def therm_calc(the_row):
rT = cloud_vars['wt']
S = the_row[-1]
temp, press,height = the_row[-4:-1]
esat = find_esat(temp)
e = S*esat
rv = c.eps*e/(press - e) #Thompkins eqn 2.20
Td = find_Td(rv,press)
thetae = find_thetaet(Td,rT,temp,press)
hm = c.cpd*temp + find_lv(temp)*rv + c.g0*height
return (thetae, hm)
def makeSkewWet(ax, corners=[-30, 25], skew=30):
"""
draw a skew-T lnP diagram on an axis
Parameters
----------
ax : matplotlib.axes
matplotlib figure axis
corners : [float]
x axis temperature limits (degC)
skew : float
adjustable coefficient to make isotherms slope
compared to adiabats
Returns
-------
ax : matplotlib.axes
the modified figure axis
"""
yplot = range(1000, 190, -6) #
xcorners = find_corners(corners, skew=skew)
xplot = list(np.linspace(xcorners[0], xcorners[1], 45))
pvals = np.size(yplot)
tvals = np.size(xplot)
temp = np.zeros([pvals, tvals])
theTheta = np.zeros_like(temp)
the_rsat = np.zeros_like(temp)
theThetae = np.zeros([pvals, tvals])
# lay down a reference grid that labels xplot,yplot points
# in the new (skewT-lnP) coordinate system .
# Each value of the temp matrix holds the actual (data)
# temperature label (in deg C) of the xplot, yplot coordinate.
# pairs. The transformation is given by W&H 3.56, p. 78. Note
# that there is a sign difference, because rather than
# taking y= -log(P) like W&H, I take y= +log(P) and
# then reverse the y axis
for presshPa in yplot: #loop over pressures
for skewed in xplot: #loop over skewed xcoords
# Note that we don't have to transform the y
# coordinate, as it is still pressure.
iInd = yplot.index(presshPa)
jInd = xplot.index(skewed)
temp[iInd, jInd] = convertSkewToTemp(skewed, presshPa, skew)
Tk = c.Tc + temp[iInd, jInd]
pressPa = presshPa * 100.
theTheta[iInd, jInd] = find_theta(Tk, pressPa)
rs = find_rsat(Tk, pressPa)
the_rsat[iInd, jInd] = rs
theThetae[iInd, jInd] = find_thetaet(Tk,rs,Tk, pressPa)
#
# Contour the temperature matrix.
#
# First, make sure that all plotted lines are solid.
mpl.rcParams['contour.negative_linestyle'] = 'solid'
tempLabels = range(-40, 50, 5)
ax.contour(xplot, yplot, temp, tempLabels, \
colors='k')
#
# contour theta
#
thetaLabels = list(range(200, 390, 10))
thetaLevs = ax.contour(xplot, yplot, theTheta, thetaLabels, \
colors='b')
#
# contour rsat
#
rsLabels = [0.1, 0.25, 0.5, 1, 2, 3] + list(range(4, 28, 2)) #+ [26, 28]
rsLevs = ax.contour(xplot,
yplot,
the_rsat * 1.e3,
levels=rsLabels,
colors='g',
linewidths=.5)
thetaeLabels = np.arange(250, 410, 10)
thetaeLevs = ax.contour(xplot, yplot, theThetae, thetaeLabels, \
colors='r')
#
# Customize the plot
#
ax.set_yscale('log')
locs = np.array(range(100, 1100, 100))
labels = locs
ax.set_yticks(locs)
ax.set_yticklabels(labels) # Conventionally labels semilog graph.
ax.set_ybound((200, 1000))
plt.setp(ax.get_xticklabels(), weight='bold')
plt.setp(ax.get_yticklabels(), weight='bold')
ax.yaxis.grid(True)
ax.set_title('skew T - lnp chart')
ax.set_ylabel('pressure (hPa)')
ax.set_xlabel('temperature (deg C)')
#
# Crop image to a more usable size
#
TempTickLabels = range(-30, 40, 5)
TempTickCoords = TempTickLabels
skewTickCoords = convertTempToSkew(TempTickCoords, 1.e3, skew)
ax.set_xticks(skewTickCoords)
ax.set_xticklabels(TempTickLabels)
skewLimits = convertTempToSkew([-15, 35], 1.e3, skew)
ax.axis([skewLimits[0], skewLimits[1], 300, 1.e3])
#
# Create line labels
#
fntsz = 9 # Handle for 'fontsize' of the line label.
