def test_chem_sulfate_formation(data):
"""
sulfate formation vs water-weighted average pH
sulfate formed from H2O2 vs sulfate formed from O3
"""
p = data.variables["p"][-1]
T = data.variables["T"][-1]
rhod = data.variables["rhod"][-1]
# water weighted average pH at the end of the simulation
r3 = data.variables["radii_m3"][-1,:]
n_H = data.variables["chem_H"][-1,:] / cm.M_H
nom = 0
for it, val in enumerate(r3):
if val > 0:
nom += (n_H[it] / (4./3 * math.pi * val * 1e3)) * val
den = np.sum(r3[:]) # to liters
pH = -1 * np.log10(nom / den)
print " "
print " "
print "water weighted average pH = ", pH, " vs 4.82-4.85 from size resolved models "
s6_ini = data.variables["chemd_S_VI"][0, :]
s6_end = data.variables["chemd_S_VI"][-1, :]
sulf_ppt = fn.mix_ratio_to_mole_frac((np.sum(s6_end) - np.sum(s6_ini)), p, cm.M_H2SO4, T, rhod) * 1e12
print " "
print "sulfate formation (ppt) = ", sulf_ppt, " vs 170-180 from size resolved models"
ini_O3 = data.variables["O3_g"][0] / cm.M_O3 + \
data.variables["chemd_O3_a"][0, :].sum() / cm.M_O3
ini_H2O2 = data.variables["H2O2_g"][0] / cm.M_H2O2 + \
data.variables["chemd_H2O2_a"][0, :].sum() / cm.M_H2O2
end_O3 = data.variables["O3_g"][-1] / cm.M_O3 + \
data.variables["chemd_O3_a"][-1, :].sum() / cm.M_O3
end_H2O2 = data.variables["H2O2_g"][-1] / cm.M_H2O2 + \
data.variables["chemd_H2O2_a"][-1, :].sum() / cm.M_H2O2
sulf_ppt_H2O2 = fn.mix_ratio_to_mole_frac((np.sum(ini_H2O2) - np.sum(end_H2O2)) * cm.M_H2SO4, p, cm.M_H2SO4, T, rhod) * 1e12
sulf_ppt_O3 = fn.mix_ratio_to_mole_frac((np.sum(ini_O3) - np.sum(end_O3)) * cm.M_H2SO4, p, cm.M_H2SO4, T, rhod) * 1e12
print " "
print "sulfate formation from H2O2 (ppt) = ", sulf_ppt_H2O2, " vs 85-105 from size resolved models"
print "sulfate formation from O3 (ppt) = ", sulf_ppt_O3, " vs 70-85 from size resolved models"
print " "
n_tot = data.variables["acti_m0"][12, 0] * rhod * 1e-6
print "N of droplets = ", n_tot, " in cm3"
print "maximum supersaturation = ", (data.RH_max - 1) * 100, "%"
def test_chem_sulfate_formation(data):
"""
sulfate formation vs water-weighted average pH
sulfate formed from H2O2 vs sulfate formed from O3
"""
p = data.variables["p"][-1]
T = data.variables["T"][-1]
rhod = data.variables["rhod"][-1]
# water weighted average pH at the end of the simulation
r3 = data.variables["radii_m3"][-1,:]
n_H = data.variables["chem_H"][-1,:] / cm.M_H
nom = 0
for it, val in enumerate(r3):
if val > 0:
nom += (n_H[it] / (4./3 * math.pi * val * 1e3)) * val
den = np.sum(r3[:]) # to liters
pH = -1 * np.log10(nom / den)
print(" ")
print(" ")
print("water weighted average pH = ", pH, " vs 4.82-4.85 from size resolved models ")
s6_ini = data.variables["chemd_S_VI"][0, :]
s6_end = data.variables["chemd_S_VI"][-1, :]
sulf_ppt = fn.mix_ratio_to_mole_frac((np.sum(s6_end) - np.sum(s6_ini)), p, cm.M_H2SO4, T, rhod) * 1e12
print(" ")
print("sulfate formation (ppt) = ", sulf_ppt, " vs 170-180 from size resolved models")
ini_O3 = data.variables["O3_g"][0] / cm.M_O3 + \
data.variables["chemd_O3_a"][0, :].sum() / cm.M_O3
ini_H2O2 = data.variables["H2O2_g"][0] / cm.M_H2O2 + \
