def from_era(ident, alt=0, lon=0, lat=0, savename='extract_ERA', verbose=0):
""" Updated Version of profiles and surface
# Can read ERA data from retrieved and converted MARS Requests
ERADIR
Parameters
----------
ident str/int/float Radiosonde ID
alt int/float Radiosonde Station Altitude
lon int/float Radiosonde Station Longitude
lat int/float Radiosonde Station Latitude
savename str Extract savename
verbose int verboseness
Returns
-------
"""
from raso.support import print_verbose, panel_to_database
from raso.met.conversion import sh2ppmv, sh2vap, bolton_inverse, vap2sh
from raso.met.esat_functions import foeewm
from raso.config import rasodir # eradir
from raso.qc import profile_limits
# read new era file
if isinstance(ident, (int, float)):
ident = "%06d" % ident
filename = rasodir + "/%s/%s.h5" % (ident, savename)
cnames = ['t2m', 'q2m', 'msl', 'u10', 'v10', 'fetch', 'skt', 'FASTEM_1', 'FASTEM_2', 'FASTEM_3',
'FASTEM_4', 'FASTEM_5', 'surf_type', 'water_type', 'elev', 'lat', 'lon', 'sat_zen', 'sat_azi',
'sol_zen', 'sol_azi', 'cltp', 'clf']
surface = {} # pd.DataFrame(columns=cnames)
era = {}
if not os.path.isfile(filename):
raise IOError('File not found: %s' % filename)
with pd.HDFStore(filename, 'r') as store:
for ikey in store.keys():
iname = ikey.replace('/', '')
if ikey[1:] in ['t', 'q', 'o3']:
# profile data
tmp = store[ikey]
tmp.index.name = 'date'
tmp.columns.name = 'p'
# tmp = tmp.reset_index().drop_duplicates('date').set_index('date') # remove duplicates
era[iname] = tmp # DataFrame (time x p)
else:
# surface data
if iname == 'sp':
surface['msl'] = store[ikey][0] / 100. # [dates] / 100. # hPa
elif iname == 'ci':
# sea ice cover
surface['surf_type'] = store[ikey][0]
elif iname == 'u10':
surface['u10'] = store[ikey][0]
elif iname == 'v10':
surface['v10'] = store[ikey][0]
else:
surface[iname] = store[ikey][0]
era = pd.Panel(era)
era = panel_to_database(era, remove=False)
# ERA can have negative Q values !?
qerror = era.query('q<=0').q.count()
print_verbose("[%s] Found %d negative Q-values!" % (ident, qerror), verbose)
# Interpolate limits
rt = profile_limits(tohpa=True, simple_names=True)
rt['qmin'] = vap2sh(rt.vpmin.values, rt.p.values) # both missing hPa factor
qmins = np.interp(np.log(era.p.values), np.log(rt.p.values*100.), rt.qmin.values, left=rt.qmin.min(), right=rt.qmin.max()) # Interpolate Minimum Q
era['q'] = np.where(era.q < qmins, qmins, era.q) # replace with RTTOV limit
# Make a dataframe
surface = pd.DataFrame(surface)
surface['q2m'] = sh2ppmv(era.query('p==100000')['q'], 100000.) # ppmv
surface['fetch'] = 100000. # m
# FASTEM Surface model Parameters:
surface['FASTEM_1'] = 3.0
surface['FASTEM_2'] = 5.0
surface['FASTEM_3'] = 15.0
surface['FASTEM_4'] = 0.1
surface['FASTEM_5'] = 0.3
# surface properties
if 'surf_type' in surface.columns:
if surface['surf_type'].isnull().any(0):
if surface['surf_type'].count() > 0:
surface['surf_type'].fillna(method='bfill') # backwards fill
surface['surf_type'].fillna(method='ffill') # forward fill
else:
surface['surf_type'] = 0. # surface type (0=land, 1=sea, 2=sea-ice)
else:
surface['surf_type'] = 0. # surface type (0=land, 1=sea, 2=sea-ice)
surface['water_type'] = 0. # water type (0=fresh, 1=ocean)
surface['elev'] = alt / 1000. # ELEVATION km
surface['lat'] = lat # LAT
surface['lon'] = lon # LON
surface['sat_azi'] = 0. # Satellite Azimuth Angle
surface['sat_zen'] = 53.1 # OSCAR , SSMIS
surface['sol_zen'] = 0. # Solar Zenith Angle
surface['sol_azi'] = 0. # Solar Azimuth Angle
surface['cltp'] = 500. # Cloud top pressure
surface['clf'] = 0. # Cloud fraction
# right order
surface = surface[cnames].copy()
# CONVERT TO WATER VAPOR PRESSURE in Pa
era['vp'] = sh2vap(era['q'], era['p']) # formula
era['td'] = bolton_inverse(era.vp.values) # default method from Murphy and Koop (iterative)
logic = era['td'] > era['t'] # impossible
era.loc[:, 'td'] = np.where(logic, era['t'].values, era['td'].values)
era.loc[:, 'vp'] = np.where(logic, foeewm(era['t'].values), era['vp'].values) # ? fixes some problems ?
return surface, era
