pdf_RegCM(rcp2.6)Miroc5+Mpi+Noresm.ipynb

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   "source": [
    "#writing some conda package or module\n",
    "#!pip install netCDF4\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "from netCDF4 import Dataset\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "#!pip install cartopy\n",
    "from scipy import stats\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.mpl.ticker as cticker\n",
    "from cartopy.util import add_cyclic_point\n",
    "\n",
    "\n",
    "import cf_units\n",
    "import glob\n",
    "import datetime\n",
    "import numpy as np\n",
    "import netCDF4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b4e84e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "rain_minimum = 1.0\n",
    "rain_maximum = 250.0\n",
    "rain_step = 1.0\n",
    "spd = 86400.0\n",
    "sph = 3600.0\n",
    "mpy = 30.4167\n",
    "\n",
    "nrainbin = int((rain_maximum-rain_minimum)/rain_step) + 1\n",
    "\n",
    "rain_pdf = np.linspace(rain_minimum,rain_maximum, num=nrainbin, endpoint=True)\n",
    "rain_bin = np.zeros((nrainbin-1))\n",
    "\n",
    "dt1 = datetime.datetime(1981,1,15,12,0,0)\n",
    "dt2 = datetime.datetime(2015,12,16,0,0,0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e0ae7567",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/mkibria/vms/ICTP RegCM/MIROC5/pr/daily/pr_WAS-22_MIROC5_historical_r1i1p1_ICTP-RegCM4-7_v0_day_1994-2014.nc\n"
     ]
    }
   ],
   "source": [
    "#for the regcm miroc5  present 1994-2014\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "prmiroc = glob.glob('/scratch/mkibria/vms/ICTP RegCM/MIROC5/pr/daily/pr_WAS-22_MIROC5_historical_r1i1p1_ICTP-RegCM4-7_v0_day_1994-2014.nc')\n",
    " \n",
    "prmiroc.sort( )\n",
    "\n",
    "rain_bin_mi = rain_bin \n",
    "\n",
    "dt1 = datetime.datetime(1994,1,1,12,0,0)\n",
    "dt2 = datetime.datetime(2014,12,31,0,0,0)\n",
    "\n",
    "\n",
    "for file in prmiroc:\n",
    "    print(file)\n",
    "    fch = netCDF4.Dataset(file)\n",
    "    time = fch.variables['time'][:]\n",
    "    tunits = fch.variables['time'].units\n",
    "    st1 = cf_units.date2num(dt1, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    st2 = cf_units.date2num(dt2, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    itime = np.argwhere(np.logical_and(time > st1, time < st2))\n",
    "    if len(itime) == 0:\n",
    "        continue\n",
    "    lat = fch.variables['lat'][:]\n",
    "    lon = fch.variables['lon'][:]\n",
    "    ipnt = np.argwhere(np.logical_and(\n",
    "                       np.logical_and(lat > 18.0, lat < 28.0),\n",
    "                       np.logical_and(lon > 86.0, lon < 94.0)))\n",
    "    la1 = min(ipnt, key = lambda t: t[0])[0]\n",
    "    la2 = max(ipnt, key = lambda t: t[0])[0]\n",
    "    lo1 = min(ipnt, key = lambda t: t[1])[1]\n",
    "    lo2 = max(ipnt, key = lambda t: t[1])[1]\n",
    "    pr = fch.variables['pr']\n",
    "    for it in itime:\n",
    "        #print(it)\n",
    "        data = pr[it,la1:la2,lo1:lo2] * 86400.0\n",
    "        nzd = data[tuple(np.where(data >= rain_minimum))]\n",
    "        hist = np.histogram(nzd,\n",
    "                            bins = nrainbin-1,\n",
    "                            range = (rain_minimum, rain_maximum))\n",
    "        rain_bin_mi = rain_bin_mi + hist[0]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "116bf5ab",
   "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/mkibria/vms/ICTP RegCM/MIROC5/rcp2.6/pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2041-2060.nc\n"
     ]
    }
   ],
   "source": [
    "#for the regcm miroc5 rcp26 mid future(2041-2060)\n",
    "\n",
    "prmirocm = glob.glob('/scratch/mkibria/vms/ICTP RegCM/MIROC5/rcp2.6/pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2041-2060.nc')\n",
    "\n",
    "prmirocm.sort( )\n",
    "\n",
    "rain_bin_mid = rain_bin \n",
    "dt1 = datetime.datetime(2041,1,1,12,0,0)\n",
    "dt2 = datetime.datetime(2060,12,31,0,0,0)\n",
    "\n",
    "for file in prmirocm:\n",
    "    print(file)\n",
    "    fch = netCDF4.Dataset(file)\n",
    "    time = fch.variables['time'][:]\n",
    "    tunits = fch.variables['time'].units\n",
    "    st1 = cf_units.date2num(dt1, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    st2 = cf_units.date2num(dt2, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    itime = np.argwhere(np.logical_and(time > st1, time < st2))\n",
    "    if len(itime) == 0:\n",
    "        continue\n",
    "    lat = fch.variables['lat'][:]\n",
    "    lon = fch.variables['lon'][:]\n",
    "    ipnt = np.argwhere(np.logical_and(\n",
    "                       np.logical_and(lat > 18.0, lat < 28.0),\n",
    "                       np.logical_and(lon > 86.0, lon < 94.0)))\n",
    "    la1 = min(ipnt, key = lambda t: t[0])[0]\n",
    "    la2 = max(ipnt, key = lambda t: t[0])[0]\n",
    "    lo1 = min(ipnt, key = lambda t: t[1])[1]\n",
    "    lo2 = max(ipnt, key = lambda t: t[1])[1]\n",
    "    pr = fch.variables['pr']\n",
    "    for it in itime:\n",
    "        #print(it)\n",
    "        data = pr[it,la1:la2,lo1:lo2] * 86400.0\n",
    "        nzd = data[tuple(np.where(data >= rain_minimum))]\n",
    "        hist = np.histogram(nzd,\n",
    "                            bins = nrainbin-1,\n",
    "                            range = (rain_minimum, rain_maximum))\n",
    "        rain_bin_mid = rain_bin_mid + hist[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5789275c",
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       "Data variables:\n",
       "    time_bnds  (time, bnds) object 2081-01-01 00:00:00 ... 2100-01-01 00:00:00\n",
       "    crs        |S1 b&#x27;&#x27;\n",
       "    pr         (time, y, x) float32 ...\n",
       "Attributes: (12/165)\n",
       "    CDI:                                             Climate Data Interface v...\n",
       "    Conventions:                                     CF-1.7\n",
       "    history:                                         Wed Aug 03 11:20:40 2022...\n",
       "    source:                                          RegCM Model output file\n",
       "    institution:                                     Oak Ridge National Labor...\n",
       "    project_id:                                      CORDEX\n",
       "    ...                                              ...\n",
       "    uwpbl_cloud_evap_entr_incr_efficiency:           15.0\n",
       "    uwpbl_eddy_LS_stable_PBL_scaling:                1.5\n",
       "    uwpbl_czero:                                     5.869\n",
       "    uwpbl_nuk:                                       5.0\n",
       "    frequency:                                       day\n",
       "    CDO:                                             Climate Data Operators v...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-92ec168a-92ff-4326-8103-a01263443d42' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-92ec168a-92ff-4326-8103-a01263443d42' 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'>time</span>: 6935</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>y</span>: 337</li><li><span class='xr-has-index'>x</span>: 429</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8bdd5bf1-cb3c-40e0-b8b0-be4987b22536' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8bdd5bf1-cb3c-40e0-b8b0-be4987b22536' class='xr-section-summary' >Coordinates: <span>(5)</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'>object</div><div class='xr-var-preview xr-preview'>2081-01-01 12:00:00 ... 2099-12-...</div><input id='attrs-ff0eb528-afb1-4e50-a562-b1c5f08837ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff0eb528-afb1-4e50-a562-b1c5f08837ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4792c56e-5dd1-4163-8997-a2121d6697fa' class='xr-var-data-in' type='checkbox'><label for='data-4792c56e-5dd1-4163-8997-a2121d6697fa' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2081, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2081, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2081, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2099, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2099, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2099, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b35e3384-e610-4956-9a8d-1ff279c2cc74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b35e3384-e610-4956-9a8d-1ff279c2cc74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc6e6e27-ccc9-4410-95d6-a46f6138d7e2' class='xr-var-data-in' type='checkbox'><label for='data-fc6e6e27-ccc9-4410-95d6-a46f6138d7e2' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude on Cross Points</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>[144573 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b303a0f3-6029-491b-8930-a20964aa9612' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b303a0f3-6029-491b-8930-a20964aa9612' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90a888d7-494c-4e7f-a1d0-baf32e2ab3d3' class='xr-var-data-in' type='checkbox'><label for='data-90a888d7-494c-4e7f-a1d0-baf32e2ab3d3' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude on Cross Points</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>[144573 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.35e+06 -5.325e+06 ... 5.35e+06</div><input id='attrs-652025ef-09be-4782-af3c-4adf87395dc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-652025ef-09be-4782-af3c-4adf87395dc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3d8aeb7-325e-498c-8ce9-96f6222b4a6a' class='xr-var-data-in' type='checkbox'><label for='data-f3d8aeb7-325e-498c-8ce9-96f6222b4a6a' 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>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>long_name :</span></dt><dd>x-coordinate in Cartesian system</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-5350000., -5325000., -5300000., ...,  5300000.,  5325000.,  5350000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.2e+06 -4.175e+06 ... 