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PseudoNetCDFCMAQ.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "PseudoNetCDFCMAQ.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/barronh/6a5f56c23cc34ecf1d10073e578f2c32/pseudonetcdfcmaq.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "wl-_umWffWlh", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# Plotting CMAQ with Cartopy\n", | |
| "## Example: CMAQ Test Case\n", | |
| "\n", | |
| " author: Barron Henderson\n", | |
| " date: 2019-09-25" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "FZ5VeVuofj2Z", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# Intalling Libraries\n", | |
| "\n", | |
| "At the time of writing, Colab includes most libraries we need but not all. The first thing we need to do is install `basemap`. Unlike many libraries, cartopy has several dependencies that are most easily met using `apt-get` before `pip`. The commands below acheive a full installation of `basemap`.\n", | |
| "\n", | |
| "This make take a minute." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "zUh8t2LM-uwl", | |
| "colab_type": "code", | |
| "outputId": "091e6c10-3391-4594-fe73-38ff01767e45", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| } | |
| }, | |
| "source": [ | |
| "# Install basemap using pip\n", | |
| "!apt-get install libgeos-3.5.0\n", | |
| "!apt-get install libgeos-dev\n", | |
| "!pip install https://github.com/matplotlib/basemap/archive/master.zip\n", | |
| "!pip install https://github.com/barronh/pseudonetcdf/archive/master.zip" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Reading package lists... Done\n", | |
| "Building dependency tree \n", | |
| "Reading state information... Done\n", | |
| "E: Unable to locate package libgeos-3.5.0\n", | |
| "E: Couldn't find any package by glob 'libgeos-3.5.0'\n", | |
| "E: Couldn't find any package by regex 'libgeos-3.5.0'\n", | |
| "Reading package lists... Done\n", | |
| "Building dependency tree \n", | |
| "Reading state information... Done\n", | |
| "The following package was automatically installed and is no longer required:\n", | |
| " libnvidia-common-430\n", | |
| "Use 'apt autoremove' to remove it.\n", | |
| "Suggested packages:\n", | |
| " libgdal-doc\n", | |
| "The following NEW packages will be installed:\n", | |
| " libgeos-dev\n", | |
| "0 upgraded, 1 newly installed, 0 to remove and 32 not upgraded.\n", | |
| "Need to get 73.1 kB of archives.\n", | |
| "After this operation, 486 kB of additional disk space will be used.\n", | |
| "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libgeos-dev amd64 3.6.2-1build2 [73.1 kB]\n", | |
| "Fetched 73.1 kB in 1s (53.8 kB/s)\n", | |
| "Selecting previously unselected package libgeos-dev.\n", | |
| "(Reading database ... 147935 files and directories currently installed.)\n", | |
| "Preparing to unpack .../libgeos-dev_3.6.2-1build2_amd64.deb ...\n", | |
| "Unpacking libgeos-dev (3.6.2-1build2) ...\n", | |
| "Setting up libgeos-dev (3.6.2-1build2) ...\n", | |
| "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n", | |
| "Collecting https://github.com/matplotlib/basemap/archive/master.zip\n", | |
| "\u001b[?25l Downloading https://github.com/matplotlib/basemap/archive/master.zip (133.1MB)\n", | |
| "\u001b[K |████████████████████████████████| 133.1MB 27kB/s \n", | |
| "\u001b[?25hRequirement already satisfied: matplotlib!=3.0.1,>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from basemap==1.2.1) (3.1.1)\n", | |
| "Requirement already satisfied: numpy>=1.2.1 in /usr/local/lib/python3.6/dist-packages (from basemap==1.2.1) (1.17.4)\n", | |
| "Collecting pyproj>=1.9.3\n", | |
| "\u001b[?25l Downloading https://files.pythonhosted.org/packages/d6/70/eedc98cd52b86de24a1589c762612a98bea26cde649ffdd60c1db396cce8/pyproj-2.4.2.post1-cp36-cp36m-manylinux2010_x86_64.whl (10.1MB)\n", | |
| "\u001b[K |████████████████████████████████| 10.1MB 2.8MB/s \n", | |
| "\u001b[?25hCollecting pyshp>=1.2.0\n", | |
| "\u001b[?25l Downloading https://files.pythonhosted.org/packages/27/16/3bf15aa864fb77845fab8007eda22c2bd67bd6c1fd13496df452c8c43621/pyshp-2.1.0.tar.gz (215kB)\n", | |
| "\u001b[K |████████████████████████████████| 225kB 45.8MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from basemap==1.2.1) (1.12.0)\n", | |
| "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.1,>=1.0.0->basemap==1.2.1) (2.6.1)\n", | |
| "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.1,>=1.0.0->basemap==1.2.1) (0.10.0)\n", | |
| "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.1,>=1.0.0->basemap==1.2.1) (1.1.0)\n", | |
| "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.1,>=1.0.0->basemap==1.2.1) (2.4.5)\n", | |
| "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib!=3.0.1,>=1.0.0->basemap==1.2.1) (41.6.0)\n", | |
| "Building wheels for collected packages: basemap, pyshp\n", | |
| " Building wheel for basemap (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for basemap: filename=basemap-1.2.1-cp36-cp36m-linux_x86_64.whl size=121756046 sha256=eaaed567275b56f70d65446139a009ff7c3df16abc3513e1701c202769024a5b\n", | |
| " Stored in directory: /tmp/pip-ephem-wheel-cache-v46a3m6e/wheels/98/4a/fc/ce719b75d97e646645c225f3332b1b217536100314922e9572\n", | |
| " Building wheel for pyshp (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for pyshp: filename=pyshp-2.1.0-cp36-none-any.whl size=32607 sha256=4c3f1271cd97edf1e38a70454bbf318b79294d0480b439e150af4af1fee6cbe5\n", | |
| " Stored in directory: /root/.cache/pip/wheels/a6/0c/de/321b5192ad416b328975a2f0385f72c64db4656501eba7cc1a\n", | |
| "Successfully built basemap pyshp\n", | |
| "Installing collected packages: pyproj, pyshp, basemap\n", | |
| "Successfully installed basemap-1.2.1 pyproj-2.4.2.post1 pyshp-2.1.0\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.colab-display-data+json": { | |
| "pip_warning": { | |
| "packages": [ | |
| "mpl_toolkits" | |
| ] | |
| } | |
| } | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Collecting https://github.com/barronh/pseudonetcdf/archive/master.zip\n", | |
| "\u001b[?25l Downloading https://github.com/barronh/pseudonetcdf/archive/master.zip\n", | |
| "\u001b[K / 3.5MB 916kB/s\n", | |
| "\u001b[?25hRequirement already satisfied (use --upgrade to upgrade): PseudoNetCDF==3.1.0 from https://github.com/barronh/pseudonetcdf/archive/master.zip in /usr/local/lib/python3.6/dist-packages\n", | |
| "Requirement already satisfied: numpy>=1.2 in /usr/local/lib/python3.6/dist-packages (from PseudoNetCDF==3.1.0) (1.17.4)\n", | |
| "Requirement already satisfied: netCDF4 in /usr/local/lib/python3.6/dist-packages (from PseudoNetCDF==3.1.0) (1.5.3)\n", | |
| "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from PseudoNetCDF==3.1.0) (1.3.2)\n", | |
| "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from PseudoNetCDF==3.1.0) (3.1.1)\n", | |
| "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from PseudoNetCDF==3.1.0) (3.13)\n", | |
| "Requirement already satisfied: cftime in /usr/local/lib/python3.6/dist-packages (from netCDF4->PseudoNetCDF==3.1.0) (1.0.4.2)\n", | |
| "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->PseudoNetCDF==3.1.0) (1.1.0)\n", | |
| "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->PseudoNetCDF==3.1.0) (2.4.5)\n", | |
| "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->PseudoNetCDF==3.1.0) (0.10.0)\n", | |
| "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->PseudoNetCDF==3.1.0) (2.6.1)\n", | |
| "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->PseudoNetCDF==3.1.0) (41.6.0)\n", | |
| "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib->PseudoNetCDF==3.1.0) (1.12.0)\n", | |
| "Building wheels for collected packages: PseudoNetCDF\n", | |
| " Building wheel for PseudoNetCDF (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for PseudoNetCDF: filename=PseudoNetCDF-3.1.0-cp36-none-any.whl size=384385 sha256=491af480ed8020931ba5c20efb38bb14f0b15fbffae3da9edb6a887d550068d5\n", | |
| " Stored in directory: /tmp/pip-ephem-wheel-cache-c687c1_f/wheels/5b/52/0b/989688b50b0b749d011eb30603cb24ad57a5830368bda3a29b\n", | |
| "Successfully built PseudoNetCDF\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "yMxvEMG9f9ga", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# Downloading CMAQ Data\n", | |
| "\n", | |
