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 thetaeq(self, filled=True, cmap=None, **kwargs):
self.initialize_plot()
self.level = kwargs['level']*100
clevels = kwargs['clevels']
t_arrays = read_files(self, 'temperature') # [C]
q_arrays = read_files(self, 'sphum') # [kg/kg]
rh_arrays = read_files(self, 'relhumid')
press = np.zeros(rh_arrays[0].shape)+kwargs['level'] # [hPa]
X, Y = np.meshgrid(self.lons, self.lats)
self.series['thetaeq'] = []
for i in range(len(self.dates)):
mixr = thermo.mixing_ratio(specific_humidity=q_arrays[i])
theta = thermo.theta_equiv2(C=t_arrays[i], hPa=press,
mixing_ratio=mixr, relh=rh_arrays[i])
val = get_value_at(-123., 38.5, theta, self) # closest to BBY
self.series['thetaeq'].append(val)
if filled:
cf = self.axes[i].contourf(X, Y, theta, clevels, cmap=cmap)
try:
self.axes[i].cax.colorbar(cf, ticks=clevels[::4])
except AttributeError:
add_colorbar(self.axes[i], cf)
else:
cs = self.axes[i].contour(X, Y, theta, clevels, colors='r',
linewidths=0.6)
clabels = self.axes[i].clabel(cs, clevels[::5],
fontweight='bold',
fontsize=10,
fmt='%1.0f')
[txt.set_color('r') for txt in clabels]
set_limits(self, i)
self.add_date(i)
txt = 'Equivalent Potential Temperature [K] at {} hPa\n'
self.title += txt.format(str(self.level/100))
def get_meteo(self,start_time, end_time):
start = self.df.index.searchsorted(start_time)
end = self.df.index.searchsorted(end_time)
# print meteo.columns.tolist()
cols=[0,1,2,3,6,7,8,9,13,14,15]
meteo=self.df.ix[start:end,cols].copy()
''' pressure '''
pres = meteo.apres.values
''' add relative humidity '''
temp = meteo.atemp.values
dewp = meteo.dewp.values
relh = thermo.relative_humidity(C=temp,Dewp=dewp) # [%]
meteo.loc[:,'relh'] = pd.Series(relh,index=meteo.index)
''' mixing ratio '''
satmixr=thermo.sat_mix_ratio(C=temp,hPa=pres)
mixr=relh*satmixr/100
''' add theta '''
theta = thermo.theta2(C=temp,hPa=pres,mixing_ratio=mixr)
meteo.loc[:,'theta'] = pd.Series(theta,index=meteo.index)
''' add thetav '''
thetav = thermo.virtual_temperature(theta=theta,mixing_ratio=mixr)
meteo.loc[:,'thetav'] = pd.Series(thetav,index=meteo.index)
''' add thetae '''
thetaeq = thermo.theta_equiv2(C=temp,hPa=pres,
mixing_ratio=mixr,relh=relh)
meteo.loc[:,'thetaeq'] = pd.Series(thetaeq,index=meteo.index)
return meteo
def air_density():
Rd = 287. # [J K-1 kg-1]
# density values do not vary significantly
Tv = tm.virtual_temperature(C=stemp, mixing_ratio=smixr / 1000.) + 273.15
air_density1 = (spress * 100.) / (Rd * Tv)
Tv = tm.virtual_temperature(C=mtemp, mixing_ratio=smixr / 1000.) + 273.15
air_density2 = (mpress * 100.) / (Rd * Tv)
def air_density():
Rd = 287. # [J K-1 kg-1]
# density values do not vary significantly
Tv = tm.virtual_temperature(C=stemp, mixing_ratio=smixr/1000.)+273.15
air_density1 = (spress*100.)/(Rd*Tv)
Tv = tm.virtual_temperature(C=mtemp, mixing_ratio=smixr/1000.)+273.15
air_density2 = (mpress*100.)/(Rd*Tv)
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 theta(self, **kwargs):
self.initialize_plot()
self.level = kwargs['level']*100
cmap = kwargs['cmap']
t_arrays = read_files(self, 'temperature') # [C]
q_arrays = read_files(self, 'sphum') # [kg/kg]
X, Y = np.meshgrid(self.lons, self.lats)
clevels = kwargs['clevels']
self.series['theta'] = []
for i in range(len(self.dates)):
mixr = thermo.mixing_ratio(specific_humidity=q_arrays[i])
press = np.zeros(mixr.shape)+kwargs['level'] # [hPa]
theta = thermo.theta2(C=t_arrays[i], hPa=press, mixing_ratio=mixr)
val = get_value_at(-123., 38.5, theta, self) # closest to BBY
self.series['theta'].append(val)
cf = self.axes[i].contourf(X, Y, theta, clevels, cmap=cmap)
self.axes[i].cax.colorbar(cf, ticks=clevels[::4])
set_limits(self, i)
self.add_date(i)
self.l1 = 'Potential Temperature [K] at ' + \
