'red': [6, 7, 8],
'orange': [9, 10, 11]
}
month_ls = {'-': [1, 4, 7, 10], '--': [2, 5, 8, 11], '-.': [12, 3, 6, 9]}
year = 2015
# ---------------------------------------------------
# Read, Process and save f(RH)
# ---------------------------------------------------
# RH
RH_paths = [
datadir + 'WXT_KSSW_' + y + '_15min.nc' for y in ['2014', '2015']
]
RH_obs = eu.netCDF_read(RH_paths)
# N(D) #use the Dn_accum.
N_path = N_dir + 'accum_Ntot_Dn_NK_SMPS_' + str(year) + '.npy'
N = np.load(N_path).flat[0]
r_accum_m = N['Dv_accum'] / 2 / 1e9 # conv. to [m] from [nm]
# f(RH) read in
f_RH_data = {}
rel_vol_species = {}
rel_vol_time = {}
for site in ['NK', 'Ch', 'Ha']:
# read in f(RH) LUTs for each site
path = fRHdir + dataRes + '_f(RH)_' + site + '_905.0nm.nc'
def read_wxt_obs(years, time):
"""
Read in RH observations from KSSW, time match them to the model data, and extend them in height to match the
dimensions of model RH
:param years:
:param time:
:return wxt_obs:
"""
met_vars = ['RH', 'Tair', 'press']
vars = met_vars + ['time']
filepath = ['C:/Users/Elliott/Documents/PhD Reading/PhD Research/Aerosol Backscatter/MorningBL/data/L1/' + \
'Davis_BGH_' + str(i) + '_15min.nc' for i in years]
wxt_obs_raw = eu.netCDF_read(filepath, vars=vars)
# set up array to be filled
wxt_obs = {}
for met_var in met_vars:
wxt_obs[met_var] = np.empty(len(time))
wxt_obs[met_var][:] = np.nan
wxt_obs['time'] = time
# find data region and create an average if appropriate
print_step = range(1000,20000, 1000)
for t, time_t in enumerate(time):
if t in print_step:
print 't ='+str(t)
# time t-1 (start of original time period, as all data is relevent for time ENDING at time_t)
tm1 = t-1
time_tm1 = time_t - dt.timedelta(minutes=60)
# # start of time period
# idx_extent = 8000
# s_idx = int(eu.binary_search(wxt_obs_raw['time'], time_tm1, lo=max(0, tm1 - idx_extent),
# hi=min(tm1 + idx_extent, len(wxt_obs_raw['time']))))
# # end of time period
# e_idx = int(eu.binary_search(wxt_obs_raw['time'], time_t, lo=max(0, t - idx_extent),
# hi=min(t + idx_extent, len(wxt_obs_raw['time']))))
s_idx = int(eu.binary_search(wxt_obs_raw['time'], time_tm1))
# end of time period
e_idx = int(eu.binary_search(wxt_obs_raw['time'], time_t))
# if the time_range time and data['time'] found in this iteration are within an acceptable range (15 mins)
tm1_diff = time_tm1 - wxt_obs_raw['time'][s_idx]
t_diff = time_t - wxt_obs_raw['time'][e_idx]
# _, s_idx, tm1_diff = eu.nearest(wxt_obs_raw['time'], time_tm1)
# _, e_idx, t_diff = eu.nearest(wxt_obs_raw['time'], time_t)
if (tm1_diff.total_seconds() <= 15 * 60) & (t_diff.total_seconds() <= 15 * 60):
for met_var in met_vars:
wxt_obs[met_var][t] = np.nanmean(wxt_obs_raw[met_var][s_idx:e_idx+1])
# create RH_frac using RH data
wxt_obs['RH_frac'] = wxt_obs['RH'] / 100.0
# calculate extra variables
e_s_hpa = 6.112 * (np.exp((17.67 * wxt_obs['Tair']) / (wxt_obs['Tair'] + 243.5))) # [hPa] # sat. v. pressure
e_s = e_s_hpa * 100.0 # [Pa] # sat. v. pressure
wxt_obs['e'] = wxt_obs['RH_frac'] * e_s # [Pa] # v. pressure
wxt_obs['r_v'] = wxt_obs['e'] / (1.61 * ((wxt_obs['press']*100.0) - wxt_obs['e'])) # water_vapour mixing ratio [kg kg-1]
wxt_obs['q'] = wxt_obs['e'] / ((1.61 * ((wxt_obs['press']*100.0) - wxt_obs['e'])) + wxt_obs['e']) # specific humidity [kg kg-1]
wxt_obs['Tv'] = (1 + (0.61 * wxt_obs['q'])) * (wxt_obs['Tair'] + 273.15) # virtual temp [K]
wxt_obs['air_density'] = (wxt_obs['press']*100.0) / (286.9 * wxt_obs['Tv'])# [kg m-3]
return wxt_obs
def main():
