obs_assimilate.value = obs_assimilate.value * 100 # to Pa
# 20CRv3 data
prmsl = twcr.load('prmsl', dte, version='4.5.1')
# Get the observations used in 20CRv3
obs_t = twcr.load_observations_fortime(dte, version='4.5.1')
# Filter to those assimilated and near the UK
obs_s = obs_t.loc[((obs_t['Latitude'] > 0) & (obs_t['Latitude'] < 90)) & (
(obs_t['Longitude'] > 240) | (obs_t['Longitude'] < 100))].copy()
# Update mslp by assimilating Fort William ob.
prmsl2 = DIYA.constrain_cube(
prmsl,
lambda dte: twcr.load('prmsl', dte, version='4.5.1'),
obs=obs_assimilate,
obs_error=100,
random_state=RANDOM_SEED,
model=sklearn.linear_model.LinearRegression(),
lat_range=(20, 85),
lon_range=(280, 60))
mg.observations.plot(ax_one, obs_s, radius=0.15)
mg.observations.plot(ax_one,
obs_assimilate,
radius=0.15,
facecolor='red',
lat_label='latitude',
lon_label='longitude')
# For each ensemble member, make a contour plot
mg.pressure.plot(ax_one,
numpy.array((fw_extent[0] * 100, fw_extent[1] * 100)).reshape(-1, 1))
ax_scp.plot(fw_extent, pre / 100, color='black', lw=3)
ax_scp2 = fig.add_axes([0.55, 0.07, 0.43, 0.89])
# x-axis
ax_scp2.set_xlim(fw_extent)
ax_scp2.set_xlabel('original MSLP at Fort William (hPa)')
# y-axis
ax_scp2.set_ylim(sw_extent)
ax_scp2.set_ylabel(
'MSLP at Stornoway after assimilating Fort William observation (hPa)')
# Adjust the mslp by assimilating the FW ob
ST_adjusted = DIYA.constrain_point(ens_ST.data,
ens_FW.data.reshape(-1, 1),
model=model,
obs=fwi * 100)
ax_scp2.plot((fwi, fwi), sw_extent, color='red', lw=3)
ax_scp2.scatter(
ens_FW.data / 100,
ST_adjusted / 100,
500, # Point size
'blue', # Color
marker='.',
edgecolors='face',
linewidths=0.0,
alpha=1,
zorder=5)
# Output as png
# Month to show
year = 1903
month = 10
dte = datetime.datetime(year, month, 1, 0)
# Get the DWR observations within +- 15 hours
obs = DWR.load_observations(
'prmsl', dte, dte + relativedelta(days=1) - relativedelta(minutes=1))
# sort them from north to south
obs = obs.sort_values(by='latitude', ascending=True)
# Get the list of stations - preserving order
stations = list(collections.OrderedDict.fromkeys(obs.loc[:, 'name']).keys())
# Add quality control flags
obs['plausible'] = DIYA.qc_plausible_range(obs, min=880, max=1060)
obs['background'] = DIYA.qc_first_guess(obs, nsd=3, osd=1, version='2c')
# Portrait page
fig = Figure(
figsize=(15, len(stations)), # Width, Height (inches)
dpi=100,
facecolor=(0.88, 0.88, 0.88, 1),
edgecolor=None,
linewidth=0.0,
frameon=False,
subplotpars=None,
tight_layout=None)
canvas = FigureCanvas(fig)
font = {
'family': 'sans-serif',
obs = DWR.load_observations('prmsl', dte - datetime.timedelta(hours=0.1),
dte + datetime.timedelta(hours=0.1))
# Throw out the ones already used in 20CRv3
obs = obs[~obs['name'].isin(['ABERDEEN', 'VALENCIA', 'JERSEY'])]
obs_assimilate = obs # Use deep copy instead?
# Convert to normalised value same as reanalysis
obs_assimilate.value = obs_assimilate.value * 100
# Update mslp by assimilating all obs.
prmsl2 = DIYA.constrain_cube(
prmsl,
lambda dte: twcr.load('prmsl', dte, version='4.5.1'),
obs=obs_assimilate,
obs_error=obs_error,
random_state=RANDOM_SEED,
model=model,
lat_range=(20, 85),
lon_range=(280, 60))
mg.observations.plot(ax_centre, obs_s, radius=0.15)
mg.observations.plot(ax_centre,
obs_assimilate,
radius=0.15,
facecolor='red',
lat_label='latitude',
lon_label='longitude')
# PRMSL spaghetti plot
mg.pressure.plot(ax_centre,
obs['dtm'].append(mslp['dtm'].values[0])
obs = pandas.DataFrame(obs)
# Throw out the suspected duds
obs = obs[~obs['name'].isin(skip_stations)]
# Throw out the specified station
if args.omit is not None:
obs = obs[~obs['name'].isin([args.omit])]
# Convert to normalised value same as reanalysis
obs.value = obs.value * 100
# Update mslp by assimilating all obs.
prmsl2 = DIYA.constrain_cube(
prmsl,
lambda dte: twcr.load('prmsl', dte, version='4.5.1'),
obs=obs,
obs_error=obs_error,
random_state=RANDOM_SEED,
model=model,
lat_range=(-80, 10),
lon_range=(250, 350))
dumpfile = "%s/%04d%02d%02d%02d.pkl" % (opdir, args.year, args.month, args.day,
args.hour)
if args.omit is not None:
dumpfile = "%s/%04d%02d%02d%02d_%s.pkl" % (opdir, args.year, args.month,
args.day, args.hour, args.omit)
pickle.dump(prmsl2, open(dumpfile, "wb"))