4.2e+06</div><input id='attrs-da79deff-38fa-4694-99b9-5b1d58fb3625' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da79deff-38fa-4694-99b9-5b1d58fb3625' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d60f0d96-006b-4770-9bab-b13011a3f939' class='xr-var-data-in' type='checkbox'><label for='data-d60f0d96-006b-4770-9bab-b13011a3f939' 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>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>long_name :</span></dt><dd>y-coordinate in Cartesian system</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-4200000., -4175000., -4150000., ...,  4150000.,  4175000.,  4200000.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8c14021f-6e04-41eb-b46b-912def6c8e10' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8c14021f-6e04-41eb-b46b-912def6c8e10' class='xr-section-summary' >Data variables: <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>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-44b41722-c88e-47cb-ae9c-a86bd5b3d3b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44b41722-c88e-47cb-ae9c-a86bd5b3d3b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13c6320e-b9e1-4f8b-8973-cdd1dafc05da' class='xr-var-data-in' type='checkbox'><label for='data-13c6320e-b9e1-4f8b-8973-cdd1dafc05da' 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([[cftime.DatetimeNoLeap(2081, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2081, 1, 2, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2081, 1, 2, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2081, 1, 3, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2081, 1, 3, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2081, 1, 4, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       ...,\n",
       "       [cftime.DatetimeNoLeap(2099, 12, 29, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2099, 12, 30, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2099, 12, 30, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2099, 12, 31, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2099, 12, 31, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2100, 1, 1, 0, 0, 0, 0, has_year_zero=True)]],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-439252a4-9ab0-4f97-a40f-6e4304f3cb44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-439252a4-9ab0-4f97-a40f-6e4304f3cb44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae5a1753-2936-4401-9518-445cae4f16f2' class='xr-var-data-in' type='checkbox'><label for='data-ae5a1753-2936-4401-9518-445cae4f16f2' 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>proj4_params :</span></dt><dd>+proj=omerc +lat_0=13.00 +alpha=89.999999 +lonc=70.00 +x_0=-12500. +y_0=-12500. +ellps=sphere +a=6371229. +b=6371229. +units=m +no_defs</dd><dt><span>semi_major_axis :</span></dt><dd>6371229.0</dd><dt><span>inverse_flattening :</span></dt><dd>0.0</dd><dt><span>grid_mapping_name :</span></dt><dd>oblique_mercator</dd><dt><span>azimuth_of_central_line :</span></dt><dd>89.999999</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>13.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>70.0</dd><dt><span>scale_factor_at_projection_origin :</span></dt><dd>1.0</dd><dt><span>false_easting :</span></dt><dd>-12500.0</dd><dt><span>false_northing :</span></dt><dd>-12500.0</dd></dl></div><div class='xr-var-data'><pre>array(b&#x27;&#x27;, dtype=&#x27;|S1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8a197702-2858-4f6a-8a43-914535d333d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a197702-2858-4f6a-8a43-914535d333d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a792e03-3f4d-4beb-8b88-4335330837fa' class='xr-var-data-in' type='checkbox'><label for='data-4a792e03-3f4d-4beb-8b88-4335330837fa' 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>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>[1002613755 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77ff1c7c-9920-4c39-ac66-ef5967732108' class='xr-section-summary-in' type='checkbox'  ><label for='section-77ff1c7c-9920-4c39-ac66-ef5967732108' class='xr-section-summary' >Attributes: <span>(165)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>history :</span></dt><dd>Wed Aug 03 11:20:40 2022: cdo mergetime pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20810102-20810131.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20810202-20810228.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20810302-20810331.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20810402-20810430.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20810502-20810531.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20810602-20810630.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20810702-20810731.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20810802-20810831.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20810902-20810930.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20811002-20811031.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20811102-20811130.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20811202-20811231.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20820102-20820131.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20820202-20820228.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20820302-20820331.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20820402-20820430.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20820502-20820531.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20820602-20820630.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20820702-20820731.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20820802-20820831.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20820902-20820930.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20821002-20821031.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20821102-20821130.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20821202-20821231.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20830102-20830131.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20830202-20830228.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20830302-20830331.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20830402-20830430.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20830502-20830531.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20830602-20830630.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20830702-20830731.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20830802-20830831.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20830902-20830930.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20831002-20831031.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20831102-20831130.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20831202-20831231.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20840102-20840131.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20840202-20840228.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20840302-20840331.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20840402-20840430.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20840502-20840531.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20840602-20840630.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20840702-20840731.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20840802-20840831.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20840902-20840930.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20841002-20841031.nc 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pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20940802-20940831.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20940902-20940930.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20941002-20941031.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20941102-20941130.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20941202-20941231.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20950102-20950131.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20950202-20950228.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20950302-20950331.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20950402-20950430.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20950502-20950531.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20950602-20950630.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20950702-20950731.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20950802-20950831.nc 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pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20961002-20961031.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20961102-20961130.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20961202-20961231.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20970102-20970131.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20970202-20970228.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20970302-20970331.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20970402-20970430.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20970502-20970531.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20970602-20970630.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20970702-20970731.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20970802-20970831.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20970902-20970930.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20971002-20971031.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20971102-20971130.