def data(request):
# initial values
RH_init = .99999
T_init = 280.
p_init = 100000.
r_init = common.eps * RH_init * common.p_vs(T_init) / (p_init - RH_init * common.p_vs(T_init))
# lists to store RH_max and N at the end of the simulation from each test run
RH_list = []
N_list = []
for dt in Dt_list:
print "\nt time step", dt
outfile_nc = "timesteptest_dt=" + str(dt) + ".nc"
parcel(dt=dt, outfreq = int(100/dt), outfile = outfile_nc,\
w = 1., T_0 = T_init, p_0 = p_init, r_0 = r_init, z_max = 200, \
mean_r = 5e-8, gstdev = 1.5, n_tot = 1e9, sd_conc = 1000, \
out_bin = '{"radii": {"rght": 1, "moms": [0], "drwt": "wet", "nbin": 1, "lnli": "lin", "left": 1e-06}}'
)
f_out = netcdf.netcdf_file(outfile_nc, "r")
RH_max = f_out.RH_max
N_end = f_out.variables["radii_m0"][-1,0] # concentration of drops > 1e-6 m
RH_list.append((RH_max - 1)*100) # [%]
N_list.append(N_end / 1e6) # [1/mg]
data = {"RH" : RH_list, "N" : N_list, "dt" : Dt_list}
# removing all netcdf files after all tests
def removing_files():
for file in glob.glob("timesteptest_dt*"):
subprocess.call(["rm", file])
request.addfinalizer(removing_files)
return data
def main(dt_list = [1e-3, 1.5e-3, 2e-3, 3e-3, 4e-3, 8e-3, 1e-2, 2e-2, 4e-2, 8e-2, 1e-1, 2e-1, 4e-1, 8e-1, 1.]):
# initial values
z_max = 200.
w = 1.
RH_init = .99999
T_init = 280.
p_init = 100000.
r_init = common.eps * RH_init * common.p_vs(T_init) / (p_init - RH_init * common.p_vs(T_init))
# lists to store RH_max and N at the end of the simulation from each test run
RH_list = []
N_list = []
for dt in dt_list:
print("\nt time step", dt)
outfile_nc = "timesteptest_dt=" + str(dt) + ".nc"
parcel(dt=dt, outfreq = int(z_max/w/dt), outfile = outfile_nc,\
w = w, T_0 = T_init, p_0 = p_init, r_0 = r_init, z_max = z_max, \
mean_r = 5e-8, gstdev = 1.5, n_tot = 1e9, sd_conc = 1000, \
radii = 1e-6 * pow(10, -3 + np.arange(26) * .2)
)
f_out = netcdf.netcdf_file(outfile_nc, "r")
RH_max = f_out.RH_max
N_end = sum(f_out.variables["conc"][-1, -9:]) # concentration of drops > 1e-6 m
RH_list.append((RH_max - 1)*100) # [%]
N_list.append(N_end / 1e6) # [1/mg]
subprocess.call(["rm", outfile_nc])
data = {"RH" : RH_list, "N" : N_list, "dt" : dt_list}
timestep_plot(data)
def data(request):
# initial condition
RH_init = .999999
T_init = 300.
p_init = 100000.
r_init = cm.eps * RH_init * cm.p_vs(T_init) / (p_init - RH_init * cm.p_vs(T_init))
# calculate rhod for gas init cond
th_0 = T_init * (cm.p_1000 / p_init)**(cm.R_d / cm.c_pd)
rhod_init = cm.rhod(p_init, th_0, r_init)
# init cond for trace gases
SO2_g_init = fn.mole_frac_to_mix_ratio(200e-12, p_init, cm.M_SO2, T_init, rhod_init)
O3_g_init = fn.mole_frac_to_mix_ratio(50e-9, p_init, cm.M_O3, T_init, rhod_init)
H2O2_g_init = fn.mole_frac_to_mix_ratio(500e-12, p_init, cm.M_H2O2, T_init, rhod_init)
CO2_g_init = fn.mole_frac_to_mix_ratio(360e-6, p_init, cm.M_CO2, T_init, rhod_init)
NH3_g_init = fn.mole_frac_to_mix_ratio(100e-12, p_init, cm.M_NH3, T_init, rhod_init)
HNO3_g_init = fn.mole_frac_to_mix_ratio(100e-12, p_init, cm.M_HNO3, T_init, rhod_init)
# aerosol size distribution
mean_r = .08e-6 / 2
gstdev = 2.
n_tot = 566.e6
# output
sd_conc = 1
outfreq = 50
z_max = 300.
outfile = "test_chem_henry.nc"
dt = 0.1
wait = 100
parcel(dt = dt, z_max = z_max, outfreq = outfreq, wait=wait,\
T_0 = T_init, p_0 = p_init, r_0 = r_init,\
SO2_g = SO2_g_init, O3_g = O3_g_init, H2O2_g = H2O2_g_init,\
CO2_g = CO2_g_init, NH3_g = NH3_g_init, HNO3_g = HNO3_g_init,\
outfile = outfile,\
chem_dsl = True, chem_dsc = True, chem_rct = False,\
aerosol = \
'{"test": {"kappa": 0.5, "mean_r": [' + str(mean_r) + '], "gstdev": [' + str(gstdev) + '], "n_tot": [' + str(n_tot) + ']}}',\
sd_conc = sd_conc,\
out_bin = \
'{"radii": {"rght": 1.0, "left": 0.0, "drwt": "wet", "lnli": "lin", "nbin": 1, "moms": [0, 3]},\
"chem" : {"rght": 1.0, "left": 0.0, "drwt": "wet", "lnli": "lin", "nbin": 1,\
"moms": ["O3_a", "H2O2_a", "SO2_a", "CO2_a", "NH3_a", "HNO3_a", "H"]}}')
data = netcdf.netcdf_file("test_chem_henry.nc", "r")
# removing all netcdf files after all tests
def removing_files():
subprocess.call(["rm", "test_chem_henry.nc"])
request.addfinalizer(removing_files)
return data
def main():
RH_init = .99999
T_init = 300.
