ex_swot_l2_basic_startup_aviso_ftp.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://img.shields.io/badge/CNES_AVISO-Contribution-%20?color=grey&labelColor=blue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F5l9prJtHsMh"
   },
   "source": [
    "# SWOT L2 KaRIn LR Ocean Data Products\n",
    "\n",
    "This tutorial will introduce you to some sample SWOT L2 KaRIn LR data products and show you how to download these data from AVISO and perform basic plots using Python related libraries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial Objectives\n",
    "- Present SWOT sample L2 KaRIn LR data products (Basic version)\n",
    "- Show you how to find and visualize SWOT Sea Level Anomaly (SLA) data sets from AVISO FTP server\n",
    "- Download SWOT KaRIn (2D swaths) data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zOy0yTp080D5"
   },
   "source": [
    "## Import + code\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install Cartopy with mamba to avoid discrepancies\n",
    "# ! mamba install -q -c conda-forge cartopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "d_vjHJj1xd5v"
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "import os\n",
    "import ftplib\n",
    "from getpass import getpass\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cft\n",
    "import cartopy.mpl.geoaxes as cmplgeo\n",
    "import cartopy.mpl.gridliner as cmplgrid\n",
    "import matplotlib.pyplot as plt\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "OJa5QDMPdWeG",
    "jupyter": {
     "source_hidden": true
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   "outputs": [],
   "source": [
    "def ftp_data_access(ftp_path, filename, username=None, password=None, local_filepath=None):\n",
    "    # Set up FTP server details\n",
    "    ftpAVISO = 'ftp-access.aviso.altimetry.fr'\n",
    "    \n",
    "    try:\n",
    "        # Prompt for username and password\n",
    "        if not username:\n",
    "            username = input(\"Enter username for AVISO: \")\n",
    "        if not password:\n",
    "            password = getpass(prompt=f\"Enter password for {username}: \")\n",
    "\n",
    "        # Logging into FTP server using provided credentials\n",
    "        with ftplib.FTP(ftpAVISO) as ftp:\n",
    "            ftp.login(username, password)\n",
    "            ftp.cwd(ftp_path)\n",
    "            print(f\"Connection Established {ftp.getwelcome()}\")\n",
    "\n",
    "            # Check if the file exists in the directory\n",
    "            if filename in ftp.nlst():\n",
    "                if not local_filepath: local_filepath = input(\"Enter the local directory to save the file: \")\n",
    "                return download_file_from_ftp(ftp, filename, local_filepath)\n",
    "            else:\n",
    "                print(f\"File {filename} does not exist in the directory {ftp_path}.\")\n",
    "    except ftplib.error_perm as e:\n",
    "        print(f\"FTP error: {e}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error: {e}\")\n",
    "\n",
    "def download_file_from_ftp(ftp, filename, target_directory):\n",
    "    try:\n",
    "        local_filepath = os.path.join(target_directory, filename)\n",
    "        with open(local_filepath, 'wb') as file:\n",
    "            ftp.retrbinary('RETR %s' % filename, file.write)\n",
    "            print(f\"Downloaded {filename} to {local_filepath}\")\n",
    "            return local_filepath\n",
    "    except Exception as e:\n",
    "        print(f\"Error downloading {filename}: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters\n",
    "Define a local filepath to download files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_filepath='downloads'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Authentication parameters\n",
    "Enter your AVISO+ credentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter username: aviso-swot@altimetry.fr\n"
     ]
    }
   ],
   "source": [
    "username = input(\"Enter username:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter password for aviso-swot@altimetry.fr: ········\n"
     ]
    }
   ],
   "source": [
    "password = getpass(f\"Enter password for {username}:\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FTP download\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connection Established 220 192.168.10.119 FTP server ready\n",
      "Downloaded SWOT_L2_LR_SSH_Basic_007_317_20231204T170132_20231204T175300_PGC0_01.nc to downloads/SWOT_L2_LR_SSH_Basic_007_317_20231204T170132_20231204T175300_PGC0_01.nc\n"
     ]
    },
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     "data": {
      "text/plain": [
       "'downloads/SWOT_L2_LR_SSH_Basic_007_317_20231204T170132_20231204T175300_PGC0_01.nc'"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Define directories\n",
    "ftp_path_basic = '/swot_products/l2_karin/l2_lr_ssh/PGC0/Basic/cycle_007/'\n",
    "filename_basic = 'SWOT_L2_LR_SSH_Basic_007_317_20231204T170132_20231204T175300_PGC0_01.nc'\n",
    "\n",
    "# FTP download\n",
    "half_orbit_basic = ftp_data_access(ftp_path_basic, filename_basic, username, password, local_filepath)\n",
    "half_orbit_basic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select regional boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "localbox = [233, 239, 35, 42] # California"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discover SWOT L2 KaRIn Ocean Basic product\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YPpXONFikAJX"
   },
   "source": [
    "### Basic product content\n",
    "This product contains two versions of SLA (ssha in the datasets) with different wet troposhere and sea state bias (SSB) corrections:\n",
    "- ssha_karin relies more on observations (e.g., wet tropo corrected using radiometer data)\n",
    "- ssha_karin_2 relies more on model for corrections (e.g., wet tropo corrected using ECMWF model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
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    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1712669566536,
     "user": {
      "displayName": "Cyril Germineaud",
      "userId": "08934926211399639299"
     },
     "user_tz": -120
    },
    "id": "YZGMuJmFkE7T",
    "outputId": "20cd29d6-cad4-4cf5-d307-7dbf94c4c0cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['time',\n",
       " 'time_tai',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'ssh_karin',\n",
       " 'ssh_karin_qual',\n",
       " 'ssh_karin_uncert',\n",
       " 'ssha_karin',\n",
       " 'ssha_karin_qual',\n",
       " 'ssh_karin_2',\n",
       " 'ssh_karin_2_qual',\n",
       " 'ssha_karin_2',\n",
       " 'ssha_karin_2_qual',\n",
       " 'num_pt_avg',\n",
       " 'distance_to_coast',\n",
       " 'heading_to_coast',\n",
       " 'ancillary_surface_classification_flag',\n",
       " 'dynamic_ice_flag',\n",
       " 'rain_flag',\n",
       " 'rad_surface_type_flag',\n",
       " 'mean_sea_surface_cnescls',\n",
       " 'mean_sea_surface_cnescls_uncert',\n",
       " 'geoid',\n",
       " 'internal_tide_hret',\n",
       " 'height_cor_xover',\n",
       " 'height_cor_xover_qual']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_basic =  xr.open_dataset(half_orbit_basic)\n",
    "[v for v in ds_basic.variables]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select data over the region\n",
    "localsubset = (\n",
    "(ds_basic.longitude > localbox[0]) &\n",
    "(ds_basic.longitude < localbox[1]) &\n",
    "(ds_basic.latitude > localbox[2]) &\n",
    "(ds_basic.latitude < localbox[3]))\n",
    "#\n",
    "ds_basic_sub = ds_basic.where(localsubset, drop=True)\n",
    "ds_basic_sub = ds_basic_sub.assign_coords(longitude=(((ds_basic_sub.longitude + 180) % 360) - 180))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Application of Crossover Calibration Correction (XOverCal or xover)\n",
    "To get corrected SSHA, user must apply the xover as follows:\n",
    "- ssha_karin_corrected = ssha_karin + height_cor_xover\n",
    "- ssha_karin_2_corrected = ssha_karin_2 + height_cor_xover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                                (num_lines: 414, num_pixels: 69,\n",
       "                                            num_sides: 2)\n",
       "Coordinates:\n",
       "    latitude                               (num_lines, num_pixels) float64 35...\n",
       "    longitude                              (num_lines, num_pixels) float64 -1...\n",
       "Dimensions without coordinates: num_lines, num_pixels, num_sides\n",