# Read in the mass data for 2016
# Read in RH data for 2016
# convert gases and such into the aerosol particles
# swell the particles based on the CLASSIC scheme stuff
# use Mie code to calculate the backscatter and extinction
# calculate lidar ratio
# plot lidar ratio
# ==============================================================================
# Setup
# ==============================================================================
# which modelled data to read in
model_type = 'UKV'
# directories
maindir = '/home/nerc/Documents/MieScatt/'
datadir = '/home/nerc/Documents/MieScatt/data/'
savedir = maindir + 'figures/LidarRatio/'
# data
wxtdatadir = datadir
massdatadir = datadir
ffoc_gfdir = datadir
# RH data
wxt_inst_site = 'WXT_KSSW'
# data year
year = '2016'
# aerosol particles to calculate (OC = Organic carbon, CBLK = black carbon, both already measured)
# match dictionary keys further down
aer_particles = ['(NH4)2SO4', 'NH4NO3', 'NaCl', 'CORG', 'CBLK']
all_species = ['(NH4)2SO4', 'NH4NO3', 'NaCl', 'CORG', 'CBLK', 'H2O']
# aer names in the complex index of refraction files
aer_names = {
'(NH4)2SO4': 'Ammonium sulphate',
'NH4NO3': 'Ammonium nitrate',
'CORG': 'Organic carbon',
'NaCl': 'Generic NaCl',
'CBLK': 'Soot',
'MURK': 'MURK'
}
# density of molecules [kg m-3]
# CBLK: # Zhang et al., (2016) Measuring the morphology and density of internally mixed black carbon
# with SP2 and VTDMA: New insight into the absorption enhancement of black carbon in the atmosphere
# ORG: Range of densities for organic carbon is mass (0.625 - 2 g cm-3)
# Haywood et al 2003 used 1.35 g cm-3 but Schkolink et al., 2006 claim the average is 1.1 g cm-3 after a lit review
aer_density = {
'(NH4)2SO4': 1770.0,
'NH4NO3': 1720.0,
'NaCl': 2160.0,
'CORG': 1100.0,
'CBLK': 1200.0
}
# Organic carbon growth curve (Assumed to be the same as aged fossil fuel organic carbon
# pure water density
water_density = 1000.0 # kg m-3
# wavelength to aim for
ceil_lambda = [0.905e-06]
# ==============================================================================
# Read data
# ==============================================================================
# read in the complex index of refraction data for the aerosol species (can include water)
n_species = read_n_data(aer_particles, aer_names, ceil_lambda, getH2O=True)
# Read in physical growth factors (GF) for organic carbon (assumed to be the same as aged fossil fuel OC)
gf_ffoc_raw = eu.csv_read(ffoc_gfdir + 'GF_fossilFuelOC_calcS.csv')
gf_ffoc_raw = np.array(gf_ffoc_raw)[1:, :] # skip header
gf_ffoc = {
'RH_frac': np.array(gf_ffoc_raw[:, 0], dtype=float),
'GF': np.array(gf_ffoc_raw[:, 1], dtype=float)
}
# Read in species by mass data
# Units are grams m-3
mass_in = read_mass_data(massdatadir, year)
# Read WXT data
wxtfilepath = wxtdatadir + wxt_inst_site + '_' + year + '_15min.nc'
WXT_in = eu.netCDF_read(wxtfilepath, vars=['RH', 'Tair', 'press', 'time'])
WXT_in['RH_frac'] = WXT_in['RH'] * 0.01
WXT_in['time'] -= dt.timedelta(
minutes=15
) # change time from 'obs end' to 'start of obs', same as the other datasets
# Trim times
# as WXT and mass data are 15 mins and both line up exactly already
# therefore trim WXT to match mass time
mass_in, WXT_in = trim_mass_wxt_times(mass_in, WXT_in)
# Time match so mass and WXT times line up INTERNALLY as well
date_range = eu.date_range(WXT_in['time'][0], WXT_in['time'][-1], 15,
'minutes')
# make sure there are no time stamp gaps in the data so mass and WXT will match up perfectly, timewise.
print 'beginning time matching for WXT...'
