def test_precipitable_water_descriptive_bound_error():
"""Test that error is raised when bound is outside profile after nan have been removed."""
pressure = np.array([
1001, 1000, 997, 977.9, 977, 957, 937.8, 925, 906, 899.3, 887, 862.5,
854, 850, 800, 793.9, 785, 777, 771, 762, 731.8, 726, 703, 700, 655,
630, 621.2, 602, 570.7, 548, 546.8, 539, 513, 511, 485, 481, 468, 448,
439, 424, 420, 412
]) * units.hPa
dewpoint = np.array([
np.nan, np.nan, -26.8, np.nan, -27.3, -28.2, np.nan, -27.2, -26.6,
np.nan, -27.4, np.nan, -23.5, -23.5, -25.1, np.nan, -22.9, -17.8,
-16.6, np.nan, np.nan, -16.4, np.nan, -18.5, -21., -23.7, np.nan,
-28.3, np.nan, -32.6, np.nan, -33.8, -35., -35.1, -38.1, -40., -43.3,
-44.6, -46.4, np.nan, np.nan, np.nan
]) * units.degC
# Top bound is above highest pressure in profile
with pytest.raises(ValueError,
match='The pressure and dewpoint profile ranges from'):
precipitable_water(pressure, dewpoint, top=units.Quantity(415, 'hPa'))
# Bottom bound is below lowest pressure in profile
with pytest.raises(ValueError,
match='The pressure and dewpoint profile ranges from'):
precipitable_water(pressure,
dewpoint,
bottom=units.Quantity(999, 'hPa'))
def wrap_xr_metpy_pw(dewpt,
pressure,
bottom=None,
top=None,
verbose=False,
cumulative=False):
from metpy.calc import precipitable_water
from metpy.units import units
import numpy as np
# try:
# T_unit = dewpt.attrs['units']
# assert T_unit == 'degC'
# except KeyError:
# T_unit = 'degC'
# if verbose:
# print('assuming dewpoint units are degC...')
dew_values = dewpt.values * units('K')
try:
P_unit = pressure.attrs['units']
assert P_unit == 'hPa'
except KeyError:
P_unit = 'hPa'
if verbose:
print('assuming pressure units are hPa...')
if top is not None:
top_with_units = top * units(P_unit)
else:
top_with_units = None
if bottom is not None:
bottom_with_units = bottom * units(P_unit)
else:
bottom_with_units = None
pressure_values = pressure.values * units(P_unit)
if cumulative:
pw_list = []
# first value is nan:
pw_list.append(np.nan)
for pre_val in pressure_values[1:]:
if np.isnan(pre_val):
pw_list.append(np.nan)
continue
pw = precipitable_water(pressure_values,
dew_values,
bottom=None,
top=pre_val)
pw_units = pw.units.format_babel('~P')
pw_list.append(pw.magnitude)
pw = np.array(pw_list)
return pw, pw_units
else:
pw = precipitable_water(pressure_values,
dew_values,
bottom=bottom_with_units,
top=top_with_units)
pw_units = pw.units.format_babel('~P')
return pw.magnitude, pw_units
def calculate_basic_thermo(self):
#Enclose in try, except because not every sounding will have a converging parcel path or CAPE.
try:
#Precipitable Water
self.pw = mc.precipitable_water(self.sounding["pres"],
self.sounding["dewp"])
#Lifting condensation level
self.lcl_pres, self.lcl_temp = mc.lcl(self.sounding["pres"][0],
self.sounding["temp"][0],
self.sounding["dewp"][0])
#Surface-based CAPE and CIN
self.parcel_path = mc.parcel_profile(self.sounding["pres"],
self.sounding["temp"][0],
self.sounding["dewp"][0])
self.sfc_cape, self.sfc_cin = mc.cape_cin(self.sounding["pres"],
self.sounding["temp"],
self.sounding["dewp"],
self.parcel_path)
#Do this when parcel path fails to converge
except Exception as e:
print("WARNING: No LCL, CAPE, or PW stats because:\n{}.".format(e))
self.parcel_path = numpy.nan
self.pw = numpy.nan
self.lcl_pres = numpy.nan
self.lcl_temp = numpy.nan
self.sfc_cape = numpy.nan
self.sfc_cin = numpy.nan
#Returning
return
def test_precipitable_water():
"""Test precipitable water with observed sounding."""
data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC')
pw = precipitable_water(data['dewpoint'], data['pressure'],
top=400 * units.hPa)
truth = (0.8899441949243486 * units('inches')).to('millimeters')
assert_array_equal(pw, truth)
def test_precipitable_water():
"""Test precipitable water with observed sounding."""
with UseSampleData():
data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC', source='wyoming')
pw = precipitable_water(data.variables['dewpoint'][:], data.variables['pressure'][:])
truth = (0.8899441949243486 * units('inches')).to('millimeters')
assert_array_equal(pw, truth)
def do_profile(cursor, fid, gdf):
"""Process this profile."""
profile = gdf[pd.notnull(gdf['tmpc']) & pd.notnull(gdf['dwpc'])]
if profile.empty:
LOG.info("profile %s is empty, skipping", fid)
return
if profile['pressure'].min() > 400:
raise ValueError("Profile only up to %s mb" %
(profile['pressure'].min(), ))
pwater = precipitable_water(profile['dwpc'].values * units.degC,
profile['pressure'].values * units.hPa)
(sbcape, sbcin) = surface_based_cape_cin(
profile['pressure'].values * units.hPa,
profile['tmpc'].values * units.degC,
profile['dwpc'].values * units.degC,
)
(mucape, mucin) = most_unstable_cape_cin(
profile['pressure'].values * units.hPa,
profile['tmpc'].values * units.degC,
profile['dwpc'].values * units.degC,
)
cursor.execute(
"""
UPDATE raob_flights SET sbcape_jkg = %s, sbcin_jkg = %s,
mucape_jkg = %s, mucin_jkg = %s, pwater_mm = %s,
computed = 't' WHERE fid = %s
""", (nonull(sbcape.to(units('joules / kilogram')).m),
nonull(sbcin.to(units('joules / kilogram')).m),
nonull(mucape.to(units('joules / kilogram')).m),
nonull(mucin.to(units('joules / kilogram')).m),
nonull(pwater.to(units('mm')).m), fid))
def test_precipitable_water_xarray():
"""Test precipitable water with xarray input."""
data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC')
press = xr.DataArray(data['pressure'], attrs={'units': str(data['pressure'].units)})
dewp = xr.DataArray(data['dewpoint'], dims=('press',), coords=(press,))
pw = precipitable_water(press, dewp, top=400 * units.hPa)
truth = 22.60430651 * units.millimeters
assert_almost_equal(pw, truth)
def test_precipitable_water():
"""Test precipitable water with observed sounding."""
data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC')
pw = precipitable_water(data['pressure'],
data['dewpoint'],
top=400 * units.hPa)
truth = 22.60430651 * units.millimeters
assert_array_almost_equal(pw, truth, 4)
def test_precipitable_water():
"""Test precipitable water with observed sounding."""
data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC')
pw = precipitable_water(data['pressure'],
data['dewpoint'],
top=400 * units.hPa)
truth = (0.8899441949243486 * units('inches')).to('millimeters')
assert_array_equal(pw, truth)
def test_precipitable_water_bound_error():
"""Test with no top bound given and data that produced floating point issue #596."""
pressure = np.array([993., 978., 960.5, 927.6, 925., 895.8, 892., 876., 45.9, 39.9, 36.,
36., 34.3]) * units.hPa
dewpoint = np.array([25.5, 24.1, 23.1, 21.2, 21.1, 19.4, 19.2, 19.2, -87.1, -86.5, -86.5,
-86.5, -88.1]) * units.degC
pw = precipitable_water(pressure, dewpoint)
truth = 89.86846252697836 * units('millimeters')
assert_almost_equal(pw, truth, 5)
def test_precipitable_water_no_bounds():
"""Test precipitable water with observed sounding and no bounds given."""