ovrlp = True # Handle for 'inline'. Any integer other than 0
# creates a white space around the label.
#tempLevs.clabel(inline=ovrlp, inline_spacing=0,fmt='%2d', fontsize=fntsz,use_clabeltext=True)
thetaLevs.clabel(inline=ovrlp,
inline_spacing=0,
fmt='%3d',
fontsize=fntsz,
use_clabeltext=True)
rsLevs.clabel(inline=ovrlp,
inline_spacing=0,
fmt='%3.2g',
fontsize=fntsz,
use_clabeltext=True)
thetaeLevs.clabel(thetaeLabels,
inline_spacing=0,
inline=ovrlp,
fmt='%5g',
fontsize=fntsz,
use_clabeltext=True)
ax.invert_yaxis()
#ax.figure.canvas.draw()
xcorners = find_corners(corners, skew=skew)
ax.set(ylim=(1000, 300), xlim=xcorners)
return ax, skew
"""
return the_string.format_map(the_tup._asdict())
get_ipython().magic('matplotlib inline')
pa2hPa = 1.e-2
A_press = 1.e5 #Pa
B_press = 4.e4 #Pa
A_temp = 300 #K
RH = 0.8
e = find_esat(A_temp)*RH
A_rv = c.eps*e/(A_press - e)
A_Td = find_Td(A_rv,A_press)
print('tropical surface dewpoint: {} K'.format(A_Td))
A_thetae=find_thetaet(A_Td,A_rv,A_temp,A_press)
print('tropical surface thetae: {} K'.format(A_thetae))
A_temp,A_rv,A_rl = tinvert_thetae(A_thetae,A_rv,A_press)
fields=['id','temp','rv','rl','press']
A_dict = dict(zip(fields,('A',A_temp,A_rv,A_rl,A_press)))
A_tup = calc_enthalpy(A_dict)
print(format_tup(A_tup))
# ### B. Lift to 400 hPa and remove 80% of the liquied water
# In[201]:
plt.close('all')
fig,ax = plt.subplots(1,1,figsize=[10,8])
ax,skew = makeSkewWet(ax,corners=[-15,35])
def makeSkewWet(ax, corners=[-30, 25], skew=30):
"""
draw a skew-T lnP diagram on an axis
Parameters
----------
ax : matplotlib.axes
matplotlib figure axis
corners : [float]
x axis temperature limits (degC)
skew : float
adjustable coefficient to make isotherms slope
compared to adiabats
Returns
-------
ax : matplotlib.axes
the modified figure axis
"""
yplot = range(1000, 190, -6) #
xcorners = find_corners(corners, skew=skew)
xplot = list(np.linspace(xcorners[0], xcorners[1], 45))
pvals = np.size(yplot)
tvals = np.size(xplot)
temp = np.zeros([pvals, tvals])
theTheta = np.zeros_like(temp)
the_rsat = np.zeros_like(temp)
theThetae = np.zeros([pvals, tvals])
# lay down a reference grid that labels xplot,yplot points
# in the new (skewT-lnP) coordinate system .
# Each value of the temp matrix holds the actual (data)
# temperature label (in deg C) of the xplot, yplot coordinate.
# pairs. The transformation is given by W&H 3.56, p. 78. Note
# that there is a sign difference, because rather than
# taking y= -log(P) like W&H, I take y= +log(P) and
# then reverse the y axis
for presshPa in yplot: #loop over pressures
for skewed in xplot: #loop over skewed xcoords
# Note that we don't have to transform the y
# coordinate, as it is still pressure.
iInd = yplot.index(presshPa)
jInd = xplot.index(skewed)
temp[iInd, jInd] = convertSkewToTemp(skewed, presshPa, skew)
Tk = c.Tc + temp[iInd, jInd]
pressPa = presshPa * 100.
