def test(self):
from netCDF4 import Dataset as NetCDF
timeidx = 0
in_wrfnc = NetCDF(wrf_in)
if (varname == "interplevel"):
ref_ht_850 = _get_refvals(referent, "interplevel", repeat, multi)
hts = getvar(in_wrfnc, "z", timeidx=timeidx)
p = getvar(in_wrfnc, "pressure", timeidx=timeidx)
hts_850 = interplevel(hts, p, 850)
nt.assert_allclose(to_np(hts_850), ref_ht_850)
elif (varname == "vertcross"):
ref_ht_cross = _get_refvals(referent, "vertcross", repeat, multi)
hts = getvar(in_wrfnc, "z", timeidx=timeidx)
p = getvar(in_wrfnc, "pressure", timeidx=timeidx)
pivot_point = CoordPair(hts.shape[-1] // 2, hts.shape[-2] // 2)
ht_cross = vertcross(hts, p, pivot_point=pivot_point, angle=90.)
nt.assert_allclose(to_np(ht_cross), ref_ht_cross, rtol=.01)
elif (varname == "interpline"):
ref_t2_line = _get_refvals(referent, "interpline", repeat, multi)
t2 = getvar(in_wrfnc, "T2", timeidx=timeidx)
pivot_point = CoordPair(t2.shape[-1] // 2, t2.shape[-2] // 2)
t2_line1 = interpline(t2, pivot_point=pivot_point, angle=90.0)
nt.assert_allclose(to_np(t2_line1), ref_t2_line)
elif (varname == "vinterp"):
# Tk to theta
fld_tk_theta = _get_refvals(referent, "vinterp", repeat, multi)
fld_tk_theta = np.squeeze(fld_tk_theta)
tk = getvar(in_wrfnc, "temp", timeidx=timeidx, units="k")
interp_levels = [200, 300, 500, 1000]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
tol = 5 / 100.
atol = 0.0001
field = np.squeeze(field)
nt.assert_allclose(to_np(field), fld_tk_theta, tol, atol)
def run_post(conf):
FIXdir = conf.get("DEFAULT","FIXbwrf")
sfc_switch = conf.getint("post","plot_sfc")
cloud_switch = conf.getint("post","plot_clouds")
sfcdiags_switch = conf.getint("post","plot_sfcdiags")
xcdiags_switch = conf.getint("post","plot_xcdiags")
switch_700mb = conf.getint("post","plot_700mb")
switch_500mb = conf.getint("post","plot_500mb")
switch_300mb = conf.getint("post","plot_300mb")
# sfc_switch = 0
# cloud_switch = 0
# sfcdiags_switch = 0
# xcdiags_switch = 0
# switch_700mb = 0
# switch_500mb = 0
# switch_300mb = 0
blat = conf.getfloat("post","blat")
blon = conf.getfloat("post","blon")
dx = conf.getfloat("wrf","dx")
POSTwork = conf.get("DEFAULT","POSTwork")
os.chdir(POSTwork)
file_wrf = glob.glob("wrfout*")[0]
print("Processing "+file_wrf)
init_time = file_wrf[11:21]+" "+file_wrf[22:24]+"Z"
# Download the states and coastlines
states = cfeature.NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',
name='admin_1_states_provinces_shp')
# Get counties.
reader = shpreader.Reader(FIXdir+'/shapefiles/countyp010g.shp')
counties = list(reader.geometries())
COUNTIES = cfeature.ShapelyFeature(counties, crs.PlateCarree())
# Open the NetCDF file
ncfile = Dataset(file_wrf)
# Get the times and convert to datetimes
times = getvar(ncfile, "Times", timeidx=ALL_TIMES, meta=False)
dtimes = [datetime.strptime(str(atime), '%Y-%m-%dT%H:%M:%S.000000000') for atime in times]
numTimes = len(times)
# Reflectivity
ref = getvar(ncfile, "REFL_10CM", timeidx=ALL_TIMES)
ref[0,0,0,0]=-21.0 # hack to plot a blank contour plot at the initial time
# Get upper-air quantities.
p = getvar(ncfile, "pressure", timeidx=ALL_TIMES)
z = getvar(ncfile, "z", units="dm", timeidx=ALL_TIMES)
u, v = getvar(ncfile, "uvmet", units="kt", timeidx=ALL_TIMES, meta=False)
w = getvar(ncfile, "wa", units="m s-1", timeidx=ALL_TIMES)*100. # m/s to cm/s
rh = getvar(ncfile, "rh", timeidx=ALL_TIMES)
wspeed = (u**2.0+v**2.0)**0.5
tc = getvar(ncfile, "tc", timeidx=ALL_TIMES)
dewT = getvar(ncfile, "td", units="degC", timeidx=ALL_TIMES)
cloudfrac = getvar(ncfile, "cloudfrac", low_thresh=20.,
mid_thresh=955., high_thresh=4500., timeidx=ALL_TIMES)
total_cloudfrac=np.max(cloudfrac,axis=0)
low_cloudfrac = cloudfrac[0,:,:,:]
mid_cloudfrac = cloudfrac[1,:,:,:]
high_cloudfrac = cloudfrac[2,:,:,:]
# Interpolate
z_500 = smooth2d(interplevel(z, p, 500), 3)
tc_500 = smooth2d(interplevel(tc, p, 500), 3)
u_500 = interplevel(u, p, 500)
v_500 = interplevel(v, p, 500)
wspeed_500 = interplevel(wspeed, p, 500)
z_300 = smooth2d(interplevel(z, p, 300), 3)
u_300 = interplevel(u, p, 300)
v_300 = interplevel(v, p, 300)
z_700 = interplevel(z, p, 700)
tc_700 = interplevel(tc, p, 700)
u_700 = interplevel(u, p, 700)
v_700 = interplevel(v, p, 700)
w_700 = interplevel(w, p, 700)
rh_700 = interplevel(rh, p, 700)
if switch_700mb == 1:
# Interpolate over NaNs.
for itime in range(numTimes):
z_700[itime,:,:] = interpnan(z_700[itime,:,:])
tc_700[itime,:,:] = interpnan(tc_700[itime,:,:])
rh_700[itime,:,:] = interpnan(rh_700[itime,:,:])
u_700[itime,:,:] = interpnan(u_700[itime,:,:])
v_700[itime,:,:] = interpnan(v_700[itime,:,:])
w_700[itime,:,:] = interpnan(w_700[itime,:,:])
z_700 = smooth2d(z_700, 3)
tc_700 = smooth2d(tc_700, 3)
w_700 = smooth2d(w_700, 3)
div_300 = smooth2d(divergence([u_300*0.514444, v_300*0.514444], dx), 3) # kt to m/s
# Get the sea level pressure
slp = getvar(ncfile, "slp", timeidx=ALL_TIMES)
# Get the wet bulb temperature
twb = getvar(ncfile, "twb", units="degC", timeidx=ALL_TIMES)
slp_levels=np.arange(980,1040,2)
z_levels=np.arange(504,620,3)
z_levels_300=np.arange(804,996,6)
z_levels_700=np.arange(285,351,3)
t2_levels=np.arange(-20,125,5)
tc_levels=np.arange(-40,30,2)
wspeed_levels=np.arange(40,150,10)
ref_levels=np.arange(-20,60,5)
rh_levels=np.arange(70,105,5)
cldfrac_levels=np.arange(0.,1.1,0.1)
twb_levels=np.arange(0,1,1)
wup_levels=np.arange(5,55,10)
wdown_levels=np.arange(-55,-5,10)
div_levels=np.arange(-110,120,10)
# Get the 10-m u and v wind components.
u_10m, v_10m = getvar(ncfile, "uvmet10", units="kt", timeidx=ALL_TIMES)
wind_10m = (u_10m**2.0+v_10m**2.0)**0.5
# Smooth the sea level pressure since it tends to be noisy near the mountains
smooth_slp = smooth2d(slp, 3)
twb=twb[:,0,:,:] # lowest model level
twb=smooth2d(twb,3)
# Get the latitude and longitude points
lats, lons = latlon_coords(slp)
# Get the cartopy mapping object
cart_proj = get_cartopy(slp)
if sfcdiags_switch == 1:
bx, by = ll_to_xy(ncfile, blat, blon, meta=False, as_int=True)
# Create a figure
fig = plt.figure(figsize=(12,9))
fileout = "precip.png"
# should be variable ACSNOW
# accumulated_snow = getvar(ncfile, "SNOWNC", timeidx=ALL_TIMES)
liq_equiv = (getvar(ncfile, "RAINC", timeidx=ALL_TIMES) +
getvar(ncfile, "RAINNC", timeidx=ALL_TIMES))/25.4 # mm to inches
liq_equiv_bdu = liq_equiv[:,by,bx]
prate = np.zeros(numTimes)
for itime in range(numTimes):
if itime > 0 and itime < numTimes-1:
prate[itime] = 0.5*((liq_equiv_bdu[itime+1]+liq_equiv_bdu[itime]) -
(liq_equiv_bdu[itime-1]+liq_equiv_bdu[itime]))
elif itime == 0:
prate[0] = liq_equiv_bdu[0]
else:
prate[numTimes-1] = liq_equiv_bdu[numTimes-1]-liq_equiv_bdu[numTimes-2]
plt.bar(times, prate, width=0.0425, color="black")
plt.plot(times, liq_equiv_bdu, color="blue")
plt.xlim(min(times), max(times))
plt.title("Forecast Precipitation: Boulder, CO")
plt.xlabel("Time (UTC)")
plt.ylabel("Running total (line) and rate (bars, inches of liquid)")
plt.grid(b=True, which="major", axis="both", color="gray", linestyle="--")
fig.savefig("sfcdiags/"+fileout,bbox_inches='tight')
plt.close(fig)
# Create a figure
fig, ax1 = plt.subplots(figsize=(12,9))
fileout = "t2m_td2m_wind10m_prate.png"
t2m = 1.8*(getvar(ncfile, "T2", timeidx=ALL_TIMES)-273.15)+32.
td2m = getvar(ncfile, "td2", units="degF", timeidx=ALL_TIMES)
color = 'tab:blue'
ax1.bar(times, prate, width=0.0425, color="blue")
ax1.plot(times, liq_equiv_bdu, color="blue")
ax1.set_ylim(0.,max([max(prate)/0.1+0.01,max(liq_equiv_bdu)+0.1]))
ax1.set_xlabel("Time (UTC)")
ax1.set_ylabel("Precipitation liquid amount (in) and rate (bars, in/hr)", color=color)
ax1.tick_params(axis="y", labelcolor=color)
ax1.grid(b=True, which="major", axis="x", color="gray", linestyle="--")
ax2=ax1.twinx()
ax2.plot(times, wind_10m[:,by,bx], color="black")
ax2.barbs(times, wind_10m[:,by,bx], u_10m[:,by,bx], v_10m[:,by,bx])
ax2.plot(times, t2m[:,by,bx], color="red")
ax2.plot(times, td2m[:,by,bx], color="green")
ax2.set_xlim(min(times), max(times))
plt.title("Forecast near-surface variables: Boulder, CO")
ax2.set_ylabel("2-m T (red) and 2-m Td (green, degF), 10-m wind (black, kt)")
ax2.grid(b=True, which="major", axis="y", color="gray", linestyle="--")
fig.tight_layout()
fig.savefig("sfcdiags/"+fileout,bbox_inches='tight')
plt.close(fig)
if xcdiags_switch == 1:
zinterp = np.arange(550, 880, 10)
u_xc = vertcross(u, p, levels=zinterp, wrfin=ncfile,
stagger='u', pivot_point=CoordPair(lat=blat,lon=blon), angle=90., meta=False)
w_xc = vertcross(w, p, levels=zinterp, wrfin=ncfile,
stagger='u', pivot_point=CoordPair(lat=blat,lon=blon), angle=90., meta=False)
tc_xc = vertcross(tc, p, levels=zinterp, wrfin=ncfile,
stagger='u', pivot_point=CoordPair(lat=blat,lon=blon), angle=90., meta=False)
rh_xc = vertcross(rh, p, levels=zinterp, wrfin=ncfile,
stagger='u', pivot_point=CoordPair(lat=blat,lon=blon), angle=90., meta=False)
nx = np.shape(u_xc)[-1]
xinterp = np.arange(0,nx,1)
bx, by = ll_to_xy(ncfile, blat, blon, meta=False, as_int=True)
for itime in range(numTimes):
# Create a figure
fig = plt.figure(figsize=(12,9))
fileout = "mtnwave_xc"+str(itime).zfill(2)+".png"
ax=plt.gca()
ax.set_facecolor("black")
plt.contourf(xinterp, zinterp, u_xc[itime,:,:],
cmap=get_cmap("seismic"), levels=np.arange(-50,55,5))
plt.colorbar(shrink=.62)
w_contour = plt.contour(xinterp, zinterp, w_xc[itime,:,:],
"--", levels=np.arange(-120,-20,20),colors="black")
plt.clabel(w_contour, inline=1, fontsize=10, fmt="%i")
w_contour = plt.contour(xinterp, zinterp, w_xc[itime,:,:],
levels=np.arange(20,120,20),colors="black")
plt.clabel(w_contour, inline=1, fontsize=10, fmt="%i")
t_contour = plt.contour(xinterp, zinterp, tc_xc[itime,:,:],
levels=[-20,-10,0], colors="yellow")
plt.clabel(t_contour, inline=1, fontsize=10, fmt="%i")
# Add location of Boulder to plot.
plt.scatter(bx,np.max(zinterp),c='r',marker='+')
plt.ylim([np.max(zinterp),np.min(zinterp)])
# plt.yscale("log")
# plt.xticks([np.arange(900,475,-25)], ["900", "875",
# "850", "825", "800", "775", "750", "725", "700", "675", "650",
# "625", "600", "575", "550", "525", "500"])
plt_time=str(dtimes[itime])
plt_time=plt_time[0:13]
plt.title(plt_time+"Z fhr "+str(itime).zfill(2)+": zonal wind (fill, kt), temp (yellow lines, degC), VV (black lines, cm/s)")
fig.savefig("xcdiags/"+fileout,bbox_inches='tight')
plt.close(fig)
os.system("convert -delay 90 -dispose background xcdiags/mtnwave_xc*.png -loop 0 xcdiags/mtnwave_xc.gif")
for itime in range(numTimes):
# Create a figure
fig = plt.figure(figsize=(12,9))
fileout = "mtnwave_xc_rh"+str(itime).zfill(2)+".png"
ax=plt.gca()
ax.set_facecolor("black")
plt.contourf(xinterp, zinterp, rh_xc[itime,:,:],
cmap=get_cmap("Greens"), levels=rh_levels, extend='both')
plt.colorbar(shrink=.62)
skip=2
plt.quiver(xinterp[::skip], zinterp[::skip], u_xc[itime,::skip,::skip], w_xc[itime,::skip,::skip]/2.,
scale=500, headwidth=3, color='black', pivot='middle')
t_contour = plt.contour(xinterp, zinterp, tc_xc[itime,:,:],
levels=[-20,-10,0], colors="yellow")
plt.clabel(t_contour, inline=1, fontsize=10, fmt="%i")
# Add location of Boulder to plot.
plt.scatter(bx,np.max(zinterp),c='r',marker='+')
plt.ylim([np.max(zinterp),np.min(zinterp)])
plt_time=str(dtimes[itime])
plt_time=plt_time[0:13]
plt.title(plt_time+"Z fhr "+str(itime).zfill(2)+": rh (fill), temp (yellow lines, degC), wind (arrows)")
fig.savefig("xcdiags_rh/"+fileout,bbox_inches='tight')
plt.close(fig)
os.system("convert -delay 90 -dispose background xcdiags_rh/mtnwave_xc_rh*.png -loop 0 xcdiags_rh/mtnwave_xc_rh.gif")
zinterp = np.arange(150, 900, 25)
rh_xc = vertcross(rh, p, levels=zinterp, wrfin=ncfile,
stagger='u', pivot_point=CoordPair(lat=blat,lon=blon), angle=90., meta=False)
tc_xc = vertcross(tc, p, levels=zinterp, wrfin=ncfile,
stagger='u', pivot_point=CoordPair(lat=blat,lon=blon), angle=90., meta=False)
nx = np.shape(u_xc)[-1]
xinterp = np.arange(0,nx,1)
bx, by = ll_to_xy(ncfile, blat, blon, meta=False, as_int=True)
for itime in range(numTimes):
# Create a figure
fig = plt.figure(figsize=(12,9))
fileout = "mtnwave_xc_rh_big"+str(itime).zfill(2)+".png"
ax=plt.gca()
ax.set_facecolor("black")
plt.contourf(xinterp, zinterp, rh_xc[itime,:,:],
cmap=get_cmap("Greens"), levels=rh_levels, extend='both')
plt.colorbar(shrink=.62)
t_contour = plt.contour(xinterp, zinterp, tc_xc[itime,:,:],
levels=[-20,-10,0], colors="yellow")
plt.clabel(t_contour, inline=1, fontsize=10, fmt="%i")
# Add location of Boulder to plot.
plt.scatter(bx,np.max(zinterp),c='r',marker='+')
plt.ylim([np.max(zinterp),np.min(zinterp)])
plt_time=str(dtimes[itime])
plt_time=plt_time[0:13]
plt.title(plt_time+"Z fhr "+str(itime).zfill(2)+": rh (fill), temp (yellow lines, degC)")
fig.savefig("xcdiags_rh_big/"+fileout,bbox_inches='tight')
plt.close(fig)
os.system("convert -delay 90 -dispose background xcdiags_rh_big/mtnwave_xc_rh_big*.png -loop 0 xcdiags_rh_big/mtnwave_xc_rh_big.gif")
if sfc_switch == 1:
bx, by = ll_to_xy(ncfile, blat, blon, meta=False, as_int=True)
for itime in range(numTimes):
ps = p[itime,:,by,bx]
ps_temp = ps
T = tc[itime,:,by,bx]
Td = dewT[itime,:,by,bx]
us = u[itime,:,by,bx]
vs = v[itime,:,by,bx]
ps = ps[ps_temp >= 100.]