data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC')
dewpoint = data['dewpoint']
pressure = data['pressure']
inds = pressure >= 400 * units.hPa
pw = precipitable_water(pressure[inds], dewpoint[inds])
truth = (0.8899441949243486 * units('inches')).to('millimeters')
assert_array_equal(pw, truth)
def test_precipitable_water_bound_error():
"""Test with no top bound given and data that produced floating point issue #596."""
pressure = np.array([993., 978., 960.5, 927.6, 925., 895.8, 892., 876., 45.9, 39.9, 36.,
36., 34.3]) * units.hPa
dewpoint = np.array([25.5, 24.1, 23.1, 21.2, 21.1, 19.4, 19.2, 19.2, -87.1, -86.5, -86.5,
-86.5, -88.1]) * units.degC
pw = precipitable_water(dewpoint, pressure)
truth = 89.86955998646951 * units('millimeters')
assert_almost_equal(pw, truth, 8)
def get_skewt_vars(p, tc, tdc, pro):
"""This function processes the dataset values and returns a string element
which can be used as a subtitle to replicate the styles of NCL Skew-T
Diagrams.
Args:
p (:class: `pint.quantity.build_quantity_class.<locals>.Quantity`):
Pressure level input from dataset
tc (:class: `pint.quantity.build_quantity_class.<locals>.Quantity`):
Temperature for parcel from dataset
tdc (:class: `pint.quantity.build_quantity_class.<locals>.Quantity`):
Dew point temperature for parcel from dataset
pro (:class: `pint.quantity.build_quantity_class.<locals>.Quantity`):
Parcel profile temperature converted to degC
Returns:
:class: 'str'
"""
# CAPE
cape = mpcalc.cape_cin(p, tc, tdc, pro)
cape = cape[0].magnitude
# Precipitable Water
pwat = mpcalc.precipitable_water(p, tdc)
pwat = (pwat.magnitude / 10) * units.cm # Convert mm to cm
pwat = pwat.magnitude
# Pressure and temperature of lcl
lcl = mpcalc.lcl(p[0], tc[0], tdc[0])
plcl = lcl[0].magnitude
tlcl = lcl[1].magnitude
# Showalter index
shox = showalter_index(p, tc, tdc)
shox = shox[0].magnitude
# Place calculated values in iterable list
vals = [plcl, tlcl, shox, pwat, cape]
vals = [round(num) for num in vals]
# Define variable names for calculated values
names = ['Plcl=', 'Tlcl[C]=', 'Shox=', 'Pwat[cm]=', 'Cape[J]=']
# Combine the list of values with their corresponding labels
lst = list(chain.from_iterable(zip(names, vals)))
lst = map(str, lst)
# Create one large string for later plotting use
joined = ' '.join(lst)
return joined
def test_precipitable_water_no_bounds():
"""Test precipitable water with observed sounding and no bounds given."""
data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC')
dewpoint = data['dewpoint']
pressure = data['pressure']
inds = pressure >= 400 * units.hPa
pw = precipitable_water(dewpoint[inds], pressure[inds])
truth = (0.8899441949243486 * units('inches')).to('millimeters')
assert_array_equal(pw, truth)
def test_precipitable_water_no_bounds():
"""Test precipitable water with observed sounding and no bounds given."""
data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC')
dewpoint = data['dewpoint']
pressure = data['pressure']
inds = pressure >= 400 * units.hPa
pw = precipitable_water(pressure[inds], dewpoint[inds])
truth = 22.60430651 * units.millimeters
assert_array_almost_equal(pw, truth, 4)
def tpw(self, top=None):
""" Calculates total precipitable water taking only water vapors into the account"""
if top is None:
top = np.min(self.p)*units.hectopascal + .01 * units.hPa
self.total_pw = mcalc.precipitable_water(dewpt=self.dew *units.K, pressure=self.p *units.hectopascal, top=top)
# has a padding factor to avoid error due to float point comparision
return self.total_pw
def test_precipitable_water_nans():
"""Test that PW returns appropriate number if NaNs are present."""
pressure = np.array([1001, 1000, 997, 977.9, 977, 957, 937.8, 925, 906, 899.3, 887, 862.5,
854, 850, 800, 793.9, 785, 777, 771, 762, 731.8, 726, 703, 700, 655,
630, 621.2, 602, 570.7, 548, 546.8, 539, 513, 511, 485, 481, 468,
448, 439, 424, 420, 412]) * units.hPa
dewpoint = np.array([-25.1, -26.1, -26.8, np.nan, -27.3, -28.2, np.nan, -27.2, -26.6,
np.nan, -27.4, np.nan, -23.5, -23.5, -25.1, np.nan, -22.9, -17.8,
-16.6, np.nan, np.nan, -16.4, np.nan, -18.5, -21., -23.7, np.nan,
-28.3, np.nan, -32.6, np.nan, -33.8, -35., -35.1, -38.1, -40.,
-43.3, -44.6, -46.4, -47., -49.2, -50.7]) * units.degC
pw = precipitable_water(pressure, dewpoint)
truth = 4.003660322395436 * units.mm
assert_almost_equal(pw, truth, 5)
def calculate_pwv_from_kit_rs(path=des_path):
""" for kit_rs that go up to 12kms"""
from metpy.calc import precipitable_water
import xarray as xr
ds = read_kit_rs(path)
dew = ds['DewPoint']
P = ds['Pressure']
times = []
for dt in P.time:
d = dew.sel(time=dt)
p = P.sel(time=dt)
pwv = precipitable_water(p, d).magnitude
times.append(pwv)
pwv_rs = xr.DataArray(times, dims=['time'])
pwv_rs['time'] = P.time
pwv_rs.name = 'pwv'
pwv_rs.attrs['long_name'] = 'precipitable water'
pwv_rs.attrs['units'] = 'mm'
pwv_rs.attrs['action'] = 'proccesed by metpy.calc on KIT-RS data'
return pwv_rs
def do_profile(cursor, fid, gdf, nt):
"""Process this profile."""