p_init = 100000.
r_init = cm.eps * RH_init * cm.p_vs(T_init) / (p_init -
RH_init * cm.p_vs(T_init))
# calculate rhod for initial gas mixing ratio
th_0 = T_init * (cm.p_1000 / p_init)**(cm.R_d / cm.c_pd)
rhod_init = cm.rhod(p_init, th_0, r_init)
SO2_g_init = fn.mole_frac_to_mix_ratio(200e-12, p_init, cm.M_SO2, T_init,
rhod_init)
O3_g_init = fn.mole_frac_to_mix_ratio(50e-9, p_init, cm.M_O3, T_init,
rhod_init)
H2O2_g_init = fn.mole_frac_to_mix_ratio(500e-12, p_init, cm.M_H2O2, T_init,
rhod_init)
CO2_g_init = fn.mole_frac_to_mix_ratio(360e-6, p_init, cm.M_CO2, T_init,
rhod_init)
NH3_g_init = fn.mole_frac_to_mix_ratio(100e-12, p_init, cm.M_NH3, T_init,
rhod_init)
HNO3_g_init = fn.mole_frac_to_mix_ratio(100e-12, p_init, cm.M_HNO3, T_init,
rhod_init)
outfreq = 50
z_max = 50.
outfile = "test_chem_henry.nc"
dt = 0.1
wait = 1000
sd_conc = 1
# run parcel run!
parcel(dt = dt, z_max = z_max, outfreq = outfreq, wait=wait,\
T_0 = T_init, p_0 = p_init, r_0 = r_init, sd_conc=sd_conc,\
SO2_g = SO2_g_init, O3_g = O3_g_init, H2O2_g = H2O2_g_init,\
CO2_g = CO2_g_init, NH3_g = NH3_g_init, HNO3_g = HNO3_g_init,\
outfile = outfile,\
chem_dsl = True, chem_dsc = False, chem_rct = False,\
out_bin = \
'{"radii": {"rght": 1, "left": 0, "drwt": "wet", "lnli": "lin", "nbin": 1, "moms": [0, 3]},\
"chem" : {"rght": 1, "left": 0, "drwt": "wet", "lnli": "lin", "nbin": 1,\
"moms": ["O3_a", "H2O2_a", "SO2_a", "CO2_a", "NH3_a", "HNO3_a", "H"]}}' )
data = netcdf.netcdf_file(outfile, "r")
#plot
plot_henry(data, output_folder="../outputs")
#cleanup
data.close()
subprocess.call(["rm", "test_chem_henry.nc"])
def main():
RH_init = .99999
T_init = 300.
p_init = 100000.
r_init = cm.eps * RH_init * cm.p_vs(T_init) / (p_init - RH_init * cm.p_vs(T_init))
# calculate rhod for initial gas mixing ratio
th_0 = T_init * (cm.p_1000 / p_init)**(cm.R_d / cm.c_pd)
rhod_init = cm.rhod(p_init, th_0, r_init)
SO2_g_init = fn.mole_frac_to_mix_ratio(200e-12, p_init, cm.M_SO2, T_init, rhod_init)
O3_g_init = fn.mole_frac_to_mix_ratio(50e-9, p_init, cm.M_O3, T_init, rhod_init)
H2O2_g_init = fn.mole_frac_to_mix_ratio(500e-12, p_init, cm.M_H2O2, T_init, rhod_init)
CO2_g_init = fn.mole_frac_to_mix_ratio(360e-6, p_init, cm.M_CO2, T_init, rhod_init)
NH3_g_init = fn.mole_frac_to_mix_ratio(100e-12, p_init, cm.M_NH3, T_init, rhod_init)
HNO3_g_init = fn.mole_frac_to_mix_ratio(100e-12, p_init, cm.M_HNO3, T_init, rhod_init)
outfreq = 50
z_max = 50.
outfile = "test_chem_henry.nc"
