{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [ "EMC²", "Simulator", "Radar", "Lidar" ] }, "source": [ "# _EMC²_ Demo Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook we show an example of how to run $EMC^2$ using ModelE3 climate model output and high spectral resolution lidar (HSRL) data, and demonstrate some of the framework's plotting capabilities." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import emc2\n", "import matplotlib.dates as mdates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we load model data (in this case, ModelE3) using the ModelE subclass object" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "model_path = 'allsteps.allmergeSCM_AWARE_788.nc'\n", "my_model = emc2.core.model.ModelE(model_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After that, we load in the HSRL data using the HSRL subclass object." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "HSRL = emc2.core.instruments.HSRL('nsa')\n", "HSRL.read_arm_netcdf_file('awrhsrlM1.20160816.100000.nc') # raw or processed ARM or ARM-like data file" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "Dimensions:                                (time: 120, mean_time: 120,\n",
       "                                            altitude: 334, profile_time: 1)\n",
       "Coordinates:\n",
       "  * time                                   (time) datetime64[ns] 2016-08-16T1...\n",
       "  * mean_time                              (mean_time) object 2016-08-16 10:0...\n",
       "  * altitude                               (altitude) float32 0.0 ... 9.99e+03\n",
       "Dimensions without coordinates: profile_time\n",
       "Data variables: (12/26)\n",
       "    base_time                              object ...\n",
       "    first_time                             object ...\n",
       "    last_time                              object ...\n",
       "    latitude                               (time) float32 dask.array<chunksize=(120,), meta=np.ndarray>\n",
       "    longitude                              (time) float32 dask.array<chunksize=(120,), meta=np.ndarray>\n",
       "    od                                     (time, altitude) float32 dask.array<chunksize=(120, 334), meta=np.ndarray>\n",
       "    ...                                     ...\n",
       "    profile_beta_a_backscat_parallel       (profile_time, altitude) float32 dask.array<chunksize=(1, 334), meta=np.ndarray>\n",
       "    beta_a_backscat_perpendicular          (time, altitude) float32 dask.array<chunksize=(120, 334), meta=np.ndarray>\n",
       "    profile_beta_a_backscat_perpendicular  (profile_time, altitude) float32 dask.array<chunksize=(1, 334), meta=np.ndarray>\n",
       "    beta_a_backscat                        (time, altitude) float32 dask.array<chunksize=(120, 334), meta=np.ndarray>\n",
       "    profile_beta_a_backscat                (profile_time, altitude) float32 dask.array<chunksize=(1, 334), meta=np.ndarray>\n",
       "    qc_mask                                (time, altitude) float64 dask.array<chunksize=(120, 334), meta=np.ndarray>\n",
       "Attributes: (12/94)\n",
       "    dpl_py_template:                                                         ...\n",
       "    dpl_py_template_version:                                                 ...\n",
       "    time_zone:                                                               ...\n",
       "    codeversion:                                                             ...\n",
       "    codedate:                                                                ...\n",
       "    hsrl_instrument:                                                         ...\n",
       "    ...                                                                                   ...\n",
       "    hsrl_processing_parameter__wfov_corr__window_durration:                  ...\n",
       "    hsrl_processing_parameter__wfov_corr__z_norm_interval:                   ...\n",
       "    _file_dates:                                                             ...\n",
       "    _file_times:                                                             ...\n",
       "    _datastream:                                                             ...\n",
       "    _arm_standards_flag:                                                     ...