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20971202-20971231.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20980102-20980131.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20980202-20980228.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20980302-20980331.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20980402-20980430.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20980502-20980531.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20980602-20980630.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20980702-20980731.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20980802-20980831.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20980902-20980930.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20981002-20981031.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20981102-20981130.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20981202-20981231.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20990102-20990131.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20990202-20990228.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20990302-20990331.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20990402-20990430.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20990502-20990531.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20990602-20990630.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20990702-20990731.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20990802-20990831.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20990902-20990930.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20991002-20991031.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20991102-20991130.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_20991202-20991231.nc pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2081-2100.nc\n",
       "2019-02-07 11:34:50 : Created by RegCM RegCM Model program</dd><dt><span>source :</span></dt><dd>RegCM Model output file</dd><dt><span>institution :</span></dt><dd>Oak Ridge National Laboratory</dd><dt><span>project_id :</span></dt><dd>CORDEX</dd><dt><span>ipcc_scenario_code :</span></dt><dd>RCP26</dd><dt><span>institute_id :</span></dt><dd>ORNL</dd><dt><span>note :</span></dt><dd>The domain is larger than WAS-22</dd><dt><span>comment :</span></dt><dd>RegCM CORDEX SAsia25 run</dd><dt><span>experiment :</span></dt><dd>WAS-22</dd><dt><span>experiment_id :</span></dt><dd>rcp26</dd><dt><span>driving_experiment :</span></dt><dd>MIROC5, rcp26, r1i1p1</dd><dt><span>driving_model_id :</span></dt><dd>MIROC5</dd><dt><span>driving_model_ensemble_member :</span></dt><dd>r1i1p1</dd><dt><span>driving_experiment_name :</span></dt><dd>rcp26</dd><dt><span>model_id :</span></dt><dd>ICTP-RegCM4-7</dd><dt><span>creation_date :</span></dt><dd>2019-06-08T07:53:39Z</dd><dt><span>CORDEX_domain :</span></dt><dd>WAS-22</dd><dt><span>rcm_version_id :</span></dt><dd>v0</dd><dt><span>ICTP_version_note :</span></dt><dd>Model run at ORNL on Titan</dd><dt><span>contact :</span></dt><dd>mashfaq@ornl.gov</dd><dt><span>product :</span></dt><dd>output</dd><dt><span>tracking_id :</span></dt><dd>0e72f22e-89e4-11e9-a513-7845c4fb261b</dd><dt><span>title :</span></dt><dd>ICTP Regional Climatic model V4</dd><dt><span>references :</span></dt><dd>http://gforge.ictp.it/gf/project/regcm</dd><dt><span>model_revision :</span></dt><dd>tag-4.7.0</dd><dt><span>projection :</span></dt><dd>ROTMER</dd><dt><span>grid_size_in_meters :</span></dt><dd>25000.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>13.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>70.0</dd><dt><span>grid_north_pole_latitude :</span></dt><dd>13.0</dd><dt><span>grid_north_pole_longitude :</span></dt><dd>70.0</dd><dt><span>grid_factor :</span></dt><dd>0.0</dd><dt><span>landsurface_model :</span></dt><dd>clm4.5</dd><dt><span>model_icbc_data_source :</span></dt><dd>MI_26</dd><dt><span>model_sst_data_source :</span></dt><dd>MI_26</dd><dt><span>boundary_nspgx :</span></dt><dd>30</dd><dt><span>boundary_nspgd :</span></dt><dd>30</dd><dt><span>boundary_high_nudge :</span></dt><dd>8.0</dd><dt><span>boundary_medium_nudge :</span></dt><dd>6.0</dd><dt><span>boundary_low_nudge :</span></dt><dd>4.0</dd><dt><span>boundary_nm :</span></dt><dd>-1.0</dd><dt><span>boundary_dm :</span></dt><dd>-1.0</dd><dt><span>model_is_restarted :</span></dt><dd>Yes</dd><dt><span>model_simulation_initial_start :</span></dt><dd> 1970-01-01 00:00:00 UTC</dd><dt><span>model_simulation_start :</span></dt><dd> 2080-11-01 00:00:00 UTC</dd><dt><span>model_simulation_end :</span></dt><dd> 2100-01-01 00:00:00 UTC</dd><dt><span>atmosphere_time_step_in_seconds :</span></dt><dd>40.0</dd><dt><span>surface_interaction_time_step_in_seconds :</span></dt><dd>480.0</dd><dt><span>convection_time_step_in_seconds :</span></dt><dd>240.0</dd><dt><span>radiation_scheme_time_step_in_seconds :</span></dt><dd>1800.0</dd><dt><span>absorption_emission_time_step_in_seconds :</span></dt><dd>64800.0</dd><dt><span>dynamical_core :</span></dt><dd>1</dd><dt><span>asselin_filter_nu_1 :</span></dt><dd>0.0625</dd><dt><span>asselin_filter_nu_2 :</span></dt><dd>0.0625</dd><dt><span>diffusion_hgt_factor :</span></dt><dd>1</dd><dt><span>upstream_mode :</span></dt><dd>Yes</dd><dt><span>off_centering_max :</span></dt><dd>0.25</dd><dt><span>stability_enhance :</span></dt><dd>Yes</dd><dt><span>temperature_extreme_gradient :</span></dt><dd>5.0</dd><dt><span>cloud_extreme_gradient_fraction :</span></dt><dd>0.5</dd><dt><span>vapor_extreme_gradient_fraction :</span></dt><dd>0.2</dd><dt><span>tracer_extreme_gradient_fraction :</span></dt><dd>0.5</dd><dt><span>lateral_boundary_condition_scheme :</span></dt><dd>5</dd><dt><span>semi_lagrangian_advection_scheme :</span></dt><dd>0</dd><dt><span>boundary_layer_scheme :</span></dt><dd>2</dd><dt><span>cumulus_convection_scheme_lnd :</span></dt><dd>4</dd><dt><span>cumulus_convection_scheme_ocn :</span></dt><dd>5</dd><dt><span>moisture_scheme :</span></dt><dd>1</dd><dt><span>ocean_flux_scheme :</span></dt><dd>2</dd><dt><span>zeng_ocean_roughness_formula :</span></dt><dd>1</dd><dt><span>zeng_ocean_roughness_method :</span></dt><dd>1</dd><dt><span>pressure_gradient_scheme :</span></dt><dd>0</dd><dt><span>surface_emissivity_factor_computed :</span></dt><dd>0</dd><dt><span>lake_model_activated :</span></dt><dd>0</dd><dt><span>chemical_aerosol_scheme_activated :</span></dt><dd>0</dd><dt><span>diurnal_cycle_sst_scheme :</span></dt><dd>0</dd><dt><span>ocean_precipitaion_cooling_effect :</span></dt><dd>0</dd><dt><span>ocean_albedo_from_whitecapping :</span></dt><dd>0</dd><dt><span>simple_sea_ice_scheme :</span></dt><dd>0</dd><dt><span>seasonal_desert_albedo :</span></dt><dd>0</dd><dt><span>convective_lwp_as_large_scale :</span></dt><dd>0</dd><dt><span>large_scale_cloud_fraction_scheme :</span></dt><dd>0</dd><dt><span>ocean_stratiform_clouds_scheme :</span></dt><dd>1</dd><dt><span>rrtm_radiation_scheme_activated :</span></dt><dd>0</dd><dt><span>climatic_ozone_input_dataset :</span></dt><dd>0</dd><dt><span>climatic_aerosol_input_dataset :</span></dt><dd>0</dd><dt><span>static_solar_constant_used :</span></dt><dd>0</dd><dt><span>cumulus_cloud_model :</span></dt><dd>1</dd><dt><span>sun_year_offset :</span></dt><dd>0.0</dd><dt><span>fix_solar_parameters :</span></dt><dd>0</dd><dt><span>cloud_bottom_level_with_no_clouds :</span></dt><dd>1</dd><dt><span>cloud_rh_with_fcc_one :</span></dt><dd>1.01</dd><dt><span>cloud_rh_threshold_for_land :</span></dt><dd>0.8</dd><dt><span>cloud_rh_threshold_for_ocean :</span></dt><dd>0.9</dd><dt><span>cloud_rh1_limit_temperature :</span></dt><dd>238.0</dd><dt><span>cloud_cloud_fraction_maximum :</span></dt><dd>0.75</dd><dt><span>cloud_condensation_threshold :</span></dt><dd>1.0</dd><dt><span>cloud_cloud_fraction_max_for_convection :</span></dt><dd>1.0</dd><dt><span>cloud_cloud_liqwat_max_for_convection :</span></dt><dd>0.0003</dd><dt><span>cloud_shallow_convective_cf_param :</span></dt><dd>0.07</dd><dt><span>cloud_deep_convective_cf_param :</span></dt><dd>0.14</dd><dt><span>cloud_k2_convective_cf_param :</span></dt><dd>500.0</dd><dt><span>cloud_surface_radiation_hack :</span></dt><dd>No</dd><dt><span>cloud_arctic_cloud_correction :</span></dt><dd>Yes</dd><dt><span>subex_auto_conversion_rate_for_land :</span></dt><dd>0.00025</dd><dt><span>subex_auto_conversion_rate_for_ocean :</span></dt><dd>0.00025</dd><dt><span>subex_gultepe_factor_when_rain_for_land :</span></dt><dd>0.4</dd><dt><span>subex_gultepe_factor_when_rain_for_ocean :</span></dt><dd>0.4</dd><dt><span>subex_land_raindrop_evaporation_rate :</span></dt><dd>0.0002</dd><dt><span>subex_ocean_raindrop_evaporation_rate :</span></dt><dd>0.0001</dd><dt><span>subex_land_raindrop_accretion_rate :</span></dt><dd>3.0</dd><dt><span>subex_ocean_raindrop_accretion_rate :</span></dt><dd>3.0</dd><dt><span>mit_lowest_convection_sigma :</span></dt><dd>0.95</dd><dt><span>mit_autoconversion_threshold_mixing_over_ocean :</span></dt><dd>0.00011</dd><dt><span>mit_autoconversion_threshold_mixing_over_land :</span></dt><dd>0.00011</dd><dt><span>mit_autoconversion_threshold_temperature :</span></dt><dd>-55.0</dd><dt><span>mit_mixing_coefficient_in_entrainment :</span></dt><dd>1.5</dd><dt><span>mit_fractional_area_unsaturated_downdraft :</span></dt><dd>0.05</dd><dt><span>mit_fractional_precip_outside_cloud :</span></dt><dd>0.12</dd><dt><span>mit_pressure_velocity_of_rain :</span></dt><dd>50.0</dd><dt><span>mit_pressure_velocity_of_snow :</span></dt><dd>5.5</dd><dt><span>mit_rain_evaporation_coefficient :</span></dt><dd>1.0</dd><dt><span>mit_snow_evaporation_coefficient :</span></dt><dd>0.8</dd><dt><span>mit_momentum_transport_coefficient :</span></dt><dd>0.7</dd><dt><span>mit_downdraft_velocity_coefficient :</span></dt><dd>10.0</dd><dt><span>mit_max_parcel_neg_temp_perturbation :</span></dt><dd>0.9</dd><dt><span>mit_approach_rate_quasi_eq_coeff_a :</span></dt><dd>0.2</dd><dt><span>mit_approach_rate_quasi_eq_coeff_d :</span></dt><dd>0.1</dd><dt><span>mit_maximum_land_precipitation_efficiency :</span></dt><dd>0.999</dd><dt><span>mit_maximum_ocean_precipitation_efficiency :</span></dt><dd>0.999</dd><dt><span>tiedtke_actual_scheme :</span></dt><dd>4</dd><dt><span>tiedtke_entrainment_rate_downdraft :</span></dt><dd>0.0003</dd><dt><span>tiedtke_entrainment_rate_deep_land :</span></dt><dd>0.00075</dd><dt><span>tiedtke_entrainment_rate_deep_ocean :</span></dt><dd>0.00075</dd><dt><span>tiedtke_penetrative :</span></dt><dd>Yes</dd><dt><span>tiedtke_midlevel :</span></dt><dd>Yes</dd><dt><span>tiedtke_shallow :</span></dt><dd>Yes</dd><dt><span>tiedtke_cumulus_downdraft :</span></dt><dd>Yes</dd><dt><span>tiedtke_prognostic_cloud :</span></dt><dd>Yes</dd><dt><span>tiedtke_cumulus_friction :</span></dt><dd>Yes</dd><dt><span>tiedtke_ke_dissipation :</span></dt><dd>Yes</dd><dt><span>tiedtke_tracer_transport :</span></dt><dd>Yes</dd><dt><span>tiedtke_tracer_smooth_massflux :</span></dt><dd>No</dd><dt><span>tiedtke_shallow_wstar_closure :</span></dt><dd>No</dd><dt><span>tiedtke_detrainment_rate_deep_land :</span></dt><dd>7.5e-05</dd><dt><span>tiedtke_detrainment_rate_deep_ocean :</span></dt><dd>7.5e-05</dd><dt><span>tiedtke_shallow_entrainment :</span></dt><dd>2.0</dd><dt><span>tiedtke_cloud_cover_evap_over_land :</span></dt><dd>0.05</dd><dt><span>tiedtke_cloud_cover_evap_over_ocean :</span></dt><dd>0.05</dd><dt><span>tiedtke_coeff_evap_over_land :</span></dt><dd>5.55e-05</dd><dt><span>tiedtke_coeff_evap_over_ocean :</span></dt><dd>5.55e-05</dd><dt><span>tiedtke_critical_rh_over_land :</span></dt><dd>0.9</dd><dt><span>tiedtke_critical_rh_over_ocean :</span></dt><dd>0.9</dd><dt><span>tiedtke_cloud_water_conv_over_land :</span></dt><dd>0.03</dd><dt><span>tiedtke_cloud_water_conv_over_ocean :</span></dt><dd>0.0014</dd><dt><span>tiedtke_cape_adjustment_timescale :</span></dt><dd>2400.0</dd><dt><span>uwpbl_advection_scheme :</span></dt><dd>0</dd><dt><span>uwpbl_cloud_evap_entr_incr_efficiency :</span></dt><dd>15.0</dd><dt><span>uwpbl_eddy_LS_stable_PBL_scaling :</span></dt><dd>1.5</dd><dt><span>uwpbl_czero :</span></dt><dd>5.869</dd><dt><span>uwpbl_nuk :</span></dt><dd>5.0</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 6935, bnds: 2, y: 337, x: 429)\n",
       "Coordinates:\n",
       "  * time       (time) object 2081-01-01 12:00:00 ... 2099-12-31 12:00:00\n",
       "    lon        (y, x) float64 ...\n",
       "    lat        (y, x) float64 ...\n",
       "  * x          (x) float64 -5.35e+06 -5.325e+06 -5.3e+06 ... 5.325e+06 5.35e+06\n",
       "  * y          (y) float64 -4.2e+06 -4.175e+06 -4.15e+06 ... 4.175e+06 4.2e+06\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) object ...\n",
       "    crs        |S1 ...\n",
       "    pr         (time, y, x) float32 ...\n",
       "Attributes: (12/165)\n",
       "    CDI:                                             Climate Data Interface v...\n",
       "    Conventions:                                     CF-1.7\n",
       "    history:                                         Wed Aug 03 11:20:40 2022...\n",
       "    source:                                          RegCM Model output file\n",
       "    institution:                                     Oak Ridge National Labor...\n",
       "    project_id:                                      CORDEX\n",
       "    ...                                              ...\n",
       "    uwpbl_cloud_evap_entr_incr_efficiency:           15.0\n",
       "    uwpbl_eddy_LS_stable_PBL_scaling:                1.5\n",
       "    uwpbl_czero:                                     5.869\n",
       "    uwpbl_nuk:                                       5.0\n",
       "    frequency:                                       day\n",
       "    CDO:                                             Climate Data Operators v..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prmirocf = xr.open_dataset('/scratch/mkibria/vms/ICTP RegCM/MIROC5/rcp2.6/pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2081-2100.nc')\n",
    "prmirocf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "89ffda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/mkibria/vms/ICTP RegCM/MIROC5/rcp2.6/pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2081-2100.nc\n"
     ]
    }
   ],
   "source": [
    "#for the regcm miroc5 rcp26 far future(2081-2100)\n",
    "\n",
    "prmirocf = glob.glob('/scratch/mkibria/vms/ICTP RegCM/MIROC5/rcp2.6/pr_WAS-22_MIROC5_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2081-2100.nc')\n",
    "\n",
    "prmirocf.sort( )\n",
    "\n",
    "rain_bin_mif = rain_bin \n",
    "dt1 = datetime.datetime(2081,1,1,12,0,0)\n",
    "dt2 = datetime.datetime(2100,12,31,0,0,0)\n",
    "\n",
    "for file in prmirocf:\n",
    "    print(file)\n",
    "    fch = netCDF4.Dataset(file)\n",
    "    time = fch.variables['time'][:]\n",
    "    tunits = fch.variables['time'].units\n",
    "    st1 = cf_units.date2num(dt1, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    st2 = cf_units.date2num(dt2, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    itime = np.argwhere(np.logical_and(time > st1, time < st2))\n",
    "    if len(itime) == 0:\n",
    "        continue\n",
    "    lat = fch.variables['lat'][:]\n",
    "    lon = fch.variables['lon'][:]\n",
    "    ipnt = np.argwhere(np.logical_and(\n",
    "                       np.logical_and(lat > 18.0, lat < 28.0),\n",
    "                       np.logical_and(lon > 86.0, lon < 94.0)))\n",
    "    la1 = min(ipnt, key = lambda t: t[0])[0]\n",
    "    la2 = max(ipnt, key = lambda t: t[0])[0]\n",
    "    lo1 = min(ipnt, key = lambda t: t[1])[1]\n",
    "    lo2 = max(ipnt, key = lambda t: t[1])[1]\n",
    "    pr = fch.variables['pr']\n",
    "    for it in itime:\n",
    "        #print(it)\n",
    "        data = pr[it,la1:la2,lo1:lo2] * 86400.0\n",
    "        nzd = data[tuple(np.where(data >= rain_minimum))]\n",
    "        hist = np.histogram(nzd,\n",
    "                            bins = nrainbin-1,\n",
    "                            range = (rain_minimum, rain_maximum))\n",
    "        rain_bin_mif = rain_bin_mif + hist[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a82d6a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "prmpi = xr.open_dataset('/scratch/mkibria/vms/ICTP RegCM/MPI-ESM-MR/pr/daily/pr_WAS-22_MPI-M-MPI-ESM-MR_historical_r1i1p1_ICTP-RegCM4-7_v0_daily_1994-2014.nc')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e206fe03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/mkibria/vms/ICTP RegCM/MPI-ESM-MR/pr/daily/pr_WAS-22_MPI-M-MPI-ESM-MR_historical_r1i1p1_ICTP-RegCM4-7_v0_daily_1994-2014.nc\n"
     ]
    }
   ],
   "source": [
    "#for the regcm mpiesm present day 1994-2014\n",
    "\n",
    "prmpi = glob.glob('/scratch/mkibria/vms/ICTP RegCM/MPI-ESM-MR/pr/daily/pr_WAS-22_MPI-M-MPI-ESM-MR_historical_r1i1p1_ICTP-RegCM4-7_v0_daily_1994-2014.nc')\n",
    "\n",
    "prmpi.sort( )\n",
    "\n",
    "rain_bin_mp = rain_bin \n",
    "dt1 = datetime.datetime(1994,1,1,12,0,0)\n",
    "dt2 = datetime.datetime(2014,12,31,0,0,0)\n",
    "\n",
    "for file in prmpi:\n",
    "    print(file)\n",
    "    fch = netCDF4.Dataset(file)\n",
    "    time = fch.variables['time'][:]\n",
    "    tunits = fch.variables['time'].units\n",
    "    st1 = cf_units.date2num(dt1, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    st2 = cf_units.date2num(dt2, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    itime = np.argwhere(np.logical_and(time > st1, time < st2))\n",
    "    if len(itime) == 0:\n",
    "        continue\n",
    "    lat = fch.variables['lat'][:]\n",
    "    lon = fch.variables['lon'][:]\n",
    "    ipnt = np.argwhere(np.logical_and(\n",
    "                       np.logical_and(lat > 18.0, lat < 28.0),\n",
    "                       np.logical_and(lon > 86.0, lon < 94.0)))\n",
    "    la1 = min(ipnt, key = lambda t: t[0])[0]\n",
    "    la2 = max(ipnt, key = lambda t: t[0])[0]\n",
    "    lo1 = min(ipnt, key = lambda t: t[1])[1]\n",
    "    lo2 = max(ipnt, key = lambda t: t[1])[1]\n",
    "    pr = fch.variables['pr']\n",
    "    for it in itime:\n",
    "        #print(it)\n",
    "        data = pr[it,la1:la2,lo1:lo2] * 86400.0\n",
    "        nzd = data[tuple(np.where(data >= rain_minimum))]\n",
    "        hist = np.histogram(nzd,\n",
    "                            bins = nrainbin-1,\n",
    "                            range = (rain_minimum, rain_maximum))\n",
    "        rain_bin_mp = rain_bin_mp + hist[0]\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "54706696",
   "metadata": {},
   "outputs": [],
   "source": [
    "prmpim = xr.open_dataset('/scratch/mkibria/vms/ICTP RegCM/MPI-ESM-MR/rcp2.6/pr_WAS-22_MPI-M-MPI-ESM-MR_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2041-2060.nc')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a92ddf9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/mkibria/vms/ICTP RegCM/MPI-ESM-MR/rcp2.6/pr_WAS-22_MPI-M-MPI-ESM-MR_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2041-2060.nc\n"
     ]
    }
   ],
   "source": [
    "#for the regcm mpiesm rcp26 mid future (2041-2060)\n",
    "\n",
    "prmpim = glob.glob('/scratch/mkibria/vms/ICTP RegCM/MPI-ESM-MR/rcp2.6/pr_WAS-22_MPI-M-MPI-ESM-MR_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2041-2060.nc')\n",
    "\n",
    "prmpim.sort( )\n",
    "\n",
    "rain_bin_mpm = rain_bin \n",
    "dt1 = datetime.datetime(2041,1,1,12,0,0)\n",
    "dt2 = datetime.datetime(2060,12,31,0,0,0) \n",
    "\n",
    "for file in prmpim:\n",