| "CMAQ test case data is available on Google Drive and is most easily downloaded with the download google drive script.\n", | |
| "\n", | |
| " * The first cell below downloads the download script.\n", | |
| " * The second cell uses that script to download the 2Day Benchmark Output (takes a little time).\n", | |
| " * The third cell uses `tar` to extract just the average concentration file (`ACONC`)\n", | |
| " " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "proBPz5D3j2u", | |
| "colab_type": "code", | |
| "outputId": "53d2fe78-39e3-4da8-8758-44eb509ecc4c", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 228 | |
| } | |
| }, | |
| "source": [ | |
| "!wget https://raw.githubusercontent.com/chentinghao/download_google_drive/master/download_gdrive.py" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "--2019-12-02 15:41:37-- https://raw.githubusercontent.com/chentinghao/download_google_drive/master/download_gdrive.py\n", | |
| "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", | |
| "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 1763 (1.7K) [text/plain]\n", | |
| "Saving to: ‘download_gdrive.py’\n", | |
| "\n", | |
| "\rdownload_gdrive.py 0%[ ] 0 --.-KB/s \rdownload_gdrive.py 100%[===================>] 1.72K --.-KB/s in 0s \n", | |
| "\n", | |
| "2019-12-02 15:41:38 (179 MB/s) - ‘download_gdrive.py’ saved [1763/1763]\n", | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "UcMlDKDq3rev", | |
| "colab_type": "code", | |
| "outputId": "42172abf-4809-4af7-b5fe-44eafb23df24", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| } | |
| }, | |
| "source": [ | |
| "!python download_gdrive.py 1r7KnaVfzlF2XuID_Fti8Y9Hz-EB6FDeh CMAQv5.3_Benchmark_2Day_Output.tar.gz" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "563MB [00:12, 45.6MB/s]\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "wqoLlxlw1-zw", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "!tar xzf CMAQv5.3_Benchmark_2Day_Output.tar.gz CMAQv5.3_Benchmark_2Day_Output/CCTM_ACONC_v53_gcc_Bench_2016_12SE1_20160702.nc" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "40ElR1c1ggdV", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# Review of Prerequisites\n", | |
| "\n", | |
| "1. Install PseudoNetCDF - check\n", | |
| "2. Download CMAQ test case - check\n", | |
| "\n", | |
| "The two lines below test the prerequisites. You should not get any errors. Warnings are fine." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "BHYCvvb-grRn", | |
| "colab_type": "code", | |
| "outputId": "4b3252d9-0e66-4c3f-d88b-1ce2c1a319d9", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 89 | |
| } | |
| }, | |
| "source": [ | |
| "!ls CMAQv5.3_Benchmark_2Day_Output/CCTM_ACONC_v53_gcc_Bench_2016_12SE1_20160702.nc\n", | |
| "!python -c \"import PseudoNetCDF as pnc\"" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "CMAQv5.3_Benchmark_2Day_Output/CCTM_ACONC_v53_gcc_Bench_2016_12SE1_20160702.nc\n", | |
| "**PNC:/usr/local/lib/python3.6/dist-packages/PseudoNetCDF/pncwarn.py:24:UserWarning:\n", | |
| " pyproj could not be found, so IO/API coordinates cannot be converted to lat/lon; to fix, install pyproj or basemap (e.g., `pip install pyproj)`\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "DuW7LDEOll-6", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# Import Libraries\n", | |
| "\n", | |
| "* PseudoNetCDF (as pnc) for NetCDF objects opening\n", | |
| "* matplotlib's pyplot module (as plt) for plotting.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "B25_fOR6xLBb", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "import PseudoNetCDF as pnc" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "hjeZ57A2l-sF", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# Open the NetCDF Dataset\n", | |
| "\n", | |
| "We uses the `pnc` `pncopen` function to create `afile`. afile has two dictionaries: `dimensions` and `variables`. In addition, attributes of the file are available via `ncatts` or the standard attribute interface.\n", | |
| "\n", | |
| " `dimensions`: has dimension lengths by name (e.g. TSTEP=24)\n", | |
| " `variables`: has all the data variables.\n", | |
| "\n", | |
| "We use the `format` keyword to tell PseudoNetCDF that this file is an `ioapi` file. There are many other possible formats (bpch: geos-chem, uamiv: CAMx, arlpackedbit: HYSPLIT, etc).\n", | |
| "\n", | |
| "After the file is open, the `repr`esentation of the file is a Common Data Language (CDL) header of the file." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "lNU0lWje41UZ", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "afile = pnc.pncopen(\n", | |
| " 'CMAQv5.3_Benchmark_2Day_Output/' +\n", | |
| " 'CCTM_ACONC_v53_gcc_Bench_2016_12SE1_20160702.nc',\n", | |
| " format='ioapi'\n", | |
| ")" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "KdcbBcZ_-1yc", | |
| "colab_type": "code", | |
| "outputId": "5ef91280-856c-4919-a6f7-6a060d74ff4f", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| } | |
| }, | |
| "source": [ | |
| "afile" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "PseudoNetCDF.cmaqfiles._ioapi.ioapi unknown {\n", | |
| "dimensions:\n", | |
| " TSTEP = UNLIMITED // (24 currently) \n", | |
| " DATE-TIME = 2 ;\n", | |
| " LAY = 1 ;\n", | |
| " VAR = 226 ;\n", | |
| " ROW = 80 ;\n", | |
| " COL = 100 ;\n", | |
| "\n", | |
| "variables:\n", | |
| " integer TFLAG(TSTEP, VAR, DATE-TIME);\n", | |
| " TFLAG:units = \"<YYYYDDD,HHMMSS>\" ;\n", | |
| " TFLAG:long_name = \"TFLAG \" ;\n", | |
| " TFLAG:var_desc = \"Timestep-valid flags: (1) YYYYDDD or (2) HHMMSS \" ;\n", | |
| " float NO2(TSTEP, LAY, ROW, COL);\n", | |
| " NO2:long_name = \"NO2 \" ;\n", | |
| " NO2:units = \"ppmV \" ;\n", | |
| " NO2:var_desc = \"Average Molar Mixing Ratio of NO2 \" ;\n", | |
| " float NO(TSTEP, LAY, ROW, COL);\n", | |
| " NO:long_name = \"NO \" ;\n", | |
| " NO:units = \"ppmV \" ;\n", | |
| " NO:var_desc = \"Average Molar Mixing Ratio of NO \" ;\n", | |
| " float O(TSTEP, LAY, ROW, COL);\n", | |
| " O:long_name = \"O \" ;\n", | |
| " O:units = \"ppmV \" ;\n", | |
| " O:var_desc = \"Average Molar Mixing Ratio of O \" ;\n", | |
| " float O3(TSTEP, LAY, ROW, COL);\n", | |
| " O3:long_name = \"O3 \" ;\n", | |
| " O3:units = \"ppmV \" ;\n", | |
| " O3:var_desc = \"Average Molar Mixing Ratio of O3 \" ;\n", | |
| " float NO3(TSTEP, LAY, ROW, COL);\n", | |
| " NO3:long_name = \"NO3 \" ;\n", | |
| " NO3:units = \"ppmV \" ;\n", | |
| " NO3:var_desc = \"Average Molar Mixing Ratio of NO3 \" ;\n", | |
| " float O1D(TSTEP, LAY, ROW, COL);\n", | |
| " O1D:long_name = \"O1D \" ;\n", | |
| " O1D:units = \"ppmV \" ;\n", | |
| " O1D:var_desc = \"Average Molar Mixing Ratio of O1D \" ;\n", | |
| " float OH(TSTEP, LAY, ROW, COL);\n", | |
| " OH:long_name = \"OH \" ;\n", | |
| " OH:units = \"ppmV \" ;\n", | |
| " OH:var_desc = \"Average Molar Mixing Ratio of OH \" ;\n", | |
| " float HO2(TSTEP, LAY, ROW, COL);\n", | |
| " HO2:long_name = \"HO2 \" ;\n", | |
| " HO2:units = \"ppmV \" ;\n", | |
| " HO2:var_desc = \"Average Molar Mixing Ratio of HO2 \" ;\n", | |
| " float H2O2(TSTEP, LAY, ROW, COL);\n", | |
| " H2O2:long_name = \"H2O2 \" ;\n", | |
| " H2O2:units = \"ppmV \" ;\n", | |
| " H2O2:var_desc = \"Average Molar Mixing Ratio of H2O2 \" ;\n", | |
| " float N2O5(TSTEP, LAY, ROW, COL);\n", | |
| " N2O5:long_name = \"N2O5 \" ;\n", | |
| " N2O5:units = \"ppmV \" ;\n", | |
| " N2O5:var_desc = \"Average Molar Mixing Ratio of N2O5 \" ;\n", | |
| " float HNO3(TSTEP, LAY, ROW, COL);\n", | |
| " HNO3:long_name = \"HNO3 \" ;\n", | |
| " HNO3:units = \"ppmV \" ;\n", | |
| " HNO3:var_desc = \"Average Molar Mixing Ratio of HNO3 \" ;\n", | |
| " float HONO(TSTEP, LAY, ROW, COL);\n", | |
| " HONO:long_name = \"HONO \" ;\n", | |
| " HONO:units = \"ppmV \" ;\n", | |
| " HONO:var_desc = \"Average Molar Mixing Ratio of HONO \" ;\n", | |
| " float PNA(TSTEP, LAY, ROW, COL);\n", | |
| " PNA:long_name = \"PNA \" ;\n", | |
| " PNA:units = \"ppmV \" ;\n", | |
| " PNA:var_desc = \"Average Molar Mixing Ratio of PNA \" ;\n", | |
| " float SO2(TSTEP, LAY, ROW, COL);\n", | |
| " SO2:long_name = \"SO2 \" ;\n", | |
| " SO2:units = \"ppmV \" ;\n", | |
| " SO2:var_desc = \"Average Molar Mixing Ratio of SO2 \" ;\n", | |
| " float SULF(TSTEP, LAY, ROW, COL);\n", | |
| " SULF:long_name = \"SULF \" ;\n", | |
| " SULF:units = \"ppmV \" ;\n", | |
