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@JiaweiZhuang
Created January 18, 2018 21:55
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Compare two diag formats in GEOS-Chem v11-02d
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compare two diag formats in GEOS-Chem v11-02d"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import cartopy.crs as ccrs\n",
"import numpy as np\n",
"import xarray as xr\n",
"import xbpch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NC diag"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The default 'SpeciesConc_?ADV?'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"rundir_nc = '../geosfp_4x5_standard_NCdiag'\n",
"ds_nc = xr.open_dataset(rundir_nc+'/outputdir/GEOSChem.inst.20130701.nc4')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'SpeciesConc_O3' (time: 9, lev: 72, lat: 46, lon: 72)>\n",
"[2146176 values with dtype=float64]\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2013-07-01T00:20:00 2013-07-01T00:40:00 ...\n",
" * lev (lev) float64 0.9925 0.9775 0.9625 0.9475 0.9325 0.9175 0.9025 ...\n",
" * lat (lat) float64 -89.0 -86.0 -82.0 -78.0 -74.0 -70.0 -66.0 -62.0 ...\n",
" * lon (lon) float64 -180.0 -175.0 -170.0 -165.0 -160.0 -155.0 -150.0 ...\n",
"Attributes:\n",
" long_name: Dry mixing ratio of species O3\n",
" units: mol mol-1 dry\n",
" averaging_method: instantaneous"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dr_nc = ds_nc['SpeciesConc_O3']\n",
"dr_nc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bpch diag"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ND49 time series"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ubuntu/miniconda/lib/python3.6/site-packages/pandas/io/parsers.py:741: UserWarning: Duplicate names specified. This will raise an error in the future.\n",
" return _read(filepath_or_buffer, kwds)\n",
"/home/ubuntu/miniconda/lib/python3.6/site-packages/xbpch/core.py:91: FutureWarning: iteration over an xarray.Dataset will change in xarray v0.11 to only include data variables, not coordinates. Iterate over the Dataset.variables property instead to preserve existing behavior in a forwards compatible manner.\n",
" for v in ds:\n"
]
}
],
"source": [
"rundir_bp = '../geosfp_4x5_standard/'\n",
"ds_bp = xbpch.open_bpchdataset(rundir_bp+'outputdir/CHEM_only_ts20130701.bpch',\n",
" tracerinfo_file=rundir_bp+'tracerinfo.dat', \n",
" diaginfo_file=rundir_bp+'diaginfo.dat')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'IJ_AVG_S_O3' (time: 9, lon: 72, lat: 46, lev: 72)>\n",
"dask.array<shape=(9, 72, 46, 72), dtype=float32, chunksize=(1, 72, 46, 72)>\n",
"Coordinates:\n",
" * lev (lev) float64 0.9925 0.9775 0.9624 0.9473 0.9322 0.9171 0.902 ...\n",
" * lon (lon) float64 -180.0 -175.0 -170.0 -165.0 -160.0 -155.0 -150.0 ...\n",
" * lat (lat) float64 -89.0 -86.0 -82.0 -78.0 -74.0 -70.0 -66.0 -62.0 ...\n",
" * time (time) datetime64[ns] 2013-07-01T00:19:41.250000 ...\n",
"Attributes:\n",
" number: 2\n",
" category: IJ-AVG-$\n",
" name: O3\n",
" full_name: O3 tracer\n",
" molwt: 0.048\n",
" C: 1\n",
" tracer: 2\n",
" hydrocarbon: False\n",
" chemical: True\n",
" original_shape: (72, 46, 72)\n",
" origin: (1, 1, 1)\n",
" scale_factor: 1000000000.0\n",
" units: ppbv"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dr_bp = ds_bp['IJ_AVG_S_O3']\n",
"dr_bp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(('time', 'lon', 'lat', 'lev'), ('time', 'lev', 'lat', 'lon'))"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# xbpch swaps lon and lev dimensions\n",
"dr_bp.dims, dr_nc.dims"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'IJ_AVG_S_O3' (time: 9, lev: 72, lat: 46, lon: 72)>\n",
"dask.array<shape=(9, 72, 46, 72), dtype=float32, chunksize=(1, 72, 46, 72)>\n",
"Coordinates:\n",
" * lev (lev) float64 0.9925 0.9775 0.9624 0.9473 0.9322 0.9171 0.902 ...\n",
" * lon (lon) float64 -180.0 -175.0 -170.0 -165.0 -160.0 -155.0 -150.0 ...\n",
" * lat (lat) float64 -89.0 -86.0 -82.0 -78.0 -74.0 -70.0 -66.0 -62.0 ...\n",
" * time (time) datetime64[ns] 2013-07-01T00:19:41.250000 ...\n",
"Attributes:\n",
" number: 2\n",
" category: IJ-AVG-$\n",
" name: O3\n",
" full_name: O3 tracer\n",
" molwt: 0.048\n",
" C: 1\n",
" tracer: 2\n",
" hydrocarbon: False\n",
" chemical: True\n",
" original_shape: (72, 46, 72)\n",
" origin: (1, 1, 1)\n",
" scale_factor: 1000000000.0\n",
" units: ppbv"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# swap it back\n",
"dr_bp = dr_bp.transpose(*dr_nc.dims)\n",
"dr_bp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compare the two"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ubuntu/miniconda/lib/python3.6/site-packages/xarray/plot/utils.py:51: FutureWarning: 'pandas.tseries.converter.register' has been moved and renamed to 'pandas.plotting.register_matplotlib_converters'. \n",
" converter.register()\n",
"/home/ubuntu/miniconda/lib/python3.6/site-packages/cartopy/mpl/geoaxes.py:1539: RuntimeWarning: invalid value encountered in greater\n",
" to_mask = ((np.abs(dx_horizontal) > np.pi / 2) |\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b0df4be9470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(1,2,figsize=[10, 4],\n",
" subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
"\n",
"for dr, ax in zip([dr_nc, dr_bp], axes):\n",
" dr.isel(time=0, lev=0).plot(ax=ax, cbar_kwargs=dict(shrink=0.4))\n",
" ax.coastlines()\n",
"\n",
"axes[0].set_title('NC diag')\n",
"axes[1].set_title('BPCH diag');"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('mol mol-1 dry', 'ppbv')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# notice that units are different\n",
"dr_nc.units, dr_bp.units"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# correct within 0.02%\n",
"np.allclose(dr_bp, dr_nc*1e9, rtol=2e-3)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# but not within within 0.01%\n",
"np.allclose(dr_bp, dr_nc*1e9, rtol=1e-4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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