Mediterranean Sea — surface temperature map¶

Demonstrates the regional-reanalysis map pattern: pull one summer day of the Mediterranean physics reanalysis potential temperature (thetao), clipped to the surface layer, and render the basin-wide SST field.

Dataset cmems_mod_med_phy-temp_my_4.2km_P1D-m is a multi-year reanalysis on a ~4 km grid (stable historical coverage, so a fixed date works). It is 4-D, so we clip minimum_depth/maximum_depth to the top few metres to fetch just the surface field.

Reads credentials from COPERNICUSMARINE_SERVICE_USERNAME / COPERNICUSMARINE_SERVICE_PASSWORD.

In [1]:

import os
from pathlib import Path

import xarray as xr

from earthlens import EarthLens
from earthlens.cmems import Catalog
from pyramids.netcdf import NetCDF

OUT_DIR = Path('data/cmems-medsst')
OUT_DIR.mkdir(parents=True, exist_ok=True)

DATASET_ID = 'cmems_mod_med_phy-temp_my_4.2km_P1D-m'
DAY = '2020-08-15'           # mid-summer, strong basin-wide SST gradient

ds_meta = Catalog().get_dataset(DATASET_ID)
print(f'{DATASET_ID}: domain={ds_meta.domain}, cadence={ds_meta.cadence}')
print('thetao units:', ds_meta.variables['thetao'].units)

import os from pathlib import Path import xarray as xr from earthlens import EarthLens from earthlens.cmems import Catalog from pyramids.netcdf import NetCDF OUT_DIR = Path('data/cmems-medsst') OUT_DIR.mkdir(parents=True, exist_ok=True) DATASET_ID = 'cmems_mod_med_phy-temp_my_4.2km_P1D-m' DAY = '2020-08-15' # mid-summer, strong basin-wide SST gradient ds_meta = Catalog().get_dataset(DATASET_ID) print(f'{DATASET_ID}: domain={ds_meta.domain}, cadence={ds_meta.cadence}') print('thetao units:', ds_meta.variables['thetao'].units)

2026-05-20 19:43:34 | INFO | pyramids.base.config | Logging is configured.

cmems_mod_med_phy-temp_my_4.2km_P1D-m: domain=mediterranean, cadence=daily
thetao units: degrees_C

Download one day, surface layer, whole basin¶

In [2]:

el = EarthLens(
    data_source='cmems',
    start=DAY,
    end=DAY,
    temporal_resolution='daily',
    variables={DATASET_ID: ['thetao']},
    lat_lim=[30.0, 46.0],
    lon_lim=[-6.0, 36.5],
    path=str(OUT_DIR),
    minimum_depth=0.0,
    maximum_depth=2.0,
    service_username=os.environ.get('COPERNICUSMARINE_SERVICE_USERNAME'),
    service_password=os.environ.get('COPERNICUSMARINE_SERVICE_PASSWORD'),
)
paths = el.download()
print(paths)

el = EarthLens( data_source='cmems', start=DAY, end=DAY, temporal_resolution='daily', variables={DATASET_ID: ['thetao']}, lat_lim=[30.0, 46.0], lon_lim=[-6.0, 36.5], path=str(OUT_DIR), minimum_depth=0.0, maximum_depth=2.0, service_username=os.environ.get('COPERNICUSMARINE_SERVICE_USERNAME'), service_password=os.environ.get('COPERNICUSMARINE_SERVICE_PASSWORD'), ) paths = el.download() print(paths)

INFO - 2026-05-20T17:43:37Z - Checking if credentials are valid.

2026-05-20 17:43:37 | INFO | copernicusmarine | Checking if credentials are valid.

INFO - 2026-05-20T17:43:38Z - Valid credentials from input username and password.

2026-05-20 17:43:38 | INFO | copernicusmarine | Valid credentials from input username and password.

