From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.

Download and Access SWOT Oceanography Cloud-hosted Data

Summary

This notebook will show direct download of PO.DAAC archived products hosted in the Earthdata Cloud. In this demo, we will showcase the usage of SWOT Level 2 Low Rate products:

  1. SWOT Level 2 KaRIn Low Rate Sea Surface Height Data Product - shortname SWOT_L2_LR_SSH_2.0
  2. SWOT Level 2 Nadir Altimeter Interim Geophysical Data Record with Waveforms - SSHA Version 2.0 - shortname SWOT_L2_NALT_IGDR_SSHA_2.0
    • This is a subcollection of the parent collection: SWOT_L2_NALT_IGDR_2.0

We will download the data and load a time series made of multiple netCDF files into a single xarray dataset.

Requirements

Local compute environment e.g. laptop, server: this tutorial can be run on your local machine.

Learning Objectives

  • authenticate for earthaccess Python Library using your NASA Earthdata Login
  • access DAAC data by downloading directly into your local machine and operating on those files.
  • plot the first time step in the data

Import Libraries

import xarray as xr
import s3fs
import cartopy.crs as ccrs
from matplotlib import pyplot as plt
import earthaccess
from earthaccess import Auth, DataCollections, DataGranules, Store
%matplotlib inline

Earthdata Login

An Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up. We use earthaccess to authenticate your login credentials below.

auth = earthaccess.login()

1. SWOT Level 2 KaRIn Low Rate Sea Surface Height Data Product

Search and Download Data!

It’s time to find our data! Below we are searching using earthaccess Python library.

