Discover SWOT L3 Unsmoothed data, and compare with CMEMS L4 SLA and IFREMER SST data

Tutorial Objectives

Select Swot data with cycle/passes numbers, and download it through AVISO’s FTP
Select data intersecting a geographical area
Download CMEMS map using copernicusmarine client, and plot Swot SLA with CMEMS SLA
Download SST map using copernicusmarine client and plot Swot SLA with SST

Environment needed to run this notebook:

ftplib
xarray
numpy
matplotlib+cartopy+cmocean
copernicusmarine

Import + code

[1]:

import warnings
warnings.filterwarnings('ignore')

[2]:

import os
from getpass import getpass
import ftplib

import xarray as xr
import numpy as np

import cartopy.crs as ccrs
import matplotlib.cm as mplcm
import matplotlib.pyplot as plt
import cmocean

import copernicusmarine

[3]:

def _download_file(ftp:str, filename:str, target_directory:str):
    print(f"Download file: {filename}")
    try:
        local_filepath = os.path.join(target_directory, filename)
        with open(local_filepath, 'wb') as file:
            ftp.retrbinary('RETR %s' % filename, file.write)
            return local_filepath
    except Exception as e:
        print(f"Error downloading {filename}: {e}")

def _get_last_version_filename(filenames):
    versions = {int(f[-5:-3]): f for f in filenames}
    return versions[max(versions.keys())]

def _select_filename(filenames, only_last):
    if not only_last: return filenames
    return [_get_last_version_filename(filenames)]

def ftp_download_files(ftp_path, level, variant, cycle_numbers, half_orbits, output_dir, only_last=True):
    """ Download half orbits files from AVISO's FTP Server.
    Args:
        ftp_path
            path of the FTP fileset
        level
            L2 or L3
        variant
            Basic, Expert, WindWave or Unsmoothed
        cycle_numbers
            list of cycles numbers
        half_orbits
            list of passes numbers
        output_dir
            output directory
        only_last
            if True (Default), downloads only the last version of a file if several versions exist.
            Downloads all versions otherwise.
    Returns:
        The list of local files.
    """
    # Set up FTP server details
    ftpAVISO = 'ftp-access.aviso.altimetry.fr'
    try:
        # Logging into FTP server using provided credentials
        with ftplib.FTP(ftpAVISO) as ftp:
            ftp.login(username, password)
            ftp.cwd(ftp_path)
            print(f"Connection Established {ftp.getwelcome()}")
            downloaded_files = []

            for cycle in cycle_numbers:
                cycle_str = '{:03d}'.format(cycle)
                cycle_dir = f'cycle_{cycle_str}'
                print(ftp_path+cycle_dir)
                ftp.cwd(cycle_dir)

                for half_orbit in half_orbits:
                    half_orbit_str = '{:03d}'.format(half_orbit)
                    pattern = f'SWOT_{level}_LR_SSH_{variant}_{cycle_str}_{half_orbit_str}'
                    filenames = []
                    try:
                        filenames = ftp.nlst(f'{pattern}_*')
                        # No version in L3 filenames
                        if level=="L3": only_last=False
                        filenames = _select_filename(filenames, only_last)
                    except Exception as e:
                        print(f"No pass {half_orbit}")

                    local_files = [_download_file(ftp, f, output_dir) for f in filenames]
                    downloaded_files += local_files

                ftp.cwd('../')

            return downloaded_files

    except ftplib.error_perm as e:
        print(f"FTP error: {e}")
    except Exception as e:
        print(f"Error: {e}")

def _normalized_ds(ds, lon_min, lon_max):
    lon = ds.longitude.values
    lon[lon < lon_min] += 360
    lon[lon > lon_max] -= 360
    ds.longitude.values = lon
    return ds

def _subset_ds(file, variables, lon_range, lat_range, output_dir):
    print(f"Subsetting dataset: {file}")
    swot_ds = xr.open_dataset(file)
    swot_ds = swot_ds[variables]
    swot_ds.load()

