MICS to CMAQ

MICS.ipynb

{
 "cells": [
  {
    "cell_type": "markdown",
    "metadata": {
      "id": "view-in-github"
    },
    "source": [
      "<a href=\"https://colab.research.google.com/gist/barronh/97633470997ca77b4bb949df20d9a0a3/MICS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yeGUm97BDjT9"
   },
   "source": [
    "# Convert MICS to CMAQ\n",
    "\n",
    "    author: Barron H. Henderson\n",
    "    contributors: hlbutterfly, Gwang-Jin \n",
    "    date: 2021-05-24\n",
    "\n",
    "Convert MICS[1,2] files to CMAQ-ready format with monthly representative days. Creates 25h files for each month with mid-month start dates [Jan 15 0Z, ..., Dec 15 0Z].\n",
    "\n",
    "Steps:\n",
    "1. Convert from mole-rates to mole-fluxes\n",
    "2. Regrid from lat/lon to Lambert Conic Conformal\n",
    "3. Add vertical distribution and temporal distribution.\n",
    "4. Convert from mole-fluxes to mechanism speciation in CMAQ-ready file (i.e., IOAPI-like)\n",
    "\n",
    "\n",
    "[1] Li, M., Zhang, Q., Kurokawa, J.-I., Woo, J.-H., He, K., Lu, Z., Ohara, T., Song, Y., Streets, D. G., Carmichael, G. R., Cheng, Y., Hong, C., Huo, H., Jiang, X., Kang, S., Liu, F., Su, H., and Zheng, B.: MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17, 935-963, doi:10.5194/acp-17-935-2017, 2017.\n",
    "\n",
    "[2] http://meicmodel.org/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sCNCjt12GDB9"
   },
   "source": [
    "# Install and Load Prerequisites\n",
    "\n",
    "* Auto-matic installation is for ubuntu or debian.\n",
    "  * To enable, change `installcdo` and `installlibs` to True\n",
    "  * Works on Google Colab or systems with admin access.\n",
    "* If you are on another system\n",
    "  * install the `cdo` and `pseudonetcdf` and `pyproj` before\n",
    "  * change `installcdo` and `installlibs` to `False`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "YE-gWn4NF-8L"
   },
   "outputs": [],
   "source": [
    "installcdo = False\n",
    "installlibs = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Oj0j5tgBzdLE"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "if installcdo:\n",
    "  !apt-get install cdo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "vL9Mb-HovvS8"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "if installlibs:\n",
    "  !pip install pseudonetcdf pyproj pycno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "dt-dcj1KvyPj"
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import shutil\n",
    "import os\n",
    "import zipfile\n",
    "import calendar\n",
    "import io\n",
    "from glob import glob\n",
    "from datetime import datetime\n",
    "import functools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import cdo\n",
    "import PseudoNetCDF as pnc\n",
    "import pycno\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On atmos with Polar Stereographic support\n",
    "cdopath = '/work/ROMO/anaconda3/envs/geo/bin/cdo'\n",
    "if not os.path.exists(cdopath):\n",
    "    # Default to system path\n",
    "    cdopath = None\n",
    "    \n",
    "cdoo = cdo.Cdo(cdopath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define some download convenience functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "tp6Y4Z-mX7Ja"
   },
   "outputs": [],
   "source": [
    "def downloadurl(url, destpath=None):\n",
    "    \"\"\"Download a url to local destination. Skip if present\n",
    "\n",
    "    Arguments\n",
    "    ---------\n",
    "    url : str\n",
    "        path to file online\n",
    "    destpath : str\n",
    "        path for storing file (default basename(url))\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    destpath : str\n",
    "    \"\"\"\n",
    "    if destpath is None:\n",
    "        destpath = os.path.basename(url)\n",
    "    if not os.path.exists(destpath):\n",
    "        r = requests.get(url, stream=True)\n",
    "        if r.status_code == 200:\n",
    "            with open(destpath, 'wb') as f:\n",
    "                r.raw.decode_content = True\n",
    "                shutil.copyfileobj(r.raw, f)        \n",
    "    return destpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extracturl(zfurl):\n",
    "    \"\"\"Download and then extract all files (if not present)\n",
    "\n",
    "    Arguments\n",
    "    ---------\n",
    "    zfurl : str\n",
    "        path to zipfile online\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    None\n",
    "    \"\"\"\n",
    "    destpath = downloadurl(zfurl)\n",
    "    zf = zipfile.ZipFile(destpath)\n",
    "    for path in zf.namelist():\n",
    "        if path.endswith('.nc'):\n",
    "            if not os.path.exists(path):\n",
    "                print(f'extract {path}')\n",
    "                zf.extract(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rzFtvVLFIXDM"
   },
   "source": [
    "# Specify which species to process and Download\n",
    "\n",
    "* Downloads all CB05 or SAPRC99 speciated VOCs\n",
    "* Downlaods all specified species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "0KrOe7DmGgpt"
   },
   "outputs": [],
   "source": [
    "# spckeys will each be downloaded and later processed\n",
    "spckeys = ['NOx', 'CO', 'SO2', 'NH3', 'BC', 'OC', 'PM2.5', 'PM10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Q7wMmdwuX2Jh"
   },
   "outputs": [],
   "source": [
    "VOCMECH = 'CB05'\n",
    "#VOCMECH = 'SAPRC99'\n",
    "extracturl(\n",
    "    f'https://meicmodel-website.oss-cn-beijing.aliyuncs.com/wp-cache/meicmodel/download/MIX_v1.1/{VOCMECH}.zip'\n",
    ")\n",
    "vocpaths = sorted(glob(f'{VOCMECH}/*/MICS_Asia_{VOCMECH}_*_*_0.25x0.25.nc'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "-NTYYqWTv38p"
   },
   "outputs": [],
   "source": [
    "infilepaths = [p for p in vocpaths]\n",
    "# for each species, download the files and extract\n",
    "for spckey in spckeys:\n",
    "    extracturl(f'https://meicmodel-website.oss-cn-beijing.aliyuncs.com/wp-cache/meicmodel/download/MIX_v1.1/{spckey}.zip')\n",
    "    infilepaths.extend(sorted(glob(f'{spckey}/*.nc')))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define PMFINE\n",
    "\n",
    "* PM2.5 is the sum of species including OC and EC.\n",
    "* MICS provides OC and EC explicitly.\n",
    "* So we must remove OC and EC to just the fraction that we speciate.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pm25paths = [p for p in infilepaths if p.startswith('PM2.5')]\n",
    "os.makedirs('PMFINE', exist_ok=True)\n",
    "for pm25path in pm25paths:\n",
    "    pmfinepath = pm25path.replace('PM2.5', 'PMFINE')\n",
    "    pm25f = pnc.pncopen(pm25path, format='netcdf').copy()\n",
    "    ocf = pnc.pncopen(pm25path.replace('PM2.5', 'OC'), format='netcdf')\n",
    "    bcf = pnc.pncopen(pm25path.replace('PM2.5', 'BC'), format='netcdf')\n",
    "    tmpf = pm25f.subset(['time', 'lat', 'lon'])\n",
