def mean_vertical_eddy_flux(cubelist, quantity, weight_by_density=True, coord=UM_HGT):
vf = spatial(vertical_eddy_flux(cubelist, quantity, w_dens=True), "mean")
if weight_by_density:
weight_by = spatial(cubelist.extract_strict("air_density"), "mean")
else:
weight_by = None
mean_vf = vertical_mean(vf, coord=coord, weight_by=weight_by)
return mean_vf
def mean_dry_lapse_rate(cubelist, coord=UM_HGT):
temp = cubelist.extract_strict("air_temperature")
rho = cubelist.extract_strict("air_density")
temp = spatial(temp, "mean")
rho = spatial(rho, "mean")
dtdz = differentiate(temp, coord)
dtdz = dtdz.interpolate([(coord, rho.coord(coord).points)], iris.analysis.Linear())
res = vertical_mean(dtdz, coord=coord, weight_by=rho)
return res
def vertical_eddy_flux(cubelist, quantity, w_dens=True):
"""Vertical flux."""
w = cubelist.extract_strict("upward_air_velocity")
q = cubelist.extract_strict(quantity)
w_eddy = w - spatial(w, "mean")
q_eddy = q - spatial(q, "mean")
vf = w_eddy * q_eddy
if w_dens:
vf *= cubelist.extract_strict("air_density")
vf.rename(f"vertical_flux_of_{quantity}")
return vf
def nondim_rossby_deformation_radius(cubelist, const=None, method="direct"):
r"""
Estimate the non-dimensional Rossby radius of deformation.
Parameters
----------
cubelist: iris.cube.CubeList
Input cubelist.
const: aeolus.const.const.ConstContainer, optional
If not given, constants are attempted to be retrieved from
attributes of a cube in the cube list.
method: str, optional
Method of calculation.
"direct" (default): estimate scale height and BV frequency from air temperature.
"leconte2013": use isothermal approximation.
Returns
-------
iris.cube.Cube
Cube with collapsed spatial dimensions.
References
----------
* Leconte et al. (2013), https://doi.org/10.1051/0004-6361/201321042
(for `method=leconte2013`)
.. math::
\lambda_{Rossby} = \sqrt{\frac{NH}{2\Omega R_p}}
\approx \sqrt{\frac{R}{c_p^{1/2}} \frac{T^{1/2}}{2\Omega R_p}}
"""
if const is None:
const = cubelist[0].attributes["planet_conf"]
omega = (const.day / (2 * np.pi)) ** (-1)
double_omega_radius = ScalarCube.from_cube(2 * omega * const.radius)
if method == "direct":
rho = cubelist.extract_strict("air_density").copy()
bv_freq_proxy = spatial(vertical_mean(bv_freq_sq(cubelist), weight_by=rho), "mean") ** 0.5
temp_proxy = spatial(
vertical_mean(cubelist.extract_strict("air_temperature"), weight_by=rho), "mean"
)
scale_height = const.dry_air_gas_constant.asc * temp_proxy / const.gravity.asc
nondim_rossby = (bv_freq_proxy * scale_height / double_omega_radius.asc) ** 0.5
elif method == "leconte2013":
temp_proxy = toa_eff_temp(cubelist)
_const_term = ScalarCube.from_cube(const.dry_air_gas_constant / double_omega_radius)
sqrt_t_over_cp = (temp_proxy / const.dry_air_spec_heat_press.asc) ** 0.5
nondim_rossby = (sqrt_t_over_cp * _const_term.asc) ** 0.5
nondim_rossby.convert_units("1")
nondim_rossby.rename("nondimensional_rossby_deformation_radius")
return nondim_rossby
def mse_hdiv_mean(cubelist, zcoord=UM_HGT):
rho = cubelist.extract_strict("air_density")
ensure_bounds(rho, [zcoord])
u = cubelist.extract_strict("x_wind")
ensure_bounds(u, [zcoord])
v = cubelist.extract_strict("y_wind")
ensure_bounds(v, [zcoord])
mse_cmpnts = moist_static_energy(cubelist)
mse_hdiv_cmpnts = {}
for key, cmpnt in mse_cmpnts.items():
ensure_bounds(cmpnt, [zcoord])
flux_x = u * cmpnt
flux_x.rename(f"eastward_{cmpnt.name()}_flux")
flux_y = v * cmpnt
flux_y.rename(f"northward_{cmpnt.name()}_flux")
fluxes_vec = iris.cube.CubeList([flux_x, flux_y])
flux_div = hdiv(fluxes_vec, *[i.name() for i in fluxes_vec])
flux_div_vmean = integrate(rho * flux_div, zcoord)
flux_div_mean = spatial(flux_div_vmean, "mean")
try:
flux_div_mean = flux_div_mean.collapsed(UM_TIME, iris.analysis.MEAN)
except iris.exceptions.CoordinateCollapseError:
pass
flux_div_mean.rename(f"integrated_horizontal_divergence_of_{cmpnt.name()}")
flux_div_mean.convert_units("W m^-2")