    ds = _normalized_ds(swot_ds.copy(), -180, 180)

    mask = (
        (ds.longitude <= lon_range[1])
        & (ds.longitude >= lon_range[0])
        & (ds.latitude <= lat_range[1])
        & (ds.latitude >= lat_range[0])
    ).compute()

    swot_ds_area = swot_ds.where(mask, drop=True)

    if swot_ds_area.sizes['num_lines'] == 0:
        print(f'Dataset {file} not matching geographical area.')
        return None

    for var in list(swot_ds_area.keys()):
        swot_ds_area[var].encoding = {'zlib':True, 'complevel':5}

    filename = "subset_"+file[10:]
    filepath = os.path.join(output_dir, filename)
    swot_ds_area.to_netcdf(filepath)
    print(f"Subset file created: {filename}")

    return filepath


def subset_files(filenames, variables, lon_range, lat_range, output_dir):
    """ Subset datasets with geographical area.
    Args:
        filenames
            the filenames of datasets to subset
        variables
            variables to select
        lon_range
            the longitude range
        lat_range
            the latitude range
        output_dir
            output directory
    Returns:
        The list of subsets files.
    """
    return [subset_file for subset_file in [_subset_ds(f, variables, lon_range, lat_range, output_dir) for f in filenames] if subset_file is not None]

[4]:

def plot_datasets(datasets, variable, vminmax, title, extent=None):
    cb_args = dict(
        add_colorbar=True,
        cbar_kwargs={"shrink": 0.3}
    )

    plot_kwargs = dict(
        x="longitude",
        y="latitude",
        cmap="Spectral_r",
        vmin=vminmax[0],
        vmax=vminmax[1],
    )

    fig, ax = plt.subplots(figsize=(12, 8), subplot_kw=dict(projection=ccrs.PlateCarree()))
    if extent: ax.set_extent(extent)

    for ds in datasets:
        ds[variable].plot.pcolormesh(
            ax=ax,
            **plot_kwargs,
            **cb_args)
        cb_args=dict(add_colorbar=False)

    ax.set_title(title)
    ax.coastlines()
    gls = ax.gridlines(draw_labels=True)
    gls.top_labels=False
    gls.right_labels=False

    return ax

Parameters

Define existing output folder to save results

[5]:

output_dir = "downloads"

Enter your AVISO+ credentials

[6]:

username = input("Enter username:")

Enter username: aviso-swot@altimetry.fr

[7]:

password = getpass(f"Enter password for {username}:")

Enter password for aviso-swot@altimetry.fr: ········

Define data parameters

[8]:

# Basic, Expert, WindWave, Unsmoothed
dataset = "Unsmoothed"
# L2, L3
level = "L3"

Set the FTP files path

[9]:

ftp_path = f'/swot_products/l3_karin_nadir/l3_lr_ssh/v1_0_2/{dataset}/'

Define cycles and half_orbits numbers to download

Note

To look for Swot passes in a predefined geographical area and temporal period, use Search Swot tool.

[10]:

cycle_numbers = [2, 3]
pass_numbers = [14, 169]

Retrieve SWOT data

Download files through FTP

[11]:

downloaded_files = ftp_download_files(ftp_path, level, dataset, cycle_numbers, pass_numbers, output_dir, only_last=True)

Connection Established 220 192.168.10.119 FTP server ready
/swot_products/l3_karin_nadir/l3_lr_ssh/v1_0_2/Unsmoothed/cycle_002
Download file: SWOT_L3_LR_SSH_Unsmoothed_002_014_20230811T132741_20230811T141908_v1.0.2.nc
Download file: SWOT_L3_LR_SSH_Unsmoothed_002_169_20230817T022159_20230817T031326_v1.0.2.nc
/swot_products/l3_karin_nadir/l3_lr_ssh/v1_0_2/Unsmoothed/cycle_003
Download file: SWOT_L3_LR_SSH_Unsmoothed_003_014_20230901T101247_20230901T110413_v1.0.2.nc
Download file: SWOT_L3_LR_SSH_Unsmoothed_003_169_20230906T230705_20230906T235831_v1.0.2.nc