WXT = internal_time_completion(WXT_in, date_range)
print 'end time matching for WXT...'
# same but for mass data
print 'beginning time matching for mass...'
mass = internal_time_completion(mass_in, date_range)
print 'end time matching for mass...'
# Create idealised number distribution for now... (dry distribution)
# idealised dist is equal for all particle types for now.
step = 0.005
r_range_um = np.arange(0.000 + step, 5.000 + step, step)
r_range_m = r_range_um * 1.0e-06
r_mean = 0.11e-06
sigma = 0
# ==============================================================================
# Process data
# ==============================================================================
# molecular mass of each molecule
mol_mass_amm_sulp = 132
mol_mass_amm_nit = 80
mol_mass_nh4 = 18
mol_mass_n03 = 62
mol_mass_s04 = 96
# Convert into moles
# calculate number of moles (mass [g] / molar mass)
# 1e-06 converts from micrograms to grams.
moles = {
'SO4': mass['SO4'] / mol_mass_s04,
'NO3': mass['NO3'] / mol_mass_n03,
'NH4': mass['NH4'] / mol_mass_nh4
}
# calculate ammonium sulphate and ammonium nitrate from gases
# adds entries to the existing dictionary
mass = calc_amm_sulph_and_amm_nit_from_gases(moles, mass)
# convert chlorine into sea salt assuming all chlorine is sea salt, and enough sodium is present.
# potentially weak assumption for the chlorine bit due to chlorine depletion!
mass['NaCl'] = mass['CL'] * 1.65
# convert masses from g m-3 to kg kg-1_air for swelling.
# Also creates the air density and is stored in WXT
mass_kg_kg, WXT = convert_mass_to_kg_kg(mass, WXT, aer_particles)
# start with just 0.11 microns as the radius - can make it more fancy later...
r_d_microns = 0.11 # [microns]
r_d_m = r_d_microns * 1.0e-6 # [m]
# calculate the number of particles for each species using radius_m and the mass
# Hopefull not needed!
num_part = {}
for aer_i in aer_particles:
num_part[aer_i] = mass_kg_kg[aer_i] / (
(4.0 / 3.0) * np.pi * (aer_density[aer_i] / WXT['dryair_rho']) *
(r_d_m**3.0))
# calculate dry volume
V_dry_from_mass = {}
for aer_i in aer_particles:
# V_dry[aer_i] = (4.0/3.0) * np.pi * (r_d_m ** 3.0)
V_dry_from_mass[aer_i] = mass_kg_kg[aer_i] / aer_density[aer_i] # [m3]
# if np.nan (i.e. there was no mass therefore no volume) make it 0.0
bin = np.isnan(V_dry_from_mass[aer_i])
V_dry_from_mass[aer_i][bin] = 0.0
# ---------------------------------------------------------
# Swell the particles (r_md,aer_i) [microns]
# set up dictionary
r_md = {}
# calculate the swollen particle size for these three aerosol types
# Follows CLASSIC guidence, based off of Fitzgerald (1975)
for aer_i in ['(NH4)2SO4', 'NH4NO3', 'NaCl']:
r_md[aer_i] = calc_r_md_species(r_d_microns, WXT, aer_i)
# set r_md for black carbon as r_d, assuming black carbon is completely hydrophobic
r_md['CBLK'] = np.empty(len(date_range))
r_md['CBLK'][:] = r_d_microns
# calculate r_md for organic carbon using the MO empirically fitted g(RH) curves
r_md['CORG'] = np.empty(len(date_range))
r_md['CORG'][:] = np.nan
for t, time_t in enumerate(date_range):
_, idx, _ = eu.nearest(gf_ffoc['RH_frac'], WXT['RH_frac'][t])
r_md['CORG'][t] = r_d_microns * gf_ffoc['GF'][idx]