    "    print(file)\n",
    "    fch = netCDF4.Dataset(file)\n",
    "    time = fch.variables['time'][:]\n",
    "    tunits = fch.variables['time'].units\n",
    "    st1 = cf_units.date2num(dt1, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    st2 = cf_units.date2num(dt2, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    itime = np.argwhere(np.logical_and(time > st1, time < st2))\n",
    "    if len(itime) == 0:\n",
    "        continue\n",
    "    lat = fch.variables['lat'][:]\n",
    "    lon = fch.variables['lon'][:]\n",
    "    ipnt = np.argwhere(np.logical_and(\n",
    "                       np.logical_and(lat > 18.0, lat < 28.0),\n",
    "                       np.logical_and(lon > 86.0, lon < 94.0)))\n",
    "    la1 = min(ipnt, key = lambda t: t[0])[0]\n",
    "    la2 = max(ipnt, key = lambda t: t[0])[0]\n",
    "    lo1 = min(ipnt, key = lambda t: t[1])[1]\n",
    "    lo2 = max(ipnt, key = lambda t: t[1])[1]\n",
    "    pr = fch.variables['pr']\n",
    "    for it in itime:\n",
    "        #print(it)\n",
    "        data = pr[it,la1:la2,lo1:lo2] * 86400.0\n",
    "        nzd = data[tuple(np.where(data >= rain_minimum))]\n",
    "        hist = np.histogram(nzd,\n",
    "                            bins = nrainbin-1,\n",
    "                            range = (rain_minimum, rain_maximum))\n",
    "        rain_bin_mpm = rain_bin_mpm + hist[0]\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "34a2f614",
   "metadata": {},
   "outputs": [],
   "source": [
    "prmpif = xr.open_dataset('/scratch/mkibria/vms/ICTP RegCM/MPI-ESM-MR/rcp2.6/pr_WAS-22_MPI-M-MPI-ESM-MR_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2081-2100.nc')\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5aa744f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/mkibria/vms/ICTP RegCM/MPI-ESM-MR/rcp2.6/pr_WAS-22_MPI-M-MPI-ESM-MR_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2081-2100.nc\n"
     ]
    }
   ],
   "source": [
    "#for the regcm mpiesm rcp26 far future (2081-2100)\n",
    "\n",
    "prmpif = glob.glob('/scratch/mkibria/vms/ICTP RegCM/MPI-ESM-MR/rcp2.6/pr_WAS-22_MPI-M-MPI-ESM-MR_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2081-2100.nc')\n",
    "\n",
    "prmpif.sort( )\n",
    "\n",
    "rain_bin_mpf = rain_bin \n",
    "dt1 = datetime.datetime(2081,1,1,12,0,0)\n",
    "dt2 = datetime.datetime(2100,12,31,0,0,0) \n",
    "\n",
    "for file in prmpif:\n",
    "    print(file)\n",
    "    fch = netCDF4.Dataset(file)\n",
    "    time = fch.variables['time'][:]\n",
    "    tunits = fch.variables['time'].units\n",
    "    st1 = cf_units.date2num(dt1, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    st2 = cf_units.date2num(dt2, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    itime = np.argwhere(np.logical_and(time > st1, time < st2))\n",
    "    if len(itime) == 0:\n",
    "        continue\n",
    "    lat = fch.variables['lat'][:]\n",
    "    lon = fch.variables['lon'][:]\n",
    "    ipnt = np.argwhere(np.logical_and(\n",
    "                       np.logical_and(lat > 18.0, lat < 28.0),\n",
    "                       np.logical_and(lon > 86.0, lon < 94.0)))\n",
    "    la1 = min(ipnt, key = lambda t: t[0])[0]\n",
    "    la2 = max(ipnt, key = lambda t: t[0])[0]\n",
    "    lo1 = min(ipnt, key = lambda t: t[1])[1]\n",
    "    lo2 = max(ipnt, key = lambda t: t[1])[1]\n",
    "    pr = fch.variables['pr']\n",
    "    for it in itime:\n",
    "        #print(it)\n",
    "        data = pr[it,la1:la2,lo1:lo2] * 86400.0\n",
    "        nzd = data[tuple(np.where(data >= rain_minimum))]\n",
    "        hist = np.histogram(nzd,\n",
    "                            bins = nrainbin-1,\n",
    "                            range = (rain_minimum, rain_maximum))\n",
    "        rain_bin_mpf = rain_bin_mpf + hist[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "072289c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "prnor = xr.open_dataset('/scratch/mkibria/vms/ICTP RegCM/NorESM1-M/pr/daily/pr_WAS-22_NCC-NORESM1-M_historical_r1i1p1_ICTP-RegCM4-7_v0_day_1994-2014.nc')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c1d8e393",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/mkibria/vms/ICTP RegCM/NorESM1-M/pr/daily/pr_WAS-22_NCC-NORESM1-M_historical_r1i1p1_ICTP-RegCM4-7_v0_day_1994-2014.nc\n"
     ]
    }
   ],
   "source": [
    "#for the regcm noresm present day (1994-2014)\n",
    "\n",
    "prnor = glob.glob('/scratch/mkibria/vms/ICTP RegCM/NorESM1-M/pr/daily/pr_WAS-22_NCC-NORESM1-M_historical_r1i1p1_ICTP-RegCM4-7_v0_day_1994-2014.nc')\n",
    "prnor.sort( )\n",
    "\n",
    "rain_bin_nr = rain_bin \n",
    "dt1 = datetime.datetime(1994,1,1,12,0,0)\n",
    "dt2 = datetime.datetime(2014,12,31,0,0,0)\n",
    "\n",
    "for file in prnor:\n",
    "    print(file)\n",
    "    fch = netCDF4.Dataset(file)\n",
    "    time = fch.variables['time'][:]\n",
    "    tunits = fch.variables['time'].units\n",
    "    st1 = cf_units.date2num(dt1, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    st2 = cf_units.date2num(dt2, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    itime = np.argwhere(np.logical_and(time > st1, time < st2))\n",
    "    if len(itime) == 0:\n",
    "        continue\n",
    "    lat = fch.variables['lat'][:]\n",
    "    lon = fch.variables['lon'][:]\n",
    "    ipnt = np.argwhere(np.logical_and(\n",
    "                       np.logical_and(lat > 18.0, lat < 28.0),\n",
    "                       np.logical_and(lon > 86.0, lon < 94.0)))\n",
    "    la1 = min(ipnt, key = lambda t: t[0])[0]\n",
    "    la2 = max(ipnt, key = lambda t: t[0])[0]\n",
    "    lo1 = min(ipnt, key = lambda t: t[1])[1]\n",
    "    lo2 = max(ipnt, key = lambda t: t[1])[1]\n",
    "    pr = fch.variables['pr']\n",
    "    for it in itime:\n",
    "        #print(it)\n",
    "        data = pr[it,la1:la2,lo1:lo2] * 86400.0\n",
    "        nzd = data[tuple(np.where(data >= rain_minimum))]\n",
    "        hist = np.histogram(nzd,\n",
    "                            bins = nrainbin-1,\n",
    "                            range = (rain_minimum, rain_maximum))\n",
    "        rain_bin_nr = rain_bin_nr + hist[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2e8b8f3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "prnorm = xr.open_dataset('/scratch/mkibria/vms/ICTP RegCM/NorESM1-M/rcp2.6/pr_WAS-22_NCC-NORESM1-M_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2041-2060.nc')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5fcf0fa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/mkibria/vms/ICTP RegCM/NorESM1-M/rcp2.6/pr_WAS-22_NCC-NORESM1-M_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2041-2060.nc\n"
     ]
    }
   ],
   "source": [
    "#for the regcm noresm rcp2.6 mid future (2041-2060)\n",
    "\n",
    "prnorm = glob.glob('/scratch/mkibria/vms/ICTP RegCM/NorESM1-M/rcp2.6/pr_WAS-22_NCC-NORESM1-M_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2041-2060.nc')\n",
    "prnorm.sort( )\n",
    "\n",
    "rain_bin_nrm = rain_bin \n",
    "dt1 = datetime.datetime(2041,1,1,12,0,0)\n",
    "dt2 = datetime.datetime(2060,12,31,0,0,0)\n",
    "\n",
    "for file in prnorm:\n",
    "    print(file)\n",
    "    fch = netCDF4.Dataset(file)\n",
    "    time = fch.variables['time'][:]\n",
    "    tunits = fch.variables['time'].units\n",
    "    st1 = cf_units.date2num(dt1, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    st2 = cf_units.date2num(dt2, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    itime = np.argwhere(np.logical_and(time > st1, time < st2))\n",
    "    if len(itime) == 0:\n",
    "        continue\n",
    "    lat = fch.variables['lat'][:]\n",
    "    lon = fch.variables['lon'][:]\n",
    "    ipnt = np.argwhere(np.logical_and(\n",
    "                       np.logical_and(lat > 18.0, lat < 28.0),\n",
    "                       np.logical_and(lon > 86.0, lon < 94.0)))\n",
    "    la1 = min(ipnt, key = lambda t: t[0])[0]\n",
    "    la2 = max(ipnt, key = lambda t: t[0])[0]\n",
    "    lo1 = min(ipnt, key = lambda t: t[1])[1]\n",
    "    lo2 = max(ipnt, key = lambda t: t[1])[1]\n",
    "    pr = fch.variables['pr']\n",
    "    for it in itime:\n",
    "        #print(it)\n",
    "        data = pr[it,la1:la2,lo1:lo2] * 86400.0\n",
    "        nzd = data[tuple(np.where(data >= rain_minimum))]\n",
    "        hist = np.histogram(nzd,\n",
    "                            bins = nrainbin-1,\n",
    "                            range = (rain_minimum, rain_maximum))\n",
    "        rain_bin_nrm = rain_bin_nrm + hist[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "47d842e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "prnorf = xr.open_dataset('/scratch/mkibria/vms/ICTP RegCM/NorESM1-M/rcp2.6/pr_WAS-22_NCC-NORESM1-M_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2081-2100.nc')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e80cbedc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/mkibria/vms/ICTP RegCM/NorESM1-M/rcp2.6/pr_WAS-22_NCC-NORESM1-M_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2081-2100.nc\n"
     ]
    }
   ],
   "source": [
    "#for the regcm noresm rcp2.6 far future (2081-2100)\n",
    "\n",
    "prnorf = glob.glob('/scratch/mkibria/vms/ICTP RegCM/NorESM1-M/rcp2.6/pr_WAS-22_NCC-NORESM1-M_rcp26_r1i1p1_ICTP-RegCM4-7_v0_day_2081-2100.nc')\n",
    "prnorf.sort( )\n",
    "\n",
    "rain_bin_nrf = rain_bin \n",
    "dt1 = datetime.datetime(2081,1,1,12,0,0)\n",
    "dt2 = datetime.datetime(2100,12,31,0,0,0)\n",
    "\n",
    "for file in prnorf:\n",
    "    print(file)\n",
    "    fch = netCDF4.Dataset(file)\n",
    "    time = fch.variables['time'][:]\n",
    "    tunits = fch.variables['time'].units\n",