| " SULF:var_desc = \"Average Molar Mixing Ratio of SULF \" ;\n", | |
| " float SULRXN(TSTEP, LAY, ROW, COL);\n", | |
| " SULRXN:long_name = \"SULRXN \" ;\n", | |
| " SULRXN:units = \"ppmV \" ;\n", | |
| " SULRXN:var_desc = \"Average Molar Mixing Ratio of SULRXN \" ;\n", | |
| " float C2O3(TSTEP, LAY, ROW, COL);\n", | |
| " C2O3:long_name = \"C2O3 \" ;\n", | |
| " C2O3:units = \"ppmV \" ;\n", | |
| " C2O3:var_desc = \"Average Molar Mixing Ratio of C2O3 \" ;\n", | |
| " float MEO2(TSTEP, LAY, ROW, COL);\n", | |
| " MEO2:long_name = \"MEO2 \" ;\n", | |
| " MEO2:units = \"ppmV \" ;\n", | |
| " MEO2:var_desc = \"Average Molar Mixing Ratio of MEO2 \" ;\n", | |
| " float RO2(TSTEP, LAY, ROW, COL);\n", | |
| " RO2:long_name = \"RO2 \" ;\n", | |
| " RO2:units = \"ppmV \" ;\n", | |
| " RO2:var_desc = \"Average Molar Mixing Ratio of RO2 \" ;\n", | |
| " float PAN(TSTEP, LAY, ROW, COL);\n", | |
| " PAN:long_name = \"PAN \" ;\n", | |
| " PAN:units = \"ppmV \" ;\n", | |
| " PAN:var_desc = \"Average Molar Mixing Ratio of PAN \" ;\n", | |
| " float PACD(TSTEP, LAY, ROW, COL);\n", | |
| " PACD:long_name = \"PACD \" ;\n", | |
| " PACD:units = \"ppmV \" ;\n", | |
| " PACD:var_desc = \"Average Molar Mixing Ratio of PACD \" ;\n", | |
| " float AACD(TSTEP, LAY, ROW, COL);\n", | |
| " AACD:long_name = \"AACD \" ;\n", | |
| " AACD:units = \"ppmV \" ;\n", | |
| " AACD:var_desc = \"Average Molar Mixing Ratio of AACD \" ;\n", | |
| " float CXO3(TSTEP, LAY, ROW, COL);\n", | |
| " CXO3:long_name = \"CXO3 \" ;\n", | |
| " CXO3:units = \"ppmV \" ;\n", | |
| " CXO3:var_desc = \"Average Molar Mixing Ratio of CXO3 \" ;\n", | |
| " float ALD2(TSTEP, LAY, ROW, COL);\n", | |
| " ALD2:long_name = \"ALD2 \" ;\n", | |
| " ALD2:units = \"ppmV \" ;\n", | |
| " ALD2:var_desc = \"Average Molar Mixing Ratio of ALD2 \" ;\n", | |
| " float XO2H(TSTEP, LAY, ROW, COL);\n", | |
| " XO2H:long_name = \"XO2H \" ;\n", | |
| " XO2H:units = \"ppmV \" ;\n", | |
| " XO2H:var_desc = \"Average Molar Mixing Ratio of XO2H \" ;\n", | |
| " float PANX(TSTEP, LAY, ROW, COL);\n", | |
| " PANX:long_name = \"PANX \" ;\n", | |
| " PANX:units = \"ppmV \" ;\n", | |
| " PANX:var_desc = \"Average Molar Mixing Ratio of PANX \" ;\n", | |
| " float FORM(TSTEP, LAY, ROW, COL);\n", | |
| " FORM:long_name = \"FORM \" ;\n", | |
| " FORM:units = \"ppmV \" ;\n", | |
| " FORM:var_desc = \"Average Molar Mixing Ratio of FORM \" ;\n", | |
| " float MEPX(TSTEP, LAY, ROW, COL);\n", | |
| " MEPX:long_name = \"MEPX \" ;\n", | |
| " MEPX:units = \"ppmV \" ;\n", | |
| " MEPX:var_desc = \"Average Molar Mixing Ratio of MEPX \" ;\n", | |
| " float MEOH(TSTEP, LAY, ROW, COL);\n", | |
| " MEOH:long_name = \"MEOH \" ;\n", | |
| " MEOH:units = \"ppmV \" ;\n", | |
| " MEOH:var_desc = \"Average Molar Mixing Ratio of MEOH \" ;\n", | |
| " float ROOH(TSTEP, LAY, ROW, COL);\n", | |
| " ROOH:long_name = \"ROOH \" ;\n", | |
| " ROOH:units = \"ppmV \" ;\n", | |
| " ROOH:var_desc = \"Average Molar Mixing Ratio of ROOH \" ;\n", | |
| " float XO2(TSTEP, LAY, ROW, COL);\n", | |
| " XO2:long_name = \"XO2 \" ;\n", | |
| " XO2:units = \"ppmV \" ;\n", | |
| " XO2:var_desc = \"Average Molar Mixing Ratio of XO2 \" ;\n", | |
| " float XO2N(TSTEP, LAY, ROW, COL);\n", | |
| " XO2N:long_name = \"XO2N \" ;\n", | |
| " XO2N:units = \"ppmV \" ;\n", | |
| " XO2N:var_desc = \"Average Molar Mixing Ratio of XO2N \" ;\n", | |
| " float XPAR(TSTEP, LAY, ROW, COL);\n", | |
| " XPAR:long_name = \"XPAR \" ;\n", | |
| " XPAR:units = \"ppmV \" ;\n", | |
| " XPAR:var_desc = \"Average Molar Mixing Ratio of XPAR \" ;\n", | |
| " float XPRP(TSTEP, LAY, ROW, COL);\n", | |
| " XPRP:long_name = \"XPRP \" ;\n", | |
| " XPRP:units = \"ppmV \" ;\n", | |
| " XPRP:var_desc = \"Average Molar Mixing Ratio of XPRP \" ;\n", | |
| " float NTR1(TSTEP, LAY, ROW, COL);\n", | |
| " NTR1:long_name = \"NTR1 \" ;\n", | |
| " NTR1:units = \"ppmV \" ;\n", | |
| " NTR1:var_desc = \"Average Molar Mixing Ratio of NTR1 \" ;\n", | |
| " float NTR2(TSTEP, LAY, ROW, COL);\n", | |
| " NTR2:long_name = \"NTR2 \" ;\n", | |
| " NTR2:units = \"ppmV \" ;\n", | |
| " NTR2:var_desc = \"Average Molar Mixing Ratio of NTR2 \" ;\n", | |
| " float FACD(TSTEP, LAY, ROW, COL);\n", | |
| " FACD:long_name = \"FACD \" ;\n", | |
| " FACD:units = \"ppmV \" ;\n", | |
| " FACD:var_desc = \"Average Molar Mixing Ratio of FACD \" ;\n", | |
| " float CO(TSTEP, LAY, ROW, COL);\n", | |
| " CO:long_name = \"CO \" ;\n", | |
| " CO:units = \"ppmV \" ;\n", | |
| " CO:var_desc = \"Average Molar Mixing Ratio of CO \" ;\n", | |
| " float HCO3(TSTEP, LAY, ROW, COL);\n", | |
| " HCO3:long_name = \"HCO3 \" ;\n", | |
| " HCO3:units = \"ppmV \" ;\n", | |
| " HCO3:var_desc = \"Average Molar Mixing Ratio of HCO3 \" ;\n", | |
| " float ALDX(TSTEP, LAY, ROW, COL);\n", | |
| " ALDX:long_name = \"ALDX \" ;\n", | |
| " ALDX:units = \"ppmV \" ;\n", | |
| " ALDX:var_desc = \"Average Molar Mixing Ratio of ALDX \" ;\n", | |
| " float GLYD(TSTEP, LAY, ROW, COL);\n", | |
| " GLYD:long_name = \"GLYD \" ;\n", | |
| " GLYD:units = \"ppmV \" ;\n", | |
| " GLYD:var_desc = \"Average Molar Mixing Ratio of GLYD \" ;\n", | |
| " float GLY(TSTEP, LAY, ROW, COL);\n", | |
| " GLY:long_name = \"GLY \" ;\n", | |
| " GLY:units = \"ppmV \" ;\n", | |
| " GLY:var_desc = \"Average Molar Mixing Ratio of GLY \" ;\n", | |
| " float MGLY(TSTEP, LAY, ROW, COL);\n", | |
| " MGLY:long_name = \"MGLY \" ;\n", | |
| " MGLY:units = \"ppmV \" ;\n", | |
| " MGLY:var_desc = \"Average Molar Mixing Ratio of MGLY \" ;\n", | |
| " float ETHA(TSTEP, LAY, ROW, COL);\n", | |
| " ETHA:long_name = \"ETHA \" ;\n", | |
| " ETHA:units = \"ppmV \" ;\n", | |
| " ETHA:var_desc = \"Average Molar Mixing Ratio of ETHA \" ;\n", | |
| " float ETOH(TSTEP, LAY, ROW, COL);\n", | |
| " ETOH:long_name = \"ETOH \" ;\n", | |
| " ETOH:units = \"ppmV \" ;\n", | |
| " ETOH:var_desc = \"Average Molar Mixing Ratio of ETOH \" ;\n", | |
| " float KET(TSTEP, LAY, ROW, COL);\n", | |
| " KET:long_name = \"KET \" ;\n", | |
| " KET:units = \"ppmV \" ;\n", | |
| " KET:var_desc = \"Average Molar Mixing Ratio of KET \" ;\n", | |
| " float PAR(TSTEP, LAY, ROW, COL);\n", | |
| " PAR:long_name = \"PAR \" ;\n", | |
| " PAR:units = \"ppmV \" ;\n", | |
| " PAR:var_desc = \"Average Molar Mixing Ratio of PAR \" ;\n", | |
| " float ACET(TSTEP, LAY, ROW, COL);\n", | |
| " ACET:long_name = \"ACET \" ;\n", | |
| " ACET:units = \"ppmV \" ;\n", | |
| " ACET:var_desc = \"Average Molar Mixing Ratio of ACET \" ;\n", | |
| " float PRPA(TSTEP, LAY, ROW, COL);\n", | |
| " PRPA:long_name = \"PRPA \" ;\n", | |
| " PRPA:units = \"ppmV \" ;\n", | |
| " PRPA:var_desc = \"Average Molar Mixing Ratio of PRPA \" ;\n", | |
| " float ROR(TSTEP, LAY, ROW, COL);\n", | |
| " ROR:long_name = \"ROR \" ;\n", | |
| " ROR:units = \"ppmV \" ;\n", | |
| " ROR:var_desc = \"Average Molar Mixing Ratio of ROR \" ;\n", | |
| " float ETHY(TSTEP, LAY, ROW, COL);\n", | |
| " ETHY:long_name = \"ETHY \" ;\n", | |
| " ETHY:units = \"ppmV \" ;\n", | |
| " ETHY:var_desc = \"Average Molar Mixing Ratio of ETHY \" ;\n", | |
| " float ETH(TSTEP, LAY, ROW, COL);\n", | |
| " ETH:long_name = \"ETH \" ;\n", | |
| " ETH:units = \"ppmV \" ;\n", | |
| " ETH:var_desc = \"Average Molar Mixing Ratio of ETH \" ;\n", | |
| " float OLE(TSTEP, LAY, ROW, COL);\n", | |
| " OLE:long_name = \"OLE \" ;\n", | |
| " OLE:units = \"ppmV \" ;\n", | |
| " OLE:var_desc = \"Average Molar Mixing Ratio of OLE \" ;\n", | |
| " float IOLE(TSTEP, LAY, ROW, COL);\n", | |
| " IOLE:long_name = \"IOLE \" ;\n", | |
| " IOLE:units = \"ppmV \" ;\n", | |
| " IOLE:var_desc = \"Average Molar Mixing Ratio of IOLE \" ;\n", | |
| " float ISOP(TSTEP, LAY, ROW, COL);\n", | |
| " ISOP:long_name = \"ISOP \" ;\n", | |
| " ISOP:units = \"ppmV \" ;\n", | |
| " ISOP:var_desc = \"Average Molar Mixing Ratio of ISOP \" ;\n", | |
| " float ISO2(TSTEP, LAY, ROW, COL);\n", | |
| " ISO2:long_name = \"ISO2 \" ;\n", | |
| " ISO2:units = \"ppmV \" ;\n", | |
| " ISO2:var_desc = \"Average Molar Mixing Ratio of ISO2 \" ;\n", | |
| " float ISOPRXN(TSTEP, LAY, ROW, COL);\n", | |
| " ISOPRXN:long_name = \"ISOPRXN \" ;\n", | |
| " ISOPRXN:units = \"ppmV \" ;\n", | |
| " ISOPRXN:var_desc = \"Average Molar Mixing Ratio of ISOPRXN \" ;\n", | |
| " float ISPD(TSTEP, LAY, ROW, COL);\n", | |
| " ISPD:long_name = \"ISPD \" ;\n", | |
| " ISPD:units = \"ppmV \" ;\n", | |
