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MatfiletoNetCDF_CL_ARTofMELT_v1.ipynb 485 KiB
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This python script converts the processed .mat file for native Ceilometer observations into a user-friendly NetCDF file. It also converts all time averaged .mat files into their respecitve netCDF files. These final (four) netCDF files are found in the Bolin Centre Database. Data from the ARTofMELT expedition in Spring 2023. This code is written by Sonja Murto (2024)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import modules\n",
    "import sys,glob,os\n",
    "import numpy as np\n",
    "import math\n",
    "import pandas as pd\n",
    "import matplotlib.dates as mdates\n",
    "import xarray as xr\n",
    "import time,datetime\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import string\n",
    "import scipy.io\n",
    "import matplotlib.ticker as mticker\n",
    "from collections import Counter\n",
    "\n",
    "\n",
    "#metpy functions for thermodynamical variable conversions\n",
    "import metpy.calc as mpcalc\n",
    "from metpy.units import units\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define functions\n",
    "def datestring_fromtuple(date=(2001,2,15,12)):\n",
    "    '''\n",
    "    Input: a date tuple (YYYY,M,D,h)\n",
    "    Output: Date string: 'YYYYMMDD_hh'\n",
    "    '''\n",
    "    strdate=str(date[0])+\"{:0>2}\".format(str(date[1]))+\"{:0>2}\".format(str(date[2]))+'_'+\"{:0>2}\".format(str(date[3]))\n",
    "    return strdate\n",
    "\n",
    "def datetuple(date):\n",
    "    '''date is as datetime object, i.e. from pandas using .to_pydatetime()\n",
    "    '''\n",
    "    return date.year,date.month,date.day,date.hour\n",
    "\n",
    "def strtodatetuple(datestr='19900109_12',format_st='%Y%m%d_%H',returntuple=True):\n",
    "    if returntuple:   \n",
    "        return datetuple(datetime.datetime.strptime(datestr, format_st))\n",
    "    else:\n",
    "        return datetime.datetime.strptime(datestr, format_st)\n",
    "\n",
    "\n",
    "def returndatetime_fromdoy(doys, year=2023):\n",
    "    '''\n",
    "    fucntion to return datetime from DOY dates. \n",
    "    Takes the leap year into account if given for a leap year\n",
    "    Input: list of DOYs\n",
    "    Output: List of Dates (in datetime) corresponding to the given doys\n",
    "    Note, returns in microseconds.\n",
    "    If second rounded: [pd.to_datetime(T).round('1s') for T in date]\n",
    "    \n",
    "    '''\n",
    "    jdate_frac=doys\n",
    "    date=[]\n",
    "    for d in jdate_frac:\n",
    "        year,julian = [year,d]\n",
    "        date.append(datetime.datetime(year, 1, 1)+datetime.timedelta(days=julian -1))\n",
    "    return date\n",
    "\n",
    "def getList(dict): \n",
    "    '''\n",
    "    This function returns a list of keys for a pandas dictionary\n",
    "    '''\n",
    "    list = [] \n",
    "    for key in dict.keys(): \n",
    "        list.append(key) \n",
    "    return list\n",
    "\n",
    "def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100,returncolors=False):\n",
    "    '''\n",
    "    This function creates a new colormap from the cmap chosen, taking the limits from minval and maxval, and the number of colors as n.\n",
    "    The default is a colormap as LinearSegmentedColormap, which can be used as colormap for imshow, contourf...\n",
    "    If you assign returncolors as True, the there will be an array of n amount of colors from the chosen colormap. This can be used in plots when each event has a color (n=50)\n",
    "    '''\n",
    "    new_cmap = mcolors.LinearSegmentedColormap.from_list('new_cmap', plt.get_cmap(cmap)(np.linspace(minval, maxval, n)))\n",
    "    if returncolors:\n",
    "        new_cmap=plt.get_cmap(cmap)(np.linspace(minval, maxval, n))\n",
    "    return new_cmap   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define directories - change according to your computer settings\n",
    "cwd = os.getcwd() \n",
    "savefigpath = cwd + '/FIGS/Example_figs/' #directory for figures\n",
    "#directory where to find the data (.mat files)\n",
    "load_data = r'/Volumes/My Passport for Mac/MISU_sticka/WORK/viktigapapper/ARTofMELT_2023/AoM_data_mod/WX/cl31_output/'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert ceilometer data from .mat files into .nc files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create secondary dimensions\n",
    "cloud_layer_levs=np.arange(1,4,1).astype('int32') # 3 levels\n",
    "sc_layer_levs=np.arange(1,6,1).astype('int32') # 5 levels\n",
    "range_levs=np.arange(1,771,1).astype('int32') # 770 levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3] [1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "print(cloud_layer_levs,sc_layer_levs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 30s data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load 30s native data\n",
    "CL_SM_org = scipy.io.loadmat(load_data + 'CL31_ceilometer_ARTofMELT_20230507_20230613_30s_v01.mat',struct_as_record=True) #30s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['__header__', '__version__', '__globals__', 'cl31'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CL_SM_org.keys()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('mday', 'doy', 'cloudcode', 'base_ht', 'vert_vis', 'high_sig', 'sc_frac', 'sc_ht', 'bs_prof', 'ceil_range', 'time')\n"
     ]
    }
   ],
   "source": [
    "# get the data in the mat file\n",
    "Names=CL_SM_org['cl31'].dtype.names\n",
    "ndata = {n: CL_SM_org['cl31'][n][0, 0] for n in Names}\n",
    "print(Names)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create time dimension\n",
    "Time_steps=returndatetime_fromdoy(np.array(list(itertools.chain.from_iterable(ndata['doy'])),dtype=float))\n",
    "Times_nomicrosec=[pd.to_datetime(T).round('1s') for T in Time_steps]\n",
    "Time_steps_dt64_org=[np.datetime64(t) for t in Time_steps]\n",
    "Time_steps_dt64_org=np.array(Time_steps_dt64_org,dtype='datetime64[ns]') #time dimension"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doy</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "      <th>cloudcode</th>\n",
       "      <th>vert_vis</th>\n",
       "      <th>high_sig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>127.000258</td>\n",
       "      <td>2023</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>127.000605</td>\n",
       "      <td>2023</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>127.000952</td>\n",
       "      <td>2023</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>127.001299</td>\n",
       "      <td>2023</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>127.001647</td>\n",
       "      <td>2023</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          doy  year  month  day  hour  minute  second  cloudcode  vert_vis  \\\n",
       "0  127.000258  2023      5    7     0       0      22          1       NaN   \n",
       "1  127.000605  2023      5    7     0       0      52          1       NaN   \n",
       "2  127.000952  2023      5    7     0       1      22          2       NaN   \n",
       "3  127.001299  2023      5    7     0       1      52          2       NaN   \n",
       "4  127.001647  2023      5    7     0       2      22          1       NaN   \n",
       "\n",
       "   high_sig  \n",
       "0       NaN  \n",
       "1       NaN  \n",
       "2       NaN  \n",
       "3       NaN  \n",
       "4       NaN  "
      ]
     },
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert data into pandas dataframes\n",
    "DF_30s=pd.DataFrame(index=range(len(ndata['doy'])),)\n",
    "DF_30s['doy']=np.array(list(itertools.chain.from_iterable(ndata['doy'])),dtype=float)\n",
    "DF_30s['year']=np.array([Times_nomicrosec[i].year for i in range(len(Time_steps))],dtype=int)\n",
    "DF_30s['month']=np.array([Times_nomicrosec[i].month for i in range(len(Time_steps))],dtype=int)\n",
    "DF_30s['day']=np.array([Times_nomicrosec[i].day for i in range(len(Time_steps))],dtype=int)\n",
    "DF_30s['hour']=np.array([Times_nomicrosec[i].hour for i in range(len(Time_steps))],dtype=int)\n",
    "DF_30s['minute']=np.array([Times_nomicrosec[i].minute for i in range(len(Time_steps))],dtype=int)\n",
    "DF_30s['second']=np.array([Times_nomicrosec[i].second for i in range(len(Time_steps))],dtype=int)\n",
    "DF_30s['cloudcode']=np.array(list(itertools.chain.from_iterable(ndata['cloudcode'])),dtype=int)\n",
    "DF_30s['vert_vis']=np.array(list(itertools.chain.from_iterable(ndata['vert_vis'])),dtype=float)\n",
    "DF_30s['high_sig']=np.array(list(itertools.chain.from_iterable(ndata['high_sig'])),dtype=float)\n",
    "\n",
    "DF_30s.head()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doy</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "      <th>cloudcode</th>\n",
       "      <th>vert_vis</th>\n",
       "      <th>high_sig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [doy, year, month, day, hour, minute, second, cloudcode, vert_vis, high_sig]\n",
       "Index: []"
      ]
     },
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for nans - no nans!\n",
    "DF_30s.iloc[DF_30s[DF_30s.doy.isna()].index]"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['doy', 'year', 'month', 'day', 'hour', 'minute', 'second', 'cloudcode',\n",
       "       'vert_vis', 'high_sig'],\n",
       "      dtype='object')"
      ]
     },
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get a list of columns\n",
    "DF_30s.columns"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save each variable into Data Arrays, with attribute data\n",
    "\n",
    "#1D variables: dimension time\n",
    "\n",
    "da_doy=xr.DataArray(data=np.array(DF_30s['doy']).astype('float32'),name=\"day_of_year\",\n",
    "                dims=[\"time\"],coords=dict(time=Time_steps_dt64_org),\n",
    "                  attrs=dict(type=\"float32\",dimension=\"time\",units=\"1\",long_name=\"Day of Year\",\n",
    "                             description=\"time as decimal day of year\"),) #added as 1D\n",
    "\n",
    "#date and time in separate arrays; microseconds approximated to seconds\n",
    "da_year=xr.DataArray(data=np.array(DF_30s['year']).astype('int32'),name=\"year\",dims=[\"time\"],\n",
    "                  coords=dict(time=Time_steps_dt64_org),\n",
    "                  attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",long_name=\"Year\"),) #added as 1D\n",
    "\n",
    "da_month=xr.DataArray(data=np.array(DF_30s['month']).astype('int32'),name=\"month\",dims=[\"time\"],\n",
    "                  coords=dict(time=Time_steps_dt64_org),\n",
    "                      attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\", long_name=\"Month\"),) #added as 1D\n",
    "\n",
    "da_day=xr.DataArray(data=np.array(DF_30s['day']).astype('int32'),name=\"day\",dims=[\"time\"],\n",
    "                  coords=dict(time=Time_steps_dt64_org),\n",
    "                    attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",long_name=\"Day\"),) #added as 1D\n",
    "\n",
    "da_hour=xr.DataArray(data=np.array(DF_30s['hour']).astype('int32'),name=\"hour\",dims=[\"time\"],\n",
    "                  coords=dict(time=Time_steps_dt64_org),\n",
    "                     attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",long_name=\"Hour\"),) #added as 1D\n",
    "\n",
    "da_min=xr.DataArray(data=np.array(DF_30s['minute']).astype('int32'),name=\"minute\",dims=[\"time\"],\n",
    "                  coords=dict(time=Time_steps_dt64_org),\n",
    "                    attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",long_name=\"Minute\"),) #added as 1D\n",
    "\n",
    "da_sec=xr.DataArray(data=np.array(DF_30s['second']).astype('int32'),name=\"second\",dims=[\"time\"],\n",
