def test(RH_formula, step_count, substep_count, exact_substep, constp):
print "[RH_formula = ", RH_formula, "]"
print "step_count = ", step_count, " substep_count = ", substep_count, "exact substepping = ", exact_substep, "constp = ", constp
opts_init.sstp_cond = substep_count
opts_init.exact_sstp_cond = exact_substep
opts_init.RH_formula = RH_formula
rhod, th, rv, p = initial_state()
rv_init = rv.copy()
# in constp mode, th_std is expected instead of th_dry
if constp == True:
# dry/std conversions assume p = rhod (Rd + rv * Rv) T
# which in general is not true in constp, but is true at init so we use it here
th[0] = common.th_dry2std(th[0], rv[0])
th_init = th.copy()
prtcls = lgrngn.factory(backend, opts_init)
if constp == False:
prtcls.init(th, rv, rhod)
else:
prtcls.init(th, rv, rhod, p)
ss = supersaturation(prtcls)
print "initial supersaturation", ss
exectime = 0
# first step without condesnation just to see diag output
opts.cond = False
for step in arange(step_count):
wrapped = wrapper(prtcls.step_sync, opts, th, rv, rhod)
exectime += timeit.timeit(wrapped, number=1)
prtcls.step_async(opts)
opts.cond = True
# print step, supersaturation(prtcls), temperature(prtcls), pressure(prtcls), th[0], rv[0]
ss_post_cond = supersaturation(prtcls)
print "supersaturation after condensation", ss_post_cond, th[0], rv[0]
assert (abs(th[0] - exp_th[constp]) < 1e-4 * exp_th[constp])
assert (abs(rv[0] - exp_rv[constp]) < 1e-3 * exp_rv[constp])
rv_diff = rv_init.copy() - rv[0].copy()
# change to subsaturated air - test evaporation
rv[0] = 0.002
rv_init = rv.copy()
for step in arange(step_count):
wrapped = wrapper(prtcls.step_sync, opts, th, rv, rhod)
exectime += timeit.timeit(wrapped, number=1)
prtcls.step_async(opts)
# print step, supersaturation(prtcls), temperature(prtcls), pressure(prtcls), th[0], rv[0]
ss_post_evap = supersaturation(prtcls)
print "supersaturation after evaporation", ss_post_evap, th[0], rv[0]
print 'execution time: ', exectime
return ss_post_cond, th[0] - th_init[0], rv[0] - rv_init[0] - rv_diff[0]
def test(RH_formula, step_count, substep_count, exact_substep, constp):
print "[RH_formula = ", RH_formula,"]"
print "step_count = ", step_count, " substep_count = ", substep_count, "exact substepping = ", exact_substep, "constp = ", constp
opts_init.sstp_cond=substep_count
opts_init.exact_sstp_cond=exact_substep
opts_init.RH_formula = RH_formula
rhod, th, rv, p = initial_state()
rv_init = rv.copy()
# in constp mode, th_std is expected instead of th_dry
if constp == True:
# dry/std conversions assume p = rhod (Rd + rv * Rv) T
# which in general is not true in constp, but is true at init so we use it here
th[0] = common.th_dry2std(th[0], rv[0])
th_init = th.copy()
prtcls = lgrngn.factory(backend, opts_init)
if constp == False:
prtcls.init(th, rv, rhod)
else:
prtcls.init(th, rv, rhod, p)
ss = supersaturation(prtcls)
print "initial supersaturation", ss
exectime = 0
# first step without condesnation just to see diag output
opts.cond = False
for step in arange(step_count):
wrapped = wrapper(prtcls.step_sync, opts, th, rv, rhod)
exectime += timeit.timeit(wrapped, number=1)
prtcls.step_async(opts)
opts.cond = True
# print step, supersaturation(prtcls), temperature(prtcls), pressure(prtcls), th[0], rv[0]
ss_post_cond = supersaturation(prtcls)
print "supersaturation after condensation", ss_post_cond, th[0], rv[0]
assert(abs(th[0] - exp_th[constp]) < 1e-4 * exp_th[constp])
assert(abs(rv[0] - exp_rv[constp]) < 1e-3 * exp_rv[constp])
rv_diff = rv_init.copy() - rv[0].copy()
# change to subsaturated air - test evaporation
rv[0] = 0.002
rv_init = rv.copy()
for step in arange(step_count):
wrapped = wrapper(prtcls.step_sync, opts, th, rv, rhod)
exectime += timeit.timeit(wrapped, number=1)
prtcls.step_async(opts)
# print step, supersaturation(prtcls), temperature(prtcls), pressure(prtcls), th[0], rv[0]
ss_post_evap = supersaturation(prtcls)