T = T[ps_temp >= 100.]
Td = Td[ps_temp >= 100.]
us = us[ps_temp >= 100.]
vs = vs[ps_temp >= 100.]
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig, rotation=45)
fileout = "sounding"+str(itime).zfill(2)+".png"
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot.
skew.plot(ps, T, 'r')
skew.plot(ps, Td, 'g')
skew.plot_barbs(ps, us, vs)
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)
# Calculate LCL height and plot as black dot. Because `p`'s first value is
# ~1000 mb and its last value is ~250 mb, the `0` index is selected for
# `p`, `T`, and `Td` to lift the parcel from the surface. If `p` was inverted,
# i.e. start from low value, 250 mb, to a high value, 1000 mb, the `-1` index
# should be selected.
lcl_pressure, lcl_temperature = mpcalc.lcl(ps[0], T[0], Td[0])
skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')
# Calculate full parcel profile and add to plot as black line
prof = mpcalc.parcel_profile(ps, T[0], Td[0]).to('degC')
skew.plot(ps, prof, 'k', linewidth=2)
# Shade areas of CAPE and CIN
# skew.shade_cin(ps, T, prof, Td)
[cape, cin] = mpcalc.cape_cin(ps, T, Td, prof)
# skew.shade_cape(ps, T, prof)
# An example of a slanted line at constant T -- in this case the 0
# isotherm
skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
plt_time=str(dtimes[itime])
plt_time=plt_time[0:13]
plt.title(plt_time+"Z fhr "+str(itime).zfill(2)+": Boulder, CO, CAPE: "+str(round(cape.magnitude))+" J/kg CIN: "+str(round(cin.magnitude))+" J/kg")
fig.savefig("sounding/"+fileout,bbox_inches='tight')
plt.close(fig)
os.system("convert -delay 90 -dispose background sounding/sounding*.png -loop 0 sounding/sounding.gif")
for itime in range(numTimes):
# Create a figure
fig = plt.figure(figsize=(12,9))
fileout = "sfc_fhr"+str(itime).zfill(2)+".png"
# Set the GeoAxes to the projection used by WRF
ax = plt.axes(projection=cart_proj)
# Add the states and coastlines
ax.add_feature(states, linewidth=0.8, edgecolor='gray')
ax.add_feature(COUNTIES, linewidth=0.4, facecolor='none', edgecolor='gray')
ax.coastlines('50m', linewidth=0.8)
# Reflectivity at the lowest model level.
plt.contourf(to_np(lons), to_np(lats), to_np(ref[itime,0,:,:]), transform=crs.PlateCarree(),
cmap=get_cmap("jet"), levels=ref_levels)
# Add a color bar
plt.colorbar(ax=ax, shrink=.62)
# Contour the wetbulb temperature at 0 degC.
c_p = plt.contour(to_np(lons), to_np(lats), to_np(twb[itime,:,:]), transform=crs.PlateCarree(),
colors="red", levels=twb_levels)
plt.clabel(c_p, inline=1, fontsize=10, fmt="%i")
# Make the contour outlines and filled contours for the smoothed sea level pressure.
c_p = plt.contour(to_np(lons), to_np(lats), to_np(smooth_slp[itime,:,:]), transform=crs.PlateCarree(),
colors="black", levels=slp_levels)
plt.clabel(c_p, inline=1, fontsize=10, fmt="%i")
# Add location of Boulder to plot.
plt.scatter(blon,blat,c='r',marker='+',transform=crs.PlateCarree())
# Add the 10-m wind barbs, only plotting every other data point.
skip=2
plt.barbs(to_np(lons[::skip,::skip]), to_np(lats[::skip,::skip]), to_np(u_10m[itime, ::skip, ::skip]),
to_np(v_10m[itime, ::skip, ::skip]), transform=crs.PlateCarree(), length=5.25, linewidth=0.5)
# Set the map limits.
ax.set_xlim(cartopy_xlim(smooth_slp))
ax.set_ylim(cartopy_ylim(smooth_slp))
plt_time=str(dtimes[itime])
plt_time=plt_time[0:13]
plt.title(plt_time+"Z fhr "+str(itime).zfill(2)+": SLP (fill, hPa), 10-m wind (barbs, kt), LML Ref (fill, dBZ), LML WBT (red, degC)")
fig.savefig("sfc/"+fileout,bbox_inches='tight')
plt.close(fig)
os.system("convert -delay 90 -dispose background sfc/sfc*.png -loop 0 sfc/sfc.gif")
for itime in range(numTimes):
# Create a figure
fig = plt.figure(figsize=(12,9))
fileout = "sfc_temp_fhr"+str(itime).zfill(2)+".png"
# Set the GeoAxes to the projection used by WRF
ax = plt.axes(projection=cart_proj)
# Add the states and coastlines
ax.add_feature(states, linewidth=0.8, edgecolor='gray')
ax.add_feature(COUNTIES, linewidth=0.4, facecolor='none', edgecolor='gray')
ax.coastlines('50m', linewidth=0.8)
# 2-m air temperature
t2 = 1.8*(getvar(ncfile, "T2", timeidx=ALL_TIMES) - 273.15)+32.
plt.contourf(to_np(lons), to_np(lats), to_np(t2[itime,:,:]), transform=crs.PlateCarree(),
cmap=get_cmap("jet"), levels=t2_levels)
# Add a color bar
plt.colorbar(ax=ax, shrink=.62)
# Make the contour outlines and filled contours for the smoothed sea level pressure.
c_p = plt.contour(to_np(lons), to_np(lats), to_np(smooth_slp[itime,:,:]), transform=crs.PlateCarree(),
colors="black", levels=slp_levels)
plt.clabel(c_p, inline=1, fontsize=10, fmt="%i")
# Add location of Boulder to plot.
plt.scatter(blon,blat,c='r',marker='+',transform=crs.PlateCarree())
# Add the 10-m wind barbs, only plotting every other data point.
skip=2
plt.barbs(to_np(lons[::skip,::skip]), to_np(lats[::skip,::skip]), to_np(u_10m[itime, ::skip, ::skip]),
to_np(v_10m[itime, ::skip, ::skip]), transform=crs.PlateCarree(), length=5.25, linewidth=0.5)
# Set the map limits.
ax.set_xlim(cartopy_xlim(smooth_slp))
ax.set_ylim(cartopy_ylim(smooth_slp))
plt_time=str(dtimes[itime])
plt_time=plt_time[0:13]
plt.title(plt_time+"Z fhr "+str(itime).zfill(2)+": SLP (contours, hPa), 10-m wind (barbs, kt), 2-m T (fill, degF)")
fig.savefig("sfc_temp/"+fileout,bbox_inches='tight')
plt.close(fig)
os.system("convert -delay 90 -dispose background sfc_temp/sfc_temp*.png -loop 0 sfc_temp/sfc_temp.gif")
if cloud_switch == 1:
for itime in range(numTimes):
# Create a figure
fig = plt.figure(figsize=(12,9))
fileout = "cldfrac_fhr"+str(itime).zfill(2)+".png"
# Set the GeoAxes to the projection used by WRF
ax = plt.axes(projection=cart_proj)
# Add the states and coastlines
ax.add_feature(states, linewidth=0.8, edgecolor='gray')
ax.add_feature(COUNTIES, linewidth=0.4, facecolor='none', edgecolor='gray')
ax.coastlines('50m', linewidth=0.8)
# Compute and plot the total cloud fraction.
plt.contourf(to_np(lons), to_np(lats), to_np(total_cloudfrac[itime,:,:]), transform=crs.PlateCarree(),
cmap=get_cmap("Greys"), levels=cldfrac_levels, extend='both')
# Add a color bar
plt.colorbar(ax=ax, shrink=.62)
# Add location of Boulder to plot.
plt.scatter(blon,blat,c='r',marker='+',transform=crs.PlateCarree())
# Set the map limits.
ax.set_xlim(cartopy_xlim(cloudfrac))
ax.set_ylim(cartopy_ylim(cloudfrac))
plt_time=str(dtimes[itime])
plt_time=plt_time[0:13]
plt.title(plt_time+"Z fhr "+str(itime).zfill(2)+": Total cloud fraction (fill)")
fig.savefig("cldfrac/"+fileout,bbox_inches='tight')
plt.close(fig)
os.system("convert -delay 90 -dispose background cldfrac/cldfrac*.png -loop 0 cldfrac/cldfrac.gif")
for itime in range(numTimes):
# Create a figure
fig = plt.figure(figsize=(12,9))
fileout = "low_cldfrac_fhr"+str(itime).zfill(2)+".png"
# Set the GeoAxes to the projection used by WRF
ax = plt.axes(projection=cart_proj)
# Add the states and coastlines
ax.add_feature(states, linewidth=0.8, edgecolor='gray')
ax.add_feature(COUNTIES, linewidth=0.4, facecolor='none', edgecolor='gray')
ax.coastlines('50m', linewidth=0.8)
# Compute and plot the cloud fraction.
plt.contourf(to_np(lons), to_np(lats), to_np(low_cloudfrac[itime,:,:]), transform=crs.PlateCarree(),
cmap=get_cmap("Greys"), levels=cldfrac_levels, extend='both')
# Add a color bar
plt.colorbar(ax=ax, shrink=.62)
# Add location of Boulder to plot.
plt.scatter(blon,blat,c='r',marker='+',transform=crs.PlateCarree())
# Set the map limits.
ax.set_xlim(cartopy_xlim(cloudfrac))
ax.set_ylim(cartopy_ylim(cloudfrac))
plt_time=str(dtimes[itime])
plt_time=plt_time[0:13]
plt.title(plt_time+"Z fhr "+str(itime).zfill(2)+": Low cloud fraction (fill)")
fig.savefig("low_cldfrac/"+fileout,bbox_inches='tight')
plt.close(fig)
os.system("convert -delay 90 -dispose background low_cldfrac/low_cldfrac*.png -loop 0 low_cldfrac/low_cldfrac.gif")
for itime in range(numTimes):
# Create a figure
fig = plt.figure(figsize=(12,9))
fileout = "mid_cldfrac_fhr"+str(itime).zfill(2)+".png"
# Set the GeoAxes to the projection used by WRF
ax = plt.axes(projection=cart_proj)
# Add the states and coastlines
ax.add_feature(states, linewidth=0.8, edgecolor='gray')
ax.add_feature(COUNTIES, linewidth=0.4, facecolor='none', edgecolor='gray')
ax.coastlines('50m', linewidth=0.8)
# Compute and plot the cloud fraction.
plt.contourf(to_np(lons), to_np(lats), to_np(mid_cloudfrac[itime,:,:]), transform=crs.PlateCarree(),
cmap=get_cmap("Greys"), levels=cldfrac_levels, extend='both')
# Add a color bar
plt.colorbar(ax=ax, shrink=.62)
# Add location of Boulder to plot.
plt.scatter(blon,blat,c='r',marker='+',transform=crs.PlateCarree())
# Set the map limits.
ax.set_xlim(cartopy_xlim(cloudfrac))
ax.set_ylim(cartopy_ylim(cloudfrac))
plt_time=str(dtimes[itime])
plt_time=plt_time[0:13]
plt.title(plt_time+"Z fhr "+str(itime).zfill(2)+": Mid cloud fraction (fill)")
fig.savefig("mid_cldfrac/"+fileout,bbox_inches='tight')
plt.close(fig)
os.system("convert -delay 90 -dispose background mid_cldfrac/mid_cldfrac*.png -loop 0 mid_cldfrac/mid_cldfrac.gif")
for itime in range(numTimes):
# Create a figure
fig = plt.figure(figsize=(12,9))
fileout = "high_cldfrac_fhr"+str(itime).zfill(2)+".png"
# Set the GeoAxes to the projection used by WRF
ax = plt.axes(projection=cart_proj)
# Add the states and coastlines
ax.add_feature(states, linewidth=0.8, edgecolor='gray')
ax.add_feature(COUNTIES, linewidth=0.4, facecolor='none', edgecolor='gray')
ax.coastlines('50m', linewidth=0.8)
# Compute and plot the cloud fraction.
plt.contourf(to_np(lons), to_np(lats), to_np(high_cloudfrac[itime,:,:]), transform=crs.PlateCarree(),
cmap=get_cmap("Greys"), levels=cldfrac_levels, extend='both')
# Add a color bar
plt.colorbar(ax=ax, shrink=.62)
# Add location of Boulder to plot.
plt.scatter(blon,blat,c='r',marker='+',transform=crs.PlateCarree())
# Set the map limits.
ax.set_xlim(cartopy_xlim(cloudfrac))
ax.set_ylim(cartopy_ylim(cloudfrac))
plt_time=str(dtimes[itime])
plt_time=plt_time[0:13]
plt.title(plt_time+"Z fhr "+str(itime).zfill(2)+": High cloud fraction (fill)")
fig.savefig("high_cldfrac/"+fileout,bbox_inches='tight')
plt.close(fig)
os.system("convert -delay 90 -dispose background high_cldfrac/high_cldfrac*.png -loop 0 high_cldfrac/high_cldfrac.gif")
if switch_500mb == 1:
for itime in range(numTimes):
# Create a figure
fig = plt.figure(figsize=(12,9))
fileout = "500mb_fhr"+str(itime).zfill(2)+".png"
# Set the GeoAxes to the projection used by WRF
ax = plt.axes(projection=cart_proj)
# Add the states and coastlines
ax.add_feature(states, linewidth=0.8, edgecolor='gray')
ax.add_feature(COUNTIES, linewidth=0.4, facecolor='none', edgecolor='gray')
ax.coastlines('50m', linewidth=0.8)
# wind color fill
if np.max(wspeed_500[itime,:,:]) > np.min(wspeed_levels):
plt.contourf(to_np(lons), to_np(lats), to_np(wspeed_500[itime,:,:]), transform=crs.PlateCarree(),
cmap=get_cmap("rainbow"), levels=wspeed_levels)
# Make the 500 mb height contours.
c_z = plt.contour(to_np(lons), to_np(lats), to_np(z_500[itime,:,:]), transform=crs.PlateCarree(),
colors="black", levels=z_levels)
plt.clabel(c_z, inline=1, fontsize=10, fmt="%i")