data.variables["chemd_H2O2_a"][0, :].sum() / cm.M_H2O2
end_O3 = data.variables["O3_g"][-1] / cm.M_O3 + \
data.variables["chemd_O3_a"][-1, :].sum() / cm.M_O3
end_H2O2 = data.variables["H2O2_g"][-1] / cm.M_H2O2 + \
data.variables["chemd_H2O2_a"][-1, :].sum() / cm.M_H2O2
sulf_ppt_H2O2 = fn.mix_ratio_to_mole_frac((np.sum(ini_H2O2) - np.sum(end_H2O2)) * cm.M_H2SO4, p, cm.M_H2SO4, T, rhod) * 1e12
sulf_ppt_O3 = fn.mix_ratio_to_mole_frac((np.sum(ini_O3) - np.sum(end_O3)) * cm.M_H2SO4, p, cm.M_H2SO4, T, rhod) * 1e12
print(" ")
print("sulfate formation from H2O2 (ppt) = ", sulf_ppt_H2O2, " vs 85-105 from size resolved models")
print("sulfate formation from O3 (ppt) = ", sulf_ppt_O3, " vs 70-85 from size resolved models")
print(" ")
n_tot = data.variables["acti_m0"][12, 0] * rhod * 1e-6
print("N of droplets = ", n_tot, " in cm3")
print("maximum supersaturation = ", (data.RH_max - 1) * 100, "%")
def plot_fig1(data, output_folder = '', output_title = ''):
import matplotlib
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
import matplotlib.pyplot as plt
# plot settings
plt.figure(1)
plt.rcParams.update({'font.size': 8})
plots = []
for i in range(3):
plots.append(plt.subplot(1,3,i+1))
#(rows, columns, number)
for ax in plots:
ax.set_ylabel('t [s]')
ax.set_ylim([0, 2400])
ax.set_yticks([0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400])
#plt.tight_layout()
spn_idx = 0#4
# read in y-axis (time)
t = data.variables["t"][spn_idx:] - data.variables["t"][spn_idx]
# calculate lwc
plots[0].set_xlabel('lwc g/kg dry air')
plots[0].grid()
#plots[0].set_xlim([0., 2.5])
#plots[0].set_xticks([0., 0.5, 1, 1.5, 2, 2.5])
plots[0].plot(np.sum(data.variables["radii_m3"][spn_idx:], axis=1) * 4. / 3 * math.pi * 998.2 * 1e3, t, "b.-")
# calculate SO2 gas volume concentration
p = data.variables["p"][spn_idx:]
T = data.variables["T"][spn_idx:]
rhod = data.variables["rhod"][spn_idx:]
plots[1].grid()
plots[1].set_xlabel('gas vol.conc SO2 [ppb]')
plots[1].set_xticks([0., 0.05, 0.1, 0.15, 0.2])
plots[1].set_xticklabels(['0', '0.05', '0.1', '0.15', '0.2'])
plots[1].set_xlim([0., 0.2])
tmp1 = fn.mix_ratio_to_mole_frac(data.variables["SO2_g"][spn_idx:], p, cm.M_SO2, T, rhod)
tmp2 = fn.mix_ratio_to_mole_frac(\
np.squeeze(data.variables["plt_ch_SO2_a"][spn_idx:]), p, cm.M_SO2_H2O, T, rhod)
#tmp2 = fn.mix_ratio_to_mole_frac(data.variables["SO2_a"][spn_idx:], p, cm.M_SO2_H2O, T, rhod)
plots[1].plot((tmp1 + tmp2) * 1e9, t, "g.-")
#plots[1].plot((tmp2) * 1e9, t, "r.-")
#plots[1].plot((tmp1) * 1e9, t, "b.-")
# calculate average pH
# (weighted with volume of cloud droplets)
plots[2].set_xlabel('average pH')
plots[2].grid()
#plots[2].set_xlim([3.8, 5])
#plots[2].set_xticks([3.8, 4, 4.2, 4.4, 4.6, 4.8, 5])
r3 = data.variables["radii_m3"][spn_idx:]
n_H = data.variables["chem_H"][spn_idx:] / cm.M_H
nom = np.zeros(t.shape[0])
den = np.zeros(t.shape[0])
for time in range(t.shape[0]):
for it, val in enumerate(r3[time,:]):
if val > 0:
nom[time] += (n_H[time, it] / (4./3 * math.pi * val * 1e3)) * val
den[time] = np.sum(r3[time,:]) # to liters
pH = -1 * np.log10(nom / den)
