def load_di_icec(dte):
dte1 = datetime.datetime(dte.year, dte.month, dte.day, 0)
dte2 = dte1 + datetime.timedelta(days=1)
try:
icec = opfc.load('icec', dte1, model='global')
tmp = opfc.load('icec', dte2, model='global')
tmp.attributes = icec.attributes
icec = iris.cube.CubeList((icec, tmp)).merge_cube()
icec = icec.interpolate([('time', dte)], iris.analysis.Linear())
except:
icec = opfc.load('icec',
dte1 - datetime.timedelta(days=1),
model='global')
return icec
def load_recent_temperatures(
dte, # current time (datetime.datetime)
before=5, # use this many days before
after=5): # use this many days after
stt = dte - datetime.timedelta(days=before)
ent = dte + datetime.timedelta(days=after)
ct = stt
tcount = 0
dcount = 0
tavg = None
davg = None
while ct < ent:
try:
ttmp = opfc.load('air.2m', ct, model='global')
tcount += 1
if tavg is None:
tavg = ttmp.copy()
else:
tavg.data += ttmp.data
if ct.hour == dte.hour:
dcount += 1
if davg is None:
davg = ttmp.copy()
else:
davg.data += ttmp.data
except:
ct += datetime.timedelta(hours=1)
continue
ct += datetime.timedelta(hours=1)
tavg.data /= tcount
davg.data /= dcount
davg.data -= tavg.data
return (tavg, davg)
def load_li_precip(dte): # Time to plot (datetime.datetime)
if dte.minute < 30:
prevt = dte - datetime.timedelta(minutes=30 + dte.minute)
else:
prevt = dte - datetime.timedelta(minutes=dte.minute - 30)
nextt = prevt + datetime.timedelta(hours=1)
prevp = opfc.load('prate_a', prevt, model='global')
prevp.data += 1.0e-7
prevp.data = numpy.log(prevp.data)
nextp = opfc.load('prate_a', nextt, model='global')
nextp.data += 1.0e-7
nextp.data = numpy.log(nextp.data)
w = (dte - prevt).total_seconds() / (nextt - prevt).total_seconds()
precip = prevp.copy()
precip.data = prevp.data * (1 - w) + nextp.data * w
precip.data += 15
precip.data /= 11
precip.data[precip.data < 0] = 0
precip.data[precip.data > 1] = 1
return precip
# Damp the latitude variation
#t2m=damp_lat(t2m,factor=0.25)
u10m = twcr.load('uwnd.10m', dte, version='2c')
u10m = u10m.extract(iris.Constraint(member=1))
v10m = twcr.load('vwnd.10m', dte, version='2c')
v10m = v10m.extract(iris.Constraint(member=1))
precip = twcr.load('prate', dte, version='2c')
precip = precip.extract(iris.Constraint(member=1))
prmsl = twcr.load('prmsl', dte, version='2c')
prmsl = prmsl.collapsed('member', iris.analysis.STD_DEV)
precip = normalise_precip(precip)
dte1 = datetime.datetime(2019, 2, 25, 12)
try:
mask = opfc.load('lsmask', dte1, model='global')
except:
mask = opfc.load('lsmask',
dte1 - datetime.timedelta(days=1),
model='global')
# Define the figure (page size, background color, resolution, ...
fig = Figure(
figsize=(19.2, 10.8), # Width, Height (inches)
dpi=100,
facecolor=(0.5, 0.5, 0.5, 1),
edgecolor=None,
linewidth=0.0,
frameon=False, # Don't draw a frame
subplotpars=None,
tight_layout=None)
dte = datetime.datetime(args.year, args.month, args.day, int(args.hour),
int(args.hour % 1 * 60))
# In the temperature field, damp the diurnal cycle, and
# boost the short-timescale variability. Load the
# recent data to calculate this.
stt = dte - datetime.timedelta(days=5)
ent = dte + datetime.timedelta(days=5)
ct = stt
tcount = 0
dcount = 0
tavg = None
davg = None
while ct < ent:
try:
ttmp = opfc.load('air.2m', ct, model='global')
tcount += 1
if tavg is None:
tavg = ttmp.copy()
else:
tavg.data += ttmp.data
if ct.hour == dte.hour:
dcount += 1
if davg is None:
davg = ttmp.copy()
else:
davg.data += ttmp.data
except:
ct += datetime.timedelta(hours=1)
continue
ct += datetime.timedelta(hours=1)
type=str,required=False)
args = parser.parse_args()
if not os.path.isdir(args.opdir):
os.makedirs(args.opdir)
dte = datetime.datetime(args.year, args.month, args.day, int(args.hour),
int(args.hour % 1 * 60))