print "supersaturation after evaporation", ss_post_evap, th[0], rv[0]
print 'execution time: ', exectime
return ss_post_cond, th[0] - th_init[0], rv[0] - rv_init[0] - rv_diff[0]
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_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 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=-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)
# lgrngn-specific parameters
sd_conc = 44
def lognormal(lnr):
mean_r = .08e-6 / 2
stdev = 2
n_tot = 566e6
return n_tot * exp(-pow(
(lnr - log(mean_r)), 2) / 2 / pow(log(stdev), 2)) / log(stdev) / sqrt(
2 * pi)
kappa = .61 #TODO - check
# rho = 1.8 # TODO
chem_gas = {
chem_species_t.SO2: 200e-12,
chem_species_t.O3: 50e-9,
chem_species_t.H2O2: 500e-12
}
# running all three
for rhs in [
rhs_blk_2m(dt),
rhs_lgrngn(dt, sd_conc, {kappa: lognormal}, chem_gas)
]:
parcel(p_d, th_d, r_v, w, dt, nt, rhs)
print p_d, arr_t([th_dry2std(th_d[0], r_v[0])]), r_v
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)
# just testing if the density is > 1 kg / m3
assert common.rhod(100000, 300, 0) > 1
# blk_2m-specific parameter
# TODO: spectrum
# lgrngn-specific parameters
sd_conc = 44
def lognormal(lnr):
mean_r = .08e-6 / 2
stdev = 2
n_tot = 566e6
return n_tot * exp(
-pow((lnr - log(mean_r)), 2) / 2 / pow(log(stdev),2)
) / log(stdev) / sqrt(2*pi);
kappa = .61 #TODO - check
# rho = 1.8 # TODO
chem_gas = {
chem_species_t.SO2 : 200e-12,
chem_species_t.O3 : 50e-9,
chem_species_t.H2O2 : 500e-12
}
# running all three
for rhs in [
rhs_blk_2m(dt),
rhs_lgrngn(dt, sd_conc, {kappa:lognormal}, chem_gas)
]:
parcel(p_d, th_d, r_v, w, dt, nt, rhs)
print p_d, arr_t([th_dry2std(th_d[0], r_v[0])]), r_v
def test(RH_formula, step_count, substep_count, exact_substep, constp):
print("[RH_formula = ", RH_formula, "]")
print("step_count = ", step_count, " substep_count = ", substep_count,
"exact substepping = ", exact_substep, "constp = ", constp)
opts_init.sstp_cond = substep_count
opts_init.exact_sstp_cond = exact_substep
opts_init.RH_formula = RH_formula
rhod, th, rv, p = initial_state()
rhod_ss, th_ss, rv_ss, p_ss = supersat_state()
# in constp mode, th_std is expected instead of th_dry
if constp == True:
# dry/std conversions assume p = rhod (Rd + rv * Rv) T
# which in general is not true in constp, but is true at init so we use it here
th[0] = common.th_dry2std(th[0], rv[0])
th_ss[0] = common.th_dry2std(th_ss[0], rv_ss[0])
prtcls = lgrngn.factory(backend, opts_init)
if constp == False:
prtcls.init(th, rv, rhod)
else:
prtcls.init(th, rv, rhod, p_ss)
# go to supersaturated air, density changes to test density substepping too
rhod[0] = rhod_ss
th[0] = th_ss
rv[0] = rv_ss
rv_init = rv.copy()
th_init = th.copy()
exectime = 0
opts.cond = 0
for step in arange(step_count):
wrapped = wrapper(prtcls.step_sync, opts, th, rv, rhod)
exectime += timeit.timeit(wrapped, number=1)
prtcls.step_async(opts)
if step == 9:
# some parameters are analyzed after 10 steps, before small CCNs evaporate
act_conc_post_cond = act_conc(prtcls)
mean_r_post_cond = mean_r(prtcls)
second_r_post_cond = second_r(prtcls)
third_r_post_cond = third_r(prtcls)
if step == 0:
print("initial supersaturation", supersaturation(prtcls))
opts.cond = 1
# print step, supersaturation(prtcls), th[0], rv[0], mean_r(prtcls), second_r(prtcls), third_r(prtcls), act_conc(prtcls)
ss_post_cond = supersaturation(prtcls)
print("supersaturation after condensation", ss_post_cond, th[0], rv[0],
mean_r(prtcls), second_r(prtcls), third_r(prtcls), act_conc(prtcls))
assert (abs(th[0] - exp_th[constp]) < 1e-4 * exp_th[constp])
assert (abs(rv[0] - exp_rv[constp]) < 1e-3 * exp_rv[constp])
rv_diff = rv_init.copy() - rv[0].copy()
th_diff = th_init.copy() - th[0].copy()
# change to subsaturated air - test evaporation
rhod[0] = 1.1
th[0] = 305
rv[0] = 0.0085
rv_init = rv.copy()
th_init = th.copy()
