AR_notrack_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Atmospheric River identification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First part of the algorithm for tracking atmospheric rivers, adapted from Erik Swenson's code.\n",
    "\n",
    "1. Read in moisture fluxes\n",
    "2. Calculate moisture flux magnitudes\n",
    "3. Determine the probability distribution of the flux magnitudes\n",
    "4. Using probability distribution, determine appropriate threshold for identifying AR (e.g. 95 percentile)\n",
    "5. Identify AR using:\n",
    "    \n",
    "    a. Flux magnitude threshold to obtain AR objects\n",
    "    \n",
    "    b. Check net direction of moisture flux in object is poleward and eastward\n",
    "    \n",
    "    c. Check object shape has length > 2000km and width < 1000 km\n",
    "    \n",
    "    d. Check median latitude poleward of 20N\n",
    "6. Obtain count of AR occurrences at each grid point\n",
    "7. Plot AR occurrences\n",
    "\n",
    "* Input:\n",
    "    Moisture flux\n",
    "    \n",
    "    `/glade/work/mkorendy/metisregimes/resolution/metis.variable_pna_months.1986-2016.nc`\n",
    "    \n",
    "* Output:\n",
    "    Atmospheric river maps\n",
    "    \n",
    "    `metisres.AR.threshold.PNA.months.png`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Haversine distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For calculating the length and width of the atmospheric rivers in kilometers\n",
    "# Converts coordinates into kilometers\n",
    "\n",
    "def haversine(coord1, coord2):\n",
    "    R = 6372.800  # Earth radius in kilometers\n",
    "    lat1, lon1 = coord1\n",
    "    lat2, lon2 = coord2\n",
    "    \n",
    "    phi1, phi2 = math.radians(lat1), math.radians(lat2) \n",
    "    dphi       = math.radians(lat2 - lat1)\n",
    "    dlambda    = math.radians(lon2 - lon1)\n",
    "    \n",
    "    a = math.sin(dphi/2)**2 + \\\n",
    "        math.cos(phi1)*math.cos(phi2)*math.sin(dlambda/2)**2\n",
    "    \n",
    "    return 2*R*math.atan2(math.sqrt(a), math.sqrt(1 - a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import cartopy.crs as ccrs\n",
    "from skimage.segmentation import watershed\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# method for detecting AR\n",
    "imethod = 0\n",
    "\n",
    "#resolution\n",
    "res = '199'\n",
    "resolution = 'tco' + res\n",
    "\n",
    "#ERA5?\n",
    "era5 = True\n",
    "\n",
    "threshold = '10%'\n",
    "crit_val = 259.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Path\n",
    "if resolution == 'tco199' or era5 == True:\n",
    "    path = '/glade/work/mkorendy/metisregimes/'\n",
    "else:\n",
    "    path = '/glade/work/mkorendy/metisregimes/' + resolution + '/'\n",
    "\n",
    "# Moisture flux input\n",
    "if era5 == False:\n",
    "    if res != '1279':\n",
    "        f_u = path + 'metis.uqvi_pna_NDJFM.1986-2016.nc'\n",
    "        f_v = path + 'metis.vqvi_pna_NDJFM.1986-2016.nc'\n",
    "    else:\n",
    "        f_u = path + 'metis.uqvi_pna_ND.1986-2016.nc'\n",
    "        f_v = path + 'metis.vqvi_pna_ND.1986-2016.nc'\n",
    "else:\n",
    "    f_v = path + 'era5.AR.vqvi_pna_NDJFM.1986-2016.nc'\n",
    "    #2016 data:\n",
    "    f_v = path + 'era5.AR.vqvi_pna_NDJFM.2016.nc'\n",
    "\n",
    "# Grab filter times\n",
    "if res != '1279':\n",
    "    f_times = path + 'metis.z500_pna_NDJFM_lpfilter_twicedaily_10days.1986-2016.nc'\n",
    "else:\n",
    "    f_times = path + 'metis.z500_pna_ND_lpfilter_twicedaily_10days.1986-2016.nc'\n",
    "\n",
    "# Output\n",
    "if era5 == False:\n",
    "    if res != '1279':\n",
    "        f_pic = '/glade/u/home/mkorendy/metisregimes/metis' + res + '.AR.' + threshold + '.PNA.NDJFM.png'\n",
    "        f_out = path + 'metis' + res + '.AR.' + threshold + '.PNA.NDJFM.nc'\n",
    "    else:\n",
    "        f_pic = '/glade/u/home/mkorendy/metisregimes/metis' + res + '.AR.' + threshold + '.PNA.ND.png'\n",
    "        f_out = path + 'metis' + res + '.AR.' + threshold + '.PNA.ND.nc'\n",
    "else:\n",
    "    f_pic = 'era5.AR.' + threshold + '.PNA.NDJFM.png'\n",
    "    f_out = path + 'era5.AR.' + threshold + '.PNA.NDJFM.nc'\n",
    "    \n",
    "    #2016 data\n",
    "    f_out = path + 'era5.AR.' + threshold + '.PNA.NDJFM.2016.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/glade/work/mkorendy/metisregimes/era5.AR.vqvi_pna_NDJFM.2016.nc\n",
      "/glade/work/mkorendy/metisregimes/era5.AR.10%.PNA.NDJFM.2016.nc\n",
      "era5.AR.10%.PNA.NDJFM.png\n"
     ]
    }
   ],
   "source": [
    "#print(f_u)\n",
    "print(f_v)\n",
    "print(f_out)\n",
    "print(f_pic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in moisture flux\n",
    "if era5 == False:\n",
    "    ds_u = xr.open_dataset(f_u)\n",
    "    ds_v = xr.open_dataset(f_v)\n",
    "    \n",
    "    # Merge the datasets into one\n",
