def load_det_cape_cin(datadir, date, t, return_array=False):
topdir = datadir + '/' + date + '/'
gribfn = gribpref + t + precsuf
detfn = topdir + 'det/' + gribfn
capefobj = getfobj(detfn, fieldn='CAPE_ML_S')
cinfobj = getfobj(detfn, fieldn='CIN_ML_S')
# Minus one hour
# gribfnm1 = gribpref + ddhhmmss(ddhhmmss_strtotime(t) -
# timedelta(hours = 1)) + precsuf
# detfnm1 = topdir + 'det/' + gribfnm1
# detfobjm1 = getfobj(detfnm1, fieldn = 'TOT_PREC')
# detfobj.data = detfobj.data - detfobjm1.data
if return_array:
return capefobj.data, cinfobj.data
else:
return capefobj, cinfobj
def load_det_da(datadir, t, return_array=False):
detfn = datadir + gribpref_da + t + precsuf_da + '.det'
detfobj = getfobj(detfn, fieldn='TOT_PREC_S')
if return_array:
return detfobj.data
else:
return detfobj
def load_det(datadir, date, t, return_array=False):
topdir = datadir + '/' + date + '/'
gribfn = gribpref + t + precsuf
detfn = topdir + 'det/' + gribfn
detfobj = getfobj(detfn, fieldn='PREC_PERHOUR')
# Minus one hour
# gribfnm1 = gribpref + ddhhmmss(ddhhmmss_strtotime(t) -
# timedelta(hours = 1)) + precsuf
# detfnm1 = topdir + 'det/' + gribfnm1
# detfobjm1 = getfobj(detfnm1, fieldn = 'TOT_PREC')
# detfobj.data = detfobj.data - detfobjm1.data
if return_array:
return detfobj.data
else:
return detfobj
def _calc_cape(obj):
"""
TODO
make type of Cosmo file clever!
"""
outint = 60
HH = cu.derive.hl_to_fl(pwg.getfobj(obj.cfile, 'HH').data)
#P0 = cosmo_ref_p(HH) / 100. # hPa
# Create new filelist
newlist = []
for trjfn in [obj.trjfiles[0]]:
newfn = trjfn.rstrip('.nc') + '_CAPE' + '.nc'
newlist.append(newfn)
os.system('cp ' + trjfn + ' ' + newfn)
for fn in [newlist[0]]:
print 'Open file:', fn
rootgrp = nc.Dataset(fn, 'a')
tarray = rootgrp.variables['time'][:]
lonmat = rootgrp.variables['longitude'][:, :]
latmat = rootgrp.variables['latitude'][:, :]
pmat = rootgrp.variables['P'][:, :]
tempmat = rootgrp.variables['T'][:, :]
qvmat = rootgrp.variables['QV'][:, :]
newcape = rootgrp.createVariable('CAPE', 'f4', ('time', 'id'))
newcin = rootgrp.createVariable('CIN', 'f4', ('time', 'id'))
# Retrieve trajectory start time
trjstart = rootgrp.variables['time'][0] / 60 # In mins
for t in range(tarray.shape[0]):
# Only interpolate if outint
if (rootgrp.variables['time'][t] / 60) % outint == 0:
# Get COSMO Index
icosmo = int((t * obj.dtrj + trjstart) / obj.dacosmo)
print obj.afiles[icosmo]
# Retrieve COSMO fields
print 'get cosmo vars'
T = pwg.getfobj(obj.afiles[icosmo], 'T')
clons = T.rlons[0, :]
clats = T.rlats[:, 0]
TC = T.data - 273.15 # Convert to Celcius
#try:
PS = pwg.getfobj(obj.afiles[icosmo], 'PS').data / 100. # hPa
#PP = pwg.getfobj(obj.afiles[icosmo], 'PP').data / 100. # hPa
#PS = P0 + PP
print 'calc td'
QV = pwg.getfobj(obj.afiles[icosmo], 'QV')
e = cu.thermodyn.MixR2VaporPress(QV.data, PS * 100.)