str(self.level/100) + ' hPa'
def parse_sounding(file_sound):
col_names = get_var_names(file_sound)
# col_units = get_var_units(file_sound)
''' read tabular file '''
raw_sounding = pd.read_table(file_sound, skiprows=36, header=None)
raw_sounding.drop(19, axis=1, inplace=True)
raw_sounding.columns = col_names
sounding = raw_sounding[
['Height', 'TE', 'TD', 'RH', 'u', 'v', 'P', 'MR', 'DD']]
sounding.units = {'Height': 'm', 'TE': 'K', 'TD': 'K',
'RH': '%', 'u': 'm s-1', 'v': 'm s-1',
'P': 'hPa', 'MR': 'g kg-1'}
''' replace nan values '''
nan_value = -32768.00
sounding = sounding.applymap(lambda x: np.nan if x == nan_value else x)
''' QC soundings that include descening trayectories;
criteria is 3 consecutive values descending
'''
sign = np.sign(np.diff(sounding['Height']))
rep = find_repeats(sign.tolist(), -1, 3)
try:
lastgood = np.where(rep)[0][0]-1
sounding = sounding.ix[0:lastgood]
except IndexError:
''' all good'''
pass
''' set index '''
sounding = sounding.set_index('Height')
''' QC '''
sounding = sounding.groupby(sounding.index).first()
sounding.dropna(how='all', inplace=True)
sounding.RH = sounding.RH.apply(lambda x: 100 if x > 100 else x)
u_nans = nan_fraction(sounding.u)
v_nans = nan_fraction(sounding.v)
if u_nans > 0. or v_nans > 0.:
sounding.u.interpolate(method='linear', inplace=True)
sounding.v.interpolate(method='linear', inplace=True)
rh_nans = nan_fraction(sounding.RH)
td_nans = nan_fraction(sounding.TD)
mr_nans = nan_fraction(sounding.MR)
if rh_nans < 5. and td_nans > 50. and mr_nans > 50.:
sat_mixr = tm.sat_mix_ratio(K=sounding.TE, hPa=sounding.P)
mixr = (sounding.RH/100)*sat_mixr*1000
sounding.loc[:, 'MR'] = mixr # [g kg-1]
''' add potential temperature '''
theta = tm.theta2(
K=sounding.TE, hPa=sounding.P, mixing_ratio=sounding.MR/1000)
thetaeq = tm.theta_equiv2(K=sounding.TE, hPa=sounding.P,
relh=sounding.RH, mixing_ratio=sounding.MR/1000)
sounding.loc[:, 'theta'] = pd.Series(theta, index=sounding.index)
sounding.loc[:, 'thetaeq'] = pd.Series(thetaeq, index=sounding.index)
''' add Brunt-Vaisala frequency '''
hgt = sounding.index.values
bvf_dry = tm.bv_freq_dry(theta=sounding.theta, agl_m=hgt,
depth_m=100, centered=True)
bvf_moist = tm.bv_freq_moist(K=sounding.TE, hPa=sounding.P,
mixing_ratio=sounding.MR/1000,
agl_m=hgt, depth_m=100, centered=True)
sounding = pd.merge(
sounding, bvf_dry, left_index=True, right_index=True, how='outer')
sounding = pd.merge(
sounding, bvf_moist, left_index=True, right_index=True, how='outer')
''' interpolate between layer-averaged values '''
sounding.bvf_dry.interpolate(method='linear', inplace=True)
sounding.bvf_moist.interpolate(method='linear', inplace=True)
sounding.loc[sounding.MR.isnull(), 'bvf_dry'] = np.nan
sounding.loc[sounding.MR.isnull(), 'bvf_moist'] = np.nan
''' NOTE: if sounding hgt jumps from 12 to 53m then 12m
bvf values are NaN since there are no data between 12
and 53m to calculate a layer-based value.
'''
return sounding
def parse_sounding2(file_sound):
'''
This version make a resample of vertical gates
and uses make_layer2 in bvf calculations
(Thermodyn module)
'''
col_names = get_var_names(file_sound)
# col_units = get_var_units(file_sound)
''' read tabular file '''
raw_sounding = pd.read_table(file_sound, skiprows=36, header=None)
raw_sounding.drop(19, axis=1, inplace=True)
raw_sounding.columns = col_names
sounding = raw_sounding[
['Height', 'TE', 'TD', 'RH', 'u', 'v', 'P', 'MR', 'DD']]
sounding.units = {'Height': 'm', 'TE': 'K', 'TD': 'K',
'RH': '%', 'u': 'm s-1', 'v': 'm s-1',
'P': 'hPa', 'MR': 'g kg-1'}
''' replace nan values '''
nan_value = -32768.00
sounding = sounding.applymap(lambda x: np.nan if x == nan_value else x)
''' QC soundings that include descening trayectories;
criteria is 3 consecutive values descending