# The inbound profile may contain mandatory level data that is below
# the surface. It seems the best we can do here is to ensure both
# temperature and dewpoint are valid and call that the bottom.
td_profile = gdf[pd.notnull(gdf["tmpc"]) & pd.notnull(gdf["dwpc"])]
wind_profile = gdf[pd.notnull(gdf["u"])]
# Presently we are all or nothing here. The length is arb
if len(td_profile.index) < 5 or len(wind_profile.index) < 5:
msg = ("quorum fail td: %s wind: %s, skipping") % (
len(td_profile.index),
len(wind_profile.index),
)
raise ValueError(msg)
if gdf["pressure"].min() > 500:
raise ValueError("Profile only up to %s mb" %
(gdf["pressure"].min(), ))
# Does a crude check that our metadata station elevation is within 50m
# of the profile bottom, otherwise we ABORT
station_elevation_m = get_station_elevation(td_profile, nt)
# get surface wind
u_sfc, v_sfc = get_surface_winds(wind_profile)
u_1km, v_1km = get_aloft_winds(wind_profile, station_elevation_m + 1000.0)
u_3km, v_3km = get_aloft_winds(wind_profile, station_elevation_m + 3000.0)
u_6km, v_6km = get_aloft_winds(wind_profile, station_elevation_m + 6000.0)
shear_sfc_1km_smps = np.sqrt((u_1km - u_sfc)**2 + (v_1km - v_sfc)**2)
shear_sfc_3km_smps = np.sqrt((u_3km - u_sfc)**2 + (v_3km - v_sfc)**2)
shear_sfc_6km_smps = np.sqrt((u_6km - u_sfc)**2 + (v_6km - v_sfc)**2)
total_totals = compute_total_totals(td_profile)
sweat_index = compute_sweat_index(td_profile, total_totals)
try:
bunkers_rm, bunkers_lm, mean0_6_wind = bunkers_storm_motion(
wind_profile["pressure"].values * units.hPa,
wind_profile["u"].values * units("m/s"),
wind_profile["v"].values * units("m/s"),
wind_profile["height"].values * units("m"),
)
except ValueError:
# Profile may not go up high enough
bunkers_rm = [np.nan * units("m/s"), np.nan * units("m/s")]
bunkers_lm = [np.nan * units("m/s"), np.nan * units("m/s")]
mean0_6_wind = [np.nan * units("m/s"), np.nan * units("m/s")]
bunkers_rm_smps = wind_speed(bunkers_rm[0], bunkers_rm[1])
bunkers_rm_drct = wind_direction(bunkers_rm[0], bunkers_rm[1])
bunkers_lm_smps = wind_speed(bunkers_lm[0], bunkers_lm[1])
bunkers_lm_drct = wind_direction(bunkers_lm[0], bunkers_lm[1])
mean0_6_wind_smps = wind_speed(mean0_6_wind[0], mean0_6_wind[1])
mean0_6_wind_drct = wind_direction(mean0_6_wind[0], mean0_6_wind[1])
try:
(
srh_sfc_1km_pos,
srh_sfc_1km_neg,
srh_sfc_1km_total,
) = storm_relative_helicity(
wind_profile["u"].values * units("m/s"),
wind_profile["v"].values * units("m/s"),
wind_profile["height"].values * units("m"),
1000.0 * units("m"),
)
except ValueError:
srh_sfc_1km_pos = np.nan * units("m") # blah
srh_sfc_1km_neg = np.nan * units("m") # blah
srh_sfc_1km_total = np.nan * units("m") # blah
try:
(
srh_sfc_3km_pos,
srh_sfc_3km_neg,
srh_sfc_3km_total,
) = storm_relative_helicity(
wind_profile["u"].values * units("m/s"),
wind_profile["v"].values * units("m/s"),
wind_profile["height"].values * units("m"),
3000.0 * units("m"),
)
except ValueError:
srh_sfc_3km_pos = np.nan * units("m") # blah
srh_sfc_3km_neg = np.nan * units("m") # blah
srh_sfc_3km_total = np.nan * units("m") # blah
pwater = precipitable_water(
td_profile["dwpc"].values * units.degC,
td_profile["pressure"].values * units.hPa,
)
(sbcape, sbcin) = surface_based_cape_cin(
td_profile["pressure"].values * units.hPa,
td_profile["tmpc"].values * units.degC,
td_profile["dwpc"].values * units.degC,
)
(mucape, mucin) = most_unstable_cape_cin(
td_profile["pressure"].values * units.hPa,
td_profile["tmpc"].values * units.degC,
td_profile["dwpc"].values * units.degC,
)
el_p, el_t = el(
td_profile["pressure"].values * units.hPa,
td_profile["tmpc"].values * units.degC,
td_profile["dwpc"].values * units.degC,
)
lfc_p, lfc_t = lfc(
td_profile["pressure"].values * units.hPa,
td_profile["tmpc"].values * units.degC,
td_profile["dwpc"].values * units.degC,
)
(lcl_p, lcl_t) = lcl(
td_profile["pressure"].values[0] * units.hPa,
td_profile["tmpc"].values[0] * units.degC,
td_profile["dwpc"].values[0] * units.degC,
)
vals = [
el_p.to(units("hPa")).m,
lfc_p.to(units("hPa")).m,
lcl_p.to(units("hPa")).m,
]
[el_hght, lfc_hght, lcl_hght] = log_interp(
np.array(vals, dtype="f"),
td_profile["pressure"].values[::-1],
td_profile["height"].values[::-1],
)
el_agl = gt1(el_hght - station_elevation_m)
lcl_agl = gt1(lcl_hght - station_elevation_m)
lfc_agl = gt1(lfc_hght - station_elevation_m)
args = (
nonull(sbcape.to(units("joules / kilogram")).m),
nonull(sbcin.to(units("joules / kilogram")).m),
nonull(mucape.to(units("joules / kilogram")).m),
nonull(mucin.to(units("joules / kilogram")).m),
nonull(pwater.to(units("mm")).m),
nonull(el_agl),
nonull(el_p.to(units("hPa")).m),
nonull(el_t.to(units.degC).m),
nonull(lfc_agl),
nonull(lfc_p.to(units("hPa")).m),
nonull(lfc_t.to(units.degC).m),
nonull(lcl_agl),
nonull(lcl_p.to(units("hPa")).m),
nonull(lcl_t.to(units.degC).m),
nonull(total_totals),
nonull(sweat_index),
nonull(bunkers_rm_smps.m),
nonull(bunkers_rm_drct.m),
nonull(bunkers_lm_smps.m),
nonull(bunkers_lm_drct.m),
nonull(mean0_6_wind_smps.m),
nonull(mean0_6_wind_drct.m),
nonull(srh_sfc_1km_pos.m),
nonull(srh_sfc_1km_neg.m),
nonull(srh_sfc_1km_total.m),
nonull(srh_sfc_3km_pos.m),
nonull(srh_sfc_3km_neg.m),
nonull(srh_sfc_3km_total.m),
nonull(shear_sfc_1km_smps),
nonull(shear_sfc_3km_smps),
nonull(shear_sfc_6km_smps),
fid,
)
cursor.execute(
"""
UPDATE raob_flights SET sbcape_jkg = %s, sbcin_jkg = %s,
mucape_jkg = %s, mucin_jkg = %s, pwater_mm = %s,
el_agl_m = %s, el_pressure_hpa = %s, el_tmpc = %s,
lfc_agl_m = %s, lfc_pressure_hpa = %s, lfc_tmpc = %s,
lcl_agl_m = %s, lcl_pressure_hpa = %s, lcl_tmpc = %s,
total_totals = %s, sweat_index = %s,
bunkers_rm_smps = %s, bunkers_rm_drct = %s,
bunkers_lm_smps = %s, bunkers_lm_drct = %s,
mean_sfc_6km_smps = %s, mean_sfc_6km_drct = %s,
srh_sfc_1km_pos = %s, srh_sfc_1km_neg = %s, srh_sfc_1km_total = %s,
srh_sfc_3km_pos = %s, srh_sfc_3km_neg = %s, srh_sfc_3km_total = %s,
shear_sfc_1km_smps = %s, shear_sfc_3km_smps = %s,
shear_sfc_6km_smps = %s,
computed = 't' WHERE fid = %s
""",
args,
)
def fmi2skewt(station, time, img_name):
apikey = 'e72a2917-1e71-4d6f-8f29-ff4abfb8f290'
url = 'http://data.fmi.fi/fmi-apikey/' + str(
apikey
) + '/wfs?request=getFeature&storedquery_id=fmi::observations::weather::sounding::multipointcoverage&fmisid=' + str(
station) + '&starttime=' + str(time) + '&endtime=' + str(time) + '&'
req = requests.get(url)
xmlstring = req.content
tree = ET.ElementTree(ET.fromstring(xmlstring))
root = tree.getroot()
#reading location and time data to "positions" from XML
positions = ""
for elem in root.getiterator(
tag='{http://www.opengis.net/gmlcov/1.0}positions'):
positions = elem.text
#'positions' is string type variable