mse_hdiv_cmpnts[key] = flux_div_mean
return mse_hdiv_cmpnts
def _wspd_typical(cubelist, aggr="mean"):
u = cubelist.extract_strict("x_wind")
v = cubelist.extract_strict("y_wind")
return spatial((u ** 2 + v ** 2) ** 0.5, aggr)
def wspd_typical(cubelist, aggr="median"):
u = cubelist.extract_strict("x_wind")
v = cubelist.extract_strict("y_wind")
return spatial((u ** 2 + v ** 2) ** 0.5, aggr).collapsed(
[UM_TIME, UM_HGT], getattr(iris.analysis, aggr.upper())
)
MODEL_TIMESTEP = 86400 / 72
FILE_REGEX = r"umglaa.p[a-z]{1}[0]{6}(?P<timestamp>[0-9]{2,4})_00"
ONLY_GLOBAL = ["eta", "b_alb", "t_sfc_diff_dn", "nondim_rossby", "nondim_rhines"]
ONLY_LAM = ["hflux_q", "hflux_t"]
DIAGS = {
"mse_hdiv": lambda cl: mse_hdiv_mean(cl)["mse"],
"dse_hdiv": lambda cl: mse_hdiv_mean(cl)["dse"],
"lse_hdiv": lambda cl: mse_hdiv_mean(cl)["lse"],
"nondim_rossby": lambda cl: nondim_rossby_deformation_radius(cl),
"nondim_rhines": lambda cl: nondim_rhines_number(cl.extract(hgt_cnstr_5_20km)),
"e_net_toa": toa_net_energy,
"eta": lambda cl: heat_redist_eff(cl, NIGHTSIDE, DAYSIDE),
"b_alb": bond_albedo,
"toa_olr": lambda cl: spatial(cl.extract_strict("toa_outgoing_longwave_flux"), "mean"),
"toa_osr": lambda cl: spatial(cl.extract_strict("toa_outgoing_shortwave_flux"), "mean"),
"cre_sw": lambda cl: toa_cloud_radiative_effect(cl, kind="sw"),
"cre_lw": lambda cl: toa_cloud_radiative_effect(cl, kind="lw"),
"cre_tot": lambda cl: toa_cloud_radiative_effect(cl, kind="total"),
"gh_norm": ghe_norm,
"e_net_sfc": sfc_net_energy,
"t_sfc_diff_dn": lambda cl: region_mean_diff(cl, "surface_temperature", DAYSIDE, NIGHTSIDE),
"t_sfc": lambda cl: spatial(cl.extract_strict("surface_temperature"), "mean"),
"t_sfc_min": lambda cl: spatial(cl.extract_strict("surface_temperature"), "min"),
"t_sfc_max": lambda cl: spatial(cl.extract_strict("surface_temperature"), "max"),
"t_sfc_extremes": lambda cl: minmaxdiff(cl, name="surface_temperature"),
"t_eff": toa_eff_temp,
"wspd_rms": lambda cl: wspd_typical(cl, "rms"),
"wspd_rms_0_15km": lambda cl: wspd_typical(cl.extract(hgt_cnstr_0_15km), "rms"),
"wspd_mean": lambda cl: wspd_typical(cl, "mean"),
def main():
"""Main function."""
# Create a subdirectory for processed data
outdir = mypaths.nsdir / "_processed"
outdir.mkdir(parents=True, exist_ok=True)
for planet in PLANET_ALIASES.keys():
L.info(f"planet = {planet}")
for run_key in NS_RUN_ALIASES.keys():
L.info(f"run_key = {run_key}")
subdir = f"{planet}_{run_key}"
for model_type, model_specs in NS_MODEL_TYPES.items():
L.info(f"model_type = {model_type}")
label = f"{planet}_{run_key}_{model_type}"
tmp_cl = iris.cube.CubeList()
for i in range(NS_CYCLE_TIMES[subdir]["ndays"]):
_cycle = (NS_CYCLE_TIMES[subdir]["start"] +
timedelta(days=i)).strftime(DT_FMT)
L.info(f"_cycle = {_cycle}")
fpath = (mypaths.nsdir / subdir / _cycle /
model_specs["path"].parent / "_processed" /
f"{label}_{_cycle}.nc")
L.info(f"fpath = {fpath}")
# Initialise a `Run` by loading processed data
run = Run(
files=fpath,
name=label,
planet=planet,
model_type=model_type,
timestep=model_specs["timestep"],
processed=True,
)
# Derive additional fields
run.add_data(calc_derived_cubes)
for cube in run.proc.extract(DIM_CONSTR_ZYX):
L.info(f"cube = {cube.name()}")
ave_cube = spatial(cube.extract(SS_REGION.constraint),
"mean")
tmp_cl.append(ave_cube)
for cube in tmp_cl:
try:
cube.attributes.pop("planet_conf")
except KeyError:
pass
tmp_cl = tmp_cl.merge()
L.debug(f"merged cubelist = {tmp_cl}")
_dict = {}
for cube in tmp_cl:
# cube.attributes = {
# k: v for k, v in cube.attributes.items() if k != "planet_conf"
# }
_dict[cube.name()] = xr.DataArray.from_iris(cube)
ds = xr.merge([_dict], compat="override")
L.debug(f"dataset = {ds}")
ds.attrs.update(
{
"region": SS_REGION.to_str(),
"model_type": run.model_type,
"run_key": run_key,
"planet": planet,
}, )
fname_out = outdir / f"ns_area_mean_vprof_{run.name}.nc"
ds.to_netcdf(fname_out)
L.success(f"Saved to {fname_out}")