'''
sign = np.sign(np.diff(sounding['Height']))
rep = find_repeats(sign.tolist(), -1, 3)
try:
lastgood = np.where(rep)[0][0]-1
sounding = sounding.ix[0:lastgood]
except IndexError:
''' all good'''
pass
# print sounding
''' resample to constant height values '''
resamp = 2 # [m]
sounding = sounding.set_index('Height')
hgt = sounding.index.values[-1]
resample = np.arange(10, hgt, resamp)
soundres = sounding.loc[resample]
soundres.iloc[0] = sounding.iloc[0] # copy first value
soundres.iloc[-1] = sounding.iloc[-1] # copy last value
soundres.interpolate(
method='linear', inplace=True, limit=80, limit_direction='backward')
soundres = soundres.reset_index().drop_duplicates(
subset='Height', keep='first')
soundres = soundres.set_index('Height')
''' QC '''
soundres.RH = soundres.RH.apply(lambda x: 100 if x > 100 else x)
rh_nans = nan_fraction(soundres.RH) # [%]
td_nans = nan_fraction(soundres.TD) # [%]
mr_nans = nan_fraction(soundres.MR) # [%]
if rh_nans < 5. and td_nans > 50. and mr_nans > 50.:
sat_mixr = tm.sat_mix_ratio(K=soundres.TE, hPa=soundres.P)
mixr = (soundres.RH/100.)*sat_mixr*1000
soundres.loc[:, 'MR'] = mixr # [g kg-1]
''' add potential temperature '''
theta = tm.theta2(K=soundres.TE, hPa=soundres.P, mixing_ratio=soundres.MR/1000)
thetaeq = tm.theta_equiv2(K=soundres.TE, hPa=soundres.P,
relh=soundres.RH, mixing_ratio=soundres.MR/1000)
soundres.loc[:, 'theta'] = pd.Series(theta, index=soundres.index)
soundres.loc[:, 'thetaeq'] = pd.Series(thetaeq, index=soundres.index)
''' add Brunt-Vaisala frequency '''
hgt = soundres.index.values
depth = 100 # [m]
bvf_dry = tm.bv_freq_dry(theta=soundres.theta, agl_m=hgt, depth_m=depth)
bvf_moist = tm.bv_freq_moist(K=soundres.TE,
hPa=soundres.P,
mixing_ratio=soundres.MR/1000,
agl_m=hgt, depth_m=depth)
soundres = pd.merge(
soundres, bvf_dry, left_index=True, right_index=True, how='outer')
soundres = pd.merge(
soundres, bvf_moist, left_index=True, right_index=True, how='outer')
''' interpolate between layer-averaged values '''
soundres.bvf_dry.interpolate(method='cubic', inplace=True)
soundres.bvf_moist.interpolate(method='cubic', inplace=True)
return soundres
def plot_thermo(**kwargs):
hgt = kwargs['hgt'] #[hPa]
pres = kwargs['press'] #[hPa]
TE = kwargs['temp'] # [C]
TD = kwargs['dewp'] # [C]
U = kwargs['u'] # [m s-1]
V = kwargs['v'] # [m s-1]
date = kwargs['date']
loc = kwargs['loc']
theta = kwargs['theta']
thetaeq = kwargs['thetaeq']
BVFd = kwargs['bvf_dry']
BVFm = kwargs['bvf_moist']
hgt_lim = kwargs['top']
relh = thermo.relative_humidity(C=TE, Dewp=TD)
sat_mixr = thermo.sat_mix_ratio(C=TD, hPa=pres)
mixr = relh * sat_mixr / 100.
fig, ax = plt.subplots(1, 5, sharey=True, figsize=(11, 8.5))
n = 0
ax[n].plot(TE, hgt, label='Temp')
ax[n].plot(TD, hgt, label='Dewp')
ax[n].legend()
ax[n].set_xlim([-30, 20])
ax[n].set_ylim([0, hgt_lim])
add_minor_grid(ax[n])
ax[n].set_xlabel('T [C]')
ax[n].set_ylabel('Altitude [m]')
n = 1
ln1 = ax[n].plot(mixr * 1000., hgt, label='mixr')
ax[n].set_xlim([0, 8])
ax[n].set_ylim([0, hgt_lim])
add_minor_grid(ax[n])
for label in ax[n].xaxis.get_ticklabels()[::2]:
label.set_visible(False)
ax[n].set_xlabel('MR [g kg-1]')
axt = ax[n].twiny()
ln2 = axt.plot(relh, hgt, 'g', label='relh')
axt.set_xlim([0, 100])
axt.set_ylim([0, hgt_lim])
axt.set_xlabel('RH [%]')
axt.xaxis.set_label_coords(0.5, 1.04)
axt.grid(False)
lns = ln1 + ln2
labs = [l.get_label() for l in lns]
axt.legend(lns, labs, loc=0)
n = 2
ax[n].plot(theta, hgt, label='Theta')
ax[n].plot(thetaeq, hgt, label='ThetaEq')
ax[n].legend()
ax[n].set_xlim([280, 320])
ax[n].set_ylim([0, hgt_lim])
add_minor_grid(ax[n])
for label in ax[n].xaxis.get_ticklabels()[::2]:
label.set_visible(False)
ax[n].set_xlabel('Theta [K]')
n = 3
ax[n].plot(U, hgt, label='u')
