{ "cells": [ { "cell_type": "markdown", "id": "db9d24de", "metadata": {}, "source": [ "# AirNow and CAM-chem\n", "\n", "First we need to import the driver." ] }, { "cell_type": "code", "execution_count": 2, "id": "a467f463", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Please install h5py to open files from the Amazon S3 servers.\n", "Please install h5netcdf to open files from the Amazon S3 servers.\n" ] } ], "source": [ "from melodies_monet import driver" ] }, { "cell_type": "code", "execution_count": 3, "id": "a73507fa-e8a5-4006-bb96-91d4b6d3a77d", "metadata": {}, "outputs": [], "source": [ "# Needed if you want to make changes to `melodies_monet` and don't want to restart kernel:\n", "%load_ext autoreload\n", "\n", "%autoreload 2" ] }, { "cell_type": "markdown", "id": "e5ff022d", "metadata": {}, "source": [ "## Initiate the analysis class\n", "\n", "Now lets create an instance of the {mod}`melodies_monet.driver` {class}`~melodies_monet.driver.analysis` class.\n", "It consists of 4 main parts: model instances, observation instances, a paired instance of both.\n", "This will allow us to move things around the plotting function for spatial and overlays and more complex plots." ] }, { "cell_type": "code", "execution_count": 4, "id": "4d778036", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "analysis(\n", " control='control.yaml',\n", " control_dict=None,\n", " models={},\n", " obs={},\n", " paired={},\n", " start_time=None,\n", " end_time=None,\n", " download_maps=True,\n", " output_dir=None,\n", " debug=False,\n", ")" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "an = driver.analysis()\n", "an" ] }, { "cell_type": "markdown", "id": "d1b59360", "metadata": {}, "source": [ "## Control File\n", "\n", "Read in the required yaml control file that sets up all the definitions of what we want to pair and plot." ] }, { "cell_type": "code", "execution_count": 5, "id": "17488a58", "metadata": { "tags": [ "hide-output" ] }, "outputs": [ { "data": { "text/plain": [ "{'analysis': {'start_time': '2019-09-01-00:00:00',\n", " 'end_time': '2019-09-09-00:00:00',\n", " 'output_dir': './output/camchem',\n", " 'download_maps': False,\n", " 'debug': False},\n", " 'model': {'cam-chem': {'files': 'example:camchem:fv',\n", " 'mod_type': 'cesm_fv',\n", " 'radius_of_influence': 150000.0,\n", " 'mapping': {'airnow': {'O3': 'OZONE'}},\n", " 'projection': 'None',\n", " 'plot_kwargs': {'color': 'dodgerblue', 'marker': '+', 'linestyle': '-.'}}},\n", " 'obs': {'airnow': {'use_airnow': True,\n", " 'filename': 'example:airnow:2019-09',\n", " 'obs_type': 'pt_sfc',\n", " 'variables': {'PM2.5': {'unit_scale': 1,\n", " 'unit_scale_method': '*',\n", " 'nan_value': -1.0,\n", " 'ylabel_plot': 'PM2.5 (ug/m3)',\n", " 'ty_scale': 2.0,\n", " 'vmin_plot': 0.0,\n", " 'vmax_plot': 22.0,\n", " 'vdiff_plot': 15.0,\n", " 'nlevels_plot': 23},\n", " 'OZONE': {'unit_scale': 1,\n", " 'unit_scale_method': '*',\n", " 'nan_value': -1.0,\n", " 'ylabel_plot': 'Ozone (ppbv)',\n", " 'vmin_plot': 15.0,\n", " 'vmax_plot': 55.0,\n", " 'vdiff_plot': 20.0,\n", " 'nlevels_plot': 21}}}},\n", " 'plots': {'plot_grp1': {'type': 'timeseries',\n", " 'fig_kwargs': {'figsize': [12, 6]},\n", " 'default_plot_kwargs': {'linewidth': 2.0, 'markersize': 10.0},\n", " 'text_kwargs': {'fontsize': 18.0},\n", " 'domain_type': ['all', 'epa_region'],\n", " 'domain_name': ['CONUS', 'R1'],\n", " 'data': ['airnow_cam-chem'],\n", " 'data_proc': {'rem_obs_nan': True,\n", " 