    "                  coords=dict(time=Time_steps_dt64_org),\n",
    "                     attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",long_name=\"Second\",\n",
    "                               description=\"Time averaged to closest second\"),) #added as 1D\n",
    "\n",
    "#add cloudcode flag\n",
    "da_cloudcode=xr.DataArray(data=np.array(DF_30s['cloudcode']).astype('int32'),name=\"flag_cloudcode\",dims=[\"time\"],\n",
    "                        coords=dict(time=Time_steps_dt64_org),\n",
    "                        attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",\n",
    "                                   long_name='Data flag: Cloudcode',\n",
    "                                   flag_values=\"-1,0,1,2,3,4\", \n",
    "                                   flag_meanings=\"missing_data\\nno_significant_backscatter\\none_cloud_base_detected\\ntwo_cloud_bases_detected\\nthree_cloud_bases_detected\\nfull_obscuration\",\n",
    "                                   description=\"Code for number of cloud bases detected; see Readme document for more information\"),)\n",
    "\n",
    "da_vertvis=xr.DataArray(data=np.array(DF_30s['vert_vis']).astype('float32'),\n",
    "                    name=\"vertical_visibility\",dims=[\"time\"],coords=dict(time=Time_steps_dt64_org),\n",
    "                   attrs=dict(type=\"float32\",dimension=\"time\", units=\"m\",\n",
    "                              long_name=\"Vertical Visibilility\",\n",
    "                             description=\"Vertical visibility given in case of obscured cloud base (at flag_cloudcode 4), else NaN\"),) #added as 1D\n",
    "\n",
    "da_high_sig=xr.DataArray(data=np.array(DF_30s['high_sig']).astype('float32'),\n",
    "                    name=\"highest_detected_signal\",dims=[\"time\"],coords=dict(time=Time_steps_dt64_org),\n",
    "                   attrs=dict(type=\"float32\",dimension=\"time\", units=\"m\",\n",
    "                              long_name=\"Highest Signal Detected\",\n",
    "                             description=\"Highest signal detected given in case of obscured cloud base (at flag_cloudcode 4), else NaN\"),) #added as 1D\n",
    "\n",
    "\n",
    "#1D: dimension range_levels\n",
    "\n",
    "da_ceilrange=xr.DataArray(data=np.array(list(itertools.chain.from_iterable(ndata['ceil_range'])),dtype=int).astype('int32'),\n",
    "                    name=\"ceilometer_range\",dims=[\"range_levels\"],coords=dict(range_levels=range_levs),\n",
    "                          attrs=dict(type=\"int32\",dimension=\"range_levels\",units=\"m\",\n",
    "                                     long_name=\"Ceilometer Range\",\n",
    "                                     description=\"Ranges for the ceilometer backscatter profile, including the instrument height\",),) #added as 1D\n",
    "\n",
    "\n",
    "#2D variable: dimension time/cloud_layer\n",
    "\n",
    "\n",
    "da_baseht=xr.DataArray(data=np.array(ndata['base_ht'],dtype=float).astype('float32'),\n",
    "                    name=\"cloud_base_altitude\",dims=[\"time\",\"cloud_layer\"],\n",
    "                       coords=dict(time=Time_steps_dt64_org,cloud_layer=cloud_layer_levs),\n",
    "                   attrs=dict(type=\"float32\",dimension=\"time, cloud_layer\", units=\"m\",\n",
    "                              long_name=\"Cloud Base Altitude\",\n",
    "                              description=\"cloud base height of 1-3 cloud layers; NaN if no layer detected. \" +\\\n",
    "                              \"Instrument height incorporated.\"),) #added as 2D\n",
    "\n",
    "#2D variables: dimension time/sky_condition_layer\n",
    "\n",
    "da_scfrac=xr.DataArray(data=np.array(ndata['sc_frac'],dtype=float).astype('float32'),\n",
    "                    name=\"sky_condition_cloud_fraction\",dims=[\"time\",\"sky_condition_layer\"],\n",
    "                       coords=dict(time=Time_steps_dt64_org,sky_condition_layer=sc_layer_levs),\n",
    "                   attrs=dict(type=\"float32\",dimension=\"time, sky_condition_layer\", units=\"octal\",\n",
    "                              long_name=\"Sky Condition Cloud Fraction\",\n",
    "                            description=\"Cloud fraction calculated with the sky condition algorithm. \"+\\\n",
    "                             \"0-8 = cloud coverage of up to 5 levels; 9 = obscuration. \" +\\\n",
    "                             \"NaN = missing data or no detected layer.\"),) #added as 2D\n",
    "\n",
    "da_scht=xr.DataArray(data=np.array(ndata['sc_ht'],dtype=float).astype('float32'),\n",
    "                    name=\"sky_condition_cloud_altitude\",dims=[\"time\",\"sky_condition_layer\"],\n",
    "                     coords=dict(time=Time_steps_dt64_org,sky_condition_layer=sc_layer_levs),\n",
    "                   attrs=dict(type=\"float32\",dimension=\"time, sky_condition_layer\", units=\"m\",\n",
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    "                              long_name=\"Sky Condition Cloud Altitude\",\n",
    "                              description = \"Cloud layer height calculated with the sky condition algorithm. \"+\\\n",
    "                             \"Cloud layer height given for 1-5 sky condition layers; NaN if no layer detected. \"+\\\n",
    "                             \"Vertical visibility is reported as height if obscuration (at sky_condition_cloud_fraction 9). \"+\\\n",
    "                             \"Instrument height incorporated.\"),) #added as 2D\n",
    "\n",
    "\n",
    "#2D variable: dimension time/range\n",
    "\n",
    "da_bsprof=xr.DataArray(data=np.array(ndata['bs_prof'],dtype=float).astype('float32'),\n",
    "                    name=\"backscatter_profile\",dims=[\"time\",\"range_levels\"],\n",
    "                       coords=dict(time=Time_steps_dt64_org,range_levels=range_levs),\n",
    "                   attrs=dict(type=\"float32\",dimension=\"time, range_levels\", units=\"1 km-1 steradians-1\",\n",
    "                              long_name=\"Backscatter Profile\", description=\"backscatter coefficient profile\"),) #added as 2D\n"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# quick plot of cloud base heights\n",
    "\n",
    "da_baseht.sel(cloud_layer=1).plot(color='r',label='cloud_layer = 1')\n",
    "da_baseht.sel(cloud_layer=2).plot(color='b',label='cloud_layer = 2')\n",
    "da_baseht.sel(cloud_layer=3).plot(color='y',label='cloud_layer = 3')\n",
    "plt.title('')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge all arrays into one\n",
    "\n",
    "ds_all=xr.merge([da_doy,da_year,da_month,da_day,da_hour,da_min,da_sec,\n",
    "                da_ceilrange,da_cloudcode,da_vertvis,da_high_sig,\n",
    "                da_baseht,da_scfrac,da_scht,da_bsprof])\n",
    "\n",
    "len(ds_all)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "<matplotlib.colorbar.Colorbar at 0x7fad6ddbd6d0>"
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     "execution_count": 15,
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     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(ds_all.time,ds_all.vertical_visibility,c=ds_all.flag_cloudcode)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-05-07 00:00:22')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the time ranges: start\n",
    "Times_nomicrosec[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-06-13 16:24:22')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the time ranges: end\n",
    "Times_nomicrosec[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add global attribute data. Note that the geospatial_bounds are taken from the gps data at the weather station sensor\n",
    "# (Weather station data: start: 2023-05-01 7:00:05, end = 2023-06-13 16:24:35)\n",
    "\n",
    "ds_all.attrs = {\"Conventions\" :\"CF-1.8\",\n",
    "                \"source\" : \"Ceilometer\",\n",
    "                \"instrument_model\" : \"Vaisala Ceilometer CL31\",\n",
    "                \"creator_name\" : \"Sonja Murto\",\n",
    "                \"creator_email\" : \"sonja.murto@misu.su.se\",\n",
    "                \"creator_url\" : \"https://orcid.org/0000-0002-4966-9077\",\n",
    "                \"institution\" : \"Stockholm University\",  \n",
    "                \"processing_software\" : \"Matlab (for creating the matlab file) and a jupyter notebook script (netCDF)\",\n",
    "                \"sampling_interval\": \"30s\", \n",
    "                \"product_version\" : \"v01\",\n",
    "                \"last_revised_date\" : \"2024-05-31T15:00:00\", \n",
    "                \"project\" : \"ARTofMELT\",\n",
    "                \"project_principal_investigator\" : \"Michael Tjernström\",\n",
    "                \"project_principal_investigator_email\" : \"michaelt@misu.su.se\",\n",
    "                \"project_principal_investigator_url\" : \"https://orcid.org/0000-0002-6908-7410\",                \n",
    "                \"acknowledgement\" : \" Knut och Alice Wallenbergs Stiftelse, Grant 2016-0024\",\n",
    "                \"platform\" : \"Swedish Icebreaker Oden\",\n",
    "                \"platform_type\" : \"On Oden's 7th deck above the bridge\",\n",
    "                \"deployment_mode\" : \"ship\",\n",
    "                \"title\" : \"Ceilometer cloud base height, vertical visibility and backscatter profiles\",\n",
    "                \"feature_type\" : \"time series\",   \n",
    "                \"time_coverage_start\" : \"2023-05-07T00:00:22\",\n",
    "                \"time_coverage_end\" : \"2023-06-13T16:24:22\",\n",
    "                \"geospatial_bounds\" : \"80.52392166666667N, -3.8737749999999997E, 78.04355166666666N, 15.660881666666667E\",\n",
    "                \"platform_altitude\" : \"Located at approximately 25 m a.s.l\",\n",
    "                \"location_keywords\": \"Oden, Arctic Ocean, Fram Strait, atmosphere, on the ship\",\n",
    "                \"comments\" : \"This file consists of ceilometer data \" +\\\n",
    "                \"measured with the Vaisala Ceilometer CL31 that was located on the 7th deck, \"+\\\n",
    "                \"above the bridge (at approximately 25m).\" + \\\n",
    "                \"The sky condition measurements are time averages to represent an area average. \" + \\\n",
    "                \"The vertical resolution is 10m * 770, but the measurement height (25m) is included in the backscatter profile ranges, \" + \\\n",
    "                \"as well as in the cloud base heights (cloud_base_altitude and sky_condition_cloud_altitude). \" + \\\n",
    "                \"Geospatial bounds are taken from the gps location of the weather station dataset located on Oden. \" +\\\n",
    "                \"Time variables month, day, hour, minute and second are approximated to the nearest second. \" +\\\n",
    "                \"Data produced by Sonja Murto. See the document - Readme_CL.txt - for more details.\"}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "\n",
       ".xr-section-summary,\n",
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       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
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       "\n",
       ".xr-section-inline-details {\n",
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       "\n",
       ".xr-section-details {\n",
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       "\n",
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       "\n",
       ".xr-array-preview,\n",
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       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
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       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
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       "\n",
       ".xr-has-index {\n",
