def standard_water_vapor(data, tvar='t', dpdvar='dpd', vpvar='vp', rvar='r', qvar='q', update=False, replace=False,
method='murphy_koop', inplace=False, report=None, verbose=0, **kwargs):
""" Convert humidity variables to water vapor pressure
1. RH (Esat)
2. Q (P)
3. DPD (Esat)
--> VP
Limits are from the RTTOV Coefficient file table 54 levels
Notes
-----
http://nwpsaf.eu/oldsite/deliverables/rtm/rttov11_coefficients.html#54L_reg_limits
Parameters
----------
data DataFrame Input database
tvar str
dpdvar str
vpvar str
rvar str
qvar str
update bool
esat str
inplace bool
verbose int
kwargs **
Returns
-------
data
"""
from raso.met.conversion import sh2vap
from raso.met import esat_functions
from raso.qc import profile_limits
############################################################################
rt = profile_limits(tohpa=True, simple_names=True) # RTTOV Variable Limits
rt['p'] *= 100. # hPa to Pa
rt['vpmin'] *= 100. # hPa to Pa
rt['vpmax'] *= 100. # hPa to Pa
############################################################################
funcid = "[SV] "
if not isinstance(data, (pd.DataFrame, pd.Panel)):
raise ValueError(funcid + "Requires a Dataframe or Panel")
if not inplace:
data = data.copy()
if not hasnames(data, 'qual'):
data['qual'] = ''
if hasnames(data, [rvar, qvar, dpdvar], value=0):
raise ValueError(funcid + " Requires a humidity variable: r, q or dpd")
if hasnames(data, tvar, value=0):
raise ValueError(funcid + " Requires variable: t")
vpfunc = getattr(esat_functions, method)
if not hasnames(data, vpvar) or update:
remove_pressure = False
if isinstance(data, pd.Panel):
data['p'] = 0. # add minor_axis as p
data.loc['p', :, :] = np.asarray(data.minor_axis)[np.newaxis, np.newaxis, :]
remove_pressure = True
ndates = data.shape[1] * data.shape[2]
else:
ndates = data.shape[0]
############################################################################
# First use R, which includes Q
# R
#
if hasnames(data, rvar):
journal(funcid + "Using r (%s) and t (%s) for vp (%s)" % (rvar, tvar, vpvar), report, verbose)
data[vpvar] = data[rvar].values * vpfunc(data[tvar].values) # Convert r,t to vp
############################################################################
#
# Q
#
elif hasnames(qvar, data):
journal(funcid + "Using q (%s) and p to fill up gaps for vp (%s)" % (qvar, vpvar), report, verbose)
vp = sh2vap(data[qvar].values, data['p'].values) # Convert q,p to vp
if hasnames(data, vpvar):
logic = (np.isfinite(vp) & ~np.isfinite(data[vpvar].values))
data[vpvar] = np.where(logic, vp, data[vpvar].values)
# TODO missing else
# data.loc[:, vpvar] = np.where(logic, vp, data[vpvar].values)
# data.loc[:, 'qual'] = np.where(logic, data.qual.values + 'Q', data.qual.values) # FLAG: F fill
############################################################################
#
# Second use DPD (IGRA)
# DPD
elif hasnames(data, dpdvar):
journal(funcid + "Warning using dpd (%s) for vp (%s)" % (dpdvar, vpvar), report, verbose)
vp = vpfunc((data[tvar] - data[dpdvar]).values) # Convert Td to vp
if hasnames(data, vpvar):
logic = (np.isfinite(vp) & ~np.isfinite(data[vpvar].values)) # Update? GOOD, BAD
data[vpvar] = np.where(logic, vp, data[vpvar].values) # Update? NEW, OLD
else:
data[vpvar] = vp
else:
raise RuntimeError("No humidity variable found!")