    "    ds = xr.merge([ds_u, ds_v])\n",
    "    \n",
    "else:\n",
    "    ds = xr.open_dataset(f_v)\n",
    "\n",
    "# Read in times\n",
    "ds_times = xr.open_dataset(f_times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop times to fit with filtered dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "if era5 == False:\n",
    "    ds = ds.sel(time = ds_times.time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collapse ensembles along the time dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ds.vqvi.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "if era5 == False:\n",
    "    ds_uqvi = xr.DataArray(ds['uqvi'].values.flatten().reshape((len(ds.time)*len(ds.ensemble),22,53)), \n",
    "                 coords={'time':np.arange(len(ds.time)*len(ds.ensemble)), 'lat':ds['lat'].values, 'lon':ds['lon'].values},\n",
    "                         dims=['time', 'lat', 'lon']).to_dataset(name='uqvi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "if era5 == False:\n",
    "    ds_vqvi = xr.DataArray(ds['vqvi'].values.flatten().reshape((len(ds.time)*len(ds.ensemble),22,53)), \n",
    "                 coords={'time':np.arange(len(ds.time)*len(ds.ensemble)), 'lat':ds['lat'].values, 'lon':ds['lon'].values},\n",
    "                         dims=['time', 'lat', 'lon']).to_dataset(name='vqvi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "if era5 == False:\n",
    "    ds = xr.merge([ds_uqvi,ds_vqvi])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "</style><div class='xr-wrap'><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-60771ace-5221-40e7-9234-cc9532bb1667' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-60771ace-5221-40e7-9234-cc9532bb1667' 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'>lat</span>: 22</li><li><span class='xr-has-index'>lon</span>: 53</li><li><span class='xr-has-index'>time</span>: 304</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-078d70a7-8807-4b21-8bfa-3edf40d7fd68' class='xr-section-summary-in' type='checkbox'  checked><label for='section-078d70a7-8807-4b21-8bfa-3edf40d7fd68' class='xr-section-summary' >Coordinates: <span>(3)</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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>151.9 154.7 157.5 ... 295.3 298.1</div><input id='attrs-9995588b-cc57-4bca-8a4a-b59a2029f38b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9995588b-cc57-4bca-8a4a-b59a2029f38b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cdaa003-cfcd-42ee-a83b-d3b157a61511' class='xr-var-data-in' type='checkbox'><label for='data-6cdaa003-cfcd-42ee-a83b-d3b157a61511' 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><pre class='xr-var-data'>array([151.875 , 154.6875, 157.5   , 160.3125, 163.125 , 165.9375, 168.75  ,\n",
       "       171.5625, 174.375 , 177.1875, 180.    , 182.8125, 185.625 , 188.4375,\n",
       "       191.25  , 194.0625, 196.875 , 199.6875, 202.5   , 205.3125, 208.125 ,\n",
       "       210.9375, 213.75  , 216.5625, 219.375 , 222.1875, 225.    , 227.8125,\n",
       "       230.625 , 233.4375, 236.25  , 239.0625, 241.875 , 244.6875, 247.5   ,\n",
       "       250.3125, 253.125 , 255.9375, 258.75  , 261.5625, 264.375 , 267.1875,\n",
       "       270.    , 272.8125, 275.625 , 278.4375, 281.25  , 284.0625, 286.875 ,\n",
       "       289.6875, 292.5   , 295.3125, 298.125 ])</pre></li><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'>2016-01-01 ... 2016-12-31T12:00:00</div><input id='attrs-b52047f8-2d73-4309-8c51-096499fd50fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b52047f8-2d73-4309-8c51-096499fd50fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2770fe26-09dc-4e75-9d38-ee5f21a51697' class='xr-var-data-in' type='checkbox'><label for='data-2770fe26-09dc-4e75-9d38-ee5f21a51697' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-02T00:00:00.000000000&#x27;, ..., &#x27;2016-12-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-31T00:00:00.000000000&#x27;, &#x27;2016-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>79.53 76.74 73.95 ... 23.72 20.93</div><input id='attrs-b3075e16-d4ca-471b-9584-9f149baf49e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b3075e16-d4ca-471b-9584-9f149baf49e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-520ec24f-76e5-4196-a8ab-f08411b7729d' class='xr-var-data-in' type='checkbox'><label for='data-520ec24f-76e5-4196-a8ab-f08411b7729d' 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><pre class='xr-var-data'>array([79.53, 76.74, 73.95, 71.16, 68.37, 65.58, 62.79, 60.  , 57.21, 54.42,\n",
       "       51.63, 48.84, 46.04, 43.25, 40.46, 37.67, 34.88, 32.09, 29.3 , 26.51,\n",
