def test_adiab_path(save_file=None, **kwargs):
sounding = snd.Sounding(None, None)
sounding.from_lapse_rate(trial_lapse_rate, 0, 20e3, 10000)
parcel = CloudParcel(T0=sounding.surface_temperature + 1,
q0=0.01,
mix_len=np.inf,
w0=0.0,
method='RK4')
water_core = WaterManager(WaterManager.tanh_vapour_flow, None)
parcel.run(0.5, 6800, sounding, WM=water_core)
zA = np.copy(parcel.storage['z'])
water_core = WaterManager(WaterManager.step_vapour_flow, None)
parcel.run(0.5, 6800, sounding, WM=water_core)
zB = np.copy(parcel.storage['z'])
water_core = WaterManager(WaterManager.step_vapour_flow, None, mult=0.5)
parcel.run(0.5, 6800, sounding, WM=water_core)
zC = np.copy(parcel.storage['z'])
f, ax = plt.subplots()
ax.plot(np.arange(parcel.NT) * parcel.dt,
zA * 1e-3,
label='Hyperbolic Tangent Flow')
ax.plot(np.arange(parcel.NT) * parcel.dt,
zB * 1e-3,
'--',
label='Step Flow')
ax.plot(np.arange(parcel.NT) * parcel.dt,
zC * 1e-3,
':',
label='Dampened Step Flow')
ax.plot([0.0, parcel.NT * parcel.dt], [0, 0], 'k--', alpha=0.4)
ax.plot(
[0.0, parcel.NT * parcel.dt],
[parcel.storage['z'].max() * 1e-3, parcel.storage['z'].max() * 1e-3],
'k--',
alpha=0.4)
ax.grid()
ax.set_xlabel("Time (s)")
ax.set_ylabel("Height (km)")
ax.set_xlim(0, parcel.NT * parcel.dt)
plt.show()
if save_file:
f.savefig(save_file, dpi=300, **kwargs)
def test_model_stability(dt=0.5, save_file=None, **kwargs):
from scipy.signal import find_peaks
sounding = snd.Sounding(None, None)
sounding.from_lapse_rate(trial_lapse_rate, 0, 20e3, 10000)
methods = ["Euler", "Matsuno", "RK4"]
parcels = [
CloudParcel(T0=sounding.surface_temperature + 1.,
q0=0.02,
mix_len=np.inf,
w0=0.0,
method=method) for method in methods
]
DWM = WaterManager(WaterManager.tanh_vapour_flow, None)
f, ax = plt.subplots(1, 2, sharey=True, figsize=(9, 4))
for i, mstr, parcel in zip(range(3), methods, parcels):
parcel.run(dt, 10000, sounding, WM=DWM)
z = parcel.storage['z']
zpeaki = find_peaks(z)[0]
zpeaks = z[zpeaki] / z[zpeaki[0]]
zmin = -z + z[zpeaki[0]]
zthroughi = find_peaks(zmin)[0]
zthroughs = zmin[zthroughi] / zmin[zthroughi[0]]
ax[0].plot(zpeaki * dt, zpeaks, color='C%i' % i)
ax[1].plot(zthroughi * dt, zthroughs, color='C%i' % i, label=mstr)
ax[0].grid()
ax[1].grid()
ax[0].set_xlabel("Time (s)")
ax[1].set_xlabel("Time (s)")
ax[0].set_ylabel("Relative Peak/Through Height")
ax[0].set_xlim(500, dt * z.size)
ax[1].set_xlim(500, dt * z.size)
ax[0].legend(methods)
plt.tight_layout()
plt.show()
if save_file is not None and type(save_file) is str:
f.savefig(save_file, **kwargs)
def test_precip_modules(save_file=None, **kwargs):
sounding = snd.Sounding(None, None)
sounding.from_lapse_rate(trial_lapse_rate, 0, 20e3, 10000)
parcel = CloudParcel(T0=sounding.surface_temperature + 1,
q0=sounding.surface_humidity,
mix_len=np.inf,
w0=0.0,
method='RK4')
w1 = WaterManager(WaterManager.tanh_vapour_flow,
WaterManager.precip_fixed_rate)
w2 = WaterManager(WaterManager.tanh_vapour_flow,
WaterManager.precip_fixed_amount)
w3 = WaterManager(WaterManager.tanh_vapour_flow,
WaterManager.precip_tanh_rate)
parcel.run(0.5, 7000, sounding, WM=w1)
p1 = parcel.storage['p_gkg'] * 1e3
parcel.run(0.5, 7000, sounding, WM=w2)
p2 = parcel.storage['p_gkg'] * 1e3
parcel.run(0.5, 7000, sounding, WM=w3)
p3 = parcel.storage['p_gkg'] * 1e3
f, ax = plt.subplots()
ax.plot(np.arange(parcel.NT) * parcel.dt, p1, ':')
ax.plot(np.arange(parcel.NT) * parcel.dt, p2, '-')
ax.plot(np.arange(parcel.NT) * parcel.dt, p3, '--')
ax.grid()