plots[2].plot(pH, t, "b.-")
plt.savefig(output_folder + output_title + ".pdf")
def plot_fig1(data, output_folder = '', output_title = ''):
# read in data
spn_idx = 2 * 5
t = data.variables["t"][:] #- data.variables["t"][spn_idx]
p = data.variables["p"][:]
T = data.variables["T"][:]
rhod = data.variables["rhod"][:]
r_v = data.variables["r_v"][:]
rho = fn.rho_calc(T, p, r_v)
import matplotlib
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
import matplotlib.pyplot as plt
# plot settings
fig = plt.figure(figsize=(28,13))
plt.rcParams.update({'font.size': 30})
y_labels = [0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400] #time above CB
y_labels2 = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3 ] #height above CB
# calculate lwc
ax = fig.add_subplot(131)
ax.set_xlim([0., 2.5])
ax.set_ylim([200, 2600])
ax.set_yticks([200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400, 2600])
ax.set_yticklabels(y_labels)
ax.tick_params(axis='x', pad=15)
ax.tick_params(axis='y', pad=15)
ax.set_xlabel('LWC [g/kg]')
ax.set_ylabel('time above cloud base [s]')
plt.grid()
plt.plot(data.variables["acti_m3"][:] * 4./3 * math.pi * 999.5 * 1e3 * rhod / rho, t,"b", lw=4.)
# calculate SO2 gas volume concentration
tmp1 = fn.mix_ratio_to_mole_frac(data.variables["SO2_g"][:], p, cm.M_SO2, T, rhod)
tmp2 = fn.mix_ratio_to_mole_frac(\
np.squeeze(data.variables["plt_ch_SO2_a"][:]), p, cm.M_SO2_H2O, T, rhod)
ax = fig.add_subplot(132)
ax.yaxis.set_major_formatter(plt.NullFormatter())
ax.set_ylim([200, 2600])
ax.set_yticks([200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400, 2600])
ax.set_xlim([0., 0.2])
ax.set_xticks([0., 0.05, 0.1, 0.15, 0.2])
ax.set_xticklabels(['0', '0.05', '0.1', '0.15', '0.2'])
ax.set_xlabel('$\mathrm{SO_2}$ conc. [ppb]') #gas volume concentration
plt.grid()
plt.plot((tmp1 + tmp2) * 1e9 * rhod / rho, t, "b", lw=4.)
print "total % of SO2 that was converted to H2SO4 = ", (1 - (tmp1[-1] + tmp2[-1]) / (tmp1[0] + tmp2[0])) * 100
# calculate average pH
# (weighted with volume of cloud droplets)
r3 = data.variables["radii_m3"][:]
n_H = data.variables["chem_H"][:] / cm.M_H
nom = np.zeros(t.shape[0])
den = np.zeros(t.shape[0])
for time in range(t.shape[0]):
for it, val in enumerate(r3[time,:]):
if val > 0:
nom[time] += (n_H[time, it] / (4./3 * math.pi * val * 1e3)) * val
den[time] = np.sum(r3[time,:]) # to liters
pH = -1 * np.log10(nom / den)
ax = fig.add_subplot(133)
ax.set_ylim([200, 2600])
ax.set_yticks([200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400, 2600])
ax.set_yticklabels(y_labels2)
ax.yaxis.tick_right()
ax.set_xlabel('average pH')
ax.set_ylabel('height above cloud base [km]')
ax.yaxis.set_label_position("right")
ax.set_xlim([3.6, 5])
ax.set_xticks([3.8, 4.0, 4.2, 4.4, 4.6, 4.8, 5])
plt.grid()
plt.plot(pH, t, "b", lw=4.)
plt.savefig(output_folder + output_title + ".pdf")
def plot_chem(data, output_folder = '', output_title = ''):
def dn_calc(m_end, m_beg, M):
return (m_end - m_beg) / M
import matplotlib
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
import matplotlib.pyplot as plt
style = "b."
