def run_calcs(df):
"""Do our maths"""
df['mixingratio'] = meteorology.mixing_ratio(
temperature(df['dwpf'].values, 'F')).value('KG/KG')
df['vapor_pressure'] = mcalc.vapor_pressure(
1000. * units.mbar,
df['mixingratio'].values * units('kg/kg')).to(units('kPa'))
df['saturation_mixingratio'] = (meteorology.mixing_ratio(
temperature(df['tmpf'].values, 'F')).value('KG/KG'))
df['saturation_vapor_pressure'] = mcalc.vapor_pressure(
1000. * units.mbar,
df['saturation_mixingratio'].values * units('kg/kg')).to(units('kPa'))
df['vpd'] = df['saturation_vapor_pressure'] - df['vapor_pressure']
group = df.groupby('year')
df = group.aggregate(np.average)
df['dwpf'] = meteorology.dewpoint_from_pq(
pressure(1000, 'MB'), mixingratio(df['mixingratio'].values,
'KG/KG')).value('F')
return df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
month = ctx['month']
nt = NetworkTable(network)
if month == 'all':
months = range(1, 13)
elif month == 'fall':
months = [9, 10, 11]
elif month == 'winter':
months = [12, 1, 2]
elif month == 'spring':
months = [3, 4, 5]
elif month == 'summer':
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-"+month+"-01", '%Y-%b-%d')
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
cursor.execute("""
SELECT tmpf::int as t, dwpf from alldata where station = %s
and tmpf is not null and dwpf is not null and dwpf <= tmpf
and tmpf >= 0 and tmpf <= 140
and extract(month from valid) in %s
""", (station, tuple(months)))
sums = np.zeros((140,), 'f')
counts = np.zeros((140,), 'f')
for row in cursor:
r = mixing_ratio(temperature(row[1], 'F')).value('KG/KG')
sums[row[0]] += r
counts[row[0]] += 1
rows = []
for i in range(140):
if counts[i] < 3:
continue
r = sums[i] / float(counts[i])
d = dewpoint_from_pq(pressure(1000, 'MB'),
mixingratio(r, 'KG/KG')
).value('F')
rh = relh(temperature(i, 'F'), temperature(d, 'F')).value('%')
rows.append(dict(tmpf=i, dwpf=d, rh=rh))
df = pd.DataFrame(rows)
tmpf = df['tmpf']
dwpf = df['dwpf']
rh = df['rh']
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
ax.bar(tmpf-0.5, dwpf, ec='green', fc='green', width=1)
ax.grid(True, zorder=11)
ax.set_title(("%s [%s]\nAverage Dew Point by Air Temperature (month=%s) "
"(%s-%s)\n"
"(must have 3+ hourly observations at the given temperature)"
) % (nt.sts[station]['name'], station, month.upper(),
nt.sts[station]['archive_begin'].year,
datetime.datetime.now().year), size=10)
ax.plot([0, 140], [0, 140], color='b')
ax.set_ylabel("Dew Point [F]")
y2 = ax.twinx()
y2.plot(tmpf, rh, color='k')
y2.set_ylabel("Relative Humidity [%] (black line)")
y2.set_yticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
y2.set_ylim(0, 100)
ax.set_ylim(0, max(tmpf)+2)
ax.set_xlim(0, max(tmpf)+2)
ax.set_xlabel("Air Temperature $^\circ$F")
return fig, df
def test_dewpoint_from_pq():
""" See if we can produce dew point from pressure and mixing ratio """
p = datatypes.pressure(1013.25, "MB")
mr = datatypes.mixingratio(0.012, "kg/kg")
dwpk = meteorology.dewpoint_from_pq(p, mr)
assert abs(dwpk.value("C") - 16.84) < 0.01
def to_dwpf(val):
return meteorology.dewpoint_from_pq(pressure(1000, 'MB'),
mixingratio(val,
'KG/KG')).value('F')
def test_dewpoint_from_pq(self):
""" See if we can produce dew point from pressure and mixing ratio """
p = datatypes.pressure(1013.25, "MB")
mr = datatypes.mixingratio(0.012, "kg/kg")
dwpk = meteorology.dewpoint_from_pq(p, mr)
self.assertAlmostEqual(dwpk.value("C"), 16.84, 2)
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
month = ctx['month']
nt = NetworkTable(network)
if month == 'all':
months = range(1, 13)
elif month == 'fall':
months = [9, 10, 11]
elif month == 'winter':
months = [12, 1, 2]
elif month == 'spring':
months = [3, 4, 5]
elif month == 'summer':
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01", '%Y-%b-%d')