# -----------------------------------------------------------
# calculate abs volume of wetted particles (V_abs,wet,aer_i)
# use the growth factors calculated based on r_d to calc V_wet from V_dry(mass, density)
# calculate the physical growth factor, wetted particle density, wetted particle volume, ...
# and water volume (V_wet - Vdry)
GF = {}
# aer_wet_density = {}
V_wet_from_mass = {}
V_water_i = {}
for aer_i in aer_particles: # aer_particles:
# physical growth factor
GF[aer_i] = r_md[aer_i] / r_d_microns
# # wet aerosol density
# aer_wet_density[aer_i] = (aer_density[aer_i] / (GF[aer_i]**3.0)) + \
# (water_density * (1.0 - (1.0 / (GF[aer_i]**3.0))))
# wet volume, using the growth rate from r_d to r_md
# if np.nan (i.e. there was no mass therefore no volume) make it 0.0
V_wet_from_mass[aer_i] = V_dry_from_mass[aer_i] * (GF[aer_i]**3.0)
bin = np.isnan(V_wet_from_mass[aer_i])
V_wet_from_mass[aer_i][bin] = 0.0
# water volume contribution from just this aer_i
V_water_i[aer_i] = V_wet_from_mass[aer_i] - V_dry_from_mass[aer_i]
# ---------------------------
# Calculate relative volume of all aerosol AND WATER (to help calculate n_mixed)
# calculate total water volume
V_water_2d = np.array(V_water_i.values(
)) # turn into a 2D array (does not matter column order)
V_water_tot = np.nansum(V_water_2d, axis=0)
# combine volumes of the DRY aerosol and the water into a single 2d array shape=(time, substance)
# V_abs = np.transpose(np.vstack([np.array(V_dry_from_mass.values()),V_water_tot]))
# shape = (time, species)
V_abs = np.transpose(
np.vstack([
np.array([V_dry_from_mass[i] for i in aer_particles]), V_water_tot
]))
# now calculate the relative volume of each of these (V_rel)
# scale absolute volume to find relative volume of each (such that sum(all substances for time t = 1))
vol_sum = np.nansum(V_abs, axis=1)
vol_sum[vol_sum == 0.0] = np.nan
scaler = 1.0 / (vol_sum) # a value for each time step
# if there is no mass data for time t, and therefore no volume data, then set scaler to np.nan
bin = np.isinf(scaler)
scaler[bin] = np.nan
# Relative volumes
V_rel = {'H2O': scaler * V_water_tot}
for aer_i in aer_particles:
V_rel[aer_i] = scaler * V_dry_from_mass[aer_i]
# --------------------------------------------------------------
# Calculate relative volume of the swollen aerosol (to weight and calculate r_md)
# V_wet_from_mass
V_abs_aer_only = np.transpose(
np.array([V_wet_from_mass[aer_i] for aer_i in aer_particles]))
# now calculate the relative volume of each of these (V_rel_Aer_only)
# scale absolute volume to find relative volume of each (such that sum(all substances for time t = 1))
vol_sum_aer_only = np.nansum(V_abs_aer_only, axis=1)
vol_sum_aer_only[vol_sum_aer_only == 0.0] = np.nan
scaler = 1.0 / (vol_sum_aer_only) # a value for each time step
# if there is no mass data for time t, and therefore no volume data, then set scaler to np.nan
bin = np.isinf(scaler)
scaler[bin] = np.nan
# Relative volumes
V_rel_aer_only = {}
for aer_i in aer_particles:
V_rel_aer_only[aer_i] = scaler * V_wet_from_mass[aer_i]
# for aer_i in aer_particles:
# print aer_i
# print V_rel[aer_i][-1]
# --------------------------------------------------------------
# calculate n_mixed using volume mixing method
# volume mixing for CIR (eq. 12, Liu and Daum 2008)
n_mixed = np.array([V_rel[i] * n_species[i] for i in V_rel.iterkeys()])
n_mixed = np.sum(n_mixed, axis=0)
# calculate volume mean radii from r_md,aer_i (weighted by V_rel,wet,aer_i)
r_md_avg = np.array(
[V_rel_aer_only[aer_i] * r_md[aer_i] for aer_i in aer_particles])