       "Data variables: (12/26)\n",
       "    time                                   (num_lines, num_pixels) datetime64[ns] ...\n",
       "    time_tai                               (num_lines, num_pixels) datetime64[ns] ...\n",
       "    ssh_karin                              (num_lines, num_pixels) float64 na...\n",
       "    ssh_karin_qual                         (num_lines, num_pixels) float64 2....\n",
       "    ssh_karin_uncert                       (num_lines, num_pixels) float32 na...\n",
       "    ssha_karin                             (num_lines, num_pixels) float64 na...\n",
       "    ...                                     ...\n",
       "    geoid                                  (num_lines, num_pixels) float64 -4...\n",
       "    internal_tide_hret                     (num_lines, num_pixels) float32 -0...\n",
       "    height_cor_xover                       (num_lines, num_pixels) float64 -0...\n",
       "    height_cor_xover_qual                  (num_lines, num_pixels) float32 0....\n",
       "    ssha_karin_corrected                   (num_lines, num_pixels) float64 na...\n",
       "    ssha_karin_2_corrected                 (num_lines, num_pixels) float64 na...\n",
       "Attributes: (12/62)\n",
       "    Conventions:                                   CF-1.7\n",
       "    title:                                         Level 2 Low Rate Sea Surfa...\n",
       "    institution:                                   JPL\n",
       "    source:                                        Ka-band radar interferometer\n",
       "    history:                                       2024-04-03T10:59:55Z : Cre...\n",
       "    platform:                                      SWOT\n",
       "    ...                                            ...\n",
       "    ellipsoid_semi_major_axis:                     6378137.0\n",
       "    ellipsoid_flattening:                          0.0033528106647474805\n",
       "    good_ocean_data_percent:                       65.90908627485365\n",
       "    ssha_variance:                                 0.23576802674130506\n",
       "    references:                                    V1.2.1\n",
       "    equator_longitude:                             -130.51</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c67c7984-a08a-4828-89a7-cb690b6ea8ce' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c67c7984-a08a-4828-89a7-cb690b6ea8ce' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>num_lines</span>: 414</li><li><span>num_pixels</span>: 69</li><li><span>num_sides</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8b4bfded-7b04-4102-919d-7beca9710f85' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8b4bfded-7b04-4102-919d-7beca9710f85' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.0 35.0 35.0 ... 42.0 41.99 41.99</div><input id='attrs-6102d056-03e7-48f2-96ee-feee0bc7101e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6102d056-03e7-48f2-96ee-feee0bc7101e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b08a0d00-dea2-4c66-8ecd-a1f012cddd65' class='xr-var-data-in' type='checkbox'><label for='data-b08a0d00-dea2-4c66-8ecd-a1f012cddd65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude (positive N, negative S)</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-80000000</dd><dt><span>valid_max :</span></dt><dd>80000000</dd><dt><span>comment :</span></dt><dd>Latitude of measurement [-80,80]. Positive latitude is North latitude, negative latitude is South latitude.</dd></dl></div><div class='xr-var-data'><pre>array([[35.003274, 34.999701, 34.996125, ..., 34.759368, 34.755547,\n",
       "        34.751721],\n",
       "       [35.020959, 35.017385, 35.013807, ..., 34.776971, 34.773148,\n",
       "        34.769321],\n",
       "       [35.038643, 35.035068, 35.031489, ..., 34.794574, 34.790749,\n",
       "        34.786921],\n",
       "       ...,\n",
       "       [42.24424 , 42.240103, 42.235961, ..., 41.960827, 41.956372,\n",
       "        41.951913],\n",
       "       [42.261781, 42.257642, 42.253498, ..., 41.978256, 41.973799,\n",
       "        41.969338],\n",
       "       [42.279321, 42.27518 , 42.271035, ..., 41.995683, 41.991225,\n",
       "        41.986762]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-125.0 -125.0 ... -121.4 -121.4</div><input id='attrs-28b69b86-3602-440e-a816-1257dd436661' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28b69b86-3602-440e-a816-1257dd436661' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8e1bc66-a69e-4fa0-8bc1-81b7b9f6c063' class='xr-var-data-in' type='checkbox'><label for='data-c8e1bc66-a69e-4fa0-8bc1-81b7b9f6c063' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-125.036847, -125.015372, -124.993899, ..., -123.623605,\n",
       "        -123.602257, -123.580911],\n",
       "       [-125.0325  , -125.011021, -124.989544, ..., -123.618977,\n",
       "        -123.597625, -123.576275],\n",
       "       [-125.028151, -125.006668, -124.985186, ..., -123.614347,\n",
       "        -123.59299 , -123.571636],\n",
       "       ...,\n",
       "       [-123.017764, -122.994181, -122.970601, ..., -121.467992,\n",
       "        -121.444618, -121.421247],\n",
       "       [-123.01219 , -122.9886  , -122.965014, ..., -121.462029,\n",
       "        -121.438649, -121.415272],\n",
       "       [-123.006611, -122.983015, -122.959423, ..., -121.456061,\n",
       "        -121.432675, -121.409293]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e72aa80-d39b-4832-b3ce-35c35fb42231' class='xr-section-summary-in' type='checkbox'  ><label for='section-9e72aa80-d39b-4832-b3ce-35c35fb42231' class='xr-section-summary' >Data variables: <span>(26)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-12-04T17:37:29.583258624 .....</div><input id='attrs-592d15a6-e6f6-40f9-89d3-2ff3bf16c98c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-592d15a6-e6f6-40f9-89d3-2ff3bf16c98c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76c30c94-ac76-49c1-ae1b-e2c40198d54d' class='xr-var-data-in' type='checkbox'><label for='data-76c30c94-ac76-49c1-ae1b-e2c40198d54d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time in UTC</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>tai_utc_difference :</span></dt><dd>37.0</dd><dt><span>leap_second :</span></dt><dd>0000-00-00T00:00:00Z</dd><dt><span>comment :</span></dt><dd>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.</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2023-12-04T17:37:29.583258624&#x27;,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;, ...,\n",
       "                                  &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;],\n",
       "       [&#x27;2023-12-04T17:37:29.895654528&#x27;, &#x27;2023-12-04T17:37:29.895654528&#x27;,\n",
       "        &#x27;2023-12-04T17:37:29.895654528&#x27;, ...,\n",
       "                                  &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;],\n",
       "       [&#x27;2023-12-04T17:37:30.208146560&#x27;, &#x27;2023-12-04T17:37:30.208146560&#x27;,\n",
       "        &#x27;2023-12-04T17:37:30.208146560&#x27;, ...,\n",
       "                                  &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;],\n",
       "       ...,\n",
       "       [                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;, ...,\n",
       "        &#x27;2023-12-04T17:39:38.019324160&#x27;, &#x27;2023-12-04T17:39:38.019324160&#x27;,\n",
       "        &#x27;2023-12-04T17:39:38.019324160&#x27;],\n",
       "       [                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;, ...,\n",
       "        &#x27;2023-12-04T17:39:38.331900288&#x27;, &#x27;2023-12-04T17:39:38.331900288&#x27;,\n",
       "        &#x27;2023-12-04T17:39:38.331900288&#x27;],\n",
       "       [                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;, ...,\n",
       "        &#x27;2023-12-04T17:39:38.644480256&#x27;, &#x27;2023-12-04T17:39:38.644480256&#x27;,\n",
       "        &#x27;2023-12-04T17:39:38.644480256&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_tai</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-12-04T17:38:06.583259008 .....</div><input id='attrs-211b78f3-f9c2-4ed8-b583-56aaaaa8f114' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-211b78f3-f9c2-4ed8-b583-56aaaaa8f114' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c23559a6-94a2-4f7f-ba73-903842e0eafe' class='xr-var-data-in' type='checkbox'><label for='data-c23559a6-94a2-4f7f-ba73-903842e0eafe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time in TAI</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>tai_utc_difference :</span></dt><dd>37.0</dd><dt><span>comment :</span></dt><dd>Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [time:tai_utc_difference].</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2023-12-04T17:38:06.583259008&#x27;,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;, ...,\n",
