def plot_T2(loc,timetuple,lats,lons,dom):
fig = plt.figure()
for i,ens in enumerate(ensnames):
# Load data from nc file
time = convert_time(dom,timetuple)
x,y,exactlat,exactlon = gridded_data.getXY(lats,lons,loc[0],loc[1])
ncpath = ncdir+ens+'/'+ncfname
nc = Dataset(ncpath,'r')
ens_T2 = nc.variables['T2'][time,y,x] - 273.15
plt.bar(i,ens_T2)
# if obs == 1:
# # Include observed shear
# D = load_sounding_data(obspath)
# obs_T2 = D['thta'][0]
# plt.bar(len(ensnames),obs_T2,color='black')
# xlabelnames = ensnames + ['OBS']
# xticklocs = [0.5+n for n in range(len(ensnames)+1)]
# else:
xlabelnames = ensnames
xticklocs = [0.5+n for n in range(len(ensnames))]
fname = '_'.join(('T2',nicetime))
plt.gca().autoscale(enable=True,axis='y',tight=True)
plt.xticks(xticklocs,xlabelnames)
plt.ylabel('2m potential temperature (C)')
fig.savefig(outdir+fname, bbox_inches='tight', pad_inches=0.5)
fig.clf()
def return_shear(lev1,lev2,ncpath,ens,loc,timetuple,lats,lons,dom):
nc = Dataset(ncpath,'r')
time = convert_time(dom,timetuple)
x,y,exactlat,exactlon = gridded_data.getXY(lats,lons,loc[0],loc[1])
u = nc.variables['U'][time,:,y,x][:]
v = nc.variables['V'][time,:,y,x][:]
#wind = N.sqrt(u**2 + v**2)
geopot = nc.variables['PH'][time,:,y,x][:] + nc.variables['PHB'][time,:,y,x][:]
Z = geopot/9.81
zAGL = Z - nc.variables['HGT'][time,y,x] # Height AGL
# Compute the height, AGL, of the u/v surfaces
zAGL_wind = N.array([(z0+z1)/2.0 for z0,z1 in zip(zAGL[:-1],zAGL[1:])])
# Find wind at lev2 via interpolation
lev2 *= 1000 # get into metres
u2 = N.interp(lev2,zAGL_wind,u)
v2 = N.interp(lev2,zAGL_wind,v)
if lev1 == 0:
u1 = nc.variables['U10'][time,y,x]
v1 = nc.variables['V10'][time,y,x]
else:
# Find wind at lev1 via interpolation
pass
shear = N.sqrt((u2-u1)**2 + (v2-v1)**2)
return shear
def plot_Q2(loc,timetuple,lats,lons,dom):
fig = plt.figure()
for i,ens in enumerate(ensnames):
# Load data from nc file
time = convert_time(dom,timetuple)
x,y,exactlat,exactlon = gridded_data.getXY(lats,lons,loc[0],loc[1])
ncpath = ncdir+ens+'/'+ncfname
nc = Dataset(ncpath,'r')
ens_Q2 = nc.variables['Q2'][time,y,x]*1000
plt.bar(i,ens_Q2)
if obs == 1:
# Include observed shear
D = load_sounding_data(obspath)
obs_Q2 = D['mixr'][0]
plt.bar(len(ensnames),obs_Q2,color='black')
xlabelnames = ensnames + ['OBS']
xticklocs = [0.5+n for n in range(len(ensnames)+1)]
else:
xlabelnames = ensnames
xticklocs = [0.5+n for n in range(len(ensnames))]
fname = '_'.join(('Q2',nicetime))
plt.xticks(xticklocs,xlabelnames)
plt.ylabel('Mixing ratio at surface (g kg$^{-1}$)')
fig.savefig(outdir+fname, bbox_inches='tight', pad_inches=0.5)
fig.clf()
def skewTPlot(nest, timetuple):
"""
This is the method to use from the outside
nest: The nesting level of the nc-file from WRF
time: The time for which to plot
"""
nc = openWRF(nest)
time = convert_time(nest, timetuple)
_Nx, _Ny, _Nz, _longs, _lats, _dx, _dy, x, y = getDimensions(nc)
plt.figure()
_isotherms()
_isobars()
_dry_adiabats()
_moist_adiabats()
P = nc.variables["P"][time, :, y, x] + nc.variables["PB"][time, :, y, x]
_windbarbs(nc, time, y, x, P)
_temperature(nc, time, y, x, P)
_dewpoint(nc, time, y, x, P)
plt.axis([-40, 50, P_b, P_t])
plt.xlabel("Temperature ($^{\circ}\! C$) at 1000hPa")
xticks = np.arange(-40, 51, 5)
plt.xticks(xticks, ["" if tick % 10 != 0 else str(tick) for tick in xticks])
plt.ylabel("Pressure (hPa)")
yticks = np.arange(P_bot, P_t - 1, -10 ** 4)
plt.yticks(yticks, yticks / 100)
sfcT = nc.variables["T2"][time, y, x] - T_zero
sfcP = nc.variables["PSFC"][time, y, x]
sfcW = nc.variables["Q2"][time, y, x]
sfcTd = td(e(sfcW, sfcP))
plt.suptitle(
"Drybulb: %4.1f$^{\circ}\! C$ Dewpoint: %3.1f$^{\circ}\! C$ Pressure: %5.1f hPa" % (sfcT, sfcTd, 0.01 * sfcP),
fontsize=10,
x=0.5,
y=0.03,
)
# plt.show()
plt.savefig(outdir + naming + str(nest) + "_skewT.png")
plt.title("Skew-T plot for (location)")
plt.close()
def gettime():
t = convert_time(dom, timetuple)
return t