r_md_avg = np.nansum(r_md_avg, axis=0)
r_md_avg[r_md_avg == 0.0] = np.nan
# convert from microns to m
r_md_avg_m = r_md_avg * 1e-6
# calculate the size parameter for the average aerosol size
x_wet_mixed = (2.0 * np.pi * r_md_avg_m) / ceil_lambda[0]
# --------------------------
# calculate Q_back and Q_ext from the avergae r_md and n_mixed
S = np.empty(len(date_range))
S[:] = np.nan
for t, time_t in enumerate(date_range):
x_i = x_wet_mixed[t] # size parameter_i
n_i = n_mixed[t] # complex index of refraction i
if t in np.arange(0, 35000, 500):
print t
if np.logical_and(~np.isnan(x_i), ~np.isnan(n_i)):
particle = Mie(x=x_i, m=n_i)
Q_ext = particle.qext()
Q_back = particle.qb()
# calculate the lidar ratio
S_t = Q_ext / Q_back
S[t] = Q_ext / Q_back
# ---------------------
# simple plot of S
fig, ax = plt.subplots(1, 1, figsize=(6, 6))
plt.plot_date(date_range, S)
plt.savefig(savedir + 'quickplot.png')
plt.close(fig)
# --------------------------
# Testing lidar ratio computation
# read in Franco's computation of the lidar ratio CIR=1.47 + 0.099i, lambda=905nm
lr_f = eu.netCDF_read(
'/home/nerc/Documents/MieScatt/testing/lr_1.47_0.099_0.905.nc',
['DIAMETER', 'LIDAR_RATIO'])
step = 0.005
r_range_um = np.arange(0.000 + step, 10.000 + step, step)
r_range_m = r_range_um * 1.0e-06
x_range = (2.0 * np.pi * r_range_m) / ceil_lambda[0]
# calculate Q_back and Q_ext from the avergae r_md and n_mixed
#S_r = lidar ratio
S_r = np.empty(len(r_range_m))
S_r[:] = np.nan
for r_idx, r_i in enumerate(r_range_m):
x_i = x_range[r_idx] # size parameter_i
n_i = complex(1.47 + 0.099j) # fixed complex index of refraction i
# print loop progress
if r_idx in np.arange(0, 2100, 100):
print r_idx
particle = Mie(x=x_i, m=n_i)
Q_ext = particle.qext()
Q_back = particle.qb()
Q_back_alt = Q_back / (4.0 * np.pi)
# #Q_back = particle.qb()
# S12 = particle.S12(-1)
# S11 = S12[0].imag
# S22 = S12[1].imag
# Q_back_fancy = ((np.abs(S11)**2) + (np.abs(S22)**2))/(2 * np.pi * (x_i**2))
# calculate the lidar ratio
# S_t = Q_ext / Q_back
S_r[r_idx] = Q_ext / Q_back_alt
# simple plot of S
fig, ax = plt.subplots(1, 1, figsize=(6, 5))
plt.loglog(r_range_um * 2, S_r, label='mine') # diameter [microns]
plt.loglog(lr_f['DIAMETER'], lr_f['LIDAR_RATIO'], label='Franco' 's')
plt.xlim([0.01, 100.0])
plt.ylim([1.0, 10.0e7])
plt.ylabel('Lidar Ratio')
plt.xlabel('Diameter [microns]')
plt.legend()
plt.tight_layout()
plt.savefig(savedir + 'quickplot_S_vs_r.png')
plt.close(fig)
# -----------------------------------------------
d_test = 0.001e-06
r_test = d_test / 2.0
r_test_microns = r_test * 1.0e6
x_i = (2.0 * np.pi * r_test) / ceil_lambda[0] # size parameter_i
n_i = complex(1.47 + 0.099j) # fixed complex index of refraction i
particle = Mie(x=x_i, m=n_i)
Q_ext = particle.qext()
Q_back = particle.qb()
Q_back_alt = Q_back / (4.0 * np.pi)
# calculate extinction and scattering cross section
C_ext = Q_ext * np.pi * (r_test_microns**2.0)
C_back = Q_back * np.pi * (r_test_microns**2.0)
C_back_alt = Q_back_alt * np.pi * (r_test_microns**2.0)
S12 = particle.S12(-1)
S11 = S12[0].imag
S22 = S12[1].imag
Q_back_fancy = ((np.abs(S11)**2) + (np.abs(S22)**2)) / (2 * np.pi *
(x_i**2))
# calculate the lidar ratio
S_t = Q_ext / Q_back
S_test = Q_ext / Q_back_alt
S_c_test = C_ext / C_back
S_c_alt = C_ext / C_back_alt
return
def test_lidar_computation(ceil_lambda, r_md_m):
"""
Test my computation of the lidar ratio against Franco's. Done for a monodisperse, soot(like?) aerosol
:param ceil_lambda:
:param r_md_m:
:return:
"""
import ellUtils as eu
