def calc_enthalpy(point_dict):
"""
Calculate the enthalpy and total water mixing ratio
for an air parcel, given a dictionary or parameters
Parameters
----------
point_dict: dictionary
dictionary with the following keys
['id','temp','rv','rl','press']
units: point letter, K kg/kg kg/kg Pa
Returns
-------
vals: named tuple
tuple with dictionary values plus rt (kg/kg) and enthalpy (J/kg)
"""
point_dict['rt'] = point_dict['rv'] + point_dict['rl']
vals = make_tuple(point_dict)
cp = c.cpd + vals.rt*c.cl
point_dict['enthalpy'] = cp*vals.temp + find_lv(vals.temp)*vals.rv
vals = make_tuple(point_dict)
return vals
def find_interval(the_func, x, *args):
"""
starting from a 2% difference, move out from a
point until the_func changes sign
Parameters
----------
the_func : function
function that returns zero when on root
x : float
argument to the_func
*args : tuple
additional arguments for the_func
Returns
-------
brackets : [left,right]
left,right brackets for root
"""
if x == 0.0:
dx = 1.0 / 50.0
else:
dx = x / 50.0
maxiter = 40
twosqrt = np.sqrt(2)
failed = True
for i in range(maxiter):
dx = dx * twosqrt
a = x - dx
fa = the_func(a, *args)
b = x + dx
fb = the_func(b, *args)
if fa * fb < 0.0:
failed = False
break
if failed:
#
# load the debugging information into the BracketError exception as a
# namedtuple
#
extra_info = make_tuple(dict(a=a, b=b, fa=fa, fb=fb, x=x, dx=dx, args=args))
raise BracketError("Couldn't find a suitable range. Providing extra_info", extra_info=extra_info)
return (a, b)
import pandas as pd
from matplotlib import pyplot as plt
import datetime, h5py
# ### Read in the yaml file and set the koehler function for this aerosol
# In[39]:
util_dir = a405utils.__path__[0]
data_dir = Path(util_dir).joinpath('../data')
yaml_file = data_dir.joinpath('dropgrow.yaml')
with yaml_file.open('r') as f:
input_dict=ry.load(f,Loader=ry.RoundTripLoader)
aero=make_tuple(input_dict['aerosol'])
parcel=make_tuple(input_dict['initial_conditions'])
koehler_fun = create_koehler(aero,parcel)
# ### initialize the lognormal mass and number distributions for 30 bins
# In[40]:
#
#set the edges of the mass bins
#31 edges means we have 30 droplet bins
#
numrads = 30
g = make_format()
info = make_format(format_string="debugging {}")
a=10
b=1.546e-23
print(info(a), g(b))
# ### Calculating the equilibrium size distribution for unactivated aerosols
#
# Below we use the rootfinder to search on log(radius) to find the equilibrium
# drop size for unactivaed aerosols on the left side of their Koehler curves
# In[55]:
aero=make_tuple(input_dict['aerosol'])
parcel=make_tuple(input_dict['initial_conditions'])
a, b = find_koehler_coeffs(aero,parcel)
#
# sanity check
#
m=1.e-18
Scrit=(4.*a**3./(27.*b*m))**0.5;
rcrit = (3.*m*b/a)**0.5
print(("for aerosol with mass = {} kg, "
"SScrit,rcrit are {:8.3g}, {:8.3g} microns")
.format(m,Scrit,rcrit*1.e6))
mass_vals = np.linspace(-20, -16, numrads + 1)
mass_vals = 10 ** mass_vals
mu = input_dict["aerosol"]["themean"]
sigma = input_dict["aerosol"]["sd"]
totmass = input_dict["aerosol"]["totmass"]
mdist = totmass * lognormal(mass_vals, np.log(mu), np.log(sigma))
mdist = find_centers(mdist) * np.diff(mass_vals)
center_mass = find_centers(mass_vals)
ndist = mdist / center_mass
cloud_vars = od()
cloud_vars["mdist"] = mdist
cloud_vars["ndist"] = ndist
cloud_vars["center_mass"] = center_mass
aero = make_tuple(input_dict["aerosol"])
parcel = make_tuple(input_dict["initial_conditions"])
koehler_fun = create_koehler(aero, parcel)
S_target = parcel.Sinit
logr_start = np.log(0.1e-6)
initial_radius = []
dry_radius = []
for mass in center_mass:
brackets = np.array(find_interval(find_diff, logr_start, S_target, mass, koehler_fun))
left_bracket, right_bracket = np.exp(brackets) * 1.0e6 # get brackets in microns for printing
equil_rad = np.exp(fzero(find_diff, brackets, S_target, mass, koehler_fun))
initial_radius.append(equil_rad)