| " ISPD:var_desc = \"Average Molar Mixing Ratio of ISPD \" ;\n", | |
| " float INTR(TSTEP, LAY, ROW, COL);\n", | |
| " INTR:long_name = \"INTR \" ;\n", | |
| " INTR:units = \"ppmV \" ;\n", | |
| " INTR:var_desc = \"Average Molar Mixing Ratio of INTR \" ;\n", | |
| " float ISPX(TSTEP, LAY, ROW, COL);\n", | |
| " ISPX:long_name = \"ISPX \" ;\n", | |
| " ISPX:units = \"ppmV \" ;\n", | |
| " ISPX:var_desc = \"Average Molar Mixing Ratio of ISPX \" ;\n", | |
| " float HPLD(TSTEP, LAY, ROW, COL);\n", | |
| " HPLD:long_name = \"HPLD \" ;\n", | |
| " HPLD:units = \"ppmV \" ;\n", | |
| " HPLD:var_desc = \"Average Molar Mixing Ratio of HPLD \" ;\n", | |
| " float OPO3(TSTEP, LAY, ROW, COL);\n", | |
| " OPO3:long_name = \"OPO3 \" ;\n", | |
| " OPO3:units = \"ppmV \" ;\n", | |
| " OPO3:var_desc = \"Average Molar Mixing Ratio of OPO3 \" ;\n", | |
| " float EPOX(TSTEP, LAY, ROW, COL);\n", | |
| " EPOX:long_name = \"EPOX \" ;\n", | |
| " EPOX:units = \"ppmV \" ;\n", | |
| " EPOX:var_desc = \"Average Molar Mixing Ratio of EPOX \" ;\n", | |
| " float IEPOXP(TSTEP, LAY, ROW, COL);\n", | |
| " IEPOXP:long_name = \"IEPOXP \" ;\n", | |
| " IEPOXP:units = \"ppmV \" ;\n", | |
| " IEPOXP:var_desc = \"Average Molar Mixing Ratio of IEPOXP \" ;\n", | |
| " float EPX2(TSTEP, LAY, ROW, COL);\n", | |
| " EPX2:long_name = \"EPX2 \" ;\n", | |
| " EPX2:units = \"ppmV \" ;\n", | |
| " EPX2:var_desc = \"Average Molar Mixing Ratio of EPX2 \" ;\n", | |
| " float TERP(TSTEP, LAY, ROW, COL);\n", | |
| " TERP:long_name = \"TERP \" ;\n", | |
| " TERP:units = \"ppmV \" ;\n", | |
| " TERP:var_desc = \"Average Molar Mixing Ratio of TERP \" ;\n", | |
| " float APIN(TSTEP, LAY, ROW, COL);\n", | |
| " APIN:long_name = \"APIN \" ;\n", | |
| " APIN:units = \"ppmV \" ;\n", | |
| " APIN:var_desc = \"Average Molar Mixing Ratio of APIN \" ;\n", | |
| " float TERPNRO2(TSTEP, LAY, ROW, COL);\n", | |
| " TERPNRO2:long_name = \"TERPNRO2 \" ;\n", | |
| " TERPNRO2:units = \"ppmV \" ;\n", | |
| " TERPNRO2:var_desc = \"Average Molar Mixing Ratio of TERPNRO2 \" ;\n", | |
| " float MTNO3(TSTEP, LAY, ROW, COL);\n", | |
| " MTNO3:long_name = \"MTNO3 \" ;\n", | |
| " MTNO3:units = \"ppmV \" ;\n", | |
| " MTNO3:var_desc = \"Average Molar Mixing Ratio of MTNO3 \" ;\n", | |
| " float TRPRXN(TSTEP, LAY, ROW, COL);\n", | |
| " TRPRXN:long_name = \"TRPRXN \" ;\n", | |
| " TRPRXN:units = \"ppmV \" ;\n", | |
| " TRPRXN:var_desc = \"Average Molar Mixing Ratio of TRPRXN \" ;\n", | |
| " float BENZENE(TSTEP, LAY, ROW, COL);\n", | |
| " BENZENE:long_name = \"BENZENE \" ;\n", | |
| " BENZENE:units = \"ppmV \" ;\n", | |
| " BENZENE:var_desc = \"Average Molar Mixing Ratio of BENZENE \" ;\n", | |
| " float CRES(TSTEP, LAY, ROW, COL);\n", | |
| " CRES:long_name = \"CRES \" ;\n", | |
| " CRES:units = \"ppmV \" ;\n", | |
| " CRES:var_desc = \"Average Molar Mixing Ratio of CRES \" ;\n", | |
| " float BZO2(TSTEP, LAY, ROW, COL);\n", | |
| " BZO2:long_name = \"BZO2 \" ;\n", | |
| " BZO2:units = \"ppmV \" ;\n", | |
| " BZO2:var_desc = \"Average Molar Mixing Ratio of BZO2 \" ;\n", | |
| " float OPEN(TSTEP, LAY, ROW, COL);\n", | |
| " OPEN:long_name = \"OPEN \" ;\n", | |
| " OPEN:units = \"ppmV \" ;\n", | |
| " OPEN:var_desc = \"Average Molar Mixing Ratio of OPEN \" ;\n", | |
| " float BENZRO2(TSTEP, LAY, ROW, COL);\n", | |
| " BENZRO2:long_name = \"BENZRO2 \" ;\n", | |
| " BENZRO2:units = \"ppmV \" ;\n", | |
| " BENZRO2:var_desc = \"Average Molar Mixing Ratio of BENZRO2 \" ;\n", | |
| " float TOL(TSTEP, LAY, ROW, COL);\n", | |
| " TOL:long_name = \"TOL \" ;\n", | |
| " TOL:units = \"ppmV \" ;\n", | |
| " TOL:var_desc = \"Average Molar Mixing Ratio of TOL \" ;\n", | |
| " float TO2(TSTEP, LAY, ROW, COL);\n", | |
| " TO2:long_name = \"TO2 \" ;\n", | |
| " TO2:units = \"ppmV \" ;\n", | |
| " TO2:var_desc = \"Average Molar Mixing Ratio of TO2 \" ;\n", | |
| " float TOLRO2(TSTEP, LAY, ROW, COL);\n", | |
| " TOLRO2:long_name = \"TOLRO2 \" ;\n", | |
| " TOLRO2:units = \"ppmV \" ;\n", | |
| " TOLRO2:var_desc = \"Average Molar Mixing Ratio of TOLRO2 \" ;\n", | |
| " float XOPN(TSTEP, LAY, ROW, COL);\n", | |
| " XOPN:long_name = \"XOPN \" ;\n", | |
| " XOPN:units = \"ppmV \" ;\n", | |
| " XOPN:var_desc = \"Average Molar Mixing Ratio of XOPN \" ;\n", | |
| " float XYLMN(TSTEP, LAY, ROW, COL);\n", | |
| " XYLMN:long_name = \"XYLMN \" ;\n", | |
| " XYLMN:units = \"ppmV \" ;\n", | |
| " XYLMN:var_desc = \"Average Molar Mixing Ratio of XYLMN \" ;\n", | |
| " float XLO2(TSTEP, LAY, ROW, COL);\n", | |
| " XLO2:long_name = \"XLO2 \" ;\n", | |
| " XLO2:units = \"ppmV \" ;\n", | |
| " XLO2:var_desc = \"Average Molar Mixing Ratio of XLO2 \" ;\n", | |
| " float XYLRO2(TSTEP, LAY, ROW, COL);\n", | |
| " XYLRO2:long_name = \"XYLRO2 \" ;\n", | |
| " XYLRO2:units = \"ppmV \" ;\n", | |
| " XYLRO2:var_desc = \"Average Molar Mixing Ratio of XYLRO2 \" ;\n", | |
| " float NAPH(TSTEP, LAY, ROW, COL);\n", | |
| " NAPH:long_name = \"NAPH \" ;\n", | |
| " NAPH:units = \"ppmV \" ;\n", | |
| " NAPH:var_desc = \"Average Molar Mixing Ratio of NAPH \" ;\n", | |
| " float PAHRO2(TSTEP, LAY, ROW, COL);\n", | |
| " PAHRO2:long_name = \"PAHRO2 \" ;\n", | |
| " PAHRO2:units = \"ppmV \" ;\n", | |
| " PAHRO2:var_desc = \"Average Molar Mixing Ratio of PAHRO2 \" ;\n", | |
| " float CRO(TSTEP, LAY, ROW, COL);\n", | |
| " CRO:long_name = \"CRO \" ;\n", | |
| " CRO:units = \"ppmV \" ;\n", | |
| " CRO:var_desc = \"Average Molar Mixing Ratio of CRO \" ;\n", | |
| " float CAT1(TSTEP, LAY, ROW, COL);\n", | |
| " CAT1:long_name = \"CAT1 \" ;\n", | |
| " CAT1:units = \"ppmV \" ;\n", | |
| " CAT1:var_desc = \"Average Molar Mixing Ratio of CAT1 \" ;\n", | |
| " float CRON(TSTEP, LAY, ROW, COL);\n", | |
| " CRON:long_name = \"CRON \" ;\n", | |
| " CRON:units = \"ppmV \" ;\n", | |
| " CRON:var_desc = \"Average Molar Mixing Ratio of CRON \" ;\n", | |
| " float OPAN(TSTEP, LAY, ROW, COL);\n", | |
| " OPAN:long_name = \"OPAN \" ;\n", | |
| " OPAN:units = \"ppmV \" ;\n", | |
| " OPAN:var_desc = \"Average Molar Mixing Ratio of OPAN \" ;\n", | |
| " float ECH4(TSTEP, LAY, ROW, COL);\n", | |
| " ECH4:long_name = \"ECH4 \" ;\n", | |
| " ECH4:units = \"ppmV \" ;\n", | |
| " ECH4:var_desc = \"Average Molar Mixing Ratio of ECH4 \" ;\n", | |
| " float CL2(TSTEP, LAY, ROW, COL);\n", | |
| " CL2:long_name = \"CL2 \" ;\n", | |
| " CL2:units = \"ppmV \" ;\n", | |
| " CL2:var_desc = \"Average Molar Mixing Ratio of CL2 \" ;\n", | |
| " float CL(TSTEP, LAY, ROW, COL);\n", | |
| " CL:long_name = \"CL \" ;\n", | |
| " CL:units = \"ppmV \" ;\n", | |
| " CL:var_desc = \"Average Molar Mixing Ratio of CL \" ;\n", | |
| " float HOCL(TSTEP, LAY, ROW, COL);\n", | |
| " HOCL:long_name = \"HOCL \" ;\n", | |
| " HOCL:units = \"ppmV \" ;\n", | |
| " HOCL:var_desc = \"Average Molar Mixing Ratio of HOCL \" ;\n", | |
| " float CLO(TSTEP, LAY, ROW, COL);\n", | |
| " CLO:long_name = \"CLO \" ;\n", | |
| " CLO:units = \"ppmV \" ;\n", | |
| " CLO:var_desc = \"Average Molar Mixing Ratio of CLO \" ;\n", | |
| " float FMCL(TSTEP, LAY, ROW, COL);\n", | |
| " FMCL:long_name = \"FMCL \" ;\n", | |
| " FMCL:units = \"ppmV \" ;\n", | |
| " FMCL:var_desc = \"Average Molar Mixing Ratio of FMCL \" ;\n", | |
| " float HCL(TSTEP, LAY, ROW, COL);\n", | |
| " HCL:long_name = \"HCL \" ;\n", | |
| " HCL:units = \"ppmV \" ;\n", | |
| " HCL:var_desc = \"Average Molar Mixing Ratio of HCL \" ;\n", | |
| " float CLNO2(TSTEP, LAY, ROW, COL);\n", | |
| " CLNO2:long_name = \"CLNO2 \" ;\n", | |
| " CLNO2:units = \"ppmV \" ;\n", | |
| " CLNO2:var_desc = \"Average Molar Mixing Ratio of CLNO2 \" ;\n", | |
| " float CLNO3(TSTEP, LAY, ROW, COL);\n", | |
| " CLNO3:long_name = \"CLNO3 \" ;\n", | |
| " CLNO3:units = \"ppmV \" ;\n", | |
| " CLNO3:var_desc = \"Average Molar Mixing Ratio of CLNO3 \" ;\n", | |
| " float SESQ(TSTEP, LAY, ROW, COL);\n", | |
| " SESQ:long_name = \"SESQ \" ;\n", | |
| " SESQ:units = \"ppmV \" ;\n", | |
| " SESQ:var_desc = \"Average Molar Mixing Ratio of SESQ \" ;\n", | |
| " float SESQRXN(TSTEP, LAY, ROW, COL);\n", | |
| " SESQRXN:long_name = \"SESQRXN \" ;\n", | |
| " SESQRXN:units = \"ppmV \" ;\n", | |
| " SESQRXN:var_desc = \"Average Molar Mixing Ratio of SESQRXN \" ;\n", | |
| " float SOAALK(TSTEP, LAY, ROW, COL);\n", | |
| " SOAALK:long_name = \"SOAALK \" ;\n", | |
| " SOAALK:units = \"ppmV \" ;\n", | |
| " SOAALK:var_desc = \"Average Molar Mixing Ratio of SOAALK \" ;\n", | |