TD = cu.thermodyn.DewPoint(e)
TD = np.nan_to_num(TD) # nan to zero
TD = np.where(TD == 0., -150., TD) # zero to -150 C
# Filter COSMO fields
for itrj in range(lonmat.shape[1]):
print itrj
# Get parcel variables
lontrj = lonmat[t, itrj]
lattrj = latmat[t, itrj]
ptrj = pmat[t, itrj]
temptrj = tempmat[t, itrj] - 273.15
qvtrj = qvmat[t, itrj]
etrj = cu.thermodyn.MixR2VaporPress(qvtrj, ptrj * 100.)
tempdtrj = cu.thermodyn.DewPoint(etrj)
# Get closest lat lon from cosmo
lonid = np.abs(clons - lontrj).argmin()
latid = np.abs(clats - lontrj).argmin()
mydata = dict(zip(('hght','pres','temp','dwpt'),
(HH[:, latid, lonid],
PS[:, latid, lonid],
TC[:, latid, lonid],
TD[:, latid, lonid])))
# Initialize sounding
S = SkewT.Sounding(soundingdata=mydata)
# calculate CAPE and CIN
try:
trjparcel = (ptrj, temptrj, tempdtrj, 'parcel')
P_lcl, P_lfc, P_el, CAPE, CIN = S.get_cape(*trjparcel)
except AssertionError:
print ptrj, temptrj, tempdtrj
print mydata
x = breakbreak
#print mydata
print ptrj, temptrj, tempdtrj
print P_lcl, P_lfc, P_el, CAPE, CIN
# Write new value
newcape[t, itrj] = CAPE
newcin[t, itrj] = CIN
else:
newcape[t, :] = np.nan
newcin[t, :] = np.nan
rootgrp.close()
return newlist
def _interpolate_3d(obj, varname, outint = 60):
"""
TODO
"""
# Get rotated lon/lat and height fields for interpolation
hhobj = pwg.getfobj(obj.cfile, 'HH')
clons = hhobj.rlons[0, :]
clats = hhobj.rlats[:, 0]
chhfield = hhobj.data
hlevhh = []
# Use cu.derive.hl_to_fl
for lev in range(chhfield.shape[0]-1):
hlevhh.append((chhfield[lev] + chhfield[lev+1]) / 2)
hlevhh = np.array(hlevhh)
# Create new filelist
newlist = []
for trjfn in obj.trjfiles:
newfn = trjfn.rstrip('.nc') + '_' + varname + '.nc'
newlist.append(newfn)
os.system('cp ' + trjfn + ' ' + newfn)
# Iterate over trajectory files
for fn in newlist:
print 'Opening file:', fn
# Open Trajectory file
rootgrp = nc.Dataset(fn, 'a')
# Read position matrices
lon = rootgrp.variables['longitude'][:, :]
lat = rootgrp.variables['latitude'][:, :]
z = rootgrp.variables['z'][:, :]
# Allocate new netCDF array
newvar = rootgrp.createVariable(varname, 'f4', ('time', 'id'))
# Retrieve trajectory start time
trjstart = rootgrp.variables['time'][0] / 60 # In mins
# Iterate over trj times
for itrj in range(lon.shape[0]):
# Only interpolate if outint
if (rootgrp.variables['time'][itrj] / 60) % outint == 0:
# Get cosmo index
icosmo = int((itrj * obj.dtrj + trjstart) / obj.dacosmo)
print icosmo, itrj, rootgrp.variables['time'][itrj] / 60,obj.afiles[icosmo]
field = pwg.getfield(obj.afiles[icosmo], varname)
# Iterate over individual trajectories
for trjid in range(lon.shape[1]):
# NOTE: Nearest Neighbor method!
ilon = lon[itrj, trjid]
ilat = lat[itrj, trjid]
iz = z[itrj, trjid]
# Get nearest lat, lon
lonid = np.abs(clons - ilon).argmin()
latid = np.abs(clats - ilat).argmin()
# Get closest vertical coordinate
zid = np.abs(hlevhh[:, latid, lonid] - iz).argmin()
# Get nearest neighbor
newvar[itrj, trjid] = field[zid, latid, lonid]
else:
newvar[itrj, :] = np.nan
# Clean up
rootgrp.close()
return newlist
'PSP_TL500': 'orange',
'DA_REF': 'blue',
'DA_REF_TL500': 'cyan',
'DA_PSP_TL500': 'red',
'DA_PSPv2_TL500': 'magenta',
'DA_PSP': 'maroon',
}
expid_str = ''
for i, expid in enumerate(args.expid):
expid_str += expid + '_'
print 'Expid:', expid
# Load precipitation
print 'Load precipitation'
precfn = datadir + expid + '/' + args.date + '/det/' + gribpref + t + precsuf
precfobj = getfobj(precfn, fieldn='PREC_PERHOUR')
maskfobj = deepcopy(precfobj)
maskfobj.data = mask
# Plot precipitation plus box
print 'Plot precipitation'
plt.sca(axmat1[0, i])
cf, tmp = ax_contourf(
axmat1[0, i],
precfobj,
Basemap_drawrivers=False,
npars=0,
nmers=0,
colors=cmPrec,
pllevels=levelsPrec,