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       "}\n",
       "\n",
       ".xr-var-list,\n",
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       "\n",
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       ".xr-var-item > .xr-var-name span {\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
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       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
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       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
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       "\n",
       ".xr-var-name {\n",
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       "\n",
       ".xr-var-dims {\n",
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       "\n",
       ".xr-var-dtype {\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
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       "\n",
       ".xr-var-data > table {\n",
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       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
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       "\n",
       ".xr-attrs,\n",
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       ".xr-var-data {\n",
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       "\n",
       "dl.xr-attrs {\n",
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       "\n",
       ".xr-attrs dt,\n",
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       "}\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",
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       "\n",
       ".xr-attrs dd {\n",
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       "  white-space: pre-wrap;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                       (cloud_layer: 3, range_levels: 770, sky_condition_layer: 5, time: 108477)\n",
       "Coordinates:\n",
       "  * time                          (time) datetime64[ns] 2023-05-07T00:00:22.2...\n",
       "  * range_levels                  (range_levels) int32 1 2 3 4 ... 768 769 770\n",
       "  * cloud_layer                   (cloud_layer) int32 1 2 3\n",
       "  * sky_condition_layer           (sky_condition_layer) int32 1 2 3 4 5\n",
       "Data variables: (12/15)\n",
       "    day_of_year                   (time) float32 127.0 127.0 ... 164.7 164.7\n",
       "    year                          (time) int32 2023 2023 2023 ... 2023 2023 2023\n",
       "    month                         (time) int32 5 5 5 5 5 5 5 5 ... 6 6 6 6 6 6 6\n",
       "    day                           (time) int32 7 7 7 7 7 7 ... 13 13 13 13 13 13\n",
       "    hour                          (time) int32 0 0 0 0 0 0 ... 16 16 16 16 16 16\n",
       "    minute                        (time) int32 0 0 1 1 2 2 ... 21 22 22 23 23 24\n",
       "    ...                            ...\n",
       "    vertical_visibility           (time) float32 nan nan nan nan ... nan nan nan\n",
       "    highest_detected_signal       (time) float32 nan nan nan nan ... nan nan nan\n",
       "    cloud_base_altitude           (time, cloud_layer) float32 915.0 nan ... nan\n",
       "    sky_condition_cloud_fraction  (time, sky_condition_layer) float32 6.0 ......\n",
       "    sky_condition_cloud_altitude  (time, sky_condition_layer) float32 775.0 ....\n",
       "    backscatter_profile           (time, range_levels) float32 0.00041 ... -0...\n",
       "Attributes: (12/27)\n",
       "    Conventions:                           CF-1.8\n",
       "    source:                                Ceilometer\n",
       "    instrument_model:                      Vaisala Ceilometer CL31\n",
       "    creator_name:                          Sonja Murto\n",
       "    creator_email:                         sonja.murto@misu.su.se\n",
       "    creator_url:                           https://orcid.org/0000-0002-4966-9077\n",
       "    ...                                    ...\n",
       "    time_coverage_start:                   2023-05-07T00:00:22\n",
       "    time_coverage_end:                     2023-06-13T16:24:22\n",
       "    geospatial_bounds:                     80.52392166666667N, -3.87377499999...\n",
       "    platform_altitude:                     Located at approximately 25 m a.s.l\n",
       "    location_keywords:                     Oden, Arctic Ocean, Fram Strait, a...\n",
Sonja Murto's avatar
Sonja Murto committed
       "    comments:                              This file consists of ceilometer d...</pre><div class='xr-wrap' hidden><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-2595830e-84c7-4bc6-80bb-6faf43cca029' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2595830e-84c7-4bc6-80bb-6faf43cca029' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>cloud_layer</span>: 3</li><li><span class='xr-has-index'>range_levels</span>: 770</li><li><span class='xr-has-index'>sky_condition_layer</span>: 5</li><li><span class='xr-has-index'>time</span>: 108477</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c6b5e696-3756-4540-9952-9896b17a74b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c6b5e696-3756-4540-9952-9896b17a74b8' class='xr-section-summary' >Coordinates: <span>(4)</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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-05-07T00:00:22.266003 ... 2...</div><input id='attrs-991fef4d-194d-42a3-a4c7-076a067a88db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-991fef4d-194d-42a3-a4c7-076a067a88db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be261f4b-8bc1-4326-a818-3a6b26a5c1b8' class='xr-var-data-in' type='checkbox'><label for='data-be261f4b-8bc1-4326-a818-3a6b26a5c1b8' 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([&#x27;2023-05-07T00:00:22.266003000&#x27;, &#x27;2023-05-07T00:00:52.264995000&#x27;,\n",
       "       &#x27;2023-05-07T00:01:22.265003000&#x27;, ..., &#x27;2023-06-13T16:23:22.296001000&#x27;,\n",
       "       &#x27;2023-06-13T16:23:52.296000000&#x27;, &#x27;2023-06-13T16:24:22.295002000&#x27;],\n",
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Sonja Murto committed
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_levels</span></div><div class='xr-var-dims'>(range_levels)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 766 767 768 769 770</div><input id='attrs-ce3989d0-c035-4a6d-a3b7-7a72d01f6835' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce3989d0-c035-4a6d-a3b7-7a72d01f6835' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ee1e740-c001-40b3-827c-d6af04db85d4' class='xr-var-data-in' type='checkbox'><label for='data-9ee1e740-c001-40b3-827c-d6af04db85d4' 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([  1,   2,   3, ..., 768, 769, 770], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cloud_layer</span></div><div class='xr-var-dims'>(cloud_layer)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-9a589097-d840-4cb2-ac55-ac07c99fa45a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a589097-d840-4cb2-ac55-ac07c99fa45a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65dd0ad0-bd35-4759-850d-821d8eded4bd' class='xr-var-data-in' type='checkbox'><label for='data-65dd0ad0-bd35-4759-850d-821d8eded4bd' 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([1, 2, 3], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sky_condition_layer</span></div><div class='xr-var-dims'>(sky_condition_layer)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5</div><input id='attrs-f8f3940a-a211-415c-8b22-caf67c5e147c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8f3940a-a211-415c-8b22-caf67c5e147c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bba2ffd-ed9b-4ed6-a567-a13d75134fe0' class='xr-var-data-in' type='checkbox'><label for='data-3bba2ffd-ed9b-4ed6-a567-a13d75134fe0' 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([1, 2, 3, 4, 5], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5c4b4bac-7157-4bd0-b537-8c97f513eda9' class='xr-section-summary-in' type='checkbox'  ><label for='section-5c4b4bac-7157-4bd0-b537-8c97f513eda9' class='xr-section-summary' >Data variables: <span>(15)</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>day_of_year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>127.0 127.0 127.0 ... 164.7 164.7</div><input id='attrs-abb5a7fb-774f-4da2-a03a-c0dc942df841' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abb5a7fb-774f-4da2-a03a-c0dc942df841' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-005e1ad2-515f-4c8a-8eed-a307b6acd595' class='xr-var-data-in' type='checkbox'><label for='data-005e1ad2-515f-4c8a-8eed-a307b6acd595' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Day of Year</dd><dt><span>description :</span></dt><dd>time as decimal day of year</dd></dl></div><div class='xr-var-data'><pre>array([127.00026, 127.0006 , 127.00095, ..., 164.68289, 164.68324,\n",
       "       164.6836 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>2023 2023 2023 ... 2023 2023 2023</div><input id='attrs-f78d9d87-9c44-4536-bca5-c5c496b3e2e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f78d9d87-9c44-4536-bca5-c5c496b3e2e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a2daeaf-fb4f-405d-bffe-1dc0c8cd247b' class='xr-var-data-in' type='checkbox'><label for='data-9a2daeaf-fb4f-405d-bffe-1dc0c8cd247b' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Year</dd></dl></div><div class='xr-var-data'><pre>array([2023, 2023, 2023, ..., 2023, 2023, 2023], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>5 5 5 5 5 5 5 5 ... 6 6 6 6 6 6 6 6</div><input id='attrs-94989e29-7ffb-47e8-a9d0-d915fbc2b340' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94989e29-7ffb-47e8-a9d0-d915fbc2b340' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77bda41d-a5e3-4960-bf4f-84751081fe08' class='xr-var-data-in' type='checkbox'><label for='data-77bda41d-a5e3-4960-bf4f-84751081fe08' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Month</dd></dl></div><div class='xr-var-data'><pre>array([5, 5, 5, ..., 6, 6, 6], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>day</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>7 7 7 7 7 7 7 ... 13 13 13 13 13 13</div><input id='attrs-0d552525-696f-45e5-9261-7927609a9a4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d552525-696f-45e5-9261-7927609a9a4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb2a6a18-6d22-4a6c-b517-77cc8dd4ae98' class='xr-var-data-in' type='checkbox'><label for='data-fb2a6a18-6d22-4a6c-b517-77cc8dd4ae98' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Day</dd></dl></div><div class='xr-var-data'><pre>array([ 7,  7,  7, ..., 13, 13, 13], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hour</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 ... 16 16 16 16 16 16</div><input id='attrs-5674d3a9-0665-406b-a5f6-5fb38c55235f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5674d3a9-0665-406b-a5f6-5fb38c55235f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8aa8892a-dcec-4025-89a4-8d6c165148cd' class='xr-var-data-in' type='checkbox'><label for='data-8aa8892a-dcec-4025-89a4-8d6c165148cd' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Hour</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  0,  0, ..., 16, 16, 16], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>minute</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 1 1 2 2 3 ... 21 22 22 23 23 24</div><input id='attrs-a72888bf-8faa-4638-9925-e1defdfece87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a72888bf-8faa-4638-9925-e1defdfece87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea82698c-1504-4df1-b9c5-31fe5469e5ee' class='xr-var-data-in' type='checkbox'><label for='data-ea82698c-1504-4df1-b9c5-31fe5469e5ee' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Minute</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  0,  1, ..., 23, 23, 24], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>second</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>22 52 22 52 22 ... 22 52 22 52 22</div><input id='attrs-c809e45a-48a7-4a89-9f28-919b541076c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c809e45a-48a7-4a89-9f28-919b541076c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b76f7f4-ad30-4dd7-8e8c-67298d648cfb' class='xr-var-data-in' type='checkbox'><label for='data-7b76f7f4-ad30-4dd7-8e8c-67298d648cfb' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Second</dd><dt><span>description :</span></dt><dd>Time averaged to closest second</dd></dl></div><div class='xr-var-data'><pre>array([22, 52, 22, ..., 22, 52, 22], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ceilometer_range</span></div><div class='xr-var-dims'>(range_levels)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>35 45 55 65 ... 7695 7705 7715 7725</div><input id='attrs-fdb4f47c-a272-4a3c-a0a3-37fd93b1beaa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdb4f47c-a272-4a3c-a0a3-37fd93b1beaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5664c11-31e9-49b4-a9b7-d271f07ecb4f' class='xr-var-data-in' type='checkbox'><label for='data-f5664c11-31e9-49b4-a9b7-d271f07ecb4f' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>range_levels</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Ceilometer Range</dd><dt><span>description :</span></dt><dd>Ranges for the ceilometer backscatter profile, including the instrument height</dd></dl></div><div class='xr-var-data'><pre>array([  35,   45,   55,   65,   75,   85,   95,  105,  115,  125,  135,\n",
