def compare_potential_temp(sounding, date):
theta1 = thermo.theta1(K=sounding.TE, hPa=sounding.P)
theta2 = thermo.theta2(K=sounding.TE,
hPa=sounding.P,
mixing_ratio=sounding.MR / 1000)
thetaeq1 = thermo.theta_equiv1(K=sounding.TE, hPa=sounding.P)
thetaeq2 = thermo.theta_equiv2(K=sounding.TE,
hPa=sounding.P,
relh=sounding.RH,
mixing_ratio=sounding.MR / 1000)
foo = pd.DataFrame({
'theta1': theta1,
'thetaeq1': thetaeq1,
'theta2': theta2,
'thetaeq2': thetaeq2
})
y = foo.index.values
fig, ax = plt.subplots(figsize=(8.5, 11))
ln1 = ax.plot(theta1, y, label='theta=f(temp,press) - W&H')
ln2 = ax.plot(theta2, y, label='theta=f(temp,press, w) - Bolton 1980')
ln3 = ax.plot(thetaeq1, y, label='thetaeq=f(temp,press,ws) - W&H')
ln4 = ax.plot(thetaeq2,
y,
label='thetaeq=f(temp,press, w, rh) - Bolton 1980')
ax.set_xlim([280, 320])
ax.set_ylim([0, 5000])
ax.set_xlabel('Temperature [K]')
ax.set_ylabel('Altitude [m]')
ax2 = ax.twiny()
ln5 = ax2.plot(sounding.MR, y, sns.xkcd_rgb["amber"], label='mixing ratio')
ax2.set_xlim([0, 8])
ax2.set_ylim([0, 5000])
ax2.grid(False)
ax2.set_xlabel('Mixing ratio [g kg-1]')
ax3 = ax.twiny()
ln6 = ax3.plot(sounding.RH,
y,
sns.xkcd_rgb["plum purple"],
label='relative humidity')
ax3.set_xlim([0, 100])
ax3.set_ylim([0, 5000])
ax3.grid(False)
ax3.set_xlabel('Relative humidity [%]')
ax3.xaxis.set_label_coords(0.5, 1.07)
ax3.tick_params(direction='out', pad=35)
lns = ln1 + ln2 + ln3 + ln4 + ln5 + ln6
labs = [l.get_label() for l in lns]
ax3.legend(lns, labs, loc=0)
plt.subplots_adjust(bottom=0.05, top=0.89)
datestr = date.strftime('%Y%m%d_%H%M%S')
plt.title('Comparison of Potential Temperature BBY sounding ' + datestr,
y=0.99)
plt.draw()
def compare_potential_temp(sounding, date):
theta1 = thermo.theta1(K=sounding.TE, hPa=sounding.P)
theta2 = thermo.theta2(
K=sounding.TE, hPa=sounding.P, mixing_ratio=sounding.MR/1000)
thetaeq1 = thermo.theta_equiv1(K=sounding.TE, hPa=sounding.P)
thetaeq2 = thermo.theta_equiv2(K=sounding.TE, hPa=sounding.P,
relh=sounding.RH, mixing_ratio=sounding.MR/1000)
foo = pd.DataFrame(
{'theta1': theta1, 'thetaeq1': thetaeq1, 'theta2': theta2, 'thetaeq2': thetaeq2})
y = foo.index.values
fig, ax = plt.subplots(figsize=(8.5, 11))
ln1 = ax.plot(theta1, y, label='theta=f(temp,press) - W&H')
ln2 = ax.plot(theta2, y, label='theta=f(temp,press, w) - Bolton 1980')
ln3 = ax.plot(thetaeq1, y, label='thetaeq=f(temp,press,ws) - W&H')
ln4 = ax.plot(
thetaeq2, y, label='thetaeq=f(temp,press, w, rh) - Bolton 1980')
ax.set_xlim([280, 320])
ax.set_ylim([0, 5000])
ax.set_xlabel('Temperature [K]')
ax.set_ylabel('Altitude [m]')
ax2 = ax.twiny()
ln5 = ax2.plot(sounding.MR, y, sns.xkcd_rgb["amber"], label='mixing ratio')
ax2.set_xlim([0, 8])
ax2.set_ylim([0, 5000])
ax2.grid(False)
ax2.set_xlabel('Mixing ratio [g kg-1]')
ax3 = ax.twiny()
ln6 = ax3.plot(
sounding.RH, y, sns.xkcd_rgb["plum purple"], label='relative humidity')
ax3.set_xlim([0, 100])
ax3.set_ylim([0, 5000])
ax3.grid(False)
ax3.set_xlabel('Relative humidity [%]')
ax3.xaxis.set_label_coords(0.5, 1.07)
