def initial_state():
rhod = arr_t([1.])
th = arr_t([300.])
rv = arr_t([0.02])
T = common.T(th[0], rhod[0])
p = arr_t([common.p(rhod[0], rv[0], T)])
return rhod, th, rv, p
def supersat_state():
rhod = arr_t([1. ])
th = arr_t([300.])
rv = arr_t([0.0091])
T = common.T(th[0], rhod[0])
p = arr_t([common.p(rhod[0], rv[0], T)])
return rhod, th, rv, p
def supersat_state():
rhod = arr_t([1.])
th = arr_t([300.])
rv = arr_t([0.0091])
T = common.T(th[0], rhod[0])
p = arr_t([common.p(rhod[0], rv[0], T)])
return rhod, th, rv, p
def initial_state():
rhod = arr_t([1. ])
th = arr_t([300.])
rv = arr_t([0.02])
T = common.T(th[0], rhod[0])
p = arr_t([common.p(rhod[0], rv[0], T)])
return rhod, th, rv, p
def initial_state():
# a little below saturation
rhod = arr_t([1.1])
th = arr_t([305.])
rv = arr_t([0.0085])
T = common.T(th[0], rhod[0])
p = arr_t([common.p(rhod[0], rv[0], T)])
return rhod, th, rv, p
def initial_state():
# a little below saturation
rhod = arr_t([1.1 ])
th = arr_t([305.])
rv = arr_t([0.0085])
T = common.T(th[0], rhod[0])
p = arr_t([common.p(rhod[0], rv[0], T)])
return rhod, th, rv, p
def test_adj_cellwise(init_sup_sat, r_eps = r_eps_def):
opts.r_eps = r_eps
print "[standard adj_cellwise]"
rhod, th, rv, rc, rr, dt = initial_state(init_sup_sat)
blk_1m.adj_cellwise(opts, rhod, th, rv, rc, rr, dt)
T = common.T(th[0], rhod[0])
p = common.p(rhod[0], rv[0], T)
ss = supersaturation(T, p, rv[0])
print "final supersaturation", ss, th[0], rv[0]
return ss
def test_adj_cellwise(init_sup_sat, r_eps=r_eps_def):
opts.r_eps = r_eps
print("[standard adj_cellwise]")
rhod, th, rv, rc, rr, dt = initial_state(init_sup_sat)
blk_1m.adj_cellwise(opts, rhod, th, rv, rc, rr, dt)
T = common.T(th[0], rhod[0])
p = common.p(rhod[0], rv[0], T)
ss = supersaturation(T, p, rv[0])
print("final supersaturation", ss, th[0], rv[0])
return ss
def test_adj_cellwise_nwtrph(init_sup_sat, nwtrph_iters = nwtrph_iters_def):
opts.nwtrph_iters = nwtrph_iters
print "[nwtrph adj_cellwise]"
rhod, th, rv, rc, rr, dt = initial_state(init_sup_sat)
# define pressure consistent with adj_cellwise to compare results
p = arr_t([common.p(rhod[0], rv[0], common.T(th[0], rhod[0]))])
#nwtrph requires th_std input
th_std = arr_t([common.th_dry2std(th[0], rv[0])])
blk_1m.adj_cellwise_nwtrph(opts, p, th_std, rv, rc, dt)
T = common.exner(p[0]) * th_std[0]
ss = supersaturation(T, p[0], rv[0])
print "final supersaturation", ss, th_std[0], rv[0]
return ss
def test_adj_cellwise_constp(init_sup_sat, r_eps = r_eps_def):
opts.r_eps = r_eps
print "[constp adj_cellwise]"
rhod, th, rv, rc, rr, dt = initial_state(init_sup_sat)
# define pressure consistent with adj_cellwise to compare results
p = arr_t([common.p(rhod[0], rv[0], common.T(th[0], rhod[0]))])
#constp requires th_std input
th_std = arr_t([common.th_dry2std(th[0], rv[0])])
blk_1m.adj_cellwise_constp(opts, rhod, p, th_std, rv, rc, rr, dt)
T = common.exner(p[0]) * th_std[0]
ss = supersaturation(T, p[0], rv[0])
print "final supersaturation", ss, th_std[0], rv[0]
return ss
def test_adj_cellwise_nwtrph(init_sup_sat, nwtrph_iters=nwtrph_iters_def):
opts.nwtrph_iters = nwtrph_iters
print("[nwtrph adj_cellwise]")
rhod, th, rv, rc, rr, dt = initial_state(init_sup_sat)
# define pressure consistent with adj_cellwise to compare results
p = arr_t([common.p(rhod[0], rv[0], common.T(th[0], rhod[0]))])
#nwtrph requires th_std input
th_std = arr_t([common.th_dry2std(th[0], rv[0])])
blk_1m.adj_cellwise_nwtrph(opts, p, th_std, rv, rc, dt)
T = common.exner(p[0]) * th_std[0]
ss = supersaturation(T, p[0], rv[0])
print("final supersaturation", ss, th_std[0], rv[0])
return ss
def initial_state(init_sup_sat):
rhod = arr_t([1. ])
th = arr_t([300.])