theTheta[iInd, jInd] = find_theta(Tk, pressPa)
rs = find_rsat(Tk, pressPa)
the_rsat[iInd, jInd] = rs
theThetae[iInd, jInd] = find_thetaet(Tk, rs, Tk, pressPa)
#
# Contour the temperature matrix.
#
# First, make sure that all plotted lines are solid.
mpl.rcParams['contour.negative_linestyle'] = 'solid'
tempLabels = range(-40, 50, 10)
ax.contour(xplot, yplot, temp, tempLabels, \
colors='k')
#
# contour theta
#
thetaLabels = list(range(200, 390, 10))
thetaLevs = ax.contour(xplot, yplot, theTheta, thetaLabels, \
colors='b')
#
# contour rsat
#
rsLabels = [0.1, 0.25, 0.5, 1, 2, 3] + list(range(4, 28, 2)) #+ [26, 28]
rsLevs = ax.contour(xplot,
yplot,
the_rsat * 1.e3,
levels=rsLabels,
colors='g',
linewidths=.5)
thetaeLabels = np.arange(250, 410, 10)
thetaeLevs = ax.contour(xplot, yplot, theThetae, thetaeLabels, \
colors='r')
#
# Customize the plot
#
ax.set_yscale('log')
locs = np.array(range(100, 1100, 100))
labels = locs
ax.set_yticks(locs)
ax.set_yticklabels(labels) # Conventionally labels semilog graph.
ax.set_ybound((200, 1000))
plt.setp(ax.get_xticklabels(), weight='bold')
plt.setp(ax.get_yticklabels(), weight='bold')
ax.yaxis.grid(True)
ax.set_title('skew T - lnp chart')
ax.set_ylabel('pressure (hPa)')
ax.set_xlabel('temperature (deg C)')
#
# Crop image to a more usable size
#
TempTickLabels = range(-30, 40, 5)
TempTickCoords = TempTickLabels
skewTickCoords = convertTempToSkew(TempTickCoords, 1.e3, skew)
ax.set_xticks(skewTickCoords)
ax.set_xticklabels(TempTickLabels)
skewLimits = convertTempToSkew([-15, 35], 1.e3, skew)
ax.axis([skewLimits[0], skewLimits[1], 300, 1.e3])
#
# Create line labels
#
fntsz = 9 # Handle for 'fontsize' of the line label.
ovrlp = True # Handle for 'inline'. Any integer other than 0
# creates a white space around the label.
#tempLevs.clabel(inline=ovrlp, inline_spacing=0,fmt='%2d', fontsize=fntsz,use_clabeltext=True)
thetaLevs.clabel(inline=ovrlp,
inline_spacing=0,
fmt='%3d',
fontsize=fntsz,
use_clabeltext=True)
rsLevs.clabel(inline=ovrlp,
inline_spacing=0,
fmt='%3.2g',
fontsize=fntsz,
use_clabeltext=True)
thetaeLevs.clabel(thetaeLabels,
inline_spacing=0,
inline=ovrlp,
fmt='%5g',
fontsize=fntsz,
use_clabeltext=True)
ax.invert_yaxis()
#ax.figure.canvas.draw()
xcorners = find_corners(corners, skew=skew)
ax.set(ylim=(1000, 300), xlim=xcorners)
return ax, skew
end_z = np.searchsorted(z, 1200.0)
bot_z = np.searchsorted(z, 1000.0)
qv_sound = qv.mean(axis=(1, 2))[bot_z:end_z] * 1.0e-3 # kg/kg
temp_sound = temp.mean(axis=(1, 2))[bot_z:end_z]
press_sound = np.squeeze(press)[bot_z:end_z]
z_sound = z[bot_z:end_z]
# print(qv_sound.shape,temp_sound.shape,z_sound.shape,press_sound.shape)
thetal_sound = [
find_thetal(the_press, the_temp, the_qv) for the_press, the_temp, the_qv in zip(press_sound, temp_sound, qv_sound)
]
Td_sound = find_Td(qv_sound, press_sound)
thetae_sound = [
find_thetaet(the_Td, the_qv, the_temp, the_press)
for the_Td, the_press, the_temp, the_qv in zip(Td_sound, press_sound, temp_sound, qv_sound)
]
fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(16, 8))
ax1.plot(qv_sound * 1.0e3, z_sound)
ax2.plot(temp_sound, press_sound * 1.0e-2)
ax3.plot(thetal_sound, press_sound * 1.0e-2)
ax4.plot(thetae_sound, press_sound * 1.0e-2)
axes = [ax2, ax3, ax4]
[ax.invert_yaxis() for ax in axes]
p0 = press_sound[0] * 1.0e-2
[ax.set(ylim=(p0, 750)) for ax in axes]
axes = [ax1, ax2, ax3, ax4]
ax1.set(ylabel="heigth (m)")
ax2.set(ylabel="pressure (hPa)")
titles = ["$q_v\ (g/kg)$", "$temperature\ (K)$", r"$\theta_l\ (K)$", r"$\theta_e\ (K)$"]
