# Close netcdf file f.close # Convert wind to geostrophic wind if compute_geostrophic_wind == 'true' : uin1,vin1 = wm.geostrophic_latlon(uin1, vin1, ghgtin1, lats, lons) uin2,vin2 = wm.geostrophic_latlon(uin2, vin2, ghgtin2, lats, lons) # Compute vertical voriticity zeta_a1 = wm.vertical_vorticity_latlon(uin1, vin1, lats, lons, 1) zeta_a2 = wm.vertical_vorticity_latlon(uin2, vin2, lats, lons, 1) # Make all fields to be plotted cyclic lonin = lons u_plot1, lons = um.addcyclic(uin1, lonin) v_plot1, lons = um.addcyclic(vin1, lonin) u_plot2, lons = um.addcyclic(uin2, lonin) v_plot2, lons = um.addcyclic(vin2, lonin) zeta_a1, lons = um.addcyclic(zeta_a1, lonin) zeta_a2, lons = um.addcyclic(zeta_a2, lonin) omegain, lons = um.addcyclic(omegain, lonin) # For the title levelh1 = levels_x1[0] / 100 # Convert level to hPa for title levelh2 = levels_x1[1] / 100 # Convert level to hPa for title # Set global figure properties golden = (np.sqrt(5)+1.)/2. figprops = dict(figsize=(8., 16./golden), dpi=128)
cbar_max_trth = 360
cint_trth = 2
cflevs_trth = np.arange(cbar_min_trth, cbar_max_trth, cint_trth)
cflevs_trth_ticks = np.arange(cbar_min_trth, cbar_max_trth, 4 * cint_trth)
base_cntr_slp = 1012
cint_slp = 2
cbar_min_slp = base_cntr_slp - 20 * cint_slp
cbar_max_slp = base_cntr_slp + 20 * cint_slp
cbar_max_slp = cbar_max_slp + (cint_slp / 2)
cflevs_slp = np.arange(cbar_min_slp, cbar_max_slp, cint_slp)
cflevs_slp_ticks = np.arange(cbar_min_slp, cbar_max_slp, 4 * cint_slp)
lonin = lons
trth, lons = um.addcyclic(trth, lonin)
# slp, lons = um.addcyclic(slp, lonin)
X, Y = np.meshgrid(lons, lats)
titletext1 = "Tropopause potential temperature valid %s at %s UTC" % (dt.strftime("%d %b %Y"), dt.strftime("%H00"))
# Set global figure properties
golden = (np.sqrt(5) + 1.0) / 2.0
figprops = dict(figsize=(8.0, 16.0 / golden), dpi=128)
# Figure 1
fig = plt.figure(figsize=(8.0, 16.0 / golden), dpi=128) # New figure
ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])
if map_projection == "ortho":
m = Basemap(projection="ortho", lat_0=50, lon_0=260, resolution="l", area_thresh=1000.0, ax=ax1)
slp = f.variables['MSLET_3_MSL_10'][record_numnow,::-1,:].squeeze()/10**2
except:
slp = f.variables['PRMSL_3_MSL_10'][record_numnow,::-1,:].squeeze()/10**2
lons = f.variables['lon_3'][:]
lats = f.variables['lat_3'][::-1] # Read in reverse direction
levs = f.variables['lv_ISBL3'][:]
f.close
if data_gridnum > -1:
if ( (data_gridnum != 11) & (data_gridnum != 12) ):
trth = wm.temp_to_theta(trtempin, trpresin)
lonin = lons
trth, lons = um.addcyclic(trth, lonin)
if plot_field == 'trtemp':
del trth
trth, dummy = um.addcyclic(trte, lonin)
slp, dummy = um.addcyclic(slp, lonin)
if data_gridnum != 11 :
X, Y = np.meshgrid(lons, lats)
else:
xlons, dummy = um.addcyclic(xlons, lonin)
xlats, dummy = um.addcyclic(xlats, lonin)
X = xlons
Y = xlats
if map_projection == 'ortho' :