       "       23.72, 20.93])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-c966b591-9d07-407b-84fd-e409ab7f5557' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c966b591-9d07-407b-84fd-e409ab7f5557' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>uqvi</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-07886a6d-f3f5-4659-a396-b6d984be5a0d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07886a6d-f3f5-4659-a396-b6d984be5a0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc27b95f-23a5-4f5f-83b8-71cb01216c37' class='xr-var-data-in' type='checkbox'><label for='data-dc27b95f-23a5-4f5f-83b8-71cb01216c37' 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><pre class='xr-var-data'>[354464 values with dtype=float32]</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>vqvi</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-28.964739 -29.610226 ... -76.16892</div><input id='attrs-748f9c5f-83e3-4b75-b627-3c79708f2870' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-748f9c5f-83e3-4b75-b627-3c79708f2870' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-feca9f56-d06d-4c13-9bff-8785362853e8' class='xr-var-data-in' type='checkbox'><label for='data-feca9f56-d06d-4c13-9bff-8785362853e8' 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><pre class='xr-var-data'>array([[[ -28.964739,  -29.610226, ...,    4.731083,   10.646955],\n",
       "        [ -17.251543,  -16.04421 , ...,    3.744371,   -2.086428],\n",
       "        ...,\n",
       "        [  50.547085,   51.35883 , ...,   20.1368  ,    9.452066],\n",
       "        [  27.711891,   12.416772, ...,   -8.058575,  -23.42587 ]],\n",
       "\n",
       "       [[ -20.51543 ,  -23.352509, ...,    4.08454 ,    8.473943],\n",
       "        [ -19.772657,  -25.400085, ...,   -3.888993,   -8.47819 ],\n",
       "        ...,\n",
       "        [-149.83046 ,  -14.931952, ...,   43.455395,   64.37123 ],\n",
       "        [ -68.29114 ,  -28.526346, ...,   52.63696 ,   58.662956]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ -14.518544,  -14.717672, ...,    3.114754,    3.546069],\n",
       "        [ -11.629329,  -12.200406, ...,    3.399454,    2.006624],\n",
       "        ...,\n",
       "        [-151.02924 , -141.37965 , ...,   71.9747  ,   72.45177 ],\n",
       "        [-188.70009 , -152.74521 , ...,   23.600454,   31.169125]],\n",
       "\n",
       "       [[  -8.823926,   -9.114   , ...,    1.291559,    1.700757],\n",
       "        [  -6.68086 ,   -7.789764, ...,    3.379832,    0.579063],\n",
       "        ...,\n",
       "        [-128.16025 , -144.94347 , ..., -102.14604 ,  -90.90045 ],\n",
       "        [-166.039   , -208.43117 , ...,  -39.573658,  -76.16892 ]]],\n",
       "      dtype=float32)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-398c3007-09ec-455c-8087-7b125b1499b0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-398c3007-09ec-455c-8087-7b125b1499b0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 22, lon: 53, time: 304)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 151.9 154.7 157.5 160.3 ... 289.7 292.5 295.3 298.1\n",
       "  * time     (time) datetime64[ns] 2016-01-01 ... 2016-12-31T12:00:00\n",
       "  * lat      (lat) float64 79.53 76.74 73.95 71.16 ... 29.3 26.51 23.72 20.93\n",
       "Data variables:\n",
       "    uqvi     (time, lat, lon) float32 ...\n",
       "    vqvi     (time, lat, lon) float32 -28.964739 -29.610226 ... -76.16892"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate moisture flux magnitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       ".xr-var-dtype:hover,\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".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",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\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",
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       "\n",
       ".xr-attrs dd {\n",
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       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 304</li><li><span class='xr-has-index'>lat</span>: 22</li><li><span class='xr-has-index'>lon</span>: 53</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-60831a36-4dce-419d-bf30-3ef819a69c12' class='xr-array-in' type='checkbox' ><label for='section-60831a36-4dce-419d-bf30-3ef819a69c12' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>28.986565 29.720213 27.727932 24.56742 ... 193.21722 143.52797 128.335</span></div><pre class='xr-array-data'>array([[[ 28.986565 ,  29.720213 ,  27.727932 , ...,   7.5572186,\n",
       "           6.6779613,  11.20266  ],\n",
       "        [ 23.09429  ,  25.88862  ,  27.761316 , ...,   6.002184 ,\n",
       "           5.520198 ,   3.4457626],\n",
       "        [ 45.21445  ,  41.248356 ,  37.507545 , ...,   6.1300373,\n",
       "           5.1198964,   3.7094538],\n",
       "        ...,\n",
       "        [356.72037  , 395.60233  , 348.69858  , ...,  86.72419  ,\n",
       "          72.25473  ,  90.27169  ],\n",
       "        [ 64.77156  ,  54.702583 , 105.10732  , ..., 304.7579   ,\n",
       "         254.81163  , 184.447    ],\n",