# Make the 500 mb temp contours.
c_t = plt.contour(to_np(lons), to_np(lats), to_np(tc_500[itime,:,:]), transform=crs.PlateCarree(),
colors="red", levels=tc_levels)
plt.clabel(c_t, inline=1, fontsize=10, fontcolor="red", fmt="%i")
# Add a color bar
# plt.colorbar(ax=ax, shrink=.62)
# Add location of Boulder to plot.
plt.scatter(blon,blat,c='r',marker='+',transform=crs.PlateCarree())
# Add the 500mb wind barbs, only plotting every other data point.
skip=3
plt.barbs(to_np(lons[::skip,::skip]), to_np(lats[::skip,::skip]), to_np(u_500[itime, ::skip, ::skip]),
to_np(v_500[itime, ::skip, ::skip]), transform=crs.PlateCarree(), length=5.25, linewidth=0.5)
# Set the map limits.
ax.set_xlim(cartopy_xlim(z_500))
ax.set_ylim(cartopy_ylim(z_500))
plt_time=str(dtimes[itime])
plt_time=plt_time[0:13]
plt.title(plt_time+"Z fhr "+str(itime).zfill(2)+": 500-mb height (black, dm), temp (red, degC), and wind (fill/barbs, kt)")
fig.savefig("500mb/"+fileout,bbox_inches='tight')
plt.close(fig)
os.system("convert -delay 90 -dispose background 500mb/500mb*.png -loop 0 500mb/500mb.gif")
if switch_700mb == 1:
for itime in range(numTimes):
# Create a figure
fig = plt.figure(figsize=(12,9))
fileout = "700mb_fhr"+str(itime).zfill(2)+".png"
# Set the GeoAxes to the projection used by WRF
ax = plt.axes(projection=cart_proj)
# Add the states and coastlines
ax.add_feature(states, linewidth=0.8, edgecolor='gray')
ax.add_feature(COUNTIES, linewidth=0.4, facecolor='none', edgecolor='gray')
ax.coastlines('50m', linewidth=0.8)
# rh color fill
plt.contourf(to_np(lons), to_np(lats), to_np(rh_700[itime,:,:]), transform=crs.PlateCarree(),
cmap=get_cmap("Greens"), levels=rh_levels, extend='both')
# Add a color bar
plt.colorbar(ax=ax, shrink=.62)
# Make the 700 mb height contours.
c_z = plt.contour(to_np(lons), to_np(lats), to_np(z_700[itime,:,:]), transform=crs.PlateCarree(),
colors="black", levels=z_levels_700)
plt.clabel(c_z, inline=1, fontsize=10, fmt="%i")
# Make the 700 mb temp contours.
c_t = plt.contour(to_np(lons), to_np(lats), to_np(tc_700[itime,:,:]), transform=crs.PlateCarree(),
colors="red", levels=tc_levels)
plt.clabel(c_t, inline=1, fontsize=10, fontcolor="red", fmt="%i")
# Make the 700 mb VV contours.
c_d = plt.contour(to_np(lons), to_np(lats), to_np(w_700[itime,:,:]), transform=crs.PlateCarree(),
colors="magenta", levels=wup_levels, linewidths=0.9)
plt.clabel(c_d, inline=1, fontsize=10, fontcolor="magenta", fmt="%i")
c_c = plt.contour(to_np(lons), to_np(lats), to_np(w_700[itime,:,:]), transform=crs.PlateCarree(),
colors="blue", levels=wdown_levels, linewidths=0.9)
plt.clabel(c_c, inline=1, fontsize=10, fontcolor="blue", fmt="%i")
# Add location of Boulder to plot.
plt.scatter(blon,blat,c='r',marker='+',transform=crs.PlateCarree())
# Add the 700mb wind barbs, only plotting every other data point.
skip=3
plt.barbs(to_np(lons[::skip,::skip]), to_np(lats[::skip,::skip]), to_np(u_700[itime, ::skip, ::skip]),
to_np(v_700[itime, ::skip, ::skip]), transform=crs.PlateCarree(), length=5.25, linewidth=0.5)
# Set the map limits.
ax.set_xlim(cartopy_xlim(z_700))
ax.set_ylim(cartopy_ylim(z_700))
plt_time=str(dtimes[itime])
plt_time=plt_time[0:13]
plt.title(plt_time+"Z fhr "+str(itime).zfill(2)+": 700-mb hgt (black, dm), T (red, degC), wind (barbs, kt), VV (cm/s), rh (fill, %)")
fig.savefig("700mb/"+fileout,bbox_inches='tight')
plt.close(fig)
os.system("convert -delay 90 -dispose background 700mb/700mb*.png -loop 0 700mb/700mb.gif")
if switch_300mb == 1:
for itime in range(numTimes):
# Create a figure
fig = plt.figure(figsize=(12,9))
fileout = "300mb_fhr"+str(itime).zfill(2)+".png"
# Set the GeoAxes to the projection used by WRF
ax = plt.axes(projection=cart_proj)
# Add the states and coastlines
ax.add_feature(states, linewidth=0.8, edgecolor='gray')
ax.add_feature(COUNTIES, linewidth=0.4, facecolor='none', edgecolor='gray')
ax.coastlines('50m', linewidth=0.8)
# 300 mb divergence
plt.contourf(to_np(lons), to_np(lats), to_np(div_300[itime,:,:]), transform=crs.PlateCarree(),
cmap=get_cmap("seismic"), levels=div_levels)
# Add a color bar
plt.colorbar(ax=ax, shrink=.62)
# Make the 300 mb height contours.
c_z = plt.contour(to_np(lons), to_np(lats), to_np(z_300[itime,:,:]), transform=crs.PlateCarree(),
colors="black", levels=z_levels_300)
plt.clabel(c_z, inline=1, fontsize=10, fmt="%i")
# Make the 300 mb divergence contours.
# c_d = plt.contour(to_np(lons), to_np(lats), to_np(div_300[itime,:,:]), transform=crs.PlateCarree(),
# colors="red", levels=div_levels, linewidths=0.8)
# plt.clabel(c_d, inline=1, fontsize=10, fontcolor="red", fmt="%i")
#
# c_c = plt.contour(to_np(lons), to_np(lats), to_np(div_300[itime,:,:]), transform=crs.PlateCarree(),
# colors="blue", levels=conv_levels, linewidths=0.8)
# plt.clabel(c_c, inline=1, fontsize=10, fontcolor="blue", fmt="%i")
# Add location of Boulder to plot.
plt.scatter(blon,blat,c='r',marker='+',transform=crs.PlateCarree())
# Add the 300mb wind barbs, only plotting every other data point.
skip=3
plt.barbs(to_np(lons[::skip,::skip]), to_np(lats[::skip,::skip]), to_np(u_300[itime, ::skip, ::skip]),
to_np(v_300[itime, ::skip, ::skip]), transform=crs.PlateCarree(), length=5.25, linewidth=0.5)
# Set the map limits.
ax.set_xlim(cartopy_xlim(z_300))
ax.set_ylim(cartopy_ylim(z_300))
plt_time=str(dtimes[itime])
plt_time=plt_time[0:13]
plt.title(plt_time+"Z fhr "+str(itime).zfill(2)+": 300-mb height (black, dm), wind (barbs, kt), and divergence x 10^5 (red/blue, s^-1)")
fig.savefig("300mb/"+fileout,bbox_inches='tight')
plt.close(fig)
os.system("convert -delay 90 -dispose background 300mb/300mb*.png -loop 0 300mb/300mb.gif")
def interpolate_vert(ncfile, start_lat, end_lat, start_lon, end_lon, var, time,
start_p, end_p, interval_p):
startpoint = CoordPair(lat=start_lat, lon=start_lon)
endpoint = CoordPair(lat=end_lat, lon=end_lon)
height = getvar(ncfile, 'height')
hgt = getvar(ncfile, 'HGT')
p = getvar(ncfile, 'pressure', timeidx=time)
f = getvar(ncfile, var, timeidx=time)
p_height = p[:, 0, 0]
bool, start_layer, end_layer = get_pressure_layer(p_height, start_p, end_p)
p_level = np.mgrid[p_height[start_layer].values:p_height[end_layer].
values:complex(str(end_layer - start_layer + 1) + 'j')]
f_vert = vertcross(f,
p,
wrfin=ncfile,
levels=p_level,
start_point=startpoint,
end_point=endpoint,
latlon=True)
if var == 'wa':
f_vert = f_vert * 200
print(f_vert)
#处理插值数据的维度
p_vert_list = f_vert.coords['vertical'].values
print("插值的压力为：", end='')
print(p_vert_list)
lon_vert_list, lat_vert_list = [], []
for i in range(len(f_vert.coords['xy_loc'])):
s = str(f_vert.coords['xy_loc'][i].values)
lon_vert_list.append(float(s[s.find('lon=') + 4:s.find('lon=') + 16]))
lat_vert_list.append(float(s[s.find('lat=') + 4:s.find('lat=') + 16]))
lon_vert_list = np.array(lon_vert_list)
lat_vert_list = np.array(lat_vert_list)
lon_vert_list = np.around(lon_vert_list, decimals=2)
lat_vert_list = np.around(lat_vert_list, decimals=2)
h_vert_list = []
h2h = height - hgt
for i in range(end_p, start_p - interval_p, -interval_p):
h_vert_list.append(float(np.max(interplevel(h2h, p, i)).values))
print("对应的高度为：", end='')
print(h_vert_list)
lat_vert_list = np.mgrid[lat_vert_list[0]:lat_vert_list[-1]:complex(
str(len(lat_vert_list)) + 'j')]
lon_vert_list = np.mgrid[lon_vert_list[0]:lon_vert_list[-1]:complex(
str(len(lon_vert_list)) + 'j')]
return f_vert, lat_vert_list, lon_vert_list, p_vert_list, h_vert_list
def make_result_file(opts):
infilename = os.path.expanduser(
os.path.join(opts.outdir, "ci_test_file.nc"))
outfilename = os.path.expanduser(
os.path.join(opts.outdir, "ci_result_file.nc"))
with Dataset(infilename) as infile, Dataset(outfilename, "w") as outfile:
for varname in WRF_DIAGS:
var = getvar(infile, varname)
add_to_ncfile(outfile, var, varname)
for interptype in INTERP_METHS:
if interptype == "interpline":
hts = getvar(infile, "z")
p = getvar(infile, "pressure")
hts_850 = interplevel(hts, p, 850.)
add_to_ncfile(outfile, hts_850, "interplevel")
if interptype == "vertcross":
hts = getvar(infile, "z")
p = getvar(infile, "pressure")
pivot_point = CoordPair(hts.shape[-1] // 2, hts.shape[-2] // 2)
ht_cross = vertcross(hts,
p,
pivot_point=pivot_point,
angle=90.)
add_to_ncfile(outfile, ht_cross, "vertcross")
if interptype == "interpline":
t2 = getvar(infile, "T2")
pivot_point = CoordPair(t2.shape[-1] // 2, t2.shape[-2] // 2)
t2_line = interpline(t2, pivot_point=pivot_point, angle=90.0)
add_to_ncfile(outfile, t2_line, "interpline")
if interptype == "vinterp":
tk = getvar(infile, "temp", units="k")
interp_levels = [200, 300, 500, 1000]
field = vinterp(infile,
field=tk,
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
log_p=True)
add_to_ncfile(outfile, field, "vinterp")
for latlonmeth in LATLON_METHS:
if latlonmeth == "xy":
# Hardcoded values from other unit tests
lats = [-55, -60, -65]
lons = [25, 30, 35]
xy = ll_to_xy(infile, lats[0], lons[0])
add_to_ncfile(outfile, xy, "xy")
else:
# Hardcoded from other unit tests
i_s = np.asarray([10, 100, 150], int) - 1
j_s = np.asarray([10, 100, 150], int) - 1
ll = xy_to_ll(infile, i_s[0], j_s[0])
add_to_ncfile(outfile, ll, "ll")
def horizontal_map(variable_name, date, start_hour,
end_hour, pressure_level=False,
subset=False, initiation=False,
save=False, gif=False):
'''This function plots the chosen variable for the analysis
of the initiation environment on a horizontal (2D) map. Supported variables for plotting
procedure are updraft, reflectivity, helicity, pw, cape, cin, ctt, temperature_surface,
wind_shear, updraft_reflectivity, rh, omega, pvo, avo, theta_e, water_vapor, uv_wind and
divergence.'''
