flatten_era5_land.ipynb

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   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
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   "source": [
    "dsi = xr.open_dataset('era5_land_invariant.nc')\n",
    "dsi"
   ]
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   "cell_type": "code",
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   "source": [
    "dsif = dsi.stack(points=('latitude', 'longitude')).reset_index(('time', 'points'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "dsif"
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   "source": [
    "ds = xr.open_dataset('era5_land_monthly_t2m_1981-2018.nc')\n",
    "ds"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "dsifs = dsif.where(np.isfinite(ds.t2m.isel(time=[0]).stack(points=('latitude', 'longitude')).reset_index(('time', 'points'))), drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsifs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "dsifs.to_netcdf('era5_land_invariant_flat.nc', encoding={'z':dsi.z.encoding, 'lsm':dsi.lsm.encoding})"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "for t in ds.time.values:\n",
    "    sel = ds.sel(time=[t])\n",
    "    sel = sel.stack(points=('latitude', 'longitude')).reset_index(('time', 'points'))\n",
    "    sel = sel.where(np.isfinite(sel.t2m), drop=True)\n",
    "    sel.to_netcdf('tmp_{}.nc'.format(str(t)[:10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "dso = xr.open_mfdataset('tmp_*.nc', concat_dim='time', combine='nested').rename_vars({'time_':'time'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dso.to_netcdf('era5_land_monthly_t2m_1981-2018_flat.nc', encoding={'t2m':ds.t2m.encoding})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "%rm tmp_*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset('era5_land_monthly_prcp_1981-2018.nc')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in ds.time.values:\n",
    "    sel = ds.sel(time=[t])\n",
    "    sel = sel.stack(points=('latitude', 'longitude')).reset_index(('time', 'points'))\n",
    "    sel = sel.where(np.isfinite(sel.tp), drop=True)\n",
    "    sel.to_netcdf('tmp_{}.nc'.format(str(t)[:10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dso = xr.open_mfdataset('tmp_*.nc', concat_dim='time', combine='nested').rename_vars({'time_':'time'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dso.to_netcdf('era5_land_monthly_prcp_1981-2018_flat.nc', encoding={'tp':ds.tp.encoding})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%rm tmp_*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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