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
cursor.execute(
"""
SELECT drct::int as t, dwpf from alldata where station = %s
and drct is not null and dwpf is not null and dwpf <= tmpf
and sknt > 3 and drct::int %% 10 = 0
and extract(month from valid) in %s
""", (station, tuple(months)))
sums = np.zeros((361, ), 'f')
counts = np.zeros((361, ), 'f')
for row in cursor:
r = mixing_ratio(temperature(row[1], 'F')).value('KG/KG')
sums[row[0]] += r
counts[row[0]] += 1
sums[0] = sums[360]
counts[0] = counts[360]
rows = []
for i in range(361):
if counts[i] < 3:
continue
r = sums[i] / float(counts[i])
d = dewpoint_from_pq(pressure(1000, 'MB'),
mixingratio(r, 'KG/KG')).value('F')
rows.append(dict(drct=i, dwpf=d))
df = pd.DataFrame(rows)
drct = df['drct']
dwpf = df['dwpf']
(fig, ax) = plt.subplots(1, 1)
ax.bar(drct, dwpf, ec='green', fc='green', width=10, align='center')
ax.grid(True, zorder=11)
ax.set_title(("%s [%s]\nAverage Dew Point by Wind Direction (month=%s) "
"(%s-%s)\n"
"(must have 3+ hourly obs > 3 knots at given direction)") %
(nt.sts[station]['name'], station, month.upper(),
max([1973, nt.sts[station]['archive_begin'].year
]), datetime.datetime.now().year),
size=10)
ax.set_ylabel("Dew Point [F]")
ax.set_ylim(min(dwpf) - 5, max(dwpf) + 5)
ax.set_xlim(-5, 365)
ax.set_xticks([0, 45, 90, 135, 180, 225, 270, 315, 360])
ax.set_xticklabels(['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW', 'N'])
ax.set_xlabel("Wind Direction")
return fig, df
def to_dwpf(val):
return meteorology.dewpoint_from_pq(pressure(1000, 'MB'),
mixingratio(val, 'KG/KG')
).value('F')
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
ASOS = psycopg2.connect(database='asos', host='iemdb', user='******')
cursor = ASOS.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('zstation', 'AMW')
network = fdict.get('network', 'IA_ASOS')
month = fdict.get('month', 'all')
nt = NetworkTable(network)
if month == 'all':
months = range(1, 13)
elif month == 'fall':
months = [9, 10, 11]
elif month == 'winter':
months = [12, 1, 2]
elif month == 'spring':
months = [3, 4, 5]
elif month == 'summer':
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-"+month+"-01", '%Y-%b-%d')
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
cursor.execute("""
SELECT drct::int as t, dwpf from alldata where station = %s
and drct is not null and dwpf is not null and dwpf <= tmpf
and sknt > 3 and drct::int %% 10 = 0
and extract(month from valid) in %s
""", (station, tuple(months)))
sums = np.zeros((361,), 'f')
counts = np.zeros((361,), 'f')
for row in cursor:
r = mixing_ratio(temperature(row[1], 'F')).value('KG/KG')
sums[row[0]] += r
counts[row[0]] += 1
sums[0] = sums[360]
counts[0] = counts[360]
rows = []
for i in range(361):
if counts[i] < 3:
continue
r = sums[i] / float(counts[i])
d = dewpoint_from_pq(pressure(1000, 'MB'),
mixingratio(r, 'KG/KG')
).value('F')
rows.append(dict(drct=i, dwpf=d))
df = pd.DataFrame(rows)
drct = df['drct']
dwpf = df['dwpf']
(fig, ax) = plt.subplots(1, 1)
ax.bar(drct-5, dwpf, ec='green', fc='green', width=10)
ax.grid(True, zorder=11)
ax.set_title(("%s [%s]\nAverage Dew Point by Wind Direction (month=%s) "
"(%s-%s)\n"
"(must have 3+ hourly obs > 3 knots at given direction)"
) % (nt.sts[station]['name'], station, month.upper(),
max([1973, nt.sts[station]['archive_begin'].year]),
datetime.datetime.now().year), size=10)
ax.set_ylabel("Dew Point [F]")
ax.set_ylim(min(dwpf)-5, max(dwpf)+5)
ax.set_xlim(-5, 365)
ax.set_xticks([0, 45, 90, 135, 180, 225, 270, 315, 360])
ax.set_xticklabels(['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW', 'N'])
ax.set_xlabel("Wind Direction")
return fig, df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('zstation', 'AMW')
network = fdict.get('network', 'IA_ASOS')
month = fdict.get('month', 'all')