" ], "text/plain": [ "\n", "Dimensions: (time: 120, mean_time: 120,\n", " altitude: 334, profile_time: 1)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2016-08-16T1...\n", " * mean_time (mean_time) object 2016-08-16 10:0...\n", " * altitude (altitude) float32 0.0 ... 9.99e+03\n", "Dimensions without coordinates: profile_time\n", "Data variables: (12/26)\n", " base_time object ...\n", " first_time object ...\n", " last_time object ...\n", " latitude (time) float32 dask.array\n", " longitude (time) float32 dask.array\n", " od (time, altitude) float32 dask.array\n", " ... ...\n", " profile_beta_a_backscat_parallel (profile_time, altitude) float32 dask.array\n", " beta_a_backscat_perpendicular (time, altitude) float32 dask.array\n", " profile_beta_a_backscat_perpendicular (profile_time, altitude) float32 dask.array\n", " beta_a_backscat (time, altitude) float32 dask.array\n", " profile_beta_a_backscat (profile_time, altitude) float32 dask.array\n", " qc_mask (time, altitude) float64 dask.array\n", "Attributes: (12/94)\n", " dpl_py_template: ...\n", " dpl_py_template_version: ...\n", " time_zone: ...\n", " codeversion: ...\n", " codedate: ...\n", " hsrl_instrument: ...\n", " ... ...\n", " hsrl_processing_parameter__wfov_corr__window_durration: ...\n", " hsrl_processing_parameter__wfov_corr__z_norm_interval: ...\n", " _file_dates: ...\n", " _file_times: ...\n", " _datastream: ...\n", " _arm_standards_flag: ..." ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HSRL.ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following command will generate and process 8 subcolumns per time period of simulated HSRL data using the default radiation approach and classify the simulator output." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "## Creating subcolumns...\n", "Now performing parallel stratiform hydrometeor allocation in subcolumns\n", "Fully overcast cl & ci in 276 voxels\n", "Done! total processing time = 4.77s\n", "Now performing parallel strat precipitation allocation in subcolumns\n", "Fully overcast pl & pi in 385 voxels\n", "Done! total processing time = 7.19s\n", "Now performing parallel conv precipitation allocation in subcolumns\n", "Fully overcast pl & pi in 0 voxels\n", "Done! total processing time = 7.37s\n", "Generating lidar moments...\n", "Generating stratiform lidar variables using radiation logic\n", "Done! total processing time = 0.49s\n", "Generating convective lidar variables using radiation logic\n", "Done! total processing time = 0.52s\n" ] } ], "source": [ "my_model = emc2.simulator.main.make_simulated_data(my_model, HSRL, 8, do_classify=True, convert_zeros_to_nan=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "Dimensions:                     (time: 48, p: 110, subcolumn: 8)\n",
       "Coordinates:\n",
       "  * time                        (time) datetime64[ns] 2016-08-16T01:15:00 ......\n",
       "  * p                           (p) float32 979.0 969.0 959.0 ... 0.0075 0.0035\n",
       "  * subcolumn                   (subcolumn) int64 0 1 2 3 4 5 6 7\n",
       "    lon                         float32 166.7\n",
       "    lat                         float32 -77.85\n",
       "Data variables: (12/170)\n",
       "    axyp                        float32 dask.array<chunksize=(), meta=np.ndarray>\n",
       "    prsurf                      (time) float32 dask.array<chunksize=(48,), meta=np.ndarray>\n",
       "    gtempr                      (time) float32 dask.array<chunksize=(48,), meta=np.ndarray>\n",
       "    shflx                       (time) float32 dask.array<chunksize=(48,), meta=np.ndarray>\n",
       "    lhflx                       (time) float32 dask.array<chunksize=(48,), meta=np.ndarray>\n",
       "    ustar                       (time) float32 dask.array<chunksize=(48,), meta=np.ndarray>\n",
       "    ...                          ...\n",
       "    strat_phase_mask_HSRL       (subcolumn, time, p) float64 nan nan ... nan nan\n",
       "    conv_phase_mask_HSRL        (subcolumn, time, p) float64 nan nan ... nan nan\n",
       "    phase_mask_HSRL_all_hyd     (subcolumn, time, p) float64 nan nan ... nan nan\n",
       "    conv_COSP_phase_mask        (subcolumn, time, p) float64 nan nan ... nan nan\n",
       "    strat_COSP_phase_mask       (subcolumn, time, p) float64 nan nan ... nan nan\n",
       "    COSP_phase_mask_all_hyd     (subcolumn, time, p) float64 nan nan ... nan nan\n",
       "Attributes:\n",
       "    xlabel:               SCM_AWARE_788 SCM_AWARE (AWARE case using the Singl...\n",
       "    _file_dates:          ['20160816']\n",
       "    _file_times:          ['011500']\n",
       "    _datastream:          act_datastream\n",
       "    _arm_standards_flag:  0
