Catalog explorer — browse CMEMS without downloading anything¶

This notebook needs no credentials and no network. It reads the bundled earthlens.cmems catalog (a directory of per-domain YAML files) to answer the first questions every CMEMS user has: what's in here, how is it organised, and what's the dataset id / variable name I need to pass to download().

The catalog has two tiers, mirroring the GEE and ECMWF backends:

• available_datasets — the informational index of every dataset id the Copernicus Marine toolbox publishes (~1,251).
• datasets — the curated map earthlens models in detail (variable lists, units, cadence, domain). A subset of available_datasets.

Uncurated ids still work — pass any id copernicusmarine.describe() recognises straight to CMEMS(variables={id: [...]}).

In [1]:

from collections import Counter

from earthlens.cmems import Catalog

cat = Catalog()
print(f'available_datasets (index): {len(cat.available_datasets)}')
print(f'curated datasets:           {len(cat.datasets)}')
print(f'curated is subset of index: {set(cat.datasets) <= set(cat.available_datasets)}')

from collections import Counter from earthlens.cmems import Catalog cat = Catalog() print(f'available_datasets (index): {len(cat.available_datasets)}') print(f'curated datasets: {len(cat.datasets)}') print(f'curated is subset of index: {set(cat.datasets) <= set(cat.available_datasets)}')

available_datasets (index): 1251
curated datasets:           1141
curated is subset of index: True

Curated datasets by domain¶

Every curated row carries a domain label inferred from its CMEMS product id. This is also how the catalog files are split on disk (catalog/global-*.yaml, catalog/mediterranean.yaml, …).

In [2]:

by_domain = Counter(d.domain for d in cat.datasets.values())
for domain, n in by_domain.most_common():
    print(f'  {domain:15s} {n:4d}')
by_cadence = Counter(d.cadence for d in cat.datasets.values())
print('\nby cadence:')
for cadence, n in by_cadence.most_common():
    print(f'  {cadence:12s} {n:4d}')

by_domain = Counter(d.domain for d in cat.datasets.values()) for domain, n in by_domain.most_common(): print(f' {domain:15s} {n:4d}') by_cadence = Counter(d.cadence for d in cat.datasets.values()) print('\nby cadence:') for cadence, n in by_cadence.most_common(): print(f' {cadence:12s} {n:4d}')

  global           424
  ibi              146
  mediterranean    131
  black-sea        112
  nw-shelf          95
  indicator         80
  polar             68
  baltic-sea        52
  arctic            33

by cadence:
  daily         430
  irregular     378
  monthly       193
  hourly         69
  annual         41
  climatology    22
  6hourly         4
  weekly          4

Search the index by keyword¶

available_datasets is just a list of ids — grep it for the basin / theme / cadence token you want. CMEMS encodes a lot in the id itself (_phy physics, _bgc biogeochem, _wav wave, _P1D-m daily mean, med Mediterranean, …).

In [3]:

def search(token, limit=8):
    hits = [d for d in cat.available_datasets if token in d]
    print(f'{token!r}: {len(hits)} matches')
    for d in hits[:limit]:
        curated = ' (curated)' if d in cat.datasets else ''
        print(f'  {d}{curated}')
    if len(hits) > limit:
        print(f'  ... and {len(hits) - limit} more')

search('glo_phy_my')
print()
search('_wav_')

def search(token, limit=8): hits = [d for d in cat.available_datasets if token in d] print(f'{token!r}: {len(hits)} matches') for d in hits[:limit]: curated = ' (curated)' if d in cat.datasets else '' print(f' {d}{curated}') if len(hits) > limit: print(f' ... and {len(hits) - limit} more') search('glo_phy_my') print() search('_wav_')

'glo_phy_my': 32 matches
  c3s_obs-si_glo_phy_my_nh-l3_P1M (curated)
  cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m (curated)
  cmems_mod_glo_phy_my_0.083deg_P1D-m (curated)
  cmems_mod_glo_phy_my_0.083deg_P1M-m (curated)
  cmems_mod_glo_phy_my_0.083deg_static (curated)
  cmems_obs-mob_glo_phy_my_0.125deg_P1D-m (curated)
  cmems_obs-mob_glo_phy_my_0.125deg_P1M-m (curated)
  cmems_obs-mob_glo_phy_mynrt_0.125deg-climatology-uncertainty_P1M-m (curated)
  ... and 24 more

'_wav_': 34 matches
  cmems_mod_arc_wav_my_3km-climatology_P1M-m
  cmems_mod_arc_wav_my_3km_PT1H-i
  cmems_mod_bal_wav_anfc_PT1H-i (curated)
  cmems_mod_bal_wav_anfc_static (curated)
  cmems_mod_bal_wav_my_2km-climatology_P1M-m (curated)
  cmems_mod_bal_wav_my_PT1H-i (curated)
  cmems_mod_bal_wav_my_aflux_PT1H-i (curated)
  cmems_mod_bal_wav_my_static (curated)
  ... and 26 more

Inspect one curated dataset¶

Once you have an id, get_dataset gives the full curated row and get_variable resolves a single (dataset, variable) pair — units and long-name without ever touching the network.

In [4]:

ds = cat.get_dataset('cmems_mod_glo_phy_my_0.083deg_P1D-m')
print(f'product:  {ds.product}')
print(f'title:    {ds.title}')
print(f'cadence:  {ds.cadence}   domain: {ds.domain}')
print(f'temporal: {ds.temporal.start} -> {ds.temporal.end}')
print('variables:')
for name, var in sorted(ds.variables.items()):
    print(f'  {name:10s} {var.units:10s} {var.long_name}')

ds = cat.get_dataset('cmems_mod_glo_phy_my_0.083deg_P1D-m') print(f'product: {ds.product}') print(f'title: {ds.title}') print(f'cadence: {ds.cadence} domain: {ds.domain}') print(f'temporal: {ds.temporal.start} -> {ds.temporal.end}') print('variables:') for name, var in sorted(ds.variables.items()): print(f' {name:10s} {var.units:10s} {var.long_name}')

product:  GLOBAL_MULTIYEAR_PHY_001_030
title:    daily mean fields from Global Ocean Physics Analysis and Forecast updated Daily
cadence:  daily   domain: global
temporal: 1993-01-01 -> None
variables:
  bottomT    degrees_C  Sea water potential temperature at sea floor
  mlotst     m          Ocean mixed layer thickness defined by sigma theta
  siconc     1          Sea ice area fraction
  sithick    m          Sea ice thickness
  so         1e-3       Sea water salinity
  thetao     degrees_C  Sea water potential temperature
  uo         m s-1      Eastward sea water velocity
  usi        m s-1      Eastward sea ice velocity
  vo         m s-1      Northward sea water velocity
  vsi        m s-1      Northward sea ice velocity
  zos        m          Sea surface height above geoid

Next steps¶

Pick an id + variable from above and hand it to a download — see the quickstart for the facade call. The other notebooks in this folder show concrete workflows: GLORYS thermocline (depth axis), PISCES chlorophyll (multi-variable seasonal cycle), altimetry SLA (coastal time-series), Arctic sea ice (polar map), global waves, and Mediterranean SST (regional map).