    "    for pm25k, pm25v in pm25f.variables.items():\n",
    "        if pm25k not in ('time', 'lon', 'lat'):\n",
    "            ocv = ocf.variables[pm25k.replace('PM2.5', 'OC')]\n",
    "            bcv = bcf.variables[pm25k.replace('PM2.5', 'BC')]\n",
    "            outv = tmpf.copyVariable(pm25v, key=pm25k.replace('PM2.5', 'PMFINE'))\n",
    "            outv[:] = np.maximum(pm25v[:] - ocv[:] - bcv[:], 0)\n",
    "    tmpf.save(pmfinepath, verbose=0).close()\n",
    "    # PMFINE is added by replacing PM2.5 with PMFINE later\n",
    "    # infilepaths.append(pmfinepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define PMC\n",
    "\n",
    "* PM10 implicitly includes PM2.5\n",
    "* PM2.5 is already handled directly\n",
    "* PMC is just the coarse fraction.\n",
    "* So, we remove PM2.5 from PM10 to get PMC.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pm10paths = [p for p in infilepaths if p.startswith('PM10')]\n",
    "os.makedirs('PMC', exist_ok=True)\n",
    "for pm10path in pm10paths:\n",
    "    pmcpath = pm10path.replace('PM10', 'PMC')\n",
    "    pm10f = pnc.pncopen(pm10path, format='netcdf').copy()\n",
    "    pm25f = pnc.pncopen(pm10path.replace('PM10', 'PM2.5'), format='netcdf')\n",
    "    tmpf = pm10f.subset(['time', 'lat', 'lon'])\n",
    "    for pm10k, pm10v in pm10f.variables.items():\n",
    "        if pm10k not in ('time', 'lon', 'lat'):\n",
    "            pm25v = pm25f.variables[pm10k.replace('PM10', 'PM2.5')]\n",
    "            outv = tmpf.copyVariable(pm10v, key=pm10k.replace('PM10', 'PMC'))\n",
    "            outv[:] = np.maximum(pm10v[:] - pm25v[:], 0)\n",
    "    tmpf.save(pmcpath, verbose=0).close()\n",
    "    # PMC is added by replacing PM10 with PMC later\n",
    "    # infilepaths.append(pmcpath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove PM2.5 and PM10\n",
    "\n",
    "* PM2.5 and PM10 are now represetned by PMFINE and PMC\n",
    "* We remove these files so they are not processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hG8wRICUdQ7o",
    "outputId": "b2877d7a-1585-42ce-baa0-d8454a02aa78"
   },
   "outputs": [],
   "source": [
    "infilepaths = [p.replace('PM2.5', 'PMFINE').replace('PM10', 'PMC') for p in infilepaths]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Group Inpaths By Year\n",
    "\n",
    "* We will process files later by year-month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "4WBE0X_agHnz"
   },
   "outputs": [],
   "source": [
    "yearpaths = {}\n",
    "for inpath in infilepaths:\n",
    "  year = inpath.split('_')[-2]\n",
    "  yearpaths.setdefault(year, []).append(inpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6gQjv9YgiOuV",
    "outputId": "925c3f8e-b635-4675-9036-a3ba6aa3c297"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2008': ['CB05/2008/MICS_Asia_CB05_ALD2_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_ALDX_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_CH4_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_ETHA_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_ETH_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_ETOH_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_FORM_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_IOLE_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_ISOP_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_MEOH_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_NVOL_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_OLE_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_PAR_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_TERP_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_TOL_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_UNR_2008_0.25x0.25.nc',\n",
       "  'CB05/2008/MICS_Asia_CB05_XYL_2008_0.25x0.25.nc',\n",
       "  'NOx/MICS_Asia_NOx_2008_0.25x0.25.nc',\n",
       "  'CO/MICS_Asia_CO_2008_0.25x0.25.nc',\n",
       "  'SO2/MICS_Asia_SO2_2008_0.25x0.25.nc',\n",
       "  'NH3/MICS_Asia_NH3_2008_0.25x0.25.nc',\n",
       "  'BC/MICS_Asia_BC_2008_0.25x0.25.nc',\n",
       "  'OC/MICS_Asia_OC_2008_0.25x0.25.nc',\n",
       "  'PMFINE/MICS_Asia_PMFINE_2008_0.25x0.25.nc',\n",
       "  'PMC/MICS_Asia_PMC_2008_0.25x0.25.nc',\n",
       "  'PMFINE/MICS_Asia_PMFINE_2008_0.25x0.25.nc',\n",
       "  'PMC/MICS_Asia_PMC_2008_0.25x0.25.nc'],\n",
       " '2010': ['CB05/2010/MICS_Asia_CB05_ALD2_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_ALDX_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_CH4_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_ETHA_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_ETH_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_ETOH_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_FORM_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_IOLE_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_ISOP_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_MEOH_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_NVOL_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_OLE_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_PAR_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_TERP_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_TOL_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_UNR_2010_0.25x0.25.nc',\n",
       "  'CB05/2010/MICS_Asia_CB05_XYL_2010_0.25x0.25.nc',\n",
       "  'NOx/MICS_Asia_NOx_2010_0.25x0.25.nc',\n",
       "  'CO/MICS_Asia_CO_2010_0.25x0.25.nc',\n",
       "  'SO2/MICS_Asia_SO2_2010_0.25x0.25.nc',\n",
       "  'NH3/MICS_Asia_NH3_2010_0.25x0.25.nc',\n",
       "  'BC/MICS_Asia_BC_2010_0.25x0.25.nc',\n",
       "  'OC/MICS_Asia_OC_2010_0.25x0.25.nc',\n",
       "  'PMFINE/MICS_Asia_PMFINE_2010_0.25x0.25.nc',\n",
       "  'PMC/MICS_Asia_PMC_2010_0.25x0.25.nc',\n",
       "  'PMFINE/MICS_Asia_PMFINE_2010_0.25x0.25.nc',\n",
       "  'PMC/MICS_Asia_PMC_2010_0.25x0.25.nc']}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearpaths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define the Chemical Speciation\n",
    "\n",
    "* First define a speciation splitter class, then define speciation by species/process (e.g., PMFINE_TRANSPORT) or species (e.g., NOx)\n",
    "* If the species/process is not available, the species will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class emis2cmaq:\n",
    "    def __init__(self, key, factor=1, units='moles/s'):\n",
    "        self.key = key\n",
    "        self.factor = factor\n",
    "        self.units = units\n",
    "\n",
    "    def __repr__(self):\n",
    "        return f'{self.key} {self.units} = {self.factor} x '\n",
    "    \n",
    "    def __call__(self, rhs):\n",
    "        return self.factor * rhs\n",
    "\n",
    "e2g = functools.partial(emis2cmaq, units='moles/s')\n",
    "e2p = functools.partial(emis2cmaq, units='g/s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "J-pyd39AFPPV"
   },
   "outputs": [],