#spn_idx = 30
spn_idx = 0
#-----------------------------------------------------------------------
# plot p, T, RH and dry/wet moments
plt.figure(1, figsize=(18,14))
plots = []
for i in range(9):
plots.append(plt.subplot(3,3,i+1))
#(rows, columns, number)
plots[0].set_xlabel('p [hPa]')
plots[1].set_xlabel('T [K]')
plots[2].set_xlabel('RH')
plots[3].set_xlabel('m0 1/kg dry air ')
plots[4].set_xlabel('m1 um/kg dry air ')
plots[5].set_xlabel('lwc g/kg dry air')
plots[6].set_xlabel('m0_dry 1/kg dry air ')
plots[7].set_xlabel('m1_dry um/kg dry air ')
plots[8].set_xlabel('mass cont ug/kg dry air')
#plots[7].set_xlim([0.02, 0.04])
#plots[7].set_xticks([0.02, 0.025, 0.03, 0.035, 0.04])
for ax in plots:
ax.set_ylabel('t [s]')
t = data.variables["t"][spn_idx:]
plots[0].plot(data.variables["p"][spn_idx:] / 100. , t, style)
#plots[0].legend(loc='upper right')
plots[1].plot(data.variables["T"][spn_idx:] , t, style)
plots[2].plot(
data.variables["RH"][spn_idx:] , t, style,
[data.variables["RH"][spn_idx:].max()] * t.shape[0], t, style
)
plots[3].plot(np.squeeze(data.variables["plt_rw_m0"][spn_idx:]), t, style)
plots[4].plot(\
np.squeeze(data.variables["plt_rw_m1"][spn_idx:]) / np.squeeze(data.variables["plt_rw_m0"][spn_idx:]) * 1e6, t, style)
plots[5].plot(\
np.squeeze(data.variables["plt_rw_m3"][spn_idx:]) * 4. / 3 * math.pi * 998.2 * 1e3, t, style)
plots[6].plot(np.squeeze(data.variables["plt_rd_m0"][spn_idx:]), t, style)
plots[7].plot(\
np.squeeze(data.variables["plt_rd_m1"][spn_idx:]) / np.squeeze(data.variables["plt_rd_m0"][spn_idx:]) * 1e6, t, style)
plots[8].plot(\
np.squeeze(data.variables["plt_rd_m3"][spn_idx:]) * 4./ 3 * math.pi * getattr(data, 'chem_rho') * 1e9, t, style)
plt.savefig(output_folder + output_title + "stat.pdf")
#-----------------------------------------------------------------------
# plot non-dissociating chem species
plt.figure(2, figsize=(18,14))
plots = []
for i in range(6):
plots.append(plt.subplot(2,3,i+1))
#(rows, columns, number)
plots[0].set_xlabel('n O3g 1/kg dry air')
plots[1].set_xlabel('n O3a 1/kg dry air')
plots[2].set_xlabel('n O3 - init 1/kg dry air')
plots[3].set_xlabel('n H2O2g 1/kg dry air')
plots[4].set_xlabel('n H2O2a 1/kg dry air')
plots[5].set_xlabel('n H2O2 - init 1/kg dry air')
for ax in plots:
ax.set_ylabel('t [s]')
t = data.variables["t"][spn_idx:]
n_o3g = data.variables["O3_g"][spn_idx:] / cm.M_O3
n_o3a = data.variables["O3_a"][spn_idx:] / cm.M_O3
n_h2o2g = data.variables["H2O2_g"][spn_idx:] / cm.M_H2O2
n_h2o2a = data.variables["H2O2_a"][spn_idx:] / cm.M_H2O2
plots[0].plot(n_o3g, t, style)
#plots[0].legend(loc='upper right')
plots[1].plot(n_o3a, t, style)
plots[2].plot(n_o3g + n_o3a - n_o3g[0] - n_o3a[0], t, style)
plots[3].plot(n_h2o2g, t, style)
plots[4].plot(n_h2o2a, t, style)
plots[5].plot(n_h2o2g + n_h2o2a - n_h2o2g[0] - n_h2o2a[0], t, style)
plt.savefig(output_folder + output_title + "O3_H2O2.pdf")
#-----------------------------------------------------------------------
# plot pH , H+, OH-
plt.figure(3, figsize=(12,8))
plots = []
for i in range(3):
plots.append(plt.subplot(1,3,i+1))