| " float H2NO3PIJ(TSTEP, LAY, ROW, COL);\n", | |
| " H2NO3PIJ:long_name = \"H2NO3PIJ \" ;\n", | |
| " H2NO3PIJ:units = \"ppmV \" ;\n", | |
| " H2NO3PIJ:var_desc = \"Average Molar Mixing Ratio of H2NO3PIJ \" ;\n", | |
| " float H2NO3PK(TSTEP, LAY, ROW, COL);\n", | |
| " H2NO3PK:long_name = \"H2NO3PK \" ;\n", | |
| " H2NO3PK:units = \"ppmV \" ;\n", | |
| " H2NO3PK:var_desc = \"Average Molar Mixing Ratio of H2NO3PK \" ;\n", | |
| " float VLVPO1(TSTEP, LAY, ROW, COL);\n", | |
| " VLVPO1:long_name = \"VLVPO1 \" ;\n", | |
| " VLVPO1:units = \"ppmV \" ;\n", | |
| " VLVPO1:var_desc = \"Average Molar Mixing Ratio of VLVPO1 \" ;\n", | |
| " float VSVPO1(TSTEP, LAY, ROW, COL);\n", | |
| " VSVPO1:long_name = \"VSVPO1 \" ;\n", | |
| " VSVPO1:units = \"ppmV \" ;\n", | |
| " VSVPO1:var_desc = \"Average Molar Mixing Ratio of VSVPO1 \" ;\n", | |
| " float VSVPO2(TSTEP, LAY, ROW, COL);\n", | |
| " VSVPO2:long_name = \"VSVPO2 \" ;\n", | |
| " VSVPO2:units = \"ppmV \" ;\n", | |
| " VSVPO2:var_desc = \"Average Molar Mixing Ratio of VSVPO2 \" ;\n", | |
| " float VSVPO3(TSTEP, LAY, ROW, COL);\n", | |
| " VSVPO3:long_name = \"VSVPO3 \" ;\n", | |
| " VSVPO3:units = \"ppmV \" ;\n", | |
| " VSVPO3:var_desc = \"Average Molar Mixing Ratio of VSVPO3 \" ;\n", | |
| " float VIVPO1(TSTEP, LAY, ROW, COL);\n", | |
| " VIVPO1:long_name = \"VIVPO1 \" ;\n", | |
| " VIVPO1:units = \"ppmV \" ;\n", | |
| " VIVPO1:var_desc = \"Average Molar Mixing Ratio of VIVPO1 \" ;\n", | |
| " float VLVOO1(TSTEP, LAY, ROW, COL);\n", | |
| " VLVOO1:long_name = \"VLVOO1 \" ;\n", | |
| " VLVOO1:units = \"ppmV \" ;\n", | |
| " VLVOO1:var_desc = \"Average Molar Mixing Ratio of VLVOO1 \" ;\n", | |
| " float VLVOO2(TSTEP, LAY, ROW, COL);\n", | |
| " VLVOO2:long_name = \"VLVOO2 \" ;\n", | |
| " VLVOO2:units = \"ppmV \" ;\n", | |
| " VLVOO2:var_desc = \"Average Molar Mixing Ratio of VLVOO2 \" ;\n", | |
| " float VSVOO1(TSTEP, LAY, ROW, COL);\n", | |
| " VSVOO1:long_name = \"VSVOO1 \" ;\n", | |
| " VSVOO1:units = \"ppmV \" ;\n", | |
| " VSVOO1:var_desc = \"Average Molar Mixing Ratio of VSVOO1 \" ;\n", | |
| " float VSVOO2(TSTEP, LAY, ROW, COL);\n", | |
| " VSVOO2:long_name = \"VSVOO2 \" ;\n", | |
| " VSVOO2:units = \"ppmV \" ;\n", | |
| " VSVOO2:var_desc = \"Average Molar Mixing Ratio of VSVOO2 \" ;\n", | |
| " float VSVOO3(TSTEP, LAY, ROW, COL);\n", | |
| " VSVOO3:long_name = \"VSVOO3 \" ;\n", | |
| " VSVOO3:units = \"ppmV \" ;\n", | |
| " VSVOO3:var_desc = \"Average Molar Mixing Ratio of VSVOO3 \" ;\n", | |
| " float PCVOC(TSTEP, LAY, ROW, COL);\n", | |
| " PCVOC:long_name = \"PCVOC \" ;\n", | |
| " PCVOC:units = \"ppmV \" ;\n", | |
| " PCVOC:var_desc = \"Average Molar Mixing Ratio of PCVOC \" ;\n", | |
| " float PCSOARXN(TSTEP, LAY, ROW, COL);\n", | |
| " PCSOARXN:long_name = \"PCSOARXN \" ;\n", | |
| " PCSOARXN:units = \"ppmV \" ;\n", | |
| " PCSOARXN:var_desc = \"Average Molar Mixing Ratio of PCSOARXN \" ;\n", | |
| " float FORM_PRIMARY(TSTEP, LAY, ROW, COL);\n", | |
| " FORM_PRIMARY:long_name = \"FORM_PRIMARY \" ;\n", | |
| " FORM_PRIMARY:units = \"ppmV \" ;\n", | |
| " FORM_PRIMARY:var_desc = \"Average Molar Mixing Ratio of FORM_PRIMARY \" ;\n", | |
| " float ALD2_PRIMARY(TSTEP, LAY, ROW, COL);\n", | |
| " ALD2_PRIMARY:long_name = \"ALD2_PRIMARY \" ;\n", | |
| " ALD2_PRIMARY:units = \"ppmV \" ;\n", | |
| " ALD2_PRIMARY:var_desc = \"Average Molar Mixing Ratio of ALD2_PRIMARY \" ;\n", | |
| " float BUTADIENE13(TSTEP, LAY, ROW, COL);\n", | |
| " BUTADIENE13:long_name = \"BUTADIENE13 \" ;\n", | |
| " BUTADIENE13:units = \"ppmV \" ;\n", | |
| " BUTADIENE13:var_desc = \"Average Molar Mixing Ratio of BUTADIENE13 \" ;\n", | |
| " float ACROLEIN(TSTEP, LAY, ROW, COL);\n", | |
| " ACROLEIN:long_name = \"ACROLEIN \" ;\n", | |
| " ACROLEIN:units = \"ppmV \" ;\n", | |
| " ACROLEIN:var_desc = \"Average Molar Mixing Ratio of ACROLEIN \" ;\n", | |
| " float ACRO_PRIMARY(TSTEP, LAY, ROW, COL);\n", | |
| " ACRO_PRIMARY:long_name = \"ACRO_PRIMARY \" ;\n", | |
| " ACRO_PRIMARY:units = \"ppmV \" ;\n", | |
| " ACRO_PRIMARY:var_desc = \"Average Molar Mixing Ratio of ACRO_PRIMARY \" ;\n", | |
| " float TOLU(TSTEP, LAY, ROW, COL);\n", | |
| " TOLU:long_name = \"TOLU \" ;\n", | |
| " TOLU:units = \"ppmV \" ;\n", | |
| " TOLU:var_desc = \"Average Molar Mixing Ratio of TOLU \" ;\n", | |
| " float HG(TSTEP, LAY, ROW, COL);\n", | |
| " HG:long_name = \"HG \" ;\n", | |
| " HG:units = \"ppmV \" ;\n", | |
| " HG:var_desc = \"Average Molar Mixing Ratio of HG \" ;\n", | |
| " float HGIIAER(TSTEP, LAY, ROW, COL);\n", | |
| " HGIIAER:long_name = \"HGIIAER \" ;\n", | |
| " HGIIAER:units = \"ppmV \" ;\n", | |
| " HGIIAER:var_desc = \"Average Molar Mixing Ratio of HGIIAER \" ;\n", | |
| " float HGIIGAS(TSTEP, LAY, ROW, COL);\n", | |
| " HGIIGAS:long_name = \"HGIIGAS \" ;\n", | |
| " HGIIGAS:units = \"ppmV \" ;\n", | |
| " HGIIGAS:var_desc = \"Average Molar Mixing Ratio of HGIIGAS \" ;\n", | |
| " float SVAVB1(TSTEP, LAY, ROW, COL);\n", | |
| " SVAVB1:long_name = \"SVAVB1 \" ;\n", | |
| " SVAVB1:units = \"ppmV \" ;\n", | |
| " SVAVB1:var_desc = \"Average Molar Mixing Ratio of SVAVB1 \" ;\n", | |
| " float SVAVB2(TSTEP, LAY, ROW, COL);\n", | |
| " SVAVB2:long_name = \"SVAVB2 \" ;\n", | |
| " SVAVB2:units = \"ppmV \" ;\n", | |
| " SVAVB2:var_desc = \"Average Molar Mixing Ratio of SVAVB2 \" ;\n", | |
| " float SVAVB3(TSTEP, LAY, ROW, COL);\n", | |
| " SVAVB3:long_name = \"SVAVB3 \" ;\n", | |
| " SVAVB3:units = \"ppmV \" ;\n", | |
| " SVAVB3:var_desc = \"Average Molar Mixing Ratio of SVAVB3 \" ;\n", | |
| " float SVAVB4(TSTEP, LAY, ROW, COL);\n", | |
| " SVAVB4:long_name = \"SVAVB4 \" ;\n", | |
| " SVAVB4:units = \"ppmV \" ;\n", | |
| " SVAVB4:var_desc = \"Average Molar Mixing Ratio of SVAVB4 \" ;\n", | |
| " float ASO4J(TSTEP, LAY, ROW, COL);\n", | |
| " ASO4J:long_name = \"ASO4J \" ;\n", | |
| " ASO4J:units = \"ug m-3 \" ;\n", | |
| " ASO4J:var_desc = \"Average Concentrations of ASO4J \" ;\n", | |
| " float ASO4I(TSTEP, LAY, ROW, COL);\n", | |
| " ASO4I:long_name = \"ASO4I \" ;\n", | |
| " ASO4I:units = \"ug m-3 \" ;\n", | |
| " ASO4I:var_desc = \"Average Concentrations of ASO4I \" ;\n", | |
| " float ANH4J(TSTEP, LAY, ROW, COL);\n", | |
| " ANH4J:long_name = \"ANH4J \" ;\n", | |
| " ANH4J:units = \"ug m-3 \" ;\n", | |
| " ANH4J:var_desc = \"Average Concentrations of ANH4J \" ;\n", | |
| " float ANH4I(TSTEP, LAY, ROW, COL);\n", | |
| " ANH4I:long_name = \"ANH4I \" ;\n", | |
| " ANH4I:units = \"ug m-3 \" ;\n", | |
| " ANH4I:var_desc = \"Average Concentrations of ANH4I \" ;\n", | |
| " float ANO3J(TSTEP, LAY, ROW, COL);\n", | |
| " ANO3J:long_name = \"ANO3J \" ;\n", | |
| " ANO3J:units = \"ug m-3 \" ;\n", | |
| " ANO3J:var_desc = \"Average Concentrations of ANO3J \" ;\n", | |
| " float ANO3I(TSTEP, LAY, ROW, COL);\n", | |
| " ANO3I:long_name = \"ANO3I \" ;\n", | |
| " ANO3I:units = \"ug m-3 \" ;\n", | |
| " ANO3I:var_desc = \"Average Concentrations of ANO3I \" ;\n", | |
| " float AISO1J(TSTEP, LAY, ROW, COL);\n", | |
| " AISO1J:long_name = \"AISO1J \" ;\n", | |
| " AISO1J:units = \"ug m-3 \" ;\n", | |
| " AISO1J:var_desc = \"Average Concentrations of AISO1J \" ;\n", | |
| " float AISO2J(TSTEP, LAY, ROW, COL);\n", | |
| " AISO2J:long_name = \"AISO2J \" ;\n", | |
| " AISO2J:units = \"ug m-3 \" ;\n", | |
| " AISO2J:var_desc = \"Average Concentrations of AISO2J \" ;\n", | |
| " float ASQTJ(TSTEP, LAY, ROW, COL);\n", | |
| " ASQTJ:long_name = \"ASQTJ \" ;\n", | |
| " ASQTJ:units = \"ug m-3 \" ;\n", | |
| " ASQTJ:var_desc = \"Average Concentrations of ASQTJ \" ;\n", | |
| " float AORGCJ(TSTEP, LAY, ROW, COL);\n", | |
| " AORGCJ:long_name = \"AORGCJ \" ;\n", | |
| " AORGCJ:units = \"ug m-3 \" ;\n", | |
| " AORGCJ:var_desc = \"Average Concentrations of AORGCJ \" ;\n", | |
| " float AECJ(TSTEP, LAY, ROW, COL);\n", | |
| " AECJ:long_name = \"AECJ \" ;\n", | |
| " AECJ:units = \"ug m-3 \" ;\n", | |
| " AECJ:var_desc = \"Average Concentrations of AECJ \" ;\n", | |
| " float AECI(TSTEP, LAY, ROW, COL);\n", | |
| " AECI:long_name = \"AECI \" ;\n", | |
| " AECI:units = \"ug m-3 \" ;\n", | |
| " AECI:var_desc = \"Average Concentrations of AECI \" ;\n", | |
| " float AOTHRJ(TSTEP, LAY, ROW, COL);\n", | |
| " AOTHRJ:long_name = \"AOTHRJ \" ;\n", | |
| " AOTHRJ:units = \"ug m-3 \" ;\n", | |
| " AOTHRJ:var_desc = \"Average Concentrations of AOTHRJ \" ;\n", | |
| " float AOTHRI(TSTEP, LAY, ROW, COL);\n", | |