   "source": [
    "# PMFINE and PMC from gspro_HTAP_CB06_from_2011ei_28apr2017_v0.txt\n",
    "# NMVOC could similarly be process, but this notbook uses the CB5 speciation from MICS\n",
    "splitfactors = {\n",
    "    'NOx': [e2g('NO', 0.9 / 46.00550), e2g('NO2', 0.1 / 46.00550)], # NO and NO2 in cmaq should be moles, but MICS files have Mg units\n",
    "    'CO': [e2g('CO', 1. / 28.0101)], # CO in cmaq should be moles, but MICS files have Mg units\n",
    "    'SO2': [e2g('SO2', 0.97 / 64.0638), e2p('PSO4', 0.03 * 96. / 64.0638)], # 3% of moles are converted to PSO4 (Mw 96g/mole)\n",
    "    'NH3': [e2g('NH3', 1. / 17)], # NH3 in cmaq is carried in mole, but the MICS file has Mg\n",
    "    'BC': [e2p('PEC')],\n",
    "    'OC': [e2p('POC')],\n",
    "    'PM2.5': [e2p('PM25')],\n",
    "    'PM10': [e2p('PM10')],\n",
    "    'PMC': [e2p('PMC')],\n",
    "    'PMFINE': [e2p('PMOTHR')],\n",
    "    'PMFINE_RESIDENTIAL': [\n",
    "        e2p(\"PAL\",9.059984890153869E-4), e2p(\"PCA\",0.0034297278364788506), e2p(\"PCL\",0.026757569821944986),\n",
    "        e2p(\"PFE\",6.747975186045601E-4), e2p(\"PH2O\",2.8623111229696884E-5), e2p(\"PK\",0.037723252780690636),\n",
    "        e2p(\"PMG\",4.886475383743535E-4), e2p(\"PMN\",2.4839678574308852E-5), e2p(\"PMOTHR\",0.0753847334041736),\n",
    "        e2p(\"PNA\",0.005562459454327978), e2p(\"PNCOM\",0.7932242255778433), e2p(\"PNH4\",0.006773223914233244),\n",
    "        e2p(\"PNO3\",0.006223289781278002), e2p(\"PSI\",0.015033354681951088), e2p(\"PSO4\",0.027661957713335183),\n",
    "        e2p(\"PTI\",1.0329869794470812E-4),\n",
    "    ] ,\n",
    "    'PMFINE_INDUSTRY': [\n",
    "        e2p(\"PAL\",0.030837976232004703), e2p(\"PCA\",0.04691978993604533), e2p(\"PCL\",0.005622635954552769),\n",
    "        e2p(\"PFE\",0.029830933564959652), e2p(\"PH2O\",0.007782126502007649), e2p(\"PK\",0.02063533585236529),\n",
    "        e2p(\"PMG\",0.0014877457975388893), e2p(\"PMN\",0.0013678183686060667), e2p(\"PMOTHR\",0.5697048834417312),\n",
    "        e2p(\"PNA\",0.010449730723527754), e2p(\"PNCOM\",0.07891155914607309), e2p(\"PNH4\",0.0018193703309040423),\n",
    "        e2p(\"PNO3\",0.00444376674366932), e2p(\"PSI\",0.089134857153437), e2p(\"PSO4\",0.09694122781646801),\n",
    "        e2p(\"PTI\",0.004110242436109103),\n",
    "    ],\n",
    "    'PMFINE_POWER': [\n",
    "        e2p(\"PAL\",0.05023462620436187), e2p(\"PCA\",0.034628121559832664), e2p(\"PCL\",0.001151793879901178),\n",
    "        e2p(\"PFE\",0.025103697649528918), e2p(\"PH2O\",3.952198796631878E-5), e2p(\"PK\",0.0048438809096782495),\n",
    "        e2p(\"PMG\",4.588736423222851E-4), e2p(\"PMN\",2.4849891826460237E-4), e2p(\"PMOTHR\",0.5889290704763706),\n",
    "        e2p(\"PNA\",1.1609404225005764E-4), e2p(\"PNCOM\",0.04390264380863414), e2p(\"PNH4\",0.003698874777325087),\n",
    "        e2p(\"PNO3\",0.006200971191188791), e2p(\"PSI\",0.07744371753307334), e2p(\"PSO4\",0.15912226512059632),\n",
    "        e2p(\"PTI\",0.003877348298705523),\n",
    "    ],\n",
    "    'PMFINE_TRANSPORT': [\n",
    "        e2p(\"PAL\",0.005602119588710436), e2p(\"PCA\",0.0333032298061885), e2p(\"PCL\",0.0091794527310865),\n",
    "        e2p(\"PFE\",0.062219609556697524), e2p(\"PH2O\",0.0037056954503465974), e2p(\"PK\",0.0022756587444511174),\n",
    "        e2p(\"PMG\",0.0407291019544641), e2p(\"PMN\",4.997777955064257E-4), e2p(\"PMOTHR\",0.2643678647790108),\n",
    "        e2p(\"PNA\",0.002986272952331283), e2p(\"PNCOM\",0.3751065749747639), e2p(\"PNH4\",0.04136881934732051),\n",
    "        e2p(\"PNO3\",0.016702731773643745), e2p(\"PSI\",0.043675932619087886), e2p(\"PSO4\",0.0963104141025025),\n",
    "        e2p(\"PTI\",0.001966743823888066),\n",
    "    ],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "FxYha81iYhcA"
   },
   "outputs": [],
   "source": [
    "# Adding MICS VOCMECH gas-phase speciation\n",
    "cbspcs = sorted({k.split(VOCMECH + '_')[1].split('_')[0] for k in vocpaths})\n",
    "for cbspc in cbspcs:\n",
    "    splitfactors[cbspc] = [emis2cmaq(cbspc)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FlNOClCXIxr1"
   },
   "source": [
    "# Define the CMAQ Output Grid\n",
    "\n",
    "* Specify the name of the projected grid (`dom = '27CN8'`)\n",
    "* Define the output grid using a IOAPI GRIDDESC formatted file.\n",
    "* This will automatically be converted to a Climate Data Operator Grid file (`dom.grid`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "eCDL48ofy8K2"
   },
   "outputs": [],
   "source": [
    "dom = '27CN8'\n",
    "\n",
    "with open('GRIDDESC', 'w') as gdf:\n",
    "    gdf.write(\"\"\"' '\n",
    "'LAM_34N110E'\n",
    "  2        25.000        40.000       110.000       110.000        34.000\n",
    "' '\n",
    "'27CN8'\n",
    "'LAM_34N110E'  -3132000.000  -2457000.000     27000.000     27000.000 232 182   1\n",
    "' '\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5CoHXt2aJa1E",
    "outputId": "96a4901e-c4a5-48a0-d36f-537ebc43829c"
   },
   "outputs": [],
   "source": [
    "gf = pnc.pncopen('GRIDDESC', format='griddesc', GDNAM=dom)\n",
    "\n",
    "with open(f'{dom}.grid', 'w') as gdf:\n",
    "    gdf.write(f\"\"\"gridtype = projection\n",
    "gridsize = {gf.NCOLS * gf.NROWS}\n",
    "xsize = {gf.NCOLS}\n",
    "ysize = {gf.NROWS}\n",
    "xinc = {gf.XCELL}\n",
    "yinc = {gf.YCELL}\n",
    "xname = x\n",
    "yname = y\n",
    "grid_mapping_name = lambert_conformal_conic\n",
    "latitude_of_projection_origin = {gf.YCENT}\n",
    "longitude_of_projection_origin = {gf.XCENT}\n",
    "longitude_of_central_meridian = {gf.P_GAM}\n",
    "standard_parallel = {gf.P_ALP}, {gf.P_BET}\n",
    "earth_radius = 6370000.\n",
    "semi_major_axis = 6370000.\n",
    "semi_minor_axis = 6370000.\n",
    "false_easting = {-gf.XORIG}\n",
    "false_northing = {-gf.YORIG}\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qNCrDYDqJ9Hz"
   },
   "source": [
    "# Get latlongrid area\n",
    "\n",
    "* Define a function (reusable elsewhere)\n",
    "* Call that function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "qhsbaGUvxH5o"
   },
   "outputs": [],
   "source": [
    "def getlatlonarea(latlonpath):\n",
    "    latlonf = pnc.pncopen(latlonpath, format='netcdf')\n",
    "    lat = latlonf.variables['lat']\n",
    "    lon = latlonf.variables['lon']\n",
    "    dlat, = np.unique(np.diff(lat[:]))\n",
    "    dlon, = np.unique(np.diff(lon[:]))\n",
    "    latb = np.append(lat[:] - dlat / 2, lat[-1] + dlat / 2)\n",
    "    nlon = 360. / dlon\n",
    "    Re = 6371000.\n",
    "    latr = np.pi / 180. * latb\n",
    "    area = 2. * np.pi * Re * Re / \\\n",
    "        (nlon) * (np.sin(latr[1:]) - np.sin(latr[:-1]))\n",
    "    area = area[:, None].repeat(lon.size, 1)\n",
    "    return area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "FzlLjhENxu5R"
   },
   "outputs": [],
