Consolidation of Data Sources

This example shows how to use ACT to combine multiple datasets to support ARM’s AMF3.

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[DOWNLOADING] bnfmetM1.b1.20241023.000000.cdf
[DOWNLOADING] bnfmetM1.b1.20241019.000000.cdf
[DOWNLOADING] bnfmetM1.b1.20241020.000000.cdf
[DOWNLOADING] bnfmetM1.b1.20241024.000000.cdf
[DOWNLOADING] bnfmetM1.b1.20241022.000000.cdf
[DOWNLOADING] bnfmetM1.b1.20241021.000000.cdf

If you use these data to prepare a publication, please cite:

Kyrouac, J., Shi, Y., & Tuftedal, M. Surface Meteorological Instrumentation
(MET). Atmospheric Radiation Measurement (ARM) User Facility.
https://doi.org/10.5439/1786358

[DOWNLOADING] bnfaoso3M1.b1.20241021.000001.nc
[DOWNLOADING] bnfaoso3M1.b1.20241020.000000.nc
[DOWNLOADING] bnfaoso3M1.b1.20241022.000000.nc
[DOWNLOADING] bnfaoso3M1.b1.20241023.000000.nc
[DOWNLOADING] bnfaoso3M1.b1.20241019.000000.nc

If you use these data to prepare a publication, please cite:

Springston, S., Koontz, A., & Trojanowski, R. Ozone Monitor (AOSO3). Atmospheric
Radiation Measurement (ARM) User Facility. https://doi.org/10.5439/1346692

[DOWNLOADING] bnfaossmpsM1.b1.20241020.000459.nc
[DOWNLOADING] bnfaossmpsM1.b1.20241022.000459.nc
[DOWNLOADING] bnfaossmpsM1.b1.20241023.000459.nc
[DOWNLOADING] bnfaossmpsM1.b1.20241021.000459.nc
[DOWNLOADING] bnfaossmpsM1.b1.20241019.000459.nc

If you use these data to prepare a publication, please cite:

Kuang, C., Singh, A., Howie, J., Salwen, C., & Hayes, C. Scanning mobility
particle sizer (AOSSMPS). Atmospheric Radiation Measurement (ARM) User Facility.
https://doi.org/10.5439/1476898

import act
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import os

# Get Surface Meteorology data from the ASOS stations
station = '1M4'
time_window = [datetime(2024, 10, 19), datetime(2024, 10, 24)]
ds_asos = act.discovery.get_asos_data(time_window, station=station, regions='AL')[station]
ds_asos = ds_asos.where(~np.isnan(ds_asos.tmpf), drop=True)
ds_asos['tmpf'].attrs['units'] = 'degF'
ds_asos.utils.change_units(variables='tmpf', desired_unit='degC', verbose=True)

# Pull EPA data from AirNow
# You need an account and token from https://docs.airnowapi.org/ first
airnow_token = os.getenv('AIRNOW_API')
if airnow_token is not None and len(airnow_token) > 0:
    latlon = '-87.453,34.179,-86.477,34.787'
    ds_airnow = act.discovery.get_airnow_bounded_obs(
        airnow_token, '2024-10-19T00', '2024-10-24T23', latlon, 'OZONE,PM25', data_type='B'
    )
    ds_airnow = act.utils.convert_2d_to_1d(ds_airnow, parse='sites')
    sites = ds_airnow['sites'].values
    airnow = True

# Get NOAA PSL Data from Courtland
results = act.discovery.download_noaa_psl_data(
    site='ctd', instrument='Temp/RH', startdate='20241019', enddate='20241024'
)
ds_noaa = act.io.read_psl_surface_met(results)

# Place your username and token here
username = os.getenv('ARM_USERNAME')
token = os.getenv('ARM_PASSWORD')

# Download ARM data for the MET, OZONE, and SMPS
if username is not None and token is not None and len(username) > 1:
    # Example to show how easy it is to download ARM data if a username/token are set
    results = act.discovery.download_arm_data(
        username, token, 'bnfmetM1.b1', '2024-10-19', '2024-10-24'
    )
    ds_arm = act.io.arm.read_arm_netcdf(results)

    results = act.discovery.download_arm_data(
        username, token, 'bnfaoso3M1.b1', '2024-10-19', '2024-10-24'
    )
    ds_o3 = act.io.arm.read_arm_netcdf(results, cleanup_qc=True)
    ds_o3.qcfilter.datafilter('o3', rm_assessments=['Suspect', 'Bad'], del_qc_var=False)

    results = act.discovery.download_arm_data(
        username, token, 'bnfaossmpsM1.b1', '2024-10-19', '2024-10-24'
    )
    ds_smps = act.io.arm.read_arm_netcdf(results)

    # Set up display and plot all the data
    display = act.plotting.TimeSeriesDisplay(
        {'ASOS': ds_asos, 'ARM': ds_arm, 'EPA': ds_airnow, 'NOAA': ds_noaa, 'ARM_O3': ds_o3},
        figsize=(12, 10),
        subplot_shape=(3,),
    )
    # Plot surface temperature from ASOS, NOAA, and ARM sites
    title = 'Comparison of ARM MET, NOAA Courtland, and Haleyville ASOS Station'
    display.plot('tmpf', dsname='ASOS', label='ASOS', subplot_index=(0,))
    display.plot('Temperature', dsname='NOAA', label='NOAA', subplot_index=(0,))
    display.plot('temp_mean', dsname='ARM', label='ARM', subplot_index=(0,), set_title=title)
    display.day_night_background(dsname='ARM', subplot_index=(0,))

    # Plot ARM and EPA Ozone data
    title = 'Comparison of ARM and EPA Ozone Measurements'
    display.plot('o3', dsname='ARM_O3', label='ARM', subplot_index=(1,))
    if airnow:
        display.plot('OZONE_sites_1', dsname='EPA', label='EPA' + sites[1], subplot_index=(1,))
        display.plot(
            'OZONE_sites_2',
            dsname='EPA',
            label='EPA' + sites[2],
            subplot_index=(1,),
            set_title=title,
        )
    display.set_yrng([0, 70], subplot_index=(1,))
    display.day_night_background(dsname='ARM', subplot_index=(1,))

    # Plot ARM SMPS Concentrations and EPA PM2.5 data on different axes
    title = 'ARM SMPS Concentrations and EPA PM2.5'
    if airnow:
        display.plot('PM2.5_sites_0', dsname='EPA', label='EPA ' + sites[0], subplot_index=(2,))
        display.plot(
            'PM2.5_sites_2',
            dsname='EPA',
            label='EPA ' + sites[2],
            subplot_index=(2,),
            set_title=title,
        )
    display.set_yrng([0, 25], subplot_index=(2,))
    ax2 = display.axes[2].twinx()
    ax2.plot(ds_smps['time'], ds_smps['total_N_conc'], label='ARM SMPS', color='purple')
    ax2.set_ylabel('ARM SMPS (' + ds_smps['total_N_conc'].attrs['units'] + ')')
    ax2.set_ylim([0, 7000])
    ax2.legend(loc=1)
    display.day_night_background(dsname='ARM', subplot_index=(2,))

    # Set legends
    for ax in display.axes:
        ax.legend(loc=2)

    plt.show()
else:
    pass