def __init__(self, Acc_Forg: float = 0.2, Acc_N2: float = 134):
Aitken = {
"palmitic": 0.2,
"(NH4)2SO4": 0.8,
"NaCl": 0,
}
Accumulation = {
"palmitic": Acc_Forg,
"(NH4)2SO4": 0,
"NaCl": (1 - Acc_Forg),
}
super().__init__(
ionic_dissociation_phi={
"palmitic": 1,
"(NH4)2SO4": 3,
"NaCl": 2,
},
is_soluble={
"palmitic": False,
"(NH4)2SO4": True,
"NaCl": True,
},
densities={
"palmitic": 0.852 * si.g / si.cm**3,
"(NH4)2SO4": 1.77 * si.g / si.cm**3,
"NaCl": 2.16 * si.g / si.cm**3,
},
compounds=("palmitic", "(NH4)2SO4", "NaCl"),
molar_masses={
"palmitic": 256.4 * si.g / si.mole,
"(NH4)2SO4": Substance.from_formula("(NH4)2SO4").mass
* si.gram
/ si.mole,
"NaCl": Substance.from_formula("NaCl").mass * si.gram / si.mole,
},
)
self.modes = (
{
"f_org": 1 - self.f_soluble_volume(Aitken),
"kappa": self.kappa(Aitken),
"nu_org": self.nu_org(Aitken),
"spectrum": spectra.Lognormal(
norm_factor=226 / si.cm**3, m_mode=19.6 * si.nm, s_geom=1.71
),
},
{
"f_org": 1 - self.f_soluble_volume(Accumulation),
"kappa": self.kappa(Accumulation),
"nu_org": self.nu_org(Accumulation),
"spectrum": spectra.Lognormal(
norm_factor=Acc_N2 / si.cm**3, m_mode=69.5 * si.nm, s_geom=1.7
),
},
)
def __init__(self, Acc_Forg: float = 0.3, Acc_N2: float = 30):
Ultrafine = {
"SOA1": 0.52,
"SOA2": 0,
"(NH4)2SO4": 0.48,
}
Accumulation = {
"SOA1": 0,
"SOA2": Acc_Forg,
"(NH4)2SO4": (1 - Acc_Forg),
}
super().__init__(
ionic_dissociation_phi={
"SOA1": 1,
"SOA2": 1,
"(NH4)2SO4": 3,
},
molar_masses={
"SOA1": 190 * si.g / si.mole,
"SOA2": 368.4 * si.g / si.mole,
"(NH4)2SO4": Substance.from_formula("(NH4)2SO4").mass
* si.gram
/ si.mole,
},
densities={
"SOA1": 1.24 * si.g / si.cm**3,
"SOA2": 1.2 * si.g / si.cm**3,
"(NH4)2SO4": 1.77 * si.g / si.cm**3,
},
compounds=("SOA1", "SOA2", "(NH4)2SO4"),
is_soluble={
"SOA1": False,
"SOA2": False,
"(NH4)2SO4": True,
},
)
self.modes = (
{
"f_org": 1 - self.f_soluble_volume(Ultrafine),
"kappa": self.kappa(Ultrafine),
"nu_org": self.nu_org(Ultrafine),
"spectrum": spectra.Lognormal(
norm_factor=2000 / si.cm**3, m_mode=11.5 * si.nm, s_geom=1.71
),
},
{
"f_org": 1 - self.f_soluble_volume(Accumulation),
"kappa": self.kappa(Accumulation),
"nu_org": self.nu_org(Accumulation),
"spectrum": spectra.Lognormal(
norm_factor=Acc_N2 / si.cm**3, m_mode=100 * si.nm, s_geom=1.70
),
},
)
def __init__(
self,
M2_sol: float = 0,
M2_N: float = 100 / si.cm**3,
M2_rad: float = 50 * si.nm,
):
super().__init__(