'ts_select_time': 'time',\n", " 'ts_avg_window': 'H',\n", " 'set_axis': False}},\n", " 'plot_grp2': {'type': 'taylor',\n", " 'fig_kwargs': {'figsize': [8, 8]},\n", " 'default_plot_kwargs': {'linewidth': 2.0, 'markersize': 10.0},\n", " 'text_kwargs': {'fontsize': 16.0},\n", " 'domain_type': ['all', 'epa_region'],\n", " 'domain_name': ['CONUS', 'R1'],\n", " 'data': ['airnow_cam-chem'],\n", " 'data_proc': {'rem_obs_nan': True, 'set_axis': True}},\n", " 'plot_grp3': {'type': 'spatial_bias',\n", " 'fig_kwargs': {'states': True, 'figsize': [10, 5]},\n", " 'text_kwargs': {'fontsize': 16.0},\n", " 'domain_type': ['all', 'epa_region'],\n", " 'domain_name': ['CONUS', 'R1'],\n", " 'data': ['airnow_cam-chem'],\n", " 'data_proc': {'rem_obs_nan': True, 'set_axis': True}},\n", " 'plot_grp4': {'type': 'spatial_overlay',\n", " 'fig_kwargs': {'states': True, 'figsize': [10, 5]},\n", " 'text_kwargs': {'fontsize': 16.0},\n", " 'domain_type': ['all', 'epa_region'],\n", " 'domain_name': ['CONUS', 'R1'],\n", " 'data': ['airnow_cam-chem'],\n", " 'data_proc': {'rem_obs_nan': True, 'set_axis': True}},\n", " 'plot_grp5': {'type': 'boxplot',\n", " 'fig_kwargs': {'figsize': [8, 6]},\n", " 'text_kwargs': {'fontsize': 20.0},\n", " 'domain_type': ['all', 'epa_region'],\n", " 'domain_name': ['CONUS', 'R1'],\n", " 'data': ['airnow_cam-chem'],\n", " 'data_proc': {'rem_obs_nan': True, 'set_axis': False}}}}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "an.control = 'control_camchem.yaml'\n", "an.read_control()\n", "an.control_dict" ] }, { "cell_type": "markdown", "id": "e75705ee", "metadata": {}, "source": [ "## Load the model data \n", "\n", "The driver will automatically loop through the \"models\" found in the model section of the control file and create model classes for each. Classes include the label, mapping information, and xarray object as well as the filenames. Note it can open multiple files easily by including wildcards. Here we are only opening one CAM-chem file." ] }, { "cell_type": "code", "execution_count": 6, "id": "3703cb9e", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Downloading data from 'https://csl.noaa.gov/groups/csl4/modeldata/melodies-monet/data/example_model_data/cesmfv_example/CAM_chem_merra2_FCSD_1deg_QFED_world_201909-01-09_small_sfc.nc' to file 'C:\\Users\\zmoon\\AppData\\Local\\pooch\\pooch\\Cache\\f57b114fb465b23ee998209b50df7d9d-CAM_chem_merra2_FCSD_1deg_QFED_world_201909-01-09_small_sfc.nc'.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cam-chem\n", "{'files': 'example:camchem:fv', 'mod_type': 'cesm_fv', 'radius_of_influence': 150000.0, 'mapping': {'airnow': {'O3': 'OZONE'}}, 'projection': 'None', 'plot_kwargs': {'color': 'dodgerblue', 'marker': '+', 'linestyle': '-.'}}\n", "example:camchem:fv\n", "**** Reading CESM model output...\n" ] } ], "source": [ "an.open_models()" ] }, { "cell_type": "code", "execution_count": 7, "id": "8a1ab3b8", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{'cam-chem': model(\n", " model='cesm_fv',\n", " radius_of_influence=150000.0,\n", " mod_kwargs={},\n", " file_str='example:camchem:fv',\n", " label='cam-chem',\n", " obj=...,\n", " mapping={'airnow': {'O3': 'OZONE'}},\n", " label='cam-chem',\n", " ...