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_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 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_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