| " AOTHRI:long_name = \"AOTHRI \" ;\n", | |
| " AOTHRI:units = \"ug m-3 \" ;\n", | |
| " AOTHRI:var_desc = \"Average Concentrations of AOTHRI \" ;\n", | |
| " float AFEJ(TSTEP, LAY, ROW, COL);\n", | |
| " AFEJ:long_name = \"AFEJ \" ;\n", | |
| " AFEJ:units = \"ug m-3 \" ;\n", | |
| " AFEJ:var_desc = \"Average Concentrations of AFEJ \" ;\n", | |
| " float AALJ(TSTEP, LAY, ROW, COL);\n", | |
| " AALJ:long_name = \"AALJ \" ;\n", | |
| " AALJ:units = \"ug m-3 \" ;\n", | |
| " AALJ:var_desc = \"Average Concentrations of AALJ \" ;\n", | |
| " float ASIJ(TSTEP, LAY, ROW, COL);\n", | |
| " ASIJ:long_name = \"ASIJ \" ;\n", | |
| " ASIJ:units = \"ug m-3 \" ;\n", | |
| " ASIJ:var_desc = \"Average Concentrations of ASIJ \" ;\n", | |
| " float ATIJ(TSTEP, LAY, ROW, COL);\n", | |
| " ATIJ:long_name = \"ATIJ \" ;\n", | |
| " ATIJ:units = \"ug m-3 \" ;\n", | |
| " ATIJ:var_desc = \"Average Concentrations of ATIJ \" ;\n", | |
| " float ACAJ(TSTEP, LAY, ROW, COL);\n", | |
| " ACAJ:long_name = \"ACAJ \" ;\n", | |
| " ACAJ:units = \"ug m-3 \" ;\n", | |
| " ACAJ:var_desc = \"Average Concentrations of ACAJ \" ;\n", | |
| " float AMGJ(TSTEP, LAY, ROW, COL);\n", | |
| " AMGJ:long_name = \"AMGJ \" ;\n", | |
| " AMGJ:units = \"ug m-3 \" ;\n", | |
| " AMGJ:var_desc = \"Average Concentrations of AMGJ \" ;\n", | |
| " float AKJ(TSTEP, LAY, ROW, COL);\n", | |
| " AKJ:long_name = \"AKJ \" ;\n", | |
| " AKJ:units = \"ug m-3 \" ;\n", | |
| " AKJ:var_desc = \"Average Concentrations of AKJ \" ;\n", | |
| " float AMNJ(TSTEP, LAY, ROW, COL);\n", | |
| " AMNJ:long_name = \"AMNJ \" ;\n", | |
| " AMNJ:units = \"ug m-3 \" ;\n", | |
| " AMNJ:var_desc = \"Average Concentrations of AMNJ \" ;\n", | |
| " float ACORS(TSTEP, LAY, ROW, COL);\n", | |
| " ACORS:long_name = \"ACORS \" ;\n", | |
| " ACORS:units = \"ug m-3 \" ;\n", | |
| " ACORS:var_desc = \"Average Concentrations of ACORS \" ;\n", | |
| " float ASOIL(TSTEP, LAY, ROW, COL);\n", | |
| " ASOIL:long_name = \"ASOIL \" ;\n", | |
| " ASOIL:units = \"ug m-3 \" ;\n", | |
| " ASOIL:var_desc = \"Average Concentrations of ASOIL \" ;\n", | |
| " float NUMATKN(TSTEP, LAY, ROW, COL);\n", | |
| " NUMATKN:long_name = \"NUMATKN \" ;\n", | |
| " NUMATKN:units = \"m-3 \" ;\n", | |
| " NUMATKN:var_desc = \"Average Concentrations of NUMATKN \" ;\n", | |
| " float NUMACC(TSTEP, LAY, ROW, COL);\n", | |
| " NUMACC:long_name = \"NUMACC \" ;\n", | |
| " NUMACC:units = \"m-3 \" ;\n", | |
| " NUMACC:var_desc = \"Average Concentrations of NUMACC \" ;\n", | |
| " float NUMCOR(TSTEP, LAY, ROW, COL);\n", | |
| " NUMCOR:long_name = \"NUMCOR \" ;\n", | |
| " NUMCOR:units = \"m-3 \" ;\n", | |
| " NUMCOR:var_desc = \"Average Concentrations of NUMCOR \" ;\n", | |
| " float SRFATKN(TSTEP, LAY, ROW, COL);\n", | |
| " SRFATKN:long_name = \"SRFATKN \" ;\n", | |
| " SRFATKN:units = \"m2 m-3 \" ;\n", | |
| " SRFATKN:var_desc = \"Average Concentrations of SRFATKN \" ;\n", | |
| " float SRFACC(TSTEP, LAY, ROW, COL);\n", | |
| " SRFACC:long_name = \"SRFACC \" ;\n", | |
| " SRFACC:units = \"m2 m-3 \" ;\n", | |
| " SRFACC:var_desc = \"Average Concentrations of SRFACC \" ;\n", | |
| " float SRFCOR(TSTEP, LAY, ROW, COL);\n", | |
| " SRFCOR:long_name = \"SRFCOR \" ;\n", | |
| " SRFCOR:units = \"m2 m-3 \" ;\n", | |
| " SRFCOR:var_desc = \"Average Concentrations of SRFCOR \" ;\n", | |
| " float AORGH2OJ(TSTEP, LAY, ROW, COL);\n", | |
| " AORGH2OJ:long_name = \"AORGH2OJ \" ;\n", | |
| " AORGH2OJ:units = \"ug m-3 \" ;\n", | |
| " AORGH2OJ:var_desc = \"Average Concentrations of AORGH2OJ \" ;\n", | |
| " float AH2OJ(TSTEP, LAY, ROW, COL);\n", | |
| " AH2OJ:long_name = \"AH2OJ \" ;\n", | |
| " AH2OJ:units = \"ug m-3 \" ;\n", | |
| " AH2OJ:var_desc = \"Average Concentrations of AH2OJ \" ;\n", | |
| " float AH2OI(TSTEP, LAY, ROW, COL);\n", | |
| " AH2OI:long_name = \"AH2OI \" ;\n", | |
| " AH2OI:units = \"ug m-3 \" ;\n", | |
| " AH2OI:var_desc = \"Average Concentrations of AH2OI \" ;\n", | |
| " float AH3OPJ(TSTEP, LAY, ROW, COL);\n", | |
| " AH3OPJ:long_name = \"AH3OPJ \" ;\n", | |
| " AH3OPJ:units = \"ug m-3 \" ;\n", | |
| " AH3OPJ:var_desc = \"Average Concentrations of AH3OPJ \" ;\n", | |
| " float AH3OPI(TSTEP, LAY, ROW, COL);\n", | |
| " AH3OPI:long_name = \"AH3OPI \" ;\n", | |
| " AH3OPI:units = \"ug m-3 \" ;\n", | |
| " AH3OPI:var_desc = \"Average Concentrations of AH3OPI \" ;\n", | |
| " float ANAJ(TSTEP, LAY, ROW, COL);\n", | |
| " ANAJ:long_name = \"ANAJ \" ;\n", | |
| " ANAJ:units = \"ug m-3 \" ;\n", | |
| " ANAJ:var_desc = \"Average Concentrations of ANAJ \" ;\n", | |
| " float ANAI(TSTEP, LAY, ROW, COL);\n", | |
| " ANAI:long_name = \"ANAI \" ;\n", | |
| " ANAI:units = \"ug m-3 \" ;\n", | |
| " ANAI:var_desc = \"Average Concentrations of ANAI \" ;\n", | |
| " float ACLJ(TSTEP, LAY, ROW, COL);\n", | |
| " ACLJ:long_name = \"ACLJ \" ;\n", | |
| " ACLJ:units = \"ug m-3 \" ;\n", | |
| " ACLJ:var_desc = \"Average Concentrations of ACLJ \" ;\n", | |
| " float ACLI(TSTEP, LAY, ROW, COL);\n", | |
| " ACLI:long_name = \"ACLI \" ;\n", | |
| " ACLI:units = \"ug m-3 \" ;\n", | |
| " ACLI:var_desc = \"Average Concentrations of ACLI \" ;\n", | |
| " float ASEACAT(TSTEP, LAY, ROW, COL);\n", | |
| " ASEACAT:long_name = \"ASEACAT \" ;\n", | |
| " ASEACAT:units = \"ug m-3 \" ;\n", | |
| " ASEACAT:var_desc = \"Average Concentrations of ASEACAT \" ;\n", | |
| " float ACLK(TSTEP, LAY, ROW, COL);\n", | |
| " ACLK:long_name = \"ACLK \" ;\n", | |
| " ACLK:units = \"ug m-3 \" ;\n", | |
| " ACLK:var_desc = \"Average Concentrations of ACLK \" ;\n", | |
| " float ASO4K(TSTEP, LAY, ROW, COL);\n", | |
| " ASO4K:long_name = \"ASO4K \" ;\n", | |
| " ASO4K:units = \"ug m-3 \" ;\n", | |
| " ASO4K:var_desc = \"Average Concentrations of ASO4K \" ;\n", | |
| " float ANH4K(TSTEP, LAY, ROW, COL);\n", | |
| " ANH4K:long_name = \"ANH4K \" ;\n", | |
| " ANH4K:units = \"ug m-3 \" ;\n", | |
| " ANH4K:var_desc = \"Average Concentrations of ANH4K \" ;\n", | |
| " float ANO3K(TSTEP, LAY, ROW, COL);\n", | |
| " ANO3K:long_name = \"ANO3K \" ;\n", | |
| " ANO3K:units = \"ug m-3 \" ;\n", | |
| " ANO3K:var_desc = \"Average Concentrations of ANO3K \" ;\n", | |
| " float AH2OK(TSTEP, LAY, ROW, COL);\n", | |
| " AH2OK:long_name = \"AH2OK \" ;\n", | |
| " AH2OK:units = \"ug m-3 \" ;\n", | |
| " AH2OK:var_desc = \"Average Concentrations of AH2OK \" ;\n", | |
| " float AH3OPK(TSTEP, LAY, ROW, COL);\n", | |
| " AH3OPK:long_name = \"AH3OPK \" ;\n", | |
| " AH3OPK:units = \"ug m-3 \" ;\n", | |
| " AH3OPK:var_desc = \"Average Concentrations of AH3OPK \" ;\n", | |
| " float AISO3J(TSTEP, LAY, ROW, COL);\n", | |
| " AISO3J:long_name = \"AISO3J \" ;\n", | |
| " AISO3J:units = \"ug m-3 \" ;\n", | |
| " AISO3J:var_desc = \"Average Concentrations of AISO3J \" ;\n", | |
| " float AOLGAJ(TSTEP, LAY, ROW, COL);\n", | |
| " AOLGAJ:long_name = \"AOLGAJ \" ;\n", | |
| " AOLGAJ:units = \"ug m-3 \" ;\n", | |
| " AOLGAJ:var_desc = \"Average Concentrations of AOLGAJ \" ;\n", | |
| " float AOLGBJ(TSTEP, LAY, ROW, COL);\n", | |
| " AOLGBJ:long_name = \"AOLGBJ \" ;\n", | |
| " AOLGBJ:units = \"ug m-3 \" ;\n", | |
| " AOLGBJ:var_desc = \"Average Concentrations of AOLGBJ \" ;\n", | |
| " float AGLYJ(TSTEP, LAY, ROW, COL);\n", | |
| " AGLYJ:long_name = \"AGLYJ \" ;\n", | |
| " AGLYJ:units = \"ug m-3 \" ;\n", | |
| " AGLYJ:var_desc = \"Average Concentrations of AGLYJ \" ;\n", | |
| " float AMTNO3J(TSTEP, LAY, ROW, COL);\n", | |
| " AMTNO3J:long_name = \"AMTNO3J \" ;\n", | |
| " AMTNO3J:units = \"ug m-3 \" ;\n", | |
| " AMTNO3J:var_desc = \"Average Concentrations of AMTNO3J \" ;\n", | |
| " float AMTHYDJ(TSTEP, LAY, ROW, COL);\n", | |
| " AMTHYDJ:long_name = \"AMTHYDJ \" ;\n", | |
| " AMTHYDJ:units = \"ug m-3 \" ;\n", | |
| " AMTHYDJ:var_desc = \"Average Concentrations of AMTHYDJ \" ;\n", | |
| " float APOCI(TSTEP, LAY, ROW, COL);\n", | |
| " APOCI:long_name = \"APOCI \" ;\n", | |
| " APOCI:units = \"ug m-3 \" ;\n", | |
| " APOCI:var_desc = \"Average Concentrations of APOCI \" ;\n", | |
| " float APOCJ(TSTEP, LAY, ROW, COL);\n", | |
| " APOCJ:long_name = \"APOCJ \" ;\n", | |
| " APOCJ:units = \"ug m-3 \" ;\n", | |
| " APOCJ:var_desc = \"Average Concentrations of APOCJ \" ;\n", | |
| " float APNCOMI(TSTEP, LAY, ROW, COL);\n", | |
| " APNCOMI:long_name = \"APNCOMI \" ;\n", | |
| " APNCOMI:units = \"ug m-3 \" ;\n", | |
| " APNCOMI:var_desc = \"Average Concentrations of APNCOMI \" ;\n", | |
