code to calculate monthly mean UpVortp from the NCEP daily pressure level U and vorticity

NCEP-covar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.1.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import xesmf as xe\n",
    "import pandas as pd\n",
    "import xgcm\n",
    "\n",
    "# to get xesmf:\n",
    "# conda install -c conda-forge esmpy\n",
    "# pip install xesmf\n",
    "\n",
    "# to get the latest xgcm:\n",
    "# pip install git+https://github.com/xgcm/xgcm.git\n",
    "\n",
    "# mkdir temp\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grid_setup(ds,pergrid):\n",
    "\n",
    "    dlon = ds.lon[1] - ds.lon[0]; dlat = ds.lat[1] - ds.lat[0]\n",
    "    lonh = ds.lon + dlon/2.0; lath = ds.lat + dlat/2.0\n",
    "    if 'lon' in pergrid:\n",
    "        ds['lonh'] = ('lonh',lonh) \n",
    "    else:\n",
    "        ds['lonh'] = ('lonh',np.concatenate(([lonh.values[0]-dlon],lonh.values))) \n",
    "    ds['lath'] = ('lath',np.concatenate(([lath.values[0]-dlat],lath.values)))\n",
    "\n",
    "def vorticity(ds, u, v, periodic = ['lon']):\n",
    "    \n",
    "    if 'lon' in periodic:\n",
    "        grid = xgcm.Grid(ds, coords={'lon': {'center': 'lon', 'right': 'lonh'},\\\n",
    "                                     'lat': {'center': 'lat', 'outer': 'lath'}}, periodic=True)\n",
    "    else:\n",
    "        grid = xgcm.Grid(ds, coords={'lon': {'center': 'lon', 'outer': 'lonh'},\\\n",
    "                                     'lat': {'center': 'lat', 'outer': 'lath'}}, periodic=False)\n",
    "\n",
    "    dlon = ds.lon[1]-ds.lon[0]; dlat = ds.lat[1]-ds.lat[0]\n",
    "\n",
    "    coslat = np.cos(np.deg2rad(ds.lat))\n",
    "    coslath = np.cos(np.deg2rad(ds.lath))\n",
    "    meterPdegree = 111000.0\n",
    "\n",
    "    dlonm = dlon*meterPdegree; dlatm = dlat*meterPdegree\n",
    "\n",
    "    # interpolate the quantities to edge of trapezoid and then compute derivatives\n",
    "\n",
    "    uh = grid.interp(u,axis='lat',boundary='extend')\n",
    "    vh = grid.interp(v,axis='lon',boundary='extend')\n",
    "    dudy = grid.diff(coslath*uh,axis='lat',boundary='extend')/dlatm  \n",
    "    dvdx = grid.diff(vh,axis='lon',boundary='extend')/dlonm\n",
    "    return (dvdx - dudy)/coslat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temp/upvortp_1950.nc\n",
      "temp/upvortp_1951.nc\n",
      "temp/upvortp_1952.nc\n",
      "temp/upvortp_1953.nc\n",
      "temp/upvortp_1954.nc\n",
      "temp/upvortp_1955.nc\n",
      "temp/upvortp_1956.nc\n",
      "temp/upvortp_1957.nc\n",
      "temp/upvortp_1958.nc\n",
      "temp/upvortp_1959.nc\n",
      "temp/upvortp_1960.nc\n",
      "temp/upvortp_1961.nc\n",
      "temp/upvortp_1962.nc\n",
      "temp/upvortp_1963.nc\n",
      "temp/upvortp_1964.nc\n",
      "temp/upvortp_1965.nc\n",
      "temp/upvortp_1966.nc\n",
      "temp/upvortp_1967.nc\n",
      "temp/upvortp_1968.nc\n",
      "temp/upvortp_1969.nc\n",
      "temp/upvortp_1970.nc\n",
      "temp/upvortp_1971.nc\n",
      "temp/upvortp_1972.nc\n",
      "temp/upvortp_1973.nc\n",
      "temp/upvortp_1974.nc\n",
      "temp/upvortp_1975.nc\n",
      "temp/upvortp_1976.nc\n",
      "temp/upvortp_1977.nc\n",
      "temp/upvortp_1978.nc\n",
      "temp/upvortp_1979.nc\n",
      "temp/upvortp_1980.nc\n",
      "temp/upvortp_1981.nc\n",
      "temp/upvortp_1982.nc\n",
      "temp/upvortp_1983.nc\n",
      "temp/upvortp_1984.nc\n",
      "temp/upvortp_1985.nc\n",
      "temp/upvortp_1986.nc\n",
      "temp/upvortp_1987.nc\n",
      "temp/upvortp_1988.nc\n",
      "temp/upvortp_1989.nc\n",
      "temp/upvortp_1990.nc\n",
      "temp/upvortp_1991.nc\n",
      "temp/upvortp_1992.nc\n",
      "temp/upvortp_1993.nc\n",
      "temp/upvortp_1994.nc\n",
      "temp/upvortp_1995.nc\n",
      "temp/upvortp_1996.nc\n",
