Altimetry — coastal sea-level anomaly time-series¶

Demonstrates the surface-only multi-mission altimetry pattern: pull five years of daily DUACS L4 sea-level anomaly (sla) and absolute dynamic topography (adt) over a coastal box, plot the box-mean SLA time-series with a 30-day rolling average.

The DUACS L4 dataset (cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D) merges every operational altimeter onto a regular 1/8° grid. It is surface-only — the depth kwargs are silently ignored — so the request stays compact.

In [1]:

import os
from pathlib import Path

import numpy as np

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

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

DATASET_ID = 'cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D'
VARIABLES = ['sla', 'adt']
BBOX = dict(lat_lim=[36.0, 41.0], lon_lim=[-9.5, -5.0])   # Iberian Atlantic coast

if DATASET_ID in Catalog().datasets:
    ds = Catalog().get_dataset(DATASET_ID)
    print(f'{DATASET_ID}: cadence={ds.cadence}, domain={ds.domain}')
else:
    print(f'{DATASET_ID} is not curated — uncurated id, request will still work')

import os from pathlib import Path import numpy as np from earthlens import EarthLens from earthlens.cmems import Catalog from pyramids.netcdf import NetCDF OUT_DIR = Path('data/cmems-altimetry') OUT_DIR.mkdir(parents=True, exist_ok=True) DATASET_ID = 'cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D' VARIABLES = ['sla', 'adt'] BBOX = dict(lat_lim=[36.0, 41.0], lon_lim=[-9.5, -5.0]) # Iberian Atlantic coast if DATASET_ID in Catalog().datasets: ds = Catalog().get_dataset(DATASET_ID) print(f'{DATASET_ID}: cadence={ds.cadence}, domain={ds.domain}') else: print(f'{DATASET_ID} is not curated — uncurated id, request will still work')

2026-05-20 19:39:18 | INFO | pyramids.base.config | Logging is configured.

cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D: cadence=daily, domain=global

Download — five years of daily SLA + ADT, coastal box¶

Multi-year × multi-variable × surface-only. Both fields are scalar (time, lat, lon) arrays so the returned NetCDF is small.

In [2]:

earthlens = EarthLens(
    data_source='cmems',
    start='2018-01-01',
    end='2022-12-31',
    temporal_resolution='daily',
    variables={DATASET_ID: VARIABLES},
    **BBOX,
    path=str(OUT_DIR),
    service_username=os.environ.get('COPERNICUSMARINE_SERVICE_USERNAME'),
    service_password=os.environ.get('COPERNICUSMARINE_SERVICE_PASSWORD'),
)
paths = earthlens.download()
print(paths)

earthlens = EarthLens( data_source='cmems', start='2018-01-01', end='2022-12-31', temporal_resolution='daily', variables={DATASET_ID: VARIABLES}, **BBOX, path=str(OUT_DIR), service_username=os.environ.get('COPERNICUSMARINE_SERVICE_USERNAME'), service_password=os.environ.get('COPERNICUSMARINE_SERVICE_PASSWORD'), ) paths = earthlens.download() print(paths)

INFO - 2026-05-20T17:39:21Z - Checking if credentials are valid.

2026-05-20 17:39:21 | INFO | copernicusmarine | Checking if credentials are valid.

INFO - 2026-05-20T17:39:21Z - Valid credentials from input username and password.

2026-05-20 17:39:21 | INFO | copernicusmarine | Valid credentials from input username and password.

2026-05-20 19:39:21.711 | INFO     | earthlens.cmems.backend:_subset_one:458 - Requesting CMEMS subset for 'cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D' variables=['sla', 'adt'] → cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D.nc

INFO - 2026-05-20T17:39:22Z - Selected dataset version: "202411"

2026-05-20 17:39:22 | INFO | copernicusmarine | Selected dataset version: "202411"

INFO - 2026-05-20T17:39:22Z - Selected dataset part: "default"

2026-05-20 17:39:22 | INFO | copernicusmarine | Selected dataset part: "default"

INFO - 2026-05-20T17:39:25Z - Total size of the download: 20.09 MB.