### Predefine some variables ###
# Get the list of all needed wrf files
data_dir = '/scratch3/thomasl/work/data/casestudy_baden/'
# Define save directory
save_dir = '/scratch3/thomasl/work/retrospective_part' '/casestudy_baden/horizontal_maps/'
# Change extent of plot
subset_extent = [6.2, 9.4, 46.5, 48.5]
# Set the location of the initiation of the thunderstorm
initiation_location = CoordPair(lat=47.25, lon=7.85)
# 2D variables:
if variable_name == 'updraft':
variable_name = 'W_UP_MAX'
title_name = 'Maximum Z-Wind Updraft'
colorbar_label = 'Max Z-Wind Updraft [$m$ $s^-$$^1$]'
save_name = 'updraft'
variable_min = 0
variable_max = 30
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'reflectivity':
variable_name = 'REFD_MAX'
title_name = 'Maximum Derived Radar Reflectivity'
colorbar_label = 'Maximum Derived Radar Reflectivity [$dBZ$]'
save_name = 'reflectivity'
variable_min = 0
variable_max = 75
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'helicity':
variable_name = 'UP_HELI_MAX'
title_name = 'Maximum Updraft Helicity'
colorbar_label = 'Maximum Updraft Helicity [$m^{2}$ $s^{-2}$]'
save_name = 'helicity'
variable_min = 0
variable_max = 140
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'pw':
title_name = 'Precipitable Water'
colorbar_label = 'Precipitable Water [$kg$ $m^{-2}$]'
save_name = 'pw'
variable_min = 0
variable_max = 50
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'cape':
variable_name = 'cape_2d'
title_name = 'CAPE'
colorbar_label = 'Convective Available Potential Energy' '[$J$ $kg^{-1}$]'
save_name = 'cape'
variable_min = 0
variable_max = 3000
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'cin':
variable_name = 'cape_2d'
title_name = 'CIN'
colorbar_label = 'Convective Inhibition [$J$ $kg^{-1}$]'
save_name = 'cin'
variable_min = 0
variable_max = 100
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'ctt':
title_name = 'Cloud Top Temperature'
colorbar_label = 'Cloud Top Temperature [$K$]'
save_name = 'cct'
variable_min = 210
variable_max = 300
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'temperature_surface':
variable_name = 'T2'
title_name = 'Temperature @ 2 m'
colorbar_label = 'Temperature [$K$]'
save_name = 'temperature_surface'
variable_min = 285
variable_max = 305
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'wind_shear':
variable_name = 'slp'
title_name = 'SLP, Wind @ 850hPa, Wind @ 500hPa\n' 'and 500-850hPa Vertical Wind Shear'
save_name = 'wind_shear'
variable_min = 1000
variable_max = 1020
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'updraft_reflectivity':
variable_name = 'W_UP_MAX'
title_name = 'Updraft and Reflectivity'
colorbar_label = 'Max Z-Wind Updraft [$m$ $s^-$$^1$]'
save_name = 'updraft_reflectivity'
variable_min = 0
variable_max = 30
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
# 3D variables:
elif variable_name == 'rh':
title_name = 'Relative Humidity'
colorbar_label = 'Relative Humidity [$pct$]'
save_name = 'rh'
variable_min = 0
variable_max = 100
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
elif variable_name == 'omega':
title_name = 'Vertical Motion'
colorbar_label = 'Omega [$Pa$ $s^-$$^1$]'
save_name = 'omega'
variable_min = -50
variable_max = 50
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
elif variable_name == 'pvo':
title_name = 'Potential Vorticity'
colorbar_label = 'Potential Vorticity [$PVU$]'
save_name = 'pvo'
variable_min = -1
variable_max = 9
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
elif variable_name == 'avo':
title_name = 'Absolute Vorticity'
colorbar_label = 'Absolute Vorticity [$10^{-5}$' '$s^{-1}$]'
save_name = 'avo'
variable_min = -250
variable_max = 250
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
elif variable_name == 'theta_e':
title_name = 'Theta-E'
colorbar_label = 'Theta-E [$K$]'
save_name = 'theta_e'
variable_min = 315
variable_max = 335
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
elif variable_name == 'water_vapor':
variable_name = 'QVAPOR'
title_name = 'Water Vapor Mixing Ratio'
colorbar_label = 'Water Vapor Mixing Ratio [$g$ $kg^{-1}$]'
save_name = 'water_vapor'
variable_min = 5
variable_max = 15
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
elif variable_name == 'uv_wind':
variable_name = 'wspd_wdir'
title_name = 'Wind Speed and Direction'
colorbar_label = 'Wind Speed [$m$ $s^{-1}$]'
save_name = 'uv_wind'
variable_min = 0
variable_max = 10
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
elif variable_name == 'divergence':
variable_name = 'ua'
title_name = 'Horizontal Wind Divergence'
colorbar_label = 'Divergence [$10^{-6}$ $s^{-1}$]'
save_name = 'divergence'
variable_min = -2.5
variable_max = 2.5
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
# Make a list of all wrf files in data directory
wrflist = list()
for (dirpath, dirnames, filenames) in os.walk(data_dir):
wrflist += [os.path.join(dirpath, file) for file in filenames]
### Plotting Iteration ###
# Iterate over a list of hourly timesteps
time = list()
for i in range(start_hour, end_hour):
time = str(i).zfill(2)
# Iterate over all 5 minutes steps of hour
for j in range(0, 60, 5):
minutes = str(j).zfill(2)
# Load the netCDF files out of the wrflist
ncfile = [Dataset(x) for x in wrflist
if x.endswith('{}_{}:{}:00'.format(date, time, minutes))]
# Load variable(s)
if title_name == 'CAPE':
variable = getvar(ncfile, variable_name)[0,:]
elif title_name == 'CIN':
variable = getvar(ncfile, variable_name)[1,:]
elif variable_name == 'ctt':
variable = getvar(ncfile, variable_name, units='K')
elif variable_name == 'wspd_wdir':
variable = getvar(ncfile, variable_name)[0,:]
elif variable_name == 'QVAPOR':
variable = getvar(ncfile, variable_name)*1000 # convert to g/kg
else:
variable = getvar(ncfile, variable_name)
if variable_name == 'slp':
slp = variable.squeeze()
ua = getvar(ncfile, 'ua')
va = getvar(ncfile, 'va')
p = getvar(ncfile, 'pressure')
u_wind850 = interplevel(ua, p, 850)
v_wind850 = interplevel(va, p, 850)
u_wind850 = u_wind850.squeeze()
v_wind850 = v_wind850.squeeze()
u_wind500 = interplevel(ua, p, 500)
v_wind500 = interplevel(va, p, 500)
u_wind500 = u_wind500.squeeze()
v_wind500 = v_wind500.squeeze()
slp = ndimage.gaussian_filter(slp, sigma=3, order=0)
# Interpolating 3d data to a horizontal pressure level
if pressure_level != False:
p = getvar(ncfile, 'pressure')
variable_pressure = interplevel(variable, p,
pressure_level)
variable = variable_pressure
if variable_name == 'wspd_wdir':
ua = getvar(ncfile, 'ua')
va = getvar(ncfile, 'va')
u_pressure = interplevel(ua, p, pressure_level)
v_pressure = interplevel(va, p, pressure_level)
elif title_name == 'Updraft and Reflectivity':
reflectivity = getvar(ncfile, 'REFD_MAX')
elif title_name == 'Difference in Theta-E values':
variable = getvar(ncfile, variable_name)
p = getvar(ncfile, 'pressure')
variable_pressure1 = interplevel(variable, p, '950')
variable_pressure2 = interplevel(variable, p, '950')
elif variable_name == 'ua':
va = getvar(ncfile, 'va')
p = getvar(ncfile, 'pressure')
v_pressure = interplevel(va, p, pressure_level)
u_wind = variable.squeeze()
v_wind = v_pressure.squeeze()
u_wind.attrs['units']='meters/second'
v_wind.attrs['units']='meters/second'
lats, lons = latlon_coords(variable)
lats = lats.squeeze()
lons = lons.squeeze()
dx, dy = mpcalc.lat_lon_grid_deltas(to_np(lons), to_np(lats))
divergence = mpcalc.divergence(u_wind, v_wind, dx, dy, dim_order='yx')
divergence = divergence*1e3
# Define cart projection
lats, lons = latlon_coords(variable)
cart_proj = ccrs.LambertConformal(central_longitude=8.722206,
central_latitude=46.73585)
bounds = geo_bounds(wrfin=ncfile)
# Create figure
fig = plt.figure(figsize=(15, 10))
if variable_name == 'slp':
fig.patch.set_facecolor('k')
ax = plt.axes(projection=cart_proj)
### Set map extent ###
domain_extent = [3.701088, 13.814863, 43.85472,49.49499]
if subset == True:
ax.set_extent([subset_extent[0],subset_extent[1],
subset_extent[2],subset_extent[3]],
ccrs.PlateCarree())
else:
ax.set_extent([domain_extent[0]+0.7,domain_extent[1]-0.7,
domain_extent[2]+0.1,domain_extent[3]-0.1],
ccrs.PlateCarree())
# Plot contour of variables
levels_num = 11
levels = np.linspace(variable_min, variable_max, levels_num)
# Creating new colormap for diverging colormaps
def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):
new_cmap = LinearSegmentedColormap.from_list(
'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval,
b=maxval),
cmap(np.linspace(minval, maxval, n)))
return new_cmap
cmap = plt.get_cmap('RdYlBu')
if title_name == 'CIN':
cmap = ListedColormap(sns.cubehelix_palette(levels_num-1,
start=.5, rot=-.75, reverse=True))
variable_plot = plt.contourf(to_np(lons), to_np(lats), to_np(variable),
levels=levels, transform=ccrs.PlateCarree(), extend='max',
cmap=cmap)
initiation_color = 'r*'
elif variable_name == 'ctt':
cmap = ListedColormap(sns.cubehelix_palette(levels_num-1,
start=.5, rot=-.75, reverse=True))
variable_plot = plt.contourf(to_np(lons), to_np(lats), to_np(variable),
levels=levels, transform=ccrs.PlateCarree(), extend='both',
cmap=cmap)
initiation_color = 'r*'
elif variable_name == 'pvo':
cmap = plt.get_cmap('RdYlBu_r')
new_cmap = truncate_colormap(cmap, 0.05, 0.9)
new_norm = DivergingNorm(vmin=-1., vcenter=2., vmax=10)
variable_plot = plt.contourf(to_np(lons), to_np(lats), to_np(variable),
levels=levels, transform=ccrs.PlateCarree(),
cmap=new_cmap, extend='both', norm=new_norm)
initiation_color = 'k*'
elif variable_name == 'avo':
cmap = plt.get_cmap('RdYlBu_r')
new_cmap = truncate_colormap(cmap, 0.05, 0.9)
new_norm = DivergingNorm(vmin=variable_min, vcenter=0, vmax=variable_max)
variable_plot = plt.contourf(to_np(lons), to_np(lats), to_np(variable),
levels=levels, transform=ccrs.PlateCarree(),
cmap=new_cmap, extend='both', norm=new_norm)
initiation_color = 'k*'
elif variable_name == 'omega':
new_cmap = truncate_colormap(cmap, 0.05, 0.9)
new_norm = DivergingNorm(vmin=variable_min, vcenter=0, vmax=variable_max)
variable_plot = plt.contourf(to_np(lons), to_np(lats), to_np(variable),
levels=levels, transform=ccrs.PlateCarree(),
cmap=new_cmap, extend='both', norm=new_norm)
initiation_color = 'k*'
elif variable_name == 'ua':
new_cmap = truncate_colormap(cmap, 0.05, 0.9)
new_norm = DivergingNorm(vmin=variable_min, vcenter=0, vmax=variable_max)
variable_plot = plt.contourf(to_np(lons), to_np(lats), divergence,
levels=levels, transform=ccrs.PlateCarree(),
cmap=new_cmap, extend='both', norm=new_norm)
initiation_color = 'k*'
elif variable_name == 'UP_HELI_MAX' or variable_name == 'W_UP_MAX' or variable_name == 'QVAPOR':
cmap = ListedColormap(sns.cubehelix_palette(levels_num-1,
start=.5, rot=-.75))
variable_plot = plt.contourf(to_np(lons), to_np(lats),
to_np(variable), levels=levels, extend='max',
transform=ccrs.PlateCarree(),cmap=cmap)
initiation_color = 'r*'
elif variable_name == 'theta_e' or variable_name == 't2':
cmap = ListedColormap(sns.cubehelix_palette(levels_num-1,
start=.5, rot=-.75))
variable_plot = plt.contourf(to_np(lons), to_np(lats),
to_np(variable), levels=levels, extend='both',
transform=ccrs.PlateCarree(),cmap=cmap)
initiation_color = 'r*'
elif variable_name == 'REFD_MAX':
levels = np.arange(5., 75., 5.)
dbz_rgb = np.array([[4,233,231],
[1,159,244], [3,0,244],
[2,253,2], [1,197,1],
[0,142,0], [253,248,2],
[229,188,0], [253,149,0],
[253,0,0], [212,0,0],
[188,0,0],[248,0,253],
[152,84,198]], np.float32) / 255.0
dbz_cmap, dbz_norm = from_levels_and_colors(levels, dbz_rgb,
extend='max')
variable_plot = plt.contourf(to_np(lons), to_np(lats),
to_np(variable), levels=levels, extend='max',
transform=ccrs.PlateCarree(), cmap=dbz_cmap,
norm=dbz_norm)
initiation_color = 'r*'
elif variable_name == 'slp':
ax.background_patch.set_fill(False)
wslice = slice(1, None, 12)
# Plot 850-hPa wind vectors
vectors850 = ax.quiver(to_np(lons)[wslice, wslice],
to_np(lats)[wslice, wslice],
to_np(u_wind850)[wslice, wslice],
to_np(v_wind850)[wslice, wslice],
headlength=4, headwidth=3, scale=400, color='gold',
label='850mb wind', transform=ccrs.PlateCarree(),
zorder=2)
# Plot 500-hPa wind vectors
vectors500 = ax.quiver(to_np(lons)[wslice, wslice],
to_np(lats)[wslice, wslice],
to_np(u_wind500)[wslice, wslice],
to_np(v_wind500)[wslice, wslice],
headlength=4, headwidth=3, scale=400,
color='cornflowerblue', zorder=2,
label='500mb wind', transform=ccrs.PlateCarree())
# Plot 500-850 shear
shear = ax.quiver(to_np(lons[wslice, wslice]),
to_np(lats[wslice, wslice]),
to_np(u_wind500[wslice, wslice]) -
to_np(u_wind850[wslice, wslice]),
to_np(v_wind500[wslice, wslice]) -
to_np(v_wind850[wslice, wslice]),
headlength=4, headwidth=3, scale=400,
color='deeppink', zorder=2,
label='500-850mb shear', transform=ccrs.PlateCarree())
contour = ax.contour(to_np(lons), to_np(lats), slp, levels=levels,
colors='lime', linewidths=2, alpha=0.5, zorder=1,
transform=ccrs.PlateCarree())
ax.clabel(contour, fontsize=12, inline=1, inline_spacing=4, fmt='%i')
# Add a legend
ax.legend(('850mb wind', '500mb wind', '500-850mb shear'), loc=4)
# Manually set colors for legend
legend = ax.get_legend()
legend.legendHandles[0].set_color('gold')
legend.legendHandles[1].set_color('cornflowerblue')
legend.legendHandles[2].set_color('deeppink')
initiation_color = 'w*'
else:
cmap = ListedColormap(sns.cubehelix_palette(10,
start=.5, rot=-.75))
variable_plot = plt.contourf(to_np(lons), to_np(lats),
to_np(variable), levels=levels,
transform=ccrs.PlateCarree(),cmap=cmap)
initiation_color = 'r*'
# Plot reflectivity contours with colorbar
if title_name == 'Updraft and Reflectivity':
dbz_levels = np.arange(35., 75., 5.)
dbz_rgb = np.array([[253,248,2],
[229,188,0], [253,149,0],
[253,0,0], [212,0,0],
[188,0,0],[248,0,253],
[152,84,198]], np.float32) / 255.0
dbz_cmap, dbz_norm = from_levels_and_colors(dbz_levels, dbz_rgb,
extend='max')
contours = plt.contour(to_np(lons), to_np(lats),
to_np(reflectivity),
levels=dbz_levels,
transform=ccrs.PlateCarree(),
cmap=dbz_cmap, norm=dbz_norm,
linewidths=1)
cbar_refl = mpu.colorbar(contours, ax, orientation='horizontal', aspect=10,
shrink=.5, pad=0.05)
cbar_refl.set_label('Maximum Derived Radar Reflectivity' '[$dBZ$]', fontsize=12.5)
colorbar_lines = cbar_refl.ax.get_children()
colorbar_lines[0].set_linewidths([10]*5)
# Add wind quivers for every 10th data point
if variable_name == 'wspd_wdir':
plt.quiver(to_np(lons[::10,::10]), to_np(lats[::10,::10]),
to_np(u_pressure[::10, ::10]),
to_np(v_pressure[::10, ::10]),
transform=ccrs.PlateCarree())
# Plot colorbar
if variable_name == 'slp':
pass
else:
cbar = mpu.colorbar(variable_plot, ax, orientation='vertical', aspect=40,
shrink=.05, pad=0.05)
cbar.set_label(colorbar_label, fontsize=15)
cbar.set_ticks(levels)
# Add borders and coastlines
if variable_name == 'slp':
ax.add_feature(cfeature.BORDERS.with_scale('10m'),
edgecolor='white', linewidth=2)
ax.add_feature(cfeature.COASTLINE.with_scale('10m'),
edgecolor='white', linewidth=2)
else:
ax.add_feature(cfeature.BORDERS.with_scale('10m'),
linewidth=0.8)
ax.add_feature(cfeature.COASTLINE.with_scale('10m'),
linewidth=0.8)
### Add initiation location ###
if initiation == True:
ax.plot(initiation_location.lon, initiation_location.lat,
initiation_color, markersize=20, transform=ccrs.PlateCarree())
# Add gridlines
lon = np.arange(0, 20, 1)
lat = np.arange(40, 60, 1)
gl = ax.gridlines(xlocs=lon, ylocs=lat, zorder=3)
# Add tick labels
mpu.yticklabels(lat, ax=ax, fontsize=12.5)
mpu.xticklabels(lon, ax=ax, fontsize=12.5)
# Make nicetime
file_name = '{}wrfout_d02_{}_{}:{}:00'.format(data_dir,
date, time, minutes)
xr_file = xr.open_dataset(file_name)
nicetime = pd.to_datetime(xr_file.QVAPOR.isel(Time=0).XTIME.values)
nicetime = nicetime.strftime('%Y-%m-%d %H:%M')
# Add plot title
if pressure_level != False:
ax.set_title('{} @ {} hPa'.format(title_name, pressure_level),
loc='left', fontsize=15)
ax.set_title('Valid time: {} UTC'.format(nicetime),
loc='right', fontsize=15)
else:
if variable_name == 'slp':
ax.set_title(title_name, loc='left', fontsize=15, color='white')
ax.set_title('Valid time: {} UTC'.format(nicetime),
loc='right', fontsize=15, color='white')
else:
ax.set_title(title_name, loc='left', fontsize=20)
ax.set_title('Valid time: {} UTC'.format(nicetime),
loc='right', fontsize=15)
plt.show()
### Save figure ###
if save == True:
if pressure_level != False:
if subset == True:
fig.savefig('{}/{}/horizontal_map_{}_subset_{}_{}_{}:{}.png'.format(
save_dir, save_name, save_name, pressure_level, date, time,
minutes), bbox_inches='tight', dpi=300)
else:
fig.savefig('{}/{}/horizontal_map_{}_{}_{}_{}:{}.png'.format(
save_dir, save_name, save_name, pressure_level, date, time,
minutes), bbox_inches='tight', dpi=300)
else:
if subset == True:
fig.savefig('{}/{}/horizontal_map_{}_subset_{}_{}:{}.png'.format(
save_dir, save_name, save_name, date, time, minutes),
bbox_inches='tight', dpi=300, facecolor=fig.get_facecolor())
else:
fig.savefig('{}/{}/horizontal_map_{}_{}_{}:{}.png'.format(
save_dir, save_name, save_name, date, time, minutes),
bbox_inches='tight', dpi=300, facecolor=fig.get_facecolor())
### Make a GIF from the plots ###
if gif == True:
# Predifine some variables
gif_data_dir = save_dir + save_name
gif_save_dir = '{}gifs/'.format(save_dir)
gif_save_name = 'horizontal_map_{}.gif'.format(save_name)
# GIF creating procedure
os.chdir(gif_data_dir)
image_folder = os.fsencode(gif_data_dir)
filenames = []
for file in os.listdir(image_folder):
filename = os.fsdecode(file)
if filename.endswith( ('.png') ):
filenames.append(filename)
filenames.sort()
images = list(map(lambda filename: imageio.imread(filename),
filenames))
imageio.mimsave(os.path.join(gif_save_dir + gif_save_name),
images, duration = 0.50)
def test(self):
try:
from netCDF4 import Dataset as NetCDF
except:
pass
try:
from PyNIO import Nio
except:
pass
if not multi:
timeidx = 0
if not pynio:
in_wrfnc = NetCDF(wrf_in)
else:
# Note: Python doesn't like it if you reassign an outer scoped
# variable (wrf_in in this case)
if not wrf_in.endswith(".nc"):
wrf_file = wrf_in + ".nc"
else:
wrf_file = wrf_in
in_wrfnc = Nio.open_file(wrf_file)
else:
timeidx = None
if not pynio:
nc = NetCDF(wrf_in)
in_wrfnc = [nc for i in xrange(repeat)]
else:
if not wrf_in.endswith(".nc"):
wrf_file = wrf_in + ".nc"
else:
wrf_file = wrf_in
nc = Nio.open_file(wrf_file)
in_wrfnc = [nc for i in xrange(repeat)]
if (varname == "interplevel"):
ref_ht_500 = _get_refvals(referent, "z_500", repeat, multi)
hts = getvar(in_wrfnc, "z", timeidx=timeidx)
p = getvar(in_wrfnc, "pressure", timeidx=timeidx)
# Make sure the numpy versions work first
hts_500 = interplevel(to_np(hts), to_np(p), 500)
hts_500 = interplevel(hts, p, 500)
nt.assert_allclose(to_np(hts_500), ref_ht_500)
elif (varname == "vertcross"):
ref_ht_cross = _get_refvals(referent, "ht_cross", repeat, multi)
ref_p_cross = _get_refvals(referent, "p_cross", repeat, multi)
hts = getvar(in_wrfnc, "z", timeidx=timeidx)
p = getvar(in_wrfnc, "pressure", timeidx=timeidx)
pivot_point = CoordPair(hts.shape[-1] / 2, hts.shape[-2] / 2)
#ht_cross = vertcross(to_np(hts), p, pivot_point=pivot_point,
# angle=90., latlon=True)
# Make sure the numpy versions work first
ht_cross = vertcross(to_np(hts),
to_np(p),
pivot_point=pivot_point,
angle=90.)