if init_sup_sat:
rv = arr_t([0.02])
else:
rv = arr_t([0.002])
rc = arr_t([0.015])
rr = arr_t([0. ])
dt = 1
T = common.T(th[0], rhod[0])
p = common.p(rhod[0], rv[0], T)
ss = supersaturation(T, p, rv[0])
print "initial supersaturation", ss
return rhod, th, rv, rc, rr, dt
def initial_state(init_sup_sat):
rhod = arr_t([1.])
th = arr_t([300.])
if init_sup_sat:
rv = arr_t([0.02])
else:
rv = arr_t([0.002])
rc = arr_t([0.015])
rr = arr_t([0.])
dt = 1
T = common.T(th[0], rhod[0])
p = common.p(rhod[0], rv[0], T)
ss = supersaturation(T, p, rv[0])
print("initial supersaturation", ss)
return rhod, th, rv, rc, rr, dt
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 test_pressure(arg, epsilon = 0.01):
rho_d = anpy.density_dry(**arg)
press_md = common.p(rho_d, arg["rv"], arg["T"])
assert abs(arg["press"] - press_md) <= epsilon * arg["press"]
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 parcel(dt=.1, z_max=200., w=1., T_0=300., p_0=101300., r_0=.022,
outfile="test.nc",
pprof="pprof_piecewise_const_rhod",
outfreq=100, sd_conc=64, kappa=.5,
mean_r = .04e-6 / 2, gstdev = 1.4, n_tot = 60.e6,
out_bin = '{"radii": {"rght": 0.0001, "moms": [0], "drwt": "wet", "nbin": 26, "lnli": "log", "left": 1e-09}}',
SO2_g_0 = 0., O3_g_0 = 0., H2O2_g_0 = 0.,
chem_sys = 'open',
chem_dsl = False, chem_dsc = False, chem_rct = False,
chem_spn = 1,
chem_rho = 1.8e3
):
"""
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]
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)
kappa (Optional[float]): kappa hygroscopicity parameter (see doi:10.5194/acp-7-1961-2007)
mean_r (Optional[float]): lognormal distribution mode diameter [m]
gstdev (Optional[float]): lognormal distribution geometric standard deviation [1]
n_tot (Optional[float]): lognormal distribution total concentration under standard
conditions (T=20C, p=1013.25 hPa, rv=0) [m-3]
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
(TODO - add chemistry output description)
SO2_g_0 (Optional[float]): initial SO2 gas volume concentration (mole fraction) [1]
O3_g_0 (Optional[float]): initial O3 gas volume concentration (mole fraction) [1]
H2O2_g_0 (Optional[float]): initial H2O2 gas volume concentration (mole fraction) [1]
chem_sys (Optional[string]): accepted values: 'open', 'closed'
(in open/closed system gas volume concentration in the air doesn't/does change
due to chemical reactions)
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
chem_spn (Optional[int]): number of spinup timesteps before enabling chemical reactions
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
"""
# 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"])
# sanity checks for arguments
_arguments_checking(opts, spectra)
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
}
info = { "RH_max" : 0, "libcloud_Git_revision" : libcloud_version,
"parcel_Git_revision" : parcel_version }
micro = _micro_init(opts, state, info)
with _output_init(micro, opts, spectra) as fout:
# adding chem state vars
if micro.opts_init.chem_switch:
state.update({ "SO2_g" : SO2_g_0, "O3_g" : O3_g_0, "H2O2_g" : H2O2_g_0 })
state.update({ "SO2_a" : 0., "O3_a" : 0., "H2O2_a" : 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: assert(False)
# 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)
# TODO: only if user wants to stop @ RH_max
#if (state["RH"] < info["RH_max"]): break
# output
if (it % outfreq == 0):
rec = it/outfreq
_output(fout, opts, micro, state, rec, spectra)
_save_attrs(fout, info)
_save_attrs(fout, opts)