       "                                  &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;],\n",
       "       [&#x27;2023-12-04T17:38:06.895654144&#x27;, &#x27;2023-12-04T17:38:06.895654144&#x27;,\n",
       "        &#x27;2023-12-04T17:38:06.895654144&#x27;, ...,\n",
       "                                  &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;],\n",
       "       [&#x27;2023-12-04T17:38:07.208147328&#x27;, &#x27;2023-12-04T17:38:07.208147328&#x27;,\n",
       "        &#x27;2023-12-04T17:38:07.208147328&#x27;, ...,\n",
       "                                  &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;],\n",
       "       ...,\n",
       "       [                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;, ...,\n",
       "        &#x27;2023-12-04T17:40:15.019323776&#x27;, &#x27;2023-12-04T17:40:15.019323776&#x27;,\n",
       "        &#x27;2023-12-04T17:40:15.019323776&#x27;],\n",
       "       [                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;, ...,\n",
       "        &#x27;2023-12-04T17:40:15.331900928&#x27;, &#x27;2023-12-04T17:40:15.331900928&#x27;,\n",
       "        &#x27;2023-12-04T17:40:15.331900928&#x27;],\n",
       "       [                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;, ...,\n",
       "        &#x27;2023-12-04T17:40:15.644480384&#x27;, &#x27;2023-12-04T17:40:15.644480384&#x27;,\n",
       "        &#x27;2023-12-04T17:40:15.644480384&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssh_karin</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-9841ee33-5dea-42c4-a102-12461b42076f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9841ee33-5dea-42c4-a102-12461b42076f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62f362c0-2bec-40ff-a3be-56e2239d0d4f' class='xr-var-data-in' type='checkbox'><label for='data-62f362c0-2bec-40ff-a3be-56e2239d0d4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea surface height above reference ellipsoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>quality_flag :</span></dt><dd>ssh_karin_qual</dd><dt><span>valid_min :</span></dt><dd>-15000000</dd><dt><span>valid_max :</span></dt><dd>150000000</dd><dt><span>comment :</span></dt><dd>Fully corrected sea surface height measured by KaRIn. The height is relative to the reference ellipsoid defined in the global attributes. This value is computed using radiometer measurements for wet troposphere effects on the KaRIn measurement (e.g., rad_wet_tropo_cor and sea_state_bias_cor).</dd></dl></div><div class='xr-var-data'><pre>array([[     nan,      nan,      nan, ...,      nan,      nan,      nan],\n",
       "       [     nan, -39.4437, -39.451 , ...,      nan,      nan,      nan],\n",
       "       [     nan, -39.4418, -39.4419, ...,      nan,      nan,      nan],\n",
       "       ...,\n",
       "       [     nan,      nan,      nan, ...,      nan,      nan,      nan],\n",
       "       [     nan,      nan,      nan, ...,      nan,      nan,      nan],\n",
       "       [     nan,      nan,      nan, ...,      nan,      nan,      nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssh_karin_qual</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.684e+09 nan ... 4.161e+09</div><input id='attrs-80e681c7-416f-4df7-8683-32cc69286aad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80e681c7-416f-4df7-8683-32cc69286aad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93eef034-d88d-4e78-aa41-8e57fab1b0b7' class='xr-var-data-in' type='checkbox'><label for='data-93eef034-d88d-4e78-aa41-8e57fab1b0b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>quality flag for sea surface height from KaRIn</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>suspect_large_ssh_delta suspect_large_ssh_std suspect_large_ssh_window_std suspect_beam_used suspect_less_than_nine_beams suspect_ssb_out_of_range suspect_pixel_used suspect_num_pt_avg suspect_karin_telem suspect_orbit_control suspect_sc_event_flag suspect_tvp_qual suspect_volumetric_corr degraded_ssb_not_computable degraded_media_delays_missing degraded_beam_used degraded_large_attitude degraded_karin_ifft_overflow bad_karin_telem bad_very_large_attitude bad_ssb_missing bad_radiometer_corr_missing bad_outside_of_range degraded bad_not_usable</dd><dt><span>flag_masks :</span></dt><dd>[         1          2          4          8         16         64\n",
       "        128        256        512       1024       2048       4096\n",
       "       8192      32768      65536     131072     262144     524288\n",
       "   16777216   33554432  134217728  268435456  536870912 1073741824\n",
       " 2147483648]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>4212113375</dd><dt><span>comment :</span></dt><dd>Quality flag for sea surface height from KaRIn in ssh_karin variable.</dd></dl></div><div class='xr-var-data'><pre>array([[2.68435482e+09,            nan,            nan, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [2.68435482e+09, 2.56000000e+02, 2.56000000e+02, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [2.68435482e+09, 2.56000000e+02, 2.56000000e+02, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       ...,\n",
       "       [           nan,            nan,            nan, ...,\n",
       "        3.62392003e+09, 4.16078260e+09, 4.16078260e+09],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "        3.62392003e+09, 4.16078260e+09, 4.16078260e+09],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "        3.62391183e+09, 4.16078260e+09, 4.16078260e+09]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssh_karin_uncert</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 0.0389 nan nan</div><input id='attrs-ad3c7832-cb21-4933-961b-89fbb7aabc43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad3c7832-cb21-4933-961b-89fbb7aabc43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2feb13be-9a97-4cc6-9cac-9c56e736a46b' class='xr-var-data-in' type='checkbox'><label for='data-2feb13be-9a97-4cc6-9cac-9c56e736a46b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface height anomaly uncertainty</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>60000</dd><dt><span>comment :</span></dt><dd>1-sigma uncertainty on the sea surface height from the KaRIn measurement.</dd></dl></div><div class='xr-var-data'><pre>array([[   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [   nan, 0.0081, 0.0044, ...,    nan,    nan,    nan],\n",
       "       [   nan, 0.0066, 0.0039, ...,    nan,    nan,    nan],\n",
       "       ...,\n",
       "       [   nan,    nan,    nan, ..., 0.0439,    nan,    nan],\n",
       "       [   nan,    nan,    nan, ..., 0.0523,    nan,    nan],\n",
       "       [   nan,    nan,    nan, ..., 0.0389,    nan,    nan]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssha_karin</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-5877d65f-3f98-4e33-95ba-3d055f719a84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5877d65f-3f98-4e33-95ba-3d055f719a84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab437282-5451-440d-b7ef-0964b9d8a285' class='xr-var-data-in' type='checkbox'><label for='data-ab437282-5451-440d-b7ef-0964b9d8a285' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface height anomaly</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>quality_flag :</span></dt><dd>ssha_karin_qual</dd><dt><span>valid_min :</span></dt><dd>-1000000</dd><dt><span>valid_max :</span></dt><dd>1000000</dd><dt><span>comment :</span></dt><dd>Sea surface height anomaly from the KaRIn measurement = ssh_karin - mean_sea_surface_cnescls - solid_earth_tide - ocean_tide_fes – internal_tide_hret - pole_tide - dac.</dd></dl></div><div class='xr-var-data'><pre>array([[   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [   nan, 0.8213, 0.8194, ...,    nan,    nan,    nan],\n",
       "       [   nan, 0.8065, 0.807 , ...,    nan,    nan,    nan],\n",
       "       ...,\n",