#retrieves granule from the day we want
karin_results = earthaccess.search_data(short_name = 'SWOT_L2_LR_SSH_2.0', 
                                        temporal = ("2024-02-01 12:00:00", "2024-02-01 19:43:00"), 
                                        granule_name = '*Expert*')  # filter by files with "Expert" in file name. This collection has subcollections of 'Basic', 'Windwave', 'Unsmoothed' and 'Expert' granules.
Granules found: 10
earthaccess.download(karin_results, "./data_downloads/SWOT_files", provider='POCLOUD')
 Getting 10 granules, approx download size: 0.32 GB
File SWOT_L2_LR_SSH_Expert_010_210_20240201T113157_20240201T122325_PIC0_01.nc already downloaded
File SWOT_L2_LR_SSH_Expert_010_211_20240201T122324_20240201T131449_PIC0_01.nc already downloaded
File SWOT_L2_LR_SSH_Expert_010_212_20240201T131459_20240201T140619_PIC0_01.nc already downloaded
File SWOT_L2_LR_SSH_Expert_010_213_20240201T140618_20240201T145746_PIC0_01.nc already downloaded
File SWOT_L2_LR_SSH_Expert_010_214_20240201T145745_20240201T154830_PIC0_01.nc already downloaded
File SWOT_L2_LR_SSH_Expert_010_215_20240201T154954_20240201T164039_PIC0_01.nc already downloaded
File SWOT_L2_LR_SSH_Expert_010_216_20240201T164038_20240201T173206_PIC0_01.nc already downloaded
File SWOT_L2_LR_SSH_Expert_010_218_20240201T182332_20240201T191500_PIC0_01.nc already downloaded
File SWOT_L2_LR_SSH_Expert_010_217_20240201T173205_20240201T182333_PIC0_01.nc already downloaded
File SWOT_L2_LR_SSH_Expert_010_219_20240201T191459_20240201T200627_PIC0_01.nc already downloaded
['SWOT_L2_LR_SSH_Expert_010_210_20240201T113157_20240201T122325_PIC0_01.nc',
 'SWOT_L2_LR_SSH_Expert_010_211_20240201T122324_20240201T131449_PIC0_01.nc',
 'SWOT_L2_LR_SSH_Expert_010_212_20240201T131459_20240201T140619_PIC0_01.nc',
 'SWOT_L2_LR_SSH_Expert_010_213_20240201T140618_20240201T145746_PIC0_01.nc',
 'SWOT_L2_LR_SSH_Expert_010_214_20240201T145745_20240201T154830_PIC0_01.nc',
 'SWOT_L2_LR_SSH_Expert_010_215_20240201T154954_20240201T164039_PIC0_01.nc',
 'SWOT_L2_LR_SSH_Expert_010_216_20240201T164038_20240201T173206_PIC0_01.nc',
 'SWOT_L2_LR_SSH_Expert_010_217_20240201T173205_20240201T182333_PIC0_01.nc',
 'SWOT_L2_LR_SSH_Expert_010_218_20240201T182332_20240201T191500_PIC0_01.nc',
 'SWOT_L2_LR_SSH_Expert_010_219_20240201T191459_20240201T200627_PIC0_01.nc']
#load downloaded data into dataset to be used for plotting
ds = xr.open_mfdataset("./data_downloads/SWOT_files/SWOT_L2_LR_SSH_Expert*.nc", combine='nested', concat_dim="num_lines", decode_times=False)
ds
<xarray.Dataset>
Dimensions:                                (num_lines: 98660, num_pixels: 69,
                                            num_sides: 2)
Coordinates:
    latitude                               (num_lines, num_pixels) float64 dask.array<chunksize=(9866, 69), meta=np.ndarray>
    longitude                              (num_lines, num_pixels) float64 dask.array<chunksize=(9866, 69), meta=np.ndarray>
    latitude_nadir                         (num_lines) float64 dask.array<chunksize=(9866,), meta=np.ndarray>
    longitude_nadir                        (num_lines) float64 dask.array<chunksize=(9866,), meta=np.ndarray>
Dimensions without coordinates: num_lines, num_pixels, num_sides
Data variables: (12/98)
    time                                   (num_lines) float64 dask.array<chunksize=(9866,), meta=np.ndarray>
    time_tai                               (num_lines) float64 dask.array<chunksize=(9866,), meta=np.ndarray>
    ssh_karin                              (num_lines, num_pixels) float64 dask.array<chunksize=(9866, 69), meta=np.ndarray>
    ssh_karin_qual                         (num_lines, num_pixels) float64 dask.array<chunksize=(9866, 69), meta=np.ndarray>
    ssh_karin_uncert                       (num_lines, num_pixels) float32 dask.array<chunksize=(9866, 69), meta=np.ndarray>
    ssha_karin                             (num_lines, num_pixels) float64 dask.array<chunksize=(9866, 69), meta=np.ndarray>
    ...                                     ...
    swh_ssb_cor_source                     (num_lines, num_pixels) float32 dask.array<chunksize=(9866, 69), meta=np.ndarray>
    swh_ssb_cor_source_2                   (num_lines, num_pixels) float32 dask.array<chunksize=(9866, 69), meta=np.ndarray>
    wind_speed_ssb_cor_source              (num_lines, num_pixels) float32 dask.array<chunksize=(9866, 69), meta=np.ndarray>
    wind_speed_ssb_cor_source_2            (num_lines, num_pixels) float32 dask.array<chunksize=(9866, 69), meta=np.ndarray>
    volumetric_correlation                 (num_lines, num_pixels) float32 dask.array<chunksize=(9866, 69), meta=np.ndarray>
    volumetric_correlation_uncert          (num_lines, num_pixels) float32 dask.array<chunksize=(9866, 69), meta=np.ndarray>
Attributes: (12/62)
    Conventions:                                   CF-1.7
    title:                                         Level 2 Low Rate Sea Surfa...
    institution:                                   CNES
    source:                                        Ka-band radar interferometer
    history:                                       2024-02-03T22:27:17Z : Cre...
    platform:                                      SWOT
    ...                                            ...
    ellipsoid_semi_major_axis:                     6378137.0
    ellipsoid_flattening:                          0.0033528106647474805
    good_ocean_data_percent:                       76.4772191457865
    ssha_variance:                                 0.4263933333980923
    references:                                    V1.2.1
    equator_longitude:                             -5.36
    • Conventions :
      CF-1.7
      title :
      Level 2 Low Rate Sea Surface Height Data Product - Expert SSH with Wind and Wave
      institution :
      CNES
      source :
      Ka-band radar interferometer
      history :
      2024-02-03T22:27:17Z : Creation
      platform :
      SWOT
      reference_document :