\n", " )}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "an.models" ] }, { "cell_type": "code", "execution_count": 8, "id": "21127894", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "\n", "Dimensions: (time: 36, z: 1, lat: 192, lon: 288)\n", "Coordinates:\n", " * lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n", " * z (z) float64 992.5\n", " * lon (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n", " * time (time) datetime64[ns] 2019-09-01T06:00:00 ... 2019-09-10\n", "Data variables:\n", " O3 (time, z, lat, lon) float32 dask.array\n", " PM25 (time, z, lat, lon) float32 dask.array\n", "Attributes:\n", " Conventions: CF-1.0\n", " source: CAM\n", " case: fmerra.2.1003.FCSD.f09.qfedcmip.56L.001.branch02\n", " logname: buchholz\n", " host: cheyenne3\n", " initial_file: /glade/p/cesmdata/cseg/inputdata/atm/cam/inic/fv/f.e20...\n", " topography_file: /glade/p/cesmdata/cseg/inputdata/atm/cam/met/MERRA2/0....\n", " model_doi_url: https://doi.org/10.5065/D67H1H0V\n", " time_period_freq: hour_6\n", " history: Mon Feb 28 16:25:23 2022: ncks -7 -L 1 --baa=4 --ppc d...\n", " NCO: netCDF Operators version 5.0.6 (Homepage = http://nco...." ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "an.models['cam-chem'].obj" ] }, { "cell_type": "code", "execution_count": 9, "id": "6ebdb85a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cam-chem\n", "{'airnow': {'O3': 'OZONE'}}\n" ] } ], "source": [ "# All the info in the model class can be called here.\n", "print(an.models['cam-chem'].label)\n", "print(an.models['cam-chem'].mapping)" ] }, { "cell_type": "code", "execution_count": 10, "id": "bf601f48", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2019-09-01 00:00:00\n", "2019-09-09 00:00:00\n", "True\n" ] } ], "source": [ "# All the info in the analysis class can also be called.\n", "print(an.start_time)\n", "print(an.end_time)\n", "print(an.download_maps)" ] }, { "cell_type": "markdown", "id": "8b93e6e0", "metadata": { "tags": [] }, "source": [ "## Open Obs\n", "\n", "Now for monet-analysis we will open preprocessed data in either netcdf icartt or some other format. We will not be retrieving data like monetio does for some observations (ie aeronet, airnow, etc....). Instead we will provide utitilies to do this so that users can add more data easily.\n", "\n", "Like models we list all obs objects in the yaml file and it will loop through and create driver.observation instances that include the model type, file, objects (i.e. data object) and label " ] }, { "cell_type": "code", "execution_count": 11, "id": "fe9036d8-43a3-4bf8-80cc-37375f2a1cdd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'airnow': {'use_airnow': True,\n", " 'filename': 'example:airnow:2019-09',\n", " 'obs_type': 'pt_sfc',\n", " 'variables': {'PM2.5': {'unit_scale': 1,\n", " 'unit_scale_method': '*',\n", " 'nan_value': -1.0,\n", " 'ylabel_plot': 'PM2.5 (ug/m3)',\n", " 'ty_scale': 2.0,\n", " 'vmin_plot': 0.0,\n", " 'vmax_plot': 22.0,\n", " 'vdiff_plot': 15.0,\n", " 'nlevels_plot': 23},\n", " 'OZONE': {'unit_scale': 1,\n", " 'unit_scale_method': '*',\n", " 'nan_value': -1.0,\n", " 'ylabel_plot': 'Ozone (ppbv)',\n", " 'vmin_plot': 15.0,\n", " 'vmax_plot': 55.0,\n", " 'vdiff_plot': 20.0,\n", " 'nlevels_plot': 21}}}}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "an.control_dict['obs']" ] }, { "cell_type": "code", "execution_count": 12, "id": "946e4837", "metadata": {}, "outputs": [], "source": [ "an.open_obs()" ] }, { "cell_type": "code", "execution_count": 13, "id": "11e5fa47", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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'UB1010001' 'UZB010001'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "an.paired['airnow_cam-chem'].obj" ] }, { "cell_type": "markdown", "id": "d2b8c241-d4cd-4e5b-852f-b02053fca16f", "metadata": { "tags": [] }, "source": [ "## Generate plots" ] }, { "cell_type": "code", "execution_count": 17, "id": "a6f3900b", "metadata": { "tags": [ "hide-output" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: ty_scale not specified for OZONE, so default used.