ax[n].plot(V, hgt, label='v')
ax[n].axvline(x=0, linestyle=':', color='r')
ax[n].legend()
ax[n].set_xlim([-10, 40])
ax[n].set_ylim([0, hgt_lim])
add_minor_grid(ax[n])
ax[n].set_xlabel('WS [ms-1]')
n = 4
ax[n].plot(BVFd * 10000., hgt, label='dry')
ax[n].plot(BVFm * 10000., hgt, label='moist')
ax[n].axvline(x=0, linestyle=':', color='r')
ax[n].legend(loc=2)
ax[n].set_xlim([-6, 6])
ax[n].set_ylim([0, hgt_lim])
add_minor_grid(ax[n])
ax[n].set_xlabel('BVF (x10^-4) [s-1]')
l1 = 'Sounding from NOAA P3'
l2 = '\nIni: ' + date[0].strftime('%Y-%b-%d %H:%M:%S UTC')
l3 = '\nEnd: ' + date[1].strftime('%Y-%b-%d %H:%M:%S UTC')
l4 = '\nLat: ' + '{:.2f}'.format(loc[0]) + ' Lon: ' + '{:.2f}'.format(
loc[1])
plt.suptitle(l1 + l2 + l3 + l4, horizontalalignment='right', x=0.9)
plt.subplots_adjust(bottom=0.06, top=0.89)
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_sounding(file_sound):
col_names = get_var_names(file_sound)
# col_units = get_var_units(file_sound)
''' read tabular file '''
raw_sounding = pd.read_table(file_sound, skiprows=36, header=None)
raw_sounding.drop(19, axis=1, inplace=True)
raw_sounding.columns = col_names
sounding = raw_sounding[[
'Height', 'TE', 'TD', 'RH', 'u', 'v', 'P', 'MR', 'DD'
]]
sounding.units = {
'Height': 'm',
'TE': 'K',
'TD': 'K',
'RH': '%',
'u': 'm s-1',
'v': 'm s-1',
'P': 'hPa',
'MR': 'g kg-1'
}
''' replace nan values '''
nan_value = -32768.00
sounding = sounding.applymap(lambda x: np.nan if x == nan_value else x)
''' QC soundings that include descening trayectories;
criteria is 3 consecutive values descending
'''
sign = np.sign(np.diff(sounding['Height']))
rep = find_repeats(sign.tolist(), -1, 3)
try:
lastgood = np.where(rep)[0][0] - 1
sounding = sounding.ix[0:lastgood]
except IndexError:
''' all good'''
pass
''' set index '''
sounding = sounding.set_index('Height')
''' QC '''
sounding = sounding.groupby(sounding.index).first()
sounding.dropna(how='all', inplace=True)
sounding.RH = sounding.RH.apply(lambda x: 100 if x > 100 else x)
u_nans = nan_fraction(sounding.u)
v_nans = nan_fraction(sounding.v)
if u_nans > 0. or v_nans > 0.:
sounding.u.interpolate(method='linear', inplace=True)
sounding.v.interpolate(method='linear', inplace=True)
rh_nans = nan_fraction(sounding.RH)
td_nans = nan_fraction(sounding.TD)
mr_nans = nan_fraction(sounding.MR)
if rh_nans < 5. and td_nans > 50. and mr_nans > 50.:
sat_mixr = tm.sat_mix_ratio(K=sounding.TE, hPa=sounding.P)
mixr = (sounding.RH / 100) * sat_mixr * 1000
sounding.loc[:, 'MR'] = mixr # [g kg-1]
''' add potential temperature '''
theta = tm.theta2(K=sounding.TE,
hPa=sounding.P,
mixing_ratio=sounding.MR / 1000)
thetaeq = tm.theta_equiv2(K=sounding.TE,
hPa=sounding.P,
relh=sounding.RH,
mixing_ratio=sounding.MR / 1000)
sounding.loc[:, 'theta'] = pd.Series(theta, index=sounding.index)
sounding.loc[:, 'thetaeq'] = pd.Series(thetaeq, index=sounding.index)
''' add Brunt-Vaisala frequency '''
hgt = sounding.index.values
bvf_dry = tm.bv_freq_dry(theta=sounding.theta,
agl_m=hgt,
depth_m=100,
centered=True)
bvf_moist = tm.bv_freq_moist(K=sounding.TE,
hPa=sounding.P,
mixing_ratio=sounding.MR / 1000,
agl_m=hgt,
depth_m=100,
centered=True)
sounding = pd.merge(sounding,
bvf_dry,
left_index=True,
right_index=True,
how='outer')
sounding = pd.merge(sounding,
bvf_moist,
left_index=True,
right_index=True,
how='outer')
''' interpolate between layer-averaged values '''
sounding.bvf_dry.interpolate(method='linear', inplace=True)
sounding.bvf_moist.interpolate(method='linear', inplace=True)
sounding.loc[sounding.MR.isnull(), 'bvf_dry'] = np.nan
sounding.loc[sounding.MR.isnull(), 'bvf_moist'] = np.nan
''' NOTE: if sounding hgt jumps from 12 to 53m then 12m
bvf values are NaN since there are no data between 12
and 53m to calculate a layer-based value.