ht_cross = vertcross(hts, p, pivot_point=pivot_point, angle=90.)
# Note: Until the bug is fixed in NCL, the wrf-python cross
# sections will contain one extra point
nt.assert_allclose(to_np(ht_cross)[..., 0:-1],
ref_ht_cross,
rtol=.01)
# Test the manual projection method with lat/lon
lats = hts.coords["XLAT"]
lons = hts.coords["XLONG"]
ll_point = ll_points(lats, lons)
pivot = CoordPair(lat=lats[int(lats.shape[-2] / 2),
int(lats.shape[-1] / 2)],
lon=lons[int(lons.shape[-2] / 2),
int(lons.shape[-1] / 2)])
v1 = vertcross(hts,
p,
wrfin=in_wrfnc,
pivot_point=pivot_point,
angle=90.0)
v2 = vertcross(hts,
p,
projection=hts.attrs["projection"],
ll_point=ll_point,
pivot_point=pivot_point,
angle=90.)
nt.assert_allclose(to_np(v1), to_np(v2), rtol=.01)
# Test opposite
p_cross1 = vertcross(p, hts, pivot_point=pivot_point, angle=90.0)
nt.assert_allclose(to_np(p_cross1)[..., 0:-1],
ref_p_cross,
rtol=.01)
# Test point to point
start_point = CoordPair(0, hts.shape[-2] / 2)
end_point = CoordPair(-1, hts.shape[-2] / 2)
p_cross2 = vertcross(p,
hts,
start_point=start_point,
end_point=end_point)
nt.assert_allclose(to_np(p_cross1), to_np(p_cross2))
elif (varname == "interpline"):
ref_t2_line = _get_refvals(referent, "t2_line", repeat, multi)
t2 = getvar(in_wrfnc, "T2", timeidx=timeidx)
pivot_point = CoordPair(t2.shape[-1] / 2, t2.shape[-2] / 2)
#t2_line1 = interpline(to_np(t2), pivot_point=pivot_point,
# angle=90.0, latlon=True)
# Make sure the numpy version works
t2_line1 = interpline(to_np(t2),
pivot_point=pivot_point,
angle=90.0)
t2_line1 = interpline(t2, pivot_point=pivot_point, angle=90.0)
# Note: After NCL is fixed, remove the slice.
nt.assert_allclose(to_np(t2_line1)[..., 0:-1], ref_t2_line)
# Test the manual projection method with lat/lon
lats = t2.coords["XLAT"]
lons = t2.coords["XLONG"]
ll_point = ll_points(lats, lons)
pivot = CoordPair(lat=lats[int(lats.shape[-2] / 2),
int(lats.shape[-1] / 2)],
lon=lons[int(lons.shape[-2] / 2),
int(lons.shape[-1] / 2)])
l1 = interpline(t2,
wrfin=in_wrfnc,
pivot_point=pivot_point,
angle=90.0)
l2 = interpline(t2,
projection=t2.attrs["projection"],
ll_point=ll_point,
pivot_point=pivot_point,
angle=90.)
nt.assert_allclose(to_np(l1), to_np(l2), rtol=.01)
# Test point to point
start_point = CoordPair(0, t2.shape[-2] / 2)
end_point = CoordPair(-1, t2.shape[-2] / 2)
t2_line2 = interpline(t2,
start_point=start_point,
end_point=end_point)
nt.assert_allclose(to_np(t2_line1), to_np(t2_line2))
elif (varname == "vinterp"):
# Tk to theta
fld_tk_theta = _get_refvals(referent, "fld_tk_theta", repeat,
multi)
fld_tk_theta = np.squeeze(fld_tk_theta)
tk = getvar(in_wrfnc, "temp", timeidx=timeidx, units="k")
interp_levels = [200, 300, 500, 1000]
# Make sure the numpy version works
field = vinterp(in_wrfnc,
field=to_np(tk),
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = vinterp(in_wrfnc,
field=tk,
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
tol = 5 / 100.
atol = 0.0001
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_tk_theta)))
nt.assert_allclose(to_np(field), fld_tk_theta, tol, atol)
# Tk to theta-e
fld_tk_theta_e = _get_refvals(referent, "fld_tk_theta_e", repeat,
multi)
fld_tk_theta_e = np.squeeze(fld_tk_theta_e)
interp_levels = [200, 300, 500, 1000]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="theta-e",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
tol = 3 / 100.
atol = 50.0001
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_tk_theta_e)/fld_tk_theta_e)*100)
nt.assert_allclose(to_np(field), fld_tk_theta_e, tol, atol)
# Tk to pressure
fld_tk_pres = _get_refvals(referent, "fld_tk_pres", repeat, multi)
fld_tk_pres = np.squeeze(fld_tk_pres)
interp_levels = [850, 500]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_tk_pres)))
nt.assert_allclose(to_np(field), fld_tk_pres, tol, atol)
# Tk to geoht_msl
fld_tk_ght_msl = _get_refvals(referent, "fld_tk_ght_msl", repeat,
multi)
fld_tk_ght_msl = np.squeeze(fld_tk_ght_msl)
interp_levels = [1, 2]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="ght_msl",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_tk_ght_msl)))
nt.assert_allclose(to_np(field), fld_tk_ght_msl, tol, atol)
# Tk to geoht_agl
fld_tk_ght_agl = _get_refvals(referent, "fld_tk_ght_agl", repeat,
multi)
fld_tk_ght_agl = np.squeeze(fld_tk_ght_agl)
interp_levels = [1, 2]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="ght_agl",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_tk_ght_agl)))
nt.assert_allclose(to_np(field), fld_tk_ght_agl, tol, atol)
# Hgt to pressure
fld_ht_pres = _get_refvals(referent, "fld_ht_pres", repeat, multi)
fld_ht_pres = np.squeeze(fld_ht_pres)
z = getvar(in_wrfnc, "height", timeidx=timeidx, units="m")
interp_levels = [500, 50]
field = vinterp(in_wrfnc,
field=z,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="ght",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_ht_pres)))
nt.assert_allclose(to_np(field), fld_ht_pres, tol, atol)
# Pressure to theta
fld_pres_theta = _get_refvals(referent, "fld_pres_theta", repeat,
multi)
fld_pres_theta = np.squeeze(fld_pres_theta)
p = getvar(in_wrfnc, "pressure", timeidx=timeidx)
interp_levels = [200, 300, 500, 1000]
field = vinterp(in_wrfnc,
field=p,
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="pressure",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_pres_theta)))
nt.assert_allclose(to_np(field), fld_pres_theta, tol, atol)
# Theta-e to pres
fld_thetae_pres = _get_refvals(referent, "fld_thetae_pres", repeat,
multi)
fld_thetae_pres = np.squeeze(fld_thetae_pres)
eth = getvar(in_wrfnc, "eth", timeidx=timeidx)
interp_levels = [850, 500, 5]
field = vinterp(in_wrfnc,
field=eth,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="theta-e",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_thetae_pres)))
nt.assert_allclose(to_np(field), fld_thetae_pres, tol, atol)
from wrf import to_np, getvar, CoordPair, vertcross import matplotlib.gridspec as gridspec from netCDF4 import Dataset, num2date from pyroms import vgrid # from octant import tools import numpy as np import matplotlib.pyplot as pltexitr ####### WRF options ####### # WRF input filename_wrf = "/media/ueslei/Ueslei/INPE/2014/Outputs/WRF/wrf_I_t01.nc" filename_wr = "/media/ueslei/Ueslei/INPE/2014/Outputs/WR/wr_I_t01.nc" filename_wrs = "/media/ueslei/Ueslei/INPE/2014/Outputs/WRS/wrs_I_t01.nc" # Create the start point and end point for the cross section start_point = CoordPair(lat=-41, lon=-60) end_point = CoordPair(lat=-41, lon=-30) # WRF max height (meters) levels = np.arange(0, 601., 1.) # WRF grid height spacing wrf_spacing = 100 # WRF timestep timeidx = 80 ####### ROMS options ####### # ROMS input path = '/media/ueslei/Ueslei/INPE/2014/Outputs/WRS/' name = 'cbm12_ocean_his.nc' # ROMS time ntime = 40
from cartopy import crs
from cartopy.feature import NaturalEarthFeature, COLORS
from netCDF4 import Dataset
from wrf import (getvar, to_np, get_cartopy, latlon_coords, vertcross,cartopy_xlim, cartopy_ylim, interpline, CoordPair, ALL_TIMES)
from datetime import datetime, timedelta
## SE CARGA LA SALIDA SIN POSTPROCESAR
wrf_file = Dataset("...")
## cantidad de tiempos
tpos = (getvar(wrf_file, "z", ALL_TIMES).shape)[0]
# agregar el inicio de la corrida, y luego ver el espaciado de cada una
t_ini = datetime.strptime('17-09-2012 18:00:00', '%d-%m-%Y %H:%M:%S')
# Definir lat y lon del corte vertical
cross_start = CoordPair(lat=-24.75, lon=-68.5)
cross_end = CoordPair(lat=-24.75, lon=-62)
if tpos == 0 :
print('ES UN SOLO TPO')
exit()
# Se consiguen las variables del WRF
for tidx in range(0,tpos) :
#tidx = 8 ## es el tiempo de la corrida, desde cero hasta el tiempo final
## PARA TERRENO
ht = getvar(wrf_file, "z", tidx)
ter = getvar(wrf_file, "ter", tidx)
w = getvar(wrf_file, 'wa', tidx)
## VARIABLE PARA GRAFICAR
## Una lista de variables : https://wrf-python.readthedocs.io/en/latest/user_api/generated/wrf.getvar.html#wrf.getvar
var = getvar(wrf_file, "theta", tidx)
def test(self):
try:
from netCDF4 import Dataset as NetCDF
except ImportError:
pass
try:
import Nio
except ImportError:
pass
timeidx = 0 if not multi else None
pat = os.path.join(dir, pattern)
wrf_files = glob.glob(pat)
wrf_files.sort()
wrfin = []
for fname in wrf_files:
if not pynio:
f = NetCDF(fname)
try:
f.set_always_mask(False)
except AttributeError:
pass
wrfin.append(f)
else:
if not fname.endswith(".nc"):
_fname = fname + ".nc"
else:
_fname = fname
f = Nio.open_file(_fname)
wrfin.append(f)
if (varname == "interplevel"):
ref_ht_500 = _get_refvals(referent, "z_500", multi)
ref_p_5000 = _get_refvals(referent, "p_5000", multi)
ref_ht_multi = _get_refvals(referent, "z_multi", multi)
ref_p_multi = _get_refvals(referent, "p_multi", multi)
ref_ht2_500 = _get_refvals(referent, "z2_500", multi)
ref_p2_5000 = _get_refvals(referent, "p2_5000", multi)
ref_ht2_multi = _get_refvals(referent, "z2_multi", multi)
ref_p2_multi = _get_refvals(referent, "p2_multi", multi)
ref_p_lev2d = _get_refvals(referent, "p_lev2d", multi)
hts = getvar(wrfin, "z", timeidx=timeidx)
p = getvar(wrfin, "pressure", timeidx=timeidx)
wspd_wdir = getvar(wrfin, "wspd_wdir", timeidx=timeidx)
# Make sure the numpy versions work first
hts_500 = interplevel(to_np(hts), to_np(p), 500)
hts_500 = interplevel(hts, p, 500)
# Note: the '*2*' versions in the reference are testing
# against the new version of interplevel in NCL
nt.assert_allclose(to_np(hts_500), ref_ht_500)
nt.assert_allclose(to_np(hts_500), ref_ht2_500)
# Make sure the numpy versions work first
p_5000 = interplevel(to_np(p), to_np(hts), 5000)
p_5000 = interplevel(p, hts, 5000)
nt.assert_allclose(to_np(p_5000), ref_p_5000)
nt.assert_allclose(to_np(p_5000), ref_p2_5000)
hts_multi = interplevel(to_np(hts), to_np(p),
[1000., 850., 500., 250.])
hts_multi = interplevel(hts, p, [1000., 850., 500., 250.])
nt.assert_allclose(to_np(hts_multi), ref_ht_multi)
nt.assert_allclose(to_np(hts_multi), ref_ht2_multi)
p_multi = interplevel(to_np(p), to_np(hts),
[500., 2500., 5000., 10000.])
p_multi = interplevel(p, hts, [500., 2500., 5000., 10000.])