       "        145,  155,  165,  175,  185,  195,  205,  215,  225,  235,  245,\n",
       "        255,  265,  275,  285,  295,  305,  315,  325,  335,  345,  355,\n",
       "        365,  375,  385,  395,  405,  415,  425,  435,  445,  455,  465,\n",
       "        475,  485,  495,  505,  515,  525,  535,  545,  555,  565,  575,\n",
       "        585,  595,  605,  615,  625,  635,  645,  655,  665,  675,  685,\n",
       "        695,  705,  715,  725,  735,  745,  755,  765,  775,  785,  795,\n",
       "        805,  815,  825,  835,  845,  855,  865,  875,  885,  895,  905,\n",
       "        915,  925,  935,  945,  955,  965,  975,  985,  995, 1005, 1015,\n",
       "       1025, 1035, 1045, 1055, 1065, 1075, 1085, 1095, 1105, 1115, 1125,\n",
       "       1135, 1145, 1155, 1165, 1175, 1185, 1195, 1205, 1215, 1225, 1235,\n",
       "       1245, 1255, 1265, 1275, 1285, 1295, 1305, 1315, 1325, 1335, 1345,\n",
       "       1355, 1365, 1375, 1385, 1395, 1405, 1415, 1425, 1435, 1445, 1455,\n",
       "       1465, 1475, 1485, 1495, 1505, 1515, 1525, 1535, 1545, 1555, 1565,\n",
       "       1575, 1585, 1595, 1605, 1615, 1625, 1635, 1645, 1655, 1665, 1675,\n",
       "       1685, 1695, 1705, 1715, 1725, 1735, 1745, 1755, 1765, 1775, 1785,\n",
       "       1795, 1805, 1815, 1825, 1835, 1845, 1855, 1865, 1875, 1885, 1895,\n",
       "       1905, 1915, 1925, 1935, 1945, 1955, 1965, 1975, 1985, 1995, 2005,\n",
       "       2015, 2025, 2035, 2045, 2055, 2065, 2075, 2085, 2095, 2105, 2115,\n",
       "       2125, 2135, 2145, 2155, 2165, 2175, 2185, 2195, 2205, 2215, 2225,\n",
       "...\n",
       "       5645, 5655, 5665, 5675, 5685, 5695, 5705, 5715, 5725, 5735, 5745,\n",
       "       5755, 5765, 5775, 5785, 5795, 5805, 5815, 5825, 5835, 5845, 5855,\n",
       "       5865, 5875, 5885, 5895, 5905, 5915, 5925, 5935, 5945, 5955, 5965,\n",
       "       5975, 5985, 5995, 6005, 6015, 6025, 6035, 6045, 6055, 6065, 6075,\n",
       "       6085, 6095, 6105, 6115, 6125, 6135, 6145, 6155, 6165, 6175, 6185,\n",
       "       6195, 6205, 6215, 6225, 6235, 6245, 6255, 6265, 6275, 6285, 6295,\n",
       "       6305, 6315, 6325, 6335, 6345, 6355, 6365, 6375, 6385, 6395, 6405,\n",
       "       6415, 6425, 6435, 6445, 6455, 6465, 6475, 6485, 6495, 6505, 6515,\n",
       "       6525, 6535, 6545, 6555, 6565, 6575, 6585, 6595, 6605, 6615, 6625,\n",
       "       6635, 6645, 6655, 6665, 6675, 6685, 6695, 6705, 6715, 6725, 6735,\n",
       "       6745, 6755, 6765, 6775, 6785, 6795, 6805, 6815, 6825, 6835, 6845,\n",
       "       6855, 6865, 6875, 6885, 6895, 6905, 6915, 6925, 6935, 6945, 6955,\n",
       "       6965, 6975, 6985, 6995, 7005, 7015, 7025, 7035, 7045, 7055, 7065,\n",
       "       7075, 7085, 7095, 7105, 7115, 7125, 7135, 7145, 7155, 7165, 7175,\n",
       "       7185, 7195, 7205, 7215, 7225, 7235, 7245, 7255, 7265, 7275, 7285,\n",
       "       7295, 7305, 7315, 7325, 7335, 7345, 7355, 7365, 7375, 7385, 7395,\n",
       "       7405, 7415, 7425, 7435, 7445, 7455, 7465, 7475, 7485, 7495, 7505,\n",
       "       7515, 7525, 7535, 7545, 7555, 7565, 7575, 7585, 7595, 7605, 7615,\n",
       "       7625, 7635, 7645, 7655, 7665, 7675, 7685, 7695, 7705, 7715, 7725],\n",
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       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flag_cloudcode</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 1 2 2 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-d799e1f3-7de1-4dd3-9ed5-c18797970841' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d799e1f3-7de1-4dd3-9ed5-c18797970841' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efcac1bd-be03-4498-b72a-5ad89c034138' class='xr-var-data-in' type='checkbox'><label for='data-efcac1bd-be03-4498-b72a-5ad89c034138' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Data flag: Cloudcode</dd><dt><span>flag_values :</span></dt><dd>-1,0,1,2,3,4</dd><dt><span>flag_meanings :</span></dt><dd>missing_data\n",
       "no_significant_backscatter\n",
       "one_cloud_base_detected\n",
       "two_cloud_bases_detected\n",
       "three_cloud_bases_detected\n",
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       "full_obscuration</dd><dt><span>description :</span></dt><dd>Code for number of cloud bases detected; see Readme document for more information</dd></dl></div><div class='xr-var-data'><pre>array([1, 1, 2, ..., 1, 1, 1], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertical_visibility</span></div><div class='xr-var-dims'>(time)</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-54a35b35-82da-444f-a86b-caef98f787b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54a35b35-82da-444f-a86b-caef98f787b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8c59782-e623-4533-a532-48d8357e02f2' class='xr-var-data-in' type='checkbox'><label for='data-c8c59782-e623-4533-a532-48d8357e02f2' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Vertical Visibilility</dd><dt><span>description :</span></dt><dd>Vertical visibility given in case of obscured cloud base (at flag_cloudcode 4), else NaN</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>highest_detected_signal</span></div><div class='xr-var-dims'>(time)</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-d5813921-3bb8-43ca-b3a3-8cfdae7bf32a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5813921-3bb8-43ca-b3a3-8cfdae7bf32a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-643fbeca-f9df-4fb0-b336-30b00265a631' class='xr-var-data-in' type='checkbox'><label for='data-643fbeca-f9df-4fb0-b336-30b00265a631' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Highest Signal Detected</dd><dt><span>description :</span></dt><dd>Highest signal detected given in case of obscured cloud base (at flag_cloudcode 4), else NaN</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cloud_base_altitude</span></div><div class='xr-var-dims'>(time, cloud_layer)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>915.0 nan nan ... 265.0 nan nan</div><input id='attrs-48d9898d-ea5a-4904-8ce4-26eb5d2ad7ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48d9898d-ea5a-4904-8ce4-26eb5d2ad7ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02774637-8d28-4358-b10a-024b57bea8f7' class='xr-var-data-in' type='checkbox'><label for='data-02774637-8d28-4358-b10a-024b57bea8f7' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time, cloud_layer</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Cloud Base Altitude</dd><dt><span>description :</span></dt><dd>cloud base height of 1-3 cloud layers; NaN if no layer detected. Instrument height incorporated.</dd></dl></div><div class='xr-var-data'><pre>array([[915.,  nan,  nan],\n",
       "       [765.,  nan,  nan],\n",
       "       [745., 945.,  nan],\n",
       "       ...,\n",
       "       [325.,  nan,  nan],\n",
       "       [325.,  nan,  nan],\n",
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       "       [265.,  nan,  nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sky_condition_cloud_fraction</span></div><div class='xr-var-dims'>(time, sky_condition_layer)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>6.0 8.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-b26e213f-9303-477a-99fa-53683dab2588' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b26e213f-9303-477a-99fa-53683dab2588' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb395ca5-285f-4bbd-ab88-eb8c7ec719f2' class='xr-var-data-in' type='checkbox'><label for='data-cb395ca5-285f-4bbd-ab88-eb8c7ec719f2' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time, sky_condition_layer</dd><dt><span>units :</span></dt><dd>octal</dd><dt><span>long_name :</span></dt><dd>Sky Condition Cloud Fraction</dd><dt><span>description :</span></dt><dd>Cloud fraction calculated with the sky condition algorithm. 0-8 = cloud coverage of up to 5 levels; 9 = obscuration. NaN = missing data or no detected layer.</dd></dl></div><div class='xr-var-data'><pre>array([[6., 8., 0., 0., 0.],\n",
       "       [6., 8., 0., 0., 0.],\n",
       "       [6., 8., 0., 0., 0.],\n",
       "       ...,\n",
       "       [8., 0., 0., 0., 0.],\n",
       "       [8., 0., 0., 0., 0.],\n",
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       "       [8., 0., 0., 0., 0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sky_condition_cloud_altitude</span></div><div class='xr-var-dims'>(time, sky_condition_layer)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>775.0 1.525e+03 nan ... nan nan nan</div><input id='attrs-9146d70b-efee-4545-8ea5-e12df1dcb1a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9146d70b-efee-4545-8ea5-e12df1dcb1a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c85c0deb-9b35-4bbf-8ab6-285b8826ff89' class='xr-var-data-in' type='checkbox'><label for='data-c85c0deb-9b35-4bbf-8ab6-285b8826ff89' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time, sky_condition_layer</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Sky Condition Cloud Altitude</dd><dt><span>description :</span></dt><dd>Cloud layer height calculated with the sky condition algorithm. Cloud layer height given for 1-5 sky condition layers; NaN if no layer detected. Vertical visibility is reported as height if obscuration (at sky_condition_cloud_fraction 9). Instrument height incorporated.</dd></dl></div><div class='xr-var-data'><pre>array([[ 775., 1525.,   nan,   nan,   nan],\n",
       "       [ 775., 1525.,   nan,   nan,   nan],\n",
       "       [ 775., 1525.,   nan,   nan,   nan],\n",
       "       ...,\n",
       "       [ 365.,   nan,   nan,   nan,   nan],\n",
       "       [ 365.,   nan,   nan,   nan,   nan],\n",
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       "       [ 365.,   nan,   nan,   nan,   nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>backscatter_profile</span></div><div class='xr-var-dims'>(time, range_levels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.00041 0.00021 ... -0.00099</div><input id='attrs-13854140-a1ce-4c87-b996-b13aa98728d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13854140-a1ce-4c87-b996-b13aa98728d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ba3a9a3-c354-4c35-be4d-179994d3474c' class='xr-var-data-in' type='checkbox'><label for='data-3ba3a9a3-c354-4c35-be4d-179994d3474c' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time, range_levels</dd><dt><span>units :</span></dt><dd>1 km-1 steradians-1</dd><dt><span>long_name :</span></dt><dd>Backscatter Profile</dd><dt><span>description :</span></dt><dd>backscatter coefficient profile</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00041,  0.00021,  0.00021, ...,  0.00481,  0.00636,  0.00369],\n",