#(rows, columns, number)
plots[0].set_xlabel('average pH')
plots[1].set_xlabel('n H 1/kg dry air')
plots[2].set_xlabel('n OH 1/kg dry air')
plots[2].ticklabel_format(axis = 'x', style='sci', scilimits=(-2,2))
for ax in plots:
ax.set_ylabel('t [s]')
t = data.variables["t"][spn_idx:]
n_H = np.squeeze(data.variables["plt_ch_H"][spn_idx:]) / cm.M_H
vol = np.squeeze(data.variables["plt_rw_m3"][spn_idx:]) * 4/3. * math.pi
pH = -1 * np.log10(n_H / vol / 1e3)
# litres
plots[0].plot(pH, t, style)
plots[1].plot(np.squeeze(data.variables["plt_ch_H"][spn_idx:]) / cm.M_H , t, style)
plots[2].plot(cm.K_H2O / np.squeeze(data.variables["plt_ch_H"][spn_idx:]) * vol , t, style)
plt.tight_layout()
plt.savefig(output_folder + output_title + "pH.pdf")
#-----------------------------------------------------------------------
# plot NH3_g, NH3_a, NH4+
plt.figure(4, figsize=(12,8))
plots = []
for i in range(3):
plots.append(plt.subplot(1,3,i+1))
#(rows, columns, number)
plots[0].set_xlabel('n NH3 g 1/kg dry air')
plots[1].set_xlabel('n N3 aq 1/kg dry air')
plots[2].set_xlabel('n - init 1/kg dry air')
for ax in plots:
ax.set_ylabel('t [s]')
t = data.variables["t"][spn_idx:]
n_nh3g = data.variables["NH3_g"][spn_idx:] / cm.M_NH3
n_N3 = np.squeeze(data.variables["plt_ch_NH3_a"][spn_idx:]) / cm.M_NH3_H2O
plots[0].plot(n_nh3g, t, style)
plots[1].plot(n_N3, t, style)
plots[2].plot(n_nh3g + n_N3 - n_nh3g[0] - n_N3[0], t, style)
plt.tight_layout()
plt.savefig(output_folder + output_title + "NH3.pdf")
#-----------------------------------------------------------------------
# plot HNO3_g, NHO3_a, NO3+
plt.figure(5, figsize=(12,8))
plots = []
for i in range(3):
plots.append(plt.subplot(1,3,i+1))
#(rows, columns, number)
plots[0].set_xlabel('n HNO3 g 1/kg dry air')
plots[1].set_xlabel('n N5 aq 1/kg dry air')
plots[2].set_xlabel('n - init 1/kg dry air')
for ax in plots:
ax.set_ylabel('t [s]')
t = data.variables["t"][spn_idx:]
n_hno3g = data.variables["HNO3_g"][spn_idx:] / cm.M_HNO3
n_N5 = np.squeeze(data.variables["plt_ch_HNO3_a"][spn_idx:]) / cm.M_HNO3
plots[0].plot(n_hno3g, t, style)
plots[1].plot(n_N5, t, style)
plots[2].plot(n_hno3g + n_N5 - n_hno3g[0] - n_N5[0], t, style)
plt.savefig(output_folder + output_title + "HNO3.pdf")
#-----------------------------------------------------------------------
# plot CO2_g, CO2_a, HCO3+, CO3--
plt.figure(6, figsize=(12,8))
plots = []
for i in range(3):
plots.append(plt.subplot(1,3,i+1))
#(rows, columns, number)
plots[0].set_xlabel('n CO2 g 1/kg dry air')
plots[1].set_xlabel('n C4 aq 1/kg dry air')
plots[2].set_xlabel('n - int 1/kg dry air')
for ax in plots:
ax.set_ylabel('t [s]')
t = data.variables["t"][spn_idx:]
n_co2g = data.variables["CO2_g"][spn_idx:] / cm.M_CO2
n_C4 = np.squeeze(data.variables["plt_ch_CO2_a"][spn_idx:]) / cm.M_CO2_H2O
plots[0].plot(n_co2g, t, style)
plots[1].plot(n_C4, t, style)
plots[2].plot(n_co2g + n_C4 - n_co2g[0] - n_C4[0], t, style)
plt.savefig(output_folder + output_title + "CO2.pdf")
#-----------------------------------------------------------------------