[12]:

downloaded_files

[12]:

['downloads/SWOT_L3_LR_SSH_Unsmoothed_002_014_20230811T132741_20230811T141908_v1.0.2.nc',
 'downloads/SWOT_L3_LR_SSH_Unsmoothed_002_169_20230817T022159_20230817T031326_v1.0.2.nc',
 'downloads/SWOT_L3_LR_SSH_Unsmoothed_003_014_20230901T101247_20230901T110413_v1.0.2.nc',
 'downloads/SWOT_L3_LR_SSH_Unsmoothed_003_169_20230906T230705_20230906T235831_v1.0.2.nc']

Explore Swot data

[13]:

ds = xr.open_dataset(downloaded_files[0])
[v for v in ds.variables]

[13]:

['time',
 'calibration',
 'dac',
 'latitude',
 'longitude',
 'mdt',
 'mss',
 'ocean_tide',
 'quality_flag',
 'sigma0',
 'ssha',
 'ssha_noiseless',
 'ssha_unedited',
 'ugos',
 'ugosa',
 'vgos',
 'vgosa']

Select data intersecting the geographical area

Define a geographical area

[14]:

# English channel
lon_range = [-7, 4]
lat_range = [46, 52]

[15]:

localbox = [lon_range[0], lon_range[1], lat_range[0], lat_range[1]]

Define variables to select

[16]:

variables = ['time', 'ssha', 'ssha_noiseless', 'ssha_unedited', 'sigma0', 'quality_flag']

Subset files

[17]:

subset_filenames = subset_files(downloaded_files, variables, lon_range, lat_range, output_dir)

Subsetting dataset: downloads/SWOT_L3_LR_SSH_Unsmoothed_002_014_20230811T132741_20230811T141908_v1.0.2.nc
Subset file created: subset_SWOT_L3_LR_SSH_Unsmoothed_002_014_20230811T132741_20230811T141908_v1.0.2.nc
Subsetting dataset: downloads/SWOT_L3_LR_SSH_Unsmoothed_002_169_20230817T022159_20230817T031326_v1.0.2.nc
Subset file created: subset_SWOT_L3_LR_SSH_Unsmoothed_002_169_20230817T022159_20230817T031326_v1.0.2.nc
Subsetting dataset: downloads/SWOT_L3_LR_SSH_Unsmoothed_003_014_20230901T101247_20230901T110413_v1.0.2.nc
Subset file created: subset_SWOT_L3_LR_SSH_Unsmoothed_003_014_20230901T101247_20230901T110413_v1.0.2.nc
Subsetting dataset: downloads/SWOT_L3_LR_SSH_Unsmoothed_003_169_20230906T230705_20230906T235831_v1.0.2.nc
Subset file created: subset_SWOT_L3_LR_SSH_Unsmoothed_003_169_20230906T230705_20230906T235831_v1.0.2.nc

[18]:

subset_filenames

[18]:

['downloads/subset_SWOT_L3_LR_SSH_Unsmoothed_002_014_20230811T132741_20230811T141908_v1.0.2.nc',
 'downloads/subset_SWOT_L3_LR_SSH_Unsmoothed_002_169_20230817T022159_20230817T031326_v1.0.2.nc',
 'downloads/subset_SWOT_L3_LR_SSH_Unsmoothed_003_014_20230901T101247_20230901T110413_v1.0.2.nc',
 'downloads/subset_SWOT_L3_LR_SSH_Unsmoothed_003_169_20230906T230705_20230906T235831_v1.0.2.nc']