    "    st1 = cf_units.date2num(dt1, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    st2 = cf_units.date2num(dt2, tunits, cf_units.CALENDAR_STANDARD)\n",
    "    itime = np.argwhere(np.logical_and(time > st1, time < st2))\n",
    "    if len(itime) == 0:\n",
    "        continue\n",
    "    lat = fch.variables['lat'][:]\n",
    "    lon = fch.variables['lon'][:]\n",
    "    ipnt = np.argwhere(np.logical_and(\n",
    "                       np.logical_and(lat > 18.0, lat < 28.0),\n",
    "                       np.logical_and(lon > 86.0, lon < 94.0)))\n",
    "    la1 = min(ipnt, key = lambda t: t[0])[0]\n",
    "    la2 = max(ipnt, key = lambda t: t[0])[0]\n",
    "    lo1 = min(ipnt, key = lambda t: t[1])[1]\n",
    "    lo2 = max(ipnt, key = lambda t: t[1])[1]\n",
    "    pr = fch.variables['pr']\n",
    "    for it in itime:\n",
    "        #print(it)\n",
    "        data = pr[it,la1:la2,lo1:lo2] * 86400.0\n",
    "        nzd = data[tuple(np.where(data >= rain_minimum))]\n",
    "        hist = np.histogram(nzd,\n",
    "                            bins = nrainbin-1,\n",
    "                            range = (rain_minimum, rain_maximum))\n",
    "        rain_bin_nrf = rain_bin_nrf + hist[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "307f9480",
   "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": [
    "#ploting miroc5 rcp26\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "#plt.plot(rain_bin_ch, 'bo',label='CHIRPS')\n",
    "#plt.plot(rain_bin_t, 'ko',label='TRMM')\n",
    "plt.plot(rain_bin_mi, 'b',label='RegCM(Miroc5) present(1994-2014)',linewidth = 3)\n",
    "plt.plot(rain_bin_mid, 'g',label='RegCM(Miroc5) mid(2041-2060)',linewidth = 3)\n",
    "plt.plot(rain_bin_mif, 'r',label='RegCM(Miroc5) far(2081-2100)',linewidth = 3)\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "ax.set_yscale('log')\n",
    "#ax.set_xscale('log')\n",
    "plt.xlabel('Precipitation [mm/day]')\n",
    "plt.ylabel('Event Number [#]')\n",
    "plt.title('Precipitation daily PDF RegCM(Miroc5 rcp2.6)')\n",
    "plt.savefig(\"/scratch/mkibria/vms/pdf_regcm_rcp2.6_miroc5.png\")\n",
    "\n",
    "\n",
    "\n",
    "plt.show( )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "06912cf7",
   "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": [
    "#ploting mpiesm rcp2.6\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "plt.plot(rain_bin_mp, 'b',label='RegCM(Mpiesm) present(1994-2014)',linewidth = 3)\n",
    "plt.plot(rain_bin_mpm, 'g',label='RegCM(Mpiesm) mid(2041-2060)',linewidth = 3)\n",
    "plt.plot(rain_bin_mpf, 'r',label='RegCM(Mpiesm) far(2081-2100)',linewidth = 3)\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "ax.set_yscale('log')\n",
    "#ax.set_xscale('log')\n",
    "plt.xlabel('Precipitation [mm/day]')\n",
    "plt.ylabel('Event Number [#]')\n",
    "plt.title('Precipitation daily PDF RegCM(Mpi rcp2.6)')\n",
    "plt.savefig(\"/scratch/mkibria/vms/pdf_regcm_rcp2.6_mpiesm.png\")\n",
    "\n",
    "\n",
    "\n",
    "plt.show( )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "532f8ca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b3LlzM3JknOch8bp9+zatW7fmpZde4tChQ2zbto2+ffsSdRVbsWJFfv/9d/v806ZNo0qVKvbhS5cusWHDBho0aGAfd/78eebPn5+keMKT6QvdunVr5syZw82bNxOeORbJFZejmIVCeHg4EyZM4KmnngKsY/HHH39E+6wBfvrJ6vlz165dLF68mLfeeovIyEguXbpE//79Wbp0KXv27OHs2bMsXbrUvtz169cZPnw4tWvHnQW7WbNm7N69m507d1KmTBk+//xzAPbu3cv06dPZs2cPCxYsoG/fvkREWLXmPXr0YMGC2DOknzp1ikWLFlGs2N1ebO/32MTkjkJhM1BaREqIiA/QBQhKzAqMMXOMMX1y5syZ6I0Xr1SNTLY7FuVDLnL0aKJXodKZunXrcvq0dV5y5MgRWrRoQfXq1alfvz779++3j69Tpw6VKlViwIABZMtm9RM/depU6tatS5s2bezra9iwof0sr3jx4ty+fZtz585hjGHBggW0bNnSPu+sWbNo0SJaWwz69+/P4MGD74nz9u3b9jPaqlWrsnz5cgAmTZpE27Ztady4MU2aNCEkJISePXtSq1YtqlatyuzZswHYs2cPtWrVIjAwkMqVK3Po0CGOHz/Ogw8+SI8ePShTpgzdunVjyZIl1KtXj9KlS7Np0ybAqptu2LAhc+fOvSeujz/+mKeffpq6detSunRp+4/sihUrqF+/Pm3btqV8+fJERETQv39/atasSeXKlfnxxx8BOHPmDA0aNLBfua1evRqARYsWUbduXapVq8aTTz7JjRs3ACu9zUcffUS1atWoVKkS+/fv5/jx44wZM4Zvv/2WwMBAVq9ezbJly6hWrRpeXtat03LlylG2bNl74t+7dy+NGzcGIH/+/OTKlYstW7Zw9OhRSpcuTVSNRNOmTZk1a5Z9uYEDB/Luu++SOXPme9YZ5dFHH7Vvv06dOgQHWw0rZ8+eTZcuXciUKRMlSpQgICDA/lk3aNCA3Llzx7q+N954gy+//DLavYL4jk1SJGuhICLTsPqaLSsiwSLyvDEmHHgFWIjVX+nvxpg9iVxvkq8UCtVsbH9f7r9wNm2/lOh1KNcRSb6XMyIiIli6dClt27YFoE+fPowYMYKtW7cybNgw+vbtC0C/fv3o168fu3btws/Pz7787t27qV69erzb6NixIzNmzGDdunVUq1aNTJky2aetXbv2nuXr1q2Lj4+P/Uc/ysiRIxERdu3axbRp03j22WftLUu2bdvGzJkzWblyJYMHD6Zx48Zs2rSJ5cuX079/f0JCQhgzZgz9+vVj+/btbNmyxb4fhw8f5q233mL//v3s37+fqVOnsmbNGoYNG8aQIUPs269Ro4b9BzumnTt3smzZMtavX8+gQYP4999/7XENHz6cgwcPMn78eHLmzMnmzZvZvHkzP/30E8eOHWPq1Kk0b96c7du3s2PHDgIDA7l48SKfffYZS5YsYdu2bdSoUYNvvvnGvr28efOybds2XnrpJYYNG4a/vz8vvvgib7zxBtu3b6d+/fqxfraxqVKlCkFBQYSHh3Ps2DG2bt3KqVOnCAgI4MCBAxw/fpzw8HD++usvTp06Zd+vU6dO0bp16wTXH2XChAn2E4LTp09TtOjdChM/Pz/7iUlcZs+eTZEiRaJdaUaJ79gkVnK3Puoax/i/sToLT+p65wBzatSo0Tuxy3oWKMjFLJ7kvRlB1jDYu2kldOqQ1FBUGnXr1i0CAwM5ffo05cqVo1mzZty4cYN169bx5JNP2ue7c+cOAOvXr+evv/4C4KmnnuLtt992eludOnWic+fO7N+/n65du0ar4jhz5gyx3RsbMGAAn332GV988YV93Jo1a3j11VcBePDBBylevDgHDx4ErGqKqLPLRYsWERQUZL9vcfv2bU6ePEndunUZPHgwwcHBdOjQgdKlSwNQokQJKlWqBECFChVo0qQJIkKlSpU4fvy4ffv58+e3/9jH1K5dO3x9ffH19aVRo0Zs2rSJXLlyUatWLXsb+UWLFrFz505mzpwJwNWrVzl06BA1a9akZ8+ehIWF0b59ewIDA1m5ciV79+6lXr16gFXvX7duXfv2OnSw/merV6/OH3/8QWzOnDlDuXLlYp3mqGfPnuzbt48aNWpQvHhxHnroITw9PXnggQcYPXo0nTt3xsPDg4ceeogjR44QGRnJm2++yaRJkxJcd5TBgwfj5eVFt27dnF7G0c2bNxkyZAiLFsXeOV58xyaxUs0TzSnpZP7c5D1u1fne2r8Sq390lZFE3VO4efMmzZs3Z+TIkfTo0YNcuXKxfft2p9dToUIFVq5cGe88BQsWxNvbm8WLFzN8+PBohYKvr2+s7cgbN27MgAED2LBhg1NxZM2a1f7eGMOsWbPuqSopV64ctWvXZt68ebRq1Yoff/yRkiVLRrty8fDwsA97eHhEuxdw+/ZtfH19Y91+zKaPUcMx4xoxYgTNmze/Z/lVq1Yxb948evTowZtvvskDDzxAs2bNmDZtWqzbi4rR09MzzvsVcX22MXl5efHtt9/ahx966CHKlCkDQJs2bexVg2PHjsXT05Pr16+ze/duewOAs2fP0rZtW4KCghg5ciT//PMPhQsX5u+/rfPeSZMmMXfuXJYuXWr/XIoUKWK/6gDrgc4iRYrEGeORI0c4duyY/SohODiYatWqsWnTJgoWLBjvsUmsVNP6KDHup/oI4FqJ4vb32c/ucFVYKgmMSb6XM7JkycL333/P119/TZYsWShRogQzZsywxWbYscP6ftSpU8denzx9+nT78k899RTr1q1j3rx59nGrVq1i9+7o/bEPGjSIL774Ak9Pz2jjy5Urx+HDh2ONbcCAAXz55Zf24fr16zNlyhTAavF08uTJWOvImzdvzogRI4jqVfGff/4B4OjRo5QsWZLXXnuNdu3asXPnTic+obsOHjwYZ4uY2bNnc/v2bS5dusSKFSuoWbNmrHGNHj2asLAw+/pCQkI4ceIEBQoUoHfv3vTq1Ytt27ZRp04d1q5da/9sQkJC7FdFccmePTvXr1+3D8f32Tq6efMmISEhACxevBgvLy/Kly8PWDf9AS5fvsyoUaPo1asXOXPm5OLFixw/fpzjx49Tp04dgoKCqFGjBhMnTmT79u32AmHBggV8+eWXBAUFkSVLFvs227Zty/Tp07lz5w7Hjh3j0KFD1KpVK84YK1WqxPnz5+3b9PPzY9u2bRQsWND+WcZ1bBIrTRYK93OjGSBzlbsfXvGrR7QFUgZXtWpVKleuzLRp05gyZQrjx4+nSpUqVKhQwX6T9rvvvuObb76hcuXKHD58mKjvnq+vL3PnzmXEiBGULl2a8uXLM2rUqHuqhB566CHat29/z7Zbt24drQmko1atWkVbT9++fYmMjKRSpUp07tyZSZMmRTvLjzJw4EDCwsLsTSkHDhwIwO+//07FihUJDAxk9+7dPPPMM4n6nJYvXx5nHXrlypVp1KgRderUYeDAgRQuXPieeXr16kX58uWpVq0aFStW5IUXXiA8PJwVK1ZQpUoVqlatym+//Ua/fv3Ily8fkyZNomvXrlSuXJm6devab/rHpU2bNvz555/2G80tW7a0N/UF+PPPP/Hz82P9+vW0bt3afsVy/vx5qlWrRrly5fjiiy+YPHmyfZl+/fpRvnx56tWrx3vvvWe/gnDWK6+8wvXr12nWrBmBgYG8+OKLgHWF2alTJ8qXL0+LFi0YOXKk/YSha9eu1K1blwMHDuDn58f48eMT3E58xybRjDFp9lW9enWTFMd//8l+Qrm2kI/5558krUYl0d69e90dQqKFhISYyMhIY4wx06ZNM23btnXZuuvVq2cuX77ssvUlh7Nnz5rGjRvHOu2jjz4yX331VQpH5Jz27dubgwcPujuMZBXfsTEm9v83rKSjsf6upskrhfutPipUu4n9ffn/Qlm0+qKrQlPp1NatW+1NOUeNGsXXX3/tsnV//fXXnDx50mXrSw4nT5506T6nlKFDh3LmzBl3h5GsXH1sxDhb+ZoK1ahRwySpO05juJzVmwduWQ8sdO88jl+nP+/i6FRc9u3b51SrEKXU/Yvt/01EthpjasQ2f5q8UrhvIhwPuNvWPOth1zz0oZRSaV3GLBQAj7p3H2qpeGMrZ8+6MRillEol0mShcL/3FAAKNL2blqD2tX9ZsTLSFaEppVSaliYLBXOfTVIBCjS+WygEnotgzvLtLohMKaXStjRZKLiC5MnDqXzW05Y+kXBtp3YJnZFo6uzEcUV65l69erF37957xk+aNIlXXnnFPuyY7jopqafBymlVtWpVHnvsMfu4H374gYCAgGhpp2PTrVs3ypYtS8WKFe3pN8Bqvv/aa68REBBA5cqV2bZtm32ZkydP8uijj1KuXDnKly9vTw9y7NgxateuTUBAAJ07dyY0NNQey4QJE5z85FJWhi0UAK7VCbS/r3VtPqm8VaByIU2dnTiuSM88btw4+5PCcYmZ7jopqacBhg8ffk+Lm3r16rFkyRKKFy9OfLp168b+/fvZtWsXt27dshfq8+fP59ChQxw6dIixY8fy0kt3ew1+5pln6N+/P/v27WPTpk3kz58fgHfffZc33niDw4cP88ADD9gfROvZsycjRoxw5mNLcWmyUHDFPQWA3B06298/+t8RFiwOjWdulV5p6uz7T5397LPPUr9+fYoXL84ff/zBO++8Q6VKlWjRooX9TLthw4b2zmgmTpxImTJlqFWrFmvXrrWvK2a666Skng4ODmbevHn06tUrWpxVq1bF39//nvhjatWqFSKCiFCrVq1o23zmmWcQEerUqcOVK1c4c+YMe/fuJTw8nGbNmgGQLVs2smTJgjGGZcuW0bFjRwCeffZZe1LFLFmy4O/vb485NUmTCfHMfWRJdVSwbTci5DU8DdQ8E8kn8xfS5/k2CS+oXEY+Sb6+ms1HCT+DE5U6+/nnredU+vTpw5gxYyhdujQbN26kb9++LFu2zJ46u2vXrowZM8a+fGJSZ1etWjXW1NlRPxpR6taty59//sny5cvJnj27fbxj6uz9+/fz6KOP2vMBbdu2jZ07d5I7d27ef/99GjduzIQJE7hy5Qq1atWiadOm9tTZ3bp1IzQ0lIiICM6dO8fhw4eZMWMGEyZMoGbNmvbU2UFBQQwZMsT+QxaVnrlTp0737OORI0dYvnw5e/fupW7dusyaNYsvv/ySxx9/nHnz5kVL8XHmzBk++ugjtm7dSs6cOWnUqBFVq1a1fx5xfZ4TJkygc2frRO706dPUqVPHPs0x9fTrr7/Ol19+GS0PUlKEhYUxefJkhg8fbt9mbOmug4ODyZUrFx06dODYsWM0bdqUoUOHcvnyZXLlymUv1GKmx476POPLeeQOafJKwVUkd26OlLIu8zyAnId/wpYXS6VzUamzCxYsyLlz5+5JnR0YGMgLL7xgfxp2/fr19pTaUVUbzurUqRMzZsxg2rRpdO0aPZt8QqmzHa1Zs4bu3bsDCafOHjp0KIGBgTRs2DBa6uwhQ4bwxRdfcOLECXtWzajU2R4eHklOnd2yZUu8vb2pVKkSERER9qufmOsA2LhxIw0bNiRfvnz4+PjYf+jj+zycTT09d+5c8ufP71Q/Cgnp27cvDRo0oH79+vHOFx4ezurVqxk2bBibN2/m6NGjTqXVdmW6a1fK0IUCgGlxN+VF25sriHG/SqVTUfcUTpw4gTGGkSNHEhkZaU+dHfXat29fvOupUKECW7dujXcex9TZTZo0iTYtvtTZt27duq/U2VH7cPLkScqVK8dTTz1FUFAQvr6+tGrVimXLlgG4JHW24zLe3t72FNEx15GQ2D6PqNTTU6ZMSTD19Nq1awkKCsLf358uXbqwbNkye0Eal+bNmxMYGBituumTTz7hwoUL0Tr2iWubfn5+BAYGUrJkSby8vGjfvj3btm0jT548XLlyxb7/MdNjuzLdtStl+EKheI/X7e8fO3GdyX9qKu2UZD4yyfZyhqbOdp6r0jPXrl2blStXcunSJcLCwuyfN9z7eSQ29fTnn39OcHAwx48fZ/r06TRu3Jhff/013ngWLlzI9u3b7TeUx40bx8KFC5k2bRoeHnd/Itu2bcsvv/yCMYYNGzaQM2dOChUqRM2aNbly5Yq9ccGyZcsoX748IkKjRo3snQr9/PPPtGvXzr4+V6a7dqUMXyhkrlaT4PzWTcPsoSA7vtYqpAxGU2c7x1XpmQsVKsTHH39M3bp1qVevXrRWQjHTXScl9XRcvv/+e/z8/AgODqZy5cr33IiO8uKLL3Lu3Dnq1q1LYGAggwYNAqzjUbJkSQICAujduzejRo0CrObNw4YNo0mTJlSqVAljDL17W7c7v/jiC7755hsCAgK4dOmS/d4VWPdPom5OpypxpU9NzS+gDTA2ICAgnoSyztvZ8zF7Ku3JpXOaKVMiXbJeFTtNnR1dWk+d7WoZId31tm3bTPfu3VNkWxkidbZxwRPNjkr2edf+/oljV5k0I/7uFVXGo6mzUy51dkZId33x4kU+/fRTd4cRq4yZOjsWR4vnpuTJywC8UPERBvy9AofWZ8qFNHW2UilHU2cnUcRzd1so9Ly0hlFjE+7wWyml0hstFGxKvTKQO7b7VLXPRLBizg+E6gPOSqkMRgsFG4+8+ThUv4