       "       [ 0.00054,  0.00023,  0.00022, ..., -0.00383, -0.00429, -0.00371],\n",
       "       [ 0.00011,  0.00021,  0.00021, ..., -0.00184, -0.00054,  0.00392],\n",
       "       ...,\n",
       "       [ 0.01576,  0.01214,  0.01054, ...,  0.00153, -0.00049, -0.00063],\n",
       "       [ 0.0024 ,  0.00564,  0.00816, ..., -0.00122,  0.00081,  0.00131],\n",
       "       [ 0.0178 ,  0.01494,  0.02054, ..., -0.00102, -0.00123, -0.00099]],\n",
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       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e9f398d8-57b8-4e95-879f-7c4213ff86da' class='xr-section-summary-in' type='checkbox'  ><label for='section-e9f398d8-57b8-4e95-879f-7c4213ff86da' class='xr-section-summary' >Attributes: <span>(27)</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-1.8</dd><dt><span>source :</span></dt><dd>Ceilometer</dd><dt><span>instrument_model :</span></dt><dd>Vaisala Ceilometer CL31</dd><dt><span>creator_name :</span></dt><dd>Sonja Murto</dd><dt><span>creator_email :</span></dt><dd>sonja.murto@misu.su.se</dd><dt><span>creator_url :</span></dt><dd>https://orcid.org/0000-0002-4966-9077</dd><dt><span>institution :</span></dt><dd>Stockholm University</dd><dt><span>processing_software :</span></dt><dd>Matlab (for creating the matlab file) and a jupyter notebook script (netCDF)</dd><dt><span>sampling_interval :</span></dt><dd>30s</dd><dt><span>product_version :</span></dt><dd>v01</dd><dt><span>last_revised_date :</span></dt><dd>2024-05-31T15:00:00</dd><dt><span>project :</span></dt><dd>ARTofMELT</dd><dt><span>project_principal_investigator :</span></dt><dd>Michael Tjernström</dd><dt><span>project_principal_investigator_email :</span></dt><dd>michaelt@misu.su.se</dd><dt><span>project_principal_investigator_url :</span></dt><dd>https://orcid.org/0000-0002-6908-7410</dd><dt><span>acknowledgement :</span></dt><dd> Knut och Alice Wallenbergs Stiftelse, Grant 2016-0024</dd><dt><span>platform :</span></dt><dd>Swedish Icebreaker Oden</dd><dt><span>platform_type :</span></dt><dd>On Oden&#x27;s 7th deck above the bridge</dd><dt><span>deployment_mode :</span></dt><dd>ship</dd><dt><span>title :</span></dt><dd>Ceilometer cloud base height, vertical visibility and backscatter profiles</dd><dt><span>feature_type :</span></dt><dd>time series</dd><dt><span>time_coverage_start :</span></dt><dd>2023-05-07T00:00:22</dd><dt><span>time_coverage_end :</span></dt><dd>2023-06-13T16:24:22</dd><dt><span>geospatial_bounds :</span></dt><dd>80.52392166666667N, -3.8737749999999997E, 78.04355166666666N, 15.660881666666667E</dd><dt><span>platform_altitude :</span></dt><dd>Located at approximately 25 m a.s.l</dd><dt><span>location_keywords :</span></dt><dd>Oden, Arctic Ocean, Fram Strait, atmosphere, on the ship</dd><dt><span>comments :</span></dt><dd>This file consists of ceilometer data measured with the Vaisala Ceilometer CL31 that was located on the 7th deck, above the bridge (at approximately 25m).The sky condition measurements are time averages to represent an area average. The vertical resolution is 10m * 770, but the measurement height (25m) is included in the backscatter profile ranges, as well as in the cloud base heights (cloud_base_altitude and sky_condition_cloud_altitude). Geospatial bounds are taken from the gps location of the weather station dataset located on Oden. Time variables month, day, hour, minute and second are approximated to the nearest second. Data produced by Sonja Murto. See the document - Readme_CL.txt - for more details.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                       (cloud_layer: 3, range_levels: 770, sky_condition_layer: 5, time: 108477)\n",
       "Coordinates:\n",
       "  * time                          (time) datetime64[ns] 2023-05-07T00:00:22.2...\n",
       "  * range_levels                  (range_levels) int32 1 2 3 4 ... 768 769 770\n",
       "  * cloud_layer                   (cloud_layer) int32 1 2 3\n",
       "  * sky_condition_layer           (sky_condition_layer) int32 1 2 3 4 5\n",
       "Data variables: (12/15)\n",
       "    day_of_year                   (time) float32 127.0 127.0 ... 164.7 164.7\n",
       "    year                          (time) int32 2023 2023 2023 ... 2023 2023 2023\n",
       "    month                         (time) int32 5 5 5 5 5 5 5 5 ... 6 6 6 6 6 6 6\n",
       "    day                           (time) int32 7 7 7 7 7 7 ... 13 13 13 13 13 13\n",
       "    hour                          (time) int32 0 0 0 0 0 0 ... 16 16 16 16 16 16\n",
       "    minute                        (time) int32 0 0 1 1 2 2 ... 21 22 22 23 23 24\n",
       "    ...                            ...\n",
       "    vertical_visibility           (time) float32 nan nan nan nan ... nan nan nan\n",
       "    highest_detected_signal       (time) float32 nan nan nan nan ... nan nan nan\n",
       "    cloud_base_altitude           (time, cloud_layer) float32 915.0 nan ... nan\n",
       "    sky_condition_cloud_fraction  (time, sky_condition_layer) float32 6.0 ......\n",
       "    sky_condition_cloud_altitude  (time, sky_condition_layer) float32 775.0 ....\n",
       "    backscatter_profile           (time, range_levels) float32 0.00041 ... -0...\n",
       "Attributes: (12/27)\n",
       "    Conventions:                           CF-1.8\n",
       "    source:                                Ceilometer\n",
       "    instrument_model:                      Vaisala Ceilometer CL31\n",
       "    creator_name:                          Sonja Murto\n",
       "    creator_email:                         sonja.murto@misu.su.se\n",
       "    creator_url:                           https://orcid.org/0000-0002-4966-9077\n",
       "    ...                                    ...\n",
       "    time_coverage_start:                   2023-05-07T00:00:22\n",
       "    time_coverage_end:                     2023-06-13T16:24:22\n",
       "    geospatial_bounds:                     80.52392166666667N, -3.87377499999...\n",
       "    platform_altitude:                     Located at approximately 25 m a.s.l\n",
       "    location_keywords:                     Oden, Arctic Ocean, Fram Strait, a...\n",
       "    comments:                              This file consists of ceilometer d..."
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to netCDF\n",
    "ds_all.to_netcdf(load_data + 'CL31_ceilometer_ARTofMELT_20230507_20230613_30s_v01.nc') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create netCDF from 1 min average files - remove times with no data points"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "CL_SM_avg = scipy.io.loadmat(load_data + 'CL31_ceilometer_ARTofMELT_20230507_20230613_1min_v01.mat',struct_as_record=True)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['__header__', '__version__', '__globals__', 'cl31_av'])"
      ]
     },
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     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CL_SM_avg.keys()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('mday', 'doy', 'cloudcode', 'base_ht', 'vert_vis', 'high_sig', 'sc_frac', 'sc_ht', 'bs_prof', 'ceil_range', 'time')\n"
     ]
    }
   ],
   "source": [
    "# get the data in the mat file\n",
    "Names=CL_SM_avg['cl31_av'].dtype.names\n",
    "ndata = {n: CL_SM_avg['cl31_av'][n][0, 0] for n in Names}\n",
    "print(Names)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create pandas dataframe from the data\n",
    "\n",
    "# 1D variables\n",
    "\n",
    "DF_1min=pd.DataFrame(index=range(len(ndata['doy'])),)\n",
    "DF_1min['doy']=np.array(list(itertools.chain.from_iterable(ndata['doy'])),dtype=float)\n",
    "DF_1min['cloudcode']=np.array(list(itertools.chain.from_iterable(ndata['cloudcode'])),dtype=float)\n",
    "DF_1min['vert_vis']=np.array(list(itertools.chain.from_iterable(ndata['vert_vis'])),dtype=float)\n",
    "DF_1min['high_sig']=np.array(list(itertools.chain.from_iterable(ndata['high_sig'])),dtype=float)\n",
    "\n",
    "\n",
    "#2D cloudlayer\n",
    "DF_1min_cloudbh=pd.DataFrame(index=range(len(ndata['doy'])),data=np.array(ndata['base_ht'],dtype=float))\n",
    "\n",
    "#2D sc_layer_ht\n",
    "DF_1min_scht=pd.DataFrame(index=range(len(ndata['doy'])),data=np.array(ndata['sc_ht'],dtype=float))\n",
    "\n",
    "#2D sc_layer_frac\n",
    "DF_1min_scfrac=pd.DataFrame(index=range(len(ndata['doy'])),data=np.array(ndata['sc_frac'],dtype=float))\n",
    "\n",
    "#2D ranges\n",
    "DF_1min_range=pd.DataFrame(index=range(len(ndata['doy'])),data=np.array(ndata['bs_prof'],dtype=float))"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54264"
      ]
     },
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(DF_1min)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54251 13\n"
     ]
    }
   ],
   "source": [
    "#check for NaNs\n",
    "selection = DF_1min.iloc[DF_1min[~DF_1min.doy.isna()].index].index.values # size with no nans\n",
    "idx_nan = DF_1min.iloc[DF_1min[DF_1min.doy.isna()].index].index.values # length of nan times\n",
    "print(len(selection),len(idx_nan))"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#select only no-nan times; for all 5 pandas dataframes\n",
    "DF_1min=DF_1min.iloc[selection].reset_index(drop=True)\n",
    "DF_1min_cloudbh=DF_1min_cloudbh.iloc[selection].reset_index(drop=True)\n",
    "DF_1min_scht=DF_1min_scht.iloc[selection].reset_index(drop=True)\n",
    "DF_1min_scfrac=DF_1min_scfrac.iloc[selection].reset_index(drop=True)\n",
    "DF_1min_range=DF_1min_range.iloc[selection].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54251 54251 54251 54251 54251\n"
     ]
    }
   ],
   "source": [
    "#check for right length\n",
    "print(len(DF_1min),len(DF_1min_cloudbh),len(DF_1min_scfrac),len(DF_1min_scht),len(DF_1min_range))"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doy</th>\n",
       "      <th>cloudcode</th>\n",
       "      <th>vert_vis</th>\n",
       "      <th>high_sig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [doy, cloudcode, vert_vis, high_sig]\n",
       "Index: []"
      ]
     },
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF_1min.iloc[DF_1min[DF_1min.doy.isna()].index] #check again for NaNs - empty!"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set time dimension\n",
    "Time_steps=returndatetime_fromdoy(np.array(DF_1min.doy.values,dtype=float))\n",
    "Times_nomicrosec=[pd.to_datetime(T).round('1s') for T in Time_steps]\n",
    "Time_steps_dt64_org=[np.datetime64(t) for t in Time_steps]\n",
    "Time_steps_dt64_org=np.array(Time_steps_dt64_org,dtype='datetime64[ns]')"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add date values\n",
    "DF_1min['year']=np.array([Times_nomicrosec[i].year for i in range(len(Time_steps))],dtype=int)\n",
    "DF_1min['month']=np.array([Times_nomicrosec[i].month for i in range(len(Time_steps))],dtype=int)\n",
    "DF_1min['day']=np.array([Times_nomicrosec[i].day for i in range(len(Time_steps))],dtype=int)\n",
    "DF_1min['hour']=np.array([Times_nomicrosec[i].hour for i in range(len(Time_steps))],dtype=int)\n",
    "DF_1min['minute']=np.array([Times_nomicrosec[i].minute for i in range(len(Time_steps))],dtype=int)\n",
    "DF_1min['second']=np.array([Times_nomicrosec[i].second for i in range(len(Time_steps))],dtype=int)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create xr DataArrays\n",
    "\n",
    "#1D: dimension time\n",
    "\n",
    "da_doy=xr.DataArray(data=np.array(DF_1min['doy']).astype('float32'),name=\"day_of_year\",\n",
