    xmax = chla_mean + chla_sd,
    xmin = chla_mean - chla_sd,
    y = depth
  ),
  alpha = 0.2) +
  scale_y_reverse()+
  facet_wrap(~year)+
  coord_cartesian(xlim = opt_xlim)+
  scale_x_continuous(breaks = opt_xbreaks)+
  labs(
    title = paste0('Yearly overall mean chlorophyll a to ', max_depth_1, 'm'),
    x = opt_measure_label,
    y = 'depth (m)'
  )

Past versions of plot_overall_mean_chla_profiles-4.png

Version	Author	Date
80c16c2	ds2n19	2023-11-15

chla_year_mean_2 %>% 
  ggplot()+
  geom_path(aes(x = chla_mean,
                y = depth))+
  geom_ribbon(aes(
    xmax = chla_mean + chla_sd,
    xmin = chla_mean - chla_sd,
    y = depth
  ),
  alpha = 0.2) +
  scale_y_reverse()+
  facet_wrap(~year)+
  coord_cartesian(xlim = opt_xlim)+
  scale_x_continuous(breaks = opt_xbreaks)+
  labs(
    title = paste0('Yearly overall mean chlorophyll a to ', max_depth_2, 'm'),
    x = opt_measure_label,
    y = 'depth (m)'
  )

Past versions of plot_overall_mean_chla_profiles-5.png

Version	Author	Date
80c16c2	ds2n19	2023-11-15

chla_year_mean_3 %>% 
  ggplot()+
  geom_path(aes(x = chla_mean,
                y = depth))+
  geom_ribbon(aes(
    xmax = chla_mean + chla_sd,
    xmin = chla_mean - chla_sd,
    y = depth
  ),
  alpha = 0.2) +
  scale_y_reverse()+
  facet_wrap(~year)+
  coord_cartesian(xlim = opt_xlim)+
  scale_x_continuous(breaks = opt_xbreaks)+
  labs(
    title = paste0('Yearly overall mean chlorophyll a to ', max_depth_3, 'm'),
    x = opt_measure_label,
    y = 'depth (m)'
  )

Past versions of plot_overall_mean_chla_profiles-6.png

Version	Author	Date
80c16c2	ds2n19	2023-11-15

Profile counts

Details of the number of profiles and to which depths over the analysis period

chla_histogram <- chla_bgc_va %>%
  group_by(year, profile_range = as.character(profile_range)) %>%
  summarise(num_profiles = n_distinct(file_id)) %>%
  ungroup()

chla_histogram %>%
  ggplot() +
  geom_bar(
    aes(
      x = year,
      y = num_profiles,
      fill = profile_range,
      group = profile_range
    ),
    position = "stack",
    stat = "identity"
  ) +
  scale_fill_viridis_d() +
  labs(title = "chlorophyll a profiles per year and profile range",
       x = "year",
       y = "profile count",
       fill = "profile range")

Past versions of chla_histogram-1.png

Version	Author	Date
80c16c2	ds2n19	2023-11-15

Session information

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.5

Matrix products: default
BLAS:   /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/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] gsw_1.1-1        gridExtra_2.3    lubridate_1.9.0  timechange_0.1.1
 [5] argodata_0.1.0   forcats_0.5.2    stringr_1.5.0    dplyr_1.1.3     
 [9] purrr_1.0.2      readr_2.1.3      tidyr_1.3.0      tibble_3.2.1    
[13] ggplot2_3.4.4    tidyverse_1.3.2 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.10         assertthat_0.2.1    rprojroot_2.0.3    
 [4] digest_0.6.30       utf8_1.2.2          R6_2.5.1           
 [7] cellranger_1.1.0    backports_1.4.1     reprex_2.0.2       
[10] evaluate_0.18       highr_0.9           httr_1.4.4         
[13] pillar_1.9.0        rlang_1.1.1         googlesheets4_1.0.1
[16] readxl_1.4.1        rstudioapi_0.15.0   whisker_0.4        
[19] jquerylib_0.1.4     rmarkdown_2.18      labeling_0.4.2     
[22] googledrive_2.0.0   munsell_0.5.0       broom_1.0.5        
[25] compiler_4.2.2      httpuv_1.6.6        modelr_0.1.10      
[28] xfun_0.35           pkgconfig_2.0.3     htmltools_0.5.3    
[31] tidyselect_1.2.0    workflowr_1.7.0     viridisLite_0.4.1  
[34] fansi_1.0.3         crayon_1.5.2        withr_2.5.0        
[37] tzdb_0.3.0          dbplyr_2.2.1        later_1.3.0        
[40] grid_4.2.2          jsonlite_1.8.3      gtable_0.3.1       
[43] lifecycle_1.0.3     DBI_1.1.3           git2r_0.30.1       
[46] magrittr_2.0.3      scales_1.2.1        cli_3.6.1          
[49] stringi_1.7.8       cachem_1.0.6        farver_2.1.1       
[52] fs_1.5.2            promises_1.2.0.1    xml2_1.3.3         
[55] bslib_0.4.1         ellipsis_0.3.2      generics_0.1.3     
[58] vctrs_0.6.4         tools_4.2.2         glue_1.6.2         
[61] RNetCDF_2.6-1       hms_1.1.2           fastmap_1.1.0      
[64] yaml_2.3.6          colorspace_2.0-3    gargle_1.2.1       
[67] rvest_1.0.3         knitr_1.41          haven_2.5.1        
[70] sass_0.4.4