nt.assert_allclose(to_np(p_multi), ref_p_multi)
nt.assert_allclose(to_np(p_multi), ref_p2_multi)
pblh = getvar(wrfin, "PBLH", timeidx=timeidx)
p_lev2d = interplevel(to_np(p), to_np(hts), to_np(pblh))
p_lev2d = interplevel(p, hts, pblh)
nt.assert_allclose(to_np(p_lev2d), ref_p_lev2d)
# Just make sure these run below
wspd_wdir_500 = interplevel(to_np(wspd_wdir), to_np(p), 500)
wspd_wdir_500 = interplevel(wspd_wdir, p, 500)
wspd_wdir_multi = interplevel(to_np(wspd_wdir), to_np(p),
[1000, 500, 250])
wdpd_wdir_multi = interplevel(wspd_wdir, p, [1000, 500, 250])
wspd_wdir_pblh = interplevel(to_np(wspd_wdir), to_np(hts), pblh)
wspd_wdir_pblh = interplevel(wspd_wdir, hts, pblh)
if multi:
wspd_wdir_pblh_2 = interplevel(to_np(wspd_wdir), to_np(hts),
pblh[0, :])
wspd_wdir_pblh_2 = interplevel(wspd_wdir, hts, pblh[0, :])
# Since PBLH doesn't change in this case, it should match
# the 0 time from previous computation. Note that this
# only works when the data has 1 time step that is repeated.
# If you use a different case with multiple times,
# this will probably fail.
nt.assert_allclose(to_np(wspd_wdir_pblh_2[:, 0, :]),
to_np(wspd_wdir_pblh[:, 0, :]))
nt.assert_allclose(to_np(wspd_wdir_pblh_2[:, -1, :]),
to_np(wspd_wdir_pblh[:, 0, :]))
elif (varname == "vertcross"):
ref_ht_cross = _get_refvals(referent, "ht_cross", multi)
ref_p_cross = _get_refvals(referent, "p_cross", multi)
ref_ht_vertcross1 = _get_refvals(referent, "ht_vertcross1", multi)
ref_ht_vertcross2 = _get_refvals(referent, "ht_vertcross2", multi)
ref_ht_vertcross3 = _get_refvals(referent, "ht_vertcross3", multi)
hts = getvar(wrfin, "z", timeidx=timeidx)
p = getvar(wrfin, "pressure", timeidx=timeidx)
pivot_point = CoordPair(hts.shape[-1] / 2, hts.shape[-2] / 2)
# Beginning in wrf-python 1.3, users can select number of levels.
# Originally, for pressure, dz was 10, so let's back calculate
# the number of levels.
p_max = np.floor(np.amax(p) / 10) * 10 # bottom value
p_min = np.ceil(np.amin(p) / 10) * 10 # top value
p_autolevels = int((p_max - p_min) / 10)
# Make sure the numpy versions work first
ht_cross = vertcross(to_np(hts),
to_np(p),
pivot_point=pivot_point,
angle=90.,
autolevels=p_autolevels)
ht_cross = vertcross(hts,
p,
pivot_point=pivot_point,
angle=90.,
autolevels=p_autolevels)
try:
nt.assert_allclose(to_np(ht_cross), ref_ht_cross, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(ht_cross) - ref_ht_cross)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
lats = hts.coords["XLAT"]
lons = hts.coords["XLONG"]
# Test the manual projection method with lat/lon
# Only do this for the non-multi case, since the domain
# might be moving
if not multi:
if lats.ndim > 2: # moving nest
lats = lats[0, :]
lons = lons[0, :]
ll_point = ll_points(lats, lons)
pivot = CoordPair(lat=lats[int(lats.shape[-2] / 2),
int(lats.shape[-1] / 2)],
lon=lons[int(lons.shape[-2] / 2),
int(lons.shape[-1] / 2)])
v1 = vertcross(hts,
p,
wrfin=wrfin,
pivot_point=pivot_point,
angle=90.0)
v2 = vertcross(hts,
p,
projection=hts.attrs["projection"],
ll_point=ll_point,
pivot_point=pivot_point,
angle=90.)
try:
nt.assert_allclose(to_np(v1), to_np(v2), atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(v1) - to_np(v2))
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Test opposite
p_cross1 = vertcross(p, hts, pivot_point=pivot_point, angle=90.0)
try:
nt.assert_allclose(to_np(p_cross1), ref_p_cross, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(p_cross1) - ref_p_cross)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Test point to point
start_point = CoordPair(0, hts.shape[-2] / 2)
end_point = CoordPair(-1, hts.shape[-2] / 2)
p_cross2 = vertcross(p,
hts,
start_point=start_point,
end_point=end_point)
try:
nt.assert_allclose(to_np(p_cross1),
to_np(p_cross2),
atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(p_cross1) - to_np(p_cross2))
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Check the new vertcross routine
pivot_point = CoordPair(hts.shape[-1] / 2, hts.shape[-2] / 2)
ht_cross = vertcross(hts,
p,
pivot_point=pivot_point,
angle=90.,
latlon=True)
try:
nt.assert_allclose(to_np(ht_cross),
to_np(ref_ht_vertcross1),
atol=.005)
except AssertionError:
absdiff = np.abs(to_np(ht_cross) - to_np(ref_ht_vertcross1))
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
levels = [1000., 850., 700., 500., 250.]
ht_cross = vertcross(hts,
p,
pivot_point=pivot_point,
angle=90.,
levels=levels,
latlon=True)
try:
nt.assert_allclose(to_np(ht_cross),
to_np(ref_ht_vertcross2),
atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(ht_cross) - to_np(ref_ht_vertcross2))
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
idxs = (0, slice(None)) if lats.ndim > 2 else (slice(None), )
start_lat = np.amin(
lats[idxs]) + .25 * (np.amax(lats[idxs]) - np.amin(lats[idxs]))
end_lat = np.amin(
lats[idxs]) + .65 * (np.amax(lats[idxs]) - np.amin(lats[idxs]))
start_lon = np.amin(
lons[idxs]) + .25 * (np.amax(lons[idxs]) - np.amin(lons[idxs]))
end_lon = np.amin(
lons[idxs]) + .65 * (np.amax(lons[idxs]) - np.amin(lons[idxs]))
start_point = CoordPair(lat=start_lat, lon=start_lon)
end_point = CoordPair(lat=end_lat, lon=end_lon)
ll_point = ll_points(lats[idxs], lons[idxs])
ht_cross = vertcross(hts,
p,
start_point=start_point,
end_point=end_point,
projection=hts.attrs["projection"],
ll_point=ll_point,
latlon=True,
autolevels=1000)
try:
nt.assert_allclose(to_np(ht_cross),
to_np(ref_ht_vertcross3),
atol=.01)
except AssertionError:
absdiff = np.abs(to_np(ht_cross) - to_np(ref_ht_vertcross3))
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
if multi:
ntimes = hts.shape[0]
for t in range(ntimes):
hts = getvar(wrfin, "z", timeidx=t)
p = getvar(wrfin, "pressure", timeidx=t)
ht_cross = vertcross(hts,
p,
start_point=start_point,
end_point=end_point,
wrfin=wrfin,
timeidx=t,
latlon=True,
autolevels=1000)
refname = "ht_vertcross_t{}".format(t)
ref_ht_vertcross = _get_refvals(referent, refname, False)
try:
nt.assert_allclose(to_np(ht_cross),
to_np(ref_ht_vertcross),
atol=.01)
except AssertionError:
absdiff = np.abs(
to_np(ht_cross) - to_np(ref_ht_vertcross))
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
elif (varname == "interpline"):
ref_t2_line = _get_refvals(referent, "t2_line", multi)
ref_t2_line2 = _get_refvals(referent, "t2_line2", multi)
ref_t2_line3 = _get_refvals(referent, "t2_line3", multi)
t2 = getvar(wrfin, "T2", timeidx=timeidx)
pivot_point = CoordPair(t2.shape[-1] / 2, t2.shape[-2] / 2)
# Make sure the numpy version works
t2_line1 = interpline(to_np(t2),
pivot_point=pivot_point,
angle=90.0)
t2_line1 = interpline(t2, pivot_point=pivot_point, angle=90.0)
nt.assert_allclose(to_np(t2_line1), ref_t2_line)
# Test the new NCL wrf_user_interplevel result
try:
nt.assert_allclose(to_np(t2_line1), ref_t2_line2)
except AssertionError:
absdiff = np.abs(to_np(t2_line1) - ref_t2_line2)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Test the manual projection method with lat/lon
lats = t2.coords["XLAT"]
lons = t2.coords["XLONG"]
if multi:
if lats.ndim > 2: # moving nest
lats = lats[0, :]
lons = lons[0, :]
ll_point = ll_points(lats, lons)
pivot = CoordPair(lat=lats[int(lats.shape[-2] / 2),
int(lats.shape[-1] / 2)],
lon=lons[int(lons.shape[-2] / 2),
int(lons.shape[-1] / 2)])
l1 = interpline(t2,
wrfin=wrfin,
pivot_point=pivot_point,
angle=90.0)
l2 = interpline(t2,
projection=t2.attrs["projection"],
ll_point=ll_point,
pivot_point=pivot_point,
angle=90.)
try:
nt.assert_allclose(to_np(l1), to_np(l2))
except AssertionError:
absdiff = np.abs(to_np(l1) - to_np(l2))
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Test point to point
start_point = CoordPair(0, t2.shape[-2] / 2)
end_point = CoordPair(-1, t2.shape[-2] / 2)
t2_line2 = interpline(t2,
start_point=start_point,
end_point=end_point)
try:
nt.assert_allclose(to_np(t2_line1), to_np(t2_line2))
except AssertionError:
absdiff = np.abs(to_np(t2_line1) - t2_line2)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Now test the start/end with lat/lon points
start_lat = float(
np.amin(lats) + .25 * (np.amax(lats) - np.amin(lats)))
end_lat = float(
np.amin(lats) + .65 * (np.amax(lats) - np.amin(lats)))
start_lon = float(
np.amin(lons) + .25 * (np.amax(lons) - np.amin(lons)))
end_lon = float(
np.amin(lons) + .65 * (np.amax(lons) - np.amin(lons)))
start_point = CoordPair(lat=start_lat, lon=start_lon)
end_point = CoordPair(lat=end_lat, lon=end_lon)
t2_line3 = interpline(t2,
wrfin=wrfin,
timeidx=0,
start_point=start_point,
end_point=end_point,
latlon=True)
try:
nt.assert_allclose(to_np(t2_line3), ref_t2_line3, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(t2_line3) - ref_t2_line3)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Test all time steps
if multi:
refnc = NetCDF(referent)
ntimes = t2.shape[0]
for t in range(ntimes):
t2 = getvar(wrfin, "T2", timeidx=t)
line = interpline(t2,
wrfin=wrfin,
timeidx=t,
start_point=start_point,
end_point=end_point,
latlon=True)
refname = "t2_line_t{}".format(t)
refline = refnc.variables[refname][:]
try:
nt.assert_allclose(to_np(line),
to_np(refline),
atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(line) - refline)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
refnc.close()
# Test NCLs single time case
if not multi:
refnc = NetCDF(referent)
ref_t2_line4 = refnc.variables["t2_line4"][:]
t2 = getvar(wrfin, "T2", timeidx=0)
line = interpline(t2,
wrfin=wrfin,
timeidx=0,
start_point=start_point,
end_point=end_point,
latlon=True)
try:
nt.assert_allclose(to_np(line),
to_np(ref_t2_line4),
atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(line) - ref_t2_line4)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
finally:
refnc.close()
elif (varname == "vinterp"):
# Tk to theta
fld_tk_theta = _get_refvals(referent, "fld_tk_theta", multi)
fld_tk_theta = np.squeeze(fld_tk_theta)
tk = getvar(wrfin, "temp", timeidx=timeidx, units="k")
interp_levels = [200, 300, 500, 1000]
# Make sure the numpy version works
field = vinterp(wrfin,
field=to_np(tk),
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = vinterp(wrfin,
field=tk,
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
tol = 0 / 100.
atol = 0.0001
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_tk_theta)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_tk_theta)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Tk to theta-e
fld_tk_theta_e = _get_refvals(referent, "fld_tk_theta_e", multi)
fld_tk_theta_e = np.squeeze(fld_tk_theta_e)
interp_levels = [200, 300, 500, 1000]
field = vinterp(wrfin,
field=tk,
vert_coord="theta-e",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_tk_theta_e, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_tk_theta_e)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Tk to pressure
fld_tk_pres = _get_refvals(referent, "fld_tk_pres", multi)
fld_tk_pres = np.squeeze(fld_tk_pres)
interp_levels = [850, 500]
field = vinterp(wrfin,
field=tk,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_tk_pres, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_tk_pres)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Tk to geoht_msl
fld_tk_ght_msl = _get_refvals(referent, "fld_tk_ght_msl", multi)
fld_tk_ght_msl = np.squeeze(fld_tk_ght_msl)
interp_levels = [1, 2]
field = vinterp(wrfin,
field=tk,
vert_coord="ght_msl",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_tk_ght_msl, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_tk_ght_msl)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Tk to geoht_agl
fld_tk_ght_agl = _get_refvals(referent, "fld_tk_ght_agl", multi)
fld_tk_ght_agl = np.squeeze(fld_tk_ght_agl)
interp_levels = [1, 2]
field = vinterp(wrfin,
field=tk,
vert_coord="ght_agl",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_tk_ght_agl, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_tk_ght_agl)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Hgt to pressure
fld_ht_pres = _get_refvals(referent, "fld_ht_pres", multi)
fld_ht_pres = np.squeeze(fld_ht_pres)
z = getvar(wrfin, "height", timeidx=timeidx, units="m")
interp_levels = [500, 50]
field = vinterp(wrfin,
field=z,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="ght",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_ht_pres, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_ht_pres)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Pressure to theta
fld_pres_theta = _get_refvals(referent, "fld_pres_theta", multi)
fld_pres_theta = np.squeeze(fld_pres_theta)
p = getvar(wrfin, "pressure", timeidx=timeidx)
interp_levels = [200, 300, 500, 1000]
field = vinterp(wrfin,
field=p,
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="pressure",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_pres_theta, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_pres_theta)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Theta-e to pres
fld_thetae_pres = _get_refvals(referent, "fld_thetae_pres", multi)
fld_thetae_pres = np.squeeze(fld_thetae_pres)
eth = getvar(wrfin, "eth", timeidx=timeidx)
interp_levels = [850, 500, 5]
field = vinterp(wrfin,
field=eth,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="theta-e",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_thetae_pres, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_thetae_pres)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
from matplotlib.colors import from_levels_and_colors
from cartopy import crs
from cartopy.feature import NaturalEarthFeature, COLORS
from netCDF4 import Dataset
from wrf import (getvar, to_np, get_cartopy, latlon_coords, vertcross,
cartopy_xlim, cartopy_ylim, interpline, CoordPair)
root_dir = '/data/mac/giyoung/MAC_WRFThompson/'
nc = Dataset(root_dir +
'2_Nisg80_ThompsonDefault/wrfout_d01_2015-11-27_00:00:00')
# Define the cross section start and end points
start_point = CoordPair(lat=-77.0, lon=-25.0)
end_point = CoordPair(lat=-72.0, lon=-25.0)
# Get the WRF variables
ht = getvar(nc, "z", timeidx=-1)
ter = getvar(nc, "ter", timeidx=-1)
dbz = getvar(nc, "dbz", timeidx=-1)
max_dbz = getvar(nc, "mdbz", timeidx=-1)
Z = 10**(dbz / 10.) # Use linear Z for interpolation
# Compute the vertical cross-section interpolation. Also, include the
# lat/lon points along the cross-section in the metadata by setting latlon
# to True.
z_cross = vertcross(Z,
ht,
wrfin=nc,
from cartopy.feature import NaturalEarthFeature
from netCDF4 import Dataset
from wrf import to_np, getvar, CoordPair, vertcross
# Open the NetCDF file
root_dir = '/data/mac/giyoung/MAC_WRFThompson/'
nc = Dataset(root_dir +
'2_Nisg80_ThompsonDefault/wrfout_d02_2015-11-27_00:00:00')
# Extract the model height and wind speed
z = getvar(nc, "z")
wspd = getvar(nc, "uvmet_wspd_wdir", units="kt")[0, :]
# Create the start point and end point for the cross section
start_point = CoordPair(lat=-74.0, lon=-27.0)
end_point = CoordPair(lat=-75.0, lon=-27.0)
# Compute the vertical cross-section interpolation. Also, include the
# lat/lon points along the cross-section.
wspd_cross = vertcross(wspd,
z,
wrfin=nc,
start_point=start_point,
end_point=end_point,
latlon=True,
meta=True)
# Create the figure
fig = plt.figure(figsize=(7, 6.5))
ax = plt.axes()
# Necessary check because if non-WRF files are present in the directory, it will try to plot them and break
if fnmatch.fnmatch(wrffile, '*[d]*'):
print "Evaluating file " + wrffile
ncfile = Dataset(wrffile)
# Get the WRF variables
slp = getvar(ncfile, "slp")
smooth_slp = smooth2d(slp, 3)
ctt = getvar(ncfile, "ctt")
z = getvar(ncfile, "z")
dbz = getvar(ncfile, "dbz")
Z = 10**(dbz / 10.)
wspd = getvar(ncfile, "wspd_wdir", units="m/s")[0, :]
# Set the start point and end point for the cross section
start_point = CoordPair(lat=36.3134, lon=-82.3535)
end_point = CoordPair(lat=35.9132, lon=-79.0558)
# Compute the vertical cross-section interpolation. Also, include the lat/lon
# points along the cross-section in the metadata by setting latlon to True.
z_cross = vertcross(Z,
z,
wrfin=ncfile,
start_point=start_point,
end_point=end_point,
latlon=True,
meta=True)
wspd_cross = vertcross(wspd,
z,
wrfin=ncfile,
start_point=start_point,
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
from netCDF4 import Dataset
from wrf import to_np, getvar, CoordPair, vertcross
# Open the NetCDF file
ncfile = Dataset("H:/Output_scenarios/2100/ref/wrfout_d03_2099-07-23_13_00_00.nc")
# Extract the model height and wind speed
z = getvar(ncfile, "z")[0:9]
wspd = getvar(ncfile, "uvmet_wspd_wdir")[0:9]
# Create the start point and end point for the cross section
start_point = CoordPair(lat=39, lon=-78.5)
end_point = CoordPair(lat=39, lon=-75.7)
# Compute the vertical cross-section interpolation. Also, include the
# lat/lon points along the cross-section.
wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point,
end_point=end_point, latlon=True, meta=True)
# Create the figure
fig = plt.figure(figsize=(12,6))
ax = plt.axes()
# Make the contour plot
wspd_contours = ax.contourf(to_np(wspd_cross), cmap=get_cmap("jet"))
# Add the color bar
def cross_section(variable_name,
date,
time,
start_lat,
start_lon,
end_lat,
end_lon,
save=False):
'''This function plots a vertical cross section of the chosen
variable. Supported variables for plotting procedure are
vertical_velocity, rh, omega, absolute_vorticity, theta_e and
reflectivity.'''
