def read_ECMWF(date):
""" Script reading the ECMWF data.
Not a generator as this is synchronized with the 1h part slice.
The data are assumed valid over the 1h period that follows the timestamp.
This is quite OK for UDR as this quantity is defined as a mean/accumuation over
this one-hour period.
Cloud-cover is from analysis, therefore as an instantaneous map, but varies less rapidly
than the UDR. """
dat = ECMWF('STC',date)
dat._get_var('T')
dat.attr['dlo'] = (dat.attr['lons'][-1] - dat.attr['lons'][0]) / (dat.nlon-1)
dat.attr['dla'] = (dat.attr['lats'][-1] - dat.attr['lats'][0]) / (dat.nlat-1)
if dat.attr['Lo1']>dat.attr['Lo2']:
dat.attr['Lo1'] = dat.attr['Lo1']-360.
#@@ test
# print('LoLa ',dat.attr['Lo1'],dat.attr['La1'],dat.attr['dlo'],dat.attr['dla'],\
# dat.attr['levs'][0],len(dat.attr['levs']))
#@@ end test
dat._get_var('UDR')
#dat._get_var('CC')
dat._mkp()
dat._mkrho()
dat.var['UDR'] /= dat.var['RHO']
dat.close()
return dat
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)
dats[i] = datr.interpolPT(pts,
varList=varList,
latRange=(20, 50),
lonRange=(-30, 90))
elif date >= datetime(2017, 9, 1, 0):
dats[i] = dat.interpolPT(pts,
varList=varList,
latRange=(30, 60),
lonRange=(0, 120))
else:
datr = dat.shift2west(-180)
dats[i] = datr.interpolPT(pts,
varList=varList,
latRange=(40, 70),
lonRange=(-40, 80))
dat.close()
date += timedelta(hours=6)
i += 1
#%%
if predates:
outbak = 'ERA5-extract-A-PT5-pre.pkl'
outfile = 'ERA5-extract-A-PT5-new.pkl'
with gzip.open(outbak, 'wb') as f:
pickle.dump(dats, f)
with gzip.open('ERA5-extract-A-PT5.pkl', 'rb') as f:
dats2 = pickle.load(f)
for i2 in range(len(dats2)):
dats[i + i2] = dats2[i2]
elif postdates:
outfile = 'ERA5-extract-A-PT5-post.pkl'
if args.month2 is not None: month2 = args.month2
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')
data, theta_level) # First interpolation to the theta level
delta_pf = ENint.total_seconds()
numpart = 0
# loop on the EN time
while date_p >= date_beg:
T_f = T_p.copy()
P_f = P_p.copy()
if not quiet:
print('load new EN file ', date_p)
data = ECMWF('FULL-EA', date_p)
data._get_var('T')
data._mkp()
data._mkthet()
T_p, P_p = interp3d_thet(data, theta_level)
data.close()
while date_current >= date_p:
# interpol the temperature in time
#print('date ',date_current)
delta_f = date_f - date_current
delta_p = date_current - date_p
T_current = (delta_p.total_seconds() / delta_pf) * T_f + (
delta_f.total_seconds() / delta_pf) * T_p
P_current = (delta_p.total_seconds() / delta_pf) * P_f + (
delta_f.total_seconds() / delta_pf) * P_p
# fill part0
ir_start = -int((date_end - date_current).total_seconds())
idx1 = numpart
numpart += bloc_size
part0['x'] = np.append(part0['x'], xg)
dtt = (mean_date-date1).total_seconds()/10800
# test whether the selected part of the orbit is totally in the interval
enter_date = ref_date + timedelta(seconds=data['time'][0])
exit_date = ref_date + timedelta(seconds=data['time'][-1])
if enter_date < date1:
print('beginning of orbit out by',date1-enter_date,dtt)
if exit_date > date2:
print('beginning of orbit out by',exit_date-date2,dtt)
# get ECMWF data for the orbit and generate a curtain over the retained segment
# read data for first bracketting date
dat1 = ECMWF('FULL-EI',date1)
dat1._get_T()
dat1._mkp()
dat1._mkz()
dat1.close()
# generate a curtain along the track for first date
sect1 = dat1.interpol_orbit(data['longitude'],data['latitude'],varList=['P','T','Z'])
del dat1
# read data for second bracketting date
dat2 = ECMWF('FULL-EI',date2)
dat2._get_T()
dat2._mkp()
dat2._mkz()
dat2.close()
# generate a curtain along the track for scond date
sect2 = dat2.interpol_orbit(data['longitude'],data['latitude'],varList=['P','T','Z'])
del dat2
sect = curtain()
#sect.x = sect1.x
#sect.y = sect1.y
part0['y'] = np.empty(0,dtype=float)
part0['t'] = np.empty(0,dtype=float)
part0['p'] = np.empty(0,dtype=float)
part0['idx_back'] = np.empty(0,dtype=int)
numpart = 0
# Loop on the values of theta
for targetTheta in [theta-dTheta, theta, theta+dTheta]:
# get the first time
date1 = date + timedelta(hours=int(begSeq/3600))
predat = ECMWF('STC',date1)
predat._get_T()
predat._mkp()
predat._mkthet()
(fT1,fP1) = interp3d_thet(predat,targetTheta)
predat.close()
#for n in range(nInt):
# finds the boundary
id1 = np.where(utc == begSeq)[0][0]
id2 = np.where(utc == endSeq)[0][0]
# get lat, lon and release time
yy = data.var['Lat'][id1:id2+1]
xx = data.var['Long'][id1:id2+1]
ir_start = utc[id1:id2+1] - 86400
ir_start = ir_start.astype(np.int)
#weigthing factor
w2 = (utc[id1:id2+1] % 3600) / 3600
# Bloc_size
block_size = len(xx)*nsel