lat_atm = infile.variables['lat'][:]
lon_atm = infile.variables['lon'][:]
ghgt_cntl = infile.variables['ghgt_cntl'][:]
ghgt_exp = infile.variables['ghgt_exp'][:]
slp_cntl = infile.variables['slp_cntl'][:]
slp_exp = infile.variables['slp_exp'][:]
trth_cntl = infile.variables['trth_cntl'][:]
trth_exp = infile.variables['trth_exp'][:]
infile.close()
ghgt_diff = ghgt_exp - ghgt_cntl
slp_diff = slp_exp - slp_cntl
trth_diff = trth_exp - trth_cntl
lonin = lon_atm
ghgt_diff, lons_atm = um.addcyclic(ghgt_diff, lonin)
trth_diff, lons_atm = um.addcyclic(trth_diff, lonin)
slp_diff, lons_atm = um.addcyclic(slp_diff, lonin)
ghgt_exp, lons_atm = um.addcyclic(ghgt_exp, lonin)
trth_exp, lons_atm = um.addcyclic(trth_exp, lonin)
slp_exp, lons_atm = um.addcyclic(slp_exp, lonin)
ghgt_cntl, lons_atm = um.addcyclic(ghgt_cntl, lonin)
trth_cntl, lons_atm = um.addcyclic(trth_cntl, lonin)
slp_cntl, lons_atm = um.addcyclic(slp_cntl, lonin)
Xatm, Yatm = np.meshgrid(lons_atm, lat_atm)
ghgt_plot = ghgt_exp
trth_plot = trth_diff
slp_plot = slp_diff
cint_ghgt = 60 #5
cbar_min_trth = 280
cbar_max_trth = 360
cint_trth = 2
cflevs_trth = np.arange(cbar_min_trth, cbar_max_trth, cint_trth)
cflevs_trth_ticks = np.arange(cbar_min_trth, cbar_max_trth, 4 * cint_trth)
base_cntr_slp = 1012
cint_slp = 2
cbar_min_slp = base_cntr_slp - 20 * cint_slp
cbar_max_slp = base_cntr_slp + 20 * cint_slp
cbar_max_slp = cbar_max_slp + (cint_slp / 2)
cflevs_slp = np.arange(cbar_min_slp, cbar_max_slp, cint_slp)
cflevs_slp_ticks = np.arange(cbar_min_slp, cbar_max_slp, 4 * cint_slp)
lonin = lons
trth, lons = um.addcyclic(trth, lonin)
#slp, lons = um.addcyclic(slp, lonin)
X, Y = np.meshgrid(lons, lats)
titletext1 = 'Tropopause potential temperature valid %s at %s UTC' % (
dt.strftime('%d %b %Y'), dt.strftime('%H00'))
# Set global figure properties
golden = (np.sqrt(5) + 1.) / 2.
figprops = dict(figsize=(8., 16. / golden), dpi=128)
# Figure 1
fig = plt.figure(figsize=(8., 16. / golden), dpi=128) # New figure
ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])
if map_projection == 'ortho':
temperature_plot = f.variables['TMP_P0_L100_GLL0'][
levelindex, ::-1, :].squeeze()
u_plot = f.variables['UGRD_P0_L100_GLL0'][levelindex, ::-1, :].squeeze()
v_plot = f.variables['VGRD_P0_L100_GLL0'][levelindex, ::-1, :].squeeze()
f.close
# Convert temperature to degrees Celsius
temperature_plot = temperature_plot - 273.15
# Convert wind to knots
u_plot = u_plot * 1.94384
v_plot = v_plot * 1.94384
lonin = lons
plotvar, lons = um.addcyclic(plotvar, lonin)
temperature_plot, lons = um.addcyclic(temperature_plot, lonin)
u_plot, lons = um.addcyclic(u_plot, lonin)
v_plot, lons = um.addcyclic(v_plot, lonin)
# Refresh the dimensions
X, Y = np.meshgrid(lons, lats)
levelh = level_option / 100 # Convert level to hPa for title
# <codecell>
# Set global figure properties
golden = (np.sqrt(5) + 1.) / 2.