dt = 0.1
wait = 1000
sd_conc = 1
# run parcel run!
parcel(dt = dt, z_max = z_max, outfreq = outfreq, wait=wait,\
T_0 = T_init, p_0 = p_init, r_0 = r_init, sd_conc=sd_conc,\
SO2_g = SO2_g_init, O3_g = O3_g_init, H2O2_g = H2O2_g_init,\
CO2_g = CO2_g_init, NH3_g = NH3_g_init, HNO3_g = HNO3_g_init,\
outfile = outfile,\
chem_dsl = True, chem_dsc = False, chem_rct = False,\
out_bin = \
'{"radii": {"rght": 1, "left": 0, "drwt": "wet", "lnli": "lin", "nbin": 1, "moms": [0, 3]},\
"chem" : {"rght": 1, "left": 0, "drwt": "wet", "lnli": "lin", "nbin": 1,\
"moms": ["O3_a", "H2O2_a", "SO2_a", "CO2_a", "NH3_a", "HNO3_a", "H"]}}')
data = netcdf.netcdf_file(outfile,"r")
#plot
plot_henry(data, output_folder = "../outputs")
#cleanup
data.close()
subprocess.call(["rm", "test_chem_henry.nc"])
def main(dt_list=[1e-3, 1.5e-3, 2e-3, 3e-3, 4e-3, 8e-3, 1e-2, 2e-2, 4e-2, 8e-2, 1e-1, 2e-1, 4e-1, 8e-1, 1.0]):
# initial values
z_max = 200.0
w = 1.0
RH_init = 0.99999
T_init = 280.0
p_init = 100000.0
r_init = common.eps * RH_init * common.p_vs(T_init) / (p_init - RH_init * common.p_vs(T_init))
# lists to store RH_max and N at the end of the simulation from each test run
RH_list = []
N_list = []
for dt in dt_list:
print "\nt time step", dt
outfile_nc = "timesteptest_dt=" + str(dt) + ".nc"
parcel(
dt=dt,
outfreq=int(z_max / w / dt),
outfile=outfile_nc,
w=w,
T_0=T_init,
p_0=p_init,
r_0=r_init,
z_max=z_max,
mean_r=5e-8,
gstdev=1.5,
n_tot=1e9,
sd_conc=1000,
radii=1e-6 * pow(10, -3 + np.arange(26) * 0.2),
)
f_out = netcdf.netcdf_file(outfile_nc, "r")
RH_max = f_out.RH_max
N_end = sum(f_out.variables["conc"][-1, -9:]) # concentration of drops > 1e-6 m
RH_list.append((RH_max - 1) * 100) # [%]
N_list.append(N_end / 1e6) # [1/mg]
subprocess.call(["rm", outfile_nc])
data = {"RH": RH_list, "N": N_list, "dt": dt_list}
timestep_plot(data)
def data(request):
# initial values
RH_init = .99999
T_init = 280.
p_init = 100000.
r_init = common.eps * RH_init * common.p_vs(T_init) / (
p_init - RH_init * common.p_vs(T_init))
# lists to store RH_max and N at the end of the simulation from each test run
RH_list = []
N_list = []
for dt in Dt_list:
print("\nt time step", dt)
outfile_nc = "timesteptest_dt=" + str(dt) + ".nc"
parcel(dt=dt, outfreq = int(100/dt), outfile = outfile_nc,\
w = 1., T_0 = T_init, p_0 = p_init, r_0 = r_init, z_max = 200, \
sd_conc = 1000, \
aerosol = '{"ammonium_sulfate": {"kappa": 0.61, "mean_r": [5e-8], "gstdev": [1.5], "n_tot": [1e9]}}',
out_bin = '{"radii": {"rght": 1, "moms": [0], "drwt": "wet", "nbin": 1, "lnli": "lin", "left": 1e-06}}'
)
f_out = netcdf.netcdf_file(outfile_nc, "r")
RH_max = f_out.RH_max
N_end = f_out.variables["radii_m0"][
-1, 0] # concentration of drops > 1e-6 m
RH_list.append((RH_max - 1) * 100) # [%]
N_list.append(N_end / 1e6) # [1/mg]
data = {"RH": RH_list, "N": N_list, "dt": Dt_list}
# removing all netcdf files after all tests
def removing_files():
for file in glob.glob("timesteptest_dt*"):
subprocess.call(["rm", file])
request.addfinalizer(removing_files)
return data
def rh_to_rv(RH, T, p):
"""
convert relative humidity [%] to water vapor mixing ratio [kg/kg]
"""
return cm.eps * RH * cm.p_vs(T) / (p - RH * cm.p_vs(T))
def test_pvs(arg, epsilon = 0.03):
pvs_md = common.p_vs(arg["T"])
pvs_anpy = anpy.press_sat(arg["T"])
assert abs(pvs_anpy - pvs_md) <= epsilon * pvs_anpy
def rh_to_rv(RH, T, p):
"""
convert relative humidity [%] to water vapor mixing ratio [kg/kg]
"""
return cm.eps * RH * cm.p_vs(T) / (p - RH * cm.p_vs(T))
def data(request):
# initial condition
RH_init = .999999
T_init = 300.
p_init = 100000.
r_init = cm.eps * RH_init * cm.p_vs(T_init) / (p_init -
RH_init * cm.p_vs(T_init))
# calculate rhod for gas init cond
th_0 = T_init * (cm.p_1000 / p_init)**(cm.R_d / cm.c_pd)
rhod_init = cm.rhod(p_init, th_0, r_init)
# init cond for trace gases
SO2_g_init = fn.mole_frac_to_mix_ratio(200e-12, p_init, cm.M_SO2, T_init,
rhod_init)
O3_g_init = fn.mole_frac_to_mix_ratio(50e-9, p_init, cm.M_O3, T_init,
rhod_init)
H2O2_g_init = fn.mole_frac_to_mix_ratio(500e-12, p_init, cm.M_H2O2, T_init,
rhod_init)
CO2_g_init = fn.mole_frac_to_mix_ratio(360e-6, p_init, cm.M_CO2, T_init,
rhod_init)
NH3_g_init = fn.mole_frac_to_mix_ratio(100e-12, p_init, cm.M_NH3, T_init,
rhod_init)
HNO3_g_init = fn.mole_frac_to_mix_ratio(100e-12, p_init, cm.M_HNO3, T_init,
rhod_init)
# aerosol size distribution
mean_r = .08e-6 / 2
gstdev = 2.
n_tot = 566.e6
# output
sd_conc = 1
outfreq = 50
z_max = 300.