#--> split positions into a list by " "
#then remove empty chars and "\n"
# from pos_split --> data into positions_data
try:
pos_split = positions.split(' ')
except NameError:
return "Sounding data not found: stationid " + station + " time " + time
pos_split = positions.split(' ')
positions_data = []
for i in range(0, len(pos_split)):
if not (pos_split[i] == "" or pos_split[i] == "\n"):
positions_data.append(pos_split[i])
#index for height: 2,6,10 etc in positions_data
height = []
myList = range(2, len(positions_data))
for i in myList[::4]:
height.append(positions_data[i])
p = []
for i in range(0, len(height)):
p.append(height2pressure(float(height[i])))
#reading wind speed, wind direction, air temperature and dew point data to 'values'
values = ""
for elem in root.getiterator(
tag='{http://www.opengis.net/gml/3.2}doubleOrNilReasonTupleList'):
values = elem.text
#split 'values' into a list by " "
#then remove empty chars and "\n"
val_split = values.split(' ')
values_data = []
for i in range(0, len(val_split)):
if not (val_split[i] == "" or val_split[i] == "\n"):
values_data.append(val_split[i])
#data in values_data: w_speed, w_dir, t_air, t_dew
wind_speed = []
wind_dir = []
T = []
Td = []
myList = range(0, len(values_data))
for i in myList[::4]:
wind_speed.append(float(values_data[i]))
wind_dir.append(float(values_data[i + 1]))
T.append(float(values_data[i + 2]))
Td.append(float(values_data[i + 3]))
if stationid == "101104":
loc_time = "Jokioinen Ilmala " + time
elif stationid == "101932":
loc_time = "Sodankyla Tahtela " + time
else:
return None
#calculate wind components u,v:
u = []
v = []
for i in range(0, len(wind_speed)):
u1, v1 = getWindComponent(wind_speed[i], wind_dir[i])
u.append(u1)
v.append(v1)
#find index for pressure < 100hPa (for number of wind bars)
if min(p) > 100:
wthin = len(p) / 20
u_plot = u
v_plot = v
p_plot = p
else:
for i in range(0, len(p)):
if p[i] - 100 <= 0:
wthin = i / 20
u_plot = u[0:i]
v_plot = v[0:i]
p_plot = p[0:i]
break
#units
wind_speed = wind_speed * units("m/s")
wind_dir = wind_dir * units.deg
T = T * units.degC
Td = Td * units.degC
p = p * units("hPa")
#calculate pwat, lcl, cape, cin and plot cape
pwat = mpcalc.precipitable_water(Td, p, bottom=None, top=None)
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
try:
cape, cin = mpcalc.cape_cin(p, T, Td, prof)
except IndexError:
cape = 0 * units("J/kg")
cin = 0 * units("J/kg")
#__________________plotting__________________
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig, rotation=45)
font_par = {
'family': 'monospace',
'color': 'darkred',
'weight': 'normal',
'size': 10,
}
font_title = {
'family': 'monospace',
'color': 'black',
'weight': 'normal',
'size': 20,
}
font_axis = {
'family': 'monospace',
'color': 'black',
'weight': 'normal',
'size': 10,
}
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p, T, 'k')
skew.plot(p, Td, 'b')
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-50, 30)
skew.plot_barbs(p_plot[0::wthin], u_plot[0::wthin], v_plot[0::wthin])
skew.plot_dry_adiabats(alpha=0.4)
skew.plot_moist_adiabats(alpha=0.4)
skew.plot_mixing_lines(alpha=0.4)
#skew.shade_cape(p, T, prof,color="orangered")
plt.title(loc_time, fontdict=font_title)
plt.xlabel("T (C)", fontdict=font_axis)
plt.ylabel("P (hPa)", fontdict=font_axis)
#round and remove units from cape,cin,plcl,tlcl,pwat
if cape.magnitude > 0:
capestr = str(np.round(cape.magnitude))
else:
capestr = "NaN"
if cin.magnitude > 0:
cinstr = str(np.round(cin.magnitude))
else:
cinstr = "NaN"
lclpstr = str(np.round(lcl_pressure.magnitude))
lclTstr = str(np.round(lcl_temperature.magnitude))
pwatstr = str(np.round(pwat.magnitude))
# str_par = "CAPE[J/kg]=" + capestr + " CIN[J/kg]=" + cinstr + " Plcl[hPa]=" + lclpstr + " Tlcl[C]=" + lclTstr + " pwat[mm]=" + pwatstr
# font = {'family': 'monospace',
# 'color': 'darkred',
# 'weight': 'normal',
# 'size': 10,
# }
# plt.text(-20,1250,str_par,fontdict=font_par)
save_file = img_name
plt.savefig(save_file)
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p, T, 'r')
skew.ax.set_xlabel('Temperature (Celsius)')
skew.plot(p, Td, 'g')
#plots the barbs
my_interval = np.arange(100, 1000, 20) * units('mbar')
ix = resample_nn_1d(p, my_interval)
skew.plot_barbs(p[ix], u[ix], v[ix])
skew.ax.set_ylim(1075, 100)
skew.ax.set_ylabel('Pressure (hPa)')
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0]) #LCL
pwat = mpcalc.precipitable_water(Td, p, 500 * units.hectopascal).to('in') #PWAT
cape, cin = mpcalc.most_unstable_cape_cin(p[:], T[:], Td[:]) #MUCAPE
cape_sfc, cin_sfc = mpcalc.surface_based_cape_cin(p, T, Td) #SBCAPE
prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC') #parcel profile
equiv_pot_temp = mpcalc.equivalent_potential_temperature(p, T, Td) #equivalent potential temperature
el_pressure, el_temperature = mpcalc.el(p, T, Td) #elevated level
lfc_pressure, lfc_temperature = mpcalc.lfc(p, T, Td) #LFC
#calculates shear
u_threekm_bulk_shear, v_threekm_bulk_shear = mpcalc.bulk_shear(p, u, v, hgt, bottom = min(hgt), depth = 3000 * units.meter)
threekm_bulk_shear = mpcalc.get_wind_speed(u_threekm_bulk_shear, v_threekm_bulk_shear)
u_onekm_bulk_shear, v_onekm_bulk_shear = mpcalc.bulk_shear(p, u, v, hgt, bottom = min(hgt), depth = 1000 * units.meter)
onekm_bulk_shear = mpcalc.get_wind_speed(u_onekm_bulk_shear, v_onekm_bulk_shear)
#shows the level of the LCL, LFC, and EL.
skew.ax.text(T[0].magnitude, p[0].magnitude + 5, str(int(np.round(T[0].to('degF').magnitude))), fontsize = 'medium', horizontalalignment = 'left', verticalalignment = 'top', color = 'red')
EL_temperature,
'bo',
markersize=8,
fillstyle='none',
label='EL')
# Calculate most unstable parcel
mup_pressure, mup_temperature, mup_dewTemperature, mup_index \
= mpcalc.most_unstable_parcel(p, T, Td, heights=prof_0, bottom=1050 * units.hPa)
skew.plot(p[mup_index - 1:mup_index + 1],
T[mup_index - 1:mup_index + 1],
'b',
label='the most unstable level')
# Calculate precipitable water
precipitable_water = mpcalc.precipitable_water(Td, p)
# An example of a slanted line at constant T -- in this case the 0
# isotherm
skew.ax.axvline(0,
color='brown',
linestyle='-',
linewidth=1,
label='isothermal')
for i in range(46):
for j in range(1, 10):
skew.ax.axvline(i * 5 - 160 + j,
color='brown',
linestyle='-',
linewidth=0.3)
skew.ax.axvline(i * 5 - 160,
def msed_plots(pressure,
temperature,
mixing_ratio,
h0_std=2000,
ensemble_size=20,
ent_rate=np.arange(0, 2, 0.05),
entrain=False):
"""
plotting the summarized static energy diagram with annotations and thermodynamic parameters
"""
p = pressure * units('mbar')
T = temperature * units('degC')
q = mixing_ratio * units('kilogram/kilogram')
qs = mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(T), p)