compounds=("(NH4)2SO4", "insoluble"),
molar_masses={
"(NH4)2SO4": 132.14 * si.g / si.mole,
"insoluble": 44 * si.g / si.mole,
},
densities={
"(NH4)2SO4": 1.77 * si.g / si.cm**3,
"insoluble": 1.77 * si.g / si.cm**3,
},
is_soluble={
"(NH4)2SO4": True,
"insoluble": False
},
ionic_dissociation_phi={
"(NH4)2SO4": 3,
"insoluble": 0
},
)
self.modes = (
{
"kappa":
self.kappa({
"(NH4)2SO4": 1.0,
"insoluble": 0.0
}),
"spectrum":
spectra.Lognormal(norm_factor=100.0 / si.cm**3,
m_mode=50.0 * si.nm,
s_geom=2.0),
},
{
"kappa":
self.kappa({
"(NH4)2SO4": M2_sol,
"insoluble": (1 - M2_sol)
}),
"spectrum":
spectra.Lognormal(norm_factor=M2_N, m_mode=M2_rad, s_geom=2.0),
},
)
def __init__(self):
nuclei = {"(NH4)2SO4": 1.0}
accum = {"(NH4)2SO4": 1.0}
coarse = {"(NH4)2SO4": 1.0}
super().__init__(
ionic_dissociation_phi={"(NH4)2SO4": 3},
molar_masses={
"(NH4)2SO4":
Substance.from_formula("(NH4)2SO4").mass * si.gram / si.mole
},
densities={"(NH4)2SO4": 1.77 * si.g / si.cm**3},
compounds=("(NH4)2SO4", ),
is_soluble={"(NH4)2SO4": True},
)
self.modes = (
{
"kappa":
self.kappa(nuclei),
"spectrum":
spectra.Lognormal(norm_factor=1000.0 / si.cm**3,
m_mode=0.008 * si.um,
s_geom=1.6),
},
{
"kappa":
self.kappa(accum),
"spectrum":
spectra.Lognormal(norm_factor=800 / si.cm**3,
m_mode=0.034 * si.um,
s_geom=2.1),
},
{
"kappa":
self.kappa(coarse),
"spectrum":
spectra.Lognormal(norm_factor=0.72 / si.cm**3,
m_mode=0.46 * si.um,
s_geom=2.2),
},
)
def __init__(
self,
n_sd: int = 100,
dt_output: float = 1 * si.second,
dt_max: float = 1 * si.second,
):
self.total_time = 3 * si.hours
self.mass_of_dry_air = (1000 * si.kilogram
) # TODO #335 doubled with jupyter si unit
self.n_steps = int(self.total_time /
(5 * si.second)) # TODO #334 rename to n_output
self.n_sd = n_sd
self.r_dry, self.n = spectral_sampling.Logarithmic(
spectrum=spectra.Lognormal(
norm_factor=1000 / si.milligram * self.mass_of_dry_air,
m_mode=50 * si.nanometre,
s_geom=1.4,
),
size_range=(10.633 * si.nanometre, 513.06 * si.nanometre),
).sample(n_sd)
self.dt_max = dt_max
self.dt_output = dt_output
self.r_bins_edges = np.linspace(0 * si.micrometre,
20 * si.micrometre,
101,
endpoint=True)
self.backend = CPU
self.coord = "VolumeLogarithm"
self.adaptive = True
self.rtol_x = condensation.DEFAULTS.rtol_x
self.rtol_thd = condensation.DEFAULTS.rtol_thd
self.dt_cond_range = condensation.DEFAULTS.cond_range
self.T0 = 284.3 * si.kelvin
self.q0 = 7.6 * si.grams / si.kilogram