############################################################################
#
# Quality control
#
vpmins = np.interp(np.log(data.p.values), np.log(rt.p.values), rt.vpmin.values, left=rt.vpmin.min(),
right=rt.vpmin.max()) # Interpolate Minimum
vpmaxs = np.interp(np.log(data.p.values), np.log(rt.p.values), rt.vpmax.values, left=rt.vpmax.min(),
right=rt.vpmax.max()) # Interpolate Maximum
# Range? BAD, GOOD
logic = ((data[vpvar].values < vpmins) | (data[vpvar].values > vpmaxs)) & np.isfinite(data[vpvar].values)
data['qual'] = np.where(logic, data.qual.values + 'V', data.qual.replace('V', '').values) # FLAG: V
# data.loc[:, 'qual'] = np.where(logic, data.qual.values + 'V', data.qual.replace('V', '').values) # FLAG: V
journal(funcid + "#%8d V flagged. (%d)" % (np.sum(np.sum(flag_inside(data.qual, 'V'))), ndates), report,
verbose)
if replace:
data[vpvar] = np.where(logic, np.nan, data[vpvar].values) # Apply? BAD, GOOD
# data.loc[:, vpvar] = np.where(logic, np.nan, data[vpvar].values) # Apply? BAD, GOOD
############################################################################
#
# Unique Flags
#
data['qual'] = unique_flags(data['qual'])
############################################################################
#
# Pressure
#
if remove_pressure:
del data['p']
if not inplace:
return data
def standard_dewpoint_depression(data, tvar='t', dpdvar='dpd', vpvar='vp', tdvar='td', update=False, replace=False,
method='murphy_koop', inplace=False, report=None, verbose=0, **kwargs):
""" Convert water vapor pressure to dewpoint depression
Parameters
----------
data pd.DataFrame Input Radiosonde Data
tvar str Temperature variable to use: t, t_cor
dpdvar str Dewpoint Dep. variable to use: dpd, dpd_mcor
vpvar str Water vapor pressure: vp
method str saturation water vapor formulation
update bool update dpd
inplace bool apply directly to input / no copy ?
verbose int verbosness
kwargs dict **
Returns
-------
pd.DataFrame / same as input (add columns)
"""
from raso.met.conversion import dewpoint
dpd_absmax = 60.
############################################################################
funcid = "[SV] "
if not isinstance(data, (pd.DataFrame, pd.Panel)):
raise ValueError(funcid + "Requires a Dataframe or Panel")
if not inplace:
data = data.copy()
if not hasnames(data,'qual'):
data['qual'] = ''
if not hasnames(data, vpvar):
raise ValueError(funcid + " Requires a humidity variable: vp")
if not hasnames(data, tvar):
raise ValueError(funcid + " Requires variable: t")
############################################################################
#
# Dewpoint / for IGRA mostly
#
if not hasnames(data, dpdvar) or update:
dpd = data[tvar] - dewpoint(data[vpvar], method=method)
if hasnames(data, dpdvar):
logic = (np.isfinite(dpd) & ~np.isfinite(data[dpdvar].values)) # Update? GOOD, BAD
data[dpdvar] = np.where(logic, dpd, data[dpdvar].values) # Update? NEW, OLD
data[tdvar] = data[tvar].values - np.where(logic, dpd, data[dpdvar].values) # Update? NEW, OLD
else:
data[dpdvar] = dpd
data[tdvar] = data[tvar].values - dpd
journal(funcid + "DPD (%s, %s) from vp (%s), replace: %s" % (dpdvar, tvar, vpvar, replace), report, verbose)
#
# DPD valid range ?
#
logic = ((data[dpdvar].values < 0) | (data[dpdvar].values > dpd_absmax)) # BAD, GOOD
if replace:
data[dpdvar] = np.where(logic, np.nan, data[dpdvar].values) # set dpd
data[vpvar] = np.where(logic, np.nan, data[vpvar].values) # set vp
data[tdvar] = np.where(logic, np.nan, data[tdvar].values) # set Td
data['qual'] = np.where(logic, data.qual.values + 'D', data.qual.replace('D', '').values) # FLAG: D