Td = mpcalc.dewpoint(mpcalc.vapor_pressure(p, q)) # dewpoint
Tp = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC') # parcel profile
# Altitude based on the hydrostatic eq.
altitude = np.zeros((np.size(T))) * units('meter') # surface is 0 meter
for i in range(np.size(T)):
altitude[i] = mpcalc.thickness_hydrostatic(
p[:i + 1], T[:i + 1]) # Hypsometric Eq. for height
# Static energy calculations
mse = mpcalc.moist_static_energy(altitude, T, q)
mse_s = mpcalc.moist_static_energy(altitude, T, qs)
dse = mpcalc.dry_static_energy(altitude, T)
# Water vapor calculations
p_PWtop = max(200 * units.mbar,
min(p) + 1 * units.mbar) # integrating until 200mb
cwv = mpcalc.precipitable_water(Td, p,
top=p_PWtop) # column water vapor [mm]
cwvs = mpcalc.precipitable_water(
T, p, top=p_PWtop) # saturated column water vapor [mm]
crh = (cwv / cwvs) * 100. # column relative humidity [%]
#================================================
# plotting MSE vertical profiles
fig = plt.figure(figsize=[12, 8])
ax = fig.add_axes([0.1, 0.1, 0.6, 0.8])
ax.plot(dse, p, '-k', linewidth=2)
ax.plot(mse, p, '-b', linewidth=2)
ax.plot(mse_s, p, '-r', linewidth=2)
# mse based on different percentages of relative humidity
qr = np.zeros((9, np.size(qs))) * units('kilogram/kilogram')
mse_r = qr * units('joule/kilogram') # container
for i in range(9):
qr[i, :] = qs * 0.1 * (i + 1)
mse_r[i, :] = mpcalc.moist_static_energy(altitude, T, qr[i, :])
for i in range(9):
ax.plot(mse_r[i, :], p[:], '-', color='grey', linewidth=0.7)
ax.text(mse_r[i, 3].magnitude / 1000 - 1, p[3].magnitude,
str((i + 1) * 10))
# drawing LCL and LFC levels
[lcl_pressure, lcl_temperature] = mpcalc.lcl(p[0], T[0], Td[0])
lcl_idx = np.argmin(np.abs(p.magnitude - lcl_pressure.magnitude))
[lfc_pressure, lfc_temperature] = mpcalc.lfc(p, T, Td)
lfc_idx = np.argmin(np.abs(p.magnitude - lfc_pressure.magnitude))
# conserved mse of air parcel arising from 1000 hpa
mse_p = np.squeeze(np.ones((1, np.size(T))) * mse[0].magnitude)
# illustration of CAPE
el_pressure, el_temperature = mpcalc.el(p, T, Td) # equilibrium level
el_idx = np.argmin(np.abs(p.magnitude - el_pressure.magnitude))
ELps = [el_pressure.magnitude
] # Initialize an array of EL pressures for detrainment profile
[CAPE, CIN] = mpcalc.cape_cin(p[:el_idx], T[:el_idx], Td[:el_idx],
Tp[:el_idx])
plt.plot(mse_p, p, color='green', linewidth=2)
ax.fill_betweenx(p[lcl_idx:el_idx + 1],
mse_p[lcl_idx:el_idx + 1],
mse_s[lcl_idx:el_idx + 1],
interpolate=True,
color='green',
alpha='0.3')
ax.fill_betweenx(p, dse, mse, color='deepskyblue', alpha='0.5')
ax.set_xlabel('Specific static energies: s, h, hs [kJ kg$^{-1}$]',
fontsize=14)
ax.set_ylabel('Pressure [hpa]', fontsize=14)
ax.set_xticks([280, 300, 320, 340, 360, 380])
ax.set_xlim([280, 390])
ax.set_ylim(1030, 120)
if entrain is True:
# Depict Entraining parcels
# Parcel mass solves dM/dz = eps*M, solution is M = exp(eps*Z)
# M=1 at ground without loss of generality
# Distribution of surface parcel h offsets
H0STDEV = h0_std # J/kg
h0offsets = np.sort(np.random.normal(
0, H0STDEV, ensemble_size)) * units('joule/kilogram')
# Distribution of entrainment rates
entrainment_rates = ent_rate / (units('km'))
for h0offset in h0offsets:
h4ent = mse.copy()
h4ent[0] += h0offset
for eps in entrainment_rates:
M = np.exp(eps * (altitude - altitude[0])).to('dimensionless')
# dM is the mass contribution at each level, with 1 at the origin level.
M[0] = 0
dM = np.gradient(M)
# parcel mass is a sum of all the dM's at each level
# conserved linearly-mixed variables like h are weighted averages
hent = np.cumsum(dM * h4ent) / np.cumsum(dM)
# Boolean for positive buoyancy, and its topmost altitude (index) where curve is clippes
posboy = (hent > mse_s)
posboy[0] = True # so there is always a detrainment level
ELindex_ent = np.max(np.where(posboy))
# Plot the curve
plt.plot(hent[0:ELindex_ent + 2],
p[0:ELindex_ent + 2],
linewidth=0.25,
color='g')
# Keep a list for a histogram plot (detrainment profile)
if p[ELindex_ent].magnitude < lfc_pressure.magnitude: # buoyant parcels only
ELps.append(p[ELindex_ent].magnitude)
# Plot a crude histogram of parcel detrainment levels
NBINS = 20
pbins = np.linspace(1000, 150,
num=NBINS) # pbins for detrainment levels
hist = np.zeros((len(pbins) - 1))
for x in ELps:
for i in range(len(pbins) - 1):
if (x < pbins[i]) & (x >= pbins[i + 1]):
hist[i] += 1
break
det_per = hist / sum(hist) * 100
# percentages of detrainment ensumbles at levels
ax2 = fig.add_axes([0.705, 0.1, 0.1, 0.8], facecolor=None)
ax2.barh(pbins[1:],
det_per,
color='lightgrey',
edgecolor='k',
height=15 * (20 / NBINS))
ax2.set_xlim([0, max(det_per)])
ax2.set_ylim([1030, 120])
ax2.set_xlabel('Detrainment [%]')
ax2.grid()
ax2.set_zorder(2)
ax.plot([400, 400], [1100, 0])
ax.annotate('Detrainment', xy=(362, 320), color='dimgrey')
ax.annotate('ensemble: ' + str(ensemble_size * len(entrainment_rates)),
xy=(364, 340),
color='dimgrey')
ax.annotate('Detrainment', xy=(362, 380), color='dimgrey')
ax.annotate(' scale: 0 - 2 km', xy=(365, 400), color='dimgrey')
# Overplots on the mess: undilute parcel and CAPE, etc.
ax.plot((1, 1) * mse[0], (1, 0) * (p[0]), color='g', linewidth=2)
# Replot the sounding on top of all that mess
ax.plot(mse_s, p, color='r', linewidth=1.5)
ax.plot(mse, p, color='b', linewidth=1.5)
# label LCL and LCF
ax.plot((mse_s[lcl_idx] + (-2000, 2000) * units('joule/kilogram')),
lcl_pressure + (0, 0) * units('mbar'),
color='orange',
linewidth=3)
ax.plot((mse_s[lfc_idx] + (-2000, 2000) * units('joule/kilogram')),
lfc_pressure + (0, 0) * units('mbar'),
color='magenta',
linewidth=3)