   "source": [
    "latlonarea = getlatlonarea(f'{spckeys[0]}/MICS_Asia_{spckeys[0]}_2010_0.25x0.25.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q779BFhkKPTe"
   },
   "source": [
    "# Define Vertical Layer Fractions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "KYk6lwy315LD"
   },
   "outputs": [],
   "source": [
    "layerfractions = pd.read_csv(io.StringIO(\"\"\"L,SigmaTop,Alt,PPCB,PRIR,PRCE,INCB,PROT\n",
    "1,0.995,35,0.00,0.06,0.06,0.50,0.06\n",
    "2,0.99,85,0.10,0.26,0.26,0.30,0.26\n",
    "3,0.98,140,0.10,0.68,0.68,0.20,0.68\n",
    "4,0.96,210,0.30,0.00,0.00,0.00,0.00\n",
    "5,0.94,310,0.20,0.00,0.00,0.00,0.00\n",
    "6,0.91,440,0.20,0.00,0.00,0.00,0.00\n",
    "7,0.86,610,0.10,0.00,0.00,0.00,0.00\n",
    "# Citation Supplemental Table 7 https://acp.copernicus.org/articles/19/3447/2019/\n",
    "# L [=] 1-based index layer\n",
    "# Sigma value [=] dry pressure based terrain following coordinate at the top of the level\n",
    "#                 VGTOP = 10000\n",
    "# Alt [=] Level height (m)\n",
    "# PPCB [=] fraction in layer, power plant combustion\n",
    "# PRIR [=] fraction in layer, iron plants\n",
    "# PRCE [=] fraction in layer, cement plants\n",
    "# INCB [=] fraction in layer, industrial boilers\n",
    "# PROT [=] fraction in layer, industrial process\"\"\"), comment='#')\n",
    "layerfractions.loc[:, 'POWER'] = layerfractions['PPCB']\n",
    "layerfractions.loc[:, 'INDUSTRY'] = layerfractions['PROT']\n",
    "layerfractions.loc[:, 'RESIDENTIAL'] = 0\n",
    "layerfractions.loc[0, 'RESIDENTIAL'] = 1\n",
    "layerfractions.loc[:, 'TRANSPORT'] = 0\n",
    "layerfractions.loc[0, 'TRANSPORT'] = 1\n",
    "layerfractions.loc[:, 'AGRICULTURE'] = 0\n",
    "layerfractions.loc[0, 'AGRICULTURE'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define HourFractions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "hourlstfactor = pd.read_csv(io.StringIO(\"\"\"hour,AGRICULTURAL_WASTE_BURNING,AGRICULTURE,AIR,AIR_CDS,AIR_CRS,AIR_LTO,ENERGY,INDUSTRY,LARGE_SCALE_BIOBURN,RESIDENTIAL,SHIPS,TRANSPORT\n",
    "0,0.6,0.6,1.333,1.333,0.5232,1.333,0.88,0.75,0.6,0.4,1.0,0.44\n",
    "1,0.6,0.6,0.0,0.0,0.4752,0.0,0.79,0.75,0.6,0.4,1.0,0.19\n",
    "2,0.6,0.6,0.0,0.0,0.4464,0.0,0.72,0.75,0.6,0.4,1.0,0.09\n",
    "3,0.6,0.6,0.0,0.0,0.4368,0.0,0.72,0.78,0.6,0.4,1.0,0.06\n",
    "4,0.6,0.6,0.0,0.0,0.4488,0.0,0.71,0.82,0.6,0.4,1.0,0.05\n",
    "5,0.6,0.6,0.0,0.0,0.504,0.0,0.74,0.88,0.6,0.4,1.0,0.09\n",
    "6,0.65,0.65,0.0,0.0,0.6,0.0,0.8,0.95,0.65,0.5,1.0,0.22\n",
    "7,0.75,0.75,1.333,1.333,0.7464,1.333,0.92,1.02,0.75,1.2,1.0,0.86\n",
    "8,0.9,0.9,1.333,1.333,0.9312,1.333,1.08,1.09,0.9,1.5,1.0,1.84\n",
    "9,1.1,1.1,1.333,1.333,1.1208,1.333,1.19,1.16,1.1,1.6,1.0,1.86\n",
    "10,1.25,1.25,1.333,1.333,1.2672,1.333,1.22,1.22,1.25,1.6,1.0,1.41\n",
    "11,1.45,1.45,1.333,1.333,1.3704,1.333,1.21,1.28,1.45,1.4,1.0,1.24\n",
    "12,1.6,1.6,1.333,1.333,1.4496,1.333,1.21,1.3,1.6,1.2,1.0,1.2\n",
    "13,1.8,1.8,1.333,1.333,1.488,1.333,1.17,1.22,1.8,1.1,1.0,1.32\n",
    "14,1.75,1.75,1.333,1.333,1.5144,1.333,1.15,1.24,1.75,1.1,1.0,1.44\n",
    "15,1.7,1.7,1.333,1.333,1.524,1.333,1.14,1.25,1.7,1.0,1.0,1.45\n",
    "16,1.55,1.55,1.333,1.333,1.4976,1.333,1.13,1.16,1.55,1.0,1.0,1.59\n",
    "17,1.35,1.35,1.333,1.333,1.4256,1.333,1.1,1.08,1.35,1.0,1.0,2.03\n",
    "18,1.1,1.1,1.333,1.333,1.3152,1.333,1.07,1.01,1.1,1.1,1.0,2.08\n",
    "19,0.9,0.9,1.333,1.333,1.2744,1.333,1.04,0.95,0.9,1.4,1.0,1.51\n",
    "20,0.75,0.75,1.333,1.333,1.2216,1.333,1.02,0.9,0.75,1.5,1.0,1.06\n",
    "21,0.65,0.65,1.333,1.333,1.02,1.333,1.02,0.85,0.65,1.4,1.0,0.74\n",
    "22,0.6,0.6,1.333,1.333,0.7848,1.333,1.01,0.81,0.6,1.4,1.0,0.62\n",
    "23,0.6,0.6,1.333,1.333,0.6168,1.333,0.96,0.78,0.6,1.0,1.0,0.61\n",
    "24,0.6,0.6,1.333,1.333,0.5232,1.333,0.88,0.75,0.6,0.4,1.0,0.44\n",
    "# Based on HTAP_2011_BaseA_tpro_hourly_21mar2016_v0\n",
    "# - Copied 24 to 0 to make 25hour file\"\"\"), comment='#')\n",
    "hourlstfactor.loc[:, 'POWER'] = hourlstfactor['ENERGY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hour.AGRICULTURAL_WASTE_BURNING.AGRICULTURE.AIR.AIR_CDS.AIR_CRS.AIR_LTO.ENERGY.INDUSTRY.LARGE_SCALE_BIOBURN.RESIDENTIAL.SHIPS.TRANSPORT.POWER.\n"
     ]
    }
   ],
   "source": [
    "# Using simple longitude based time zones\n",
    "# you can replace this with explicit utcoffset values\n",
    "utcoffset = (gf.variables['longitude'][:] / 15).round(0).astype('i')\n",
    "\n",
    "uutcoffsets, uutcidx = np.unique(utcoffset, return_inverse=True)\n",
    "hourutcfactors = dict()\n",
    "ni = gf.NROWS * gf.NCOLS * hourlstfactor.shape[1]\n",
    "\n",
    "for ki, key in enumerate(hourlstfactor.columns):\n",
    "    print(key, end='.', flush=True)\n",
    "    hourutcfactors[key] = np.zeros((25, gf.NROWS, gf.NCOLS), dtype='f')\n",
    "    for ui, uutc in enumerate(uutcoffsets):\n",
    "        hourutcfactors[key].reshape(25, -1)[:, uutcidx == ui] = np.roll(hourlstfactor[key].values, -uutc).copy()[:, None]\n",
    "\n",
    "print()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IuhqzHLrt/14McfwPtk+0fUJRTradgB4RMZeq1nIZgGlJR/6yntJRkv5tmQMT0CNi7C1buaL6tV3G7A69xztt/9/uG9s/B95Z5sAE9IgaydDFCo1vQF/au3RusfTu88ocmE7RiGidEaZTyvgq8HlJ/6N4/65i27waHdDTIRoRcxq/US5d76cTxH+3eH8b8KdlDmx0QI+IZlm2ckVnse4KjOsderEo11WUXD+9VwJ6RLTTmAZ0SavorOdyEvCC7nbbJ853bDpFGyaTiiJKKDlkcUR38f+Tzt35PuAs4Hrgf5U5sLEBPfnziDio8R3l8kLbmwDZ/rHtjwLnlTkwKZeIaCUN6QEXi/CUpCXAA5IuA3YCLy5zYGPv0COaKmPRm0nSZ4qXf05nka5/B7waeDtwUZlz5A49Itpp/DpFXy3pV4C3AdcAvwDet5ATNC6gJ3ceEfMaz4lFVwObgBN5drVF9/ycd5TLogO6pFcAn+/ZdCLwETrPwnsn8HfF9g/ZvnWx14mIGIgxC+i2rwSulHSV7d+d94BZLDqg274fWA2/XGtgJ/Al4GLgE7b/ZLHnjogYuDEL6F2LDeZQXcrlbOBB2z/uWVMmImIsibEe5bJoVY1yuQC4oef9ZZK2StpQPErpAJLWSdosafPUVAP/ZSNifFU4saiIc7slbZvj87cV8fBuSX8t6eSqm9PVd0CX9DzgTcCfFZuuAl5GJx2zC7hituNsr7c9aXtyYqKa3yvpEI22yNDFClQ3sWgjsOYgn/8I+Ce2/xHwh8D6xVZ5PlVE0nOA79t+FMD2o7b3FwvMXAOcVsE1ooRM+49YgIoCuu07gMcO8vlfFw+pALgTqPhJHc+qIqCvpSfdIumYns/eAsz6Z0hExCgtIOWyvJseLsq6Pi57CfCXlTRgFn11iko6FPindNbu7fqvklbT+d328IzPIiLGQ/lRLntsT/Z7OUln0Qnor+v3XHPpK6DbfhKYmLFtJH/3J38eEaV5uKNcJL2KzkMqzrE9NajrNG6maEREKUMahy5pJXAz8HbbPxzktRLQI6KVqpr6L+kG4Ew6ufYdwOXAIQC2r6Yzg34C+HQxT2dfFSmc2SSgR9RUd+ji8tsfGXFNaqqigG577Tyf/w7wO9Vc7eAS0COifUb38IqBakRAT4doRCyEGMvVFvvWiIAemVQUsVAJ6BERTZGAHhHREAno4yf584hYsPF8YlHf8pDoiJrLyouLVN1qi2Oj9nfoERGL0cQHXCSgR0QrNTHlkoAeEe1Tw3RKGbUO6OkQjYhFS0CPiKi/zBSNiGgQTTcvoieg11ym/Ad0hi5m1cUFSA59vCR/HhH9SMolIqIpEtAjIpohd+gREU2RgH4gSQ8DTwD7KZ6VJ+klwOeB44GHgbfa/nm/14qIqISbOfW/qsW5zrK9uufBpx8ANtleBWwq3ldiYsmh6RCNiL50x6GXKXUyqNUWzweuK15fB7x5QNeJiEJWXVwgu1ypkSoCuoGvS7pL0rpi29G2dxWvfwYcPfMgSeskbZa0eWqqgX/7RMRYq+oOXdIGSbslbZvjc0m6UtJ2SVslnVp1W7qqCOivs30qcA5wqaQzej+0PesQftvrbU/anpyYyLLsETFEZddCL3eDvhFYc5DPzwFWFWUdcNXiKj2/viOp7Z3Fz93Al4DTgEclHQNQ/Nzd73XiQJklGrF4mi5X5mP7DuCxg+xyPnC9O+4EjuzGx6r1FdAlHSrpsO5r4A3ANuAW4KJit4uAL/dzna50hkZEVRYQ0Jd308NFWTfPqWc6Fuhdl2FHsa1y/Q5bPBr4kqTuuT5n+6uSvgd8QdIlwI+Bt/Z5nYg57