nt = NetworkTable(network)
if month == 'all':
months = range(1, 13)
elif month == 'fall':
months = [9, 10, 11]
elif month == 'winter':
months = [12, 1, 2]
elif month == 'spring':
months = [3, 4, 5]
elif month == 'summer':
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-"+month+"-01", '%Y-%b-%d')
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
cursor.execute("""
SELECT tmpf::int as t, dwpf from alldata where station = %s
and tmpf is not null and dwpf is not null and dwpf <= tmpf
and tmpf >= 0 and tmpf <= 140
and extract(month from valid) in %s
""", (station, tuple(months)))
sums = np.zeros((140,), 'f')
counts = np.zeros((140,), 'f')
for row in cursor:
r = mixing_ratio(temperature(row[1], 'F')).value('KG/KG')
sums[row[0]] += r
counts[row[0]] += 1
rows = []
for i in range(140):
if counts[i] < 3:
continue
r = sums[i] / float(counts[i])
d = dewpoint_from_pq(pressure(1000, 'MB'),
mixingratio(r, 'KG/KG')
).value('F')
rh = relh(temperature(i, 'F'), temperature(d, 'F')).value('%')
rows.append(dict(tmpf=i, dwpf=d, rh=rh))
df = pd.DataFrame(rows)
tmpf = df['tmpf']
dwpf = df['dwpf']
rh = df['rh']
(fig, ax) = plt.subplots(1, 1)
ax.bar(tmpf-0.5, dwpf, ec='green', fc='green', width=1)
ax.grid(True, zorder=11)
ax.set_title(("%s [%s]\nAverage Dew Point by Air Temperature (month=%s) "
"(%s-%s)\n"
"(must have 3+ hourly observations at the given temperature)"
) % (nt.sts[station]['name'], station, month.upper(),
nt.sts[station]['archive_begin'].year,
datetime.datetime.now().year), size=10)
ax.plot([0, 140], [0, 140], color='b')
ax.set_ylabel("Dew Point [F]")
y2 = ax.twinx()
y2.plot(tmpf, rh, color='k')
y2.set_ylabel("Relative Humidity [%] (black line)")
y2.set_yticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
y2.set_ylim(0, 100)
ax.set_ylim(0, max(tmpf)+2)
ax.set_xlim(0, max(tmpf)+2)
ax.set_xlabel("Air Temperature $^\circ$F")
return fig, df
def run_model(nc, initts, ncout, oldncout):
""" Actually run the model, please """
t2 = nc.variables['t2']
u10 = nc.variables['u10']
v10 = nc.variables['v10']
tm = nc.variables['time']
lwdown = nc.variables['lwdown']
swdown = nc.variables['swdown']
q2 = nc.variables['q2']
rc = nc.variables['rain_con']
rn = nc.variables['rain_non']
lats = nc.variables['latitcrs']
lons = nc.variables['longicrs']
# keep masking in-tact as we only write data below when we have it
otmpk = ma.array(ncout.variables['tmpk'][:])
otmpk._sharedmask = False
owmps = ma.array(ncout.variables['wmps'][:])
owmps._sharedmask = False
oswout = ma.array(ncout.variables['swout'][:])
oswout._sharedmask = False
oh = ma.array(ncout.variables['h'][:])
oh._sharedmask = False
olf = ma.array(ncout.variables['lf'][:])
olf._sharedmask = False
obdeckt = ma.array(ncout.variables['bdeckt'][:])
obdeckt._sharedmask = False
osubsfct = ma.array(ncout.variables['subsfct'][:])
osubsfct._sharedmask = False
oifrost = ma.array(ncout.variables['ifrost'][:])
oifrost._sharedmask = False
odwpk = ma.array(ncout.variables['dwpk'][:])
odwpk._sharedmask = False
ofrostd = ma.array(ncout.variables['frostd'][:])
ofrostd._sharedmask = False
oicond = ma.array(ncout.variables['icond'][:])
oicond._sharedmask = False
# mini = 200
# minj = 200
# maxi = 0
# maxj = 0
errorcount = 0
cmd = "/usr/bin/python model/usr/bin/metro "
cmd += "--roadcast-start-date %s " % (initts.strftime("%Y-%m-%dT%H:%MZ"),)
cmd += "--input-forecast isumm5.xml "
cmd += "--input-observation rwis.xml "
cmd += "--input-station station.xml "
cmd += "--output-roadcast roadcast.xml "
cmd += "--log-file /dev/null "
# cmd += "--verbose-level 4 "
cmd += "--use-solarflux-forecast --use-infrared-forecast"
# We don't have pressure from MM5 (yet)
pressure = dt.pressure(1000.0, 'MB')
for i in range(len(nc.dimensions['i_cross'])):
if errorcount > 100:
print('Too many errors, aborting....')