outfile = "test_chem_henry.nc"
dt = 0.1
wait = 100
parcel(dt = dt, z_max = z_max, outfreq = outfreq, wait=wait,\
T_0 = T_init, p_0 = p_init, r_0 = r_init,\
SO2_g = SO2_g_init, O3_g = O3_g_init, H2O2_g = H2O2_g_init,\
CO2_g = CO2_g_init, NH3_g = NH3_g_init, HNO3_g = HNO3_g_init,\
outfile = outfile,\
chem_dsl = True, chem_dsc = True, chem_rct = False,\
aerosol = \
'{"test": {"kappa": 0.5, "mean_r": [' + str(mean_r) + '], "gstdev": [' + str(gstdev) + '], "n_tot": [' + str(n_tot) + ']}}',\
sd_conc = sd_conc,\
out_bin = \
'{"radii": {"rght": 1.0, "left": 0.0, "drwt": "wet", "lnli": "lin", "nbin": 1, "moms": [0, 3]},\
"chem" : {"rght": 1.0, "left": 0.0, "drwt": "wet", "lnli": "lin", "nbin": 1,\
"moms": ["O3_a", "H2O2_a", "SO2_a", "CO2_a", "NH3_a", "HNO3_a", "H"]}}' )
data = netcdf.netcdf_file("test_chem_henry.nc", "r")
# removing all netcdf files after all tests
def removing_files():
subprocess.call(["rm", "test_chem_henry.nc"])
request.addfinalizer(removing_files)
return data
from rhs_blk_2m import rhs_blk_2m from rhs_lgrngn import rhs_lgrngn from libcloudphxx.common import th_std2dry, th_dry2std from libcloudphxx.common import p_vs from libcloudphxx.common import eps, p_1000, R_d, c_pd from libcloudphxx.lgrngn import chem_species_t from numpy import array as arr_t from math import exp, log, sqrt, pi # initial parameters T = arr_t([282.2]) p = arr_t([95000.]) p_v = arr_t([0.95 * p_vs(T[0])]) p_d = p - p_v r_v = eps * p_v / p_d #th_d = arr_t([th_std2dry(300., r_v[0])]) th_d = T * pow(p_1000 / p_d[0], R_d / c_pd) w = 0.5 dt = .1 nt = int(600 / w / dt) # 600 metres # blk_2m-specific parameter # TODO: spectrum # lgrngn-specific parameters sd_conc = 44 def lognormal(lnr): mean_r = .08e-6 / 2
prsr_lgr.add_argument('--kappa', type=float, required=True, help='aerosol hygroscopicity parameter [1]')
prsr_lgr.add_argument('--n_tot', type=float, required=True, help='aerosol concentration @STP [m-3]')
prsr_lgr.add_argument('--meanr', type=float, required=True, help='aerosol mean dry radius [m]')
prsr_lgr.add_argument('--gstdv', type=float, required=True, help='aerosol geometric standard deviation [1]')
prsr_lgr.add_argument('--chem_SO2', type=float, default=0, help='SO2 volume concentration [1]')
prsr_lgr.add_argument('--chem_O3', type=float, default=0, help='O3 volume concentration [1]')
prsr_lgr.add_argument('--chem_H2O2', type=float, default=0, help='H2O2 volume concentration [1]')
## blk_2m options
prsr_b2m = sprsr.add_parser('blk_2m')
#TODO...
args = prsr.parse_args()
# computing state variables
p_v = arr_t([args.RH * p_vs(args.T)])
p_d = args.p - p_v
r_v = eps * p_v / p_d
th_d = args.T * pow(p_1000 / p_d, R_d / c_pd)
class lognormal:
def __init__(self, n_tot, meanr, gstdv):
self.meanr = meanr
self.stdev = gstdv
self.n_tot = n_tot
def __call__(self, lnr):
return self.n_tot * exp(
-pow((lnr - log(self.meanr)), 2) / 2 / pow(log(self.stdev),2)
) / log(self.stdev) / sqrt(2*pi);
prsr_lgr.add_argument('--gstdv', type=float, required=(dflts.gstdv is None), default=dflts.gstdv, help='aerosol geometric standard deviation [1]', nargs='+')
prsr_lgr.add_argument('--cloud_r_min', type=float, required=(dflts.cloud_r_min is None), default=dflts.cloud_r_min, help='minimum radius of cloud droplet range [m]')
prsr_lgr.add_argument('--cloud_r_max', type=float, required=(dflts.cloud_r_max is None), default=dflts.cloud_r_max, help='maximum radius of cloud droplet range [m]')
prsr_lgr.add_argument('--cloud_n_bin', type=int, required=(dflts.cloud_n_bin is None), default=dflts.cloud_n_bin, help='number of bins in the cloud droplet range [1]')
prsr_lgr.add_argument('--chem_SO2', type=float, default=dflts.chem_SO2, help='SO2 volume concentration [1]')
prsr_lgr.add_argument('--chem_O3', type=float, default=dflts.chem_O3, help='O3 volume concentration [1]')
prsr_lgr.add_argument('--chem_H2O2', type=float, default=dflts.chem_H2O2, help='H2O2 volume concentration [1]')
## blk_2m options
prsr_b2m = sprsr.add_parser('blk_2m')
#TODO...
