def read_ERA5(t0, dtRange, pre=False, vshift=0):
""" Generator reading the ERA5 data.
The loop is infinite; ERA5 data are called when required until the end of
the parcel loop.
The output contain a function that gives the pressure of the tropo^pause as
a function of lon and lat in the FullAMA domain"""
# initial time for internal loop
current_time = t0
while True:
try:
if (current_time in blacklist): raise BlacklistError()
# defining a new object is necessary to avoid messing dat if an error occurs
dat = ECMWF('FULL-EA', current_time)
dat._get_T()
dat._mkp()
dats = dat.shift2west(-179)
# extraction in a domain that encompasses FullAMA
datr0 = dats.extract(latRange=[-5, 55],
lonRange=[-15, 165],
varss=['P', 'T'])
del dat, dats
datr0._WMO()
datr0.fP = RegularGridInterpolator(
(np.arange(-5, 56), np.arange(-15, 166)),
datr0.d2d['pwmo'],
bounds_error=True)
print('get ERA5 tropopause for ', current_time)
try:
del datr
except:
pass # intended for the first pass when datr undefined
datr = datr0 # datr as a view of datr0
# We reproduce here the same sequence as for the satellite file
# although perhapsnot appropriate (the ERA5 data are instantaneous)
if pre:
datr.tf = current_time + dtRange
datr.ti = current_time
else:
datr.tf = current_time
datr.ti = current_time - dtRange
except BlacklistError:
print('blacklisted date for GridSat', current_time)
# extend the lease while keeping the old dat
datr.ti -= dtRange
except FileNotFoundError:
print('ERA5 file not found ', current_time)
# extend the lease while keeping the old dat
datr.ti -= dtRange
current_time -= dtRange
yield datr
def Pinterpol(date):
""" Read the ERA5 data and interpolate to the pressure levels. """
dat = ECMWF('FULL-EA', date, exp=['VOZ', 'QN'])
dat._get_T()
dat._get_var('O3')
dat._get_var('Q')
dat.close()
dat._mkp()
dat._mkz()
dat2 = dat.shift2west(-20)
dat3 = dat2.extract(lonRange=[-10, 160], latRange=[0, 50], varss='All')
dat3._WMO()
dat4 = dat3.interpolP(pressPa, varList=['Z', 'T', 'Q', 'O3'])
dat4.d2d = dat3.d2d
return (dat4)
dat._get_var('T')
dat._get_var('VO')
dat._get_var('O3')
dat._get_var('U')
dat._get_var('V')
dat._mkp()
dat._mkz()
dat._mkthet()
dat._mkpv()
if date >= datetime(2017, 10, 12, 0):
dats[i] = dat.interpolPT(pts,
varList=varList,
latRange=(5, 35),
lonRange=(100, 220))
elif date >= datetime(2017, 10, 3, 0):
datr = dat.shift2west(-180)
dats[i] = datr.interpolPT(pts,
varList=varList,
latRange=(20, 50),
lonRange=(-180, -60))
elif date >= datetime(2017, 9, 23, 0):
datr = dat.shift2west(-180)
dats[i] = datr.interpolPT(pts,
varList=varList,
latRange=(20, 50),
lonRange=(-120, 0))
elif date >= datetime(2017, 9, 11, 0):
datr = dat.shift2west(-180)
dats[i] = datr.interpolPT(pts,
varList=varList,
latRange=(20, 50),
if args.day1 is not None: day1 = args.day1
if args.day2 is not None: day2 = args.day2
if args.hour1 is not None: hour1 = args.hour1
if args.hour2 is not None: hour2 = args.hour2
date = datetime(year, month1, day1, hour1)
while date < datetime(year, month2, day2, hour2):
print('processing ', date)
outfile = date.strftime('TPP%y%m%d%H.hdf5')
fullname = os.path.join(maindir, date.strftime('%Y/%m'), outfile)
fdd = ECMWF('FULL-EA', date)
fdd._get_T()
fdd._mkp()
fdd.close()
fde = fdd.shift2west(-20)
fdf = fde.extract(lonRange=[-10, 160], latRange=[0, 50], varss='All')
fdf._CPT()
fdf._WMO()
tpp = {}
tpp['Twmo'] = fdf.d2d['Twmo']
tpp['pwmo'] = fdf.d2d['pwmo']
tpp['Tcold'] = fdf.d2d['Tcold']
tpp['pcold'] = fdf.d2d['pcold']
tpp['nlon'] = fdf.nlon
tpp['nlat'] = fdf.nlat
tpp['lats'] = fdf.attr['lats']
tpp['lons'] = fdf.attr['lons']
tpp['date'] = fdd.date
fl.save(fullname, tpp, compression='zlib')
date += timedelta(hours=3)
