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)
i = 0
else:
# Continuation
with gzip.open('ERA5-extract-PT5.pkl', 'rb') as f:
dats = pickle.load(f)
i = len(dats)
print('dats length ', len(dats))
date = day1
pts = list(np.arange(420, 610, 5))
varList = ['T', 'VO', 'O3', 'PV', 'Z', 'U', 'V']
while date < day2:
print(date)
dat = ECMWF('FULL-EA', date, exp='VOZ')
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)
part0['x'] = np.empty(0, dtype=float)
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)
# It is assumed that both date_end and date_beg are valid dates for the EN files
date_f = date_end
date_p = date_f - ENint
date_current = date_end
# first read and first interpolation
#data = read_ECMWF(ENdir,date_f)
# data is read from ERA5 data in the global domain at 1° horizontal resolution
# and longitude origin at 0°
data = ECMWF('FULL-EA', date_f)
data._get_var('T')
data._mkp(
) # Calculate pressure from surface pressure and hybrid coefficients
data._mkthet() # Calculate the potential temperature
# ACHTUNG: TO DO : ADD HERE A SHIFT IN LONGITUDE
# data -> data.shit2west(-179)
if not quiet:
print('load first EN file ', date_f)
T_p, P_p = interp3d_thet(
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()
from datetime import datetime
#import pickle,gzip
#import matplotlib.pyplot as plt
from os.path import join
from ECMWF_N import ECMWF
date = datetime(2017, 8, 23, 3)
dat = ECMWF('FULL-EA', date, exp='VOZ')
#date = datetime(2020,1,23,6)
#dat = ECMWF('OPZ',date)
#date = datetime(2017,8,10,0)
#dat = ECMWF('STC',date)
#%%
dat._get_T()
dat._get_var('VO')
dat._get_U()
dat._get_V()
dat._mkp()
dat._mkthet()
#%%
print('now call mkpv')
dat._mkpv()
#%%
#dats = dat.interpolPT([350,370,395,430],varList=['PV'],lonRange=(50,120),latRange=(0,50))
dats = dat.extract(varss='All', lonRange=(240, 330), latRange=(-80, -30))