sys.exit()
# loopstart = datetime.datetime.now()
for j in range(len(nc.dimensions['j_cross'])):
lat = lats[i, j]
lon = lons[i, j]
# Hey, we only care about Iowa data! -97 40 -90 43.75
if lat < 40 or lat > 43.75 or lon < -97 or lon > -90:
continue
make_rwis(i, j, initts, oldncout)
o = open('isumm5.xml', 'w')
o.write("""<?xml version="1.0"?>
<forecast>
<header>
<production-date>%s</production-date>
<version>1.1</version>
<filetype>forecast</filetype>
<station-id>ofr</station-id>
</header>
<prediction-list>""" % (
datetime.datetime.utcnow().strftime("%Y-%m-%dT%H:%MZ"),))
for t in range(1, len(nc.dimensions['time'])):
ts = initts + datetime.timedelta(minutes=int(tm[t]))
tmpk = dt.temperature(t2[t, i, j], 'K')
mr = dt.mixingratio(q2[t, i, j], 'KG/KG')
dwp = met.dewpoint_from_pq(pressure, mr)
sped = dt.speed((u10[t, i, j]**2 + v10[t, i, j]**2)**.5, 'MPS')
# sn - snow accumulation in cm
# ap - surface pressure in mb
o.write("""<prediction>
<forecast-time>%s</forecast-time>
<at>%.1f</at>
<td>%.1f</td>
<ra>%.1f</ra>
<sn>0.0</sn>
<ws>%.1f</ws>
<ap>993.8</ap>
<wd>300</wd>
<cc>0</cc>
<sf>%.1f</sf>
<ir>%.1f</ir>
</prediction>
""" % (ts.strftime("%Y-%m-%dT%H:%MZ"),
tmpk.value("C"), dwp.value("C"),
(rn[t, i, j] + rc[t, i, j])*10.,
sped.value("KMH"),
swdown[t, i, j], lwdown[t, i, j])
)
o.write("</prediction-list></forecast>")
o.close()
proc = subprocess.Popen(cmd, shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
se = proc.stderr.read().decode("utf-8")
if se != "":
errorcount += 1
print(('metro error i:%03i j:%03i stderr:|%s|'
) % (i, j, se.strip()))
continue
# Starts at :20 minutes after start time
tree = ET.parse('roadcast.xml')
root = tree.getroot()
tstep = 0
for c in root.findall('./prediction-list/prediction'):
tstep += 1
# Road surface temperature st Celsius
obdeckt[tstep, i, j] = float(c.find('./st').text) + 273.15
# Road sub surface temperature* (40 cm) sst Celsius
osubsfct[tstep, i, j] = float(c.find('./sst').text) + 273.15
# Air temperature at Celsius
otmpk[tstep, i, j] = float(c.find('./at').text) + 273.15
# Dew point td Celsius
odwpk[tstep, i, j] = float(c.find('./td').text) + 273.15
# Wind speed ws km/h
owmps[tstep, i, j] = float(c.find('./ws').text)
# Quantity of snow or ice on the road sn cm
# Quantity of rain on the road ra mm
# Total (1 hr) snow precipitation qp-sn cm
# Total (1 hr) rain precipitation qp-ra mm
# Solar flux sf W/m2
oswout[tstep, i, j] = float(c.find('./sf').text)
# Incident infra-red flux ir W/m2
# Vapor flux fv W/m2
# Sensible heat fc W/m2
# Anthropogenic flux fa W/m2
# Ground exchange flux fg W/m2
# Blackbody effect bb W/m2
# Phase change fp W/m2
# Road condition rc METRo code
oicond[tstep, i, j] = int(c.find('./rc').text)
# Octal cloud coverage** cc octal
ncout.variables['tmpk'][:] = otmpk
ncout.variables['wmps'][:] = dt.speed(owmps, 'KMH').value('MPS')
ncout.variables['swout'][:] = oswout
ncout.variables['h'][:] = oh
ncout.variables['lf'][:] = olf
ncout.variables['bdeckt'][:] = obdeckt
ncout.variables['subsfct'][:] = osubsfct
ncout.variables['ifrost'][:] = oifrost
ncout.variables['frostd'][:] = ofrostd
ncout.variables['dwpk'][:] = odwpk
ncout.variables['icond'][:] = oicond
def to_dwpf(val):
"""Unsure why I am doing this, like this"""
return meteorology.dewpoint_from_pq(pressure(1000, 'MB'),
mixingratio(val,
'KG/KG')).value('F')
def test_dewpoint_from_pq():
""" See if we can produce dew point from pressure and mixing ratio """
p = datatypes.pressure(1013.25, "MB")
mr = datatypes.mixingratio(0.012, "kg/kg")
dwpk = meteorology.dewpoint_from_pq(p, mr)
assert abs(dwpk.value("C") - 16.84) < 0.01
def test_dewpoint_from_pq(self):
""" See if we can produce dew point from pressure and mixing ratio """
p = datatypes.pressure(1013.25, "MB")
mr = datatypes.mixingratio(0.012, "kg/kg")
dwpk = meteorology.dewpoint_from_pq(p, mr)
self.assertAlmostEqual(dwpk.value("C"), 16.84, 2)
