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Subset SWOT LR L3 Unsmoothed data

This notebooks explains how to retrieve a geographical subset of unsmoothed (250-m) SWOT LR L3 data using altimetry_downloader_aviso.

Tutorial Objectives

• Subset Swot data with cycle/passes numbers, and a geographical area selection, using altimetry_downloader_aviso

• Open data using altimetry.io

• Visualise data using matplotlib

Note

Required environment to run this notebook:

• altimetry_downloader_aviso: see documentation.

• altimetry.io: available here.

• matplotlib<3.11+cartopy

Import + code

import altimetry_downloader_aviso as dl_aviso

from altimetry.io import AltimetryData, FileCollectionSource

from pathlib import Path
import cartopy.crs as ccrs
import matplotlib.pyplot as plt

import logging
logging.basicConfig(level=logging.INFO)

Parameters

Define the variables you want:

variables = ['longitude', 'latitude', 'time', 'sigma0', 'ssha_unfiltered', 'quality_flag']

Define output folder to save results:

output_dir= Path.home() / "TMP_DATA" / "subsets"

Define the parameters needed to retrieve data:

• geographical area

• phase: 1day-orbit (Calval) / 21day-orbit (Science)

• cycle min, max

• list of half-orbits

# Mediterranean sea
box = (-15, 25, 40, 50)

cycles = [10, 12]

half_orbits = [1, 3, 14]

Download data using altimetry_downloader_aviso

dl_aviso.subset(
    'SWOT_L3_LR_SSH_Unsmoothed', 
    output_dir=output_dir, 
    cycle_number=cycles, 
    pass_number=half_orbits,
    selected_variables=variables, 
    box=box
)

INFO:altimetry_downloader_aviso.catalog_client.client:Fetching products from Aviso's catalog...
INFO:altimetry_downloader_aviso.catalog_client.granule_discoverer:Filtering SWOT_L3_LR_SSH_Unsmoothed product with filters {'cycle_number': [10, 12], 'pass_number': [1, 3, 14]}...
INFO:altimetry_downloader_aviso.core:Subsetting 6 file(s)...

['/home/atonneau/TMP_DATA/subsets/SWOT_L3_LR_SSH_Unsmoothed_010_001_20240125T001932_20240125T011059_v3.0.nc',
 '/home/atonneau/TMP_DATA/subsets/SWOT_L3_LR_SSH_Unsmoothed_010_003_20240125T020226_20240125T025352_v3.0.nc',
 '/home/atonneau/TMP_DATA/subsets/SWOT_L3_LR_SSH_Unsmoothed_010_014_20240125T112821_20240125T121947_v3.0.nc',
 '/home/atonneau/TMP_DATA/subsets/SWOT_L3_LR_SSH_Unsmoothed_012_001_20240306T174941_20240306T184108_v3.0.nc',
 '/home/atonneau/TMP_DATA/subsets/SWOT_L3_LR_SSH_Unsmoothed_012_003_20240306T193234_20240306T202401_v3.0.nc',
 '/home/atonneau/TMP_DATA/subsets/SWOT_L3_LR_SSH_Unsmoothed_012_014_20240307T045830_20240307T054956_v3.0.nc']

Open data using altimetry.io

Open data source

alti_data = AltimetryData(
    source=FileCollectionSource(
        path=output_dir,
        ftype="SWOT_L3_LR_SSH",
        subset="Unsmoothed",
    ),
)

Query data

ds = alti_data.query_orbit(
    cycle_number=10, 
    pass_number=1, 
    variables=variables, 
)
ds

INFO:fcollections.core._filesdb:Picked subset {'version': '3.0', 'subset': <ProductSubset.Unsmoothed: 4>}
INFO:fcollections.core._readers:Files to read: 1

<xarray.Dataset>
Dimensions:          (num_lines: 12184, num_pixels: 519)
Coordinates:
    longitude        (num_lines, num_pixels) float64 dask.array<chunksize=(12184, 519), meta=np.ndarray>
    latitude         (num_lines, num_pixels) float64 dask.array<chunksize=(12184, 519), meta=np.ndarray>
    time             (num_lines) datetime64[ns] dask.array<chunksize=(12184,), meta=np.ndarray>
Dimensions without coordinates: num_lines, num_pixels
Data variables:
    sigma0           (num_lines, num_pixels) float64 dask.array<chunksize=(12184, 519), meta=np.ndarray>
    ssha_unfiltered  (num_lines, num_pixels) float64 dask.array<chunksize=(12184, 519), meta=np.ndarray>
    quality_flag     (num_lines, num_pixels) uint8 dask.array<chunksize=(12184, 519), meta=np.ndarray>
    cycle_number     (num_lines) uint8 10 10 10 10 10 10 ... 10 10 10 10 10 10
    pass_number      (num_lines) uint8 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
Attributes: (12/43)
    Conventions:                     CF-1.9
    Metadata_Conventions:            Unidata Dataset Discovery v1.0
    cdm_data_type:                   Swath
    comment:                         Sea Surface Height measured by Altimetry
    geospatial_lat_units:            degrees_north
    geospatial_lon_units:            degrees_east
    ...                              ...
    geospatial_lon_max:              359.999999
    date_modified:                   2026-08-13T20:23:55Z
    history:                         2026-08-13T20:23:55Z: Created by DUACS K...
    date_created:                    2026-08-13T20:23:55Z
    date_issued:                     2026-08-13T20:23:55Z
    temporality:                     reproc