self.p0 = 938.5 * si.hectopascals
self.z0 = 600 * si.metres
self.kappa = 0.53 # Petters and S. M. Kreidenweis mean growth-factor derived
self.t0 = 1200 * si.second
self.f0 = 1 / 1000 * si.hertz
def __init__(self, Forg: float = 0.8, N: float = 400):
mode = {
"(NH4)2SO4": (1 - Forg),
"apinene_dark": Forg,
}
super().__init__(
compounds=("(NH4)2SO4", "apinene_dark"),
molar_masses={
"(NH4)2SO4":
Substance.from_formula("(NH4)2SO4").mass * si.gram / si.mole,
"apinene_dark":
209 * si.gram / si.mole,
},
densities={
"(NH4)2SO4": 1.77 * si.g / si.cm**3,
"apinene_dark": 1.27 * si.g / si.cm**3,
},
is_soluble={
"(NH4)2SO4": False,
"apinene_dark": True,
},
ionic_dissociation_phi={
"(NH4)2SO4": 3,
"apinene_dark": 1,
},
)
self.modes = ({
"f_org":
1 - self.f_soluble_volume(mode),
"kappa":
self.kappa(mode),
"nu_org":
self.nu_org(mode),
"spectrum":
spectra.Lognormal(norm_factor=N / si.cm**3,
m_mode=50.0 * si.nm,
s_geom=1.75),
}, )
def __init__(self, Acc_Forg: float = 0.668, Acc_N2: float = 540):
# note: SOA1 or SOA2 unclear from the paper
Aitken = {
"SOA1": 0.668,
"SOA2": 0,
"(NH4)2SO4": 0.166,
"NH4NO3": 0.166,
}
Accumulation = {
"SOA1": 0,
"SOA2": Acc_Forg,
"(NH4)2SO4": (1 - Acc_Forg) / 2,
"NH4NO3": (1 - Acc_Forg) / 2,
}
super().__init__(
ionic_dissociation_phi={
"SOA1": 1,
"SOA2": 1,
"(NH4)2SO4": 3,
"NH4NO3": 2,
},
molar_masses={
"(NH4)2SO4": Substance.from_formula("(NH4)2SO4").mass
* si.gram
/ si.mole,
"NH4NO3": Substance.from_formula("NH4NO3").mass * si.gram / si.mole,
"SOA1": 190 * si.g / si.mole,
"SOA2": 368.4 * si.g / si.mole,
},
densities={
"SOA1": 1.24 * si.g / si.cm**3,
"SOA2": 1.2 * si.g / si.cm**3,
"(NH4)2SO4": 1.77 * si.g / si.cm**3,
"NH4NO3": 1.72 * si.g / si.cm**3,
},
compounds=("SOA1", "SOA2", "(NH4)2SO4", "NH4NO3"),
is_soluble={
"SOA1": False,
"SOA2": False,
"(NH4)2SO4": True,
"NH4NO3": True,
},
)
self.modes = (
{
"f_org": 1 - self.f_soluble_volume(Aitken),
"kappa": self.kappa(Aitken),
"nu_org": self.nu_org(Aitken),
"spectrum": spectra.Lognormal(
norm_factor=1100 / si.cm**3, m_mode=22.7 * si.nm, s_geom=1.75
),
},
{
"f_org": 1 - self.f_soluble_volume(Accumulation),
"kappa": self.kappa(Accumulation),
"nu_org": self.nu_org(Accumulation),
"spectrum": spectra.Lognormal(
norm_factor=Acc_N2 / si.cm**3,
m_mode=82.2 * si.nm,
s_geom=1.62,
),
},
)
def __init__(
self,
dt: float,
n_sd: int,
n_substep: int,
spectral_sampling: spec_sampling.SpectralSampling = spec_sampling.