      D-56407_SWOT_Product_Description_L2_LR_SSH
      contact :
      podaac@jpl.nasa.gov
      cycle_number :
      10
      pass_number :
      210
      equator_time :
      2024-02-01T11:57:39.935000Z
      short_name :
      L2_LR_SSH
      product_file_id :
      Expert
      crid :
      PIC0
      product_version :
      01
      pge_name :
      PGE_L2_LR_SSH
      pge_version :
      5.0.2
      time_coverage_start :
      2024-02-01T11:31:57.844839
      time_coverage_end :
      2024-02-01T12:23:25.880560
      geospatial_lon_min :
      270.91792399999997
      geospatial_lon_max :
      78.362457
      geospatial_lat_min :
      -78.271942
      geospatial_lat_max :
      78.27206799999999
      left_first_longitude :
      270.91792399999997
      left_first_latitude :
      78.27200599999999
      left_last_longitude :
      78.343086
      left_last_latitude :
      -77.05370099999999
      right_first_longitude :
      270.93575
      right_first_latitude :
      77.053837
      right_last_longitude :
      78.36245699999999
      right_last_latitude :
      -78.27186999999999
      wavelength :
      0.008385803020979021
      transmit_antenna :
      minus_y
      xref_l1b_lr_intf_file :
      SWOT_L1B_LR_INTF_010_210_20240201T113154_20240201T122329_PIC0_01.nc
      xref_l2_nalt_gdr_files :
      SWOT_IPN_2PfP010_209_20240201_104031_20240201_113158.nc, SWOT_IPN_2PfP010_210_20240201_113158_20240201_122325.nc, SWOT_IPN_2PfP010_211_20240201_122325_20240201_131452.nc
      xref_l2_rad_gdr_files :
      SWOT_IPRAD_2PaP010_209_20240201_104027_20240201_113202_PIC0_01.nc, SWOT_IPRAD_2PaP010_210_20240201_113154_20240201_122329_PIC0_01.nc, SWOT_IPRAD_2PaP010_211_20240201_122321_20240201_131455_PIC0_01.nc
      xref_int_lr_xover_cal_file :
      SWOT_INT_LR_XOverCal_20240131T233132_20240201T233223_PIC0_01.nc
      xref_statickarincal_files :
      SWOT_StaticKaRInCalAdjustableParam_20000101T000000_20991231T235959_20230823T210000_v106.nc
      xref_param_l2_lr_precalssh_file :
      SWOT_Param_L2_LR_PreCalSSH_20000101T000000_20991231T235959_20230815T120500_v301.nc
      xref_orbit_ephemeris_file :
      SWOT_POR_AXVCNE20240202_103657_20240131_225923_20240202_005923.nc
      xref_reforbittrack_files :
      SWOT_RefOrbitTrack125mPass1_Nom_20000101T000000_21000101T000000_20200617T193054_v101.txt, SWOT_RefOrbitTrack125mPass2_Nom_20000101T000000_21000101T000000_20200617T193054_v101.txt
      xref_meteorological_sealevel_pressure_files :
      SMM_PMA_AXVCNE20240201_164030_20240201_060000_20240201_060000, SMM_PMA_AXVCNE20240201_171613_20240201_120000_20240201_120000, SMM_PMA_AXVCNE20240202_030828_20240201_180000_20240201_180000
      xref_meteorological_wettroposphere_files :
      SMM_WEA_AXVCNE20240201_164030_20240201_060000_20240201_060000, SMM_WEA_AXVCNE20240201_171613_20240201_120000_20240201_120000, SMM_WEA_AXVCNE20240202_030828_20240201_180000_20240201_180000
      xref_meteorological_wind_files :
      SMM_VWA_AXVCNE20240201_164030_20240201_060000_20240201_060000, SMM_UWA_AXVCNE20240201_164030_20240201_060000_20240201_060000, SMM_UWA_AXVCNE20240201_171613_20240201_120000_20240201_120000, SMM_VWA_AXVCNE20240201_171613_20240201_120000_20240201_120000, SMM_UWA_AXVCNE20240202_030828_20240201_180000_20240201_180000, SMM_VWA_AXVCNE20240202_030828_20240201_180000_20240201_180000
      xref_meteorological_surface_pressure_files :
      SMM_PSA_AXVCNE20240201_174042_20240201_060000_20240201_060000, SMM_PSA_AXVCNE20240201_174042_20240201_120000_20240201_120000, SMM_PSA_AXVCNE20240202_054023_20240201_180000_20240201_180000
      xref_meteorological_temperature_files :
      SMM_T2M_AXPCNE20240201_174042_20240201_060000_20240201_060000.grb, SMM_T2M_AXPCNE20240201_174042_20240201_120000_20240201_120000.grb, SMM_T2M_AXPCNE20240202_054023_20240201_180000_20240201_180000.grb
      xref_meteorological_water_vapor_files :
      SMM_CWV_AXPCNE20240201_174042_20240201_060000_20240201_060000.grb, SMM_CWV_AXPCNE20240201_174042_20240201_120000_20240201_120000.grb, SMM_CWV_AXPCNE20240202_054023_20240201_180000_20240201_180000.grb
      xref_meteorological_cloud_liquid_water_files :
      SMM_CLW_AXPCNE20240201_174042_20240201_060000_20240201_060000.grb, SMM_CLW_AXPCNE20240201_174042_20240201_120000_20240201_120000.grb, SMM_CLW_AXPCNE20240202_054023_20240201_180000_20240201_180000.grb
      xref_model_significant_wave_height_files :
      SMM_SWH_AXPCNE20240201_174042_20240201_060000_20240201_060000.grb, SMM_SWH_AXPCNE20240201_174042_20240201_120000_20240201_120000.grb, SMM_SWH_AXPCNE20240202_054023_20240201_180000_20240201_180000.grb
      xref_gim_files :
      JPLQ0320.24I
      xref_pole_location_file :
      SMM_PO1_AXXCNE20240203_020000_19900101_000000_20240801_000000
      xref_dac_files :
      SMM_MOG_AXPCNE20240201_203002_20240201_060000_20240201_060000, SMM_MOG_AXPCNE20240201_203002_20240201_120000_20240201_120000, SMM_MOG_AXPCNE20240202_074502_20240201_180000_20240201_180000
      xref_precipitation_files :
      SMM_LSR_AXFCNE20240201_065551_20240201_060000_20240201_060000.grb, SMM_CRR_AXFCNE20240201_065551_20240201_060000_20240201_060000.grb, SMM_LSR_AXFCNE20240201_185554_20240201_120000_20240201_120000.grb, SMM_CRR_AXFCNE20240201_185554_20240201_120000_20240201_120000.grb, SMM_LSR_AXFCNE20240201_185554_20240201_180000_20240201_180000.grb, SMM_CRR_AXFCNE20240201_185554_20240201_180000_20240201_180000.grb
      xref_sea_ice_mask_files :
      SMM_ICS_AXFCNE20240202_042003_20240201_000000_20240201_235959.nc, SMM_ICN_AXFCNE20240202_041506_20240201_000000_20240201_235959.nc
      xref_wave_model_files :
      SMM_WMA_AXPCNE20240202_072016_20240201_030000_20240202_000000.grb
      xref_geco_database_version :
      v102
      ellipsoid_semi_major_axis :
      6378137.0
      ellipsoid_flattening :
      0.0033528106647474805
      good_ocean_data_percent :
      76.4772191457865
      ssha_variance :
      0.4263933333980923
      references :
      V1.2.1
      equator_longitude :
      -5.36

