Skip to content

ENH: add option to skip "fluffy" ice scaling factor for rad approach #141

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
isilber merged 1 commit into from
May 15, 2025
Merged

ENH: add option to skip "fluffy" ice scaling factor for rad approach #141

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 2 additions & 1 deletion emc2/core/model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -37,10 +37,11 @@ class Model():
Rho_hyd: dict
A dictionary whose keys are the names of the model's hydrometeor classes and
whose values are the density of said hydrometeors in :math:`kg\ m^{-3}`
fluffy: dict
fluffy: dict or None
A dictionary whose keys are the names of the model's ice hydrometeor classes and
whose values are the ice fluffiness factor for the fwd calculations using r_e,
where values of 0 - equal volume sphere, 1 - fluffy sphere i.e., diameter = maximum dimension.
If None, then not applying this correction factor
lidar_ratio: dict
A dictionary whose keys are the names of the model's hydrometeor classes and
whose values are the lidar_ratio of said hydrometeors.
Expand Down
22 changes: 14 additions & 8 deletions emc2/simulator/lidar_moments.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -367,7 +367,7 @@ def calc_lidar_bulk(instrument, model, is_conv, p_values, z_values, OD_from_sfc=
dz = np.tile(np.diff(z_values, axis=1, append=0.), (model.num_subcolumns, 1, 1))
for hyd_type in hyd_types:
rad_A = True # calculate total surface area using classic derivation from r_eff and q_i
if hyd_type[-1] == 'l':
if hyd_type[-1] == 'l': # liquid hydrometeors
rho_b = model.Rho_hyd[hyd_type] # bulk water
re_array = np.tile(model.ds[re_fields[hyd_type]], (model.num_subcolumns, 1, 1))
if model.lambda_field is not None: # assuming my and lambda can be provided only for liq hydrometeors
Expand Down Expand Up @@ -410,18 +410,24 @@ def calc_lidar_bulk(instrument, model, is_conv, p_values, z_values, OD_from_sfc=
rho_b = instrument.rho_i # bulk ice
re_interped = model.interpobj["bulk"]["ri_eff"]((
i_calc_kws["Fr_in"], i_calc_kws["rho_r_in"], i_calc_kws["q_norm_in"])) * 1e6 # um for now
rhoi_eff_interped = model.interpobj["bulk"]["rho_m_weight"]((
i_calc_kws["Fr_in"], i_calc_kws["rho_r_in"], i_calc_kws["q_norm_in"]))
re_array = np.tile(re_interped * rhoi_eff_interped / rho_b, (model.num_subcolumns, 1, 1))
if model.fluffy is None: # fluffy is used here only to determine if the approach is to be implemented
re_array = np.tile(re_interped, (model.num_subcolumns, 1, 1))
else:
rhoi_eff_interped = model.interpobj["bulk"]["rho_m_weight"]((
i_calc_kws["Fr_in"], i_calc_kws["rho_r_in"], i_calc_kws["q_norm_in"]))
re_array = np.tile(re_interped * rhoi_eff_interped / rho_b, (model.num_subcolumns, 1, 1))
else:
rho_b = instrument.rho_i # bulk ice
rho_hyd = model.Rho_hyd[hyd_type]
if rho_hyd == 'variable':
rho_hyd = model.ds[model.variable_density[hyd_type]].values
fi_factor = model.fluffy[hyd_type].magnitude * rho_hyd / rho_b + \
(1 - model.fluffy[hyd_type].magnitude) * (rho_hyd / rho_b) ** (1 / 3)
re_array = np.tile(model.ds[re_fields[hyd_type]].values * fi_factor,
(model.num_subcolumns, 1, 1))
if model.fluffy is None:
re_array = np.tile(model.ds[re_fields[hyd_type]].values, (n_subcolumns, 1, 1))
else:
fi_factor = model.fluffy[hyd_type].magnitude * rho_hyd / rho_b + \
(1 - model.fluffy[hyd_type].magnitude) * (rho_hyd / rho_b) ** (1 / 3)
re_array = np.tile(model.ds[re_fields[hyd_type]].values * fi_factor,
(model.num_subcolumns, 1, 1))

sub_frac_arr = model.ds["%s_frac_subcolumns_%s" % (cloud_str, hyd_type)].values
if rad_A:
Expand Down
22 changes: 14 additions & 8 deletions emc2/simulator/radar_moments.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -313,7 +313,7 @@ def calc_radar_bulk(instrument, model, is_conv, p_values, z_values, atm_ext, OD_

for hyd_type in hyd_types:
rad_A = True # calculate total surface area using classic derivation from r_eff and q_i
if hyd_type[-1] == 'l':
if hyd_type[-1] == 'l': # liquid hydrometeors
rho_b = model.Rho_hyd[hyd_type] # bulk water
re_array = np.tile(model.ds[re_fields[hyd_type]].values, (n_subcolumns, 1, 1))
if model.lambda_field is not None: # assuming my and lambda can be provided only for liq hydrometeors
Expand Down Expand Up @@ -356,19 +356,25 @@ def calc_radar_bulk(instrument, model, is_conv, p_values, z_values, atm_ext, OD_
rho_b = instrument.rho_i # bulk ice
re_interped = model.interpobj["bulk"]["ri_eff"]((
i_calc_kws["Fr_in"], i_calc_kws["rho_r_in"], i_calc_kws["q_norm_in"])) * 1e6 # um for now
rhoi_eff_interped = model.interpobj["bulk"]["rho_m_weight"]((
i_calc_kws["Fr_in"], i_calc_kws["rho_r_in"], i_calc_kws["q_norm_in"]))
re_array = np.tile(re_interped * rhoi_eff_interped / rho_b, (model.num_subcolumns, 1, 1))
if model.fluffy is None: # fluffy is used here only to determine if the approach is to be implemented
re_array = np.tile(re_interped, (model.num_subcolumns, 1, 1))
else:
rhoi_eff_interped = model.interpobj["bulk"]["rho_m_weight"]((
i_calc_kws["Fr_in"], i_calc_kws["rho_r_in"], i_calc_kws["q_norm_in"]))
re_array = np.tile(re_interped * rhoi_eff_interped / rho_b, (model.num_subcolumns, 1, 1))
else:
rho_b = instrument.rho_i.magnitude # bulk ice
if model.Rho_hyd[hyd_type] == 'variable':
rho_hyd = model.ds[model.variable_density[hyd_type]].values
else:
rho_hyd = model.Rho_hyd[hyd_type].magnitude
fi_factor = model.fluffy[hyd_type].magnitude * rho_hyd / rho_b + \
(1 - model.fluffy[hyd_type].magnitude) * (rho_hyd / rho_b) ** (1 / 3)
re_array = np.tile(model.ds[re_fields[hyd_type]].values * fi_factor,
(n_subcolumns, 1, 1))
if model.fluffy is None:
re_array = np.tile(model.ds[re_fields[hyd_type]].values, (n_subcolumns, 1, 1))
else:
fi_factor = model.fluffy[hyd_type].magnitude * rho_hyd / rho_b + \
(1 - model.fluffy[hyd_type].magnitude) * (rho_hyd / rho_b) ** (1 / 3)
re_array = np.tile(model.ds[re_fields[hyd_type]].values * fi_factor,
(n_subcolumns, 1, 1))

sub_frac_arr = model.ds["%s_frac_subcolumns_%s" % (cloud_str, hyd_type)].values
if rad_A:
Expand Down