xarray.Dataset

• Dimensions:
  • num_lines: 12184
  • num_pixels: 519
• Coordinates: (3)
  • longitude
    (num_lines, num_pixels)
    float64
    dask.array<chunksize=(12184, 519), meta=np.ndarray>

    comment :

    Longitude of measurement. East longitude relative to Greenwich meridian.

    long_name :

    longitude (degrees East)

    standard_name :

    longitude

    units :

    degrees_east

    _ChunkSizes :

    [4000 260]

    Array Chunk Bytes 48.24 MiB 48.24 MiB Shape (12184, 519) (12184, 519) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • latitude
    (num_lines, num_pixels)
    float64
    dask.array<chunksize=(12184, 519), meta=np.ndarray>

    comment :

    Latitude of measurement [-80,80]. Positive latitude is North latitude, negative latitude is South latitude.

    long_name :

    latitude (positive N, negative S)

    standard_name :

    latitude

    units :

    degrees_north

    _ChunkSizes :

    [4000 260]

    Array Chunk Bytes 48.24 MiB 48.24 MiB Shape (12184, 519) (12184, 519) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • time
    (num_lines)
    datetime64[ns]
    dask.array<chunksize=(12184,), meta=np.ndarray>

    comment :

    Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.

    leap_second :

    0000-00-00T00:00:00Z

    long_name :

    time in UTC

    standard_name :

    time

    tai_utc_difference :

    37.0

    _ChunkSizes :

    82242

    Array Chunk Bytes 95.19 kiB 95.19 kiB Shape (12184,) (12184,) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

• Data variables: (5)
  • sigma0
    (num_lines, num_pixels)
    float64
    dask.array<chunksize=(12184, 519), meta=np.ndarray>

    long_name :

    normalized radar cross section (sigma0) from KaRIn

    standard_name :

    surface_backwards_scattering_coefficient_of_radar_wave

    comment :

    KaRIn normalized radar cross section (sigma0) in linear units (not dB), corrected for calibration and model-based atmospheric attenuation. Values may be negative due to noise subtraction.

    source :

    sig0_karin_2 field in the SWOT L2_LR_SSH product.

    units :

    1

    valid_max :

    10000000

    valid_min :

    -1000

    _ChunkSizes :

    [4000 260]

    Array Chunk Bytes 48.24 MiB 48.24 MiB Shape (12184, 519) (12184, 519) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • ssha_unfiltered
    (num_lines, num_pixels)
    float64
    dask.array<chunksize=(12184, 519), meta=np.ndarray>

    long_name :

    sea surface height anomaly calibrated and edited

    standard_name :

    sea_surface_height_above_mean_sea_level

    comment :

    Height of the sea surface anomaly with all corrections applied and with calibration and data selection applied; It is equivalent to the Sea Level Anomaly (SLA) distribued in the nadir DUACS products; see the product user manual for details.

    units :

    m

    _ChunkSizes :

    [4000 260]

    Array Chunk Bytes 48.24 MiB 48.24 MiB Shape (12184, 519) (12184, 519) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_flag
    (num_lines, num_pixels)
    uint8
    dask.array<chunksize=(12184, 519), meta=np.ndarray>

    comment :

    Deduced from L3 DUACS processing; see the product user manual for details.

    credits :

    CNES and CLS are the authors of the sea ice segmentation used to fill the 'ice', 'ice_unsure' and 'ocean_unsure' flags in the Unsmoothed dataset. CLS and FLUCTUS are the authors of the precipitation rate algorithm used to fill 'rain' flag in the Basic/Expert/Technical datasets. CLS is the author of the precipitation rate algorithm used to fill the 'rain' flag in the Unsmoothed dataset.

    flag_meanings :

    good eclipse local_outliers bad_quality_coast ocean_unsure ice_unsure ice rain soft_outliers extremes mission_event bad_swath_extremities not_on_sea no_data

    flag_values :