T3vSY74Sn7ZxwKYhXR47ukZwTfW+groth8CTp5l+xRwdj/njogYpCEOSdwJ9A5BWlFsq1x6IyOinYb3kOhbgAuL0S6nA3t7RgFWqjZT/5M/j5hfltEtp8oHXEi6ATiTTq59B3A5cAiA7auBW4Fzge3AL4CLq7nygWoT0CMiKmNX9oAL22vn+dzApZVcbB4J6BHRTvWaBFpKAnpEtFLd1mkpoxYBPfnziKiUgTxTNCKiIZoXzxPQ6yrT/iP608SUS8ahRzRMltEtR9MuVeokd+gR0T7VTRoaK2Mf0NMhGhFV60wsal5EH/uAHhExEA18rk4CekS0Uu7QIyKaIDn0iIimqN8IljLGOqCnQzRicbpDF7Py4kEk5RLjIJOKIvrkcs8LrZsE9Ihop9yhD0dSLRExcM2L5+MZ0CMiBk3Tzcu5JKBHRPuYRk4sWvTiXJKOk3S7pHsl3SPpPcX2j0raKWlLUc6trroREf0TRi5X6qSfO/R9wPtsf1/SYcBdkm4rPvuE7T/pv3oREQNSs2BdxqLv0G3vsv394vUTwH3Asf1WKB2iEdXJUroHYZcrNVLJeuiSjgdOAb5TbLpM0lZJGyQdNccx6yRtlrR5aqqByayIGF/dHHqZMg9JayTdL2m7pA/M8vnKIj39gyIuDiwN3XdAl/Ri4CbgvbYfB64CXgasBnYBV8x2nO31tidtT05M5DkbETFcmp4uVQ56Dmkp8CngHOAkYK2kk2bs9p+AL9g+BbgA+PQAmgP0GdAlHUInmH/W9s0Ath+1vd/2NHANcFr/1YyIqFLJdMv8KZfTgO22H7L9NHAjcP6BF+Pw4vURwE8rbUqPRXeKShJwLXCf7Y/3bD/G9q7i7VuAbf1VMXpl2n9EBcxC8uPLJW3ueb/e9vri9bFA74I5O4DXzDj+o8DXJf0ecCjwWwuub0n9jHJ5LfB24G5JW4ptH6LzJ8dqOv9kDwPvKnvCdIhGxNCU77rbY3uyjyutBTbavkLSbwCfkfRrRRajUosO6La/RedJTjPduvjqREQMR0VjzHcCvUOJVhTbel0CrAGw/W1JLwCWA7urqECv9EZGNFyGLs6hmhz694BVkk6Q9Dw6nZ63zNjnJ8DZAJL+IfAC4O8qbg2Qqf8R0UY27O8/42F7n6TLgK8BS4ENtu+R9AfAZtu3AO8DrpH07+mkot9hD2aAewJ6RLRTRTHV9q3MSDXb/kjP63vp9DkO3NgE9HSIRsRQ1WwWaBljE9AjIobGQJ4pGhHRBIbqRw2OXAJ6jWRSUURFTCWdouMmAT0i2qmBOfSxGIe+bDyqEdFYGYs+iwYun5s79IhoofoF6zIS0COifQzkIdEREQ2RO/SIiCaoZur/uElAj4j2MQxg9dqRS0CPiHZq4EzRjBeMaIkMXZwhwxYjIhrAziiXGJ1M+4+oWM3uvstIQI+IFjLev3/UlahcAnpEtE9Dl89Np2g0wt7znhx1FaJuPF2u1MjAArqkNZLul7Rd0gcGdZ2IiIUy4GmXKnUykIAuaSnwKeAc4CRgraSTBnGtiCgvQxcLdiPv0AeVQz8N2G77IQBJNwLnA/cO6HoREQuSTtHyjgUe6Xm/A3hN7w6S1gHrirdPLT3mgW0Dqss4WQ7sWdyhH5l/l/HRRztrow1thPFs5z/o9wRP8POvfcNfXF5y93Fr/5xGNsrF9npgPYCkzbYnR1WXYUk7m6MNbYTmttP2mlHXYRAG1Sm6E+hN1q0otkVExIAMKqB/D1gl6QRJzwMuAG4Z0LUiIoIBpVxs75N0GfA1YCmwwfY9Bzlk/SDqMYbSzuZoQxuhPe1sBLmB6xlERLRRZopGRDREAnpEREOMPKA3eYkASQ9LulvSFkmbi20vkXSbpAeKn0eNup4LIWmDpN2StvVsm7VN6riy+G63Sjp1dDVfmDna+VFJO4vvc4ukc3s++2DRzvslvXE0tV44ScdJul3SvZLukfSeYnvjvtM2GGlAb8kSAWfZXt0zlvcDwCbbq4BNxfs62QjMHMM7V5vOAVYVZR1w1ZDqWIWNHNhOgE8U3+dq27cCFP9nLwBeWRzz6eL/dh3sA95n+yTgdODSoj1N/E4bb9R36L9cIsD200B3iYAmOx+4rnh9HfDm0VVl4WzfATw2Y/NcbTofuN4ddwJHSjpmKBXt0xztnMv5wI22n7L9I2A7nf/bY8/2LtvfL14/AdxHZ6Z3477TNhh1QJ9tiYBjR1SXQTDwdUl3FUsdABxte1fx+mfA0aOpWqXmalMTv9/LilTDhp50WSPaKel44BTgO7TrO22MUQf0pnud7VPp/Jl6qaQzej90Z8xoo8aNNrFNPa4CXgasBnYBV4y0NhWS9GLgJuC9th/v/azh32mjjDqgN3qJANs7i5+7gS/R+TP80e6fqMXP3aOrYWXmalOjvl/bj9reb3sauIZn0yq1bqekQ+gE88/avrnY3IrvtGlGHdAbu0SApEMlHdZ9DbwB2EanfRcVu10EfHk0NazUXG26BbiwGBlxOrC358/42pmRK34Lne8TOu28QNLzJZ1Ap8Pwu8Ou32JIEnAtcJ/tj/d81IrvtHFsj7QA5wI/BB4EPjzq+lTYrhOBvynKPd22ARN0Rg08AHwDeMmo67rAdt1AJ93wDJ386SVztQkQnVFMDwJ3A5Ojrn+f7fxM0Y6tdALbMT37f7ho5/3AOaOu/wLa+To66ZStwJainNvE77QNJVP/IyIaYtQpl4iIqEgCekREQySgR0Q0RAJ6RERDJKBHRDREAnpEREMkoEdENMT/B9nxsq39Qay/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.pcolormesh(hourutcfactors['TRANSPORT'][12])\n",
    "plt.colorbar(label='factor');\n",
    "plt.figure()\n",
    "plt.axes(ylabel='factor', xlabel='UTC');\n",
    "plt.plot(hourutcfactors['TRANSPORT'][:, 100, 100]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define Convenience Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "OcLWj6mgCKh0"
   },
   "outputs": [],
   "source": [
    "def clean(path, overwrite=True):\n",
    "    if overwrite and os.path.exists(path):\n",
    "        os.remove(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "N3MiEANJ-aBb"
   },
   "outputs": [],
   "source": [
    "def rate2flux(inpath, outpath, area, verbose=0, overwrite=False):\n",
    "    clean(outpath, overwrite=overwrite)\n",
    "    if os.path.exists(outpath):\n",
    "        return\n",
    "    molratef = pnc.pncopen(inpath, format='netcdf')\n",
    "    fluxf = molratef.subset([])\n",
    "    for k, v in molratef.variables.items():\n",
    "        outv = fluxf.copyVariable(v, key=k)\n",
    "        if v.dimensions[-2:] == ('lat', 'lon'):\n",
    "            outv[:] = v[:] / area\n",
    "            inunit = getattr(outv, 'units', getattr(outv, 'Unit', 'unknown'))\n",
    "            if inunit == 'unknown':\n",
    "                print('Unkown unit', str(outv))\n",
    "            outunit = inunit + '/m2'\n",
    "            outv.units = outunit\n",
    "        else:\n",
    "            outv[:] = v[:]\n",
    "    fluxf.save(outpath, verbose=verbose).close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fluxregrid(fluxpath, regridpath, overwrite=False):\n",
    "    clean(regridpath, overwrite=overwrite)\n",
    "\n",
    "    if not os.path.exists(regridpath):\n",
    "        cdoo.remapycon(f'{dom}.grid', input=fluxpath, output=regridpath, returnCdf=False)\n",
    "        \n",
    "    regridf = pnc.pncopen(regridpath, format='netcdf')\n",
    "    return regridf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def addvar(outf, varkey, vals, units, description):\n",
    "    if varkey not in outf.variables:\n",
    "        var = outf.createVariable(varkey, 'f', ('TSTEP', 'LAY', 'ROW', 'COL'))\n",
    "        var.units = units.ljust(16)\n",
    "        var.long_name = varkey.ljust(16)\n",
    "        var.var_desc = description.ljust(80)[:80]\n",
    "        var.description = description\n",
    "    else:\n",
    "        var = outf.variables[varkey]\n",
    "        if var.var_desc != description.ljust(80)[:80]:\n",
    "            var.var_desc = f'Sum of {varkey} described in description'.ljust(80)\n",
    "            var.description += ' + ' + description\n",
    "    var[:] += vals\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process Files\n",
    "\n",
    "Creates monthly speciated emission files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "areapath = 'flux_regrid/area.nc'\n",
    "os.makedirs('flux_regrid', exist_ok=True)\n",
    "clean(areapath, overwrite=True)\n",
    "\n",
    "cdoo.gridarea(f'-O -remapnn,{dom}.grid -stdatm,0', options='-f nc', output=areapath, returnCdf=True)\n",
    "areaf = pnc.pncopen(areapath, format='netcdf')\n",
    "projarea = areaf.variables['cell_area'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hnYmwMsk2Qe4",
    "outputId": "85ef82e5-3832-4215-d5a0-baccf74bdff5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 100.0% cmaqready/MICS_Asia_2010-12_27CN8.nc : saving                                                                                                                                                   "
     ]
    }
   ],
   "source": [
    "overwrite = True\n",
    "\n",
    "# Create directories for output\n",
    "os.makedirs('flux', exist_ok=True)\n",
    "os.makedirs('cmaqready', exist_ok=True)\n",
    "\n",
    "# Define useful constants\n",
    "nz = layerfractions.shape[0]\n",
    "vglvls = np.append(1, layerfractions.SigmaTop.values.astype('f'))\n",
    "vgtop = 5000.\n",
    "# 1-based index\n",
    "seconds_per_month = np.array(calendar.mdays) * 24 * 3600\n",
    "\n",