figprops = dict(figsize=(8., 16. / golden), dpi=128)
# Follow the example for diagnostic==2 below. The key is to make sure your variable names end up as:
# var_mid, var_lower, var_upper, var_adv_lower, var_adv_upper, and var_adv_mid
putstuffhere = 1
# compute absolute geostrophic vorticity at the middle level (call it var_mid)
# var_lower is temp_lower and var_upper is temp_upper
var_lower = temp_lower
var_upper = temp_upper
# Calculate temperature advection at lower and upper level for differential thermal advection term later
# Calculate absolute vorticity advection at middle level
# Need to wrap the tendency array
geoph_tend, lons = um.addcyclic(geoph_tend, lonin)
# Convert temperature to degrees Celsius
var_lower = var_lower - 273.15
var_upper = var_upper - 273.15
elif diagnostic == 2: # traditional omega
# Temperature at 700 hPa
var_mid = temp_mid
# Vertical relative and absolute voriticity
var_lower = wm.vertical_vorticity_latlon(uin_lower, vin_lower, lats, lons,
1)
var_upper = wm.vertical_vorticity_latlon(uin_upper, vin_upper, lats, lons,
1)
# for differential vorticity advection
# var_mid, var_lower, var_upper, var_adv_lower, var_adv_upper, and var_adv_mid putstuffhere = 1 # compute absolute geostrophic vorticity at the middle level (call it var_mid) # var_lower is temp_lower and var_upper is temp_upper var_lower = temp_lower var_upper = temp_upper # Calculate temperature advection at lower and upper level for differential thermal advection term later # Calculate absolute vorticity advection at middle level # Need to wrap the tendency array geoph_tend, lons = um.addcyclic(geoph_tend, lonin) # Convert temperature to degrees Celsius var_lower = var_lower - 273.15 var_upper = var_upper - 273.15 elif diagnostic == 2: # traditional omega # Temperature at 700 hPa var_mid = temp_mid # Vertical relative and absolute voriticity var_lower = wm.vertical_vorticity_latlon(uin_lower, vin_lower, lats, lons, 1) var_upper = wm.vertical_vorticity_latlon(uin_upper, vin_upper, lats, lons, 1) # for differential vorticity advection var_adv_lower = wm.hadvection_latlon(uin_lower, vin_lower, var_lower, lats, lons) var_adv_upper = wm.hadvection_latlon(uin_upper, vin_upper, var_upper, lats, lons)
print fpath
f = netCDF4.Dataset(fpath,'r')
lons = f.variables['lon_0'][:]
lats = f.variables['lat_0'][::-1] # Read in reverse direction
levs = f.variables['lv_ISBL0'][:]
if ( level_option == -1 ) :
plotvar = f.variables['PRMSL_P0_L101_GLL0'][::-1,:]/100
else:
levelindex = np.ravel(levs==level_option)
plotvar = f.variables['HGT_P0_L100_GLL0'][levelindex,::-1,:].squeeze() # Reverse latitude dimension
f.close
# <codecell>
lonin = lons
plotvar, lons = um.addcyclic(plotvar, lonin)
# Refresh the dimensions
X, Y = np.meshgrid(lons, lats)
levelh = level_option / 100 # Convert level to hPa for title
if (level_option == -1 ):
titletext = 'Sea level pressure valid %s at %s UTC' % (
dt.strftime('%d %b %Y'), dt.strftime('%H00'))
else:
titletext = '%s hPa geopotential heights valid %s at %s UTC' % (
levelh, dt.strftime('%d %b %Y'), dt.strftime('%H00'))
print titletext
# <codecell>
print fpath
f = netCDF4.Dataset(fpath, "r")
lons = f.variables["lon_0"][:]
lats = f.variables["lat_0"][::-1] # Read in reverse direction
levs = f.variables["lv_ISBL0"][:]
if level_option == -1:
plotvar = f.variables["PRMSL_P0_L101_GLL0"][::-1, :] / 100
else:
levelindex = np.ravel(levs == level_option)
plotvar = f.variables["HGT_P0_L100_GLL0"][levelindex, ::-1, :].squeeze() # Reverse latitude dimension
f.close
# <codecell>
lonin = lons
plotvar, lons = um.addcyclic(plotvar, lonin)
# Refresh the dimensions
X, Y = np.meshgrid(lons, lats)
levelh = level_option / 100 # Convert level to hPa for title
if level_option == -1:
titletext = "Sea level pressure valid %s at %s UTC" % (dt.strftime("%d %b %Y"), dt.strftime("%H00"))
else:
titletext = "%s hPa geopotential heights valid %s at %s UTC" % (
levelh,
dt.strftime("%d %b %Y"),
dt.strftime("%H00"),
)
print titletext
record_numnow, ::-1, :].squeeze() / 10**2
except:
slp = f.variables['PRMSL_3_MSL_10'][
record_numnow, ::-1, :].squeeze() / 10**2
lons = f.variables['lon_3'][:]
lats = f.variables['lat_3'][::-1] # Read in reverse direction
levs = f.variables['lv_ISBL3'][:]
f.close
if data_gridnum > -1:
if ((data_gridnum != 11) & (data_gridnum != 12)):
trth = wm.temp_to_theta(trtempin, trpresin)
lonin = lons
trth, lons = um.addcyclic(trth, lonin)
if plot_field == 'trtemp':
del trth
trth, dummy = um.addcyclic(trte, lonin)
slp, dummy = um.addcyclic(slp, lonin)
if data_gridnum != 11:
X, Y = np.meshgrid(lons, lats)
else:
xlons, dummy = um.addcyclic(xlons, lonin)
xlats, dummy = um.addcyclic(xlats, lonin)
X = xlons
Y = xlats
if map_projection == 'ortho':
trth_thresh = 380