       "       [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [   nan,    nan,    nan, ...,    nan,    nan,    nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssha_karin_qual</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.684e+09 nan ... 4.161e+09</div><input id='attrs-2eaf84c7-7a2f-425a-a14f-709d470b91fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2eaf84c7-7a2f-425a-a14f-709d470b91fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e1fe64e-377d-4f7a-b059-3ae2373a7735' class='xr-var-data-in' type='checkbox'><label for='data-2e1fe64e-377d-4f7a-b059-3ae2373a7735' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface height anomaly quality flag</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>suspect_large_ssh_delta suspect_large_ssh_std suspect_large_ssh_window_std suspect_beam_used suspect_less_than_nine_beams suspect_ssb_out_of_range suspect_pixel_used suspect_num_pt_avg suspect_karin_telem suspect_orbit_control suspect_sc_event_flag suspect_tvp_qual suspect_volumetric_corr degraded_ssb_not_computable degraded_media_delays_missing degraded_beam_used degraded_large_attitude degraded_karin_ifft_overflow bad_karin_telem bad_very_large_attitude bad_tide_corrections_missing bad_ssb_missing bad_radiometer_corr_missing bad_outside_of_range degraded bad_not_usable</dd><dt><span>flag_masks :</span></dt><dd>[         1          2          4          8         16         64\n",
       "        128        256        512       1024       2048       4096\n",
       "       8192      32768      65536     131072     262144     524288\n",
       "   16777216   33554432   67108864  134217728  268435456  536870912\n",
       " 1073741824 2147483648]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>4279222239</dd><dt><span>comment :</span></dt><dd>Quality flag for the SSHA from KaRIn in the ssha_karin variable.</dd></dl></div><div class='xr-var-data'><pre>array([[2.68435482e+09,            nan,            nan, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [2.68435482e+09, 2.56000000e+02, 2.56000000e+02, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [2.68435482e+09, 2.56000000e+02, 2.56000000e+02, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       ...,\n",
       "       [           nan,            nan,            nan, ...,\n",
       "        3.62392003e+09, 4.16078260e+09, 4.16078260e+09],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "        3.62392003e+09, 4.16078260e+09, 4.16078260e+09],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "        3.62391183e+09, 4.16078260e+09, 4.16078260e+09]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssh_karin_2</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 1.563e+03 nan nan</div><input id='attrs-69a21091-504c-4432-bcc6-f2912b9a336a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69a21091-504c-4432-bcc6-f2912b9a336a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f347f0f-14a5-424e-bd95-7446f86bdda6' class='xr-var-data-in' type='checkbox'><label for='data-5f347f0f-14a5-424e-bd95-7446f86bdda6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea surface height above reference ellipsoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>quality_flag :</span></dt><dd>ssh_karin_2_qual</dd><dt><span>valid_min :</span></dt><dd>-15000000</dd><dt><span>valid_max :</span></dt><dd>150000000</dd><dt><span>comment :</span></dt><dd>Fully corrected sea surface height measured by KaRIn. The height is relative to the reference ellipsoid defined in the global attributes. This value is computed using model-based estimates for wet troposphere effects on the KaRIn measurement (e.g., model_wet_tropo_cor and sea_state_bias_cor_2).</dd></dl></div><div class='xr-var-data'><pre>array([[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "              nan],\n",
       "       [      nan,  -39.4412,  -39.4484, ...,       nan,       nan,\n",
       "              nan],\n",
       "       [      nan,  -39.4387,  -39.4388, ...,       nan,       nan,\n",
       "              nan],\n",
       "       ...,\n",
       "       [      nan,       nan,       nan, ..., 1548.4925,       nan,\n",
       "              nan],\n",
       "       [      nan,       nan,       nan, ..., 1558.1347,       nan,\n",
       "              nan],\n",
       "       [      nan,       nan,       nan, ..., 1563.3064,       nan,\n",
       "              nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssh_karin_2_qual</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.684e+09 nan ... 2.684e+09</div><input id='attrs-c985632f-1ef0-4be0-b1d0-fd8ae1495203' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c985632f-1ef0-4be0-b1d0-fd8ae1495203' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-585abe55-9f6e-4b78-9230-863f5ab41b54' class='xr-var-data-in' type='checkbox'><label for='data-585abe55-9f6e-4b78-9230-863f5ab41b54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>quality flag for sea surface height from KaRIn</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>suspect_large_ssh_delta suspect_large_ssh_std suspect_large_ssh_window_std suspect_beam_used suspect_less_than_nine_beams suspect_ssb_out_of_range suspect_pixel_used suspect_num_pt_avg suspect_karin_telem suspect_orbit_control suspect_sc_event_flag suspect_tvp_qual suspect_volumetric_corr degraded_ssb_not_computable degraded_media_delays_missing degraded_beam_used degraded_large_attitude degraded_karin_ifft_overflow bad_karin_telem bad_very_large_attitude bad_outside_of_range degraded bad_not_usable</dd><dt><span>flag_masks :</span></dt><dd>[         1          2          4          8         16         64\n",
       "        128        256        512       1024       2048       4096\n",
       "       8192      32768      65536     131072     262144     524288\n",
       "   16777216   33554432  536870912 1073741824 2147483648]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3809460191</dd><dt><span>comment :</span></dt><dd>Quality flag for sea surface height from KaRIn in ssh_karin_2 variable.</dd></dl></div><div class='xr-var-data'><pre>array([[2.68435482e+09,            nan,            nan, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [2.68435482e+09, 2.56000000e+02, 2.56000000e+02, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [2.68435482e+09, 2.56000000e+02, 2.56000000e+02, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       ...,\n",
       "       [           nan,            nan,            nan, ...,\n",
       "        8.60400000e+03, 2.68435482e+09, 2.68435482e+09],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "        8.60400000e+03, 2.68435482e+09, 2.68435482e+09],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "        4.04000000e+02, 2.68435482e+09, 2.68435482e+09]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssha_karin_2</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 1.586e+03 nan nan</div><input id='attrs-ba937202-5d48-4119-b9b3-cae3c3cd1d9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba937202-5d48-4119-b9b3-cae3c3cd1d9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3edb9dfb-f88d-4ff2-a2c3-db12b74e3b02' class='xr-var-data-in' type='checkbox'><label for='data-3edb9dfb-f88d-4ff2-a2c3-db12b74e3b02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface height anomaly</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>quality_flag :</span></dt><dd>ssha_karin_2_qual</dd><dt><span>valid_min :</span></dt><dd>-1000000</dd><dt><span>valid_max :</span></dt><dd>1000000</dd><dt><span>comment :</span></dt><dd>Sea surface height anomaly from the KaRIn measurement = ssh_karin_2 - mean_sea_surface_cnescls - solid_earth_tide - ocean_tide_fes – internal_tide_hret - pole_tide - dac.</dd></dl></div><div class='xr-var-data'><pre>array([[          nan,           nan,           nan, ...,           nan,\n",
       "                  nan,           nan],\n",
       "       [          nan, 8.2380000e-01, 8.2200000e-01, ...,           nan,\n",
       "                  nan,           nan],\n",
       "       [          nan, 8.0960000e-01, 8.1010000e-01, ...,           nan,\n",
       "                  nan,           nan],\n",
       "       ...,\n",
       "       [          nan,           nan,           nan, ..., 1.5710438e+03,\n",
       "                  nan,           nan],\n",
       "       [          nan,           nan,           nan, ..., 1.5806170e+03,\n",
       "                  nan,           nan],\n",
       "       [          nan,           nan,           nan, ..., 1.5857128e+03,\n",