#
# Dewpoint larger than Temperature!
#
logic = (data[tdvar].values > data[tvar].values) # BAD, GOOD
if replace:
data[dpdvar] = np.where(logic, np.nan, data[dpdvar].values) # set dpd to small value
data[tdvar] = np.where(logic, np.nan, data[tdvar].values) # set Td to small value
data[vpvar] = np.where(logic, np.nan, data[vpvar].values) # set vp
data['qual'] = np.where(logic, data.qual.values + 'Y', data.qual.replace('Y', '').values) # FLAG: D
############################################################################
#
# Unique Flags
#
data['qual'] = unique_flags(data['qual'])
if not inplace:
return data
def standard_rel_humidity(data, rvar='r', tvar='t', dpdvar='dpd', qvar='q', update=False, replace=False,
method='murphy_koop', inplace=False, report=None, verbose=0):
""" convert humidity variables to relative humidity
1. q to vp
2. dpd to vp
3. merge vp
4. vp to rh
set quality flags
Parameters
----------
data DataFrame Input database
rvar str
tvar str
dpdvar str
qvar str
update bool Update existing
replace bool set flagged to nan
method str Saturation water vapor
inplace bool
verbose int
kwargs **
Returns
-------
data
"""
from raso.met.conversion import sh2vap
from raso.met import esat_functions
from raso.qc import profile_limits
############################################################################
rt = profile_limits(tohpa=True, simple_names=True) # RTTOV Variable Limits
rt['p'] *= 100. # hPa to Pa
rt['vpmin'] *= 100. # hPa to Pa
rt['vpmax'] *= 100. # hPa to Pa
############################################################################
funcid = "[SV] "
r_absmin = 0
r_absmax = 1
if not isinstance(data, (pd.DataFrame, pd.Panel)):
raise ValueError(funcid + "Requires a Dataframe or Panel")
if not inplace:
data = data.copy()
if not hasnames('qual', data):
data['qual'] = ''
if hasnames(data, [rvar, qvar, dpdvar], value=0):
raise ValueError(funcid + " Requires a humidity variable: %s, %s or %s" % (rvar, qvar, dpdvar))
if hasnames(data,tvar, value=0):
raise ValueError(funcid + " Requires variable: %s" % tvar)
vpfunc = getattr(esat_functions, method)
if not hasnames(data, rvar) or update:
remove_pressure = False
if isinstance(data, pd.Panel):
data['p'] = 0. # add minor_axis as p
data.loc['p', :, :] = np.asarray(data.minor_axis)[np.newaxis, np.newaxis, :]
remove_pressure = True
journal(funcid + "rel. humidity (%s) update: %s" % (rvar, update), report, verbose)
# Quality control
vpmins = np.interp(np.log(data.p.values), np.log(rt.p.values), rt.vpmin.values, left=rt.vpmin.min(),
right=rt.vpmin.max()) # Interpolate Minimum
vpmaxs = np.interp(np.log(data.p.values), np.log(rt.p.values), rt.vpmax.values, left=rt.vpmax.min(),
right=rt.vpmax.max()) # Interpolate Maximum
vpsat = vpfunc(data[tvar].values)
if hasnames(data, rvar):
vp = data[rvar].values * vpsat # Convert r,t to vp
logic = ((vp < vpmins) | (vp > vpmaxs)) & np.isfinite(vp)
data['qual'] = np.where(logic, data.qual.values + 'R', data.qual.replace('R', '').values) # FLAG: R (?)
journal(funcid + "rel. humidity (%s) available (replace: %s)" % (rvar, replace), report, verbose)
if replace:
vp = np.where(logic, np.nan, vp) # Apply? BAD, GOOD
else:
vp = np.full(data[tvar].shape, np.nan)
if hasnames(data, qvar):
qvp = sh2vap(data[qvar].values, data['p'].values) # only a formula no approximation
logic = ((qvp < vpmins) | (qvp > vpmaxs)) & np.isfinite(qvp)
data['qual'] = np.where(logic, data.qual.values + 'Q', data.qual.replace('Q', '').values) # FLAG: R (?)
journal(funcid + "spec. humidity (%s) available (replace: %s)" % (qvar, replace), report, verbose)
if replace:
qvp = np.where(logic, np.nan, qvp) # Apply? BAD, GOOD
# Fill Gaps
logic = (np.isfinite(qvp) & (~np.isfinite(vp))) # GOOD, BAD
vp = np.where(logic, qvp, vp) # UPDATE, OLD
if hasnames(data, dpdvar):
dvp = vpfunc((data[tvar] - data[dpdvar])) # Dewpoint -> vp
logic = ((dvp < vpmins) | (dvp > vpmaxs)) & np.isfinite(dvp)
data['qual'] = np.where(logic, data.qual.values + 'D', data.qual.replace('D', '').values) # FLAG: R (?)
journal(funcid + "DPD (%s) available (replace: %s)" % (dpdvar, replace), report, verbose)
if replace:
dvp = np.where(logic, np.nan, dvp) # Apply? BAD, GOOD
# fill gaps
logic = (np.isfinite(dvp) & (~np.isfinite(vp))) # GOOD, BAD
vp = np.where(logic, dvp, vp) # UPDATE, OLD
############################################################################
#
# Convert VP to RH
#
data[rvar] = vp / vpsat # rel. Humidity
logic = ((data[rvar].values < r_absmin) | (data[rvar].values > r_absmax)) & np.isfinite(
data[rvar].values) # Range? BAD, GOOD
data['qual'] = np.where(logic, data.qual.values + 'R', data.qual.replace('R', '').values) # FLAG: R
if replace:
data[rvar] = np.where(logic, np.nan, data.r.values) # Apply? BAD, GOOD
############################################################################
#
# Unique Flags
#
data['qual'] = unique_flags(data['qual'])
############################################################################
#
# Pressure
#
if remove_pressure:
del data['p']
if not inplace:
return data