# In the temperature field, damp the diurnal cycle, and
# boost the short-timescale variability. Load the
# recent data to calculate this.
(tavg, davg) = load_recent_temperatures(dte)
# Load the model data
t2m = opfc.load('air.2m', dte, model='global')
# Remove the diurnal cycle
t2m.data -= davg.data
# Double the synoptic variability
t2m.data += (t2m.data - tavg.data) * 1
# Add back a reduced diurnal cycle
t2m.data += davg.data * 0.25
u10m = opfc.load('uwnd.10m', dte, model='global')
v10m = opfc.load('vwnd.10m', dte, model='global')
# We're plotting log precip - so interpolate in log space too
precip = load_li_precip(dte)
mask = iris.load_cube("%s/fixed_fields/land_mask/opfc_global_2019.nc" %
os.getenv('SCRATCH'))
int(args.hour % 1 * 60))
# Scale down the latitudinal variation in temperature
def damp_lat(sst, factor=0.5):
s = sst.shape
mt = numpy.min(sst.data)
for lat_i in range(s[0]):
lmt = numpy.mean(sst.data[lat_i, :])
if numpy.isfinite(lmt):
sst.data[lat_i, :] -= (lmt - mt) * factor
return (sst)
# Load the Meto data
sst = opfc.load('tsurf', dte, model='global')
icec = opfc.load('icec', dte, model='global')
orog = opfc.load('orog', dte, model='global')
mask = opfc.load('lsmask', dte, model='global')
# Apply the LS mask to turn tsurf into SST
sst.data[mask.data >= 0.5] = numpy.nan
sst.data = numpy.ma.array(sst.data, mask=mask.data > 0.5)
sst = damp_lat(sst, factor=0.25)
# Define the figure (page size, background color, resolution, ...
fig = Figure(
figsize=(19.2, 10.8), # HD video
dpi=100,
facecolor=(0.5, 0.5, 0.5, 1),
return (sst)
# In the temperature field, damp the diurnal cycle, and
# boost the short-timescale variability. Load the
# recent data to calculate this.
stt = dte - datetime.timedelta(days=5)
ent = dte + datetime.timedelta(days=5)
ct = stt
tcount = 0
dcount = 0
tavg = None
davg = None
while ct < ent:
try:
ttmp = opfc.load('air.2m', ct, model='global')
tcount += 1
if tavg is None:
tavg = ttmp.copy()
else:
tavg.data += ttmp.data
if ct.hour == dte.hour:
dcount += 1
if davg is None:
davg = ttmp.copy()
else:
davg.data += ttmp.data
except:
ct += datetime.timedelta(hours=1)
continue
ct += datetime.timedelta(hours=1)
import cartopy.crs as ccrs
from pandas import qcut
# Scale down the latitudinal variation in temperature
def damp_lat(sst,factor=0.5):
s=sst.shape
mt=numpy.min(sst.data)
for lat_i in range(s[0]):
lmt=numpy.mean(sst.data[lat_i,:])
if numpy.isfinite(lmt):
sst.data[lat_i,:] -= (lmt-mt)*factor
return(sst)
# Load the Meto SST
sst=opfc.load('tsurf',dte,model='global')
# And the sea-ice
icec=opfc.load('icec',dte,model='global')
# And the orography
orog=iris.load_cube("%s/fixed_fields/orography/opfc_global_2019.nc" %
os.getenv('SCRATCH'))
# And the land-sea mask
mask=iris.load_cube("%s/fixed_fields/land_mask/opfc_global_2019.nc" %
os.getenv('SCRATCH'))
# Apply the LS mask to turn tsurf into SST
sst.data[mask.data>=0.5]=numpy.nan
sst.data=numpy.ma.array(sst.data,
mask=mask.data>0.5)
sst=damp_lat(sst,factor=0.25)