       "                  nan,           nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssha_karin_2_qual</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.684e+09 nan ... 2.684e+09</div><input id='attrs-9ced1261-cf29-4f1e-9eab-636ed71a2fd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ced1261-cf29-4f1e-9eab-636ed71a2fd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17426165-11f6-4f77-b264-e7d5d12b336f' class='xr-var-data-in' type='checkbox'><label for='data-17426165-11f6-4f77-b264-e7d5d12b336f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface height anomaly quality flag</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>suspect_large_ssh_delta suspect_large_ssh_std suspect_large_ssh_window_std suspect_beam_used suspect_less_than_nine_beams suspect_ssb_out_of_range suspect_pixel_used suspect_num_pt_avg suspect_karin_telem suspect_orbit_control suspect_sc_event_flag suspect_tvp_qual suspect_volumetric_corr degraded_ssb_not_computable degraded_media_delays_missing degraded_beam_used degraded_large_attitude degraded_karin_ifft_overflow bad_karin_telem bad_very_large_attitude bad_tide_corrections_missing bad_outside_of_range degraded bad_not_usable</dd><dt><span>flag_masks :</span></dt><dd>[         1          2          4          8         16         64\n",
       "        128        256        512       1024       2048       4096\n",
       "       8192      32768      65536     131072     262144     524288\n",
       "   16777216   33554432   67108864  536870912 1073741824 2147483648]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3876569055</dd><dt><span>comment :</span></dt><dd>Quality flag for the SSHA from KaRIn in the ssha_karin_2 variable</dd></dl></div><div class='xr-var-data'><pre>array([[2.68435482e+09,            nan,            nan, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [2.68435482e+09, 2.56000000e+02, 2.56000000e+02, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [2.68435482e+09, 2.56000000e+02, 2.56000000e+02, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       ...,\n",
       "       [           nan,            nan,            nan, ...,\n",
       "        8.60400000e+03, 2.68435482e+09, 2.68435482e+09],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "        8.60400000e+03, 2.68435482e+09, 2.68435482e+09],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "        4.04000000e+02, 2.68435482e+09, 2.68435482e+09]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_pt_avg</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 nan nan nan ... 81.0 0.0 0.0</div><input id='attrs-0725f3ac-9e96-4345-bab7-f6e8f09cec58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0725f3ac-9e96-4345-bab7-f6e8f09cec58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f4f11a3-929c-481b-8877-ad74694e508c' class='xr-var-data-in' type='checkbox'><label for='data-6f4f11a3-929c-481b-8877-ad74694e508c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of samples averaged</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>289</dd><dt><span>comment :</span></dt><dd>Number of native unsmoothed, beam-combined KaRIn samples averaged.</dd></dl></div><div class='xr-var-data'><pre>array([[  0.,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "       [  0.,  34., 170., ...,  nan,  nan,  nan],\n",
       "       [  0.,  34., 170., ...,  nan,  nan,  nan],\n",
       "       ...,\n",
       "       [ nan,  nan,  nan, ...,  85.,   0.,   0.],\n",
       "       [ nan,  nan,  nan, ...,  84.,   0.,   0.],\n",
       "       [ nan,  nan,  nan, ...,  81.,   0.,   0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>distance_to_coast</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.18e+05 nan ... -2e+04 -1.9e+04</div><input id='attrs-31b5d008-5088-45da-9c0a-6421029460be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31b5d008-5088-45da-9c0a-6421029460be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcef49f9-344f-4ba3-a71f-300c803cafd1' class='xr-var-data-in' type='checkbox'><label for='data-bcef49f9-344f-4ba3-a71f-300c803cafd1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>distance to coast</dd><dt><span>source :</span></dt><dd>MODIS/GlobCover</dd><dt><span>institution :</span></dt><dd>European Space Agency</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-21000</dd><dt><span>valid_max :</span></dt><dd>21000</dd><dt><span>comment :</span></dt><dd>Approximate distance to the nearest coast point along the Earth surface.</dd></dl></div><div class='xr-var-data'><pre>array([[318000.,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "       [317000., 316000., 313000., ...,     nan,     nan,     nan],\n",
       "       [316000., 314000., 312000., ...,     nan,     nan,     nan],\n",
       "       ...,\n",
       "       [    nan,     nan,     nan, ..., -22000., -20000., -18000.],\n",
       "       [    nan,     nan,     nan, ..., -23000., -20000., -18000.],\n",
       "       [    nan,     nan,     nan, ..., -22000., -20000., -19000.]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading_to_coast</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-10ca6318-1f6d-4e85-ac87-6d4e245bb0be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10ca6318-1f6d-4e85-ac87-6d4e245bb0be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20caf2b0-87e0-4cdd-8d22-a31ac374575f' class='xr-var-data-in' type='checkbox'><label for='data-20caf2b0-87e0-4cdd-8d22-a31ac374575f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>heading to coast</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>35999</dd><dt><span>comment :</span></dt><dd>Approximate compass heading (0-360 degrees with respect to true north) to the nearest coast point.</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ancillary_surface_classification_flag</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 nan nan nan ... nan 1.0 1.0 1.0</div><input id='attrs-bb3ea7e2-e770-48ee-9c90-5ba2f1eb9bf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb3ea7e2-e770-48ee-9c90-5ba2f1eb9bf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26699756-723a-4687-8a6f-2af8cd307235' class='xr-var-data-in' type='checkbox'><label for='data-26699756-723a-4687-8a6f-2af8cd307235' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>surface classification</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>source :</span></dt><dd>MODIS/GlobCover</dd><dt><span>institution :</span></dt><dd>European Space Agency</dd><dt><span>flag_meanings :</span></dt><dd>open_ocean land continental_water aquatic_vegetation continental_ice_snow floating_ice salted_basin</dd><dt><span>flag_values :</span></dt><dd>[0 1 2 3 4 5 6]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>6</dd><dt><span>comment :</span></dt><dd>7-state surface type classification computed from a mask built with MODIS and GlobCover data.</dd></dl></div><div class='xr-var-data'><pre>array([[ 0., nan, nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ...,  1.,  1.,  1.],\n",
       "       [nan, nan, nan, ...,  1.,  1.,  1.],\n",
       "       [nan, nan, nan, ...,  1.,  1.,  1.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dynamic_ice_flag</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 nan nan nan ... nan 0.0 0.0 0.0</div><input id='attrs-a270e791-9672-42ff-8a4d-713fbad247fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a270e791-9672-42ff-8a4d-713fbad247fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f299628-6006-4974-8aaa-4edea898986a' class='xr-var-data-in' type='checkbox'><label for='data-7f299628-6006-4974-8aaa-4edea898986a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dynamic ice flag</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>source :</span></dt><dd>EUMETSAT Ocean and Sea Ice Satellite Applications Facility</dd><dt><span>institution :</span></dt><dd>EUMETSAT</dd><dt><span>flag_meanings :</span></dt><dd>no_ice probable_ice ice no_data</dd><dt><span>flag_values :</span></dt><dd>[0 1 2 3]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3</dd><dt><span>comment :</span></dt><dd>Dynamic ice flag for the location of the KaRIn measurement.</dd></dl></div><div class='xr-var-data'><pre>array([[ 0., nan, nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ...,  0.,  0.,  0.],\n",
       "       [nan, nan, nan, ...,  0.,  0.,  0.],\n",