2026-05-20 19:43:38.037 | INFO     | earthlens.cmems.backend:_subset_one:458 - Requesting CMEMS subset for 'cmems_mod_med_phy-temp_my_4.2km_P1D-m' variables=['thetao'] → cmems_mod_med_phy-temp_my_4.2km_P1D-m.nc

INFO - 2026-05-20T17:43:38Z - Selected dataset version: "202511"

2026-05-20 17:43:38 | INFO | copernicusmarine | Selected dataset version: "202511"

INFO - 2026-05-20T17:43:38Z - Selected dataset part: "default"

2026-05-20 17:43:38 | INFO | copernicusmarine | Selected dataset part: "default"

WARNING - 2026-05-20T17:43:38Z - Some of your subset selection [30.0, 46.0] for the latitude dimension exceed the dataset coordinates [30.1875, 45.97916793823242]

2026-05-20 17:43:38 | WARNING | copernicusmarine | Some of your subset selection [30.0, 46.0] for the latitude dimension exceed the dataset coordinates [30.1875, 45.97916793823242]

WARNING - 2026-05-20T17:43:38Z - Some of your subset selection [-6.0, 36.5] for the longitude dimension exceed the dataset coordinates [-6.0, 36.29166793823242]

2026-05-20 17:43:38 | WARNING | copernicusmarine | Some of your subset selection [-6.0, 36.5] for the longitude dimension exceed the dataset coordinates [-6.0, 36.29166793823242]

WARNING - 2026-05-20T17:43:38Z - Some of your subset selection [0.0, 2.0] for the depth dimension exceed the dataset coordinates [1.0182366371154785, 5754.0439453125]

2026-05-20 17:43:38 | WARNING | copernicusmarine | Some of your subset selection [0.0, 2.0] for the depth dimension exceed the dataset coordinates [1.0182366371154785, 5754.0439453125]

INFO - 2026-05-20T17:43:40Z - Total size of the download: 1.49 MB.

2026-05-20 17:43:40 | INFO | copernicusmarine | Total size of the download: 1.49 MB.

2026-05-20 19:43:40.589 | INFO     | earthlens.cmems.backend:download:326 - CMEMS download summary: 1 files written to C:\gdrive\algorithms\remote-sensing\earthlens\docs\examples\cmems\data\cmems-medsst

[WindowsPath('C:/gdrive/algorithms/remote-sensing/earthlens/docs/examples/cmems/data/cmems-medsst/cmems_mod_med_phy-temp_my_4.2km_P1D-m.nc')]

Open and reduce to a 2-D surface field¶

In [3]:

import numpy as np

nc = NetCDF.read_file(str(paths[0]), read_only=True)
ds = xr.decode_cf(nc.to_xarray())
nc.close()
print('dims:', dict(ds.sizes))
# Top model level, single day -> 2-D (latitude, longitude).
sst = ds['thetao'].isel(time=0, depth=0)
valid = sst.values[~np.isnan(sst.values)]
print(f'surface thetao min/mean/max: {valid.min():.1f} / {valid.mean():.1f} / {valid.max():.1f} degrees_C')

import numpy as np nc = NetCDF.read_file(str(paths[0]), read_only=True) ds = xr.decode_cf(nc.to_xarray()) nc.close() print('dims:', dict(ds.sizes)) # Top model level, single day -> 2-D (latitude, longitude). sst = ds['thetao'].isel(time=0, depth=0) valid = sst.values[~np.isnan(sst.values)] print(f'surface thetao min/mean/max: {valid.min():.1f} / {valid.mean():.1f} / {valid.max():.1f} degrees_C')

dims: {'time': 1, 'depth': 1, 'latitude': 380, 'longitude': 1016}
surface thetao min/mean/max: 19.4 / 27.2 / 31.3 degrees_C

Map the surface temperature¶

Warm eastern-basin / Levantine waters versus the cooler Gulf of Lions and Aegean — the classic August Mediterranean SST signature.

In [4]:

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 4))
mesh = ax.pcolormesh(
    sst['longitude'].values, sst['latitude'].values, sst.values,
    cmap='inferno', shading='auto',
)
fig.colorbar(mesh, ax=ax, label='SST (degrees_C)')
ax.set_title(f'Mediterranean surface temperature ({DAY})')
ax.set_xlabel('lon'); ax.set_ylabel('lat')
ax.set_aspect('equal')
fig.tight_layout()

import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(10, 4)) mesh = ax.pcolormesh( sst['longitude'].values, sst['latitude'].values, sst.values, cmap='inferno', shading='auto', ) fig.colorbar(mesh, ax=ax, label='SST (degrees_C)') ax.set_title(f'Mediterranean surface temperature ({DAY})') ax.set_xlabel('lon'); ax.set_ylabel('lat') ax.set_aspect('equal') fig.tight_layout()