'''
return sounding
def parse_sounding2(file_sound):
'''
This version make a resample of vertical gates
and uses make_layer2 in bvf calculations
(Thermodyn module)
'''
col_names = get_var_names(file_sound)
# col_units = get_var_units(file_sound)
''' read tabular file '''
raw_sounding = pd.read_table(file_sound, skiprows=36, header=None)
raw_sounding.drop(19, axis=1, inplace=True)
raw_sounding.columns = col_names
sounding = raw_sounding[[
'Height', 'TE', 'TD', 'RH', 'u', 'v', 'P', 'MR', 'DD'
]]
sounding.units = {
'Height': 'm',
'TE': 'K',
'TD': 'K',
'RH': '%',
'u': 'm s-1',
'v': 'm s-1',
'P': 'hPa',
'MR': 'g kg-1'
}
''' replace nan values '''
nan_value = -32768.00
sounding = sounding.applymap(lambda x: np.nan if x == nan_value else x)
''' QC soundings that include descening trayectories;
criteria is 3 consecutive values descending
'''
sign = np.sign(np.diff(sounding['Height']))
rep = find_repeats(sign.tolist(), -1, 3)
try:
lastgood = np.where(rep)[0][0] - 1
sounding = sounding.ix[0:lastgood]
except IndexError:
''' all good'''
pass
# print sounding
''' resample to constant height values '''
resamp = 2 # [m]
sounding = sounding.set_index('Height')
hgt = sounding.index.values[-1]
resample = np.arange(10, hgt, resamp)
soundres = sounding.loc[resample]
soundres.iloc[0] = sounding.iloc[0] # copy first value
soundres.iloc[-1] = sounding.iloc[-1] # copy last value
soundres.interpolate(method='linear',
inplace=True,
limit=80,
limit_direction='backward')
soundres = soundres.reset_index().drop_duplicates(subset='Height',
keep='first')
soundres = soundres.set_index('Height')
''' QC '''
soundres.RH = soundres.RH.apply(lambda x: 100 if x > 100 else x)
rh_nans = nan_fraction(soundres.RH) # [%]
td_nans = nan_fraction(soundres.TD) # [%]
mr_nans = nan_fraction(soundres.MR) # [%]
if rh_nans < 5. and td_nans > 50. and mr_nans > 50.:
sat_mixr = tm.sat_mix_ratio(K=soundres.TE, hPa=soundres.P)
mixr = (soundres.RH / 100.) * sat_mixr * 1000
soundres.loc[:, 'MR'] = mixr # [g kg-1]
''' add potential temperature '''
theta = tm.theta2(K=soundres.TE,
hPa=soundres.P,
mixing_ratio=soundres.MR / 1000)
thetaeq = tm.theta_equiv2(K=soundres.TE,
hPa=soundres.P,
relh=soundres.RH,
mixing_ratio=soundres.MR / 1000)
soundres.loc[:, 'theta'] = pd.Series(theta, index=soundres.index)
soundres.loc[:, 'thetaeq'] = pd.Series(thetaeq, index=soundres.index)
''' add Brunt-Vaisala frequency '''
hgt = soundres.index.values
depth = 100 # [m]
bvf_dry = tm.bv_freq_dry(theta=soundres.theta, agl_m=hgt, depth_m=depth)
bvf_moist = tm.bv_freq_moist(K=soundres.TE,
hPa=soundres.P,
mixing_ratio=soundres.MR / 1000,
agl_m=hgt,
depth_m=depth)
soundres = pd.merge(soundres,
bvf_dry,
left_index=True,
right_index=True,
how='outer')
soundres = pd.merge(soundres,
bvf_moist,
left_index=True,
right_index=True,
how='outer')
''' interpolate between layer-averaged values '''
soundres.bvf_dry.interpolate(method='cubic', inplace=True)
soundres.bvf_moist.interpolate(method='cubic', inplace=True)
return soundres
def plot_thermo(**kwargs):
hgt=kwargs['hgt'] #[hPa]
pres=kwargs['press'] #[hPa]
TE=kwargs['temp'] # [C]
TD=kwargs['dewp'] # [C]
U=kwargs['u'] # [m s-1]
V=kwargs['v'] # [m s-1]
date=kwargs['date']
loc=kwargs['loc']
theta=kwargs['theta']
thetaeq=kwargs['thetaeq']
BVFd=kwargs['bvf_dry']
BVFm=kwargs['bvf_moist']
hgt_lim=kwargs['top']
relh=thermo.relative_humidity(C=TE,Dewp=TD)
sat_mixr= thermo.sat_mix_ratio(C=TD, hPa=pres)
mixr = relh*sat_mixr/100.