for step in arange(step_count):
wrapped = wrapper(prtcls.step_sync, opts, th, rv, rhod)
exectime += timeit.timeit(wrapped, number=1)
prtcls.step_async(opts)
# print step, supersaturation(prtcls), th[0], rv[0], mean_r(prtcls), second_r(prtcls), third_r(prtcls), act_conc(prtcls)
ss_post_evap = supersaturation(prtcls)
print("supersaturation after evaporation", ss_post_evap, th[0], rv[0],
mean_r(prtcls), second_r(prtcls), third_r(prtcls), act_conc(prtcls),
gccn_conc(prtcls))
# after evaporation, only larger mode particles should have r > 0.5 microns
assert (act_conc(prtcls) == gccn_conc(prtcls))
print('execution time: ', exectime)
return ss_post_cond, th[0] - th_init[0] - th_diff[0], rv[0] - rv_init[
0] - rv_diff[
0], act_conc_post_cond, mean_r_post_cond, second_r_post_cond, third_r_post_cond
def test(RH_formula, step_count, substep_count, exact_substep, constp):
print "[RH_formula = ", RH_formula,"]"
print "step_count = ", step_count, " substep_count = ", substep_count, "exact substepping = ", exact_substep, "constp = ", constp
opts_init.sstp_cond=substep_count
opts_init.exact_sstp_cond=exact_substep
opts_init.RH_formula = RH_formula
rhod, th, rv, p = initial_state()
rhod_ss, th_ss, rv_ss, p_ss = supersat_state()
# in constp mode, th_std is expected instead of th_dry
if constp == True:
# dry/std conversions assume p = rhod (Rd + rv * Rv) T
# which in general is not true in constp, but is true at init so we use it here
th[0] = common.th_dry2std(th[0], rv[0])
th_ss[0] = common.th_dry2std(th_ss[0], rv_ss[0])
prtcls = lgrngn.factory(backend, opts_init)
if constp == False:
prtcls.init(th, rv, rhod)
else:
prtcls.init(th, rv, rhod, p_ss)
# go to supersaturated air, density changes to test density substepping too
rhod[0] = rhod_ss
th[0] = th_ss
rv[0] = rv_ss
rv_init = rv.copy()
th_init = th.copy()
exectime = 0
opts.cond = 0
for step in arange(step_count):
wrapped = wrapper(prtcls.step_sync, opts, th, rv, rhod)
exectime += timeit.timeit(wrapped, number=1)
prtcls.step_async(opts)
if step == 9:
# some parameters are analyzed after 10 steps, before small CCNs evaporate
act_conc_post_cond = act_conc(prtcls)
mean_r_post_cond = mean_r(prtcls)
second_r_post_cond = second_r(prtcls)
third_r_post_cond = third_r(prtcls)
if step == 0:
print "initial supersaturation", supersaturation(prtcls)
opts.cond = 1
# print step, supersaturation(prtcls), th[0], rv[0], mean_r(prtcls), second_r(prtcls), third_r(prtcls), act_conc(prtcls)
ss_post_cond = supersaturation(prtcls)
print "supersaturation after condensation", ss_post_cond, th[0], rv[0], mean_r(prtcls), second_r(prtcls), third_r(prtcls), act_conc(prtcls)
assert(abs(th[0] - exp_th[constp]) < 1e-4 * exp_th[constp])
assert(abs(rv[0] - exp_rv[constp]) < 1e-3 * exp_rv[constp])
rv_diff = rv_init.copy() - rv[0].copy()
th_diff = th_init.copy() - th[0].copy()
# change to subsaturated air - test evaporation
rhod[0] = 1.1
th[0] = 305
rv[0] = 0.0085
rv_init = rv.copy()
th_init = th.copy()
for step in arange(step_count):
wrapped = wrapper(prtcls.step_sync, opts, th, rv, rhod)
exectime += timeit.timeit(wrapped, number=1)
prtcls.step_async(opts)
# print step, supersaturation(prtcls), th[0], rv[0], mean_r(prtcls), second_r(prtcls), third_r(prtcls), act_conc(prtcls)
ss_post_evap = supersaturation(prtcls)
print "supersaturation after evaporation", ss_post_evap, th[0], rv[0], mean_r(prtcls), second_r(prtcls), third_r(prtcls), act_conc(prtcls), gccn_conc(prtcls)
# after evaporation, only larger mode particles should have r > 0.5 microns
assert(act_conc(prtcls) == gccn_conc(prtcls))
print 'execution time: ', exectime
return ss_post_cond, th[0] - th_init[0] - th_diff[0], rv[0] - rv_init[0] - rv_diff[0], act_conc_post_cond, mean_r_post_cond, second_r_post_cond, third_r_post_cond
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) # just testing if the density is > 1 kg / m3 assert common.rhod(100000, 300, 0) > 1