PEX-and-metrics-by-time-of-day.ipynb

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   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import metpy.calc as mpcalc\n",
    "import metpy.constants as mpconstants\n",
    "import metpy\n",
    "from metpy.units import units\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "from tqdm import tqdm\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nmap = np.load(\"/home/swenson/projects/AGU2020/SOM_Zones/ERA5_1deg_regions7_EdgesRemoved.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcip_path = '/home/swenson/era5_data_direct_1.0degree_Version3/tp/'\n",
    "flist = []\n",
    "for year in tqdm(range(1980,2011)):\n",
    "    flist.append(pcip_path+'ERA5_'+str(year)+'_tp.nc')\n",
    "pcip_dat = xr.open_mfdataset(flist,concat_dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcip = pcip_dat.resample(time='3H').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcip.to_netcdf(pcip_path+'ERA5_1980-2010_tp_3H.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "WetDays = np.where(pcip['tp']>0.001/8,pcip['tp'],np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p90 = np.nanpercentile(np.where(pcip['tp']>0.001,pcip['tp'],np.nan),q=90,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/home/swenson/projects/PEX_feature_metrics/'\n",
    "cape_name = 'CAPE_consumption_percentile.nc'\n",
    "front_name = 'gradEPT_Frontal_percentile.nc'\n",
    "vort_name = 'PositiveRelativeVorticityAdvection_Percentile.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CONVECT = xr.open_dataset(path+cape_name)\n",
    "CAPE_metric = CONVECT['CAPE_consumption_percentile']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "FRONTAL = xr.open_dataset(path+front_name)\n",
    "FRT_metric = FRONTAL['gradEPT_percentile_AboveMedian']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "VORTICAL = xr.open_dataset(path+vort_name)\n",
    "VRT_metric = VORTICAL['PositiveRelativeVorticityAdvection_Percentile']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CAPE_metric.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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