    "                dims=[\"time\"],coords=dict(time=Time_steps_dt64_org),\n",
    "                  attrs=dict(type=\"float32\",dimension=\"time\",units=\"1\",long_name=\"Day of Year\",\n",
    "                             description=\"time as decimal day of year\"),) #added as 1D\n",
    "\n",
    "#date and time in separate arrays; microseconds approximated to seconds\n",
    "da_year=xr.DataArray(data=np.array(DF_1min['year']).astype('int32'),name=\"year\",dims=[\"time\"],\n",
    "                  coords=dict(time=Time_steps_dt64_org),\n",
    "                  attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",long_name=\"Year\"),) #added as 1D\n",
    "\n",
    "da_month=xr.DataArray(data=np.array(DF_1min['month']).astype('int32'),name=\"month\",dims=[\"time\"],\n",
    "                  coords=dict(time=Time_steps_dt64_org),\n",
    "                      attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\", long_name=\"Month\"),) #added as 1D\n",
    "\n",
    "da_day=xr.DataArray(data=np.array(DF_1min['day']).astype('int32'),name=\"day\",dims=[\"time\"],\n",
    "                  coords=dict(time=Time_steps_dt64_org),\n",
    "                    attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",long_name=\"Day\"),) #added as 1D\n",
    "\n",
    "da_hour=xr.DataArray(data=np.array(DF_1min['hour']).astype('int32'),name=\"hour\",dims=[\"time\"],\n",
    "                  coords=dict(time=Time_steps_dt64_org),\n",
    "                     attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",long_name=\"Hour\"),) #added as 1D\n",
    "\n",
    "da_min=xr.DataArray(data=np.array(DF_1min['minute']).astype('int32'),name=\"minute\",dims=[\"time\"],\n",
    "                  coords=dict(time=Time_steps_dt64_org),\n",
    "                    attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",long_name=\"Minute\"),) #added as 1D\n",
    "\n",
    "da_sec=xr.DataArray(data=np.array(DF_1min['second']).astype('int32'),name=\"second\",dims=[\"time\"],\n",
    "                  coords=dict(time=Time_steps_dt64_org),\n",
    "                     attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",long_name=\"Second\",\n",
    "                               description=\"Time averaged to closest second\"),) #added as 1D\n",
    "\n",
    "#add cloudcode flag\n",
    "da_cloudcode=xr.DataArray(data=np.array(DF_1min['cloudcode']).astype('int32'),name=\"flag_cloudcode\",dims=[\"time\"],\n",
    "                        coords=dict(time=Time_steps_dt64_org),\n",
    "                        attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",\n",
    "                                   long_name='Data flag: Cloudcode',\n",
    "                                   flag_values=\"-1,0,1,2,3,4\", \n",
    "                                   flag_meanings=\"missing_data\\nno_significant_backscatter\\none_cloud_base_detected\\ntwo_cloud_bases_detected\\nthree_cloud_bases_detected\\nfull_obscuration\",\n",
    "                                   description=\"Code for number of cloud bases detected; see Readme document for more information\"),)\n",
    "\n",
    "da_vertvis=xr.DataArray(data=np.array(DF_1min['vert_vis']).astype('float32'),\n",
    "                    name=\"vertical_visibility\",dims=[\"time\"],coords=dict(time=Time_steps_dt64_org),\n",
    "                   attrs=dict(type=\"float32\",dimension=\"time\", units=\"m\",\n",
    "                              long_name=\"Vertical Visibilility\",\n",
    "                             description=\"Vertical visibility given in case of obscured cloud base (at flag_cloudcode 4), else NaN\"),) #added as 1D\n",
    "\n",
    "da_high_sig=xr.DataArray(data=np.array(DF_1min['high_sig']).astype('float32'),\n",
    "                    name=\"highest_detected_signal\",dims=[\"time\"],coords=dict(time=Time_steps_dt64_org),\n",
    "                   attrs=dict(type=\"float32\",dimension=\"time\", units=\"m\",\n",
    "                              long_name=\"Highest Signal Detected\",\n",
    "                             description=\"Highest signal detected given in case of obscured cloud base (at flag_cloudcode 4), else NaN\"),) #added as 1D\n",
    "\n",
    "\n",
    "#1D: dimension range_levels\n",
    "\n",
    "da_ceilrange=xr.DataArray(data=np.array(list(itertools.chain.from_iterable(ndata['ceil_range'])),dtype=int).astype('int32'),\n",
    "                    name=\"ceilometer_range\",dims=[\"range_levels\"],coords=dict(range_levels=range_levs),\n",
    "                          attrs=dict(type=\"int32\",dimension=\"range_levels\",units=\"m\",\n",
    "                                     long_name=\"Ceilometer Range\",\n",
    "                                     description=\"Ranges for the ceilometer backscatter profile, including the instrument height\",),) #added as 1D\n",
    "\n",
    "\n",
    "#2D: dimension time/cloud_layer\n",
    "\n",
    "\n",
    "da_baseht=xr.DataArray(data=np.array(DF_1min_cloudbh,dtype=float).astype('float32'),\n",
    "                    name=\"cloud_base_altitude\",dims=[\"time\",\"cloud_layer\"],\n",
    "                       coords=dict(time=Time_steps_dt64_org,cloud_layer=cloud_layer_levs),\n",
    "                   attrs=dict(type=\"float32\",dimension=\"time, cloud_layer\", units=\"m\",\n",
    "                              long_name=\"Cloud Base Altitude\",\n",
    "                              description=\"cloud base height of 1-3 cloud layers; NaN if no layer detected. \" +\\\n",
    "                              \"Instrument height incorporated.\"),) #added as 2D\n",
    "\n",
    "#2D: dimension time/sky_condition_layer\n",
    "\n",
    "\n",
    "\n",
    "da_scfrac=xr.DataArray(data=np.array(DF_1min_scfrac,dtype=float).astype('float32'),\n",
    "                    name=\"sky_condition_cloud_fraction\",dims=[\"time\",\"sky_condition_layer\"],\n",
    "                       coords=dict(time=Time_steps_dt64_org,sky_condition_layer=sc_layer_levs),\n",
    "                   attrs=dict(type=\"float32\",dimension=\"time, sky_condition_layer\", units=\"octal\",\n",
    "                              long_name=\"Sky Condition Cloud Fraction\",\n",
    "                            description=\"Cloud fraction calculated with the sky condition algorithm. \"+\\\n",
    "                             \"0-8 = cloud coverage of up to 5 levels; 9 = obscuration. \" +\\\n",
    "                             \"NaN = missing data or no detected layer.\"),) #added as 2D\n",
    "\n",
    "da_scht=xr.DataArray(data=np.array(DF_1min_scht,dtype=float).astype('float32'),\n",
    "                    name=\"sky_condition_cloud_altitude\",dims=[\"time\",\"sky_condition_layer\"],\n",
    "                     coords=dict(time=Time_steps_dt64_org,sky_condition_layer=sc_layer_levs),\n",
    "                   attrs=dict(type=\"float32\",dimension=\"time, sky_condition_layer\", units=\"m\",\n",
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    "                              long_name=\"Sky Condition Cloud Altitude\",\n",
    "                              description = \"Cloud layer height calculated with the sky condition algorithm. \"+\\\n",
    "                             \"Cloud layer height given for 1-5 sky condition layers; NaN if no layer detected. \"+\\\n",
    "                             \"Vertical visibility is reported as height if obscuration (at sky_condition_cloud_fraction 9). \"+\\\n",
    "                             \"Instrument height incorporated.\"),) #added as 2D\n",
    "\n",
    "\n",
    "\n",
    "#2D: dimension time/range\n",
    "\n",
    "da_bsprof=xr.DataArray(data=np.array(DF_1min_range,dtype=float).astype('float32'),\n",
    "                    name=\"backscatter_profile\",dims=[\"time\",\"range_levels\"],\n",
    "                       coords=dict(time=Time_steps_dt64_org,range_levels=range_levs),\n",
    "                   attrs=dict(type=\"float32\",dimension=\"time, range_levels\", units=\"1 km-1 steradians-1\",\n",
    "                              long_name=\"Backscatter Profile\", description=\"backscatter coefficient profile\"),) #added as 2D\n"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
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     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge all arrays into one\n",
    "\n",
    "ds_all=xr.merge([da_doy,da_year,da_month,da_day,da_hour,da_min,da_sec,\n",
    "                da_ceilrange,da_cloudcode,da_vertvis,da_high_sig,\n",
    "                da_baseht,da_scfrac,da_scht,da_bsprof])\n",
    "\n",
    "len(ds_all)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-05-07 00:00:52')"
      ]
     },
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     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get time range - start\n",
    "Times_nomicrosec[0]"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-06-13 16:23:37')"
      ]
     },
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     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get time range - end\n",
    "Times_nomicrosec[-1]"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# modify attributes (start/end time and text in comments); \n",
    "# note again geospatial bounds from the weather station data\n",
    "\n",
    "ds_all.attrs = {\"Conventions\" :\"CF-1.8\",\n",
    "                \"source\" : \"Ceilometer\",\n",
    "                \"instrument_model\" : \"Vaisala Ceilometer CL31\",\n",
    "                \"creator_name\" : \"Sonja Murto\",\n",
    "                \"creator_email\" : \"sonja.murto@misu.su.se\",\n",
    "                \"creator_url\" : \"https://orcid.org/0000-0002-4966-9077\",\n",
    "                \"institution\" : \"Stockholm University\",  \n",
    "                \"processing_software\" : \"Matlab (for creating the matlab file) and a jupyter notebook script (netCDF)\",\n",
    "                \"sampling_interval\": \"original 30s; here 1min averages\", \n",
    "                \"product_version\" : \"v01\",\n",
    "                \"last_revised_date\" : \"2024-05-31T15:00:00\", \n",
    "                \"project\" : \"ARTofMELT\",\n",
    "                \"project_principal_investigator\" : \"Michael Tjernström\",\n",
    "                \"project_principal_investigator_email\" : \"michaelt@misu.su.se\",\n",
    "                \"project_principal_investigator_url\" : \"https://orcid.org/0000-0002-6908-7410\",                \n",
    "                \"acknowledgement\" : \" Knut och Alice Wallenbergs Stiftelse, Grant 2016-0024\",\n",
    "                \"platform\" : \"Swedish Icebreaker Oden\",\n",
    "                \"platform_type\" : \"On Oden's 7th deck above the bridge\",\n",
    "                \"deployment_mode\" : \"ship\",\n",
    "                \"title\" : \"Ceilometer cloud base height, vertical visibility and backscatter profiles\",\n",
    "                \"feature_type\" : \"time series\",   \n",
    "                \"time_coverage_start\" : \"2023-05-07T00:00:52\",\n",
    "                \"time_coverage_end\" : \"2023-06-13T16:23:37\",\n",
    "                \"geospatial_bounds\" : \"80.52392166666667N, -3.8737749999999997E, 78.04355166666666N, 15.660881666666667E\",\n",
    "                \"platform_altitude\" : \"Located at approximately 25 m a.s.l\",\n",
    "                \"location_keywords\": \"Oden, Arctic Ocean, Fram Strait, atmosphere, on the ship\",\n",
    "                \"comments\" : \"This file consists of 1 min averages of ceilometer data \" +\\\n",
    "                \"measured with the Vaisala Ceilometer CL31 that was located on the 7th deck, \"+\\\n",
    "                \"above the bridge (at approximately 25m).\" + \\\n",
    "                \"The sky condition measurements are time averages to represent an area average. \" + \\\n",