      • Cross Over Calibration Correction

      In order to get the corrected SSHA, we must compute a new column like the following:

      ds['ssha_karin_corrected'] = ds.ssha_karin + ds.height_cor_xover
ds.ssha_karin_corrected
      <xarray.DataArray 'ssha_karin_corrected' (num_lines: 98660, num_pixels: 69)>
dask.array<add, shape=(98660, 69), dtype=float64, chunksize=(9866, 69), chunktype=numpy.ndarray>
Coordinates:
    latitude         (num_lines, num_pixels) float64 dask.array<chunksize=(9866, 69), meta=np.ndarray>
    longitude        (num_lines, num_pixels) float64 dask.array<chunksize=(9866, 69), meta=np.ndarray>
    latitude_nadir   (num_lines) float64 dask.array<chunksize=(9866,), meta=np.ndarray>
    longitude_nadir  (num_lines) float64 dask.array<chunksize=(9866,), meta=np.ndarray>
Dimensions without coordinates: num_lines, num_pixels

        • Plot

        Now let’s plot a variable (ssha_karin_corrected) from these 10 files in a chosen projection.

        plt.figure(figsize=(15, 5))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_global()
ds.ssha_karin_corrected.plot.pcolormesh(
 ax=ax, transform=ccrs.PlateCarree(), x="longitude", y="latitude", vmin = -1, vmax=1, cmap='coolwarm', add_colorbar=True
)
ax.coastlines()

        2. SWOT Level 2 Nadir Altimeter Interim Geophysical Data Record with Waveforms - SSHA Version 1.0

        Search and Download Data!

        Below we are searching using earthaccess Python library.