    "nfiles = sum([len(v) for v in yearpaths.values()]) * 12\n",
    "ifile = 0\n",
    "for year, inpaths in sorted(yearpaths.items()):\n",
    "    for month in range(1, 13):\n",
    "        outpath = os.path.join(f'cmaqready/MICS_Asia_{year}-{month:02d}_{dom}.nc')\n",
    "        clean(outpath, overwrite=overwrite)\n",
    "        if os.path.exists(outpath):\n",
    "            # Skipping\n",
    "            continue\n",
    "        outf = pnc.pncopen('GRIDDESC', format='griddesc', GDNAM=dom, VGLVLS=vglvls, VGTOP=vgtop).subset([])\n",
    "        outf.createDimension('TSTEP', 25).setunlimited(True)\n",
    "        rootdir = os.path.dirname(os.path.dirname(inpaths[0])) + '/'\n",
    "        filepathstr = '\\n - '.join(inpaths)\n",
    "        \n",
    "        del outf.Conventions\n",
    "        outf.FILEDESC = f\"\"\"Regridded, temporally allocted, vertically allocated, and speciated emissions derived from MICS[1] files:\\n- {filepathstr}\n",
    "\n",
    "[1] Li et al.: MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17, 935-963, doi:10.5194/acp-17-935-2017, 2017.\n",
    "\"\"\"\n",
    "        outf.HISTORY = \"Created from MICS.ipynb\"\n",
    "        delattr(outf, 'VAR-LIST')\n",
    "        delattr(outf, 'NVARS')\n",
    "        for ratepath in inpaths:\n",
    "            print(f'\\r {ifile/nfiles:6.1%} {outpath} : Starting {ratepath}'.ljust(120), flush=True, end='')\n",
    "            # Define output paths\n",
    "            fluxpath = os.path.join('flux', os.path.basename(ratepath).replace('0.25x0.25', dom))\n",
    "            regridpath = os.path.join('flux_regrid', os.path.basename(fluxpath))\n",
    "\n",
    "            print(f'\\r {ifile/nfiles:6.1%} {outpath} : Converting rate to flux {fluxpath}', flush=True, end='')\n",
    "            rate2flux(ratepath, fluxpath, latlonarea, overwrite=False)\n",
    "\n",
    "            print(f'\\r {ifile/nfiles:6.1%} {outpath} : Remap to projected area {regridpath}', flush=True, end='')\n",
    "            regridf = fluxregrid(fluxpath, regridpath, overwrite=False).slice(time=month-1)\n",
    "\n",
    "            print(f'\\r {ifile/nfiles:6.1%} {ifile/nfiles:6.1%} {outpath} (4/6) Vertically/temporally allocate sectors...', flush=True, end='')\n",
    "            m2month_per_second = projarea * 1e6 / seconds_per_month[month]\n",
    "            for k, v in regridf.variables.items():\n",
    "                if k in ('time', 'lat', 'lon', 'x', 'y', 'Projection'):\n",
    "                    continue\n",
    "                \n",
    "                if k.startswith(VOCMECH + '_'):\n",
    "                    k = k[len(VOCMECH)+1:]\n",
    "                firstu = k.find('_')\n",
    "                spckey, prockey = k[:firstu], k[firstu+1:]\n",
    "                spcunit = str(v.units.strip())\n",
    "                spcval = (\n",
    "                    v[:][:, None, :, :]\n",
    "                    * layerfractions[prockey].values[None, :, None, None]\n",
    "                    * hourutcfactors[prockey][:, None, :, :]\n",
    "                    * m2month_per_second\n",
    "                )\n",
    "                if spcunit.startswith('Mg'):\n",
    "                    outunit = 'g/s'.ljust(16)\n",
    "                else:\n",
    "                    outunit = 'moles/s'.ljust(16)\n",
    "                \n",
    "                if k in splitfactors:\n",
    "                    mysplits = splitfactors[k]\n",
    "                else:\n",
    "                    mysplits = splitfactors[spckey]\n",
    "                \n",
    "                for split in mysplits:\n",
    "                    outkey = split.key\n",
    "                    outvardesc = f'{split} {spckey}_{prockey} {spcunit} x 1e6 x projarea m2 x months / seconds'\n",
    "                    print(f'\\r{\" \":200s}\\r {ifile/nfiles:6.1%} {outpath} : {outvardesc}', end='', flush=True)\n",
    "                    addvar(outf, outkey, split(spcval), split.units.strip(), outvardesc)\n",
    "                    \n",
    "            regridf.close()\n",
    "            ifile += 1\n",
    "        \n",
    "        outf.SDATE = int(datetime(int(year), month, 15).strftime('%Y%j'))\n",
    "        outf.STIME = 0\n",
    "        outf.TSTEP = 10000\n",
    "        \n",
    "        outf.updatemeta()\n",
    "        outf.updatetflag(overwrite=True)\n",
    "        outf.variables.move_to_end('TFLAG', last=False)\n",
    "        print(f'\\r{\" \":200s}\\r {ifile/nfiles:6.1%} {outpath} : saving'.ljust(120), end='', flush=True)\n",
    "        outf.save(outpath, format='NETCDF4_CLASSIC', verbose=0, complevel=1).close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visually QA a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "testf = pnc.pncopen('cmaqready/MICS_Asia_2008-01_27CN8.nc', format='ioapi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "**PNC:/home/bhenders/.local/lib/python3.6/site-packages/PseudoNetCDF/pncwarn.py:24:UserWarning:\n",
      "  NetCDF: Not a valid ID\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "norms = {\n",
    "    'CO': plt.matplotlib.colors.LogNorm(vmin=1e-9, vmax=1e-5),\n",
    "    'ETHA': plt.matplotlib.colors.LogNorm(vmin=1e-12, vmax=1.6e-8)\n",
    "}\n",
    "\n",
    "plotkey = 'ETHA'\n",
    "norm = norms.get(plotkey, None)\n",
    "newZ = testf.variables[plotkey][:].mean(0).sum(0) / projarea\n",
    "\n",
    "try:\n",
    "    origf = pnc.pncopen(f'{plotkey}/MICS_Asia_{plotkey}_2008_0.25x0.25.nc', format='netcdf')\n",
    "    plotvockey = plotkey\n",
    "except:\n",
    "    origf = pnc.pncopen(f'{VOCMECH}/2008/MICS_Asia_{VOCMECH}_{plotkey}_2008_0.25x0.25.nc', format='netcdf')\n",
    "    plotvockey = f'{VOCMECH}_{plotkey}'\n",
    "\n",
    "fig, axx = plt.subplots(1, 2, figsize=(10, 4))\n",
    "Z = (\n",
    "    origf.variables[f'{plotvockey}_TRANSPORT'][:]\n",
    "    + origf.variables[f'{plotvockey}_INDUSTRY'][:]\n",
    "    + origf.variables[f'{plotvockey}_RESIDENTIAL'][:]\n",
    "    + origf.variables[f'{plotvockey}_POWER'][:]\n",
    ") / latlonarea\n",
    "axx[0].set_title('Original in moles/s/m2')\n",
    "p = axx[0].pcolormesh(\n",
    "    origf.variables['lon'][160:420], origf.variables['lat'][125:305],\n",
    "    Z[0, 125:305, 160:420] * 1e6 / (30.5 * 24 * 3600),\n",
    "    norm=norm, shading='nearest'\n",
    ")\n",
    "plt.colorbar(p, ax=axx[0], label=plotkey)\n",
    "pycno.cno().draw(ax=axx[0]);\n",
    "axx[1].set_title('Reprojected in moles/s/m2')\n",
    "p = axx[1].pcolormesh(\n",
    "    newZ, norm=norm\n",
    ")\n",
    "plt.colorbar(p, ax=axx[1], label=plotkey)\n",
    "pycno.cno(proj=testf.getproj(withgrid=True)).draw(ax=axx[1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "MICS.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "anaconda",
   "language": "python",
   "name": "anaconda"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