args = prsr.parse_args()
# computing state variables
p_v = np.array([args.RH * libcom.p_vs(args.T)])
p_d = args.p - p_v
r_v = libcom.eps * p_v / p_d
th_d = args.T * pow(libcom.p_1000 / p_d, libcom.R_d / libcom.c_pd)
chem_gas = None
if args.chem_SO2 + args.chem_O3 + args.chem_H2O2 > 0:
chem_gas = {
libcl.lgrngn.chem_species_t.SO2 : args.chem_SO2,
libcl.lgrngn.chem_species_t.O3 : args.chem_O3,
libcl.lgrngn.chem_species_t.H2O2 : args.chem_H2O2
}
# performing the simulation
rhs = rhs_lgrngn.rhs_lgrngn(
def _stats(state, info):
state["T"] = np.array([common.T(state["th_d"][0], state["rhod"][0])])
state["RH"] = state["p"] * state["r_v"] / (
state["r_v"] + common.eps) / common.p_vs(state["T"][0])
info["RH_max"] = max(info["RH_max"], state["RH"])
from parcel import parcel from rhs_blk_2m import rhs_blk_2m from rhs_lgrngn import rhs_lgrngn from libcloudphxx.common import th_std2dry, th_dry2std from libcloudphxx.common import p_vs from libcloudphxx.common import eps, p_1000, R_d, c_pd from libcloudphxx.lgrngn import chem_species_t from numpy import array as arr_t from math import exp, log, sqrt, pi # initial parameters T = arr_t([282.2]) p = arr_t([95000.]) p_v = arr_t([0.95 * p_vs(T[0])]) p_d = p - p_v r_v = eps * p_v / p_d #th_d = arr_t([th_std2dry(300., r_v[0])]) th_d = T * pow(p_1000 / p_d[0], R_d / c_pd) w = 0.5 dt = .1 nt = int(600 / w / dt) # 600 metres # blk_2m-specific parameter # TODO: spectrum # lgrngn-specific parameters sd_conc = 44
import sys
sys.path.insert(0, "../../bindings/python/")
#<listing-1>
from libcloudphxx import common, git_revision
print(git_revision)
print("common.p_vs(273.16)=", common.p_vs(273.16))
assert abs(common.p_vs(273.16) - 611.73) < .001
#</listing-1>
print("R_d =", common.R_d)
print("c_pd =", common.c_pd)
print("g =", common.g)
print("p_1000 =", common.p_1000)
print("eps =", common.eps)
print("rho_w =", common.rho_w)
th = 300
rv = .01
print(common.th_dry2std(th, rv))
assert common.th_std2dry(common.th_dry2std(th, rv), rv) == th
rd3 = (.2e-6)**3
assert common.rw3_cr(rd3, .5, 300) > rd3
assert common.S_cr(rd3, .5, 300) > 1
# just testing if pressure at 200m is lower than at 100m
assert common.p_hydro(100, 300, .01, 0, 100000) > common.p_hydro(
def parcel(
dt=.1,
z_max=200.,
w=1.,
T_0=300.,
p_0=101300.,
r_0=-1.,
RH_0=-1., #if none specified, the default will be r_0=.022,
outfile="test.nc",
pprof="pprof_piecewise_const_rhod",
outfreq=100,
sd_conc=64,
aerosol='{"ammonium_sulfate": {"kappa": 0.61, "mean_r": [0.02e-6], "gstdev": [1.4], "n_tot": [60.0e6]}}',
out_bin='{"radii": {"rght": 0.0001, "moms": [0], "drwt": "wet", "nbin": 1, "lnli": "log", "left": 1e-09}}',
SO2_g=0.,
O3_g=0.,
H2O2_g=0.,
CO2_g=0.,
HNO3_g=0.,
NH3_g=0.,
chem_dsl=False,
chem_dsc=False,
chem_rct=False,
chem_rho=1.8e3,
sstp_cond=1,
sstp_chem=1,
wait=0):
"""
Args:
dt (Optional[float]): timestep [s]
z_max (Optional[float]): maximum vertical displacement [m]
w (Optional[float]): updraft velocity [m/s]
T_0 (Optional[float]): initial temperature [K]
p_0 (Optional[float]): initial pressure [Pa]
r_0 (Optional[float]): initial water vapour mass mixing ratio [kg/kg]
RH_0 (Optional[float]): initial relative humidity
outfile (Optional[string]): output netCDF file name
outfreq (Optional[int]): output interval (in number of time steps)
sd_conc (Optional[int]): number of moving bins (super-droplets)
aerosol (Optional[json str]): dict of dicts defining aerosol distribution, e.g.:
{"ammonium_sulfate": {"kappa": 0.61, "mean_r": [0.02e-6, 0.07e-7], "gstdev": [1.4, 1.2], "n_tot": [120.0e6, 80.0e6]}
"gccn" : {"kappa": 1.28, "mean_r": [2e-6], "gstdev": [1.6], "n_tot": [1e2]}}
where kappa - hygroscopicity parameter (see doi:10.5194/acp-7-1961-2007)
mean_r - lognormal distribution mean radius [m] (list if multimodal distribution)
gstdev - lognormal distribution geometric standard deviation (list if multimodal distribution)
n_tot - lognormal distribution total concentration under standard
conditions (T=20C, p=1013.25 hPa, rv=0) [m^-3] (list if multimodal distribution)
out_bin (Optional[json str]): dict of dicts defining spectrum diagnostics, e.g.:
{"radii": {"rght": 0.0001, "moms": [0], "drwt": "wet", "nbin": 26, "lnli": "log", "left": 1e-09},
"cloud": {"rght": 2.5e-05, "moms": [0, 1, 2, 3], "drwt": "wet", "nbin": 49, "lnli": "lin", "left": 5e-07}}
will generate five output spectra:
