Provides access to, and download from, Copernicus Marine Data Store’s catalog using R language.
Copernicus serves so many data resources; finding what you want can be a challenge. Check out the new Marine Data Store. And checkout the listing here.
Like data offerings from OBPG,
Copernicus strives to provided consistent dataset identifiers that are
easily decoded programmatically (and with practice by eye). In order to
download programmatically you must have the product_id and or
dataset_id in hand. Learn more about Copernicus nomenclature rules
here.
The Copernicus Marine
Toolbox
command-line application, copernicus-marine provides two primary
methods for donwloading data: get and subset. get is not well
documented, but subset does what it implies - subsetting resources by
variable, spatial bounding box, depth and time. This package only
supports subset.
remotes::install_github("BigelowLab/nicolaus")
If you have already installed and configured the copernicus R package then you are all set to use this package. If you have not installed and configured the copernicus R package then please do that first.
You can download a product catalog for local storage.
suppressPackageStartupMessages({
library(nicolaus)
library(dplyr)
})
ok = fetch_catalog()
ok## [1] 0
This downloads into a “catalogs” directory within your copernicus data directory Now read it in.
x = import_catalog()
dplyr::glimpse(x)## Rows: 6,594
## Columns: 11
## $ product_id <chr> "ARCTIC_ANALYSISFORECAST_BGC_002_004", "ARCTIC_ANALYSISF…
## $ dataset_id <chr> "cmems_mod_arc_bgc_anfc_ecosmo_P1D-m", "cmems_mod_arc_bg…
## $ short_name <chr> "chl", "dissic", "expc", "kd", "model_depth", "no3", "np…
## $ standard_name <chr> "mass_concentration_of_chlorophyll_a_in_sea_water", "mol…
## $ units <chr> "mg m-3", "mole m-3", "mol m-2 d-1", "m-1", "meter", "mm…
## $ start_time <dttm> 2019-03-22, 2019-03-22, 2019-03-22, 2019-03-22, NA, 201…
## $ end_time <dttm> 2025-06-21, 2025-06-21, 2025-06-21, 2025-06-21, NA, 202…
## $ time_step <dbl> 86400, 86400, 86400, 86400, NA, 86400, 86400, 86400, 864…
## $ min_depth <dbl> 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, N…
## $ max_depth <dbl> 4000, 4000, 4000, 4000, NA, 4000, 4000, 4000, 4000, 4000…
## $ n_depth <dbl> 40, 40, 40, 40, NA, 40, 40, 40, 40, 40, 40, 40, 40, 40, …
By default this provides a flattened table of available products and their datasets along with each dataset’s variables. There is a lot of information in the JSON catalog that we place in the table including:
-
depth min, max and depth layer count
-
time min, max and step size
-
short and standard name as well as unit for each variable
dplyr::count(x, product_id, dataset_id)## # A tibble: 899 × 3
## product_id dataset_id n
## <chr> <chr> <int>
## 1 ARCTIC_ANALYSISFORECAST_BGC_002_004 cmems_mod_arc_bgc_anfc_ecosmo_… 14
## 2 ARCTIC_ANALYSISFORECAST_BGC_002_004 cmems_mod_arc_bgc_anfc_ecosmo_… 14
## 3 ARCTIC_ANALYSISFORECAST_PHY_002_001 cmems_mod_arc_phy_anfc_6km_det… 18
## 4 ARCTIC_ANALYSISFORECAST_PHY_002_001 cmems_mod_arc_phy_anfc_6km_det… 18
## 5 ARCTIC_ANALYSISFORECAST_PHY_002_001 cmems_mod_arc_phy_anfc_6km_det… 11
## 6 ARCTIC_ANALYSISFORECAST_PHY_002_001 cmems_mod_arc_phy_anfc_6km_det… 18
## 7 ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011 cmems_mod_arc_phy_anfc_nextsim… 10
## 8 ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011 cmems_mod_arc_phy_anfc_nextsim… 10
## 9 ARCTIC_ANALYSIS_FORECAST_WAV_002_014 dataset-wam-arctic-1hr3km-be 24
## 10 ARCTIC_MULTIYEAR_BGC_002_005 cmems_mod_arc_bgc_my_ecosmo_P1… 10
## # ℹ 889 more rows
It doesn’t take a long time to parse the JSON list into a table, but it is worth the effort to save the table as a flat structure for later use.
x = write_catalog(x)
y = read_catalog()
identical(x,y)## [1] TRUE