       "       [nan, nan, nan, ...,  0.,  0.,  0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_flag</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 nan nan nan ... nan 0.0 0.0 0.0</div><input id='attrs-08c5f0ce-eee7-415b-8432-30666fd9b2f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08c5f0ce-eee7-415b-8432-30666fd9b2f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9507737-f3fc-4265-8b5c-38fd5031b41f' class='xr-var-data-in' type='checkbox'><label for='data-d9507737-f3fc-4265-8b5c-38fd5031b41f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>rain flag</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>no_rain probable_rain rain no_data</dd><dt><span>flag_values :</span></dt><dd>[0 1 2 3]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3</dd><dt><span>comment :</span></dt><dd>Flag indicates that signal is attenuated, probably from rain.</dd></dl></div><div class='xr-var-data'><pre>array([[ 0., nan, nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ...,  0.,  0.,  0.],\n",
       "       [nan, nan, nan, ...,  0.,  0.,  0.],\n",
       "       [nan, nan, nan, ...,  0.,  0.,  0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rad_surface_type_flag</span></div><div class='xr-var-dims'>(num_lines, num_sides, num_pixels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 nan nan nan ... nan 2.0 2.0 2.0</div><input id='attrs-7efee7b0-c780-4ed4-97b8-bcd3a387c4f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7efee7b0-c780-4ed4-97b8-bcd3a387c4f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b875e1c-102e-4cf3-8250-6ec4e9c30e3e' class='xr-var-data-in' type='checkbox'><label for='data-5b875e1c-102e-4cf3-8250-6ec4e9c30e3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>radiometer surface type flag</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>source :</span></dt><dd>Advanced Microwave Radiometer</dd><dt><span>flag_meanings :</span></dt><dd>open_ocean coastal_ocean land</dd><dt><span>flag_values :</span></dt><dd>[0 1 2]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>2</dd><dt><span>comment :</span></dt><dd>Flag indicating the validity and type of processing applied to generate the wet troposphere correction (rad_wet_tropo_cor). A value of 0 indicates that open ocean processing is used, a value of 1 indicates coastal processing, and a value of 2 indicates that rad_wet_tropo_cor is invalid due to land contamination.</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0., nan, nan, ..., nan, nan, nan],\n",
       "        [ 0., nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[ 0.,  0.,  0., ..., nan, nan, nan],\n",
       "        [ 0.,  0.,  0., ..., nan, nan, nan]],\n",
       "\n",
       "       [[ 0.,  0.,  0., ..., nan, nan, nan],\n",
       "        [ 0.,  0.,  0., ..., nan, nan, nan]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[nan, nan, nan, ...,  2.,  2.,  2.],\n",
       "        [nan, nan, nan, ...,  2.,  2.,  2.]],\n",
       "\n",
       "       [[nan, nan, nan, ...,  2.,  2.,  2.],\n",
       "        [nan, nan, nan, ...,  2.,  2.,  2.]],\n",
       "\n",
       "       [[nan, nan, nan, ...,  2.,  2.,  2.],\n",
       "        [nan, nan, nan, ...,  2.,  2.,  2.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_sea_surface_cnescls</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-40.25 nan nan ... -22.51 -22.48</div><input id='attrs-2c77c929-e95d-4082-ba02-7fac97c7cc70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c77c929-e95d-4082-ba02-7fac97c7cc70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1524f0e5-0239-4a7f-a965-3f28fb9e13b9' class='xr-var-data-in' type='checkbox'><label for='data-1524f0e5-0239-4a7f-a965-3f28fb9e13b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>mean sea surface height (CNES/CLS)</dd><dt><span>source :</span></dt><dd>CNES_CLS_2022</dd><dt><span>institution :</span></dt><dd>CNES/CLS</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-1500000</dd><dt><span>valid_max :</span></dt><dd>1500000</dd><dt><span>comment :</span></dt><dd>Mean sea surface height above the reference ellipsoid. The value is referenced to the mean tide system, i.e. includes the permanent tide (zero frequency).</dd></dl></div><div class='xr-var-data'><pre>array([[-40.2493,      nan,      nan, ...,      nan,      nan,      nan],\n",
       "       [-40.225 , -40.2211, -40.2264, ...,      nan,      nan,      nan],\n",
       "       [-40.2068, -40.2048, -40.2053, ...,      nan,      nan,      nan],\n",
       "       ...,\n",
       "       [     nan,      nan,      nan, ..., -22.6357, -22.6208, -22.6027],\n",
       "       [     nan,      nan,      nan, ..., -22.5822, -22.5649, -22.5441],\n",
       "       [     nan,      nan,      nan, ..., -22.5275, -22.5064, -22.4828]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_sea_surface_cnescls_uncert</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.432 nan nan nan ... nan nan nan</div><input id='attrs-eb77e34a-adaf-4786-8a3f-ea04e0b3d8f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb77e34a-adaf-4786-8a3f-ea04e0b3d8f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7f24203-7d49-48db-b435-e678190ba394' class='xr-var-data-in' type='checkbox'><label for='data-e7f24203-7d49-48db-b435-e678190ba394' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>mean sea surface height accuracy (CNES/CLS)</dd><dt><span>source :</span></dt><dd>CNES_CLS_2022</dd><dt><span>institution :</span></dt><dd>CNES/CLS</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>comment :</span></dt><dd>Accuracy of the mean sea surface height (mean_sea_surface_cnescls).</dd></dl></div><div class='xr-var-data'><pre>array([[0.43199998,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.43629998, 0.45569998, 0.5197    , ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.4376    , 0.45369998, 0.5119    , ...,        nan,        nan,\n",
       "               nan],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geoid</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-40.53 nan nan ... -22.37 -22.35</div><input id='attrs-00a0b83e-f7b8-422f-ba64-dbefcc86b607' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00a0b83e-f7b8-422f-ba64-dbefcc86b607' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ff669f0-65ab-4373-9df7-ea6710ee22e4' class='xr-var-data-in' type='checkbox'><label for='data-8ff669f0-65ab-4373-9df7-ea6710ee22e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>geoid height</dd><dt><span>standard_name :</span></dt><dd>geoid_height_above_reference_ellipsoid</dd><dt><span>source :</span></dt><dd>EGM2008 (Pavlis et al., 2012)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-1500000</dd><dt><span>valid_max :</span></dt><dd>1500000</dd><dt><span>comment :</span></dt><dd>Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency).</dd></dl></div><div class='xr-var-data'><pre>array([[-40.5281,      nan,      nan, ...,      nan,      nan,      nan],\n",
       "       [-40.5058, -40.5015, -40.4975, ...,      nan,      nan,      nan],\n",
       "       [-40.4853, -40.4793, -40.4742, ...,      nan,      nan,      nan],\n",
       "       ...,\n",
       "       [     nan,      nan,      nan, ..., -22.5249, -22.5148, -22.4947],\n",
       "       [     nan,      nan,      nan, ..., -22.4559, -22.4462, -22.4268],\n",
       "       [     nan,      nan,      nan, ..., -22.38  , -22.3701, -22.3514]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>internal_tide_hret</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.0015 nan nan nan ... 0.0 0.0 0.0</div><input id='attrs-246abf43-9907-466b-9e73-5b5fecfd610c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-246abf43-9907-466b-9e73-5b5fecfd610c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd5941da-c4bb-41a8-8677-3b5a385c6ec3' class='xr-var-data-in' type='checkbox'><label for='data-cd5941da-c4bb-41a8-8677-3b5a385c6ec3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coherent internal tide (HRET)</dd><dt><span>source :</span></dt><dd>Zaron (2019)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-2000</dd><dt><span>valid_max :</span></dt><dd>2000</dd><dt><span>comment :</span></dt><dd>Coherent internal ocean tide. This value is subtracted from the ssh_karin and ssh_karin_2 to compute ssha_karin and ssha_karin_2, respectively.</dd></dl></div><div class='xr-var-data'><pre>array([[-0.0015,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "       [-0.001 , -0.0011, -0.0011, ...,     nan,     nan,     nan],\n",
       "       [-0.0005, -0.0006, -0.0006, ...,     nan,     nan,     nan],\n",
       "       ...,\n",
       "       [    nan,     nan,     nan, ...,  0.    ,  0.    ,  0.    ],\n",
       "       [    nan,     nan,     nan, ...,  0.    ,  0.    ,  0.    ],\n",