Logarithmic,
):
self.formulae = Formulae(
saturation_vapour_pressure="AugustRocheMagnus",
constants={"g_std": 10 * si.m / si.s**2},
)
const = self.formulae.constants
self.DRY_RHO = 1800 * si.kg / (si.m**3)
self.dry_molar_mass = Substance.from_formula(
"NH4HSO4").mass * si.gram / si.mole
self.system_type = "closed"
self.t_max = (2400 + 196) * si.s
self.output_interval = 10 * si.s
self.dt = dt
self.w = 0.5 * si.m / si.s
self.n_sd = n_sd
self.n_substep = n_substep
self.p0 = 950 * si.mbar
self.T0 = 285.2 * si.K
pv0 = 0.95 * self.formulae.saturation_vapour_pressure.pvs_Celsius(
self.T0 - T0)
self.q0 = const.eps * pv0 / (self.p0 - pv0)
self.kappa = 0.61
self.cloud_radius_range = (0.5 * si.micrometre, 25 * si.micrometre)
self.mass_of_dry_air = 44
# note: rho is not specified in the paper
rho0 = 1
self.r_dry, self.n_in_dv = spectral_sampling(
spectrum=spectra.Lognormal(
norm_factor=566 / si.cm**3 / rho0 * self.mass_of_dry_air,
m_mode=0.08 * si.um / 2,
s_geom=2,
)).sample(n_sd)
self.ENVIRONMENT_MOLE_FRACTIONS = {
"SO2": 0.2 * PPB,
"O3": 50 * PPB,
"H2O2": 0.5 * PPB,
"CO2": 360 * PPM,
"HNO3": 0.1 * PPB,
"NH3": 0.1 * PPB,
}
self.starting_amounts = {
"moles_" + k:
self.formulae.trivia.volume(self.r_dry) * self.DRY_RHO /
self.dry_molar_mass if k in ("N_mIII",
"S_VI") else np.zeros(self.n_sd)
for k in AQUEOUS_COMPOUNDS
}
self.dry_radius_bins_edges = (np.logspace(
np.log10(0.01 * si.um), np.log10(1 * si.um), 51, endpoint=True) /
2)
def __init__(self, formulae: Formulae):
self.formulae = formulae
const = formulae.constants
self.condensation_rtol_x = condensation.DEFAULTS.rtol_x
self.condensation_rtol_thd = condensation.DEFAULTS.rtol_thd
self.condensation_adaptive = True
self.condensation_substeps = -1
self.condensation_dt_cond_range = condensation.DEFAULTS.cond_range
self.condensation_schedule = condensation.DEFAULTS.schedule
self.coalescence_adaptive = True
self.coalescence_dt_coal_range = collisions.collision.DEFAULTS.dt_coal_range
self.coalescence_optimized_random = True
self.coalescence_substeps = 1
self.kernel = Geometric(collection_efficiency=1)
self.freezing_singular = True
self.freezing_inp_spec = None
self.displacement_adaptive = displacement.DEFAULTS.adaptive
self.displacement_rtol = displacement.DEFAULTS.rtol
self.n_sd_per_gridbox = 20
self.aerosol_radius_threshold = 0.5 * si.micrometre
self.drizzle_radius_threshold = 25 * si.micrometre
self.r_bins_edges = np.logspace(
np.log10(0.001 * si.micrometre),
np.log10(100 * si.micrometre),
64,
endpoint=True,
)
self.T_bins_edges = np.linspace(const.T0 - 40,
const.T0 - 20,
64,
endpoint=True)
# TODO #599
n_bins_per_phase = 25
solid_phase_radii = (
np.linspace(-n_bins_per_phase, -1, n_bins_per_phase + 1) * si.um)
liquid_phase_radii = (
np.linspace(0, n_bins_per_phase, n_bins_per_phase + 1) * si.um)
self.terminal_velocity_radius_bin_edges = np.concatenate(
[solid_phase_radii, liquid_phase_radii])
self.output_interval = 1 * si.minute
self.spin_up_time = 0
self.mode_1 = spectra.Lognormal(
norm_factor=60 / si.centimetre**3 / const.rho_STP,
m_mode=0.04 * si.micrometre,
s_geom=1.4,
)
self.mode_2 = spectra.Lognormal(
norm_factor=40 / si.centimetre**3 / const.rho_STP,
m_mode=0.15 * si.micrometre,
s_geom=1.6,
)
self.spectrum_per_mass_of_dry_air = spectra.Sum(
(self.mode_1, self.mode_2))
self.kappa = 1 # TODO #441!