- 0-th spectrum moment for 26 bins spaced logarithmically between 0 and 1e-4 m for dry radius
- 0,1,2 & 3-rd moments for 49 bins spaced linearly between .5e-6 and 25e-6 for wet radius
It can also define spectrum diagnostics for chemical compounds, e.g.:
{"chem" : {"rght": 1e-6, "left": 1e-10, "drwt": "dry", "lnli": "log", "nbin": 100, "moms": ["S_VI", "NH4_a"]}}
will output the total mass of H2SO4 and NH4 ions in each sizedistribution bin
Valid "moms" for chemistry are:
"O3_a", "H2O2_a", "H",
"SO2_a", "S_VI",
"CO2_a",
"NH3_a", "HNO3_a",
SO2_g (Optional[float]): initial SO2 gas mixing ratio [kg / kg dry air]
O3_g (Optional[float]): initial O3 gas mixing ratio [kg / kg dry air]
H2O2_g (Optional[float]): initial H2O2 gas mixing ratio [kg / kg dry air]
CO2_g (Optional[float]): initial CO2 gas mixing ratio [kg / kg dry air]
NH3_g (Optional[float]): initial NH3 gas mixing ratio [kg / kg dry air]
HNO3_g (Optional[float]): initial HNO3 gas mixing ratio [kg / kg dry air]
chem_dsl (Optional[bool]): on/off for dissolving chem species into droplets
chem_dsc (Optional[bool]): on/off for dissociation of chem species in droplets
chem_rct (Optional[bool]): on/off for oxidation of S_IV to S_VI
pprof (Optional[string]): method to calculate pressure profile used to calculate
dry air density that is used by the super-droplet scheme
valid options are: pprof_const_th_rv, pprof_const_rhod, pprof_piecewise_const_rhod
wait (Optional[float]): number of timesteps to run parcel model with vertical velocity=0 at the end of simulation
(added for testing)
"""
# packing function arguments into "opts" dictionary
args, _, _, _ = inspect.getargvalues(inspect.currentframe())
opts = dict(zip(args, [locals()[k] for k in args]))
# parsing json specification of output spectra
spectra = json.loads(opts["out_bin"])
# parsing json specification of init aerosol spectra
aerosol = json.loads(opts["aerosol"])
# default water content
if ((opts["r_0"] < 0) and (opts["RH_0"] < 0)):
print "both r_0 and RH_0 negative, using default r_0 = 0.022"
r_0 = .022
# water coontent specified with RH
if ((opts["r_0"] < 0) and (opts["RH_0"] >= 0)):
r_0 = common.eps * opts["RH_0"] * common.p_vs(T_0) / (
p_0 - opts["RH_0"] * common.p_vs(T_0))
# sanity checks for arguments
_arguments_checking(opts, spectra, aerosol)
th_0 = T_0 * (common.p_1000 / p_0)**(common.R_d / common.c_pd)
nt = int(z_max / (w * dt))
state = {
"t": 0,
"z": 0,
"r_v": np.array([r_0]),
"p": p_0,
"th_d": np.array([common.th_std2dry(th_0, r_0)]),
"rhod": np.array([common.rhod(p_0, th_0, r_0)]),
"T": None,
"RH": None
}
if opts["chem_dsl"] or opts["chem_dsc"] or opts["chem_rct"]:
for key in _Chem_g_id.iterkeys():
state.update({key: np.array([opts[key]])})
info = {
"RH_max": 0,
"libcloud_Git_revision": libcloud_version,
"parcel_Git_revision": parcel_version
}
micro = _micro_init(aerosol, opts, state, info)
with _output_init(micro, opts, spectra) as fout:
# adding chem state vars
if micro.opts_init.chem_switch:
state.update({
"SO2_a": 0.,
"O3_a": 0.,
"H2O2_a": 0.,
})
state.update({"CO2_a": 0., "HNO3_a": 0.})
micro.diag_all() # selecting all particles
micro.diag_chem(_Chem_a_id["NH3_a"])
state.update({"NH3_a": np.frombuffer(micro.outbuf())[0]})
# t=0 : init & save
_output(fout, opts, micro, state, 0, spectra)
# timestepping
for it in range(1, nt + 1):
# diagnostics
# the reasons to use analytic solution:
# - independent of dt
# - same as in 2D kinematic model
state["z"] += w * dt
state["t"] = it * dt
# pressure
if pprof == "pprof_const_th_rv":
# as in icicle model
p_hydro = _p_hydro_const_th_rv(state["z"], p_0, th_0, r_0)
elif pprof == "pprof_const_rhod":
# as in Grabowski and Wang 2009
rho = 1.13 # kg/m3 1.13
state["p"] = _p_hydro_const_rho(state["z"], p_0, rho)
elif pprof == "pprof_piecewise_const_rhod":
# as in Grabowski and Wang 2009 but calculating pressure
# for rho piecewise constant per each time step
state["p"] = _p_hydro_const_rho(w * dt, state["p"],
state["rhod"][0])
else:
raise Exception(
"pprof should be pprof_const_th_rv, pprof_const_rhod, or pprof_piecewise_const_rhod"
)
# dry air density
if pprof == "pprof_const_th_rv":
state["rhod"][0] = common.rhod(p_hydro, th_0, r_0)
state["p"] = common.p(
state["rhod"][0], state["r_v"][0],
common.T(state["th_d"][0], state["rhod"][0]))
else:
state["rhod"][0] = common.rhod(
state["p"],
common.th_dry2std(state["th_d"][0], state["r_v"][0]),