        #retrieves granule from the day we want
nadir_results = earthaccess.search_data(short_name = 'SWOT_L2_NALT_IGDR_SSHA_2.0', temporal = ("2024-01-30 12:00:00", "2024-01-30 19:43:00"))
        Granules found: 10
        earthaccess.download(nadir_results, "./data_downloads/SWOT_files")
         Getting 10 granules, approx download size: 0.0 GB
Error while downloading the file SWOT_IPR_2PfP010_161_20240130_173104_20240130_182230.nc
Traceback (most recent call last):
  File "C:\Users\nickles\Anaconda3\envs\main\lib\site-packages\earthaccess\store.py", line 589, in _download_file
    r.raise_for_status()
  File "C:\Users\nickles\Anaconda3\envs\main\lib\site-packages\requests\models.py", line 1021, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 502 Server Error: Bad Gateway for url: https://archive.swot.podaac.earthdata.nasa.gov/podaac-swot-ops-cumulus-protected/SWOT_L2_NALT_IGDR_2.0/SWOT_IPR_2PfP010_161_20240130_173104_20240130_182230.nc

        ['SWOT_IPR_2PfP010_154_20240130_113056_20240130_122223.nc',
 'SWOT_IPR_2PfP010_155_20240130_122223_20240130_131350.nc',
 'SWOT_IPR_2PfP010_156_20240130_131350_20240130_140516.nc',
 'SWOT_IPR_2PfP010_157_20240130_140516_20240130_145643.nc',
 'SWOT_IPR_2PfP010_158_20240130_145643_20240130_154810.nc',
 'SWOT_IPR_2PfP010_159_20240130_154810_20240130_163937.nc',
 'SWOT_IPR_2PfP010_160_20240130_163937_20240130_173104.nc',
 Exception(),
 'SWOT_IPR_2PfP010_162_20240130_182230_20240130_191357.nc',
 'SWOT_IPR_2PfP010_163_20240130_191357_20240130_200524.nc']
        #load downloaded data into dataset to be used for plotting
ds_nadir = xr.open_mfdataset("./data_downloads/SWOT_files/SWOT_IPR_*.nc", combine='nested', concat_dim="time", decode_times=False, engine='h5netcdf', group='data_01')
ds_nadir
        <xarray.Dataset>
Dimensions:                            (time: 25125)
Coordinates:
  * time                               (time) float64 7.599e+08 ... 7.6e+08
    latitude                           (time) float64 dask.array<chunksize=(2806,), meta=np.ndarray>
    longitude                          (time) float64 dask.array<chunksize=(2806,), meta=np.ndarray>
Data variables: (12/31)
    time_tai                           (time) float64 dask.array<chunksize=(2806,), meta=np.ndarray>
    surface_classification_flag        (time) float32 dask.array<chunksize=(2806,), meta=np.ndarray>
    rad_side_1_surface_type_flag       (time) float32 dask.array<chunksize=(2806,), meta=np.ndarray>
    rad_side_2_surface_type_flag       (time) float32 dask.array<chunksize=(2806,), meta=np.ndarray>
    alt_qual                           (time) float32 dask.array<chunksize=(2806,), meta=np.ndarray>
    rad_qual                           (time) float32 dask.array<chunksize=(2806,), meta=np.ndarray>
    ...                                 ...
    pole_tide                          (time) float32 dask.array<chunksize=(2806,), meta=np.ndarray>
    internal_tide_hret                 (time) float32 dask.array<chunksize=(2806,), meta=np.ndarray>
    wind_speed_alt                     (time) float32 dask.array<chunksize=(2806,), meta=np.ndarray>
    wind_speed_alt_mle3                (time) float32 dask.array<chunksize=(2806,), meta=np.ndarray>
    rad_water_vapor                    (time) float32 dask.array<chunksize=(2806,), meta=np.ndarray>
    rad_cloud_liquid_water             (time) float32 dask.array<chunksize=(2806,), meta=np.ndarray>
        • time
          PandasIndex
          PandasIndex(Float64Index([759929456.5671339, 759929457.6690049,  759929458.770874,
               759929459.872746, 759929460.9746141, 759929462.0764852,
              759929463.1783538, 759929464.2802248, 759929465.3820939,
              759929466.4839649,
              ...
              759960314.5902781, 759960315.6921468, 759960316.7940178,
              759960317.8958869, 759960318.9977579,  759960320.099627,
               759960321.201498, 759960322.3033671, 759960323.4052382,
              759960324.5071082],
             dtype='float64', name='time', length=25125))

        • Plot

        plt.figure(figsize=(15, 5))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_global()
ax.coastlines()
plt.scatter(x=ds_nadir.longitude, y=ds_nadir.latitude, c=ds_nadir.depth_or_elevation, marker='.')
plt.colorbar().set_label('Depth or Elevation (m)')

        A final word…

        Accessing data completely in memory are affected by various things. Tools like xarray make a lot of assumptions about how to open and read a file. Sometimes the internals don’t fit the xarray ‘mould’ and we need to continue to work with data providers and software providers to make these two sides work together. Level 2 data (non-gridded), specifically, suffers from some of the assumptions made.