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.

Setup¶

Imports up front: pyramids provides NetCDF (reading the downloaded cube) and ColorBar (labelling the map), and earthlens provides the unified EarthLens entry point and the CMEMS Catalog. The reduction and the plot both go through pyramids, so no second array library is needed.

In [ ]:

import os
from pathlib import Path

from pyramids.netcdf import NetCDF
from pyramids.plot import ColorBar

from earthlens.cmems import Catalog
from earthlens.core import EarthLens

import os from pathlib import Path from pyramids.netcdf import NetCDF from pyramids.plot import ColorBar from earthlens.cmems import Catalog from earthlens.core import EarthLens

Request parameters¶

Pin the dataset id, the mid-summer day (strong basin-wide SST gradient), and an output directory for the downloaded NetCDF.

In [ ]:

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

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

Inspect the dataset metadata¶

Look up the dataset in the CMEMS Catalog to confirm its domain, cadence, and the units of the thetao variable before downloading.

In [ ]:

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

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

Download one day, surface layer, whole basin¶

Build the EarthLens request first — dataset, day window, basin bounding box, and the minimum_depth / maximum_depth clip that keeps only the top model level.

In [ ]:

el = EarthLens(
    data_source='cmems',
    start=DAY,
    end=DAY,
    cadence='daily',
    dataset=DATASET_ID,
    variables=['thetao'],
    aoi=[-6.0, 30.0, 36.5, 46.0],
    path=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'),
)

el = EarthLens( data_source='cmems', start=DAY, end=DAY, cadence='daily', dataset=DATASET_ID, variables=['thetao'], aoi=[-6.0, 30.0, 36.5, 46.0], path=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'), )

Run the download. download() returns the list of written file paths; this request writes a single NetCDF cube.

In [ ]:

paths = el.download()
print(paths)

paths = el.download() print(paths)

Open the cube¶

Read it with pyramids' NetCDF. It decodes the CF metadata itself, so the cube is ready to query without a second library.

In [ ]:

nc = NetCDF.read_file(paths[0], read_only=True)
print('variables :', nc.variable_names)
print('dimensions:', nc.dimension_sizes)

nc = NetCDF.read_file(paths[0], read_only=True) print('variables :', nc.variable_names) print('dimensions:', nc.dimension_sizes)

get_variable returns the georeferenced thetao field. time and depth are both length 1 here — the request already clipped to one day and the top model level — so the variable is the 2-D surface field. stats reports over the valid cells only, because the band's own no-data marks the land.

In [ ]:

sst = nc.get_variable('thetao')
stats = sst.stats(approx_ok=False)
low, mean, high = (float(stats[k].iloc[0]) for k in ('min', 'mean', 'max'))
print(f'grid: {sst.rows} x {sst.columns}, epsg {sst.epsg}')
print(f'surface thetao min/mean/max: {low:.1f} / {mean:.1f} / {high:.1f} degrees_C')

sst = nc.get_variable('thetao') stats = sst.stats(approx_ok=False) low, mean, high = (float(stats[k].iloc[0]) for k in ('min', 'mean', 'max')) print(f'grid: {sst.rows} x {sst.columns}, epsg {sst.epsg}') print(f'surface thetao min/mean/max: {low:.1f} / {mean:.1f} / {high:.1f} 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 [ ]:

sst.plot(
    cmap='inferno',
    colorbar=ColorBar(label='SST (degrees_C)'),
    title=f'Mediterranean surface temperature ({DAY})',
)
nc.close()

sst.plot( cmap='inferno', colorbar=ColorBar(label='SST (degrees_C)'), title=f'Mediterranean surface temperature ({DAY})', ) nc.close()