ax3.tick_params(direction='out', pad=35)
lns = ln1+ln2+ln3+ln4+ln5+ln6
labs = [l.get_label() for l in lns]
ax3.legend(lns, labs, loc=0)
plt.subplots_adjust(bottom=0.05, top=0.89)
datestr = date.strftime('%Y%m%d_%H%M%S')
plt.title(
'Comparison of Potential Temperature BBY sounding '+datestr, y=0.99)
plt.draw()
def parse_surface_met(file_met, index_field=None,
name_field=None, locelevation=None):
from scipy import stats
'''
Process surface meteorological files
with .met extension
Output dataframe assumes:
- Each file is a 24h period observation
- Frequency of observation is constant
'''
casename = file_met.split('/')[-2]
station_id = file_met.split('/')[-1][:3]
usr_case = str(int(casename[-2:]))
if usr_case in ['1', '2']:
index_field = {'bby': [3, 4, 10, 5, 6, 11, 13],
'czc': [3, 4, 10, 5, 6, 11, 13]}
elif usr_case in ['3', '4', '5', '6', '7']:
index_field = {'bby': [3, 5, 8, 10, 12, 17, 26],
'czc': [3, 4, 5, 6, 8, 13, 22]}
else:
index_field = {'bby': [3, 4, 5, 6, 8, 13, 15],
'czc': [3, 4, 5, 6, 8, 13, 15],
'frs': [3, 4, 5, 6, 8, 13, 15]}
elev = {'bby': 15, 'czc': 462, 'frs': 45}
name_field = ['press', 'temp', 'rh', 'wspd', 'wdir', 'precip', 'mixr']
index_field = index_field[station_id]
locelevation = elev[station_id]
''' read the csv file '''
raw_dframe = pd.read_csv(file_met, header=None)
''' parse date columns into a single date col '''
dates_col = [0, 1, 2]
raw_dates = raw_dframe.ix[:, dates_col]
raw_dates.columns = ['Y', 'j', 'HHMM']
raw_dates['HHMM'] = raw_dates['HHMM'].apply(lambda x: '{0:0>4}'.format(x))
raw_dates = raw_dates.apply(
lambda x: '%s %s %s' % (x['Y'], x['j'], x['HHMM']), axis=1)
dates_fmt = '%Y %j %H%M'
dates = raw_dates.apply(lambda x: datetime.strptime(x, dates_fmt))
''' make meteo df, assign datetime index, and name columns '''
meteo = raw_dframe.ix[:, index_field]
meteo.index = dates
meteo.columns = name_field
''' create a dataframe with regular and continuous 24h period time index
(this works as a QC for time gaps)
'''
nano = 1000000000
time_diff = np.diff(meteo.index)
sample_freq = (stats.mode(time_diff)[0][0]/nano).astype(int) # [seconds]
fidx = meteo.index[0] # (start date for dataframe)
fidx_str = pd.to_datetime(
fidx.year*10000 + fidx.month*100 + fidx.day, format='%Y%m%d')
periods = (24*60*60)/sample_freq
ts = pd.date_range(fidx_str, periods=periods, freq=str(sample_freq)+'s')
nanarray = np.empty((periods, 1))
nanarray[:] = np.nan
df = pd.DataFrame(nanarray, index=ts)
meteo = df.join(meteo, how='outer')
meteo.drop(0, axis=1, inplace=True)
''' make field with hourly acum precip '''
hour = pd.TimeGrouper('H')
preciph = meteo.precip.groupby(hour).sum()
meteo = meteo.join(preciph, how='outer', rsuffix='h')
''' add thermodynamics '''
theta = tm.theta1(C=meteo.temp, hPa=meteo.press)
thetaeq = tm.theta_equiv1(C=meteo.temp, hPa=meteo.press)
meteo.loc[:, 'theta'] = pd.Series(theta, index=meteo.index)
meteo.loc[:, 'thetaeq'] = pd.Series(thetaeq, index=meteo.index)
''' add sea level pressure '''
Tv = tm.virtual_temperature(C=meteo.temp, mixing_ratio=meteo.mixr/1000.)