fig,ax = plt.subplots(1,5,sharey=True,figsize=(11,8.5))
n=0
ax[n].plot(TE,hgt,label='Temp')
ax[n].plot(TD,hgt,label='Dewp')
ax[n].legend()
ax[n].set_xlim([-30,20])
ax[n].set_ylim([0,hgt_lim])
add_minor_grid(ax[n])
ax[n].set_xlabel('T [C]')
ax[n].set_ylabel('Altitude [m]')
n=1
ln1=ax[n].plot(mixr*1000.,hgt,label='mixr')
ax[n].set_xlim([0,8])
ax[n].set_ylim([0,hgt_lim])
add_minor_grid(ax[n])
for label in ax[n].xaxis.get_ticklabels()[::2]:
label.set_visible(False)
ax[n].set_xlabel('MR [g kg-1]')
axt=ax[n].twiny()
ln2=axt.plot(relh,hgt,'g',label='relh')
axt.set_xlim([0,100])
axt.set_ylim([0,hgt_lim])
axt.set_xlabel('RH [%]')
axt.xaxis.set_label_coords(0.5, 1.04)
axt.grid(False)
lns = ln1+ln2
labs = [l.get_label() for l in lns]
axt.legend(lns, labs, loc=0)
n=2
ax[n].plot(theta,hgt,label='Theta')
ax[n].plot(thetaeq,hgt,label='ThetaEq')
ax[n].legend()
ax[n].set_xlim([280,320])
ax[n].set_ylim([0,hgt_lim])
add_minor_grid(ax[n])
for label in ax[n].xaxis.get_ticklabels()[::2]:
label.set_visible(False)
ax[n].set_xlabel('Theta [K]')
n=3
ax[n].plot(U,hgt,label='u')
ax[n].plot(V,hgt,label='v')
ax[n].axvline(x=0,linestyle=':',color='r')
ax[n].legend()
ax[n].set_xlim([-10,40])
ax[n].set_ylim([0,hgt_lim])
add_minor_grid(ax[n])
ax[n].set_xlabel('WS [ms-1]')
n=4
ax[n].plot(BVFd*10000.,hgt,label='dry')
ax[n].plot(BVFm*10000.,hgt,label='moist')
ax[n].axvline(x=0,linestyle=':',color='r')
ax[n].legend(loc=2)
ax[n].set_xlim([-6,6])
ax[n].set_ylim([0,hgt_lim])
add_minor_grid(ax[n])
ax[n].set_xlabel('BVF (x10^-4) [s-1]')
l1='Sounding from NOAA P3'
l2='\nIni: ' + date[0].strftime('%Y-%b-%d %H:%M:%S UTC')
l3='\nEnd: ' + date[1].strftime('%Y-%b-%d %H:%M:%S UTC')
l4='\nLat: '+'{:.2f}'.format(loc[0])+' Lon: '+'{:.2f}'.format(loc[1])
plt.suptitle(l1+l2+l3+l4,horizontalalignment='right',x=0.9)
plt.subplots_adjust(bottom=0.06,top=0.89)
plt.draw()
def cross_section(self, **kwargs):
field = kwargs['field']
clevels = kwargs['clevels']
cmap = kwargs['cmap']
self.orientation = kwargs['orientation']
self.initialize_plot()
self.horizontal = False
t_arrays = read_files(self, 'temperature') # [C]
q_arrays = read_files(self, 'sphum') # [kg/kg]
rh_arrays = read_files(self, 'relhumid')
isob = get_vertical_array(self)/100. # to [hPa]
z, n = t_arrays[0].shape
press = np.tile(np.array([isob]).transpose(), (1, n))
X, Y = np.meshgrid(range(len(self.lons)), range(len(isob)))
if len(clevels) == 2:
foo = clevels[0]
boundsc = clevels[1]
clevels = foo
''' make a color map of fixed colors '''
vmin = min(clevels)
vmax = max(clevels)
bounds = clevels
snsmap = sns.color_palette(cmap, n_colors=len(bounds))
cmap = colors.ListedColormap(snsmap)
norm = colors.BoundaryNorm(bounds, cmap.N)
plot_field = []
if field == 'thetaeq':
for i in range(6):
mixr = thermo.mixing_ratio(specific_humidity=q_arrays[i])
theta = thermo.theta_equiv2(
C=t_arrays[i], hPa=press,
mixing_ratio=mixr, relh=rh_arrays[i])
theta[theta > 320] = np.nan
plot_field.append(theta)
plot_fieldc = plot_field
cboundaries = bounds
cticks = bounds
boundsc = bounds
ti = ' - Equivalent potential temperature [K]'
elif field == 'q':
for i in range(6):
q = q_arrays[i]
plot_field.append(q*1000.) # [g kg-1]
plot_fieldc = plot_field
cboundaries = bounds
cticks = bounds
boundsc = bounds
ti = ' - Specific humidity [g kg-1]'
elif field == 'U':
plot_field = read_files(self, 'u')
plot_fieldc = plot_field
cboundaries = bounds
cticks = bounds
boundsc = bounds
ti = ' - Wind speed zonal component [m s-1]'
elif field == 'V':
plot_field = read_files(self, 'v')
plot_fieldc = plot_field
cboundaries = bounds
cticks = bounds
boundsc = bounds
ti = ' - Wind speed meridional component [m s-1]'
elif field == 'thetaeq+U':
for i in range(6):
mixr = thermo.mixing_ratio(specific_humidity=q_arrays[i])
theta = thermo.theta_equiv2(
C=t_arrays[i], hPa=press,