Note

Size of a Swot L3 Unsmoothed half-orbit dataset: 400 Mb

Size of the subset after geographical selection: 25 Mb

[19]:

subsets_cycle_02 = [xr.open_dataset(file) for file in subset_filenames[:2]]
subsets_cycle_03 = [xr.open_dataset(file) for file in subset_filenames[2:]]

Visualise data

Let’s plot SLA for each cycle

[20]:

plot_datasets(subsets_cycle_02, 'ssha_noiseless', (0, 0.25), 'Filtered SLA', extent=localbox)

[20]:

<GeoAxes: title={'center': 'Filtered SLA'}, xlabel='longitude (degrees East)\n[degrees_east]', ylabel='latitude (positive N, negative\nS) [degrees_north]'>

../_images/SWOT-Oceanography_ex_swot_l3_unsmoothed_41_1.png

Let’s plot SWOT KaRIn Sigma 0 and the different SLA available

[21]:

ds = subsets_cycle_02[1]

[22]:

# Apply quality flag
ds["sigma0"] = ds.sigma0.where(ds.quality_flag==0)
# Apply log 10
ds["sigma0_log"] = 10*np.log10(ds["sigma0"])

[23]:

localbox_zoom = [-5, -1.5, 46, 51]

[24]:

# set figure
fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(25, 21), subplot_kw=dict(projection=ccrs.PlateCarree()))

plot_kwargs = dict(
    x="longitude",
    y="latitude",
    cmap="Spectral_r",
    vmin=0,
    vmax=0.3,
    cbar_kwargs={"shrink": 0.3}
)
plot_kwargs2 = dict(
    x="longitude",
    y="latitude",
    cmap="gray_r",
    vmin=14,
    vmax=19,
    cbar_kwargs={"shrink": 0.3}
)

ds.sigma0_log.plot.pcolormesh(ax=ax1, **plot_kwargs2)
ax1.set_title("Sigma 0")

ds.ssha_unedited.plot.pcolormesh(ax=ax2, **plot_kwargs)
ax2.set_title("Calibrated SLA")

ds.ssha.plot.pcolormesh(ax=ax3, **plot_kwargs)
ax3.set_title("Edited SLA")

ds.ssha_noiseless.plot.pcolormesh(ax=ax4, **plot_kwargs)
ax4.set_title("Filtered SLA")

for ax in [ax1, ax2, ax3, ax4]:
    ax.set_extent(localbox_zoom)
    ax.coastlines()
    ax.gridlines()

../_images/SWOT-Oceanography_ex_swot_l3_unsmoothed_46_0.png

Download notebook.

Discover SWOT L3 Unsmoothed data, and compare with CMEMS L4 SLA and IFREMER SST data

Tutorial Objectives

Import + code

Parameters

Retrieve SWOT data

Download files through FTP

Explore Swot data

Select data intersecting the geographical area

Visualise data

Visualise SWOT SLA with L4 CMEMS SLA map

Download L4 CMEMS SLA maps with `copernicusmarine` client

Visualise SWOT SLA with CMEMS SLA

Visualise SWOT SLA with IFREMER SST map

Download SST maps with `copernicusmarine` client

Visualise SWOT SLA with SST

Discover SWOT L3 Unsmoothed data, and compare with CMEMS L4 SLA and IFREMER SST data

Tutorial Objectives

Import + code

Parameters

Retrieve SWOT data

Download files through FTP

Explore Swot data

Select data intersecting the geographical area

Visualise data

Visualise SWOT SLA with L4 CMEMS SLA map

Download L4 CMEMS SLA maps with copernicusmarine client

Visualise SWOT SLA with CMEMS SLA

Visualise SWOT SLA with IFREMER SST map

Download SST maps with copernicusmarine client

Visualise SWOT SLA with SST

Download L4 CMEMS SLA maps with `copernicusmarine` client

Download SST maps with `copernicusmarine` client