       "        [ 81.70172  ,  51.127575 , 126.8656   , ..., 310.08636  ,\n",
       "         227.14404  , 260.8854   ]],\n",
       "\n",
       "       [[ 26.5284   ,  26.225058 ,  24.580606 , ...,   2.3645434,\n",
       "           4.688716 ,   8.990762 ],\n",
       "        [ 29.341515 ,  31.907726 ,  29.767006 , ...,   2.669944 ,\n",
       "           4.119184 ,   9.568065 ],\n",
       "        [ 22.881071 ,  22.155231 ,  22.790207 , ...,   1.5667365,\n",
       "           2.4933612,   6.21365  ],\n",
       "        ...,\n",
       "        [435.58038  , 505.2576   , 528.28345  , ...,  73.186905 ,\n",
       "          60.353657 , 102.64849  ],\n",
       "        [215.60837  , 117.82183  , 122.483795 , ..., 194.47247  ,\n",
       "         139.68997  , 245.51775  ],\n",
       "        [ 71.09461  ,  33.439434 ,  49.64001  , ..., 208.07344  ,\n",
       "         228.94144  , 272.3899   ]],\n",
       "\n",
       "       [[ 12.871257 ,  11.754836 ,  11.2593355, ...,   3.0712945,\n",
       "           4.118613 ,  15.107647 ],\n",
       "        [ 13.657824 ,  15.563998 ,  16.823595 , ...,   5.9216146,\n",
       "          11.615578 ,  15.697032 ],\n",
       "        [ 38.65335  ,  32.08377  ,  24.963963 , ...,  11.726865 ,\n",
       "          15.776307 ,   1.0599035],\n",
       "        ...,\n",
       "        [108.831795 , 129.43127  , 157.8682   , ...,  47.890102 ,\n",
       "          47.716267 ,  90.628815 ],\n",
       "        [126.5595   , 212.59561  , 209.35295  , ..., 127.227715 ,\n",
       "         188.28514  , 191.4042   ],\n",
       "        [249.38725  , 109.01997  , 122.16095  , ..., 238.667    ,\n",
       "         203.26003  , 185.66074  ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 25.587902 ,  25.488039 ,  25.489096 , ...,   6.6579556,\n",
       "           8.1851015,  12.197907 ],\n",
       "        [ 51.1165   ,  59.348454 ,  62.8273   , ...,   9.043882 ,\n",
       "           9.306919 ,  10.755818 ],\n",
       "        [ 46.96249  ,  47.836533 ,  42.97975  , ...,   4.1867056,\n",
       "           5.550316 ,   8.375095 ],\n",
       "        ...,\n",
       "        [268.47964  , 330.20068  , 428.7584   , ..., 427.03323  ,\n",
       "         240.17766  , 153.27397  ],\n",
       "        [221.92279  , 219.29784  , 182.31322  , ..., 191.2034   ,\n",
       "         108.18342  ,  44.65203  ],\n",
       "        [180.5432   , 121.822105 ,  96.45202  , ..., 138.63136  ,\n",
       "         103.67994  ,  94.7814   ]],\n",
       "\n",
       "       [[ 26.926863 ,  27.156618 ,  27.607412 , ...,   7.4562674,\n",
       "           3.4211104,   4.680943 ],\n",
       "        [ 34.950867 ,  33.472824 ,  33.88605  , ...,   5.080762 ,\n",
       "           4.1590457,   2.0102682],\n",
       "        [ 29.253181 ,  27.079857 ,  25.527964 , ...,  14.995589 ,\n",
       "          13.892163 ,  12.281354 ],\n",
       "        ...,\n",
       "        [115.69024  , 122.47773  , 175.70135  , ..., 376.18896  ,\n",
       "         602.94135  , 304.89337  ],\n",
       "        [152.0222   , 151.03343  , 179.00853  , ..., 294.11884  ,\n",
       "         194.14429  , 122.42598  ],\n",
       "        [269.37753  , 236.37239  , 129.57857  , ...,  38.899075 ,\n",
       "          29.334244 ,  70.91173  ]],\n",
       "\n",
       "       [[ 16.746979 ,  16.929173 ,  17.802343 , ...,   4.0873938,\n",
       "           2.5328133,   3.0342145],\n",
       "        [ 23.818056 ,  23.92055  ,  24.186174 , ...,   5.1852636,\n",
       "           4.042522 ,   1.2982732],\n",
       "        [ 17.27966  ,  17.65323  ,  17.956524 , ...,  31.183968 ,\n",
       "          19.409094 ,  11.744581 ],\n",
       "        ...,\n",
       "        [ 96.996    ,  83.75546  ,  99.49551  , ...,  92.176254 ,\n",
       "         110.58942  , 212.91286  ],\n",
       "        [171.63129  , 185.71828  , 169.82588  , ..., 162.23692  ,\n",
       "         103.288414 , 117.5117   ],\n",
       "        [282.61227  , 338.0157   , 295.78772  , ..., 193.21722  ,\n",
       "         143.52797  , 128.335    ]]], dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-e7e28f87-77b5-4bc9-a1b8-ae74d13a254a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e7e28f87-77b5-4bc9-a1b8-ae74d13a254a' class='xr-section-summary' >Coordinates: <span>(3)</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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>151.9 154.7 157.5 ... 295.3 298.1</div><input id='attrs-dc8d904c-9dce-43e9-85ea-b176650b2c8c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc8d904c-9dce-43e9-85ea-b176650b2c8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee5adedd-9f9b-4062-ac2e-5fb9a5574566' class='xr-var-data-in' type='checkbox'><label for='data-ee5adedd-9f9b-4062-ac2e-5fb9a5574566' 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><pre class='xr-var-data'>array([151.875 , 154.6875, 157.5   , 160.3125, 163.125 , 165.9375, 168.75  ,\n",