" ], "text/plain": [ "\n", "Dimensions: (time: 48, p: 110, subcolumn: 8)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2016-08-16T01:15:00 ......\n", " * p (p) float32 979.0 969.0 959.0 ... 0.0075 0.0035\n", " * subcolumn (subcolumn) int64 0 1 2 3 4 5 6 7\n", " lon float32 166.7\n", " lat float32 -77.85\n", "Data variables: (12/170)\n", " axyp float32 dask.array\n", " prsurf (time) float32 dask.array\n", " gtempr (time) float32 dask.array\n", " shflx (time) float32 dask.array\n", " lhflx (time) float32 dask.array\n", " ustar (time) float32 dask.array\n", " ... ...\n", " strat_phase_mask_HSRL (subcolumn, time, p) float64 nan nan ... nan nan\n", " conv_phase_mask_HSRL (subcolumn, time, p) float64 nan nan ... nan nan\n", " phase_mask_HSRL_all_hyd (subcolumn, time, p) float64 nan nan ... nan nan\n", " conv_COSP_phase_mask (subcolumn, time, p) float64 nan nan ... nan nan\n", " strat_COSP_phase_mask (subcolumn, time, p) float64 nan nan ... nan nan\n", " COSP_phase_mask_all_hyd (subcolumn, time, p) float64 nan nan ... nan nan\n", "Attributes:\n", " xlabel: SCM_AWARE_788 SCM_AWARE (AWARE case using the Singl...\n", " _file_dates: ['20160816']\n", " _file_times: ['011500']\n", " _datastream: act_datastream\n", " _arm_standards_flag: 0" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_model.ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$EMC^2$ can interacts with the [Atmospheric Community Toolkit](https://arm-doe.github.io/ACT) allowing to easily create visualizations. Alternatively, as in the observed and simulated examples below, one can use $EMC^2$'s SubcolumnDisplay subclass object (of ACT's Display class) to generate and save visualizations of both the observation and simulated variables.\n", "The SubcolumnDisplay plotting routins enable mask arrays to be applied on instrument variables; in this case, observed data is masked where the particulate optical thickness is greater than 4, providing a \"cleaner\" plots." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "display = emc2.plotting.SubcolumnDisplay(my_model, figsize=(20, 10))\n", "ax, _ = display.plot_instrument_timeseries(HSRL, \"beta_a_backscat\", log_plot=True, y_range=(0., 2000.),\n", " cmap=\"viridis\", vmin=1e-6, vmax=1e-3,\n", " Mask_array=HSRL.ds[\"od_aerosol\"] > 4.)\n", "ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n", "display.fig.savefig('HSRL_backscatter.png', dpi=200)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "model_display = emc2.plotting.SubcolumnDisplay(my_model, figsize=(20, 10))\n", "ax_mod, _ = model_display.plot_subcolumn_timeseries(\"sub_col_beta_p_tot\", 0, log_plot=True, y_range=(0., 2000.),\n", " pressure_coords=False, cmap=\"viridis\", vmin=1e-6, vmax=1e-3)\n", "ax_mod.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n", "display.fig.savefig('HSRL_backscatter_simulated.png', dpi=200)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also use $EMC^2$'s SubcolumnDisplay object to generate profile plots with shaded regions designating variable temporal or spatial (based on all subcolumns) standard deviation." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "model_display_prof = emc2.plotting.SubcolumnDisplay(my_model, figsize=(5,8))\n", "axp = model_display_prof.plot_subcolumn_mean_profile(\"sub_col_beta_p_tot\", \"2016-08-16T10:00:00\", log_plot=True,\n", " y_range=(0., 2000.), x_range=(1e-6, 1e-3), color='black',\n", " pressure_coords=False, alpha=0.2)\n", "axp = model_display_prof.plot_instrument_mean_profile(HSRL, \"beta_a_backscat\", log_plot=True,\n", " Mask_array=HSRL.ds[\"od_aerosol\"] > 4.,\n", " y_range=(0., 2000.), x_range=(1e-6, 1e-3), color=\"lime\",\n", " pressure_coords=False, alpha=0.2)\n", "display.fig.savefig('HSRL_backscatter_simulated_profile.png', dpi=200)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, $EMC^2$'s SubcolumnDisplay object also allows easy production of phase classification plots. Here we demonstrate curtain plots of lidar classificaiton for the first subcolumn and frequency phase ratio calculated using all subcolumn data. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "model_display2 = emc2.plotting.SubcolumnDisplay(my_model, figsize=(20,10), subplot_shape=(2, 1))\n", "ax2_1, cb2_1 = model_display2.plot_subcolumn_timeseries(\"phase_mask_HSRL_all_hyd\", 0, y_range=(0., 2000.),\n", " pressure_coords=False, subplot_index=0)\n", "model_display.change_plot_to_class_mask(cb2_1, variable=\"phase_mask_HSRL_all_hyd\", convert_zeros_to_nan=True)\n", "ax2_1.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n", "\n", "my_model = emc2.simulator.classification.calculate_phase_ratio(my_model, \"phase_mask_HSRL_all_hyd\", [1])\n", "ax2_2, cb2_2 = model_display2.plot_subcolumn_timeseries(\"phase_mask_HSRL_all_hyd_fpr\", 0, y_range=(0., 2000.),\n", " pressure_coords=False, cmap=\"bwr\", subplot_index=1,\n", " cbar_label='Frequency pr (reddish - more liquid)')\n", "ax2_2.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n", "display.fig.savefig('HSRL_backscatter_simulated_class.png', dpi=200)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.4" } }, "nbformat": 4, "nbformat_minor": 4 }