Phoenix AQS vs TropOMI

Timeseries comparison of NO2 from AQS and TropOMI in Phoenix.

import matplotlib.pyplot as plt
import pyrsig

# Create an RSIG api isntance
# Define a Time and Space Scope: here end of February around Phoenix
rsigapi = pyrsig.RsigApi(
    bdate='2022-01-01', edate='2022-01-15',
    bbox=(-112.3, 33.25, -111.85, 33.65)
)

# Get AQS NO2 with dates parsed and units removed from column names
aqsdf = rsigapi.to_dataframe('aqs.no2', parse_dates=True, unit_keys=False)

# Get TropOMI NO2
tomino2df = rsigapi.to_dataframe(
    'tropomi.offl.no2.nitrogendioxide_tropospheric_column',
    unit_keys=False, parse_dates=True
)


# Create spatial medians for TropOMI and AQS
tomids = tomino2df.groupby('time').median(numeric_only=True)[
    'nitrogendioxide_tropospheric_column'
]
aqsds = aqsdf.groupby(['time']).median(numeric_only=True)['no2']

# Subset AQS to overpass times
oaqsds = aqsds.loc[aqsds.index.isin(tomids.index.floor('1h'))]  # just overpass t

# Create axes with shared x
fig, ax = plt.subplots(figsize=(12, 4),
                       gridspec_kw=dict(bottom=0.25, left=0.05, right=0.95))
ax.tick_params(axis='x', labelrotation=90)
tax = ax.twinx()

# Add AQS with markers at overpasses
ax.plot(aqsds.index.values, aqsds.values, color='k')
ax.scatter(oaqsds.index.values, oaqsds.values, marker='o', color='b')

# Add TropOMI NO2
tax.plot(tomids.index.values, tomids.values, marker='s', color='r')

# Configure axes
ax.set(ylabel='AQS NO2 ppb')
tax.set(ylim=(0, 1.7e16), ylabel='TropOMI NO2 molecules/cm$^2$')

plt.show()
# Or save out figure
# fig.savefig('phoenix.png')