       "       171.5625, 174.375 , 177.1875, 180.    , 182.8125, 185.625 , 188.4375,\n",
       "       191.25  , 194.0625, 196.875 , 199.6875, 202.5   , 205.3125, 208.125 ,\n",
       "       210.9375, 213.75  , 216.5625, 219.375 , 222.1875, 225.    , 227.8125,\n",
       "       230.625 , 233.4375, 236.25  , 239.0625, 241.875 , 244.6875, 247.5   ,\n",
       "       250.3125, 253.125 , 255.9375, 258.75  , 261.5625, 264.375 , 267.1875,\n",
       "       270.    , 272.8125, 275.625 , 278.4375, 281.25  , 284.0625, 286.875 ,\n",
       "       289.6875, 292.5   , 295.3125, 298.125 ])</pre></li><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'>2016-01-01 ... 2016-12-31T12:00:00</div><input id='attrs-8ec94c0d-b2d3-4f81-a5fe-9fb4904dec81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ec94c0d-b2d3-4f81-a5fe-9fb4904dec81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a72cd4e4-b031-4dbd-aba1-d0346cbde640' class='xr-var-data-in' type='checkbox'><label for='data-a72cd4e4-b031-4dbd-aba1-d0346cbde640' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-02T00:00:00.000000000&#x27;, ..., &#x27;2016-12-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-31T00:00:00.000000000&#x27;, &#x27;2016-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>79.53 76.74 73.95 ... 23.72 20.93</div><input id='attrs-376d5a23-64e2-4243-bbd0-be30604a9901' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-376d5a23-64e2-4243-bbd0-be30604a9901' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3be5b3ef-33cf-45b7-80aa-3bd0c0433537' class='xr-var-data-in' type='checkbox'><label for='data-3be5b3ef-33cf-45b7-80aa-3bd0c0433537' 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><pre class='xr-var-data'>array([79.53, 76.74, 73.95, 71.16, 68.37, 65.58, 62.79, 60.  , 57.21, 54.42,\n",
       "       51.63, 48.84, 46.04, 43.25, 40.46, 37.67, 34.88, 32.09, 29.3 , 26.51,\n",
       "       23.72, 20.93])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-6f86cc40-82ea-496e-8578-3611bda56b7e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6f86cc40-82ea-496e-8578-3611bda56b7e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
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       "           6.6779613,  11.20266  ],\n",
       "        [ 23.09429  ,  25.88862  ,  27.761316 , ...,   6.002184 ,\n",
       "           5.520198 ,   3.4457626],\n",
       "        [ 45.21445  ,  41.248356 ,  37.507545 , ...,   6.1300373,\n",
       "           5.1198964,   3.7094538],\n",
       "        ...,\n",
       "        [356.72037  , 395.60233  , 348.69858  , ...,  86.72419  ,\n",
       "          72.25473  ,  90.27169  ],\n",
       "        [ 64.77156  ,  54.702583 , 105.10732  , ..., 304.7579   ,\n",
       "         254.81163  , 184.447    ],\n",
       "        [ 81.70172  ,  51.127575 , 126.8656   , ..., 310.08636  ,\n",
       "         227.14404  , 260.8854   ]],\n",
       "\n",
       "       [[ 26.5284   ,  26.225058 ,  24.580606 , ...,   2.3645434,\n",
       "           4.688716 ,   8.990762 ],\n",
       "        [ 29.341515 ,  31.907726 ,  29.767006 , ...,   2.669944 ,\n",
       "           4.119184 ,   9.568065 ],\n",
       "        [ 22.881071 ,  22.155231 ,  22.790207 , ...,   1.5667365,\n",
       "           2.4933612,   6.21365  ],\n",
       "        ...,\n",
       "        [435.58038  , 505.2576   , 528.28345  , ...,  73.186905 ,\n",
       "          60.353657 , 102.64849  ],\n",
       "        [215.60837  , 117.82183  , 122.483795 , ..., 194.47247  ,\n",
       "         139.68997  , 245.51775  ],\n",
       "        [ 71.09461  ,  33.439434 ,  49.64001  , ..., 208.07344  ,\n",
       "         228.94144  , 272.3899   ]],\n",
       "\n",
       "       [[ 12.871257 ,  11.754836 ,  11.2593355, ...,   3.0712945,\n",
       "           4.118613 ,  15.107647 ],\n",
       "        [ 13.657824 ,  15.563998 ,  16.823595 , ...,   5.9216146,\n",
       "          11.615578 ,  15.697032 ],\n",
       "        [ 38.65335  ,  32.08377  ,  24.963963 , ...,  11.726865 ,\n",
       "          15.776307 ,   1.0599035],\n",
       "        ...,\n",
       "        [108.831795 , 129.43127  , 157.8682   , ...,  47.890102 ,\n",
       "          47.716267 ,  90.628815 ],\n",
       "        [126.5595   , 212.59561  , 209.35295  , ..., 127.227715 ,\n",
       "         188.28514  , 191.4042   ],\n",
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       "         203.26003  , 185.66074  ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 25.587902 ,  25.488039 ,  25.489096 , ...,   6.6579556,\n",
       "           8.1851015,  12.197907 ],\n",
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       "           9.306919 ,  10.755818 ],\n",
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       "           5.550316 ,   8.375095 ],\n",
       "        ...,\n",