### Internal waves (100m adiabatic displacements, assumed adiabatic: conserves s, sv, h).
#dZ = 100 *mpunits.units.meter
dp = 1000 * units.pascal
# depict displacements at sounding levels nearest these target levels
targetlevels = [900, 800, 700, 600, 500, 400, 300, 200] * units.hPa
for ilev in targetlevels:
idx = np.argmin(np.abs(p - ilev))
# dp: hydrostatic
rho = (p[idx]) / Rd / (T[idx])
dZ = -dp / rho / g
# dT: Dry lapse rate dT/dz_dry is -g/Cp
dT = (-g / Cp_d * dZ).to('kelvin')
Tdisp = T[idx].to('kelvin') + dT
# dhsat
dqs = mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(Tdisp),
p[idx] + dp) - qs[idx]
dhs = g * dZ + Cp_d * dT + Lv * dqs
# Whiskers on the data plots
ax.plot((mse_s[idx] + dhs * (-1, 1)),
p[idx] + dp * (-1, 1),
linewidth=3,
color='r')
ax.plot((dse[idx] * (1, 1)),
p[idx] + dp * (-1, 1),
linewidth=3,
color='k')
ax.plot((mse[idx] * (1, 1)),
p[idx] + dp * (-1, 1),
linewidth=3,
color='b')
# annotation to explain it
if ilev == 400 * ilev.units:
ax.plot(360 * mse_s.units + dhs * (-1, 1) / 1000,
440 * units('mbar') + dp * (-1, 1),
linewidth=3,
color='r')
ax.annotate('+/- 10mb', xy=(362, 440), fontsize=8)
ax.annotate(' adiabatic displacement', xy=(362, 460), fontsize=8)
# Plot a crude histogram of parcel detrainment levels
# Text parts
ax.text(290, pressure[3], 'RH (%)', fontsize=11, color='k')
ax.text(285,
200,
'CAPE = ' + str(np.around(CAPE.magnitude, decimals=2)) + ' [J/kg]',
fontsize=12,
color='green')
ax.text(285,
250,
'CIN = ' + str(np.around(CIN.magnitude, decimals=2)) + ' [J/kg]',
fontsize=12,
color='green')
ax.text(285,
300,
'LCL = ' + str(np.around(lcl_pressure.magnitude, decimals=2)) +
' [hpa]',
fontsize=12,
color='darkorange')
ax.text(285,
350,
'LFC = ' + str(np.around(lfc_pressure.magnitude, decimals=2)) +
' [hpa]',
fontsize=12,
color='magenta')
ax.text(285,
400,
'CWV = ' + str(np.around(cwv.magnitude, decimals=2)) + ' [mm]',
fontsize=12,
color='deepskyblue')
ax.text(285,
450,
'CRH = ' + str(np.around(crh.magnitude, decimals=2)) + ' [%]',
fontsize=12,
color='blue')
ax.legend(['DSE', 'MSE', 'SMSE'], fontsize=12, loc=1)
ax.set_zorder(3)
return (ax)
def entropy_plots(pressure,
temperature,
mixing_ratio,
altitude,
h0_std=2000,
ensemble_size=20,
ent_rate=np.arange(0, 2, 0.05),
entrain=False):
"""
plotting the summarized entropy diagram with annotations and thermodynamic parameters
"""
p = pressure * units('mbar')
T = temperature * units('degC')
q = mixing_ratio * units('kilogram/kilogram')
qs = mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(T), p)
Td = mpcalc.dewpoint(mpcalc.vapor_pressure(p, q)) # dewpoint
Tp = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC') # parcel profile
# Altitude based on the hydrostatic eq.
if len(altitude) == len(pressure): # (1) altitudes for whole levels
altitude = altitude * units('meter')
elif len(altitude
) == 1: # (2) known altitude where the soundings was launched
z_surf = altitude.copy() * units('meter')
# given altitude
altitude = np.zeros((np.size(T))) * units('meter')
for i in range(np.size(T)):
altitude[i] = mpcalc.thickness_hydrostatic(
p[:i + 1], T[:i + 1]) + z_surf # Hypsometric Eq. for height
else:
print(
'***NOTE***: the altitude at the surface is assumed 0 meter, and altitudes are derived based on the hypsometric equation'
)
altitude = np.zeros(
(np.size(T))) * units('meter') # surface is 0 meter
for i in range(np.size(T)):
altitude[i] = mpcalc.thickness_hydrostatic(
p[:i + 1], T[:i + 1]) # Hypsometric Eq. for height
# specific entropy [joule/(kg*K)]
# sd : specific entropy of dry air
# sm1 : specific entropy of airborne mositure in state 1 (water vapor)
# sm2 : specific entropy of airborne mositure in state 2 (saturated water vapor)
sd = entropy(T.magnitude, q.magnitude * 1e-6, p.magnitude)
sm1 = entropy(T.magnitude, q.magnitude, p.magnitude)
sm2 = entropy(T.magnitude, qs.magnitude, p.magnitude)
###############################
# Water vapor calculations
p_PWtop = min(p)
#p_PWtop = max(200*units.mbar, min(p) + 1*units.mbar) # integrating until 200mb
cwv = mpcalc.precipitable_water(Td, p,
top=p_PWtop) # column water vapor [mm]
cwvs = mpcalc.precipitable_water(
T, p, top=p_PWtop) # saturated column water vapor [mm]
crh = (cwv / cwvs) * 100. # column relative humidity [%]
#================================================
# plotting MSE vertical profiles
fig = plt.figure(figsize=[12, 8])
ax = fig.add_axes([0.1, 0.1, 0.6, 0.8])
ax.plot(sd, p, '-k', linewidth=2)
ax.plot(sm1, p, '-b', linewidth=2)
ax.plot(sm2, p, '-r', linewidth=2)
# mse based on different percentages of relative humidity
qr = np.zeros((9, np.size(qs))) * units('kilogram/kilogram')
sm1_r = qr # container
for i in range(9):
qr[i, :] = qs * 0.1 * (i + 1)
sm1_r[i, :] = entropy(T.magnitude, qr[i, :].magnitude, p.magnitude)
for i in range(9):
ax.plot(sm1_r[i, :], p[:], '-', color='grey', linewidth=0.7)
ax.text(sm1_r[i, 3].magnitude - 2, p[3].magnitude, str((i + 1) * 10))
# drawing LCL and LFC levels
[lcl_pressure, lcl_temperature] = mpcalc.lcl(p[0], T[0], Td[0])
lcl_idx = np.argmin(np.abs(p.magnitude - lcl_pressure.magnitude))
[lfc_pressure, lfc_temperature] = mpcalc.lfc(p, T, Td)
lfc_idx = np.argmin(np.abs(p.magnitude - lfc_pressure.magnitude))
# conserved mse of air parcel arising from 1000 hpa
sm1_p = np.squeeze(np.ones((1, np.size(T))) * sm1[0])
# illustration of CAPE
el_pressure, el_temperature = mpcalc.el(p, T, Td) # equilibrium level
el_idx = np.argmin(np.abs(p.magnitude - el_pressure.magnitude))
ELps = [el_pressure.magnitude
] # Initialize an array of EL pressures for detrainment profile
[CAPE, CIN] = mpcalc.cape_cin(p[:el_idx], T[:el_idx], Td[:el_idx],
Tp[:el_idx])
plt.plot(sm1_p, p, color='green', linewidth=2)
#ax.fill_betweenx(p[lcl_idx:el_idx+1],sm1_p[lcl_idx:el_idx+1],sm2[lcl_idx:el_idx+1],interpolate=True
# ,color='green',alpha='0.3')
ax.fill_betweenx(p, sd, sm1, color='deepskyblue', alpha='0.5')
ax.set_xlabel('Specific entropies: sd, sm, sm_sat [J K$^{-1}$ kg$^{-1}$]',
fontsize=14)
ax.set_ylabel('Pressure [hPa]', fontsize=14)
ax.set_xticks([0, 50, 100, 150, 200, 250, 300, 350])
ax.set_xlim([0, 440])
ax.set_ylim(1030, 120)
if entrain is True:
# Depict Entraining parcels
# Parcel mass solves dM/dz = eps*M, solution is M = exp(eps*Z)
# M=1 at ground without loss of generality
# Distribution of surface parcel h offsets
h0offsets = np.sort(np.random.normal(
0, h0_std, ensemble_size)) * units('joule/kilogram')
# Distribution of entrainment rates
entrainment_rates = ent_rate / (units('km'))
for h0offset in h0offsets:
h4ent = sm1.copy()
h4ent[0] += h0offset
for eps in entrainment_rates:
M = np.exp(eps * (altitude - altitude[0])).to('dimensionless')