| " float APNCOMJ(TSTEP, LAY, ROW, COL);\n", | |
| " APNCOMJ:long_name = \"APNCOMJ \" ;\n", | |
| " APNCOMJ:units = \"ug m-3 \" ;\n", | |
| " APNCOMJ:var_desc = \"Average Concentrations of APNCOMJ \" ;\n", | |
| " float APCSOJ(TSTEP, LAY, ROW, COL);\n", | |
| " APCSOJ:long_name = \"APCSOJ \" ;\n", | |
| " APCSOJ:units = \"ug m-3 \" ;\n", | |
| " APCSOJ:var_desc = \"Average Concentrations of APCSOJ \" ;\n", | |
| " float ALVPO1I(TSTEP, LAY, ROW, COL);\n", | |
| " ALVPO1I:long_name = \"ALVPO1I \" ;\n", | |
| " ALVPO1I:units = \"ug m-3 \" ;\n", | |
| " ALVPO1I:var_desc = \"Average Concentrations of ALVPO1I \" ;\n", | |
| " float ASVPO1I(TSTEP, LAY, ROW, COL);\n", | |
| " ASVPO1I:long_name = \"ASVPO1I \" ;\n", | |
| " ASVPO1I:units = \"ug m-3 \" ;\n", | |
| " ASVPO1I:var_desc = \"Average Concentrations of ASVPO1I \" ;\n", | |
| " float ASVPO2I(TSTEP, LAY, ROW, COL);\n", | |
| " ASVPO2I:long_name = \"ASVPO2I \" ;\n", | |
| " ASVPO2I:units = \"ug m-3 \" ;\n", | |
| " ASVPO2I:var_desc = \"Average Concentrations of ASVPO2I \" ;\n", | |
| " float ALVPO1J(TSTEP, LAY, ROW, COL);\n", | |
| " ALVPO1J:long_name = \"ALVPO1J \" ;\n", | |
| " ALVPO1J:units = \"ug m-3 \" ;\n", | |
| " ALVPO1J:var_desc = \"Average Concentrations of ALVPO1J \" ;\n", | |
| " float ASVPO1J(TSTEP, LAY, ROW, COL);\n", | |
| " ASVPO1J:long_name = \"ASVPO1J \" ;\n", | |
| " ASVPO1J:units = \"ug m-3 \" ;\n", | |
| " ASVPO1J:var_desc = \"Average Concentrations of ASVPO1J \" ;\n", | |
| " float ASVPO2J(TSTEP, LAY, ROW, COL);\n", | |
| " ASVPO2J:long_name = \"ASVPO2J \" ;\n", | |
| " ASVPO2J:units = \"ug m-3 \" ;\n", | |
| " ASVPO2J:var_desc = \"Average Concentrations of ASVPO2J \" ;\n", | |
| " float ASVPO3J(TSTEP, LAY, ROW, COL);\n", | |
| " ASVPO3J:long_name = \"ASVPO3J \" ;\n", | |
| " ASVPO3J:units = \"ug m-3 \" ;\n", | |
| " ASVPO3J:var_desc = \"Average Concentrations of ASVPO3J \" ;\n", | |
| " float AIVPO1J(TSTEP, LAY, ROW, COL);\n", | |
| " AIVPO1J:long_name = \"AIVPO1J \" ;\n", | |
| " AIVPO1J:units = \"ug m-3 \" ;\n", | |
| " AIVPO1J:var_desc = \"Average Concentrations of AIVPO1J \" ;\n", | |
| " float ALVOO1I(TSTEP, LAY, ROW, COL);\n", | |
| " ALVOO1I:long_name = \"ALVOO1I \" ;\n", | |
| " ALVOO1I:units = \"ug m-3 \" ;\n", | |
| " ALVOO1I:var_desc = \"Average Concentrations of ALVOO1I \" ;\n", | |
| " float ALVOO2I(TSTEP, LAY, ROW, COL);\n", | |
| " ALVOO2I:long_name = \"ALVOO2I \" ;\n", | |
| " ALVOO2I:units = \"ug m-3 \" ;\n", | |
| " ALVOO2I:var_desc = \"Average Concentrations of ALVOO2I \" ;\n", | |
| " float ASVOO1I(TSTEP, LAY, ROW, COL);\n", | |
| " ASVOO1I:long_name = \"ASVOO1I \" ;\n", | |
| " ASVOO1I:units = \"ug m-3 \" ;\n", | |
| " ASVOO1I:var_desc = \"Average Concentrations of ASVOO1I \" ;\n", | |
| " float ASVOO2I(TSTEP, LAY, ROW, COL);\n", | |
| " ASVOO2I:long_name = \"ASVOO2I \" ;\n", | |
| " ASVOO2I:units = \"ug m-3 \" ;\n", | |
| " ASVOO2I:var_desc = \"Average Concentrations of ASVOO2I \" ;\n", | |
| " float ALVOO1J(TSTEP, LAY, ROW, COL);\n", | |
| " ALVOO1J:long_name = \"ALVOO1J \" ;\n", | |
| " ALVOO1J:units = \"ug m-3 \" ;\n", | |
| " ALVOO1J:var_desc = \"Average Concentrations of ALVOO1J \" ;\n", | |
| " float ALVOO2J(TSTEP, LAY, ROW, COL);\n", | |
| " ALVOO2J:long_name = \"ALVOO2J \" ;\n", | |
| " ALVOO2J:units = \"ug m-3 \" ;\n", | |
| " ALVOO2J:var_desc = \"Average Concentrations of ALVOO2J \" ;\n", | |
| " float ASVOO1J(TSTEP, LAY, ROW, COL);\n", | |
| " ASVOO1J:long_name = \"ASVOO1J \" ;\n", | |
| " ASVOO1J:units = \"ug m-3 \" ;\n", | |
| " ASVOO1J:var_desc = \"Average Concentrations of ASVOO1J \" ;\n", | |
| " float ASVOO2J(TSTEP, LAY, ROW, COL);\n", | |
| " ASVOO2J:long_name = \"ASVOO2J \" ;\n", | |
| " ASVOO2J:units = \"ug m-3 \" ;\n", | |
| " ASVOO2J:var_desc = \"Average Concentrations of ASVOO2J \" ;\n", | |
| " float ASVOO3J(TSTEP, LAY, ROW, COL);\n", | |
| " ASVOO3J:long_name = \"ASVOO3J \" ;\n", | |
| " ASVOO3J:units = \"ug m-3 \" ;\n", | |
| " ASVOO3J:var_desc = \"Average Concentrations of ASVOO3J \" ;\n", | |
| " float AAVB1J(TSTEP, LAY, ROW, COL);\n", | |
| " AAVB1J:long_name = \"AAVB1J \" ;\n", | |
| " AAVB1J:units = \"ug m-3 \" ;\n", | |
| " AAVB1J:var_desc = \"Average Concentrations of AAVB1J \" ;\n", | |
| " float AAVB2J(TSTEP, LAY, ROW, COL);\n", | |
| " AAVB2J:long_name = \"AAVB2J \" ;\n", | |
| " AAVB2J:units = \"ug m-3 \" ;\n", | |
| " AAVB2J:var_desc = \"Average Concentrations of AAVB2J \" ;\n", | |
| " float AAVB3J(TSTEP, LAY, ROW, COL);\n", | |
| " AAVB3J:long_name = \"AAVB3J \" ;\n", | |
| " AAVB3J:units = \"ug m-3 \" ;\n", | |
| " AAVB3J:var_desc = \"Average Concentrations of AAVB3J \" ;\n", | |
| " float AAVB4J(TSTEP, LAY, ROW, COL);\n", | |
| " AAVB4J:long_name = \"AAVB4J \" ;\n", | |
| " AAVB4J:units = \"ug m-3 \" ;\n", | |
| " AAVB4J:var_desc = \"Average Concentrations of AAVB4J \" ;\n", | |
| " float AMT1J(TSTEP, LAY, ROW, COL);\n", | |
| " AMT1J:long_name = \"AMT1J \" ;\n", | |
| " AMT1J:units = \"ug m-3 \" ;\n", | |
| " AMT1J:var_desc = \"Average Concentrations of AMT1J \" ;\n", | |
| " float AMT2J(TSTEP, LAY, ROW, COL);\n", | |
| " AMT2J:long_name = \"AMT2J \" ;\n", | |
| " AMT2J:units = \"ug m-3 \" ;\n", | |
| " AMT2J:var_desc = \"Average Concentrations of AMT2J \" ;\n", | |
| " float AMT3J(TSTEP, LAY, ROW, COL);\n", | |
| " AMT3J:long_name = \"AMT3J \" ;\n", | |
| " AMT3J:units = \"ug m-3 \" ;\n", | |
| " AMT3J:var_desc = \"Average Concentrations of AMT3J \" ;\n", | |
| " float AMT4J(TSTEP, LAY, ROW, COL);\n", | |
| " AMT4J:long_name = \"AMT4J \" ;\n", | |
| " AMT4J:units = \"ug m-3 \" ;\n", | |
| " AMT4J:var_desc = \"Average Concentrations of AMT4J \" ;\n", | |
| " float AMT5J(TSTEP, LAY, ROW, COL);\n", | |
| " AMT5J:long_name = \"AMT5J \" ;\n", | |
| " AMT5J:units = \"ug m-3 \" ;\n", | |
| " AMT5J:var_desc = \"Average Concentrations of AMT5J \" ;\n", | |
| " float AMT6J(TSTEP, LAY, ROW, COL);\n", | |
| " AMT6J:long_name = \"AMT6J \" ;\n", | |
| " AMT6J:units = \"ug m-3 \" ;\n", | |
| " AMT6J:var_desc = \"Average Concentrations of AMT6J \" ;\n", | |
| " float NH3(TSTEP, LAY, ROW, COL);\n", | |
| " NH3:long_name = \"NH3 \" ;\n", | |
| " NH3:units = \"ppmV \" ;\n", | |
| " NH3:var_desc = \"Average Molar Mixing Ratio of NH3 \" ;\n", | |
| " float SVISO1(TSTEP, LAY, ROW, COL);\n", | |
| " SVISO1:long_name = \"SVISO1 \" ;\n", | |
| " SVISO1:units = \"ppmV \" ;\n", | |
| " SVISO1:var_desc = \"Average Molar Mixing Ratio of SVISO1 \" ;\n", | |
| " float SVISO2(TSTEP, LAY, ROW, COL);\n", | |
| " SVISO2:long_name = \"SVISO2 \" ;\n", | |
| " SVISO2:units = \"ppmV \" ;\n", | |
| " SVISO2:var_desc = \"Average Molar Mixing Ratio of SVISO2 \" ;\n", | |
| " float SVSQT(TSTEP, LAY, ROW, COL);\n", | |
| " SVSQT:long_name = \"SVSQT \" ;\n", | |
| " SVSQT:units = \"ppmV \" ;\n", | |
| " SVSQT:var_desc = \"Average Molar Mixing Ratio of SVSQT \" ;\n", | |
| " float LVPCSOG(TSTEP, LAY, ROW, COL);\n", | |
| " LVPCSOG:long_name = \"LVPCSOG \" ;\n", | |
| " LVPCSOG:units = \"ppmV \" ;\n", | |
| " LVPCSOG:var_desc = \"Average Molar Mixing Ratio of LVPCSOG \" ;\n", | |
| " float SVMT1(TSTEP, LAY, ROW, COL);\n", | |
| " SVMT1:long_name = \"SVMT1 \" ;\n", | |
| " SVMT1:units = \"ppmV \" ;\n", | |
| " SVMT1:var_desc = \"Average Molar Mixing Ratio of SVMT1 \" ;\n", | |
| " float SVMT2(TSTEP, LAY, ROW, COL);\n", | |
| " SVMT2:long_name = \"SVMT2 \" ;\n", | |
| " SVMT2:units = \"ppmV \" ;\n", | |
| " SVMT2:var_desc = \"Average Molar Mixing Ratio of SVMT2 \" ;\n", | |
| " float SVMT3(TSTEP, LAY, ROW, COL);\n", | |
| " SVMT3:long_name = \"SVMT3 \" ;\n", | |
| " SVMT3:units = \"ppmV \" ;\n", | |
| " SVMT3:var_desc = \"Average Molar Mixing Ratio of SVMT3 \" ;\n", | |
| " float SVMT4(TSTEP, LAY, ROW, COL);\n", | |
| " SVMT4:long_name = \"SVMT4 \" ;\n", | |
| " SVMT4:units = \"ppmV \" ;\n", | |
| " SVMT4:var_desc = \"Average Molar Mixing Ratio of SVMT4 \" ;\n", | |
| " float SVMT5(TSTEP, LAY, ROW, COL);\n", | |
| " SVMT5:long_name = \"SVMT5 \" ;\n", | |
| " SVMT5:units = \"ppmV \" ;\n", | |
| " SVMT5:var_desc = \"Average Molar Mixing Ratio of SVMT5 \" ;\n", | |
| " float SVMT6(TSTEP, LAY, ROW, COL);\n", | |
| " SVMT6:long_name = \"SVMT6 \" ;\n", | |
| " SVMT6:units = \"ppmV \" ;\n", | |
| " SVMT6:var_desc = \"Average Molar Mixing Ratio of SVMT6 \" ;\n", | |
| " float WVEL(TSTEP, LAY, ROW, COL);\n", | |
| " WVEL:long_name = \"WVEL \" ;\n", | |