       "        [268.47964  , 330.20068  , 428.7584   , ..., 427.03323  ,\n",
       "         240.17766  , 153.27397  ],\n",
       "        [221.92279  , 219.29784  , 182.31322  , ..., 191.2034   ,\n",
       "         108.18342  ,  44.65203  ],\n",
       "        [180.5432   , 121.822105 ,  96.45202  , ..., 138.63136  ,\n",
       "         103.67994  ,  94.7814   ]],\n",
       "\n",
       "       [[ 26.926863 ,  27.156618 ,  27.607412 , ...,   7.4562674,\n",
       "           3.4211104,   4.680943 ],\n",
       "        [ 34.950867 ,  33.472824 ,  33.88605  , ...,   5.080762 ,\n",
       "           4.1590457,   2.0102682],\n",
       "        [ 29.253181 ,  27.079857 ,  25.527964 , ...,  14.995589 ,\n",
       "          13.892163 ,  12.281354 ],\n",
       "        ...,\n",
       "        [115.69024  , 122.47773  , 175.70135  , ..., 376.18896  ,\n",
       "         602.94135  , 304.89337  ],\n",
       "        [152.0222   , 151.03343  , 179.00853  , ..., 294.11884  ,\n",
       "         194.14429  , 122.42598  ],\n",
       "        [269.37753  , 236.37239  , 129.57857  , ...,  38.899075 ,\n",
       "          29.334244 ,  70.91173  ]],\n",
       "\n",
       "       [[ 16.746979 ,  16.929173 ,  17.802343 , ...,   4.0873938,\n",
       "           2.5328133,   3.0342145],\n",
       "        [ 23.818056 ,  23.92055  ,  24.186174 , ...,   5.1852636,\n",
       "           4.042522 ,   1.2982732],\n",
       "        [ 17.27966  ,  17.65323  ,  17.956524 , ...,  31.183968 ,\n",
       "          19.409094 ,  11.744581 ],\n",
       "        ...,\n",
       "        [ 96.996    ,  83.75546  ,  99.49551  , ...,  92.176254 ,\n",
       "         110.58942  , 212.91286  ],\n",
       "        [171.63129  , 185.71828  , 169.82588  , ..., 162.23692  ,\n",
       "         103.288414 , 117.5117   ],\n",
       "        [282.61227  , 338.0157   , 295.78772  , ..., 193.21722  ,\n",
       "         143.52797  , 128.335    ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 151.9 154.7 157.5 160.3 ... 289.7 292.5 295.3 298.1\n",
       "  * time     (time) datetime64[ns] 2016-01-01 ... 2016-12-31T12:00:00\n",
       "  * lat      (lat) float64 79.53 76.74 73.95 71.16 ... 29.3 26.51 23.72 20.93"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da_mag = (ds['uqvi']**2 + ds['vqvi']**2)**(1/2)\n",
    "#da_mag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "flux = da_mag.values.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identify atmospheric rivers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flux magnitude threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_ar = da_mag.where(da_mag >= crit_val)"
   ]
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       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 304</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fbb3a5f0-1067-44b6-aae2-7b9fef1ee5c8' class='xr-array-in' type='checkbox' ><label for='section-fbb3a5f0-1067-44b6-aae2-7b9fef1ee5c8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2016-01-01 2016-01-01T12:00:00 ... 2016-12-31 2016-12-31T12:00:00</span></div><pre class='xr-array-data'>array([&#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-02T00:00:00.000000000&#x27;, ..., &#x27;2016-12-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-31T00:00:00.000000000&#x27;, &#x27;2016-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-section-item'><input id='section-5537ba8e-8139-4ec3-b206-aa9cb041158c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5537ba8e-8139-4ec3-b206-aa9cb041158c' class='xr-section-summary' >Coordinates: <span>(1)</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'>2016-01-01 ... 2016-12-31T12:00:00</div><input id='attrs-3f21e879-07b5-4b34-b21b-f5bee163db45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f21e879-07b5-4b34-b21b-f5bee163db45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d026323-4a7b-4ace-98e1-89e0bf178e6d' class='xr-var-data-in' type='checkbox'><label for='data-2d026323-4a7b-4ace-98e1-89e0bf178e6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-02T00:00:00.000000000&#x27;, ..., &#x27;2016-12-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-31T00:00:00.000000000&#x27;, &#x27;2016-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-c3c6f579-217d-4f63-895b-7108557683be' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c3c6f579-217d-4f63-895b-7108557683be' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 304)>\n",
       "array(['2016-01-01T00:00:00.000000000', '2016-01-01T12:00:00.000000000',\n",
       "       '2016-01-02T00:00:00.000000000', ..., '2016-12-30T12:00:00.000000000',\n",
       "       '2016-12-31T00:00:00.000000000', '2016-12-31T12:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2016-01-01 ... 2016-12-31T12:00:00\n",
       "Attributes:\n",
       "    long_name:      initial time of forecast\n",