mixing_ratio=mixr, relh=rh_arrays[i])
theta[theta > 320] = np.nan
plot_field.append(theta)
plot_fieldc = read_files(self, 'u')
cboundaries = bounds
cticks = bounds
t0 = '\nEquivalent potential temperature [K] (color coded)'
t1 = '\nWind speed zonal component [m s-1] (contour lines)'
ti = t0+t1
elif field == 'thetaeq+V':
for i in range(6):
mixr = thermo.mixing_ratio(specific_humidity=q_arrays[i])
theta = thermo.theta_equiv2(
C=t_arrays[i], hPa=press,
mixing_ratio=mixr, relh=rh_arrays[i])
theta[theta > 320] = np.nan
plot_field.append(theta)
plot_fieldc = read_files(self, 'v')
cboundaries = bounds
cticks = bounds
t0 = '\nEquivalent potential temperature [K] (color coded)'
t1 = '\nWind speed meridional component [m s-1] (contour lines)'
ti = t0+t1
for i in range(6):
im = self.axes[i].imshow(plot_field[i],
interpolation='none',
vmin=vmin,
vmax=vmax,
cmap=cmap,
norm=norm)
self.axes[i].cax.colorbar(im,
cmap=cmap, norm=norm,
boundaries=cboundaries,
ticks=cticks[::4])
xticks = self.lons[::10]
xticklabs = [str(x) for x in xticks]
xticklabs.reverse()
xticklabs.append(' ')
xticklabs.reverse()
self.axes[i].set_xticklabels(xticklabs)
yidx = np.where(isob == self.zboundary)[0]
self.axes[i].set_ylim([36, yidx])
yticks = self.axes[i].get_yticks()
isob_yticks = [isob[y] for y in yticks]
if i == 0:
yticklabs = [str(x) for x in isob_yticks]
else:
yticklabs = [' ' for x in isob_yticks]
self.axes[i].set_yticklabels(yticklabs)
''' add contour lines '''
cs = self.axes[i].contour(X, Y, plot_fieldc[i],
origin='lower', levels=boundsc,
colors='k', linewidths=0.5)
if field in ['thetaeq+U', 'thetaeq+V']:
self.axes[i].clabel(cs, boundsc,
fmt='%1.0f',
fontsize=10)
self.axes[i].set_ylim([36, yidx])
''' add vertical line '''
xidx = np.where(self.lons == -123.0)
self.axes[i].axvline(xidx, color='k', linestyle=':')
''' add date to subplot '''
self.add_date(i)
''' add axis label '''
self.axes[i].set_xlabel('Longitude [deg]')
if i == 0:
self.axes[i].set_ylabel('Pressure level [hPa]')
t1 = 'Climate Forecast System Reanalysis'+ti
if self.orientation[0] == 'zonal':
t2 = '\nLatitude: ' + str(self.orientation[1])
elif self.orientation[0] == 'meridional':
t2 = '\nLongitude: ' + str(self.orientation[1])
plt.suptitle(t1+t2)
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_acft_sounding(flight_level_file, req_ini, req_end, return_interp):
raw = pd.read_table(flight_level_file, engine='python', delimiter='\s*')
ini_raw = raw['TIME'].iloc[0]
end_raw = raw['TIME'].iloc[-1]
timestamp = pd.date_range(
'2001-01-23 '+ini_raw, '2001-01-24 '+end_raw, freq='s')
raw.index = timestamp
raw.drop(['TIME'], axis=1, inplace=True)
''' req_ini and req_end are python datatime variables '''
st = raw.index.searchsorted(req_ini)
en = raw.index.searchsorted(req_end)
data = raw[['PRES_ALT', 'AIR_PRESS', 'AIR_TEMP', 'DEW_POINT',
'WIND_SPD', 'WIND_DIR', 'LAT', 'LON']].ix[st:en]
x = data['PRES_ALT'].values
xnew = np.linspace(min(x), max(x), x.size)
if x[0] > x[-1]:
descening = True
data = data.iloc[::-1]
if return_interp:
''' flight sounding might include constant height levels
or descening trayectories, so we interpolate data to a
common vertical grid '''
data2 = data.drop_duplicates(subset='PRES_ALT')
x2 = data2['PRES_ALT'].values
''' interpolate each field '''
for n in ['AIR_PRESS', 'AIR_TEMP', 'DEW_POINT', 'WIND_SPD', 'WIND_DIR']:
y = data2[n].values
# spl = UnivariateSpline(x2,y,k=4)
# data[n]= spl(xnew)
rbf = Rbf(x2, y, smooth=1.0)
ynew = rbf(xnew)
data[n] = ynew
'''' update values '''
data['PRES_ALT'] = xnew
''' set index '''
data = data.set_index('PRES_ALT')
''' compute thermo '''
hgt = data.index.values
Tc = data['AIR_TEMP']
Tk = Tc+273.15
press = data['AIR_PRESS']
dewp = data['DEW_POINT']
Rh = tm.relative_humidity(C=Tc, Dewp=dewp)
Sat_mixr = tm.sat_mix_ratio(C=Tc, hPa=press)
Mr = Rh*Sat_mixr/100.