# Testing lidar ratio computation
# read in Franco's computation of the lidar ratio CIR=1.47 + 0.099i, lambda=905nm
lr_f = eu.netCDF_read(
'/home/nerc/Documents/MieScatt/testing/lr_1.47_0.099_0.905.nc',
['DIAMETER', 'LIDAR_RATIO'])
step = 0.005
r_range_um = np.arange(0.000 + step, 10.000 + step, step)
r_range_m = r_range_um * 1.0e-06
x_range = (2.0 * np.pi * r_range_m) / ceil_lambda[0]
# calculate Q_back and Q_ext from the avergae r_md and n_mixed
#S_r = lidar ratio
S_r = np.empty(len(r_range_m))
S_r[:] = np.nan
for r_idx, r_i in enumerate(r_range_m):
x_i = x_range[r_idx] # size parameter_i
n_i = complex(1.47 + 0.0j) # fixed complex index of refraction i
# n_i = complex(1.47 + 0.099j) # fixed complex index of refraction i for soot
# print loop progress
if r_idx in np.arange(0, 2100, 100):
print r_idx
particle = Mie(x=x_i, m=n_i)
Q_ext = particle.qext()
Q_back = particle.qb()
Q_back_alt = Q_back / (4.0 * np.pi)
# #Q_back = particle.qb()
# S12 = particle.S12(-1)
# S11 = S12[0].imag
# S22 = S12[1].imag
# Q_back_fancy = ((np.abs(S11)**2) + (np.abs(S22)**2))/(2 * np.pi * (x_i**2))
# calculate the lidar ratio
# S_t = Q_ext / Q_back
S_r[r_idx] = Q_ext / Q_back_alt
# simple plot of S
fig, ax = plt.subplots(1, 1, figsize=(8, 7))
plt.loglog(r_range_um * 2, S_r, label='mine') # diameter [microns]
plt.loglog(lr_f['DIAMETER'], lr_f['LIDAR_RATIO'], label='Franco' 's')
for aer_i, r_md_m_aer_i in r_md_m.iteritems():
for r_i in r_md_m_aer_i:
plt.vlines(r_i, 1, 1e6, linestyle='--', alpha=0.5)
plt.xlim([0.01, 100.0])
plt.ylim([1.0, 10.0e7])
plt.ylabel('Lidar Ratio')
plt.xlabel('Diameter [microns]')
plt.legend()
plt.tight_layout()
plt.savefig(maindir + 'figures/LidarRatio/' +
'quickplot_S_vs_r_with_rbin_lines.png')
plt.close(fig)
return
high_idx = np.where(y[idx] > 4.0e-06)[0]
y[idx][high_idx]
# # check transmission - seems ok
# filename = 'C:/Users/Elliott/Documents/PhD Reading/PhD Research/Aerosol Backscatter/clearFO/data/L1/CL31-D_CCW30_NK_2015_15min.nc'
# data = eu.netCDF_read(filename)
# _, t_idx, _ = eu.nearest(data['time'], dt.datetime(2015, 6, 4))
# plt.figure()
# plt.plot_date(data['time'][t_idx-(24*4):t_idx+(48*4)], data['transmission'][t_idx-(24*4):t_idx+(48*14)])
# plt.ylabel('transmission for CL31-NK')
# plt.xlabel('date [YYYY:mm:DD]')
# check RH trend at the time
# filename = 'C:/Users/Elliott/Documents/PhD Reading/PhD Research/Aerosol Backscatter/MorningBL/data/L1/Davis_IMU_2015_15min.nc'
filename = 'C:/Users/Elliott/Documents/PhD Reading/PhD Research/Aerosol Backscatter/MorningBL/data/L1/WXT_KSSW_2015_15min.nc'
data = eu.netCDF_read(filename)
_, t_idx, _ = eu.nearest(data['time'], dt.datetime(2015, 6, 4))
all_range = np.arange(t_idx, t_idx + (110 * 1))
_, ts_idx, _ = eu.nearest(data['time'], dt.datetime(2015, 6, 4, 20, 0, 0))
_, te_idx, _ = eu.nearest(data['time'], dt.datetime(2015, 6, 5, 0, 0, 0))
focus_range = np.arange(ts_idx, te_idx)
plt.figure(figsize=(11, 4))
ax = plt.gca()
# 96 = 1 day of 15 min data
# plt.plot_date(data['time'][t_idx:t_idx+(110*1)], data['RH'][t_idx:t_idx+(110*1)], color='blue') # all data
# plt.plot_date(data['time'][ts_idx:te_idx], data['RH'][ts_idx:te_idx], color='red') # just those 5 hours
plt.plot_date(data['time'][all_range], data['RH'][all_range],
color='blue') # all data
plt.plot_date(data['time'][focus_range],
def main():
