concat_simulation_data.ipynb

{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": ["import xarray as xr"],
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "metadata": {},
            "outputs": [],
            "source": [
                "sims = []\n",
                "for sim in range(1, 56):\n",
                "    sims.extend(['t' + str(sim)])",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "#outputs = ['PM2_5_DRY', 'o3', 'bc_2p5', 'oc_2p5', 'so4_2p5', 'oin_2p5', 'AOD550_sfc', 'bsoaX_2p5', 'nh4_2p5', 'no3_2p5', 'asoaX_2p5']\n",
                "outputs = ['o3']\n",
                "months = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']\n",
                "\n",
                "for sim in sims:\n",
                "    path = f'/nfs/b0122/Users/earlacoa/paper_aia_china/{sim}'\n",
                "    for output in outputs:\n",
                "        monthly_files = []\n",
                "\n",
                "        for month in months:\n",
                "            monthly_files.append(xr.open_dataset(f'{path}/wrfout_d01_global_0.25deg_2015-{month}_{output}.nc'))\n",
                "\n",
                "        ds_annual = xr.concat(monthly_files, dim='time')\n",
                "        ds_annual.to_netcdf(f'{path}/wrfout_d01_global_0.25deg_2015-{months[0]}:{months[-1]}_{output}.nc')\n",
                "        \n",
                "        #ds_annual_mean = ds_annual.mean(dim='time')\n",
                "        #ds_annual_mean.to_netcdf(f'{path}/wrfout_d01_global_0.25deg_2015-annual-mean_{output}.nc')",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# convert aerosol units\n",
                "aerosols = ['bc_2p5', 'oc_2p5', 'so4_2p5', 'oin_2p5', 'bsoaX_2p5', 'nh4_2p5', 'no3_2p5', 'asoaX_2p5']\n",
                "\n",
                "alt = xr.open_dataset(\n",
                "    '/nfs/b0122/Users/earlacoa/paper_aia_china/wrfout_d01_global_0.25deg_alt.nc'\n",
                ")['ALT'].isel(time=0)\n",
                "\n",
                "for sim in sims:\n",
                "    path = f'/nfs/b0122/Users/earlacoa/paper_aia_china/{sim}'\n",
                "    for aerosol in aerosols:\n",
                "        for time in ['annual-mean', '01']:\n",
                "            with xr.open_dataset(f'{path}/wrfout_d01_global_0.25deg_2015-{time}_{aerosol}.nc') as ds:\n",
                "                ds_units = ds / alt\n",
                "\n",
                "            ds_units.to_netcdf(f'{path}/wrfout_d01_global_0.25deg_2015-{time}_{aerosol}_ugm-3.nc')",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [],
        },
    ],
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        },
        "language_info": {
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