self.processes = {
"particle advection": True,
"fluid advection": True,
"coalescence": True,
"condensation": True,
"sedimentation": True,
"freezing": False,
}
self.mpdata_iters = 2
self.mpdata_iga = True
self.mpdata_fct = True
self.mpdata_tot = True
key_packages = [PySDM, PyMPDATA, numba, numpy, scipy]
try:
import ThrustRTC # pylint: disable=import-outside-toplevel
key_packages.append(ThrustRTC)
except: # pylint: disable=bare-except
pass
self.versions = {}
for pkg in key_packages:
try:
self.versions[pkg.__name__] = pkg.__version__
except AttributeError:
pass
self.versions = str(self.versions)
self.dt = None
self.simulation_time = None
self.grid = None
self.p0 = None
self.qv0 = None
self.th_std0 = None
self.size = None
def __init__(
self,
*,
n_sd_per_gridbox: int,
p0: float = 1007 * si.hPa, # as used in Olesik et al. 2022 (GMD)
particle_reservoir_depth: float = 0 * si.m,
kappa: float = 1,
rho_times_w_1: float = 2 * si.m / si.s * si.kg / si.m**3,
dt: float = 1 * si.s,
dz: float = 25 * si.m,
precip: bool = True,
breakup: bool = False
):
self.formulae = Formulae()
self.n_sd_per_gridbox = n_sd_per_gridbox
self.kappa = kappa
self.wet_radius_spectrum_per_mass_of_dry_air = spectra.Lognormal(
norm_factor=50 / si.cm**3 / self.formulae.constants.rho_STP,
m_mode=0.08 / 2 * si.um,
s_geom=1.4,
)
self.particle_reservoir_depth = particle_reservoir_depth
self.dt = dt
self.dz = dz
self.precip = precip
self.breakup = breakup
self.z_max = 3000 * si.metres
self.t_max = 60 * si.minutes
t_1 = 600 * si.s
self.rho_times_w = (
lambda t: rho_times_w_1 * np.sin(np.pi * t / t_1) if t < t_1 else 0
)
self._th = interp1d(
(0.0 * si.m, 740.0 * si.m, 3260.00 * si.m),
(297.9 * si.K, 297.9 * si.K, 312.66 * si.K),
fill_value="extrapolate",
)
self.qv = interp1d(
(-max(particle_reservoir_depth, 1), 0, 740, 3260),
(0.015, 0.015, 0.0138, 0.0024),
fill_value="extrapolate",
)
self.thd = (
lambda z_above_reservoir: self.formulae.state_variable_triplet.th_dry(
self._th(z_above_reservoir), self.qv(z_above_reservoir)
)
)
g = self.formulae.constants.g_std
self.rhod0 = self.formulae.state_variable_triplet.rho_d(
p=p0,
qv=self.qv(0 * si.m),
theta_std=self._th(0 * si.m),
)
def drhod_dz(z_above_reservoir, rhod):
if z_above_reservoir < 0:
return 0
qv = self.qv(z_above_reservoir)
T = self.formulae.state_variable_triplet.T(
rhod[0], self.thd(z_above_reservoir)
)
p = self.formulae.state_variable_triplet.p(rhod[0], T, qv)
lv = self.formulae.latent_heat.lv(T)
return self.formulae.hydrostatics.drho_dz(
g, p, T, qv, lv
) # note: drho \approx drhod
z_span = (-self.particle_reservoir_depth, self.z_max)
z_points = np.linspace(*z_span, 2 * self.nz + 1)
rhod_solution = solve_ivp(
fun=drhod_dz,
t_span=z_span,
y0=np.asarray((self.rhod0,)),
t_eval=z_points,
max_step=dz / 2,
)
assert rhod_solution.success
self.rhod = interp1d(z_points, rhod_solution.y[0])
self.mpdata_settings = {"n_iters": 3, "iga": True, "fct": True, "tot": True}
self.condensation_rtol_x = condensation.DEFAULTS.rtol_x
self.condensation_rtol_thd = condensation.DEFAULTS.rtol_thd
self.condensation_adaptive = True
self.coalescence_adaptive = True
self.r_bins_edges = np.logspace(
np.log10(0.001 * si.um), np.log10(100 * si.um), 101, endpoint=True
)
self.cloud_water_radius_range = [1 * si.um, 50 * si.um]
self.rain_water_radius_range = [50 * si.um, np.inf * si.um]