       "    standard_name:  forecast_reference_time"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ds_ar.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1051\n",
      "1030\n",
      "1028\n"
     ]
    }
   ],
   "source": [
    "print(np.count_nonzero(np.isnan(ds_ar[0].values)))\n",
    "print(np.count_nonzero(np.isnan(ds_ar[1].values)))\n",
    "print(np.count_nonzero(np.isnan(ds_ar[2].values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify objects using watershed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate through time\n",
    "if era5 == False:\n",
    "    \n",
    "    for t in ds_ar['time'].values:\n",
    "\n",
    "        step = ds_ar[t]\n",
    "        obj = watershed(step)\n",
    "        nobj = np.max(obj)\n",
    "\n",
    "        # Iterate through objects for the time step; ds_ar will update automatically\n",
    "\n",
    "        for nobj in np.arange(1,np.max(obj) + 1):\n",
    "            # Check that net moisture flux is poleward and eastward\n",
    "            if (np.sum(ds['uqvi'][t][np.where(obj==nobj)]) < 0) or (np.sum(ds['vqvi'][t][np.where(obj==nobj)]) < 0):\n",
    "\n",
    "                #print('False')\n",
    "\n",
    "                # If net flux not poleward and eastward, set object to nan\n",
    "                step[np.where(obj==nobj)] = 'nan'\n",
    "\n",
    "            # Object latitudes\n",
    "            objx = ds_ar['lat'][np.unique(np.where(obj==nobj)[0])]\n",
    "\n",
    "            # Object longitudes\n",
    "            objy = ds_ar['lon'][np.unique(np.where(obj==nobj)[1])]\n",
    "\n",
    "            # Sides of the rectangle encompassing the atmospheric river object in kilometers\n",
    "            d1 = haversine([objx[0],objy[0]], [objx[0],objy[-1]])\n",
    "            d2 = haversine([objx[0],objy[-1]], [objx[-1],objy[-1]])\n",
    "\n",
    "            # Assign length and width\n",
    "            if d1 > d2:\n",
    "                length = d1\n",
    "                width = d2\n",
    "            else:\n",
    "                length = d2\n",
    "                width = d1\n",
    "\n",
    "            # Check that object width and length are consistent with atmospheric rivers\n",
    "            if (length < 2000) or (width > 1000):\n",
    "\n",
    "                #If not consistent with width and length, set object to nan\n",
    "                step[np.where(obj==nobj)] = 'nan'\n",
    "\n",
    "            #Finally, check that median latitude is north of 20N\n",
    "            if np.median(step['lat'][np.where(objx)]) < 20:\n",
    "\n",
    "                #If median latitude not north of 20N, set object to nan\n",
    "                step[np.where(obj==nobj)] = 'nan'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "if era5 == True:\n",
    "    for t in np.arange(0,len(ds_ar.time)):\n",
    "\n",
    "        step = ds_ar[t]\n",
    "        obj = watershed(step)\n",
    "        nobj = np.max(obj)\n",
    "\n",
    "        # Iterate through objects for the time step; ds_ar will update automatically\n",
    "\n",
    "        for nobj in np.arange(1,np.max(obj) + 1):\n",
    "            # Check that net moisture flux is poleward and eastward\n",
    "            if (np.sum(ds['uqvi'][t][np.where(obj==nobj)]) < 0) or (np.sum(ds['vqvi'][t][np.where(obj==nobj)]) < 0):\n",
    "\n",
    "                #print('False')\n",
    "\n",
    "                # If net flux not poleward and eastward, set object to nan\n",
    "                step[np.where(obj==nobj)] = 'nan'\n",
    "\n",
    "            # Object latitudes\n",
    "            objx = ds_ar['lat'][np.unique(np.where(obj==nobj)[0])]\n",
    "\n",
    "            # Object longitudes\n",
    "            objy = ds_ar['lon'][np.unique(np.where(obj==nobj)[1])]\n",
    "\n",
    "            # Sides of the rectangle encompassing the atmospheric river object in kilometers\n",
    "            d1 = haversine([objx[0],objy[0]], [objx[0],objy[-1]])\n",
    "            d2 = haversine([objx[0],objy[-1]], [objx[-1],objy[-1]])\n",
    "\n",
    "            # Assign length and width\n",
    "            if d1 > d2:\n",
    "                length = d1\n",
    "                width = d2\n",
    "            else:\n",
    "                length = d2\n",
    "                width = d1\n",
    "\n",
    "            # Check that object width and length are consistent with atmospheric rivers\n",
    "            if (length < 2000) or (width > 1000):\n",
    "\n",
    "                #If not consistent with width and length, set object to nan\n",
    "                step[np.where(obj==nobj)] = 'nan'\n",
    "\n",
    "            #Finally, check that median latitude is north of 20N\n",
    "            if np.median(step['lat'][np.where(objx)]) < 20:\n",
    "\n",
    "                #If median latitude not north of 20N, set object to nan\n",