### Predefine some variables ###
# Define data filename
data_dir = '/scratch3/thomasl/work/data/casestudy_baden/'
filename = '{}wrfout_d02_{}_{}:00'.format(data_dir, date, time)
# Define save directory
save_dir = '/scratch3/thomasl/work/retrospective_part/' 'casestudy_baden/cross_section/'
# Create the start point and end point for the cross section
start_point = CoordPair(lat=start_lat, lon=start_lon)
end_point = CoordPair(lat=end_lat, lon=end_lon)
### Start plotting procedure ###
# Open NetCDF file
ncfile = Dataset(filename)
# Extract the model height, terrain height and variables
ht = getvar(ncfile, 'z') / 1000 # change to km
ter = getvar(ncfile, 'ter') / 1000
if variable_name == 'vertical_velocity':
variable = getvar(ncfile, 'wa', units='kt')
title_name = 'Vertical Velocity'
colorbar_label = 'Vertical Velocity [$kn$]'
variable_min = -2
variable_max = 2
elif variable_name == 'rh':
variable = getvar(ncfile, 'rh')
title_name = 'Relative Humidity'
colorbar_label = 'Relative Humidity [$pct$]'
variable_min = 0
variable_max = 100
elif variable_name == 'omega':
variable = getvar(ncfile, 'omega')
title_name = 'Vertical Motion (Omega)'
colorbar_label = 'Omega [$Pa$ $s^-$$^1$]'
variable_min = -5
variable_max = 5
elif variable_name == 'absolute_vorticity':
variable = getvar(ncfile, 'avo')
title_name = 'Absolute Vorticity'
colorbar_label = 'Absolute Vorticity [$10^{-5}$' '$s^{-1}$]'
variable_min = -50
variable_max = 100
elif variable_name == 'theta_e':
variable = getvar(ncfile, 'theta_e')
title_name = 'Theta-E'
colorbar_label = 'Theta-E [$K$]'
variable_min = 315
variable_max = 335
elif variable_name == 'reflectivity':
variable = getvar(ncfile, 'dbz') #, timeidx=-1
title_name = 'Reflectivity'
colorbar_label = 'Reflectivity [$dBZ$]'
variable_min = 5
variable_max = 75
# Linear Z for interpolation
Z = 10**(variable / 10)
# Compute the vertical cross-section interpolation
z_cross = vertcross(Z,
ht,
wrfin=ncfile,
start_point=start_point,
end_point=end_point,
latlon=True,
meta=True)
# Convert back after interpolation
variable_cross = 10.0 * np.log10(z_cross)
# Make a copy of the z cross data
variable_cross_filled = np.ma.copy(to_np(variable_cross))
# For each cross section column, find the first index with
# non-missing values and copy these to the missing elements below
for i in range(variable_cross_filled.shape[-1]):
column_vals = variable_cross_filled[:, i]
first_idx = int(np.transpose((column_vals > -200).nonzero())[0])
variable_cross_filled[0:first_idx,
i] = variable_cross_filled[first_idx, i]
ter_line = interpline(ter,
wrfin=ncfile,
start_point=start_point,
end_point=end_point)
# Get latitude and longitude points
lats, lons = latlon_coords(variable)
# Create the figure
fig = plt.figure(figsize=(15, 10))
ax = plt.axes()
ys = to_np(variable_cross.coords['vertical'])
xs = np.arange(0, variable_cross.shape[-1], 1)
# Make contour plot
if variable_name == 'reflectivity':
levels = np.arange(variable_min, variable_max, 5)
# Create the dbz color table found on NWS pages.
dbz_rgb = np.array(
[[4, 233, 231], [1, 159, 244], [3, 0, 244], [2, 253, 2],
[1, 197, 1], [0, 142, 0], [253, 248, 2], [229, 188, 0],
[253, 149, 0], [253, 0, 0], [212, 0, 0], [188, 0, 0],
[248, 0, 253], [152, 84, 198]], np.float32) / 255.0
dbz_cmap, dbz_norm = from_levels_and_colors(levels,
dbz_rgb,
extend='max')
else:
levels = np.linspace(variable_min, variable_max, 11)
if variable_name == 'omega' or variable_name == 'vertical_velocity':
def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):
new_cmap = colors.LinearSegmentedColormap.from_list(
'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name,
a=minval,
b=maxval),
cmap(np.linspace(minval, maxval, n)))
return new_cmap
old_cmap = plt.get_cmap('RdYlBu')
cmap = truncate_colormap(old_cmap, 0.05, 0.9)
norm = colors.DivergingNorm(vmin=variable_min,
vcenter=0,
vmax=variable_max)
else:
cmap = ListedColormap(sns.cubehelix_palette(20, start=.5, rot=-.75))
if variable_name == 'omega' or variable_name == 'vertical_velocity':
variable_contours = ax.contourf(xs,
ys,
to_np(variable_cross_filled),
levels=levels,
cmap=cmap,
extend='both',
norm=norm)
elif variable_name == 'rh':
variable_contours = ax.contourf(xs,
ys,
to_np(variable_cross_filled),
levels=levels,
cmap=cmap,
extend='max')
elif variable_name == 'reflectivity':
variable_contours = ax.contourf(xs,
ys,
to_np(variable_cross_filled),
levels=levels,
cmap=dbz_cmap,
norm=dbz_norm,
extend='both')
else:
variable_contours = ax.contourf(xs,
ys,
to_np(variable_cross_filled),
levels=levels,
cmap=cmap,
extend='both')
# Plot wind barbs
if variable_name == 'vertical_velocity':
u = getvar(ncfile, 'ua', units='kt')
U = 10**(u / 10)
u_cross = vertcross(U,
ht,
wrfin=ncfile,
start_point=start_point,
end_point=end_point,
latlon=True,
meta=True)
u_cross = 10.0 * np.log10(u_cross)
u_cross_filled = np.ma.copy(to_np(u_cross))
for i in range(u_cross_filled.shape[-1]):
column_vals = u_cross_filled[:, i]
first_idx = int(np.transpose((column_vals > -200).nonzero())[0])
u_cross_filled[0:first_idx, i] = u_cross_filled[first_idx, i]
ax.barbs(xs[::3],
ys[::3],
to_np(u_cross_filled[::3, ::3]),
to_np(variable_cross_filled[::3, ::3]),
length=7,
zorder=1)
if variable_name == 'omega':
u = getvar(ncfile, 'ua', units='kt')
U = 10**(u / 10)
u_cross = vertcross(U,
ht,
wrfin=ncfile,
start_point=start_point,
end_point=end_point,
latlon=True,
meta=True)
u_cross = 10.0 * np.log10(u_cross)
u_cross_filled = np.ma.copy(to_np(u_cross))
for i in range(u_cross_filled.shape[-1]):
column_vals = u_cross_filled[:, i]
first_idx = int(np.transpose((column_vals > -200).nonzero())[0])
u_cross_filled[0:first_idx, i] = u_cross_filled[first_idx, i]
w = getvar(ncfile, 'wa', units='kt')
W = 10**(w / 10)
w_cross = vertcross(W,
ht,
wrfin=ncfile,
start_point=start_point,
end_point=end_point,
latlon=True,
meta=True)
w_cross = 10.0 * np.log10(w_cross)
w_cross_filled = np.ma.copy(to_np(w_cross))
for i in range(w_cross_filled.shape[-1]):
column_vals = w_cross_filled[:, i]
first_idx = int(np.transpose((column_vals > -200).nonzero())[0])
w_cross_filled[0:first_idx, i] = w_cross_filled[first_idx, i]
ax.barbs(xs[::3],
ys[::3],
to_np(u_cross_filled[::3, ::3]),
to_np(w_cross_filled[::3, ::3]),
length=7,
zorder=1,
color='grey')
# Add color bar
cbar = mpu.colorbar(variable_contours,
ax,
orientation='vertical',
aspect=40,
shrink=.05,
pad=0.05)
cbar.set_label(colorbar_label, fontsize=15)
cbar.set_ticks(levels)
# Set x-ticks to use latitude and longitude labels
coord_pairs = to_np(variable_cross.coords['xy_loc'])
x_ticks = np.arange(coord_pairs.shape[0])
x_labels = [
pair.latlon_str(fmt='{:.2f}, {:.2f}') for pair in to_np(coord_pairs)
]
# Set desired number of x ticks below
num_ticks = 5
thin = int((len(x_ticks) / num_ticks) + .5)
ax.set_xticks(x_ticks[::thin])
ax.set_xticklabels(x_labels[::thin], rotation=45, fontsize=10)
ax.set_xlim(x_ticks[0], x_ticks[-1])
# Set y-ticks and limit the height
ax.set_yticks(np.linspace(0, 12, 13))
ax.set_ylim(0, 12)
# Set x-axis and y-axis labels
ax.set_xlabel('Latitude, Longitude', fontsize=12.5)
ax.set_ylabel('Height [$km$]', fontsize=12.5)
# Fill in mountian area
ht_fill = ax.fill_between(xs,
0,
to_np(ter_line),
facecolor='saddlebrown',
zorder=2)
# Make nicetime
xr_file = xr.open_dataset(filename)
nicetime = pd.to_datetime(xr_file.QVAPOR.isel(Time=0).XTIME.values)
# Add title
ax.set_title('Vertical Cross-Section of {}'.format(title_name),
fontsize=20,
loc='left')
ax.set_title('Valid time: {} {}'.format(
nicetime.strftime('%Y-%m-%d %H:%M'), 'UTC'),
fontsize=15,
loc='right')
# Add grid for y axis
ax.grid(axis='y', linestyle='--', color='grey')
plt.show()
### Save figure ###
if save == True:
fig.savefig('{}cross_section_{}.png'.format(save_dir, variable_name),
bbox_inches='tight',
dpi=300)
def lagranto_plotting(traj_variable_name,
start_time,
end_time,
trajs_bunch_level='all',
subset=False,
save=False):
'''This function plots the chosen variables for the trajectories
calculated with Lagranto. Supported variables for plotting procedure
are water_vapor, updraft and height.'''