ax.set_xlabel("Time (s)")
ax.set_ylabel("Precipitated Water (g/kg)")
ax.set_xlim(0, parcel.NT * parcel.dt)
if save_file is not None:
f.savefig(save_file, dpi=300, **kwargs)
plt.show()
if test_model and __name__ == '__main__':
def trial_lapse_rate(z):
Tv = 0.0
Td = 0.0
if 0.0 <= z < 1500.0:
Tv = -10.0e-3
Td = 1e-4
elif 1500.0 <= z < 10000.0:
Tv = -6.5e-3
Td = -8e-3
else:
Tv = 1e-3
Td = -9.8e-4
return {'T' : Tv, 'Td' : Td}
sounding = snd.Sounding(None, None)
sounding.from_lapse_rate(trial_lapse_rate, 0, 2e4, 10000)
# Setup sim
parcel = CCNParcel(T0=sounding.surface_temperature + 1.0,
q0=0.01, mix_len=np.inf, ql_kernel=Long_colkernel)
parcel.run(0.2, 4500, sounding)
#parcel.write_to_netCDF("./ascent_long_kernel.nc")
f, ax = sounding.SkewT_logP(show=False)
ax.plot(parcel.T, parcel.p * 1e-2)
plt.show()
# Test Collision Growth Module
if test_collision and __name__ == '__main__':
R = prdist(69, 0.25, 0.055, mode='linexp')
def read(self, mwx_file):
def _get_flighttime(radio_time, start_time, launch_time):
"""
f_flighttime = lambda radio_time: start_time + dt.timedelta(
seconds=radio_time - np.float(launch_time)
)
"""
return start_time + dt.timedelta(seconds=radio_time -
np.float(launch_time))
with rh.MWX(mwx_file) as mwx:
decompressed_files = mwx.decompressed_files
# Get the files SynchronizedSoundingData.xml, Soundings.xml, ...
a1, sync_filename = rh.check_availability(
decompressed_files, "SynchronizedSoundingData.xml", True)
a2, snd_filename = rh.check_availability(decompressed_files,
"Soundings.xml", True)
a3, radio_filename = rh.check_availability(decompressed_files,
"Radiosondes.xml", True)
if np.any([not a1, not a2, not a3]):
logging.warning(
"No sounding data found in {}. Skipped".format(mwx_file))
return
# Read Soundings.xml to get base time
itemlist = rh.read_xml(snd_filename)
for i, item in enumerate(itemlist):
begin_time = item.attributes["BeginTime"].value
launch_time = item.attributes["LaunchTime"].value
station_altitude = item.attributes["Altitude"].value
begin_time_dt = dt.datetime.strptime(begin_time,
"%Y-%m-%dT%H:%M:%S.%f")
# Read sounding data
pd_snd = rh.get_sounding_profile(sync_filename,
self.sync_sounding_values)
# Read Radiosounding.xml to get sounding metadata
sounding_meta_dict = rh.get_sounding_metadata(
radio_filename, self.radiosondes_values)
sounding_meta_dict["source"] = str(mwx_file)
pd_snd = rh.rename_variables(pd_snd, self.variable_name_mapping)
sounding_meta_dict = rh.rename_metadata(sounding_meta_dict,
self.variable_name_mapping)
# Attach units where provided
import pandas as pd
import pint
import pint_pandas as pp
ureg = pint.UnitRegistry()
ureg.define("fraction = [] = frac")
ureg.define("percent = 1e-2 frac = pct")
pp.PintType.ureg = ureg
PA_ = pp.PintArray
pd_snd_w_units = pd.DataFrame()
for var in pd_snd.columns:
if var in self.units.keys():
pd_snd_w_units[var] = PA_(pd_snd[var].values,
dtype=self.units[var])
else:
# no units found
pd_snd_w_units[var] = pd_snd[var].values
pd_snd = pd_snd_w_units
# Create flight time
f_flighttime = partial(_get_flighttime,
start_time=begin_time_dt,
launch_time=launch_time)
pd_snd["flight_time"] = pd_snd.RadioRxTimePk.apply(f_flighttime)
# Write to class
sounding = snd.Sounding()
sounding.profile = pd_snd
sounding.meta_data = sounding_meta_dict
sounding.meta_data["launch_time"] = launch_time
sounding.meta_data["begin_time"] = begin_time_dt
sounding.meta_data["station_altitude"] = station_altitude
sounding.unitregistry = ureg
return sounding