Following the previous post. I dig more into this. It seems like the output files for all sectors are dropping the first variable, for some reason. For instance, 'ALD2' is not in the output 'VAR-LIST' and 'NVARS' is one less, although it is showing up as a variable. So in the case of agriculture where only NH3 is valid, 'VAR-LIST' is null and 'NVARS=0'. Why this is happening?

@hlbutterfly - Try manually deleting VAR-LIST and NVARS before calling updatemeta. Does that fix it?

delattr(outf, 'VAR-LIST')
delattr(outf, 'NVARS')
outf.updatemeta()
outf.updatetflag(overwrite=True)
outf.variables.move_to_end('TFLAG', last=False)

@Gwang-Jin - make sure you're aware of this.

@hlbutterfly - Try manually deleting VAR-LIST and NVARS before calling updatemeta. Does that fix it?

delattr(outf, 'VAR-LIST')
delattr(outf, 'NVARS')
outf.updatemeta()
outf.updatetflag(overwrite=True)
outf.variables.move_to_end('TFLAG', last=False)

Yes! It worked! Although the two commands should not be put right before the "updatemeta" command. In that case, I got the following error message:

if update and len(varlist) != self.NVARS:
AttributeError: 'griddesc' object has no attribute 'NVARS'

I move these two commands right after outf.HISTORY = "Created from MICS.ipynb" and this worked out fine! I am getting all the required variables in the 'VAR-LIST'.

Thank you so much!

I made the changes to the gist. Can you confirm that I made them where you did?

I made the changes to the gist. Can you confirm that I made them where you did?

Yes. That is correct!

@ barronh

Just to make you aware of this.

Another potential bug that I found is that PMC and PMFINE species are double counted. In [13] is replacing PM10 with PMC and PM2.5 with PMFINE in the filename list. However, PMFINE and PMC file has already been added to the list under In [11] and ln [12] (last command). So either remove the replacing command or remove the extend command. Otherwsie, PMC and PMFINE will be doubled counted. As least, I found this mistake with my processing.

Thanks
Ling

Thank you for your updates. With your permission, I would like to add your name at the top as a contributor.

Thank you for your updates. With your permission, I would like to add your name at the top as a contributor.

Thanks for asking! I am fine with this but I just feel my contribution is far from being qualified as a contributor.

Another thing that I forgot to point out earlier is the calculation of PMC should be PM10 - PM2.5, instead of PM10 - OC - BC in In [12].

Good catch. Clearly a copy and paste error. I used your github handle to recognize your contribution. I put this together to be helpful, but haven't tested it like you have. I appreciate your contribution!

Hi, Dr. Henderson,
An error occurred when I executed this code( correspond to code In [31]), that is:
"CDOException: (returncode:1) cdo gridarea: Started child process "remapnn,27CN8.grid -stdatm,0 (pipe1.1)".
cdo(2) remapnn: Started child process "stdatm,0 (pipe2.1)".
cdo(3) stdatm: Set default filetype to GRIB
cdo gridarea: Open failed on >flux_regrid/area.nc<
No such file or directory"
Do you know the reason and how to fix it ?

It looks like the flux_regrid folder is not being created until after the file is made. If you make the folder, it should work. We should move the os.makedirs calls in cell 32 to cell 31.

It looks like the flux_regrid folder is not being created until after the file is made. If you make the folder, it should work. We should move the os.makedirs calls in cell 32 to cell 31.

Thank you for replying, I made the "flux_regrid" folder before executed the "cdo.gridarea( )"，but there was still mistake. I was wondering if there something wrong with my CDO? And I changed the cell 5 because the original code have error when I executed, maybe related to this?
cdoo = cdo.Cdo(cdo=cdopath)==>cdoo = cdo.Cdo(cdopath)

I used the colab and executed the MICS.ipynb, the cell 31 as below:

areapath = 'flux_regrid/area.nc'
clean(areapath, overwrite=True)
**os.makedirs('flux_regrid', exist_ok=True)** # make "flux_regrid" folder

cdoo.gridarea(f'-O -remapnn,{dom}.grid -stdatm,0', output=areapath, returnCdf=True)
areaf = pnc.pncopen(areapath, format='netcdf')
projarea = areaf.variables['cell_area'][:]

The error information as below:

Could not import data from file 'flux_regrid/area.nc'
---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
<ipython-input-46-de24e1440792> in <module>()
      3 os.makedirs('flux_regrid', exist_ok=True)
      4 
----> 5 cdoo.gridarea(f'-O -remapnn,{dom}.grid -stdatm,0', output=areapath, returnCdf=True)
      6 areaf = pnc.pncopen(areapath, format='netcdf')
      7 projarea = areaf.variables['cell_area'][:]

1 frames
/usr/lib/python3/dist-packages/cdo.py in readCdf(self, iFile)
    480     """Return a cdf handle created by the available cdf library. python-netcdf4 and scipy suported (default:scipy)"""
    481     try:
--> 482         fileObj =  self.cdf(iFile, mode='r')
    483     except:
    484       print("Could not import data from file '%s'" % iFile)

src/netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Dataset.__init__()

src/netCDF4/_netCDF4.pyx in netCDF4._netCDF4._ensure_nc_success()

OSError: [Errno -101] NetCDF: HDF error: b'flux_regrid/area.nc'

@atmosxiuxiu
I had encountered similar errors with cdo, which took me quite long time to figure it our. Try the following command:

cdoo.remapycon(f'{dom}.grid', input=fluxpath, output=regridpath, returnCdf=False)

Oh, I reproduced the same NetCDF: HDF error: b'flux_regrid/area.nc' error in[31].