state["r_v"][0])
# microphysics
_micro_step(micro, state, info, opts, it, fout)
# TODO: only if user wants to stop @ RH_max
#if (state["RH"] < info["RH_max"]): break
# output
if (it % outfreq == 0):
print str(round(it / (nt * 1.) * 100, 2)) + " %"
rec = it / outfreq
_output(fout, opts, micro, state, rec, spectra)
_save_attrs(fout, info)
_save_attrs(fout, opts)
if wait != 0:
for it in range(nt + 1, nt + wait):
state["t"] = it * dt
_micro_step(micro, state, info, opts, it, fout)
if (it % outfreq == 0):
rec = it / outfreq
_output(fout, opts, micro, state, rec, spectra)
def _stats(state, info): state["T"] = np.array([common.T(state["th_d"][0], state["rhod"][0])]) state["RH"] = state["p"] * state["r_v"] / (state["r_v"] + common.eps) / common.p_vs(state["T"][0]) info["RH_max"] = max(info["RH_max"], state["RH"])
def parcel(dt=.1, z_max=200., w=1., T_0=300., p_0=101300.,
r_0=-1., RH_0=-1., #if none specified, the default will be r_0=.022,
outfile="test.nc",
pprof="pprof_piecewise_const_rhod",
outfreq=100, sd_conc=64,
aerosol = '{"ammonium_sulfate": {"kappa": 0.61, "mean_r": [0.02e-6], "gstdev": [1.4], "n_tot": [60.0e6]}}',
out_bin = '{"radii": {"rght": 0.0001, "moms": [0], "drwt": "wet", "nbin": 1, "lnli": "log", "left": 1e-09}}',
SO2_g = 0., O3_g = 0., H2O2_g = 0., CO2_g = 0., HNO3_g = 0., NH3_g = 0.,
chem_dsl = False, chem_dsc = False, chem_rct = False,
chem_rho = 1.8e3,
sstp_cond = 1,
sstp_chem = 1,
wait = 0
):
"""
Args:
dt (Optional[float]): timestep [s]
z_max (Optional[float]): maximum vertical displacement [m]
w (Optional[float]): updraft velocity [m/s]
T_0 (Optional[float]): initial temperature [K]
p_0 (Optional[float]): initial pressure [Pa]
r_0 (Optional[float]): initial water vapour mass mixing ratio [kg/kg]
RH_0 (Optional[float]): initial relative humidity
outfile (Optional[string]): output netCDF file name
outfreq (Optional[int]): output interval (in number of time steps)
sd_conc (Optional[int]): number of moving bins (super-droplets)
aerosol (Optional[json str]): dict of dicts defining aerosol distribution, e.g.:
{"ammonium_sulfate": {"kappa": 0.61, "mean_r": [0.02e-6, 0.07e-7], "gstdev": [1.4, 1.2], "n_tot": [120.0e6, 80.0e6]}
"gccn" : {"kappa": 1.28, "mean_r": [2e-6], "gstdev": [1.6], "n_tot": [1e2]}}
where kappa - hygroscopicity parameter (see doi:10.5194/acp-7-1961-2007)
mean_r - lognormal distribution mean radius [m] (list if multimodal distribution)
gstdev - lognormal distribution geometric standard deviation (list if multimodal distribution)
n_tot - lognormal distribution total concentration under standard
conditions (T=20C, p=1013.25 hPa, rv=0) [m^-3] (list if multimodal distribution)
out_bin (Optional[json str]): dict of dicts defining spectrum diagnostics, e.g.:
{"radii": {"rght": 0.0001, "moms": [0], "drwt": "wet", "nbin": 26, "lnli": "log", "left": 1e-09},
"cloud": {"rght": 2.5e-05, "moms": [0, 1, 2, 3], "drwt": "wet", "nbin": 49, "lnli": "lin", "left": 5e-07}}
will generate five output spectra:
- 0-th spectrum moment for 26 bins spaced logarithmically between 0 and 1e-4 m for dry radius
- 0,1,2 & 3-rd moments for 49 bins spaced linearly between .5e-6 and 25e-6 for wet radius
It can also define spectrum diagnostics for chemical compounds, e.g.:
{"chem" : {"rght": 1e-6, "left": 1e-10, "drwt": "dry", "lnli": "log", "nbin": 100, "moms": ["S_VI", "NH4_a"]}}
will output the total mass of H2SO4 and NH4 ions in each sizedistribution bin
Valid "moms" for chemistry are:
"O3_a", "H2O2_a", "H",
"SO2_a", "S_VI",
"CO2_a",
"NH3_a", "HNO3_a",
SO2_g (Optional[float]): initial SO2 gas mixing ratio [kg / kg dry air]
O3_g (Optional[float]): initial O3 gas mixing ratio [kg / kg dry air]
H2O2_g (Optional[float]): initial H2O2 gas mixing ratio [kg / kg dry air]
CO2_g (Optional[float]): initial CO2 gas mixing ratio [kg / kg dry air]
NH3_g (Optional[float]): initial NH3 gas mixing ratio [kg / kg dry air]
HNO3_g (Optional[float]): initial HNO3 gas mixing ratio [kg / kg dry air]
chem_dsl (Optional[bool]): on/off for dissolving chem species into droplets
chem_dsc (Optional[bool]): on/off for dissociation of chem species in droplets
chem_rct (Optional[bool]): on/off for oxidation of S_IV to S_VI
pprof (Optional[string]): method to calculate pressure profile used to calculate
dry air density that is used by the super-droplet scheme