# plot SO2_g, SO2_a, HSO3+, SO3--, HSO4-, SO4--, S_VI
plt.figure(7, figsize=(18,14))
plots = []
for i in range(5):
plots.append(plt.subplot(2,3,i+1))
#(rows, columns, number)
plots[0].set_xlabel('n SO2 g [1/kg dry air]')
plots[1].set_xlabel('n S4 aq 1/kg dry air')
plots[2].set_xlabel('gas vol.conc SO2 [ppb]')
plots[2].set_xticks([0., 0.05, 0.1, 0.15, 0.2, 0.25, 0.3])
plots[2].set_xticklabels(['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3'])
plots[2].set_xlim([0., 0.3])
plots[3].set_xlabel('n S6 [1 / kg dry air]')
plots[4].set_xlabel('n - init 1/kg dry air')
for ax in plots:
ax.set_ylabel('t [s]')
t = data.variables["t"][spn_idx:]
p = data.variables["p"][spn_idx:]
T = data.variables["T"][spn_idx:]
rhod = data.variables["rhod"][spn_idx:]
n_so2g = data.variables["SO2_g"][spn_idx:] / cm.M_SO2
n_S4 = np.squeeze(data.variables["plt_ch_SO2_a"][spn_idx:]) / cm.M_SO2_H2O
n_S6 = np.squeeze(data.variables["plt_ch_S_VI"][spn_idx:]) / cm.M_H2SO4
plots[0].plot(n_so2g, t, style)
plots[1].plot(n_S4, t, style)
plots[2].plot(fn.mix_ratio_to_mole_frac(data.variables["SO2_g"][:], p, cm.M_SO2, T, rhod) * 1e9, t)
plots[3].plot(n_S6, t, style)
plots[4].plot(n_so2g + n_S4 + n_S6 - n_so2g[0] - n_S4[0] - n_S6[0], t, style)
plt.savefig(output_folder + output_title + "SO2.pdf")
def plot_fig1(data, output_folder='', output_title=''):
# read in data
spn_idx = 2 * 5
t = data.variables["t"][:] #- data.variables["t"][spn_idx]
p = data.variables["p"][:]
T = data.variables["T"][:]
rhod = data.variables["rhod"][:]
r_v = data.variables["r_v"][:]
rho = fn.rho_calc(T, p, r_v)
import matplotlib
matplotlib.use(
'Agg') # Must be before importing matplotlib.pyplot or pylab!
import matplotlib.pyplot as plt
# plot settings
fig = plt.figure(figsize=(28, 13))
plt.rcParams.update({'font.size': 30})
y_labels = [
0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400
] #time above CB
y_labels2 = [
0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3
] #height above CB
# calculate lwc
ax = fig.add_subplot(131)
ax.set_xlim([0., 2.5])
ax.set_ylim([200, 2600])
ax.set_yticks([
200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400,
2600
])
ax.set_yticklabels(y_labels)
ax.tick_params(axis='x', pad=15)
ax.tick_params(axis='y', pad=15)
ax.set_xlabel('LWC [g/kg]')
ax.set_ylabel('time above cloud base [s]')
plt.grid()
plt.plot(data.variables["acti_m3"][:] * 4. / 3 * math.pi * 999.5 * 1e3 *
rhod / rho,
t,
"b",
lw=4.)
# calculate SO2 gas volume concentration
tmp1 = fn.mix_ratio_to_mole_frac(data.variables["SO2_g"][:], p, cm.M_SO2,
T, rhod)
tmp2 = fn.mix_ratio_to_mole_frac(\
np.squeeze(data.variables["plt_ch_SO2_a"][:]), p, cm.M_SO2_H2O, T, rhod)
ax = fig.add_subplot(132)
ax.yaxis.set_major_formatter(plt.NullFormatter())
ax.set_ylim([200, 2600])
ax.set_yticks([
200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400,
2600
])
ax.set_xlim([0., 0.2])
ax.set_xticks([0., 0.05, 0.1, 0.15, 0.2])
ax.set_xticklabels(['0', '0.05', '0.1', '0.15', '0.2'])
ax.set_xlabel('$\mathrm{SO_2}$ conc. [ppb]') #gas volume concentration
plt.grid()
plt.plot((tmp1 + tmp2) * 1e9 * rhod / rho, t, "b", lw=4.)