# dM is the mass contribution at each level, with 1 at the origin level.
M[0] = 0
dM = np.gradient(M)
# parcel mass is a sum of all the dM's at each level
# conserved linearly-mixed variables like h are weighted averages
if eps.magnitude == 0.0:
hent = np.ones(len(h4ent)) * h4ent[0] # no mixing
else:
hent = np.cumsum(dM * h4ent) / np.cumsum(dM)
# Boolean for positive buoyancy, and its topmost altitude (index) where curve is clippes
posboy = (hent > sm2)
posboy[0] = True # so there is always a detrainment level
# defining the first EL by posboy as the detrainment layer, swiching from positive buoyancy to
# negative buoyancy (0 to 1) and skipping the surface
ELindex_ent = 0
for idx in range(len(posboy) - 1):
if posboy[idx + 1] == 0 and posboy[idx] == 1 and idx > 0:
ELindex_ent = idx
break
# Plot the curve
plt.plot(hent[0:ELindex_ent + 2],
p[0:ELindex_ent + 2],
linewidth=0.6,
color='g')
#plt.plot( hent[0:], p[0:], linewidth=0.6, color='g')
# Keep a list for a histogram plot (detrainment profile)
if p[ELindex_ent].magnitude < lfc_pressure.magnitude: # buoyant parcels only
ELps.append(p[ELindex_ent].magnitude)
# Plot a crude histogram of parcel detrainment levels
NBINS = 20
pbins = np.linspace(1000, 150,
num=NBINS) # pbins for detrainment levels
hist = np.zeros((len(pbins) - 1))
for x in ELps:
for i in range(len(pbins) - 1):
if (x < pbins[i]) & (x >= pbins[i + 1]):
hist[i] += 1
break
det_per = hist / sum(hist) * 100
# percentages of detrainment ensumbles at levels
ax2 = fig.add_axes([0.705, 0.1, 0.1, 0.8], facecolor=None)
ax2.barh(pbins[1:],
det_per,
color='lightgrey',
edgecolor='k',
height=15 * (20 / NBINS))
ax2.set_xlim([0, 100])
ax2.set_xticks([0, 20, 40, 60, 80, 100])
ax2.set_ylim([1030, 120])
ax2.set_xlabel('Detrainment [%]')
ax2.grid()
ax2.set_zorder(2)
ax.plot([400, 400], [1100, 0])
ax.annotate('Detrainment', xy=(362, 320), color='dimgrey')
ax.annotate('ensemble: ' + str(ensemble_size * len(entrainment_rates)),
xy=(364, 340),
color='dimgrey')
ax.annotate('Detrainment', xy=(362, 380), color='dimgrey')
ax.annotate(' scale: 0 - 2 km', xy=(365, 400), color='dimgrey')
# Overplots on the mess: undilute parcel and CAPE, etc.
ax.plot((1, 1) * sm1[0], (1, 0) * (p[0]), color='g', linewidth=2)
# Replot the sounding on top of all that mess
ax.plot(sm2, p, color='r', linewidth=1.5)
ax.plot(sm1, p, color='b', linewidth=1.5)
# label LCL and LCF
ax.plot((sm2[lcl_idx] + (-2000, 2000) * units('joule/kilogram')),
lcl_pressure + (0, 0) * units('mbar'),
color='orange',
linewidth=3)
ax.plot((sm2[lfc_idx] + (-2000, 2000) * units('joule/kilogram')),
lfc_pressure + (0, 0) * units('mbar'),
color='magenta',
linewidth=3)
# Plot a crude histogram of parcel detrainment levels
# Text parts
ax.text(30, pressure[3], 'RH (%)', fontsize=11, color='k')
ax.text(20,
200,
'CAPE = ' + str(np.around(CAPE.magnitude, decimals=2)) + ' [J/kg]',
fontsize=12,
color='green')
ax.text(20,
250,
'CIN = ' + str(np.around(CIN.magnitude, decimals=2)) + ' [J/kg]',
fontsize=12,
color='green')
ax.text(20,
300,
'LCL = ' + str(np.around(lcl_pressure.magnitude, decimals=2)) +
' [hpa]',
fontsize=12,
color='darkorange')
ax.text(20,
350,
'LFC = ' + str(np.around(lfc_pressure.magnitude, decimals=2)) +
' [hpa]',
fontsize=12,
color='magenta')
ax.text(20,
400,
'CWV = ' + str(np.around(cwv.magnitude, decimals=2)) + ' [mm]',
fontsize=12,
color='deepskyblue')
ax.text(20,
450,
'CRH = ' + str(np.around(crh.magnitude, decimals=2)) + ' [%]',
fontsize=12,
color='blue')
ax.legend(['DEnt', 'MEnt', 'SMEnt'], fontsize=12, loc=1)
ax.set_zorder(3)
return (ax)
def EnergyMassPlot(pressure, temperature, dewpoint,
height, uwind, vwind, sphum=None, rh=None,
label='', size=(12,10), return_fig=False):
p=pressure
Z=height
T=temperature
Td=dewpoint
if isinstance(sphum,np.ndarray) and isinstance(rh,np.ndarray):
q=sphum
qs=q/rh
else:
q = mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(Td),p)
qs= mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(T),p)
s = g*Z + Cp_d*T
sv= g*Z + Cp_d*mpcalc.virtual_temperature(T,q)
h = s + Lv*q
hs= s + Lv*qs
parcel_Tprofile = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
CAPE,CIN = mpcalc.cape_cin(p,T,Td,parcel_Tprofile)
ELp,ELT = mpcalc.el(p,T,Td)
ELindex = np.argmin(np.abs(p - ELp))
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
p_PWtop = max(200*units.mbar, min(p) +1*units.mbar)
PW = mpcalc.precipitable_water(Td,p, top=p_PWtop)
PWs = mpcalc.precipitable_water(T,p, top=p_PWtop)
CRH = (PW/PWs).magnitude *100.
fig,ax=setup_fig(size=size,label=label)
ax.plot(s /1000, p, color='r', linewidth=1.5) ### /1000 for kJ/kg
ax.plot(sv /1000, p, color='r', linestyle='-.')
ax.plot(h /1000, p, color='b', linewidth=1.5)
ax.plot(hs /1000, p, color='r', linewidth=1.5)
### RH rulings between s and h lines: annotate near 800 hPa level
annot_level = 800 #hPa
idx = np.argmin(np.abs(p - annot_level *units.hPa))
right_annot_loc = 380
for iRH in np.arange(10,100,10):
ax.plot( (s+ Lv*qs*iRH/100.)/1000, p, linewidth=0.5, linestyle=':', color='k')
ax.annotate(str(iRH), xy=( (s[idx]+Lv*qs[idx]*iRH/100.)/1000, annot_level),
horizontalalignment='center',fontsize=6)
ax.annotate('RH (%)', xy=(right_annot_loc, annot_level), fontsize=10)
if not np.isnan(CAPE.magnitude) and CAPE.magnitude >10:
parcelh = h [0] # for a layer mean: np.mean(h[idx1:idx2])
parcelsv = sv[0]
parcelp0 = p[0]
# Undilute parcel
ax.plot( (1,1)*parcelh/1000., (1,0)*parcelp0, linewidth=0.5, color='g')
maxbindex = np.argmax(parcel_Tprofile - T)
ax.annotate('CAPE='+str(int(CAPE.magnitude)),
xy=(parcelh/1000., p[maxbindex]), color='g')
# Plot LCL at saturation point, above the lifted sv of the surface parcel
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
ax.annotate('LCL', xy=(sv[0]/1000., lcl_pressure), fontsize=10, color='g', horizontalalignment='right')
# Purple fill for negative buoyancy below LCL:
ax.fill_betweenx(p, sv/1000., parcelsv/1000., where=p>lcl_pressure, facecolor='purple', alpha=0.4)
# Positive moist convective buoyancy in green
# Above LCL:
ax.fill_betweenx(p, hs/1000., parcelh/1000., where= parcelh>hs, facecolor='g', alpha=0.4)
# Depict Entraining parcels
# Parcel mass solves dM/dz = eps*M, solution is M = exp(eps*Z)
# M=1 at ground without loss of generality
entrainment_distance = 10000., 5000., 2000.
