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        "\n# Planetary Boundary Layer Height Gradient Method Retrievals\n\nThis example shows how to estimate the planetary boundary layer\nheight via a gradient method retrieval\n\nAuthor: Joe O'Brien\n"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "from arm_test_data import DATASETS\n\nimport act\n\n# Read Ceilometer data for an example\nfilename_ceil = DATASETS.fetch('sgpceilC1.b1.20190101.000000.nc')\nds = act.io.arm.read_arm_netcdf(filename_ceil)\n\n# Apply corrections to the dataset\nds = act.corrections.correct_ceil(ds, var_name='backscatter')\n\n# Estimate PBL Height via a gradient method\nds = act.retrievals.pbl_lidar.calculate_gradient_pbl(ds, parm=\"backscatter\", smooth_dis=3)\n\n# Estimate PBL Height via a modified gradient method\nds = act.retrievals.pbl_lidar.calculate_modified_gradient_pbl(\n    ds, parm=\"backscatter\", threshold=1e-4, smooth_dis=3\n)\n\n# Plot the pbl height estimates\ndisplay = act.plotting.TimeSeriesDisplay(ds, subplot_shape=(2,), figsize=(10, 8))\n\n# plot the CL backscatter before overlaying the Gradient Method PBL Height\ndisplay.plot(\n    'backscatter',\n    subplot_index=(0,),\n    cmap='ChaseSpectral',\n    vmin=-6,\n    vmax=6,\n    set_title='SGP Ceilometer PBL Height Estimate via Gradient Method',\n)\n\n# overlay the PBL Height estimate, compute ~10min temporal averages\ndisplay.axes[0].plot(\n    ds['time'].values,\n    ds['pbl_gradient'].rolling(time=38, min_periods=3, center=True).mean().values,\n    color='k',\n)\n# shorten the range\ndisplay.set_yrng([0, 2000], subplot_index=(0,))\n\n# plot the CL backscatter before overlaying the Modified Gradient PBL Height\ndisplay.plot(\n    'backscatter',\n    subplot_index=(1,),\n    cmap='ChaseSpectral',\n    vmin=-6,\n    vmax=6,\n    set_title='SGP Ceilometer PBL Height Estimate via Modified Gradient Method',\n)\n\n# overlay the PBL Height estimate, compute ~10min temporal averages\ndisplay.axes[1].plot(\n    ds['time'].values,\n    ds['pbl_mod_gradient'].rolling(time=38, min_periods=3, center=True).mean().values,\n    color='k',\n)\n# shorten the range\ndisplay.set_yrng([0, 2000], subplot_index=(1,))"
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