# Read in the mass data for 2016
# Read in RH data for 2016
# convert gases and such into the aerosol particles
# swell the particles based on the CLASSIC scheme stuff
# use Mie code to calculate the backscatter and extinction
# calculate lidar ratio
# plot lidar ratio
# ==============================================================================
# Setup
# ==============================================================================
# which modelled data to read in
model_type = 'UKV'
res = FOcon.model_resolution[model_type]
# directories
maindir = 'C:/Users/Elliott/Documents/PhD Reading/PhD Research/Aerosol Backscatter/MorningBL/'
datadir = 'C:/Users/Elliott/Documents/PhD Reading/PhD Research/Aerosol Backscatter/MorningBL/data/'
massdatadir = 'C:/Users/Elliott/Documents/PhD Reading/PhD Research/Aerosol Backscatter/clearFO/data/ERG/'
savedir = maindir + 'figures/LidarRatio/'
# data
wxtdatadir = datadir + 'L1/'
# RH data
wxt_inst_site = 'WXT_KSSW'
# data year
year = '2016'
# aerosol particles to calculate (OC = Organic carbon, CBLK = black carbon, both already measured)
# match dictionary keys further down
aer_particles = ['(NH4)2SO4', 'NH4NO3', 'NaCl', 'CORG', 'CBLK']
# density of molecules [kg m-3]
# CBLK: # Zhang et al., (2016) Measuring the morphology and density of internally mixed black carbon
# with SP2 and VTDMA: New insight into the absorption enhancement of black carbon in the atmosphere
# ORG: Range of densities for organic carbon is mass (0.625 - 2 g cm-3)
# Haywood et al 2003 used 1.35 g cm-3 but Schkolink et al., 2006 claim the average is 1.1 g cm-3 after a lit review
aer_density = {
'(NH4)2SO4': 1770.0,
'NH4NO3': 1720.0,
'NaCl': 2160.0,
'CORG': 1100.0,
'CBLK': 1200.0
}
# pure water density
water_density = 1000.0 # kg m-3
# ==============================================================================
# Read data
# ==============================================================================
# Read in species by mass data
# Units are grams m-3
mass_in = read_mass_data(massdatadir, year)
# Read WXT data
wxtfilepath = wxtdatadir + wxt_inst_site + '_' + year + '_15min.nc'
WXT_in = eu.netCDF_read(wxtfilepath, vars=['RH', 'Tair', 'press', 'time'])
WXT_in['RH_frac'] = WXT_in['RH'] * 0.01
WXT_in['time'] -= dt.timedelta(
minutes=15
) # change time from 'obs end' to 'start of obs', same as the other datasets
# Trim times
# as WXT and mass data are 15 mins and both line up exactly already
# therefore trim WXT to match mass time
mass_in, WXT_in = trim_mass_wxt_times(mass_in, WXT_in)
# Time match so mass and WXT times line up INTERNALLY as well
date_range = eu.date_range(WXT_in['time'][0], WXT_in['time'][-1], 15,
'minutes')
# make sure there are no time stamp gaps in the data so mass and WXT will match up perfectly, timewise.
print 'beginning time matching for WXT...'
WXT = internal_time_completion(WXT_in, date_range)
print 'end time matching for WXT...'
# same but for mass data
print 'beginning time matching for mass...'
mass = internal_time_completion(mass_in, date_range)
print 'end time matching for mass...'
# ==============================================================================
# Process data
# ==============================================================================
# molecular mass of each molecule
mol_mass_amm_sulp = 132
mol_mass_amm_nit = 80
mol_mass_nh4 = 18
mol_mass_n03 = 62
mol_mass_s04 = 96
# Convert into moles
# calculate number of moles (mass [g] / molar mass)
# 1e-06 converts from micrograms to grams.
moles = {
'SO4': mass['SO4'] / mol_mass_s04,
'NO3': mass['NO3'] / mol_mass_n03,
'NH4': mass['NH4'] / mol_mass_nh4
}
# calculate ammonium sulphate and ammonium nitrate from gases
# adds entries to the existing dictionary
mass = calc_amm_sulph_and_amm_nit_from_gases(moles, mass)