When I read flux_regrid/area.nc with vim, I found

GRIB^@Ѷ^A^@^@4?b^???^A^A^@
The file flux_regrid/area.nc was not HDF or CDF but GRIB.

I've solved the problem by adding a option options = '-f nc',

cdoo.gridarea( f'-O -remapnn,{dom}.grid -stdatm,0', options = '-f nc', output=areapath, returnCdf=True )

How about?

Great. I made the update.

Dear Barron,

I have obtained too big value of emissions (CO : 100 times).
But I found that MICS.ipynb deal with units very seriously.
I don't know why the emissions have too big value.

Best regards,
Gwang-Jin

Dear Barron,

I found why the emissions have too big value.
When converting gram [g] to mole, should the emissions be divided by some fraction?
For example,

1 moles CO to grams = 28.0101 grams.

In case of CO, MICS has unit of mass Mg/month originally

$ ncdump -h MICS_Asia_CO_2010_0.25x0.25.nc
float CO_RESIDENTIAL(time, lat, lon) ;
CO_RESIDENTIAL:long_name = "CO_RESIDENTIAL" ;
CO_RESIDENTIAL:short_name = "MICS_Asia_CO_RES" ;
CO_RESIDENTIAL:units = "Mg/month" ;

MICS.ipynb is likely to need consideration of mole-mass conversion by each chemical species.
Fortunately, only five species (CO, CO2, NOx, SO2, and NH3) need to be converted.

CO : 1 moles = 28.0101 grams
CO2 : 1 moles = 44.0095 grams
NO : 1 moles = 30.0061 grams
NO2 : 1 moles = 46.00550 grams
SO2 : 1 moles = 64.0638 grams
NH3 : 1 moles = 17.03052 grams

Best regards,
Gwang-Jin

I found that MICS.ipynb has already considered mole-mass conversion regarding NH3.
How about modifying [17] :

splitfactors = {
'NOx': [e2g('NO', 0.9/30.0061), e2g('NO2', 0.1/46.00550)],
'CO': [e2g('CO', 1./28.0101)],
'SO2': [e2g('SO2', 0.97/64.0638), e2p('PSO4', 0.03)],
'NH3': [e2g('NH3', 1./17.03052)],
}

?

I am not sure that PSO4 need no mole-mass conversion. Or

'SO2': [e2g('SO2', 0.97/64.0638), e2p('PSO4', 0.03/64.0638 * 96.)],

?

After changing, I've got a reasonable CO emission [moles/s].

Thank you.

@Gwang-Jin

You're right. My code incorrectly assumed the units for gases (CO, NOx, and SO2) were in Mmol/month like the VOCs. I agree with all of your updates except for NOx. For NOx, the file emissions units are probably in "Mg/month as NO2." This is similar to using TgN instead of TgNOx. The benefit is that the mass does not depend on what the speciation split. This is very common.

So, the conversion for both NO and NO2 would use the Mw of NO2.

You're also correct that PSO4 does not need the adjustment because CMAQ uses g/s for aerosols.

I'll update the gist.

I got it regarding NOx. Thank you.

@barronh Hi Barron! Just wondering do you have any tools or suggestions if I need to reproject gridded emission files from one Lambert Conformal Conic (LCC) projection to another LCC project with the total emissions conserved? Say I need to reproject a 27kmx27km gridded emissions into 36kmx36km grids. Cdos' remapycon seems not working and remapbil gives me close numbers (off by 0.5%). Thanks!

The best choice is using remapycon. The results still won't be perfect, but there are three basic steps to make sure it works right.

1. Convert emissions from CMAQ-ready mass/rates and mole/rates files to flux files (moles/s to moles/m2/s).
2. Set the flux file to the projected space using cdo's grid definition file
3. Run remapycon

Steps 2 and 3 can be combined.

It would be better to ask this type of question thru the CMAS Center forum since it is more general than this gist.

A quick remind
If you are using different machines beyond google colab, different installation methods or versions of CDO could make "cdoo.remapycon" or "cdoo.gridarea" not working. The CDO version on Ubuntu with Colab is 1.9.3 and python3-CDO 1.3.5 here.

If you can't install old-version CDO like 1.9.3, the recent version of CDO I have tested is CDO 2.0.3 using conda.
Several changes to match original code with CDO 2.0.3

1. install cdo bundle:
   I suggest to create a new conda env for cdo so it won't have unexpected bugs happening for conda
   conda create -n cdo
   conda install -c conda-forge cdo python-cdo xarray

2. make sure you also install other required packages for running this gist.
   Pseudonetcdf
   pyproj

3. Now you don't have to use python-cdo interface, you can comment out like
   #import cdo
   #cdoo = cdo.Cdo(cdopath)

4. Change cdoo code
   change
   cdoo.remapycon(f'{dom}.grid', input=fluxpath, output=regridpath, returnCdf=False)
   to
   os.system(f'cdo remapycon,grid.{domain} {fluxpath} {regridpath}')

change
cdoo.gridarea(f'-O -remapnn,{dom}.grid -stdatm,0', options='-f nc', output=areapath, returnCdf=True)
to
os.system(f'cdo -O -f nc -gridarea -remapnn,grid.{domain} -stdatm,0 ../outputs/{domain}/flux_regrid/area.nc')

5. Instead of creating "Projection" variable in remapped files, newer version CDO like CDO 2.0.3 here will output "crs". You also need to change code
   if k in ('time', 'lat', 'lon', 'x', 'y', 'Projection')
   to
   if k in ('time', 'lat', 'lon', 'x', 'y', 'crs')

Dear @barronh ,

I'm using eta_levels in WRF :

1.000, 0.995, 0.985, 0.979, 0.973,
0.967, 0.960, 0.954, 0.949, 0.941,
0.934, 0.925, 0.917, 0.907, 0.897,
0.887, 0.878, 0.866, 0.855, 0.844,
0.832, 0.806, 0.778, 0.764, 0.749,
0.718, 0.687, 0.654, 0.623, 0.590,
0.559, 0.526, 0.495, 0.462, 0.431,
0.398, 0.367, 0.334, 0.304, 0.272,
0.244, 0.213, 0.187, 0.155, 0.120,
0.090, 0.065, 0.040, 0.015, 0.000

And I would like to make 17 vertically allocated emissions. Is it possible to allocate emissions on different vertical grid?

I know I should not simply interpolate the vertical fractions to my eta_levels. But, I don't know a mathematical keyword to obtain new fractions on the new vertical grid.

I've obtained the new fractions on my eta_levels by replacing layerfractions with :

layerfractions = pd.read_csv(io.StringIO("""L,SigmaTop,PPCB,PROT
1,0.995,0.00,0.06
2,0.985,0.066,0.26
3,0.979,0.067,0.34
4,0.973,0.067,0.34
5,0.967,0.1,0.0
6,0.960,0.1,0.0
7,0.954,0.1,0.0
8,0.949,0.05,0.0
9,0.941,0.05,0.0
10,0.934,0.05,0.0
11,0.925,0.05,0.0
12,0.917,0.05,0.0
13,0.907,0.05,0.0
14,0.897,0.05,0.0
15,0.887,0.05,0.0
16,0.878,0.05,0.0
17,0.866,0.05,0.0
"""), comment='#')

I distributed original fraction to new level properly, and the sum of fractions is 1.

I have one question regarding the area source of MICS-Asia RESIDENTIAL, TRANSPORT, and AGRICULTURE.
In [23], the area sources are allocated on the second vertical grids. But, the first vertical grid has also height (35m). So should be the area sources allocated on the first vertical grids?
Thank you.

I'm glad you figured it out.

You're right about the layers. When it was first made, I think that L was the index. In this version, however, the index is a zero-based value. You should change all of those to layerfractions.loc[0, '...'] = 1. I updated the notebook.

Thank you for response.