J9+Mk7P/Dnn24MSCml3EALBQf5XnnH/v7pkyf4ftxBN0ajlFIpTwsFBwXad+P8A1a773w3ocj5t9i1y81BqWShqbMTJ77U2Xfu3KFp06YEBgby22+/JWq995sm+9tvv6VChQpUrFiRrl272p+Gvt802fv376du3bpkypQp2rEC64G6smXLEhAQwNChQ+3j02Ka7FjF1VY1LbyqV6+e9Ma7cTj4ylP2ZxaWFPc03Z675vJtZHSp4TmFrFmz2t8/88wz5rPPPkvSem7dumUCAgJMUFCQfdzy5cvNrl27zPLly03FihVNs2bN7NPeeecdU6VKFfPVV18ZY4y5ePGiqV27tn36I488YooWLWr+/vtvY4wxmzdvNo888ojT8YSFhSVpPxISGRlpAgMDTUhIyD3T1q9fb5o0aZKo9YWFhZmwsDBTqVIle8wLFy60v3/nnXfMO++8Y4wxZs+ePaZy5crm9u3b5ujRo6ZkyZImPDzcBAcHG39/f3Pz5k1jjDFPPvmkmThxojHGeg7g2LFjpnjx4ubChQtxxjFv3jwTGRlpIiMjTZcuXcyoUaOMMcacO3fObNq0ybz//vv2Y2WMMeHh4aZkyZLmyJEj5s6dO6Zy5cpmz5499u1PmzbNGGPMCy+8YF9XSEiICQwMTNTn40oZ4jkFV6XOjk2pdz4nwpa4s8mJCLb88xUOiQ1VOqSps5OeOvv8+fN0796dzZs3ExgYyJEjRxg0aBA1a9akYsWK9OnTx55uo2HDhrz++uvUqFGD4cOHuyRNdnh4OLdu3SI8PJybN29SuHBh4P7TZOfPn5+aNWvi7e0dbf5NmzYREBBAyZIl8fHxoUuXLsyePTvNpsmOTZosFIyLH15z5FG0GCcfrmQffj18ON8N1/6bk41I8r2cEJU6u23btoCVOnvEiBFs3bqVYcOG0bdvXwB76uxdu3bh5+dnXz4xqbPXrVsXa+rsmMvXrVsXHx8f+49+FMfU2dOmTePZZ5+1V5ds27aNmTNnsnLlSgYPHkzjxo3ZtGkTy5cvp3///oSEhNhTZ2/fvp0tW7bY9+Pw4cO89dZb7N+/n/3799tTZw8bNowhQ4bYtx+V6tlR/vz5GTduHPXr12f79u2UKlWKV155hc2bN7N7925u3boVrSAJDQ1ly5YtvPXWWwmmyY4qPONKWV2kSBHefvttihUrRqFChciZMyePPvpovMciLlFpsmMW0DHFFculS5ecSpOdFqTJQiG5FRxwt57wuX3XmDXvV5LhokS5kabOdm3qbEfLly+ndu3aVKpUiWXLlrFnzx77NFemyb58+TKzZ8/m2LFj/Pvvv4SEhCSY/C4uzqbJTqrUmiY7NlooxMK3WUtOli0EQKYI6CMf8uOPbg5KuZSmznZt6mzHefr27cvMmTPZtWsXvXv3jrZ/jnHeb5rsJUuWUKJECfLly4e3tzcdOnRg3bp18cbnbJrsuMQVS1pNkx0bLRRiI4LvgI/tgy8dPsHo8eu5dct9IaVb9tv6yfBygqbOdp4zqZ6jfuTz5s3LjRs37GmjY3O/abKLFSvGhg0buHnzJsYYli5dmmD6FGfTZMelZs2aHDp0iGPHjhEaGsr06dNp27Ztmk2THRstFOKQ76leBBe2ukLMeQc6e76KQ8tClY5o6mznOJM6O1euXPTu3ZuKFSvSvHlzatasGee895smu3bt2nTs2JFq1apRqVIlIiMj6dOnD3D/abLPnj2Ln58f33zzDZ999hl+fn5cu3YNLy8vfvjhB5o3b065cuXo1KkTFSpYD72myTTZsYmrWVJaeCVHk1RHB79+337eeTYrpnjAAXP7drJuMkNIDU1SE0tTZydP6mxNk538MkST1JQS8PKHnHvAB4ACIdAycy8mTnRzUMotNHV28qTO1jTZqY+mzk7AwYF9KfPZaACO5oKGBY5xeKc/Pj7Jutl0TVNnK5VyNHW2i5V+90uuZrXaHpe8AvUyv8Dkye6NKT1IyycjSqUVSfk/SzWFgog0FJHVIjJGRBq6O54oki0bF57vYh9+78xiBn1zGoeWeiqRMmfOzKVLl7RgUCoZGWO4dOkSmTNnTtRyXskUDwAiMgF4DDhvjKnoML4FMBzwBMYZY4YCBrgBZAaCkzOuxCo18FtujZmKb2gkVc4bqvi9xLRpQTz9tLsjS5uiWoVcuHDB3aEola5lzpw52hP4zkjWewoi0gDrh/6XqEJBRDyBg0AzrB//zUBXYL8xJlJECgDfGGPif5yRlLmnEOVozw6UnGh1sLA9v9CxwCkO/FOEGM3OlVIq1XPbPQVjzCrgvxijawGHjTFHjTGhwHSgnTEmKsHQZeDextduVuLz0dzysT6uwPOGqp69sD3jpJRS6YY77ikUAU45DAcDRUSkg4j8CEwGfohrYRHpIyJbRGRLSlY/SIECnH72cfvwJ8ELGDjsIBERKRaCUkolu1Rzo9kY84cx5gVjTGdjzIp45htrjKlhjKkRWzKt5FRqyBhCMlv1ReUvQm2PZ0li/i2llEqV3FEonAaKOgz72cY5LTn7U4h3u3nz8t8Ld1MDfHRkAwOGb8bWwZJSSqV57igUNgOlRaSEiPgAXYCgxKzAJGN/Cgkp+vE33MhidbxR+j9oluk5xo9P8TCUUipZJGuhICLTgPVAWREJFpHnjTHhwCvAQmAf8LsxZk9864llvW65UgAgVy5uvt7XPvjhvj0MGj+fmzdTPhSllHI1TXORFDducL1IPrJfs9IEv/RwMQo0PcbHH6WaWzRKKRUnTXPhatmyEdn/bfvggB0nGf7Hr9qXs1IqzUuThYJbq49scr75P67ltnqRKnId3vHux3vvh7ktHqWUcoU0WSi480azXZYseA6525fz2/9cYcuOgbijNksppVwl3nsKIlLNiXWEGWN2uS4k57ntnkKUyEiCqwXgt+MYAL8/6MXn+U6ydUUhnOjZTyml3CK+ewoJFQrXsZqQSjzrL2GM8b+vCBNJRNoAbQICAnofOnQoJTd9j7BNG/CuXdc+XLlJK15+ch4vvODGoJRSKh73UygsM8Y0TmDlCc6TXNx+pWBzoVk98i1ZB8CCUvBkpuUcWd6Q/PndHJhSSsUiya2PnPmxd1eBkJrkGzaKSNu1VIsj0ND/Wd7qrzedlVJpj1M137b+D1RcqlThZo/u9sHv1p9kxoFvWLnSjTEppVQSxFsoiEhtW/8HQxzGub0zytTQJDWmbF9+y63svgCUugxvZP6QXm+f0LxISqk0JaErhS7ASqCkiHwhIt0AZ1okJatU0SQ1prx58R78uX1wwLpQbhV7mm++SbtPjCulMp6ECoV3jTEPAyeBucADQEER2SAivyV7dGmM10svE/JgKQCyhsEXh1bz0V8TOHHCzYEppZSTEioUFojIYiAfkBeYD5wwxtQB3kru4NIcLy+yjh5nH+y2Cx7xf40erwWThlNMKaUyEGdaH7XF6me5JPApECAif2FVLblFarynYNewIeEdn7APTvz7Jjt8evDLL1oqKKVSvwRbHxljbgGnjDFfG2OeAo4AvYFjyR1cPDGlvnsKDrx+GElonlyAlRdpzN6l9P3xZ86edW9cSimVEKeapMZ4FuEHY8wFY8ysZIop7StQAJ+Jv9gHO+2FDrlf5rnX/tVqJKVUqpboDD3GGO1nzBlt2hDWs4d98IclN9kb2p2xY7VUUEqlXgk9pzA2oRU4M09G5T18BDf9iwCQ8w5M/mc5r035kj2J6mdOKaVSjlcC09uLyO14pgvQyIXxpC/ZspFl2kwi6j2EZ6ShwUl4r8T/aPNiI/YsqoWvr7sDVEqp6BJKiPesE+u4ZYz53XUhJSw1ZUl1RvjHH+H1ySAAIgQatS9G2QL7+Wm0lgpKqZSX5CypqV1qyZKaoIgIbj1SD9+1GwE4lQMCH+7ET89Pp0OH+LKSK6WU62kfze7m6Ynv9Jnczml131n0Gkw48jvP/vgV//3n5tiUUsqBFgopxc+PTL9MtQ+2OwDPer7LE+/9rc1UlVKpRoKFgoh4isiwlAgmvZO2bYl49WX78LBF8F94J97/Zp8bo1JKqbuceaI5Ang4BWLJEDy/+pqwyhUByBwBvwWF8P3hx5gx95KbI1NKKeerj/4RkSAReVpEOkS9kjWy9CpTJrx/n0lEFqvl0YOXYMSGo3Sd3YHd++64OTilVEbnbKGQGbgENAba2F6PJVdQ6V7ZsniOHGUf7Lkdep9ZRb2hL3D5st5gUEq5jzZJdRdj4NlnYbLVkV24wHPtYeMDg9k39n08Pd0bnlIq/brvJqkiUkZElorIbttwZREZ4MogEyNVp852lgj8+COmenUAvAxM/hOqX/2Ax979XVskKaXcwtnqo5+A/wFhAMaYnbixP4XUnjrbab6+yJw5RFasYB81ah7sCH+aFz7d4MbAlFIZlbOFQhZjzKYY48JdHUyGVKgQHqvXEOFfHIAHbsOCaaEsPvUYo6Yed29sSqkMx9lC4aKIlAIMgIh0BM4kW1QZTa5ceE762T5Y+Tys+e0SQxY159eZabiKTCmV5jhbKLwM/Ag8KCKngdeBF5MrqAzpkUdg4kQivL0Bq8e2eQsO8t7UdoybGObm4JRSGUVCqbMBMMYcBZqKSFbAwxhzPXnDyqB69MDTz4+IFi3wjIigyjnYtnAljamJyTyf3l0LuTtCpVQ652zrozwi8j2wGlghIsNFJE/yhpZBNW2K57hxRHhY2VPz34Sg5Tv439IGTJ9z3s3BKaXSO2erj6YDF4AngI62978lV1AZXo8eeCxdxh1fHwBKXoGghYfpNf8hpiw44N7YlFLpmrOFQiFjzKfGmGO212dAgeQMLKOThg3JNO1u30UPBcOMxUfoufQhfluqBYNSKnk4WygsEpEuIuJhe3UCFiZnYApo1w6GD7cPtjwMv8z/j2eCmhO04pQbA1NKpVfxFgoicl1ErgG9galAqO01Hejj6mBEJKuIbBERzasU5bXX4MMP7YOd98D8oBN0n/kwf6066sbAlFLpUbyFgjEmuzEmh+2vhzHGy/byMMbkSGjlIjJBRM5HpcdwGN9CRA6IyGERec9h0rtAivb3nCZ8/LFVONg0Pg6z552k2x/1mb9Jq5KUUq7jVJNUsPIdAf6Oyxhj/khgsUnAD8AvDuvxBEYCzYBgYLOIBAFFgL1YGVmVIxH47jsoUAA++ACARsdh2vx/aedVn5+vL6Vrk0puDVEplT44VSiIyASgMrAHiLSNNkC8hYIxZpWI+McYXQs4bHv2ARGZDrQDsgFZgfLALRH52xgTibKIwPvvg4cH/O9/ALQ9CH8GXaB7eAPCIpfyTLNqbg5SKZXWOXulUMcYU95F2ywCON4lDQZqG2NeARCRHsDFuAoEEemD7X5GsWLFXBRSGvLee3D1KgwdCkDrQ7Dllys8cf0RjOcinm1c180BKqXSMmdbH60XEVcVCvEyxkwyxsyNZ/pYY0wNY0yNfPnypURIqc+QIfarBYBSl2Ht5BsMn9KYEYv/cl9cSqk0z9lC4ResguGAiOwUkV0isjOJ2zwNFHUY9rONc1q66E/hfohYBcPMmYRlyQpA1jD4Y9ZtBi1+nOem9icsQvMlKaUSz6me10TkMPAmsIu79xQwxpxwYll/YK4xpqJt2As4CDTBKgw2A08ZY/YkNvg03fOaq+zZQ2itOvjcvAHAPwXhqScgtNDDrH7tNwpnL+zmAJVSqc1997wGXDDGBNmeZj4R9XJiw9OA9UBZEQkWkeeNMeHAK1gPv+0Dfk9sgZDhrxQcVaiAz/SpGLFyJVU9C/tGwnt/rKH012X4eMXHROr9eqWUk5y9UhgF5ALmAHeixjvRJDVZ6ZWCg7Fj4YUXoo36vhb0awmfNPyMDxt+4KbAlFKpTXxXCs62PvLFKgwedRiXYJNUlYL69IHChQn/8BO8/rEKytc2wfms8EnkpzQt9hgPlazi5iCVUqmdU1cKqY2ItAHaBAQE9D506JC7w0ldIiO5/XhXMgfdfTB8QxF4uWVmunb9gbcbP+/G4JRSqUF8VwrOVh9NxNYVpyNjTM/7Dy/ptPooDnfuYFq0QFassI+65QU920HOp/vxadMPyJc1gzbnVUq55EbzXGCe7bUUyAHccE14yuUyZUJmz4Y+fQj3srr39A2HKbPg2i/DKfXtg2z9d5ubg1RKpUZJqj4SEQ9gjTHmIdeH5NT2tfrIWQcPcqVxW3KdthLnRQh0fQKCyhZgTodVNKtWxs0BKqVSmiuuFGIqDeRPekj3xxgzxxjTJ2fOnO4KIe0oU4Zc/6zmYsEKAHgamD4TRs87R8df6tDnm79Ii/eVlFLJw9k+mq+LyLWoF1bT1HeTNzTlMvnykXf7UsIDHgSsg/7cdlg/5TJnNz9OhaF1WHtyrVtDVEqlDk4VCg79KkS9yhhjZiV3cMqFChTAa80KaNPGPqr8RQiaDtO/20SX7xowfedM98WnlEoVEup5rVh8r5QKMpa49InmpChQAIKCYOJEjKenfXTl87B6QiSvTe7CkiPL3RigUsrd4r3RLCK7sJqiisNoA+QD8htjPGNdMIVok9T7sGULIV+PIfNvk/A0EQDMKQPtOuTg4QtT+KZvC2pUc7oPJqVUGpLkG83GmErGmMq2v5WANsBarOaor7s8UpVyatQg67RxeMy6W2XU5iDsGXeNmx5tqDm9CK9P/05vQiuVwTh7o7m0iEwC5gNbgfLGmBHJGZhKGfJ4e8Kf72MfLncRlv0ML+49z6Ttb/D85E+4HX7bjREqpVJSQvcUKtoync4ClgAVjTHjjDFuTdav9xRcy2vk9/Dmm0T6+ACQIxRGz4Pj30HuXz8h16A8fLT0M0IjQt0bqFIq2SV0TyECq+vMeUBEzOnGmNeSL7SE6T0FF9u1C5o0gQsXoo2eH2A98JYrUyP+7j6H8qWzuilApZQr3M/Daz2Bj4BNWNVGMV8qPalUCXbuhM8+42axEvbRLQ/D0l8g7MZyKo4pT//fRuu9BqXSqTSZJTWKXikko7AwzIcfIUM/vzvKw2qh9PVDcNnvSdb2n8gD2fSqQam0JjnSXKj0ztsb+XwIjBtn79XNOxI67IfVE6DynhkUH/QwG48ntatupVRqpIWCit/zzyPz50P9+vZRHsCUP+CDddtp9mMVHpv6mKbJUCqdcLZJaj1nxqUUbX2Uwpo3h1WrYO9eTLlygJVY7921sH0MXFo2j4cnPkzjSU05H3LezcEqpe6Hs1cKsT2T4LbnFDRLqpuUK4csWgQ17lZFlrxiVSe9uxqWH19Kg7HNuXL7ittCVErdn3jzGIhIXeAhIJ+IvOkwKQfg1hQXyk38/GDjRq7/OIXIl18lp7mKl4GhSyHXbRjYeDsBX1fm3Yav8mipR6lSUPuFViotSehKwQfIhlV4ZHd4XQM6Jm9oKtXy8CD7S09zZcUOgv3v1iK+txbODIPnV5zi07nvEPhjIDXH1mbCPxMICQ1xY8BKKWc520dzcWPMiRSIJ1G0SWoqcOcOtG4NS5dGH+0Jv1SB/s3gqi9k987Jt82/49mq3fHy0ER7SrlTfE1SnS0UygBvA/44VDkZYxq7KMYk0UIhlQgJgc8+I3Lyr3icDo426WxWGNwARtWESNt1aR2/OizsvpAcmXK4IVillCueU5gB/AMMAPo7vJSCrFnh88/xOHYURo3CVAm0TyoYAiPmw7SZ4BNujdsQvIEhq4e4J1alVLycvVLYaoypngLxJIpeKaRis2YR1vc1vM//ax91Nivszg9LS8LQhyGHKc5HDT/k9YY98BB9ZEaplOKKK4U5ItJXRAqJSO6olwtjVOnNE0/gfeII9O1rH1UwBJoeg8+XwvmvoO/aE3y0+Hm6/v6s5lJSKpVw9krhWCyjjTGmpOtDSpiItAHaBAQE9D506JA7QlDOMga++w6GDIGLF++ZvKgkNH8a8vIgJYpk50boDQY0GEDXil0RkXvXp5S6b/d9ozm10uqjNOTWLdiwgbAevfA+eTTapGfaw+TA6LO3K/0EvzwxQW9GK5UM7rv6SESyiMgAERlrGy4tIo+5MkiVzvn6QqNGeO/YCi+9FG3S6Hnwv1Xw+WLryWjfUJh9aBa1fqrN0qNLuRN+x01BK5XxOFt99BtW/wnPGGMqikgWYJ0xJjCZ44uXXimkYTduQOXKcOzemskFpeDtR+FgHgjzgsLZCzOlwxQa+jdM+TiVSodccaO5lDHmSyAMwBhzE9AKX5V02bLBggWQP/89k1ocgd2jYfNPkDcE/r3+L41+bkSl0ZWYumuqG4JVKuNw9tHSUBHxBQyAiJQC9Jpe3Z8yZWD1avj2W/D0hBMnYO5c++Qq5+DCV7CyOLzXFDawm25/dGPbmW10LN+ROn513Bi8UumTs9VHjwIfAOWBRUA9oIcxZkWyRpcArT5KZ+7cgfbtrSuIGCKBBQHw6SNwOTPc9oJPekyiS8UuZPLKlOKhKpWWuaT1kYjkAepgVRttMMbc274whWmhkE5FRMBvv2Fefx25cCHWWUI9oHsHmFER3qzzJsMeHaZNWJVykityH80BpgJBxphUk+5SC4V0LiwM9u6FAQOiVStFueMJvdvA5CoQkD2QgAKFQAz1i9Wnb82+5MqcK+VjVioNcEWh8AjQGWgNbAamA3ONMbddGWhiaaGQgRw4wK2O3fHdfe/x3lwYFpeEtcXg7zLWuAJZCzCo0SCal2pO8VzFUzhYpVI3lz28JiKeQGOgN9DCGOOyJ4tEpBzQD8gLLDXGjE5oGS0UMpjQUFi6lFsR3kQ89xzZLgbfM8tTHWBbITiQzxrOkSkHm3tvpkyeMikcrFKplyuapGJrffQE8CJQE/jZiWUmiMh5EdkdY3wLETkgIodF5D0AY8w+Y8yLQCesG9lKRefjAy1b4vtYU7Id3Uv4G28T6Rm9Ad3UP2D/SJg+A7zD4drta5QfXpbX5vZn/qH53Ay76abglUobnK0++h2oBSwAfgNWGmMinViuAXAD+MUYU9E2zhM4CDQDgrGqo7oaY/aKSFvgJWCyMSbBBul6paDYtw9+/BGGD4918g1vuJAVuj4BG4uCt4c3tYrUolulbrxQ4wXNzqoyJFdcKYzHeoDtRWPMcmcKBABjzCrgvxijawGHjTFHjTGhWPcn2tnmDzLGtAS6ORmXyujKlbMS7o0fH+vkbGFQ4gr8PQUe3wvh4WFsObqWV+b25f0lA1M0VKXSgngfXhORd4wxXxpjForIk1id7URNG2KMeT8J2ywCnHIYDgZqi0hDoAOQCfg7npj6AH0AihUrloTNq3Tpueeszn4uXoTjx2HYsGiTc9+GP36/O3wqBzT9bwh7D1/nlUcf4+MVH1M2b1lGtx5NZq/MKRu7UqlIvNVHIrLNGFMt5vvYhuNZhz9WS6Wo6qOOWDepe9mGnwZqG2NeSWzwWn2k4vTnn1a67pAQq4opFtd9oG9rmFkebntb4+p7vcncV78mhyZnVenY/VQfSRzvYxt21mmgqMOwn22c00SkjYiMvXr1ahJDUOne44/D5s3Wcw7Hj8OLL94zS/ZQmPwnbBkLpS4BBtbf+Qb/l15mzMpZXLtzLcXDVsrdEioUTBzvYxt21magtIiUEBEfoAsQlJgVGGPmGGP65MyZM4khqAyleHEYPRomTYJy5TAe0b/2FS7A4RFw6hsI+xSO/DGKxaM6UvaHsozZMobzIedZdGQRry94nR1nd7hnH5RKIQlVH0UAIVhXBb5AVHs+ATIbY7zjXbnINKAh1rMH54CPjDHjRaQV8B3gCUwwxgxOVNDa85q6X7Nnc2bMX+RdOAVvE3bP