    0 3 5 10 18 19 20 25 30 50 70 100 101 102

    institution :

    CNES/CLS/FLUCTUS

    long_name :

    quality_flag

    units :

    1

    _ChunkSizes :

    [4000 260]

    Array Chunk Bytes 6.03 MiB 6.03 MiB Shape (12184, 519) (12184, 519) Dask graph 1 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • cycle_number
    (num_lines)
    uint8
    10 10 10 10 10 ... 10 10 10 10 10

    array([10, 10, 10, ..., 10, 10, 10], dtype=uint8)

  • pass_number
    (num_lines)
    uint8
    1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1

    array([1, 1, 1, ..., 1, 1, 1], dtype=uint8)

• Indexes: (0)
• Attributes: (43)

  Conventions :

  CF-1.9

  Metadata_Conventions :

  Unidata Dataset Discovery v1.0

  cdm_data_type :

  Swath

  comment :

  Sea Surface Height measured by Altimetry

  geospatial_lat_units :

  degrees_north

  geospatial_lon_units :

  degrees_east

  geospatial_vertical_max :

  0

  geospatial_vertical_min :

  0

  geospatial_vertical_positive :

  down

  geospatial_vertical_resolution :

  point

  geospatial_vertical_units :

  m

  keywords :

  Oceans > Ocean Topography > Sea Surface Height

  keywords_vocabulary :

  NetCDF COARDS Climate and Forecast Standard Names

  processing_level :

  L3

  product_version :

  3.0

  project :

  SSALTO/DUACS

  source :

  Altimetry measurements

  ssalto_duacs_comment :

  The reference mission used for the altimeter inter-calibration processing is Sentinel-6A

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table v37

  time_coverage_resolution :

  PT0.027S

  title :

  NRT SWOT KaRIn & nadir Global Ocean swath SSALTO/DUACS Sea Surface Height L3 product

  platform :

  Swot

  institution :

  CLS, CNES

  data_used :

  SWOT KaRIn L2_LR_SSH PGC0/PIC0/PIC2/PID0 (NASA/CNES). DOI associated: https://doi.org/10.24400/527896/a01-2023.016

  reference_altimeter :

  S6

  references :

  Handbook: https://www.aviso.altimetry.fr/fileadmin/documents/data/tools/handbook_duacs_SWOT_L3.pdf

  doi :

  https://doi.org/10.24400/527896/A01-2024.003

  contact :

  aviso@altimetry.fr

  creator_email :

  aviso@altimetry.fr

  creator_name :

  DUACS - Data Unification and Altimeter Combination System

  creator_url :

  https://aviso.altimetry.fr

  license :

  https://www.aviso.altimetry.fr/fileadmin/documents/data/License_Aviso.pdf

  time_coverage_start :

  2024-01-25T00:19:32Z

  time_coverage_end :

  2024-01-25T01:10:59Z

  geospatial_lat_min :

  -78.259364

  geospatial_lat_max :

  78.256488

  geospatial_lon_min :

  1e-06

  geospatial_lon_max :

  359.999999

  date_modified :

  2026-08-13T20:23:55Z

  history :

  2026-08-13T20:23:55Z: Created by DUACS KaRIn prototype

  date_created :

  2026-08-13T20:23:55Z

  date_issued :

  2026-08-13T20:23:55Z

  temporality :

  reproc

Visualize data

Apply quality flag on Sigma 0

ds["sigma0"] = ds.sigma0.where(ds.quality_flag==0)

plot_kwargs = dict(
    x="longitude",
    y="latitude",
    cmap="gray_r",
    vmin=18,
    vmax=37,
)

fig, ax = plt.subplots(figsize=(21, 12), subplot_kw=dict(projection=ccrs.PlateCarree()))

ds.sigma0.plot.pcolormesh(ax=ax, cbar_kwargs={"shrink": 0.3}, **plot_kwargs)
plt.title("Sigma 0")

ax.gridlines(draw_labels=True)
ax.coastlines()
ax.set_extent([6, 11, 35, 45], crs=ccrs.PlateCarree())

plot_kwargs = dict(
    x="longitude",
    y="latitude",
    vmin=0,
    vmax=0.2,
)

fig, ax = plt.subplots(figsize=(21, 12), subplot_kw=dict(projection=ccrs.PlateCarree()))

ds.ssha_unfiltered.plot.pcolormesh(ax=ax, cbar_kwargs={"shrink": 0.3}, **plot_kwargs)
plt.title('SSHA')

ax.gridlines(draw_labels=True)
ax.coastlines()
ax.set_extent([6, 11, 35, 45], crs=ccrs.PlateCarree())

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