valid options are: pprof_const_th_rv, pprof_const_rhod, pprof_piecewise_const_rhod
wait (Optional[float]): number of timesteps to run parcel model with vertical velocity=0 at the end of simulation
(added for testing)
"""
# packing function arguments into "opts" dictionary
args, _, _, _ = inspect.getargvalues(inspect.currentframe())
opts = dict(zip(args, [locals()[k] for k in args]))
# parsing json specification of output spectra
spectra = json.loads(opts["out_bin"])
# parsing json specification of init aerosol spectra
aerosol = json.loads(opts["aerosol"])
# default water content
if ((opts["r_0"] < 0) and (opts["RH_0"] < 0)):
print "both r_0 and RH_0 negative, using default r_0 = 0.022"
r_0 = .022
# water coontent specified with RH
if ((opts["r_0"] < 0) and (opts["RH_0"] >= 0)):
r_0 = common.eps * opts["RH_0"] * common.p_vs(T_0) / (p_0 - opts["RH_0"] * common.p_vs(T_0))
# sanity checks for arguments
_arguments_checking(opts, spectra, aerosol)
th_0 = T_0 * (common.p_1000 / p_0)**(common.R_d / common.c_pd)
nt = int(z_max / (w * dt))
state = {
"t" : 0, "z" : 0,
"r_v" : np.array([r_0]), "p" : p_0,
"th_d" : np.array([common.th_std2dry(th_0, r_0)]),
"rhod" : np.array([common.rhod(p_0, th_0, r_0)]),
"T" : None, "RH" : None
}
if opts["chem_dsl"] or opts["chem_dsc"] or opts["chem_rct"]:
for key in _Chem_g_id.iterkeys():
state.update({ key : np.array([opts[key]])})
info = { "RH_max" : 0, "libcloud_Git_revision" : libcloud_version,
"parcel_Git_revision" : parcel_version }
micro = _micro_init(aerosol, opts, state, info)
with _output_init(micro, opts, spectra) as fout:
# adding chem state vars
if micro.opts_init.chem_switch:
state.update({ "SO2_a" : 0.,"O3_a" : 0.,"H2O2_a" : 0.,})
state.update({ "CO2_a" : 0.,"HNO3_a" : 0.})
micro.diag_all() # selecting all particles
micro.diag_chem(_Chem_a_id["NH3_a"])
state.update({"NH3_a": np.frombuffer(micro.outbuf())[0]})
# t=0 : init & save
_output(fout, opts, micro, state, 0, spectra)
# timestepping
for it in range(1,nt+1):
# diagnostics
# the reasons to use analytic solution:
# - independent of dt
# - same as in 2D kinematic model
state["z"] += w * dt
state["t"] = it * dt
# pressure
if pprof == "pprof_const_th_rv":
# as in icicle model
p_hydro = _p_hydro_const_th_rv(state["z"], p_0, th_0, r_0)
elif pprof == "pprof_const_rhod":
# as in Grabowski and Wang 2009
rho = 1.13 # kg/m3 1.13
state["p"] = _p_hydro_const_rho(state["z"], p_0, rho)
elif pprof == "pprof_piecewise_const_rhod":
# as in Grabowski and Wang 2009 but calculating pressure
# for rho piecewise constant per each time step
state["p"] = _p_hydro_const_rho(w*dt, state["p"], state["rhod"][0])
else: raise Exception("pprof should be pprof_const_th_rv, pprof_const_rhod, or pprof_piecewise_const_rhod")
# dry air density
if pprof == "pprof_const_th_rv":
state["rhod"][0] = common.rhod(p_hydro, th_0, r_0)
state["p"] = common.p(
state["rhod"][0],
state["r_v"][0],
common.T(state["th_d"][0], state["rhod"][0])
)
else:
state["rhod"][0] = common.rhod(
state["p"],
common.th_dry2std(state["th_d"][0], state["r_v"][0]),
state["r_v"][0]
)
# microphysics
_micro_step(micro, state, info, opts, it, fout)
# TODO: only if user wants to stop @ RH_max
#if (state["RH"] < info["RH_max"]): break
# output
if (it % outfreq == 0):
print str(round(it / (nt * 1.) * 100, 2)) + " %"
rec = it/outfreq
_output(fout, opts, micro, state, rec, spectra)
_save_attrs(fout, info)
_save_attrs(fout, opts)
if wait != 0:
for it in range (nt+1, nt+wait):
state["t"] = it * dt
_micro_step(micro, state, info, opts, it, fout)
if (it % outfreq == 0):
rec = it/outfreq
_output(fout, opts, micro, state, rec, spectra)
import sys sys.path.insert(0, "../../bindings/python/") #<listing-1> from libcloudphxx import common, git_revision print git_revision print "common.p_vs(273.16)=", common.p_vs(273.16) assert abs(common.p_vs(273.16) - 611.73) < .001 #</listing-1> print "R_d =", common.R_d print "c_pd =", common.c_pd print "g =", common.g print "p_1000 =", common.p_1000 print "eps =", common.eps print "rho_w =", common.rho_w th = 300 rv = .01 print common.th_dry2std(th, rv) assert common.th_std2dry(common.th_dry2std(th, rv), rv) == th rd3 = (.2e-6)**3 assert common.rw3_cr(rd3, .5, 300) > rd3 assert common.S_cr(rd3, .5, 300) > 1 # just testing if pressure at 200m is lower than at 100m assert common.p_hydro(100, 300, .01, 0, 100000) > common.p_hydro(200, 300, .01, 0, 100000)