5Eu+UPpVuJzl3kVrF6nN4w8+ztsPvY2nh2cKBKuUa2nPa0rF5fvvoV+/OCcvKgkfNoI9+aHiedhUBCJtFxof1hnGJ83fSqFAlXIdLRSUisvNm1C0KPxntZw2Pj5IaGics28uDF/Ug4I34FImb6p1WUD/zo1TKlqlXCK+QsHZ/hRSFYfqI3eHotK6LFng66+tJq1FiyJr1vDv0+9QeNVvsc5e81+YaW+YHcaPp5qQ89ADeEfkI6dvVlpXepg3676Jfy7/lNoDpVxKrxSUAvj3X6s3uBw5wBg4fx7OnOFkt/9RbO+9/TtEWzQbfNAEJlW1hr09vOlVrRcDGgygcPbCKRC8Uomj1UdKJVF4mOHnNjM5vf4kh0IK80zEBG7hSx7Pf6kXsfXufAIV+8KBvERrrJ0rcy4CCwYy88mZ5MmSJ+V3QKlYpLtCQVsfKXcwBlauhLZtIfT6bcbRi+5MiTbPsWyZ6NzlDpv9oi9bKfw5Pqn7Le1b5kT7AlLulu4KhSh6paDc5fJlqFwZSgavZCUN75m+pkA28oXfYElJ+Ooh8DRw9AF4+N/uLP1+LD6ZfVM+aKVstFBQKhmcPQsHD0L98T2QXxLMJG+3sWQuFr29gNCbBXj+ieLcyrqfIWuGUDZPWZqVbEYmr0wEFgxMvsBVhqeFglLJKTwcZsyA4GDMsGHI+fMJLvLmozCiNvjc8eFmlnubwP7v4f8xpMmQ5IhWqfRXKOg9BZVq3b4NGzdazz9MmwaTJ8c7eyQwvhqUvgQb/WBAYwi3PSQ9sd1EegT2SPaQVcaT7gqFKHqloFK9Y8es5q2PPw5nziQ4+878MLomTAq0MreWzVOW7pW78269d/H2jDerjFJOc0l3nEqpJChRAmrXhp07uf33Miqyix1UjnP2yudh9Dz4ZDkUvgaHzx9g4PKBNJrYlFUnVnEz7Caz98/m0CW9QlbJQ68UlEpBI0bAa68Z6j2wj6A+c8n9xbvxzn85M+zPa3UK1Lc1XMpqjffy8GJup9k0L9rQeipbqURId1cK2p+CSqtefRV27RJm7ClP7s/7W+k1AHr14uqC9ffM/8BtqBsMnfbC6onwwzx4ZSP4XwincIPWhGXPylvPFGTExhEpvCcqvdIrBaXcyRjr5nTmzCBC+Cv98Br5faJWcd0HyrwKfVp/yNP+Awi55k2VKta0O+F32HluJ1m8s1A+X3lEn5xT6I1mpdKOtWvh4Yftg3fe+h+Zvv48wcUO5YbfK8CWvFmZ7fUotYs8QmS539h+fiuhEVaT1+qFqjO48WCaBzRPtvBV2qCFglJpRWQkdO4MM2fCoEEwcCBnxsym0EvtnV7FopLQtSP8F8ethrp+dXmjzhs8WeFJ18Ss0hwtFJRKa0JDwcfHeh8Zybmu/fBetRTf74biu2cLfPopAGcoSH7O40lktMWXlLCeefhxLtz08ObfnGHcyASzy0JQWYjwhIcLNqdh6ZoAPFrqUR4u9rBWL2UQWigolZ4YA9Ong4cHYY93onHRQ9Q/P5PabKAdcxJc/Ia39cDc6y0AgZy34PH9cKxAc5oWm8f/3vPEU3sZTdfSXaGgTzQrddeyZTBggJWgb6jPQHKN+Myp5Zp3h41F4K/p0PAEhHhDK78BtHrxE27VGsTE7RN5unxvPm48AK802R2Xiku6KxSi6JWCUjGEh0O7dvD33/ZRF7L6s7FQe3KGXqD+ySnxLAwhXkK7evUolX0Nxa/AzPJwZccudiwpT2bfCH2qOp3QQkGpjCQ0FF58ESZOhDZt4M8/iaoPOjZ5DSWeqe/0qs5lBf/XgUzZMBLGh498yFPlO1PsrxWYvAWIbPUY3jcuw8KFUL8+FCmSPPukXEoLBaUyopAQyJo1+rjISOsJ6Dt3nF7Ncn/YUcDqD2J8NXhjPXy23JrWJvNifq74Jbm3LCaihD/s3Yun9hWR6qW7J5qVUk6IWSAAeHhAv37RRl3NH0DfHL/wZZPOsa6m0XF4fSN8vwDGzL1bIAB8FdqH3FsWA+B57Dit3irI6M2jScsnmxmdXikoldHcugXvvQfZssGHH0KmTPZJ52b9xZD3zrH5cCXWUS/Rq36juXXzenCuDjT6dLLmZUqltPpIKeW0iAg4ddJQ9JESeJ46kahlD+aG4lchUwQsKJGPX995k8+6dME/lz//Xv+XbWe20ci/Eb7evkzeMZmrd67St2ZfvDy0eVNK0kJBKZV4P/8MPXqwn7J8yxv8yIuJXkXDZ60CwjeTP/P8zhIut6l//iG+XXqEHb7neKUVvNnkAz5rbDWjXXh4IR7iQbNSzVy9N8qBFgpKqSTZuvQKjzTPTCg+HCn6CEWPr2GXlOO92oUYvX0TxW7fcHpdTZ+GpSVh/Tioc9oaN6IWfNA+OyffOMlv2yZz4b3XiBTI8/lwSmSvybfbP6RFqRa89dBbABhjuHz7Mjkz5cTTQ5+wS6p0Vyjow2tKpZzz563703lzhhFx4DBVO5dh115PcnOJBQ0+p6rZiufmDcjt20laf6NnoWrXNyg2ZCSvr7KS933Q2IshVfNBdqu3uqXPLGX1idX8svMXjl4+SuvSrZndZbYWDEmU7gqFKHqloFTKW7cOWreGvHmt9/nyARs3Etn9acKPneJ8ztLkbfcwmSeOdmp92wtAs2fgwlfRx9frCf5XYL0fHMt973IjWo7gucDnyOoTSysrFS8tFJRSLhUZaaVgijNHUmSk1S91UNB9b+tcVij/cuxZX709vFn79ApqnvPEVKnClVuZ8PQUcuS4782ma/qcglLKpTw84ikQomaYNcsqFJo2jTbpwxL1KVF8JoMY6NS2CoRAvw3Q+Ch03vYMxclln5bzehj56reAOnWYXzkred5uSNEHL7B6Ney9sJeX573M34fupvzYsgX69oXVqxOztxmLXikopZLX+vXw0EMArCndg6y/TWTVKhjw+nU2U50Hse4LRgj87dWY5mGr8SEsztUZEaZWNLzZHOZOhZr/3p2W+x24HFqcrNvep9Dj33H46j68PLzY//J+/HOWwt8fgoOtRzSOH4c8ee5df1hEGLfCb5EjU/q93NDqI6WUe/35Jxw+DC+/DFmyYAxMnQpXzt2hVo4JjJ80g8VH+3D0TBcC+YcH2ctm31IcvlU3zlXe9BKyhEf//brmA7sKwMri8E1duGS73dC3Rl+aZX2bx/v+A4dbQFgWPv3Uyi7r6NyNc1T9sSr/3fqPOV3npNumsVooKKVStYsX4YcfrN5Ilyyxxj3yCAw85UGTo3d/ow5QhrIcdGqdN7xhSUnYWhjKXYAcd+CjRrDt7PMQNI58BSI4ccyDT9d9wPh/xvP+w+9z7Moxhm8cDoB/Ln/mNTvGiBFQvjy88gqklz6ItFBQSqUJd+5Ap06wY4f17NzVw+/S9MUvuZAV2vn8yo4L3ejIDMbzPDm4nuj1h3nAwEbw1a6t9KzSmEjfq0yoCoh10zp7puz8d+s/AApfg97fDWJOZCu2UZ2gICvpbHqghYJSKk2KNJEsPbCA3Vse4M2n71Yl9X/8MINvvcml82G8tfMJpoT3jrbclUyQy8lEsC+3gmUl4GAeiLQ1vckcBntGQskrcNErCyXDz1DtkRwsWRaeLlJyaKGglErTLl2C4sWtbOBVq1qtiDw8rK4jqhb/jz1n794xNq1b0amNJzdXzqPZyjr0OruVbJEJlxATA6Fne+v9u6th6NK70zoXfIffW63F238zI1v9QP3i9Xk+6Hluh9+mdenWdK3YlT/3/0mzks2oWaSma3c+GWihoJRK89atg6VL4fnnoXDhu+P37IEKFR0q+wcNgoEDCT57h4oPZuLm1VDCC2/Ds8xfTPjvC57eGfc2srwPPhFw4jvI6VCOTK8AXZ+8O+wTDqGxXDDk8M7FyTePkzNzTqbvns6ITSPoUaUHvav3vndmm1thtzhw6QCVC1TGQ1LmKYE0UyiISHugNZADGG+MWRTf/FooKKUA6+GD0aMhZ07Yvx8KFgRg3Djo3RuyZ4e/5oTSZ3Fedg+9TuaI2Fez3g/KXoTcMTJ2XPOBfO9YBcHT22HsHNidH7o9AQfzRp/3t7bzyJfLl6aTmxJpIgFY1H1R9JZMJ07A008TmSc31R7Zz46rB3it1msMbzncRR9I/NxaKIjIBOAx4LwxpqLD+BbAcMATGGeMGeow7QFgmDHm+fjWrYWCUgqw+oiYOxcqV4ayZaNNOnIEcuSw0nGM3jyaTYP7Mi4IPBP509euCwSVhWPfgf9Va1yIN1TsC8cfuDtfPZ+XOOQ9i/Mh5+3jCmQtQIPiDTj832HGthlLjZ4DrC5MgefawaSq1nzhA8Px9PBkY/BGtp/dTtdKXZPleQl3FwoNgBvAL1GFgoh4AgeBZkAwsBnoaozZa5v+NTDFGLMtvnVroaCUSgxjDP0W9GPi6hHkzFWA7y+NpcqXb1CKo/fM+19mT/57siUBk+cCcCg3/JKtGZ+eXBxtvq/rwuga8NpGOJ4Lfq0MF7JZ06r9C9/Ph38KwhstINwT6hSpzfreG+3LrygOjZ6z3m9/YTs5MuWg/Kjy3A6/Ta+qvfip7U8u/xzcXn0kIv7AXIdCoS7wsTGmuW34f7ZZh9pei40xS+JYVx+gD0CxYsWqnziRuE5AlFJq9/ndFM5emMwmN739FzPlwqP3zHPz7Q/J0qc7lCkT77rOZLOauha7dne4/nNwJquwf6QPRa9bNyc+fgQ+aQQ1g2HTuLvLzykDbZ+Ke/2RH0YiLn5AIr5CwV1tq4oApxyGg4HawKtAUyCniAQYY8bEXNAYMxYYC9aVQgrEqpRKZyrmt9dkM2R+VXD8eSxcGIYOJctTT1kJnh55BFaujHNdhW7cOzx0CRy5Vp+i11fZxw9cBd13QsDl6POXsh6LIN8NeHUT7M8LUytb47LegSOXj1A8ewDGwH+hZ1l3ah1FcxSlUoFKZPbKnJTdj1eqanBrjPke+D6h+Rz6U0j+oJRS6Vrx6jHuFA8fDh073h1+441YC4VV1KcBsWfW67jPmsORp7m3QAAodRlKX4Q506DsJWvc2WzQeTf02QbbNj9P7vUryJb7Ft6v1ePm+aNczQx4e/H3U3+7PBWHu7KkngaKOgz72cY5xRgzxxjTJ2fOnC4PTCmVAQ0fbj340KABtGsXfVq7dlbujdmzoWtXAKbnfZljzV+KNtvJmmWYVe7eVZ/ICZHxbDpTBBz84W6BANaVRh/bHdVqS1bRMmQGZ3PP4FTIUb6fD5eHwrxJ4VQ45nzPd04zxiT7C/AHdjsMewFHgRKAD7ADqJCI9bUBxgYEBBillHKJq1eNiYxMeL4bN6y/YWHG9OxpzMMPG/PNNyby5k0z589p5oRPEWOs7ibMQQJM8Wdamm9rYx9nwIyqEX04oddpCpnsT9cwfIQJzn53fOTmzUnaVWCLieP3NSVaH00DGgJ5gXPAR8aY8SLSCvgOq0nqBGPM4MSuW1sfKaVSnZAQmDGD0IPH2VLjRXxrXqXThIeY+dM1KpyNpF/HMoyKqMyEw7/z3Pa7i52hIIU4G+dqjzwAP9SCb62WrNzOlIPMIf8l0LFF7Nze+ii5aKGglEoLwiLCEMALq3eiAQMg6/Ae/O/Gz/Z5inKSdx5ez6trOju1zrm05uLEufTokfh40l3PayLSRkTGXr161d2hKKVUgrw9vfHy9Laf1X/6KTQc0NM+fQQvE0xRHvr2SWjSxD7+WE5YUiL2dW7L3pDAQNfHmiYLBaM3mpVSaZgI1H2nPjfGTmGA79e8zdc8+ihUryEwdSrnC1ptUj/N+yQ92sPCkvc2Pe0z5ZFkKRRSVZNUpZTKMETI1vsputcH/zXwxBO28fnzs3XMZjq1v8ONI9lh7Hl2DcpN81LzoH17a57s2SnYsmqyhJUmCwV9TkEplV48+KD1clSpug838LEGQvLTqgVQri106wazZsEnn4BX8vx8641mpZRKZYyBGjVg2zaoWRM2bnToCjQiIkktjhylxjQXSiml4iACixbB8uXQsGGMvqHvs0BIiBYKSimVCuXJEz3bRkpJk62PtEmqUkoljzRZKGiTVKWUSh5pslBQSimVPLRQUEopZaeFglJKKbs0WSjojWallEoeabJQ0BvNSimVPNL0E80icgE4kcTF8wIXXRhOWpER91v3OePIiPudlH0ubozJF9uENF0o3A8R2RLXY97pWUbcb93njCMj7rer9zlNVh8ppZRKHlooKKWUssvIhcJYdwfgJhlxv3WfM46MuN8u3ecMe09BKaXUvTLylYJSSqkYtFBQSilllyELBRFpISIHROSwiLzn7niSi4gcF5FdIrJdRLbYxuUWkcUicsj29wF3x3m/RGSCiJwXkd0O42LdT7F8bzv2O0WkmvsiT7o49vljETltO97bRaSVw7T/2fb5gIg0d0/U90dEiorIchHZKyJ7RKSfbXy6Pdbx7HPyHWtjTIZ6AZ7AEaAk4APsAMq7O65k2tfjQN4Y474E3rO9fw/4wt1xumA/GwDVgN0J7SfQCpgPCFAH2Oju+F24zx8Db8cyb3nb9zwTUML2/fd09z4kYZ8LAdVs77MDB237lm6PdTz7nGzHOiNeKdQCDhtjjhpjQoHpQDs3x5SS2gE/297/DLR3XyiuYYxZBfwXY3Rc+9kO+MVYNgC5RKRQigTqQnHsc1zaAdONMXeMMceAw1j/B2mKMeaMMWab7f11YB9QhHR8rOPZ57jc97HOiIVCEeCUw3Aw8X/IaZkBFonIVhHpYxtXwBhzxvb+LFDAPaElu7j2M70f/1dsVSUTHKoG090+i4g/UBXYSAY51jH2GZLpWGfEQiEjedgYUw1oCbwsIg0cJxrrejPdt0nOKPsJjAZKAYHAGeBrt0aTTEQkGzALeN0Yc81xWno91rHsc7Id64xYKJwGijoM+9nGpTvGmNO2v+eBP7EuI89FXULb/p53X4TJKq79TLfH3xhzzhgTYYyJBH7ibrVButlnEfHG+nGcYoz5wzY6XR/r2PY5OY91RiwUNgOlRaSEiPgAXYAgN8fkciKSVUSyR70HHgV2Y+3rs7bZngVmuyfCZBfXfgYBz9haptQBrjpUPaRpMerLH8c63mDtcxcRySQiJYDSwKaUju9+iYgA44F9xphvHCal22Md1z4n67F29911N93Rb4V1F/8I8IG740mmfSyJ1QphB7Anaj+BPMBS4BCwBMjt7lhdsK/TsC6hw7DqUJ+Paz+xWqKMtB37XUANd8fvwn2ebNunnbYfh0IO839g2+cDQEt3x5/EfX4Yq2poJ7Dd9mqVno91PPucbMda01wopZSyy4jVR0oppeKghYJSSik7LRSUUkrZaaGglFLKTgsFpZRSdlooKLcQkQhbdsfdIjJDRLK4YJ2DRKRpAvO8KCLP2N73EJHCTqw32nwiMk5Eyrsg3qhMl4Pud13xbGO+iPjFGNdQROYmcX2dbRk4k7S8Sv20UFDucssYE2iMqQiEAi86ThQRr8Su0BjzoTFmSQLzjDHG/GIb7AEkWCjEnM8Y08sYszex8cXhW2PMhy5aVzQi4gvkMcYEu2qdxpjfgF6uWp9KfbRQUKnBaiDAdga7WkSCgL0i4ikiX4nIZlvirxeiFhCRd8XqK2KHiAy1jZskIh1t74+LyJe2eTaJSIBt/Mci8rZtvhrAFNsVi6+IfGjb1m4RGWt7Eja2+VaISA3b+rratrFbRL5wiO+GiAy2xbdBRBJMPGiL7WfbZ3BCRDo47MMCW7qDqH373BbPFhGpJiILReSIiDgWrg2BFbZlWojIfhHZBnRw2GYtEVkvIv+IyDoRKWsbv0pEAh3mWyMiVZw/pCqt0kJBuZXtiqAl1tOZYPUR0M8YUwbrKd2rxpiaQE2gt1jpSVpipQiubYypgpVPPzZXjTGVgB+A7xwnGGNmAluAbrYrllvAD8aYmrarF1/gsTjmi4q9MPAF0BgrMVlNEWlvm5wV2GCLbxXQ28mPpJRtfW2BX4Hltn24BbR2mO+kMSYQq0CdBHTE6jPgE4d5WgILRCQzVn6cNkB1oKDDPPuB+saYqsCHwBDb+PFYV0iISBkgszFmh5P7oNIwLRSUu/iKyHasH9yTWD9CAJuMlQcerHxNz9jm24iVzqA00BSYaIy5CWCMiatfgWkOf+s6EVMjEdkoIruwfpgrJDB/TWCFMeaCMSYcmILV+Q1YVWJR9e5bAX8ntg8w3xgThlVIegILbON3xVhHkMP4jcaY68aYC8AdEcllm1YPWAM8CBwzxhwyVgqDXx3WkxOYIVYPbt9yd59nAI/Zrk56YhU8KgNIdL2tUi5yy3ama2fl/iLEcRTwqjFmYYz5nO1i0MTx/h62s+lRWPlxTonIx0BmJ7cTmzBzN4dMBM7/r90BMMZEiojjOiJjrOOOw/g7DuMjAS8RKQmcMsaE2j7XuHyKdTXyuFj5+lfYtn9TRBZjXZF1wrrCUBmAXimo1Gwh8JJDXXoZsTK+LgaeE1uLJRHJHcfynR3+ro9l+nWsLg7hbgFwUazc9R3jmM/RJuAREckrIp5AV2ClU3uW/Fpy9ypjP+AvIqVsw10d5svJ3dTKPWKsYxzwPbDZGHM5meJUqYxeKajUbBxWlck2sU53LwDtjTELbDdBt4hIKPA38H4syz8gIjuxzqS7xjJ9EjBGRG5hVS/9hJWC+CxWivW45gOsrhJF5D1gOdZVzTxjTGpJRd4CeBXAGHNbrJ735onITaz7EFGF3JfAzyIyAJjnuAJjzFYRuQZMTLmwlbtpllSVLonIcayqoIvujiUutiqqG8aYYS5ebyZgrTGmxn2upzBWddKDxurMJWp8Q6xO4x+7n/Wr1Emrj5RynxtAH3Hxw2vG6rT9fguEZ7Bu7n8Qo0DojHXvRauT0im9UlBKKWWnVwpKKaXstFBQSillp4WCUkopOy0UlFJK2WmhoJRSyu7/dBc1qXxQ4GUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ploting noresm rcp2.6\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    " \n",
    "plt.plot(rain_bin_nr, 'b',label='RegCM(Noresm) present(1994-2014)',linewidth = 3)\n",
    "plt.plot(rain_bin_nrm, 'g',label='RegCM(Noresm) mid(2041-2060)',linewidth = 3)\n",
    "plt.plot(rain_bin_nrf, 'r',label='RegCM(Noresm) far(2081-2100)',linewidth = 3)\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "ax.set_yscale('log')\n",
    "#ax.set_xscale('log')\n",
    "plt.xlabel('Precipitation [mm/day]')\n",
    "plt.ylabel('Event Number [#]')\n",
    "plt.title('Precipitation daily PDF RegCM(Noresm rcp2.6)')\n",
    "plt.savefig(\"/scratch/mkibria/vms/pdf_regcm_rcp2.6_noresm.png\")\n",
    "\n",
    "\n",
    "\n",
    "plt.show( )"
   ]
  },
  {
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