| " WVEL:units = \"m s-1 \" ;\n", | |
| " WVEL:var_desc = \"Vertical Wind Velocity \" ;\n", | |
| " float RH(TSTEP, LAY, ROW, COL);\n", | |
| " RH:long_name = \"RH \" ;\n", | |
| " RH:units = \"1 \" ;\n", | |
| " RH:var_desc = \"Fractional Relative Humidity \" ;\n", | |
| " float TA(TSTEP, LAY, ROW, COL);\n", | |
| " TA:long_name = \"TA \" ;\n", | |
| " TA:units = \"K \" ;\n", | |
| " TA:var_desc = \"Air Temperature \" ;\n", | |
| " float PRES(TSTEP, LAY, ROW, COL);\n", | |
| " PRES:long_name = \"PRES \" ;\n", | |
| " PRES:units = \"Pa \" ;\n", | |
| " PRES:var_desc = \"Air Pressure \" ;\n", | |
| "\n", | |
| "\n", | |
| "// global properties:\n", | |
| " :IOAPI_VERSION = \"$Id: @(#) ioapi library version 3.1 $ \" ;\n", | |
| " :EXEC_ID = \"CMAQ_CCTM22august2019_snapelen_20190822_193232_977500114 \" ;\n", | |
| " :FTYPE = 1 ;\n", | |
| " :CDATE = 2019234 ;\n", | |
| " :CTIME = 202145 ;\n", | |
| " :WDATE = 2019234 ;\n", | |
| " :WTIME = 202145 ;\n", | |
| " :SDATE = 2016184 ;\n", | |
| " :STIME = 0 ;\n", | |
| " :TSTEP = 10000 ;\n", | |
| " :NTHIK = 1 ;\n", | |
| " :NCOLS = 100 ;\n", | |
| " :NROWS = 80 ;\n", | |
| " :NLAYS = 1 ;\n", | |
| " :NVARS = 226 ;\n", | |
| " :GDTYP = 2 ;\n", | |
| " :P_ALP = 33.0 ;\n", | |
| " :P_BET = 45.0 ;\n", | |
| " :P_GAM = -97.0 ;\n", | |
| " :XCENT = -97.0 ;\n", | |
| " :YCENT = 40.0 ;\n", | |
| " :XORIG = 792000.0 ;\n", | |
| " :YORIG = -1080000.0 ;\n", | |
| " :XCELL = 12000.0 ;\n", | |
| " :YCELL = 12000.0 ;\n", | |
| " :VGTYP = 7 ;\n", | |
| " :VGTOP = 5000.0 ;\n", | |
| " :VGLVLS = array([1. , 0.9975], dtype=float32) ;\n", | |
| " :GDNAM = \"2016_12SE1 \" ;\n", | |
| " :UPNAM = \"OPACONC \" ;\n", | |
| " :VAR-LIST = \"NO2 NO O O3 NO3 O1D OH HO2 H2O2 N2O5 HNO3 HONO PNA SO2 SULF SULRXN C2O3 MEO2 RO2 PAN PACD AACD CXO3 ALD2 XO2H PANX FORM MEPX MEOH ROOH XO2 XO2N XPAR XPRP NTR1 NTR2 FACD CO HCO3 ALDX GLYD GLY MGLY ETHA ETOH KET PAR ACET PRPA ROR ETHY ETH OLE IOLE ISOP ISO2 ISOPRXN ISPD INTR ISPX HPLD OPO3 EPOX IEPOXP EPX2 TERP APIN TERPNRO2 MTNO3 TRPRXN BENZENE CRES BZO2 OPEN BENZRO2 TOL TO2 TOLRO2 XOPN XYLMN XLO2 XYLRO2 NAPH PAHRO2 CRO CAT1 CRON OPAN ECH4 CL2 CL HOCL CLO FMCL HCL CLNO2 CLNO3 SESQ SESQRXN SOAALK H2NO3PIJ H2NO3PK VLVPO1 VSVPO1 VSVPO2 VSVPO3 VIVPO1 VLVOO1 VLVOO2 VSVOO1 VSVOO2 VSVOO3 PCVOC PCSOARXN FORM_PRIMARY ALD2_PRIMARY BUTADIENE13 ACROLEIN ACRO_PRIMARY TOLU HG HGIIAER HGIIGAS SVAVB1 SVAVB2 SVAVB3 SVAVB4 ASO4J ASO4I ANH4J ANH4I ANO3J ANO3I AISO1J AISO2J ASQTJ AORGCJ AECJ AECI AOTHRJ AOTHRI AFEJ AALJ ASIJ ATIJ ACAJ AMGJ AKJ AMNJ ACORS ASOIL NUMATKN NUMACC NUMCOR SRFATKN SRFACC SRFCOR AORGH2OJ AH2OJ AH2OI AH3OPJ AH3OPI ANAJ ANAI ACLJ ACLI ASEACAT ACLK ASO4K ANH4K ANO3K AH2OK AH3OPK AISO3J AOLGAJ AOLGBJ AGLYJ AMTNO3J AMTHYDJ APOCI APOCJ APNCOMI APNCOMJ APCSOJ ALVPO1I ASVPO1I ASVPO2I ALVPO1J ASVPO1J ASVPO2J ASVPO3J AIVPO1J ALVOO1I ALVOO2I ASVOO1I ASVOO2I ALVOO1J ALVOO2J ASVOO1J ASVOO2J ASVOO3J AAVB1J AAVB2J AAVB3J AAVB4J AMT1J AMT2J AMT3J AMT4J AMT5J AMT6J NH3 SVISO1 SVISO2 SVSQT LVPCSOG SVMT1 SVMT2 SVMT3 SVMT4 SVMT5 SVMT6 WVEL RH TA PRES \" ;\n", | |
| " :FILEDESC = \"Concentration file output Averaged over the synchronization time steps Timestamp represents beginning computed date/time Layer mapping (CGRID to AGRID): Layer 1 to 1 \" ;\n", | |
| " :HISTORY = \"\" ;\n", | |
| "}" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 27 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "xJatiGgmm1_I", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# Take the average across time\n", | |
| "\n", | |
| "PseudoNetCDF offers many useful methods including `subsetVariables` and `applyAlongDimensions`. `subsetVariables` extracts one or more variables. `applyAlongDimensions` applies a function across one or more named dimensions. Here we are `subset`ing the 'O3' variable and `apply`ing `mean` to the TSTEP dimension, which is time." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "f-IN9Try5DJj", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "pfile = afile.subsetVariables(['O3']).applyAlongDimensions(TSTEP='mean')" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "XowGRJm1Afer", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# Create a Map Figure\n", | |
| "\n", | |
| "1. Create figure\n", | |
| "2. Create axes on the figure\n", | |
| "3. Add the states geometries" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "eVjyq6nwIm8K", | |
| "colab_type": "code", | |
| "outputId": "c3274737-a3f6-4e0d-f85e-5d23baf445a2", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 290 | |
| } | |
| }, | |
| "source": [ | |
| "ax = pfile.plot('O3')" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "**PNC:/usr/local/lib/python3.6/dist-packages/PseudoNetCDF/pncwarn.py:24:UserWarning:\n", | |
| " IOAPI_ISPH is assumed to be 6370000.; consistent with WRF\n" | |
| ], | |
| "name": "stderr" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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rCbMiPceKpsujxKvS5XSsYcyeQma9svfls2KZsz6RX+87hsF85PIxiOc7Bh9/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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "cKMpFLBCoMXF", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# Set Color Normalizer\n", | |
| "\n", | |
| "Here we are going to reset the color bar. This could also be done using the `plot_kw` keyword in the `plot` call above. The `plot_kw` takes a dictionary of keywords that it passes to the matplotlib plotting function (here pcolormesh)." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "luSL-OJd5mr6", | |
| "colab_type": "code", | |
| "outputId": "f15a18ec-ead5-4c3e-ffa8-090392b362c0", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 259 | |
| } | |
| }, | |
| "source": [ | |
| "ax.collections[-1].set_norm(plt.Normalize(vmin=0, vmax=0.05))\n", | |
| "ax.figure" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "image/png": 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SKgegB8AAQkv6dUqpjwHoBRAopcpa63+brcEJ/AvZw2ORQzcC7P5/30FxbAyn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| |
| "text/plain": [ | |
| "<Figure size 432x288 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 30 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "SfymqW6NR0n8", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# Store the file for download\n", | |
| "\n", | |
| "Saving the figure to content allows you to download the figure. Look under \"Files\" on the left." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "caTvX6ueJ215", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "ax.figure.savefig('/content/Benchmark_DailyAvg_Ozone.png')" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "bjE0mMlrQOyP", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# Use options to configure plot\n", | |
| "\n", | |
| "The command below creates a highly configured plot. Use help to explore the options." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "_L4soSrLQO9Q", | |
| "colab_type": "code", | |
| "outputId": "1615a62e-a556-40e4-fdeb-4dde9b020f89", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 319 | |
| } | |
| }, | |
| "source": [ | |
| "plt.close()\n", | |
| "norm = plt.matplotlib.colors.BoundaryNorm(\n", | |
| " [0.01, 0.015, 0.02, 0.025, 0.03, 0.035, 0.04, 0.045, 0.05], 256\n", | |
| ")\n", | |
| "cmap = 'jet'\n", | |
| "pfile.plot('O3',\n", | |
| " plot_kw=dict(cmap=cmap, norm=norm),\n", | |
| " cbar_kw=dict(label='ozone mixing ratio ppmV', orientation='horizontal'),\n", | |
| " map_kw=dict(\n", | |
| " area_thresh=1e4,\n", | |
| " states=True,\n", | |
| " resolution='i'\n", | |
| " )\n", | |
| ")" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "**PNC:/usr/local/lib/python3.6/dist-packages/PseudoNetCDF/pncwarn.py:24:UserWarning:\n", | |
| " IOAPI_ISPH is assumed to be 6370000.; consistent with WRF\n" | |
| ], | |
| "name": "stderr" | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c860b0358>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 31 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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B2eLhl8PPzr2YmvFw46wUBjy0g6l/dfdNp7guC2HrxNxdyNsbHtRe5Olb13Ph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\nAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "yCcD1XUwQxYr", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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