      "temp/upvortp_1997.nc\n",
      "temp/upvortp_1998.nc\n",
      "temp/upvortp_1999.nc\n",
      "temp/upvortp_2000.nc\n",
      "temp/upvortp_2001.nc\n",
      "temp/upvortp_2002.nc\n",
      "temp/upvortp_2003.nc\n",
      "temp/upvortp_2004.nc\n",
      "temp/upvortp_2005.nc\n",
      "temp/upvortp_2006.nc\n",
      "temp/upvortp_2007.nc\n",
      "temp/upvortp_2008.nc\n",
      "temp/upvortp_2009.nc\n",
      "temp/upvortp_2010.nc\n",
      "temp/upvortp_2011.nc\n",
      "temp/upvortp_2012.nc\n",
      "temp/upvortp_2013.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/naomi/.conda/envs/my3.6/lib/python3.6/site-packages/xarray/coding/variables.py:135: RuntimeWarning: invalid value encountered in equal\n",
      "  condition |= data == fv\n",
      "/home/naomi/.conda/envs/my3.6/lib/python3.6/site-packages/xgcm/grid.py:997: RuntimeWarning: invalid value encountered in add\n",
      "  return 0.5*(data_left + data_right)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temp/upvortp_2014.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/naomi/.conda/envs/my3.6/lib/python3.6/site-packages/xarray/coding/variables.py:135: RuntimeWarning: invalid value encountered in equal\n",
      "  condition |= data == fv\n",
      "/home/naomi/.conda/envs/my3.6/lib/python3.6/site-packages/xgcm/grid.py:997: RuntimeWarning: invalid value encountered in add\n",
      "  return 0.5*(data_left + data_right)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temp/upvortp_2015.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/naomi/.conda/envs/my3.6/lib/python3.6/site-packages/xarray/coding/variables.py:135: RuntimeWarning: invalid value encountered in equal\n",
      "  condition |= data == fv\n",
      "/home/naomi/.conda/envs/my3.6/lib/python3.6/site-packages/xgcm/grid.py:997: RuntimeWarning: invalid value encountered in add\n",
      "  return 0.5*(data_left + data_right)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temp/upvortp_2016.nc\n",
      "temp/upvortp_2017.nc\n"
     ]
    }
   ],
   "source": [
    "url_base = 'https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.DAILY/.Intrinsic/.PressureLevel/'\n",
    "for year in np.arange(1950,2018):\n",
    "    url_sel = '/T/(Jan%20' + str(year) + ')/(Dec%20' + str(year) + ')/RANGE/dods'\n",
    "    urlu = url_base + 'u' + url_sel\n",
    "    urlv = url_base + 'v' + url_sel\n",
    "    ds = xr.open_dataset(urlu,decode_times=False)\n",
    "    ds['v'] = xr.open_dataset(urlv,decode_times=False).v\n",
    "    ds = ds.rename({'T':'time','X':'lon','Y':'lat'})\n",
    "    nt = ds.time.size\n",
    "    ds['time'] = pd.date_range(str(year)+'-01-01', periods=nt, freq='D')\n",
    "    grid_setup(ds,['lon'])\n",
    "    vort  = vorticity(ds, ds.u, ds.v)\n",
    "    um = ds.u.resample(time='M').mean('time')\n",
    "    vortm = vort.resample(time='M').mean('time')\n",
    "    upvortp = (ds.u * vort).resample(time='M').mean('time') - um * vortm\n",
    "    file_out = 'temp/upvortp_' + str(year) + '.nc'\n",
    "    print(file_out)\n",
    "    ds_out = upvortp.to_dataset(name='upvortp')\n",
    "    ds_out.sel(lat=slice(85,-85)).to_netcdf(file_out,mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "all = xr.open_mfdataset('temp/upvortp_*.nc')\n",
    "all.to_netcdf('upvortp.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fac67bfb710>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all.mean('time').sel(P=200).upvortp.plot(vmin=-0.5e-4,vmax=0.5e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# wget http://byrd.ldeo.columbia.edu/python/data/upvortp.nc"
   ]
  }
 ],
 "metadata": {
  "gist_info": {
   "gist_id": null,
   "gist_url": null
  },
  "kernelspec": {
   "display_name": "myPython3.6",
   "language": "python",
   "name": "my3.6"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}