2026-05-20 17:39:25 | INFO | copernicusmarine | Total size of the download: 20.09 MB.

2026-05-20 19:39:25.167 | INFO     | earthlens.cmems.backend:download:326 - CMEMS download summary: 1 files written to C:\gdrive\algorithms\remote-sensing\earthlens\docs\examples\cmems\data\cmems-altimetry

[WindowsPath('C:/gdrive/algorithms/remote-sensing/earthlens/docs/examples/cmems/data/cmems-altimetry/cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D.nc')]

Box-averaged SLA + ADT time-series, plus 30-day rolling mean¶

Average across (lat, lon) per day, then convolve with a 30-day boxcar for the smoothed series. Coastal SLA tracks regional ocean dynamics with a strong interannual signal.

In [3]:

import numpy as np
import xarray as xr

nc = NetCDF.read_file(str(paths[0]), read_only=True)
ds = xr.decode_cf(nc.to_xarray())   # labelled (time, latitude, longitude)
nc.close()

# Box-averaged daily series, then a 30-day rolling mean for SLA.
box = ds.mean(dim=['latitude', 'longitude'])
dates = box['time'].values
series = {v: box[v].values for v in VARIABLES}
sla_smoothed = (
    box['sla'].rolling(time=30, center=True, min_periods=1).mean().values
)
print(f'sla mean: {np.nanmean(series["sla"]):.4f} m, std: {np.nanstd(series["sla"]):.4f} m')
print(f'adt mean: {np.nanmean(series["adt"]):.4f} m, std: {np.nanstd(series["adt"]):.4f} m')

import numpy as np import xarray as xr nc = NetCDF.read_file(str(paths[0]), read_only=True) ds = xr.decode_cf(nc.to_xarray()) # labelled (time, latitude, longitude) nc.close() # Box-averaged daily series, then a 30-day rolling mean for SLA. box = ds.mean(dim=['latitude', 'longitude']) dates = box['time'].values series = {v: box[v].values for v in VARIABLES} sla_smoothed = ( box['sla'].rolling(time=30, center=True, min_periods=1).mean().values ) print(f'sla mean: {np.nanmean(series["sla"]):.4f} m, std: {np.nanstd(series["sla"]):.4f} m') print(f'adt mean: {np.nanmean(series["adt"]):.4f} m, std: {np.nanstd(series["adt"]):.4f} m')

sla mean: -151069.4834 m, std: 0.0114 m
adt mean: -152113.3869 m, std: 0.0112 m

Plot the SLA time-series¶

Raw daily anomaly plus 30-day rolling mean over the Iberian Atlantic coast — a coastal box that captures both seasonal steric variability and the regional response to North Atlantic forcing.

In [4]:

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 4))
ax.plot(dates, series['sla'], color='tab:gray', alpha=0.4, label='daily SLA')
ax.plot(dates, sla_smoothed, color='tab:blue', label='30-day rolling mean')
ax.axhline(0, color='black', lw=0.5)
ax.set_xlabel('Date')
ax.set_ylabel('Sea level anomaly (m)')
ax.set_title('DUACS L4 SLA — Iberian Atlantic coastal box (2018-2022)')
ax.legend()
ax.grid(alpha=0.3)
fig.tight_layout()

import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(10, 4)) ax.plot(dates, series['sla'], color='tab:gray', alpha=0.4, label='daily SLA') ax.plot(dates, sla_smoothed, color='tab:blue', label='30-day rolling mean') ax.axhline(0, color='black', lw=0.5) ax.set_xlabel('Date') ax.set_ylabel('Sea level anomaly (m)') ax.set_title('DUACS L4 SLA — Iberian Atlantic coastal box (2018-2022)') ax.legend() ax.grid(alpha=0.3) fig.tight_layout()

Next steps¶

• Pair the SLA series with an in-situ tide-gauge record (e.g. GLOSS / PSMSL) to validate the regional signal.
• Switch to ugos / vgos (surface geostrophic velocity) to look at the coastal current field.
• Extend the time-window — DUACS L4 covers 1993-present, suitable for multi-decadal trend analysis.