theta = tm.theta2(K=Tk, hPa=press, mixing_ratio=Mr)
thetaeq = tm.theta_equiv2(K=Tk, hPa=press, relh=Rh, mixing_ratio=Mr)
data['theta'] = theta.values
data['thetaeq'] = thetaeq.values
bvf_dry = tm.bv_freq_dry(
theta=theta, agl_m=hgt, depth_m=100, centered=True)
bvf_moist = tm.bv_freq_moist(K=Tk, hPa=press, mixing_ratio=Mr,
agl_m=hgt, depth_m=100, centered=True)
data = pd.merge(
data, bvf_dry, left_index=True, right_index=True, how='outer')
data = pd.merge(
data, bvf_moist, left_index=True, right_index=True, how='outer')
data.bvf_dry.interpolate(method='linear', inplace=True)
data.bvf_moist.interpolate(method='linear', inplace=True)
''' drop last incorrect decreasing (increasing) altitude (pressure) value '''
data2 = data.ix[:xnew[-2]]
''' return dataframe '''
return data2
else:
hgt = data['PRES_ALT']
Tc = data['AIR_TEMP']
Tk = Tc+273.15
press = data['AIR_PRESS']
dewp = data['DEW_POINT']
Rh = tm.relative_humidity(C=Tc, Dewp=dewp)
Sat_mixr = tm.sat_mix_ratio(C=Tc, hPa=press)
Mr = Rh*Sat_mixr/100.
theta = tm.theta2(K=Tk, hPa=press, mixing_ratio=Mr)
thetaeq = tm.theta_equiv2(K=Tk, hPa=press, relh=Rh, mixing_ratio=Mr)
data['theta'] = theta.values
data['thetaeq'] = thetaeq.values
data['bvf_dry'] = np.nan
data['bvf_moist'] = np.nan
return data
def parse_acft_sounding(flight_level_file, req_ini, req_end, return_interp):
raw = pd.read_table(flight_level_file, engine='python', delimiter='\s*')
ini_raw = raw['TIME'].iloc[0]
end_raw = raw['TIME'].iloc[-1]
timestamp = pd.date_range('2001-01-23 ' + ini_raw,
'2001-01-24 ' + end_raw,
freq='s')
raw.index = timestamp
raw.drop(['TIME'], axis=1, inplace=True)
''' req_ini and req_end are python datatime variables '''
st = raw.index.searchsorted(req_ini)
en = raw.index.searchsorted(req_end)
data = raw[[
'PRES_ALT', 'AIR_PRESS', 'AIR_TEMP', 'DEW_POINT', 'WIND_SPD',
'WIND_DIR', 'LAT', 'LON'
]].ix[st:en]
x = data['PRES_ALT'].values
xnew = np.linspace(min(x), max(x), x.size)
if x[0] > x[-1]:
descening = True
data = data.iloc[::-1]
if return_interp:
''' flight sounding might include constant height levels
or descening trayectories, so we interpolate data to a
common vertical grid '''
data2 = data.drop_duplicates(subset='PRES_ALT')
x2 = data2['PRES_ALT'].values
''' interpolate each field '''
for n in [
'AIR_PRESS', 'AIR_TEMP', 'DEW_POINT', 'WIND_SPD', 'WIND_DIR'
]:
y = data2[n].values
# spl = UnivariateSpline(x2,y,k=4)
# data[n]= spl(xnew)
rbf = Rbf(x2, y, smooth=1.0)
ynew = rbf(xnew)
data[n] = ynew
'''' update values '''
data['PRES_ALT'] = xnew
''' set index '''
data = data.set_index('PRES_ALT')
''' compute thermo '''
hgt = data.index.values
Tc = data['AIR_TEMP']
Tk = Tc + 273.15
press = data['AIR_PRESS']
dewp = data['DEW_POINT']
Rh = tm.relative_humidity(C=Tc, Dewp=dewp)
Sat_mixr = tm.sat_mix_ratio(C=Tc, hPa=press)
Mr = Rh * Sat_mixr / 100.
theta = tm.theta2(K=Tk, hPa=press, mixing_ratio=Mr)
thetaeq = tm.theta_equiv2(K=Tk, hPa=press, relh=Rh, mixing_ratio=Mr)
data['theta'] = theta.values
data['thetaeq'] = thetaeq.values
bvf_dry = tm.bv_freq_dry(theta=theta,
agl_m=hgt,
depth_m=100,
centered=True)
bvf_moist = tm.bv_freq_moist(K=Tk,
hPa=press,
mixing_ratio=Mr,
agl_m=hgt,
depth_m=100,
centered=True)
data = pd.merge(data,
bvf_dry,
left_index=True,
right_index=True,
how='outer')
data = pd.merge(data,
bvf_moist,
left_index=True,
right_index=True,
how='outer')
data.bvf_dry.interpolate(method='linear', inplace=True)
data.bvf_moist.interpolate(method='linear', inplace=True)
''' drop last incorrect decreasing (increasing) altitude (pressure) value '''
data2 = data.ix[:xnew[-2]]
''' return dataframe '''
return data2
else:
hgt = data['PRES_ALT']
Tc = data['AIR_TEMP']
Tk = Tc + 273.15
press = data['AIR_PRESS']
dewp = data['DEW_POINT']
Rh = tm.relative_humidity(C=Tc, Dewp=dewp)
Sat_mixr = tm.sat_mix_ratio(C=Tc, hPa=press)
Mr = Rh * Sat_mixr / 100.
theta = tm.theta2(K=Tk, hPa=press, mixing_ratio=Mr)
thetaeq = tm.theta_equiv2(K=Tk, hPa=press, relh=Rh, mixing_ratio=Mr)
data['theta'] = theta.values
data['thetaeq'] = thetaeq.values
data['bvf_dry'] = np.nan
data['bvf_moist'] = np.nan
return data