# convert chlorine into sea salt assuming all chlorine is sea salt, and enough sodium is present.
# potentially weak assumption for the chlorine bit due to chlorine depletion!
mass['NaCl'] = mass['CL'] * 1.65
# convert masses from g m-3 to kg kg-1_air for swelling.
# Also creates the air density and is stored in WXT
mass_kg_kg, WXT = convert_mass_to_kg_kg(mass, WXT, aer_particles)
# start with just 0.11 microns as the radius - can make it more fancy later...
r_d_microns = 0.11 # [microns]
r_d_m = r_d_microns * 1.0e-6 # [m]
V_dry_from_r_d = (4.0 / 3.0) * np.pi * (r_d_m**3.0) # [m3]
# calculate the number of particles for each species using radius_m and the mass
# Hopefull not needed!
num_part = {}
for aer_i in aer_particles:
num_part[aer_i] = mass_kg_kg[aer_i] / (
(4.0 / 3.0) * np.pi * (aer_density[aer_i] / WXT['dryair_rho']) *
(r_d_m**3.0))
# calculate dry volume
V_dry_from_mass = {}
for aer_i in aer_particles:
# V_dry[aer_i] = (4.0/3.0) * np.pi * (r_d_m ** 3.0)
V_dry_from_mass[aer_i] = mass_kg_kg[aer_i] / aer_density[aer_i] # [m3]
# ---------------------------------------------------------
# Swell the particles (r_md,aer_i) [microns]
# set up dictionary
r_md = {}
# calculate the swollen particle size for these three aerosol types
# Follows CLASSIC guidence, based off of Fitzgerald (1975)
for aer_i in ['(NH4)2SO4', 'NH4NO3', 'NaCl']:
r_md[aer_i] = calc_r_md_species(r_d_microns, WXT, aer_i)
# set r_md for black carbon as r_d, assuming black carbon is completely hydrophobic
r_md['CBLK'] = np.empty(len(date_range))
r_md['CBLK'][:] = r_d_microns
# calculate r_md for organic carbon using the MO empirically fitted g(RH) curves
# r_md['CORG'] = ...
# -----------------------------------------------------------
# calculate abs volume of wetted particles (V_abs,wet,aer_i)
# use the growth factors calculated based on r_d to calc V_wet from V_dry(mass, density)
# calculate the physical growth factor, wetted particle density, wetted particle volume, ...
# and water volume (V_wet - Vdry)
GF = {}
aer_wet_density = {}
V_wet_from_mass = {}
V_water_i = {}
for aer_i in ['(NH4)2SO4', 'NH4NO3', 'NaCl', 'CBLK']: # aer_particles:
# physical growth factor
GF[aer_i] = r_md[aer_i] / r_d_microns
# wet aerosol density
aer_wet_density[aer_i] = (aer_density[aer_i] / (GF[aer_i]**3.0)) + \
(water_density * (1.0 - (1.0 / (GF[aer_i]**3.0))))
# wet volume, using the growth rate from r_d to r_md
V_wet_from_mass[aer_i] = V_dry_from_mass[aer_i] * (GF[aer_i]**3.0)
# if np.nan (i.e. there was no mass therefore no volume) make it 0.0
bin = np.isnan(V_wet_from_mass[aer_i])
V_wet_from_mass[aer_i][bin] = 0.0
# water volume contribution from just this aer_i
V_water_i[aer_i] = V_wet_from_mass[aer_i] - V_dry_from_mass[aer_i]
# calculate total water volume
V_water_2d = np.array(V_water_i.values()) # turn into a 2D array
V_water_tot = np.nansum(V_water_2d, axis=0)
# combine volumes of the DRY aerosol and the water into a single 2d array shape=(time, substance)
V_abs = np.transpose(
np.vstack([np.array(V_dry_from_mass.values()), V_water_tot]))
# now calculate the relative volume of each of these
# scale absolute volume to find relative volume of each (such that sum(all substances for time t = 1))
scaler = 1.0 / (np.nansum(V_abs, axis=1)) # a value for each time step
scaler_rep = np.transpose(np.array([scaler] *
6)) # 2d array shape=(time, substance)
V_rel = scaler_rep * V_abs
# --------------------------------------------------------------
# calculate n_mixed using volume mixing method
# calculate relative volume of all wet aerosol (V_rel,wet,aer_i)
# calculate n_mixed using volume mixing and (V_rel,wet,aer_i)
# calculate volume mean radii from r_md,aer_i (weighted by V_rel,wet,aer_i)
# calculate Q_back and Q_ext from the avergae r_md and n_mixed
# calculate lidar ratio
# assume radii are 0.11 microns for now...
return