slp = tm.sea_level_press(K=Tv+273.15, Pa=meteo.press*100, m=locelevation)
meteo.loc[:, 'sea_levp'] = slp
''' assign metadata (prototype, not really used) '''
units = {'press': 'mb', 'temp': 'C', 'rh': '%', 'wspd': 'm s-1',
'wdir': 'deg', 'precip': 'mm', 'mixr': 'g kg-1'}
agl = {'press': 'NaN', 'temp': '10 m', 'rh': '10 m',
'wspd': 'NaN', 'wdir': 'NaN', 'precip': 'NaN', 'mixr': 'NaN'}
for n in name_field:
meteo[n].units = units[n]
meteo[n].agl = agl[n]
meteo[n].nan = -9999.999
meteo[n].sampling_freq = '2 minute'
return meteo
def parse_surface_met(file_met,
index_field=None,
name_field=None,
locelevation=None):
from scipy import stats
'''
Process surface meteorological files
with .met extension
Output dataframe assumes:
- Each file is a 24h period observation
- Frequency of observation is constant
'''
casename = file_met.split('/')[-2]
station_id = file_met.split('/')[-1][:3]
usr_case = str(int(casename[-2:]))
if usr_case in ['1', '2']:
index_field = {
'bby': [3, 4, 10, 5, 6, 11, 13],
'czc': [3, 4, 10, 5, 6, 11, 13]
}
elif usr_case in ['3', '4', '5', '6', '7']:
index_field = {
'bby': [3, 5, 8, 10, 12, 17, 26],
'czc': [3, 4, 5, 6, 8, 13, 22]
}
else:
index_field = {
'bby': [3, 4, 5, 6, 8, 13, 15],
'czc': [3, 4, 5, 6, 8, 13, 15],
'frs': [3, 4, 5, 6, 8, 13, 15]
}
elev = {'bby': 15, 'czc': 462, 'frs': 45}
name_field = ['press', 'temp', 'rh', 'wspd', 'wdir', 'precip', 'mixr']
index_field = index_field[station_id]
locelevation = elev[station_id]
''' read the csv file '''
raw_dframe = pd.read_csv(file_met, header=None)
''' parse date columns into a single date col '''
dates_col = [0, 1, 2]
raw_dates = raw_dframe.ix[:, dates_col]
raw_dates.columns = ['Y', 'j', 'HHMM']
raw_dates['HHMM'] = raw_dates['HHMM'].apply(lambda x: '{0:0>4}'.format(x))
raw_dates = raw_dates.apply(lambda x: '%s %s %s' %
(x['Y'], x['j'], x['HHMM']),
axis=1)
dates_fmt = '%Y %j %H%M'
dates = raw_dates.apply(lambda x: datetime.strptime(x, dates_fmt))
''' make meteo df, assign datetime index, and name columns '''
meteo = raw_dframe.ix[:, index_field]
meteo.index = dates
meteo.columns = name_field
''' create a dataframe with regular and continuous 24h period time index
(this works as a QC for time gaps)
'''
nano = 1000000000
time_diff = np.diff(meteo.index)
sample_freq = (stats.mode(time_diff)[0][0] / nano).astype(int) # [seconds]
fidx = meteo.index[0] # (start date for dataframe)
fidx_str = pd.to_datetime(fidx.year * 10000 + fidx.month * 100 + fidx.day,
format='%Y%m%d')
periods = (24 * 60 * 60) / sample_freq
ts = pd.date_range(fidx_str, periods=periods, freq=str(sample_freq) + 's')
nanarray = np.empty((periods, 1))
nanarray[:] = np.nan
df = pd.DataFrame(nanarray, index=ts)
meteo = df.join(meteo, how='outer')
meteo.drop(0, axis=1, inplace=True)
''' make field with hourly acum precip '''
hour = pd.TimeGrouper('H')
preciph = meteo.precip.groupby(hour).sum()
meteo = meteo.join(preciph, how='outer', rsuffix='h')
''' add thermodynamics '''
theta = tm.theta1(C=meteo.temp, hPa=meteo.press)
thetaeq = tm.theta_equiv1(C=meteo.temp, hPa=meteo.press)
meteo.loc[:, 'theta'] = pd.Series(theta, index=meteo.index)
meteo.loc[:, 'thetaeq'] = pd.Series(thetaeq, index=meteo.index)
''' add sea level pressure '''
Tv = tm.virtual_temperature(C=meteo.temp, mixing_ratio=meteo.mixr / 1000.)
slp = tm.sea_level_press(K=Tv + 273.15,
Pa=meteo.press * 100,
m=locelevation)
meteo.loc[:, 'sea_levp'] = slp
''' assign metadata (prototype, not really used) '''
units = {
'press': 'mb',
'temp': 'C',
'rh': '%',
'wspd': 'm s-1',
'wdir': 'deg',
'precip': 'mm',
'mixr': 'g kg-1'
}
agl = {
'press': 'NaN',
'temp': '10 m',
'rh': '10 m',
'wspd': 'NaN',
'wdir': 'NaN',
'precip': 'NaN',
'mixr': 'NaN'
}
for n in name_field:
meteo[n].units = units[n]
meteo[n].agl = agl[n]
meteo[n].nan = -9999.999
meteo[n].sampling_freq = '2 minute'
return meteo