WACCM LBC for CMAQ

This example shows how to use aqmbc with WACCM’s publicly available forecasts.

• Download from waccm (only if not available in WACCM folder).

• Define translation.

• Extract and translate.

• Display figures and statistics.

from os.path import basename
import aqmbc
import matplotlib.pyplot as plt
import glob

gdnam = '12US1'

Download from waccm via HTTP

dates = ['2023-04-15', '2023-07-15']

aqmbc.models.waccm.download(dates)

Define Translation Expressions

# In Notebooks, display available expressions
aqmbc.exprlib.avail('waccm')

exprpaths = aqmbc.exprlib.exprpaths([
    'waccm_o3so4.expr'                     # for full run, comment this
    # 'waccm_met.expr', 'waccm_cb6.expr',  # for full run, uncomment this
    # 'waccm_ae7.expr'                     # for full run, uncomment this
], prefix='waccm')

Translate WACCM for use by CMAQ

# For "real" VGLVLS use
# METBDYD_PATH = '...'
# metaf = pnc.pncopen(METBDY3D_PATH, format='ioapi')
metaf = aqmbc.options.getmetaf(bctype='bcon', gdnam=gdnam, vgnam='EPA_35L')
inpaths = sorted(glob.glob('WACCM/f.e22*.nc'))
bcpaths = []
suffix = f'_{gdnam}_BCON.nc'
gcdims = aqmbc.options.dims['waccm']
for inpath in inpaths:
    print(inpath, flush=True)
    outpath = basename(inpath).replace('.nc', suffix)
    history = f'From {outpath}'
    outf = aqmbc.bc(
        inpath, outpath, metaf, vmethod='linear', exprpaths=exprpaths,
        dimkeys=gcdims, format_kw={'format': 'waccm'}, history=history,
        clobber=True, verbose=0
    )
    bcpaths.append(outpath)

Figures and Statistics

vprof = aqmbc.report.get_vertprof(bcpaths)
statdf = aqmbc.report.getstats(bcpaths)
sumdf = aqmbc.report.summarize(statdf)
sumdf.to_csv('waccm_summary.csv')

Visualize Vertical Profiles

v1 = vprof['O3'] * 1000
v1.attrs.update(vprof['O3'].attrs)
v1.attrs['long_name'] = 'Ozone'
v2 = vprof['ASO4I'] + vprof['ASO4J']
v2.attrs.update(vprof['ASO4J'].attrs)
v2.attrs['long_name'] = 'ASO4IJ'


fig, axx = plt.subplots(1, 2, figsize=(18, 6), dpi=72, sharey=True)
cmap = plt.cm.nipy_spectral
nt = v1.sizes['TSTEP']
for ti, t in enumerate(v1.TSTEP):
    label = t.dt.strftime('%FT%H:%MZ').values
    axx[0].plot(v1.sel(TSTEP=t), v1.LAY, label=label, color=cmap(ti / nt))
axx[0].legend()
cmap = plt.cm.nipy_spectral
for ti, t in enumerate(v2.TSTEP):
    label = t.dt.strftime('%FT%H:%MZ').values
    axx[1].plot(v2.sel(TSTEP=t), v2.LAY, label=label, color=cmap(ti / nt))
axx[1].legend()
axx[0].set(
    xscale='log', xlabel='{long_name} [{units}]'.format(**v1.attrs),
    xlim=(None, 100), ylabel='VGLVLS [$(p - p_t) / (p_s - p_t)$]', ylim=(1, 0))
axx[1].set(xscale='log', xlabel='{long_name} [{units}]'.format(**v2.attrs))
fig.suptitle('WACCM Boundary Conditions for CMAQ')
fig.savefig('waccm_profiles.png')

Barplot of Concentrations

fig, axx = plt.subplots(
    2, 1, figsize=(18, 8), dpi=72, gridspec_kw=dict(hspace=.8, bottom=0.15)
)
gasds = sumdf.query('unit == "ppmV"').xs('Overall')['median']
aqmbc.report.barplot(gasds.sort_values(), bar_kw=dict(ax=axx[0]))
pmds = sumdf.query('unit == "micrograms/m**3"').xs('Overall')['median']
aqmbc.report.barplot(pmds.sort_values(), bar_kw=dict(ax=axx[1]))
fig.savefig('waccm_bar.png')