    "                \"The vertical resolution is 10m * 770, but the measurement height (25m) is included in the backscatter profile ranges, \" + \\\n",
    "                \"as well as in the cloud base heights (cloud_base_altitude and sky_condition_cloud_altitude). \" + \\\n",
    "                \"Geospatial bounds are taken from the gps location of the weather station dataset located on Oden. \" +\\\n",
    "                \"Time variables month, day, hour, minute and second are approximated to the nearest second. \" +\\\n",
    "                \"Data produced by Sonja Murto. See the document - Readme_CL.txt - for more details.\"}\n",
    "               "
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 37,
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   "metadata": {},
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       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  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",
       "  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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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",
       "  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:                       (cloud_layer: 3, range_levels: 770, sky_condition_layer: 5, time: 54251)\n",
       "Coordinates:\n",
       "  * time                          (time) datetime64[ns] 2023-05-07T00:00:52.2...\n",
       "  * range_levels                  (range_levels) int32 1 2 3 4 ... 768 769 770\n",
       "  * cloud_layer                   (cloud_layer) int32 1 2 3\n",
       "  * sky_condition_layer           (sky_condition_layer) int32 1 2 3 4 5\n",
       "Data variables: (12/15)\n",
       "    day_of_year                   (time) float32 127.0 127.0 ... 164.7 164.7\n",
       "    year                          (time) int32 2023 2023 2023 ... 2023 2023 2023\n",
       "    month                         (time) int32 5 5 5 5 5 5 5 5 ... 6 6 6 6 6 6 6\n",
       "    day                           (time) int32 7 7 7 7 7 7 ... 13 13 13 13 13 13\n",
       "    hour                          (time) int32 0 0 0 0 0 0 ... 16 16 16 16 16 16\n",
       "    minute                        (time) int32 0 2 3 4 5 6 ... 18 19 20 21 22 23\n",
       "    ...                            ...\n",
       "    vertical_visibility           (time) float32 nan nan nan nan ... nan nan nan\n",
       "    highest_detected_signal       (time) float32 nan nan nan nan ... nan nan nan\n",
       "    cloud_base_altitude           (time, cloud_layer) float32 808.3 ... nan\n",
       "    sky_condition_cloud_fraction  (time, sky_condition_layer) float32 6.0 ......\n",
       "    sky_condition_cloud_altitude  (time, sky_condition_layer) float32 775.0 ....\n",
       "    backscatter_profile           (time, range_levels) float32 0.0003533 ... ...\n",
       "Attributes: (12/27)\n",
       "    Conventions:                           CF-1.8\n",
       "    source:                                Ceilometer\n",
       "    instrument_model:                      Vaisala Ceilometer CL31\n",
       "    creator_name:                          Sonja Murto\n",
       "    creator_email:                         sonja.murto@misu.su.se\n",
       "    creator_url:                           https://orcid.org/0000-0002-4966-9077\n",
       "    ...                                    ...\n",
       "    time_coverage_start:                   2023-05-07T00:00:52\n",
       "    time_coverage_end:                     2023-06-13T16:23:37\n",
       "    geospatial_bounds:                     80.52392166666667N, -3.87377499999...\n",
       "    platform_altitude:                     Located at approximately 25 m a.s.l\n",
       "    location_keywords:                     Oden, Arctic Ocean, Fram Strait, a...\n",
Sonja Murto's avatar
Sonja Murto committed
       "    comments:                              This file consists of 1 min averag...</pre><div class='xr-wrap' hidden><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-c8a8ecf0-c432-410d-8670-7ba62f030f36' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c8a8ecf0-c432-410d-8670-7ba62f030f36' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>cloud_layer</span>: 3</li><li><span class='xr-has-index'>range_levels</span>: 770</li><li><span class='xr-has-index'>sky_condition_layer</span>: 5</li><li><span class='xr-has-index'>time</span>: 54251</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-29b2e0c2-9c68-4874-a914-4c4cafa83672' class='xr-section-summary-in' type='checkbox'  checked><label for='section-29b2e0c2-9c68-4874-a914-4c4cafa83672' class='xr-section-summary' >Coordinates: <span>(4)</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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-05-07T00:00:52.265334 ... 2...</div><input id='attrs-5821690b-40af-4d89-b65f-6bf8c1d70ec1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5821690b-40af-4d89-b65f-6bf8c1d70ec1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db5f3a96-512f-41e4-bc56-246e78e768ab' class='xr-var-data-in' type='checkbox'><label for='data-db5f3a96-512f-41e4-bc56-246e78e768ab' 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([&#x27;2023-05-07T00:00:52.265334000&#x27;, &#x27;2023-05-07T00:02:07.263497000&#x27;,\n",
       "       &#x27;2023-05-07T00:03:07.263001000&#x27;, ..., &#x27;2023-06-13T16:21:37.297999000&#x27;,\n",
       "       &#x27;2023-06-13T16:22:37.297000000&#x27;, &#x27;2023-06-13T16:23:37.296001000&#x27;],\n",
Sonja Murto's avatar
Sonja Murto committed
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_levels</span></div><div class='xr-var-dims'>(range_levels)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 766 767 768 769 770</div><input id='attrs-5348be17-6a5e-43a5-86d2-4c43befeb566' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5348be17-6a5e-43a5-86d2-4c43befeb566' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54136a38-e88e-4ed6-9de8-b740986bae82' class='xr-var-data-in' type='checkbox'><label for='data-54136a38-e88e-4ed6-9de8-b740986bae82' 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([  1,   2,   3, ..., 768, 769, 770], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cloud_layer</span></div><div class='xr-var-dims'>(cloud_layer)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-063be0c7-bba9-4140-9ddf-bc4098d2b188' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-063be0c7-bba9-4140-9ddf-bc4098d2b188' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a535b096-5490-418b-8ba0-ff7246dcbfff' class='xr-var-data-in' type='checkbox'><label for='data-a535b096-5490-418b-8ba0-ff7246dcbfff' 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([1, 2, 3], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sky_condition_layer</span></div><div class='xr-var-dims'>(sky_condition_layer)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5</div><input id='attrs-1bd83c73-d317-42c3-a0e4-acc76acad1f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1bd83c73-d317-42c3-a0e4-acc76acad1f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd5a2d91-26a2-4598-8f69-b57690996ad8' class='xr-var-data-in' type='checkbox'><label for='data-fd5a2d91-26a2-4598-8f69-b57690996ad8' 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([1, 2, 3, 4, 5], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-33b4e616-0d1e-4190-a9ff-f05f5d1e3ef7' class='xr-section-summary-in' type='checkbox'  ><label for='section-33b4e616-0d1e-4190-a9ff-f05f5d1e3ef7' class='xr-section-summary' >Data variables: <span>(15)</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>day_of_year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>127.0 127.0 127.0 ... 164.7 164.7</div><input id='attrs-6c572098-9585-4785-b9ea-25e7c49f4521' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c572098-9585-4785-b9ea-25e7c49f4521' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8a34d23-038b-4d6e-add0-d156a487e737' class='xr-var-data-in' type='checkbox'><label for='data-d8a34d23-038b-4d6e-add0-d156a487e737' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Day of Year</dd><dt><span>description :</span></dt><dd>time as decimal day of year</dd></dl></div><div class='xr-var-data'><pre>array([127.0006 , 127.00147, 127.00217, ..., 164.68169, 164.68237,\n",
       "       164.68307], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>2023 2023 2023 ... 2023 2023 2023</div><input id='attrs-c8065166-6a3b-42de-8b91-227930bdad37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8065166-6a3b-42de-8b91-227930bdad37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e52b4347-7c86-440a-96aa-dc7967cf7530' class='xr-var-data-in' type='checkbox'><label for='data-e52b4347-7c86-440a-96aa-dc7967cf7530' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Year</dd></dl></div><div class='xr-var-data'><pre>array([2023, 2023, 2023, ..., 2023, 2023, 2023], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>5 5 5 5 5 5 5 5 ... 6 6 6 6 6 6 6 6</div><input id='attrs-201157e7-3a5d-41d6-bad3-2858c587806d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-201157e7-3a5d-41d6-bad3-2858c587806d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49cc089d-5779-4c38-9041-09f3a228cef2' class='xr-var-data-in' type='checkbox'><label for='data-49cc089d-5779-4c38-9041-09f3a228cef2' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Month</dd></dl></div><div class='xr-var-data'><pre>array([5, 5, 5, ..., 6, 6, 6], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>day</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>7 7 7 7 7 7 7 ... 13 13 13 13 13 13</div><input id='attrs-5999efe8-c639-4df4-8efe-a54f46601a30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5999efe8-c639-4df4-8efe-a54f46601a30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-314ae8e3-0040-4e47-8264-cab168b2cf8d' class='xr-var-data-in' type='checkbox'><label for='data-314ae8e3-0040-4e47-8264-cab168b2cf8d' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Day</dd></dl></div><div class='xr-var-data'><pre>array([ 7,  7,  7, ..., 13, 13, 13], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hour</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 ... 16 16 16 16 16 16</div><input id='attrs-ba90909f-954b-40a9-bb1e-ebd936e72944' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba90909f-954b-40a9-bb1e-ebd936e72944' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50f7c962-b796-4f5d-8392-90399ce54909' class='xr-var-data-in' type='checkbox'><label for='data-50f7c962-b796-4f5d-8392-90399ce54909' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Hour</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  0,  0, ..., 16, 16, 16], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>minute</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 2 3 4 5 6 7 ... 18 19 20 21 22 23</div><input id='attrs-b8b05877-fcc9-4485-9be1-9135c6fab5e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8b05877-fcc9-4485-9be1-9135c6fab5e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cb8b9d3-da66-44bf-a059-559177e7b4ed' class='xr-var-data-in' type='checkbox'><label for='data-5cb8b9d3-da66-44bf-a059-559177e7b4ed' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Minute</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  2,  3, ..., 21, 22, 23], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>second</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>52 7 7 7 7 7 ... 37 37 37 37 37 37</div><input id='attrs-658ac113-7b42-40c7-86e3-a11f99c5eeb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-658ac113-7b42-40c7-86e3-a11f99c5eeb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8b7c085-d532-49a6-8e1e-350c954b3db1' class='xr-var-data-in' type='checkbox'><label for='data-c8b7c085-d532-49a6-8e1e-350c954b3db1' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Second</dd><dt><span>description :</span></dt><dd>Time averaged to closest second</dd></dl></div><div class='xr-var-data'><pre>array([52,  7,  7, ..., 37, 37, 37], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ceilometer_range</span></div><div class='xr-var-dims'>(range_levels)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>35 45 55 65 ... 7695 7705 7715 7725</div><input id='attrs-1788d758-9ff5-4a0e-899a-09aa60f49126' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1788d758-9ff5-4a0e-899a-09aa60f49126' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af4cd13f-cdaf-4c3b-9a00-f7fff1473349' class='xr-var-data-in' type='checkbox'><label for='data-af4cd13f-cdaf-4c3b-9a00-f7fff1473349' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>range_levels</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Ceilometer Range</dd><dt><span>description :</span></dt><dd>Ranges for the ceilometer backscatter profile, including the instrument height</dd></dl></div><div class='xr-var-data'><pre>array([  35,   45,   55,   65,   75,   85,   95,  105,  115,  125,  135,\n",