### Predefine some variables ###
traj_data_dir = '/scratch3/thomasl/work/retrospective_part/lagranto/' 'traj_baden_10000_every1000_area_1200.ll'
save_dir = '/scratch3/thomasl/work/retrospective_part' '/casestudy_baden/lagranto/'
start_locations = 'area' # distinction between single and multiple
# starting points of trajectories
number_trajs_plot = 1
# Location of Initiation
lat = 47.25
lon = 7.85
initiation = CoordPair(lat, lon)
# Change extent of plot
subset_extent = [7, 9, 47, 48]
# Variables for getting PBL height of WRF model data
date = '2018-05-30'
time = '16:10'
wrf_filename = '/scratch3/thomasl/work/data/casestudy_baden/' 'wrfout_d02_{}_{}:00'.format(
date, time)
# Variables:
if traj_variable_name == 'water_vapor':
traj_variable_name = 'QVAPOR'
title_name = 'Trajectories of Water Vapor'
colorbar_label_trajs = 'Water Vapor Mixing Ratio [$g$ $kg^{-1}$]'
save_name = 'trajectory_water_vapor_{}_{}'.format(start_time, end_time)
traj_variable_min = 0
traj_variable_max = 15
elif traj_variable_name == 'updraft':
traj_variable_name = 'W_UP_MAX'
title_name = 'Trajectories of Updraft'
colorbar_label_trajs = 'Max Z-Wind Updraft [$m$ $s^-$$^1$]'
save_name = 'trajectory_updraft_{}_{}'.format(start_time, end_time)
traj_variable_min = 0
traj_variable_max = 10
elif traj_variable_name == 'height':
traj_variable_name = 'z'
title_name = 'Height of Trajectories'
colorbar_label_trajs = 'Height of Trajectories [$m$]'
save_name = 'trajectory_height_{}_{}'.format(start_time, end_time)
traj_variable_min = 0
traj_variable_max = 10000
### Plotting procedure ###
trajs = Tra()
trajs.load_ascii(traj_data_dir)
# Get PBL out of WRF model data
ncfile = Dataset(wrf_filename)
wrf_file = xr.open_dataset(wrf_filename)
data = wrf_file.PBLH
location = ll_to_xy(ncfile, lat, lon)
data_point = data.sel(west_east=location[0], south_north=location[1])
pbl = data_point.values
# Separate trajectories in 3 vertical bunches
# Trajectories of pbl (according to pbl height of WRF model data)
trajspbl = []
for t in trajs:
if (t['z'][0] <= pbl):
trajspbl.append(t)
# Trajectories between pbl and 5000 m
trajs5 = []
for t in trajs:
if (t['z'][0] > pbl and t['z'][0] < 5000):
trajs5.append(t)
# Trajectories between 5000 m and 10000 m
trajs10 = []
for t in trajs:
if (t['z'][0] > 5000 and t['z'][0] <= 10000):
trajs10.append(t)
if trajs_bunch_level == 'pbl':
trajs_bunch = trajspbl
elif trajs_bunch_level == '5':
trajs_bunch = trajs5
elif trajs_bunch_level == '10':
trajs_bunch = trajs10
elif trajs_bunch_level == 'all':
trajs_bunch = trajs
# Get terrain height
terrain_height = getvar(ncfile, 'HGT') / 1000 # change to km
# Define cart projection
lats, lons = latlon_coords(terrain_height)
cart_proj = ccrs.LambertConformal(central_longitude=8.722206,
central_latitude=46.73585)
# Create figure
fig = plt.figure(figsize=(15, 10))
ax = plt.axes(projection=cart_proj)
### Set map extent ###
domain_extent = [3.701088, 13.814863, 43.85472, 49.49499]
if subset == True:
ax.set_extent([
subset_extent[0], subset_extent[1], subset_extent[2],
subset_extent[3]
], ccrs.PlateCarree())
else:
ax.set_extent([
domain_extent[0] + 0.7, domain_extent[1] - 0.7,
domain_extent[2] + 0.1, domain_extent[3] - 0.1
], ccrs.PlateCarree())
# Plot trajectories
levels_trajs = np.linspace(traj_variable_min, traj_variable_max, 11)
rounded_levels_trajs = [round(elem, 1) for elem in levels_trajs]
cmap = ListedColormap(sns.cubehelix_palette(
10,
start=.5,
rot=-.75,
))
plt_trajs = plot_trajs(ax,
trajs_bunch[0::number_trajs_plot],
traj_variable_name,
linewidth=2,
levels=rounded_levels_trajs,
cmap=cmap)
# Plot the terrain height with colorbar
levels = np.linspace(0, 4, 21)
terrain = plt.contourf(to_np(lons),
to_np(lats),
to_np(terrain_height),
levels=levels,
transform=ccrs.PlateCarree(),
cmap=get_cmap('Greys'),
alpha=0.75)
cbar = mpu.colorbar(terrain,
ax,
orientation='horizontal',
aspect=40,
shrink=.05,
pad=0.075)
cbar.set_label('Terrain Height [$km$]', fontsize=15)
cbar.set_ticks(levels)
# Make only every second color bar tick label visible
for label in cbar.ax.xaxis.get_ticklabels()[1::2]:
label.set_visible(False)
# Add color bar for trajectory variable
if traj_variable_name == 'QVAPOR':
extend = 'both'
else:
extend = 'max'
cbar_trajs = mpu.colorbar(plt_trajs,
ax,
orientation='vertical',
aspect=40,
shrink=.05,
pad=0.05,
extend=extend)
cbar_trajs.set_label(colorbar_label_trajs, fontsize=15)
cbar_trajs.set_ticks(rounded_levels_trajs)
# Add borders and coastlines
ax.add_feature(cfeature.BORDERS.with_scale('10m'), linewidth=0.8)
ax.add_feature(cfeature.COASTLINE.with_scale('10m'), linewidth=0.8)
# Add cross for initiation location
for t in trajs:
ax.plot(t['lon'][0], t['lat'][0], 'kx', transform=ccrs.PlateCarree())
# Add gridlines
lon = np.arange(0, 20, 1)
lat = np.arange(40, 60, 1)
gl = ax.gridlines(xlocs=lon, ylocs=lat, zorder=3)
# Add tick labels
mpu.yticklabels(lat, ax=ax, fontsize=12.5)
mpu.xticklabels(lon, ax=ax, fontsize=12.5)
# Set title
ax.set_title(title_name, loc='left', fontsize=20)
ax.set_title('Time Range: {} - {} UTC'.format(start_time, end_time),
loc='right',
fontsize=15)
plt.show()
### Save figure ###
if save == True:
if subset == True:
if trajs_bunch_level == 'all':
fig.savefig('{}{}_subset_{}.png'.format(
save_dir, save_name, start_locations),
bbox_inches='tight',
dpi=300)
else:
fig.savefig('{}{}_subset_{}_{}.png'.format(
save_dir, save_name, trajs_bunch_level, start_locations),
bbox_inches='tight',
dpi=300)
else:
if trajs_bunch_level == 'all':
fig.savefig('{}{}_{}.png'.format(save_dir, save_name,
start_locations),
bbox_inches='tight',
dpi=300)
else:
fig.savefig('{}{}_{}_{}.png'.format(save_dir, save_name,
trajs_bunch_level,
start_locations),
bbox_inches='tight',
dpi=300)
ncfile = nc.Dataset('D:\\wrf_simulation\\final\\wrfout_d03_2016-07-21_12')
time = 126
ua = getvar(ncfile, 'ua', timeidx=time)
va = getvar(ncfile, 'va', timeidx=time)
wa = getvar(ncfile, 'wa', timeidx=time)
wa = wa * 10
p = getvar(ncfile, 'pressure', timeidx=time)
lat = getvar(ncfile, 'lat')
lon = getvar(ncfile, 'lon')
height = getvar(ncfile, 'height')
hgt = getvar(ncfile, 'HGT')
height2earth = height - hgt
start_lat, start_lon = 31.2, 121.0
end_lat, end_lon = 31.2, 122.0
startpoint = CoordPair(lat=start_lat, lon=start_lon)
endpoint = CoordPair(lat=end_lat, lon=end_lon)
ua_vert = vertcross(ua,
p,
wrfin=ncfile,
start_point=startpoint,
end_point=endpoint,
latlon=True)
va_vert = vertcross(va,
p,
wrfin=ncfile,
start_point=startpoint,
end_point=endpoint,
latlon=True)
wa_vert = vertcross(wa,
p,
import numpy as np
from matplotlib import pyplot
from matplotlib.cm import get_cmap
from matplotlib.colors import from_levels_and_colors
from cartopy import crs
from cartopy.feature import NaturalEarthFeature, COLORS
from netCDF4 import Dataset
from wrf import (getvar, to_np, ll_to_xy, get_cartopy, latlon_coords, vertcross,
cartopy_xlim, cartopy_ylim, interpline, CoordPair)
wrf_file = Dataset("wrfout_d04_2020-06-02_09:00:00")
# Define the cross section start and end points
cross_start = CoordPair(lat=66.983306, lon = -136.45)
cross_end = CoordPair(lat=66.983306, lon = -135.7)
road1 = CoordPair(lat=66.983306, lon = -136.215596)
road2 = CoordPair(lat=66.983, lon = -136.21666666)
# Get the WRF variables
ht = getvar(wrf_file, "z", timeidx=-1)
ter = getvar(wrf_file, "ter", timeidx=-1)
wind = getvar(wrf_file, "wspd_wdir",timeidx=-1)[0,:]
theta = getvar(wrf_file, "th", timeidx=-1)
# Compute the vertical cross-section interpolation. Also, include the
# lat/lon points along the cross-section in the metadata by setting latlon
# to True.
wind_cross = vertcross(wind, ht, wrfin=wrf_file,
start_point=cross_start,
end_point=cross_end,
latlon=True, meta=True)
tc = tc1 - tc2
#file1 = open("C:/Users/Yating/Desktop/output/output_gr100.txt","w")#write mode
#for i in range(tc.shape[0]):
# for j in range(tc.shape[1]):
# for k in range(tc.shape[2]):
# if landmask[j,k]!=0 and landuse[j,k]>21 and landuse[j,k]<26:
# file1.write(np.array2string(tc[i,j,k]))
# file1.write("\n")
#file1.close()
# Create the start point and end point for the cross section
#start_point = CoordPair(lat=39, lon=-78.5)
#end_point = CoordPair(lat=39, lon=-75.7)
start_point = CoordPair(lat=38.9, lon=-77.4)
end_point = CoordPair(lat=38.9, lon=-76.8)
# Compute the vertical cross-section interpolation. Also, include the
# lat/lon points along the cross-section.
wspd_cross = vertcross(tc,
z,
wrfin=ncfile,
start_point=start_point,
end_point=end_point,
latlon=True,
meta=True)
# Create the figure
#fig = plt.figure(figsize=(7,4))
fig = plt.figure(figsize=(2, 4))
def test(self):
try:
from netCDF4 import Dataset as NetCDF
except:
pass
try:
from PyNIO import Nio
except:
pass
if not multi:
timeidx = 0
if not pynio:
in_wrfnc = NetCDF(wrf_in)
else:
# Note: Python doesn't like it if you reassign an outer scoped
# variable (wrf_in in this case)
if not wrf_in.endswith(".nc"):
wrf_file = wrf_in + ".nc"
else:
wrf_file = wrf_in
in_wrfnc = Nio.open_file(wrf_file)
else:
timeidx = None
if not pynio:
nc = NetCDF(wrf_in)
in_wrfnc = [nc for i in xrange(repeat)]
else:
if not wrf_in.endswith(".nc"):
wrf_file = wrf_in + ".nc"
else:
wrf_file = wrf_in
nc = Nio.open_file(wrf_file)
in_wrfnc = [nc for i in xrange(repeat)]
if (varname == "interplevel"):
ref_ht_500 = _get_refvals(referent, "z_500", repeat, multi)
hts = getvar(in_wrfnc, "z", timeidx=timeidx)
p = getvar(in_wrfnc, "pressure", timeidx=timeidx)
hts_500 = interplevel(hts, p, 500)
nt.assert_allclose(to_np(hts_500), ref_ht_500)
elif (varname == "vertcross"):
ref_ht_cross = _get_refvals(referent, "ht_cross", repeat, multi)
ref_p_cross = _get_refvals(referent, "p_cross", repeat, multi)
hts = getvar(in_wrfnc, "z", timeidx=timeidx)
p = getvar(in_wrfnc, "pressure", timeidx=timeidx)
pivot_point = CoordPair(hts.shape[-1] / 2, hts.shape[-2] / 2)
ht_cross = vertcross(hts, p, pivot_point=pivot_point, angle=90.)
nt.assert_allclose(to_np(ht_cross), ref_ht_cross, rtol=.01)
# Test opposite
p_cross1 = vertcross(p, hts, pivot_point=pivot_point, angle=90.0)
nt.assert_allclose(to_np(p_cross1), ref_p_cross, rtol=.01)
# Test point to point
start_point = CoordPair(0, hts.shape[-2] / 2)
end_point = CoordPair(-1, hts.shape[-2] / 2)
p_cross2 = vertcross(p,
hts,
start_point=start_point,
end_point=end_point)
nt.assert_allclose(to_np(p_cross1), to_np(p_cross2))
elif (varname == "interpline"):
ref_t2_line = _get_refvals(referent, "t2_line", repeat, multi)
t2 = getvar(in_wrfnc, "T2", timeidx=timeidx)
pivot_point = CoordPair(t2.shape[-1] / 2, t2.shape[-2] / 2)
t2_line1 = interpline(t2, pivot_point=pivot_point, angle=90.0)
nt.assert_allclose(to_np(t2_line1), ref_t2_line)
# Test point to point
start_point = CoordPair(0, t2.shape[-2] / 2)
end_point = CoordPair(-1, t2.shape[-2] / 2)
t2_line2 = interpline(t2,
start_point=start_point,
end_point=end_point)
nt.assert_allclose(to_np(t2_line1), to_np(t2_line2))
elif (varname == "vinterp"):
# Tk to theta
fld_tk_theta = _get_refvals(referent, "fld_tk_theta", repeat,
multi)
fld_tk_theta = np.squeeze(fld_tk_theta)
tk = getvar(in_wrfnc, "temp", timeidx=timeidx, units="k")
interp_levels = [200, 300, 500, 1000]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
tol = 5 / 100.
atol = 0.0001
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_tk_theta)))
nt.assert_allclose(to_np(field), fld_tk_theta, tol, atol)
# Tk to theta-e
fld_tk_theta_e = _get_refvals(referent, "fld_tk_theta_e", repeat,
multi)
fld_tk_theta_e = np.squeeze(fld_tk_theta_e)
interp_levels = [200, 300, 500, 1000]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="theta-e",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
tol = 3 / 100.
atol = 50.0001
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_tk_theta_e)/fld_tk_theta_e)*100)
nt.assert_allclose(to_np(field), fld_tk_theta_e, tol, atol)
# Tk to pressure
fld_tk_pres = _get_refvals(referent, "fld_tk_pres", repeat, multi)
fld_tk_pres = np.squeeze(fld_tk_pres)
interp_levels = [850, 500]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_tk_pres)))
nt.assert_allclose(to_np(field), fld_tk_pres, tol, atol)
# Tk to geoht_msl
fld_tk_ght_msl = _get_refvals(referent, "fld_tk_ght_msl", repeat,
multi)
fld_tk_ght_msl = np.squeeze(fld_tk_ght_msl)
interp_levels = [1, 2]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="ght_msl",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_tk_ght_msl)))
nt.assert_allclose(to_np(field), fld_tk_ght_msl, tol, atol)
# Tk to geoht_agl
fld_tk_ght_agl = _get_refvals(referent, "fld_tk_ght_agl", repeat,
multi)
fld_tk_ght_agl = np.squeeze(fld_tk_ght_agl)
interp_levels = [1, 2]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="ght_agl",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_tk_ght_agl)))
nt.assert_allclose(to_np(field), fld_tk_ght_agl, tol, atol)
# Hgt to pressure
fld_ht_pres = _get_refvals(referent, "fld_ht_pres", repeat, multi)
fld_ht_pres = np.squeeze(fld_ht_pres)
z = getvar(in_wrfnc, "height", timeidx=timeidx, units="m")
interp_levels = [500, 50]
field = vinterp(in_wrfnc,
field=z,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="ght",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_ht_pres)))
nt.assert_allclose(to_np(field), fld_ht_pres, tol, atol)
# Pressure to theta
fld_pres_theta = _get_refvals(referent, "fld_pres_theta", repeat,
multi)
fld_pres_theta = np.squeeze(fld_pres_theta)
p = getvar(in_wrfnc, "pressure", timeidx=timeidx)
interp_levels = [200, 300, 500, 1000]
field = vinterp(in_wrfnc,
field=p,
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="pressure",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_pres_theta)))
nt.assert_allclose(to_np(field), fld_pres_theta, tol, atol)
# Theta-e to pres
fld_thetae_pres = _get_refvals(referent, "fld_thetae_pres", repeat,
multi)
fld_thetae_pres = np.squeeze(fld_thetae_pres)
eth = getvar(in_wrfnc, "eth", timeidx=timeidx)
interp_levels = [850, 500, 5]
field = vinterp(in_wrfnc,
field=eth,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="theta-e",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_thetae_pres)))
nt.assert_allclose(to_np(field), fld_thetae_pres, tol, atol)
file3 = 'wrfout_d03_2012-04-22_23.nc'
mrg = ds[toinclude].to_netcdf(file3)
# Read in the data and extract variables
wrfin = Dataset(('wrfout_d03_2012-04-22_23.nc'))
z = getvar(wrfin, "z")
qv = getvar(wrfin, "QVAPOR")
# Pull lat/lon coords from QVAPOR data using wrf-python tools
lats, lons = latlon_coords(qv)
###############################################################################
# Create vertical cross section using wrf-python tools
# Define start and stop coordinates for cross section
start_point = CoordPair(lat=38, lon=-118)
end_point = CoordPair(lat=40, lon=-115)
qv_cross = vertcross(qv,
z,
wrfin=wrfin,
start_point=start_point,
end_point=end_point,
latlon=True)
# Close 'wrfin' to prevent PermissionError if code is run more than once locally
wrfin.close()
# Remove created wrfout file from local directory
os.remove('wrfout_d03_2012-04-22_23.nc')
###############################################################################
end_i, end_j = find_ind_latlon2d(lat, end_lat, lon, end_lon)
print(start_i, start_j)
print(lat[start_i, start_j], lon[start_i, start_j])
print(end_i, end_j)
print(lat[end_i, end_j], lon[end_i, end_j])
start = (start_j, start_i)
end = (end_j, end_i)
## 2. Compute the vertical cross-section interpolation
xy = xy(PS, start_point=start, end_point=end)
rh_cross = interp2dxy(RH, xy, meta=False)
print(np.nanmin(rh_cross), np.nanmax(rh_cross))
## 3. Interpolate LAT-LON along the cross section and build LAT-LON pairs (to put them on the X-axis in the final plot)
lat_interp = interpline(lat,
start_point=CoordPair(x=start_j, y=start_i),
end_point=CoordPair(x=end_j, y=end_i),
latlon=True,
meta=False)
lon_interp = interpline(lon,
start_point=CoordPair(x=start_j, y=start_i),
end_point=CoordPair(x=end_j, y=end_i),
latlon=False,
meta=False)
latlon_pairs = np.dstack((lat_interp, lon_interp))
ps_interp = interpline(PS,
start_point=CoordPair(x=start_j, y=start_i),
end_point=CoordPair(x=end_j, y=end_i),
latlon=False,
meta=False)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
import cartopy.crs as crs
from cartopy.feature import NaturalEarthFeature
from netCDF4 import Dataset
from wrf import to_np, getvar, CoordPair, vertcross
z = getvar(ncfile, "z")
wspd = getvar(ncfile, "uvmet_wspd_wdir", units="kt")[0, :]
# Create the start point and end point for the cross section
start_point = CoordPair(lat=26.76, lon=-80.0)
end_point = CoordPair(lat=26.76, lon=-77.8)
# Compute the vertical cross-section interpolation. Also, include the lat/lon
# points along the cross-section.
wspd_cross = vertcross(wspd,
z,
wrfin=ncfile,
start_point=start_point,
end_point=end_point,
latlon=True,
meta=True)
# Create the figure
fig = plt.figure(figsize=(12, 6))
ax = plt.axes()