print("total % of SO2 that was converted to H2SO4 = ",
(1 - (tmp1[-1] + tmp2[-1]) / (tmp1[0] + tmp2[0])) * 100)
# calculate average pH
# (weighted with volume of cloud droplets)
r3 = data.variables["radii_m3"][:]
n_H = data.variables["chem_H"][:] / cm.M_H
nom = np.zeros(t.shape[0])
den = np.zeros(t.shape[0])
for time in range(t.shape[0]):
for it, val in enumerate(r3[time, :]):
if val > 0:
nom[time] += (n_H[time, it] /
(4. / 3 * math.pi * val * 1e3)) * val
den[time] = np.sum(r3[time, :]) # to liters
pH = -1 * np.log10(nom / den)
ax = fig.add_subplot(133)
ax.set_ylim([200, 2600])
ax.set_yticks([
200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400,
2600
])
ax.set_yticklabels(y_labels2)
ax.yaxis.tick_right()
ax.set_xlabel('average pH')
ax.set_ylabel('height above cloud base [km]')
ax.yaxis.set_label_position("right")
ax.set_xlim([3.6, 5])
ax.set_xticks([3.8, 4.0, 4.2, 4.4, 4.6, 4.8, 5])
plt.grid()
plt.plot(pH, t, "b", lw=4.)
plt.savefig(output_folder + output_title + ".pdf")
def data(request):
# copy options from chem_conditions ...
p_dict = copy.deepcopy(parcel_dict)
# ... and modify them for the current test
p_dict['outfile'] = "test_chem_kreidenweis.nc"
p_dict['chem_dsl'] = True
p_dict['chem_dsc'] = True
p_dict['chem_rct'] = True
p_dict['outfreq'] = 10 / (p_dict['dt'] * p_dict['w'])
p_dict['out_bin'] = p_dict['out_bin'][:-1] + \
', "chem" : {"rght": 1e-4, "left": 1e-6, "drwt": "wet", "lnli": "log", "nbin": 100, "moms": ["H"]},\
"radii" : {"rght": 1e-4, "left": 1e-6, "drwt": "wet", "lnli": "log", "nbin": 100, "moms": [3]},\
"acti" : {"rght": 1e-3, "left": 1e-6, "drwt": "wet", "lnli": "log", "nbin": 1, "moms": [0]},\
"chemd" : {"rght": 1e-6, "left": 1e-8, "drwt": "dry", "lnli": "log", "nbin": 100, "moms": ["S_VI" ]}}'
# create empty lists for storing simulation results
sd_list = []
pow_list = []
RH_max_list = []
N_drop_list = []
pH_list = []
tot_S_list = []
# run parcel test for super-droplet numbers between 1 and 1024
for pw in range(11):
sd_num = pow(2, pw)
p_dict['sd_conc'] = sd_num
pp.pprint(p_dict)
# run parcel
parcel(**p_dict)
# save the simulation results ...
tmp_data = netcdf.netcdf_file(p_dict['outfile'], "r")
# ... do the initial analysis ...
p = tmp_data.variables["p"][-1]
T = tmp_data.variables["T"][-1]
rhod = tmp_data.variables["rhod"][-1]
r3 = tmp_data.variables["radii_m3"][-1, :]
n_H = tmp_data.variables["chem_H"][-1, :] / cm.M_H
# water weighted average pH at the end of the simulation:
nom = 0
for it, val in enumerate(r3):
if val > 0:
nom += (n_H[it] / (4. / 3 * math.pi * val * 1e3)) * val
den = np.sum(r3[:]) # to liters
pH = -1 * np.log10(nom / den)
# total sulfate formation:
s6_ini = tmp_data.variables["chemd_S_VI"][0, :]
s6_end = tmp_data.variables["chemd_S_VI"][-1, :]
sulf_ppt = fn.mix_ratio_to_mole_frac(
(np.sum(s6_end) - np.sum(s6_ini)), p, cm.M_H2SO4, T, rhod) * 1e12
# number of droplets at RH = max:
n_tot = tmp_data.variables["acti_m0"][12, 0] * rhod * 1e-6
# ^^ TODO - think of something better
# maximum supersaturation:
RH_max = (tmp_data.RH_max - 1) * 100
# ... save the results ...
sd_list.append(sd_num)
pow_list.append(pw)
RH_max_list.append(RH_max)
N_drop_list.append(n_tot)
pH_list.append(pH)
tot_S_list.append(sulf_ppt)
# ... and remove the simulation data files
subprocess.call(["rm", p_dict['outfile']])
# pack all the data into a dictionary
data = {}
data['number of super-droplets'] = sd_list
data['power of two'] = pow_list
data['cloud droplet conc. at CB'] = N_drop_list
data['maximum supersaturation'] = RH_max_list
data['water weighted average pH'] = pH_list
data['total sulfate production'] = tot_S_list
return data