    "                step[np.where(obj==nobj)] = 'nan'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1166\n",
      "1166\n",
      "1166\n"
     ]
    }
   ],
   "source": [
    "print(np.count_nonzero(np.isnan(ds_ar[0].values)))\n",
    "print(np.count_nonzero(np.isnan(ds_ar[1].values)))\n",
    "print(np.count_nonzero(np.isnan(ds_ar[2].values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_ar.to_dataset(name='river').to_netcdf(f_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count occurrences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "'time' not found in array dimensions ('lat', 'lon')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/gpfs/u/apps/dav/opt/python/3.6.8/gnu/7.3.0/pkg-library/20200417/lib/python3.6/site-packages/xarray/core/common.py\u001b[0m in \u001b[0;36m_get_axis_num\u001b[0;34m(self, dim)\u001b[0m\n\u001b[1;32m    172\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: tuple.index(x): x not in tuple",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-4e402940f328>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mds_ar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds_ar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mds_ar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/gpfs/u/apps/dav/opt/python/3.6.8/gnu/7.3.0/pkg-library/20200417/lib/python3.6/site-packages/xarray/core/common.py\u001b[0m in \u001b[0;36mwrapped_func\u001b[0;34m(self, dim, axis, **kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mwrapped_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped_func\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/gpfs/u/apps/dav/opt/python/3.6.8/gnu/7.3.0/pkg-library/20200417/lib/python3.6/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mreduce\u001b[0;34m(self, func, dim, axis, keep_attrs, keepdims, **kwargs)\u001b[0m\n\u001b[1;32m   2244\u001b[0m         \"\"\"\n\u001b[1;32m   2245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2246\u001b[0;31m         \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_attrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2247\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_replace_maybe_drop_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/gpfs/u/apps/dav/opt/python/3.6.8/gnu/7.3.0/pkg-library/20200417/lib/python3.6/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36mreduce\u001b[0;34m(self, func, dim, axis, keep_attrs, keepdims, allow_lazy, **kwargs)\u001b[0m\n\u001b[1;32m   1560\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1561\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1562\u001b[0;31m             \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_axis_num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mallow_lazy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/gpfs/u/apps/dav/opt/python/3.6.8/gnu/7.3.0/pkg-library/20200417/lib/python3.6/site-packages/xarray/core/common.py\u001b[0m in \u001b[0;36mget_axis_num\u001b[0;34m(self, dim)\u001b[0m\n\u001b[1;32m    167\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_axis_num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mHashable\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/gpfs/u/apps/dav/opt/python/3.6.8/gnu/7.3.0/pkg-library/20200417/lib/python3.6/site-packages/xarray/core/common.py\u001b[0m in \u001b[0;36m_get_axis_num\u001b[0;34m(self, dim)\u001b[0m\n\u001b[1;32m    173\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{dim!r} not found in array dimensions {self.dims!r}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: 'time' not found in array dimensions ('lat', 'lon')"
     ]
    }
   ],
   "source": [
    "ds_ar = ds_ar.count('time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Atmospheric River Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = 220\n",
    "\n",
    "data_crs = ccrs.PlateCarree(central_longitude=cm)\n",
    "proj = ccrs.NorthPolarStereo(central_longitude=cm)\n",
    "\n",
    "# Set the axes using the specified map projection\n",
    "fig,axs=plt.subplots(1,1,subplot_kw={'projection':proj},figsize=(20,15))\n",
    "\n",
    "cs = axs.contourf(ds_ar['lon'], ds_ar['lat'], ds_ar, transform=ccrs.PlateCarree())\n",
    "axs.coastlines()\n",
    "cbar_ax = fig.add_axes([0.3, 0.5, 0.4, 0.02])\n",
    "cbar=fig.colorbar(cs, cax=cbar_ax,orientation='horizontal')\n",
    "cbar.ax.tick_params(labelsize=14)\n",
    "fig.subplots_adjust(bottom=0, top=1.5, left=0.2, right=0.8,\n",
    "                    wspace=0.01, hspace=0.2)\n",
    "if era5 == False:\n",
    "    plt.suptitle('Metis' + res + ' Atmospheric River Count, Threshold Value = ' + str(crit_val) + ', Upper '+ threshold,\n",
    "                fontsize=20)\n",
    "else:\n",
    "    plt.suptitle('ERA5 Atmospheric River Count, Threshold Value = ' + str(crit_val) + ', Upper '+ threshold,\n",
    "                fontsize=20)\n",
    "#plt.savefig(f_pic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}