       "       [    nan,     nan,     nan, ...,  0.    ,  0.    ,  0.    ]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height_cor_xover</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.7001 nan nan ... 0.9509 0.9815</div><input id='attrs-d66d9bbf-deff-4cac-af9f-daf00e3d14f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d66d9bbf-deff-4cac-af9f-daf00e3d14f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc2f7fb8-e7e5-4d6c-beec-c6440acded55' class='xr-var-data-in' type='checkbox'><label for='data-bc2f7fb8-e7e5-4d6c-beec-c6440acded55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height correction from crossover calibration</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>quality_flag :</span></dt><dd>height_cor_xover_qual</dd><dt><span>valid_min :</span></dt><dd>-100000</dd><dt><span>valid_max :</span></dt><dd>100000</dd><dt><span>comment :</span></dt><dd>Height correction from crossover calibration. To apply this correction the value of height_cor_xover should be added to the value of ssh_karin, ssh_karin_2, ssha_karin, and ssha_karin_2.</dd></dl></div><div class='xr-var-data'><pre>array([[-0.7001,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "       [-0.7   , -0.6801, -0.6601, ...,     nan,     nan,     nan],\n",
       "       [-0.7   , -0.6801, -0.6601, ...,     nan,     nan,     nan],\n",
       "       ...,\n",
       "       [    nan,     nan,     nan, ...,  0.9207,  0.9511,  0.9816],\n",
       "       [    nan,     nan,     nan, ...,  0.9206,  0.951 ,  0.9815],\n",
       "       [    nan,     nan,     nan, ...,  0.9205,  0.9509,  0.9815]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height_cor_xover_qual</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 nan nan nan ... nan 1.0 1.0 1.0</div><input id='attrs-9f46e15f-ac8f-4eed-8155-3edd6227219c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f46e15f-ac8f-4eed-8155-3edd6227219c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a81ea061-3ca6-403e-ac62-ea8288d97b21' class='xr-var-data-in' type='checkbox'><label for='data-a81ea061-3ca6-403e-ac62-ea8288d97b21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>quality flag for height correction from crossover calibration</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>good suspect bad</dd><dt><span>flag_values :</span></dt><dd>[0 1 2]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>2</dd><dt><span>comment :</span></dt><dd>Flag indicating the quality of the height correction from crossover calibration.  Values of 0, 1, and 2 indicate that the correction is good, suspect, and bad, respectively.</dd></dl></div><div class='xr-var-data'><pre>array([[ 0., nan, nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ...,  1.,  1.,  1.],\n",
       "       [nan, nan, nan, ...,  1.,  1.,  1.],\n",
       "       [nan, nan, nan, ...,  1.,  1.,  1.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssha_karin_corrected</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-5e75cfb9-9662-4c90-9658-a7eaeb1c8297' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5e75cfb9-9662-4c90-9658-a7eaeb1c8297' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22c00827-54fb-4706-bd07-c4fbb32b0555' class='xr-var-data-in' type='checkbox'><label for='data-22c00827-54fb-4706-bd07-c4fbb32b0555' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [   nan, 0.1412, 0.1593, ...,    nan,    nan,    nan],\n",
       "       [   nan, 0.1264, 0.1469, ...,    nan,    nan,    nan],\n",
       "       ...,\n",
       "       [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [   nan,    nan,    nan, ...,    nan,    nan,    nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssha_karin_2_corrected</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 1.587e+03 nan nan</div><input id='attrs-b0c47dbd-91fe-4fde-8d14-1194eff5399c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0c47dbd-91fe-4fde-8d14-1194eff5399c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-750cbde4-378d-4c2b-bf4c-eee0de762213' class='xr-var-data-in' type='checkbox'><label for='data-750cbde4-378d-4c2b-bf4c-eee0de762213' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[          nan,           nan,           nan, ...,           nan,\n",
       "                  nan,           nan],\n",
       "       [          nan, 1.4370000e-01, 1.6190000e-01, ...,           nan,\n",
       "                  nan,           nan],\n",
       "       [          nan, 1.2950000e-01, 1.5000000e-01, ...,           nan,\n",
       "                  nan,           nan],\n",
       "       ...,\n",
       "       [          nan,           nan,           nan, ..., 1.5719645e+03,\n",
       "                  nan,           nan],\n",
       "       [          nan,           nan,           nan, ..., 1.5815376e+03,\n",
       "                  nan,           nan],\n",
       "       [          nan,           nan,           nan, ..., 1.5866333e+03,\n",
       "                  nan,           nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-840d7e1f-2e68-49a1-8268-3decb35df2e8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-840d7e1f-2e68-49a1-8268-3decb35df2e8' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-39ae6b2b-12ce-4796-90d0-c4e9c5250dd4' class='xr-section-summary-in' type='checkbox'  ><label for='section-39ae6b2b-12ce-4796-90d0-c4e9c5250dd4' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>title :</span></dt><dd>Level 2 Low Rate Sea Surface Height Data Product - Basic SSH</dd><dt><span>institution :</span></dt><dd>JPL</dd><dt><span>source :</span></dt><dd>Ka-band radar interferometer</dd><dt><span>history :</span></dt><dd>2024-04-03T10:59:55Z : Creation</dd><dt><span>platform :</span></dt><dd>SWOT</dd><dt><span>reference_document :</span></dt><dd>D-56407_SWOT_Product_Description_L2_LR_SSH</dd><dt><span>contact :</span></dt><dd>podaac@jpl.nasa.gov</dd><dt><span>cycle_number :</span></dt><dd>7</dd><dt><span>pass_number :</span></dt><dd>317</dd><dt><span>equator_time :</span></dt><dd>2023-12-04T17:27:18.247000Z</dd><dt><span>short_name :</span></dt><dd>L2_LR_SSH</dd><dt><span>product_file_id :</span></dt><dd>Basic</dd><dt><span>crid :</span></dt><dd>PGC0</dd><dt><span>product_version :</span></dt><dd>01</dd><dt><span>pge_name :</span></dt><dd>PGE_L2_LR_SSH</dd><dt><span>pge_version :</span></dt><dd>5.0.2</dd><dt><span>time_coverage_start :</span></dt><dd>2023-12-04T17:01:32.101735</dd><dt><span>time_coverage_end :</span></dt><dd>2023-12-04T17:53:00.294975</dd><dt><span>geospatial_lon_min :</span></dt><dd>145.764772</dd><dt><span>geospatial_lon_max :</span></dt><dd>313.209305</dd><dt><span>geospatial_lat_min :</span></dt><dd>-78.271942</dd><dt><span>geospatial_lat_max :</span></dt><dd>78.27206799999999</dd><dt><span>left_first_longitude :</span></dt><dd>145.784143</dd><dt><span>left_first_latitude :</span></dt><dd>-77.05370099999999</dd><dt><span>left_last_longitude :</span></dt><dd>313.209305</dd><dt><span>left_last_latitude :</span></dt><dd>78.27200599999999</dd><dt><span>right_first_longitude :</span></dt><dd>145.764772</dd><dt><span>right_first_latitude :</span></dt><dd>-78.27186999999999</dd><dt><span>right_last_longitude :</span></dt><dd>313.19147899999996</dd><dt><span>right_last_latitude :</span></dt><dd>77.053837</dd><dt><span>wavelength :</span></dt><dd>0.008385803020979021</dd><dt><span>transmit_antenna :</span></dt><dd>minus_y</dd><dt><span>xref_l1b_lr_intf_file :</span></dt><dd>SWOT_L1B_LR_INTF_007_317_20231204T170128_20231204T175303_PGC0_01.nc</dd><dt><span>xref_l2_nalt_gdr_files :</span></dt><dd>SWOT_GPN_2PfP007_316_20231204_161005_20231204_170132.nc, SWOT_GPN_2PfP007_317_20231204_170132_20231204_175259.nc, SWOT_GPN_2PfP007_318_20231204_175259_20231204_184426.nc</dd><dt><span>xref_l2_rad_gdr_files :</span></dt><dd>SWOT_GPRAD_2PaP007_316_20231204_161002_20231204_170136_PIC0_01.nc, SWOT_GPRAD_2PaP007_317_20231204_170128_20231204_175303_PIC0_01.nc, SWOT_GPRAD_2PaP007_318_20231204_175255_20231204_184430_PIC0_01.nc</dd><dt><span>xref_int_lr_xover_cal_file :</span></dt><dd>SWOT_INT_LR_XOverCal_20231203T235226_20231204T235318_PGC0_01.nc</dd><dt><span>xref_statickarincal_files :</span></dt><dd>SWOT_StaticKaRInCalAdjustableParam_20000101T000000_20991231T235959_20230823T210000_v106.nc</dd><dt><span>xref_param_l2_lr_precalssh_file :</span></dt><dd>SWOT_Param_L2_LR_PreCalSSH_20000101T000000_20991231T235959_20230815T120500_v301.nc</dd><dt><span>xref_orbit_ephemeris_file :</span></dt><dd>SWOT_VOR_AXVCNE20231228_112316_20231203_225923_20231205_005923.nc</dd><dt><span>xref_reforbittrack_files :</span></dt><dd>SWOT_RefOrbitTrack125mPass1_Nom_20000101T000000_21000101T000000_20200617T193054_v101.txt, SWOT_RefOrbitTrack125mPass2_Nom_20000101T000000_21000101T000000_20200617T193054_v101.txt</dd><dt><span>xref_meteorological_sealevel_pressure_files :</span></dt><dd>SMM_PMA_AXVCNE20231204_171336_20231204_120000_20231204_120000, SMM_PMA_AXVCNE20231205_030312_20231204_180000_20231204_180000</dd><dt><span>xref_meteorological_wettroposphere_files :</span></dt><dd>SMM_WEA_AXVCNE20231204_171336_20231204_120000_20231204_120000, SMM_WEA_AXVCNE20231205_030312_20231204_180000_20231204_180000</dd><dt><span>xref_meteorological_wind_files :</span></dt><dd>SMM_UWA_AXVCNE20231204_171336_20231204_120000_20231204_120000, SMM_VWA_AXVCNE20231204_171336_20231204_120000_20231204_120000, SMM_UWA_AXVCNE20231205_030312_20231204_180000_20231204_180000, SMM_VWA_AXVCNE20231205_030312_20231204_180000_20231204_180000</dd><dt><span>xref_meteorological_surface_pressure_files :</span></dt><dd>SMM_PSA_AXVCNE20231204_174032_20231204_120000_20231204_120000, SMM_PSA_AXVCNE20231205_054033_20231204_180000_20231204_180000</dd><dt><span>xref_meteorological_temperature_files :</span></dt><dd>SMM_T2M_AXPCNE20231204_174032_20231204_120000_20231204_120000.grb, SMM_T2M_AXPCNE20231205_054033_20231204_180000_20231204_180000.grb</dd><dt><span>xref_meteorological_water_vapor_files :</span></dt><dd>SMM_CWV_AXPCNE20231204_174032_20231204_120000_20231204_120000.grb, SMM_CWV_AXPCNE20231205_054033_20231204_180000_20231204_180000.grb</dd><dt><span>xref_meteorological_cloud_liquid_water_files :</span></dt><dd>SMM_CLW_AXPCNE20231204_174032_20231204_120000_20231204_120000.grb, SMM_CLW_AXPCNE20231205_054033_20231204_180000_20231204_180000.grb</dd><dt><span>xref_model_significant_wave_height_files :</span></dt><dd>SMM_SWH_AXPCNE20231204_174032_20231204_120000_20231204_120000.grb, SMM_SWH_AXPCNE20231205_054033_20231204_180000_20231204_180000.grb</dd><dt><span>xref_gim_files :</span></dt><dd>JPLQ3380.23I</dd><dt><span>xref_pole_location_file :</span></dt><dd>SMM_PO1_AXXCNE20231122_020000_19900101_000000_20240520_000000</dd><dt><span>xref_dac_files :</span></dt><dd>SMM_MOG_AXVCNE20231224_190002_20231204_120000_20231204_120000, SMM_MOG_AXVCNE20231225_070002_20231204_180000_20231204_180000</dd><dt><span>xref_precipitation_files :</span></dt><dd>SMM_CRR_AXFCNE20231204_185551_20231204_120000_20231204_120000.grb, SMM_LSR_AXFCNE20231204_185551_20231204_120000_20231204_120000.grb, SMM_CRR_AXFCNE20231204_185551_20231204_180000_20231204_180000.grb, SMM_LSR_AXFCNE20231204_185551_20231204_180000_20231204_180000.grb</dd><dt><span>xref_sea_ice_mask_files :</span></dt><dd>SMM_ICN_AXPCNE20231220_100319_20231204_000000_20231204_235959.nc, SMM_ICS_AXPCNE20231220_100319_20231204_000000_20231204_235959.nc</dd><dt><span>xref_wave_model_files :</span></dt><dd>SMM_WMA_AXPCNE20231205_072014_20231204_030000_20231205_000000.grb</dd><dt><span>xref_geco_database_version :</span></dt><dd>v102</dd><dt><span>ellipsoid_semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>ellipsoid_flattening :</span></dt><dd>0.0033528106647474805</dd><dt><span>good_ocean_data_percent :</span></dt><dd>65.90908627485365</dd><dt><span>ssha_variance :</span></dt><dd>0.23576802674130506</dd><dt><span>references :</span></dt><dd>V1.2.1</dd><dt><span>equator_longitude :</span></dt><dd>-130.51</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                                (num_lines: 414, num_pixels: 69,\n",
       "                                            num_sides: 2)\n",
       "Coordinates:\n",
       "    latitude                               (num_lines, num_pixels) float64 35...\n",
       "    longitude                              (num_lines, num_pixels) float64 -1...\n",
       "Dimensions without coordinates: num_lines, num_pixels, num_sides\n",
       "Data variables: (12/26)\n",
       "    time                                   (num_lines, num_pixels) datetime64[ns] ...\n",
       "    time_tai                               (num_lines, num_pixels) datetime64[ns] ...\n",
       "    ssh_karin                              (num_lines, num_pixels) float64 na...\n",
       "    ssh_karin_qual                         (num_lines, num_pixels) float64 2....\n",
       "    ssh_karin_uncert                       (num_lines, num_pixels) float32 na...\n",
       "    ssha_karin                             (num_lines, num_pixels) float64 na...\n",
       "    ...                                     ...\n",
       "    geoid                                  (num_lines, num_pixels) float64 -4...\n",
       "    internal_tide_hret                     (num_lines, num_pixels) float32 -0...\n",
       "    height_cor_xover                       (num_lines, num_pixels) float64 -0...\n",
       "    height_cor_xover_qual                  (num_lines, num_pixels) float32 0....\n",
       "    ssha_karin_corrected                   (num_lines, num_pixels) float64 na...\n",
       "    ssha_karin_2_corrected                 (num_lines, num_pixels) float64 na...\n",
       "Attributes: (12/62)\n",
       "    Conventions:                                   CF-1.7\n",
       "    title:                                         Level 2 Low Rate Sea Surfa...\n",
       "    institution:                                   JPL\n",
       "    source:                                        Ka-band radar interferometer\n",
       "    history:                                       2024-04-03T10:59:55Z : Cre...\n",
       "    platform:                                      SWOT\n",
       "    ...                                            ...\n",
       "    ellipsoid_semi_major_axis:                     6378137.0\n",
       "    ellipsoid_flattening:                          0.0033528106647474805\n",
       "    good_ocean_data_percent:                       65.90908627485365\n",
       "    ssha_variance:                                 0.23576802674130506\n",
       "    references:                                    V1.2.1\n",
       "    equator_longitude:                             -130.51"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_basic_sub['ssha_karin_corrected'] = ds_basic_sub.ssha_karin + ds_basic_sub.height_cor_xover\n",
    "ds_basic_sub['ssha_karin_2_corrected'] = ds_basic_sub.ssha_karin_2 + ds_basic_sub.height_cor_xover\n",
    "ds_basic_sub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mask invalid data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_basic_sub[\"ssha_karin_corrected\"] = ds_basic_sub.ssha_karin_corrected.where(ds_basic_sub.ancillary_surface_classification_flag==0)\n",
    "ds_basic_sub[\"ssha_karin_corrected\"] = ds_basic_sub.ssha_karin_corrected.where(ds_basic_sub.ssha_karin_qual==0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_basic_sub[\"ssha_karin_2_corrected\"] = ds_basic_sub.ssha_karin_2_corrected.where(ds_basic_sub.ancillary_surface_classification_flag==0)\n",
    "ds_basic_sub[\"ssha_karin_2_corrected\"] = ds_basic_sub.ssha_karin_2_corrected.where(ds_basic_sub.ssha_karin_2_qual==0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lZf0XTEJmteU"
   },
   "source": [
    "### Output visualisation: basic SLA plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualise SWOT KaRIn LR data using cartopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 480
    },
    "executionInfo": {
     "elapsed": 2012,
     "status": "ok",
     "timestamp": 1698934132415,
     "user": {
      "displayName": "Cyril Germineaud",
      "userId": "08934926211399639299"
     },
     "user_tz": -60
    },
    "id": "KQebq4SCmtES",
    "outputId": "85dfcd67-b180-4f8d-d2ac-a4fee802e161"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'SSHA KaRIn 2 corrected')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2100x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set figure\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(21, 12), subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
    "ax1.set_extent(localbox)\n",
    "ax2.set_extent(localbox)\n",
    "plot_kwargs = dict(\n",
    "    x=\"longitude\",\n",
    "    y=\"latitude\",\n",
    "    cmap=\"Spectral_r\",\n",
    "    vmin=-0.2,\n",
    "    vmax=0.2,\n",
    "    cbar_kwargs={\"shrink\": 0.3},)\n",
    "\n",
    "# SWOT KaRIn SLA plots\n",
    "ds_basic_sub.ssha_karin_corrected.plot.pcolormesh(ax=ax1, **plot_kwargs)\n",
    "ds_basic_sub.ssha_karin_2_corrected.plot.pcolormesh(ax=ax2, **plot_kwargs)\n",
    "#\n",
    "ax1.gridlines(draw_labels=True)\n",
    "ax1.coastlines()\n",
    "ax2.gridlines(draw_labels=True)\n",
    "ax1.set_title(\"SSHA KaRIn corrected\")\n",
    "ax2.coastlines()\n",
    "ax2.gridlines()\n",
    "ax2.set_title(\"SSHA KaRIn 2 corrected\")"
   ]
  }
 ],
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