\n", "Reference std: 16.085098346255723\n", "Warning: ty_scale not specified for OZONE, so default used.\n", "Reference std: 10.063694443664746\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\tools\\miniconda3\\envs\\melodies-monet-docs\\lib\\site-packages\\cartopy\\io\\__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_admin_0_boundary_lines_land.zip\n", " warnings.warn(f'Downloading: {url}', DownloadWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 0.1s\n", "[########################################] | 100% Completed | 0.1s\n", "[########################################] | 100% Completed | 0.2s\n", "[########################################] | 100% Completed | 0.3s\n", "[########################################] | 100% Completed | 0.1s\n", "[########################################] | 100% Completed | 0.1s\n", "[########################################] | 100% Completed | 0.1s\n", "[########################################] | 100% Completed | 0.1s\n", "Wall time: 33.4 s\n" ] } ], "source": [ "%%time\n", "\n", "an.plotting()" ] }, { "cell_type": "markdown", "id": "097635c1-6ce2-41cf-a864-e1aa810560aa", "metadata": {}, "source": [ "**10 Figures**\n", "\n", "::::{card-carousel} 10\n", "\n", ":::{card} Figure 1\n", ":img-background: output/camchem/plot_grp1.timeseries.OZONE.2019-09-01_00.2019-09-09_00.all.CONUS.png\n", ":width: 50%\n", ":::\n", "\n", ":::{card} Figure 2\n", ":img-background: output/camchem/plot_grp1.timeseries.OZONE.2019-09-01_00.2019-09-09_00.epa_region.R1.png\n", ":width: 50%\n", ":::\n", "\n", ":::{card} Figure 3\n", ":img-background: output/camchem/plot_grp2.taylor.OZONE.2019-09-01_00.2019-09-09_00.all.CONUS.png\n", ":width: 50%\n", ":::\n", "\n", ":::{card} Figure 4\n", ":img-background: output/camchem/plot_grp2.taylor.OZONE.2019-09-01_00.2019-09-09_00.epa_region.R1.png\n", ":width: 50%\n", ":::\n", "\n", ":::{card} Figure 5\n", ":img-background: output/camchem/plot_grp3.spatial_bias.OZONE.2019-09-01_00.2019-09-09_00.all.CONUS.airnow_cam-chem.png\n", ":width: 50%\n", ":::\n", "\n", ":::{card} Figure 6\n", ":img-background: output/camchem/plot_grp3.spatial_bias.OZONE.2019-09-01_00.2019-09-09_00.epa_region.R1.airnow_cam-chem.png\n", ":width: 50%\n", ":::\n", "\n", ":::{card} Figure 7\n", ":img-background: output/camchem/plot_grp4.spatial_overlay.OZONE.2019-09-01_00.2019-09-09_00.all.CONUS.airnow_cam-chem.png\n", ":width: 50%\n", ":::\n", "\n", ":::{card} Figure 8\n", ":img-background: output/camchem/plot_grp4.spatial_overlay.OZONE.2019-09-01_00.2019-09-09_00.epa_region.R1.airnow_cam-chem.png\n", ":width: 50%\n", ":::\n", "\n", ":::{card} Figure 9\n", ":img-background: output/camchem/plot_grp5.boxplot.OZONE.2019-09-01_00.2019-09-09_00.all.CONUS.png\n", ":width: 50%\n", ":::\n", "\n", ":::{card} Figure 10\n", ":img-background: output/camchem/plot_grp5.boxplot.OZONE.2019-09-01_00.2019-09-09_00.epa_region.R1.png\n", ":width: 50%\n", ":::\n", "\n", "::::" ] } ], "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": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 5 }