       "        145,  155,  165,  175,  185,  195,  205,  215,  225,  235,  245,\n",
       "        255,  265,  275,  285,  295,  305,  315,  325,  335,  345,  355,\n",
       "        365,  375,  385,  395,  405,  415,  425,  435,  445,  455,  465,\n",
       "        475,  485,  495,  505,  515,  525,  535,  545,  555,  565,  575,\n",
       "        585,  595,  605,  615,  625,  635,  645,  655,  665,  675,  685,\n",
       "        695,  705,  715,  725,  735,  745,  755,  765,  775,  785,  795,\n",
       "        805,  815,  825,  835,  845,  855,  865,  875,  885,  895,  905,\n",
       "        915,  925,  935,  945,  955,  965,  975,  985,  995, 1005, 1015,\n",
       "       1025, 1035, 1045, 1055, 1065, 1075, 1085, 1095, 1105, 1115, 1125,\n",
       "       1135, 1145, 1155, 1165, 1175, 1185, 1195, 1205, 1215, 1225, 1235,\n",
       "       1245, 1255, 1265, 1275, 1285, 1295, 1305, 1315, 1325, 1335, 1345,\n",
       "       1355, 1365, 1375, 1385, 1395, 1405, 1415, 1425, 1435, 1445, 1455,\n",
       "       1465, 1475, 1485, 1495, 1505, 1515, 1525, 1535, 1545, 1555, 1565,\n",
       "       1575, 1585, 1595, 1605, 1615, 1625, 1635, 1645, 1655, 1665, 1675,\n",
       "       1685, 1695, 1705, 1715, 1725, 1735, 1745, 1755, 1765, 1775, 1785,\n",
       "       1795, 1805, 1815, 1825, 1835, 1845, 1855, 1865, 1875, 1885, 1895,\n",
       "       1905, 1915, 1925, 1935, 1945, 1955, 1965, 1975, 1985, 1995, 2005,\n",
       "       2015, 2025, 2035, 2045, 2055, 2065, 2075, 2085, 2095, 2105, 2115,\n",
       "       2125, 2135, 2145, 2155, 2165, 2175, 2185, 2195, 2205, 2215, 2225,\n",
       "...\n",
       "       5645, 5655, 5665, 5675, 5685, 5695, 5705, 5715, 5725, 5735, 5745,\n",
       "       5755, 5765, 5775, 5785, 5795, 5805, 5815, 5825, 5835, 5845, 5855,\n",
       "       5865, 5875, 5885, 5895, 5905, 5915, 5925, 5935, 5945, 5955, 5965,\n",
       "       5975, 5985, 5995, 6005, 6015, 6025, 6035, 6045, 6055, 6065, 6075,\n",
       "       6085, 6095, 6105, 6115, 6125, 6135, 6145, 6155, 6165, 6175, 6185,\n",
       "       6195, 6205, 6215, 6225, 6235, 6245, 6255, 6265, 6275, 6285, 6295,\n",
       "       6305, 6315, 6325, 6335, 6345, 6355, 6365, 6375, 6385, 6395, 6405,\n",
       "       6415, 6425, 6435, 6445, 6455, 6465, 6475, 6485, 6495, 6505, 6515,\n",
       "       6525, 6535, 6545, 6555, 6565, 6575, 6585, 6595, 6605, 6615, 6625,\n",
       "       6635, 6645, 6655, 6665, 6675, 6685, 6695, 6705, 6715, 6725, 6735,\n",
       "       6745, 6755, 6765, 6775, 6785, 6795, 6805, 6815, 6825, 6835, 6845,\n",
       "       6855, 6865, 6875, 6885, 6895, 6905, 6915, 6925, 6935, 6945, 6955,\n",
       "       6965, 6975, 6985, 6995, 7005, 7015, 7025, 7035, 7045, 7055, 7065,\n",
       "       7075, 7085, 7095, 7105, 7115, 7125, 7135, 7145, 7155, 7165, 7175,\n",
       "       7185, 7195, 7205, 7215, 7225, 7235, 7245, 7255, 7265, 7275, 7285,\n",
       "       7295, 7305, 7315, 7325, 7335, 7345, 7355, 7365, 7375, 7385, 7395,\n",
       "       7405, 7415, 7425, 7435, 7445, 7455, 7465, 7475, 7485, 7495, 7505,\n",
       "       7515, 7525, 7535, 7545, 7555, 7565, 7575, 7585, 7595, 7605, 7615,\n",
       "       7625, 7635, 7645, 7655, 7665, 7675, 7685, 7695, 7705, 7715, 7725],\n",
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       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flag_cloudcode</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-6b982b75-f0e4-4137-b939-a37008adddce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b982b75-f0e4-4137-b939-a37008adddce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10e2fe12-abc9-42a9-b30e-2f1c667a7c00' class='xr-var-data-in' type='checkbox'><label for='data-10e2fe12-abc9-42a9-b30e-2f1c667a7c00' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Data flag: Cloudcode</dd><dt><span>flag_values :</span></dt><dd>-1,0,1,2,3,4</dd><dt><span>flag_meanings :</span></dt><dd>missing_data\n",
       "no_significant_backscatter\n",
       "one_cloud_base_detected\n",
       "two_cloud_bases_detected\n",
       "three_cloud_bases_detected\n",
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       "full_obscuration</dd><dt><span>description :</span></dt><dd>Code for number of cloud bases detected; see Readme document for more information</dd></dl></div><div class='xr-var-data'><pre>array([1, 1, 1, ..., 1, 1, 1], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertical_visibility</span></div><div class='xr-var-dims'>(time)</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-77baba13-4dc2-4941-9bec-0cbb49b17fcb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77baba13-4dc2-4941-9bec-0cbb49b17fcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-723b3125-6e36-4dd6-9679-8a89d8c6d282' class='xr-var-data-in' type='checkbox'><label for='data-723b3125-6e36-4dd6-9679-8a89d8c6d282' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Vertical Visibilility</dd><dt><span>description :</span></dt><dd>Vertical visibility given in case of obscured cloud base (at flag_cloudcode 4), else NaN</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>highest_detected_signal</span></div><div class='xr-var-dims'>(time)</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-8a64a908-defd-459e-9902-6fc78159a28c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a64a908-defd-459e-9902-6fc78159a28c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eab9109d-70fa-4aeb-b507-2596e7761fe7' class='xr-var-data-in' type='checkbox'><label for='data-eab9109d-70fa-4aeb-b507-2596e7761fe7' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Highest Signal Detected</dd><dt><span>description :</span></dt><dd>Highest signal detected given in case of obscured cloud base (at flag_cloudcode 4), else NaN</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cloud_base_altitude</span></div><div class='xr-var-dims'>(time, cloud_layer)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>808.3 945.0 nan ... 325.0 nan nan</div><input id='attrs-0793c79e-8956-4aed-8a7a-f386e06cf3d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0793c79e-8956-4aed-8a7a-f386e06cf3d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-089fc130-86f0-4e90-a862-99610fc9ddf8' class='xr-var-data-in' type='checkbox'><label for='data-089fc130-86f0-4e90-a862-99610fc9ddf8' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time, cloud_layer</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Cloud Base Altitude</dd><dt><span>description :</span></dt><dd>cloud base height of 1-3 cloud layers; NaN if no layer detected. Instrument height incorporated.</dd></dl></div><div class='xr-var-data'><pre>array([[808.3333, 945.    ,      nan],\n",
       "       [840.    , 955.    ,      nan],\n",
       "       [785.    ,      nan,      nan],\n",
       "       ...,\n",
       "       [335.    ,      nan,      nan],\n",
       "       [335.    ,      nan,      nan],\n",
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       "       [325.    ,      nan,      nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sky_condition_cloud_fraction</span></div><div class='xr-var-dims'>(time, sky_condition_layer)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>6.0 8.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-85a07185-56a9-4374-9db6-2699ee77ebdc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85a07185-56a9-4374-9db6-2699ee77ebdc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10b24ee7-7fbc-479d-9995-d66ece53ce41' class='xr-var-data-in' type='checkbox'><label for='data-10b24ee7-7fbc-479d-9995-d66ece53ce41' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time, sky_condition_layer</dd><dt><span>units :</span></dt><dd>octal</dd><dt><span>long_name :</span></dt><dd>Sky Condition Cloud Fraction</dd><dt><span>description :</span></dt><dd>Cloud fraction calculated with the sky condition algorithm. 0-8 = cloud coverage of up to 5 levels; 9 = obscuration. NaN = missing data or no detected layer.</dd></dl></div><div class='xr-var-data'><pre>array([[6., 8., 0., 0., 0.],\n",
       "       [6., 8., 0., 0., 0.],\n",
       "       [6., 8., 0., 0., 0.],\n",
       "       ...,\n",
       "       [8., 0., 0., 0., 0.],\n",
       "       [8., 0., 0., 0., 0.],\n",
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       "       [8., 0., 0., 0., 0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sky_condition_cloud_altitude</span></div><div class='xr-var-dims'>(time, sky_condition_layer)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>775.0 1.525e+03 nan ... nan nan nan</div><input id='attrs-6e2c5335-c7f7-4c53-9600-136b06a99963' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e2c5335-c7f7-4c53-9600-136b06a99963' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa4c4cda-54ea-419f-b5be-eb176dd35b05' class='xr-var-data-in' type='checkbox'><label for='data-aa4c4cda-54ea-419f-b5be-eb176dd35b05' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time, sky_condition_layer</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Sky Condition Cloud Altitude</dd><dt><span>description :</span></dt><dd>Cloud layer height calculated with the sky condition algorithm. Cloud layer height given for 1-5 sky condition layers; NaN if no layer detected. Vertical visibility is reported as height if obscuration (at sky_condition_cloud_fraction 9). Instrument height incorporated.</dd></dl></div><div class='xr-var-data'><pre>array([[ 775., 1525.,   nan,   nan,   nan],\n",
       "       [ 775., 1525.,   nan,   nan,   nan],\n",
       "       [ 775., 1525.,   nan,   nan,   nan],\n",
       "       ...,\n",
       "       [ 365.,   nan,   nan,   nan,   nan],\n",
       "       [ 365.,   nan,   nan,   nan,   nan],\n",
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       "       [ 365.,   nan,   nan,   nan,   nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>backscatter_profile</span></div><div class='xr-var-dims'>(time, range_levels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0003533 0.0002167 ... 0.00034</div><input id='attrs-b890216a-bfcc-4147-b89a-379da5816281' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b890216a-bfcc-4147-b89a-379da5816281' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee495f38-4f41-4f38-8ada-1e4e538a77e8' class='xr-var-data-in' type='checkbox'><label for='data-ee495f38-4f41-4f38-8ada-1e4e538a77e8' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time, range_levels</dd><dt><span>units :</span></dt><dd>1 km-1 steradians-1</dd><dt><span>long_name :</span></dt><dd>Backscatter Profile</dd><dt><span>description :</span></dt><dd>backscatter coefficient profile</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00035333,  0.00021667,  0.00021333, ..., -0.00028667,\n",
       "         0.00051   ,  0.0013    ],\n",
       "       [ 0.00032   ,  0.000225  ,  0.00022   , ...,  0.00276   ,\n",
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