ax.annotate('entrain: 10,5,2 km', xy=(parcelh/1000, 140), color='g',
fontsize=8, horizontalalignment='right')
ax.annotate('parcel h', xy=(parcelh/1000, 120), color='g',
fontsize=10, horizontalalignment='right')
for ED in entrainment_distance:
eps = 1.0 / (ED*units.meter)
M = np.exp(eps * (Z-Z[0]).to('m')).to('dimensionless')
# dM is the mass contribution at each level, with 1 at the origin level.
M[0] = 0
dM = np.gradient(M)
# parcel mass is a sum of all the dM's at each level
# conserved linearly-mixed variables like h are weighted averages
hent = np.cumsum(dM*h) / np.cumsum(dM)
ax.plot( hent[0:ELindex+3]/1000., p[0:ELindex+3], linewidth=0.5, color='g')
### Internal waves (100m adiabatic displacements, assumed adiabatic: conserves s, sv, h).
dZ = 100 *units.meter
# depict displacements at sounding levels nearest these target levels
targetlevels = [900,800,700,600,500,400,300,200]*units.hPa
for ilev in targetlevels:
idx = np.argmin(np.abs(p - ilev))
# dT: Dry lapse rate dT/dz_dry is -g/Cp
dT = (-g/Cp_d *dZ).to('kelvin')
Tdisp = T[idx].to('kelvin') + dT
# dp: hydrostatic
rho = (p[idx]/Rd/T[idx])
dp = -rho*g*dZ
# dhsat
#qs = mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(T) ,p)
dqs = mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(Tdisp) ,p[idx]+dp) -qs[idx]
dhs = g*dZ + Cp_d*dT + Lv*dqs
# Whiskers on the data plots
ax.plot( (hs[idx]+dhs*(-1,1))/1000, p[idx]+dp*(-1,1), linewidth=3, color='r')
ax.plot( (s [idx] *( 1,1))/1000, p[idx]+dp*(-1,1), linewidth=3, color='r')
ax.plot( (h [idx] *( 1,1))/1000, p[idx]+dp*(-1,1), linewidth=3, color='b')
# annotation to explain it
if ilev == 600*ilev.units:
ax.plot(right_annot_loc*hs.units +dhs*(-1,1)/1000, p[idx]+dp*(-1,1), linewidth=3, color='r')
ax.annotate('+/- 100m', xy=(right_annot_loc,600), fontsize=8)
ax.annotate(' internal', xy=(right_annot_loc,630), fontsize=8)
ax.annotate(' waves', xy=(right_annot_loc,660), fontsize=8)
### Blue fill proportional to precipitable water, and blue annotation
ax.fill_betweenx(p, s/1000., h/1000., where=h > s, facecolor='b', alpha=0.4)
# Have to specify the top of the PW integral.
# I want whole atmosphere of course, but 200 hPa captures it all really.
#import metpy.calc as mpcalc
p_PWtop = max(200*units.mbar, min(p) +1*units.mbar)
PW = mpcalc.precipitable_water(Td,p, top=p_PWtop)
PWs = mpcalc.precipitable_water(T,p, top=p_PWtop)
CRH = (PW/PWs).magnitude *100.
# PW annotation arrow tip at 700 mb
idx = np.argmin(np.abs(p - 700*p.units))
centerblue = (s[idx]+h[idx])/2.0 /1000.
ax.annotate('CWV='+str(round(PW.to('mm').magnitude, 1))+'mm',
xy=(centerblue, 700), xytext=(285, 200),
color='blue', fontsize=15,
arrowprops=dict(width = 1, edgecolor='blue', shrink=0.02),
)
ax.annotate('(' + str(round(CRH,1)) +'% of sat)',
xy=(285, 230), color='blue', fontsize=12)
### Surface water values at 1C intervals, for eyeballing surface fluxes
sT = np.trunc(T[0].to('degC'))
sTint = int(sT.magnitude)
for idT in [-2,0,2,4]:
ssTint = sTint + idT # UNITLESS degC integers, for labels
# Kelvin values for computations
ssTC = ssTint * units.degC
ssTK = ssTC.to('kelvin')
ss = g*Z[0] + Cp_d*ssTK
hs = ss + Lv*mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(ssTK) ,p[0])
ax.annotate(str(ssTint), xy=(ss/1000., p[0]+0*p.units),
verticalalignment='top', horizontalalignment='center',
color='red', fontsize=7)
ax.annotate(str(ssTint), xy=(hs/1000., p[0]+0*p.units),
verticalalignment='top', horizontalalignment='center',
color='red', fontsize=9)
ax.annotate('\u00b0C water', xy=(right_annot_loc, p[0]), verticalalignment='top',
fontsize=10, color='r')
if return_fig:
return ax,fig
def process_skewt(self):
# Calculation
index_p100 = get_pressure_level_index(self.p_i, 100)
lcl_p, lcl_t = mpcalc.lcl(self.p_i[0], self.t_i[0], self.td_i[0])
lfc_p, lfc_t = mpcalc.lfc(self.p_i, self.t_i, self.td_i)
el_p, el_t = mpcalc.el(self.p_i, self.t_i, self.td_i)
prof = mpcalc.parcel_profile(self.p_i, self.t_i[0], self.td_i[0]).to('degC')
cape, cin = mpcalc.cape_cin(self.p_i, self.t_i, self.td_i, prof)
mucape, mucin = mpcalc.most_unstable_cape_cin(self.p_i, self.t_i, self.td_i)
pwat = mpcalc.precipitable_water(self.td_i, self.p_i)
i8 = get_pressure_level_index(self.p_i, 850)
i7 = get_pressure_level_index(self.p_i, 700)
i5 = get_pressure_level_index(self.p_i, 500)
theta850 = mpcalc.equivalent_potential_temperature(850 * units('hPa'), self.t_i[i8], self.td_i[i5])
theta500 = mpcalc.equivalent_potential_temperature(500 * units('hPa'), self.t_i[i5], self.td_i[i5])
thetadiff = theta850 - theta500
k = self.t_i[i8] - self.t_i[i5] + self.td_i[i8] - (self.t_i[i7] - self.td_i[i7])
a = ((self.t_i[i8] - self.t_i[i5]) - (self.t_i[i8] - self.td_i[i5]) -
(self.t_i[i7] - self.td_i[i7]) - (self.t_i[i5] - self.td_i[i5]))
sw = c_sweat(np.array(self.t_i[i8].magnitude), np.array(self.td_i[i8].magnitude),
np.array(self.t_i[i5].magnitude), np.array(self.u_i[i8].magnitude),
np.array(self.v_i[i8].magnitude), np.array(self.u_i[i5].magnitude),
np.array(self.v_i[i5].magnitude))
si = showalter_index(self.t_i[i8], self.td_i[i8], self.t_i[i5])
li = lifted_index(self.t_i[0], self.td_i[0], self.p_i[0], self.t_i[i5])
srh_pos, srh_neg, srh_tot = mpcalc.storm_relative_helicity(self.u_i, self.v_i, self.alt, 1000 * units('m'))
sbcape, sbcin = mpcalc.surface_based_cape_cin(self.p_i, self.t_i, self.td_i)
shr6km = mpcalc.bulk_shear(self.p_i, self.u_i, self.v_i, heights=self.alt, depth=6000 * units('m'))
wshr6km = mpcalc.wind_speed(*shr6km)
sigtor = mpcalc.significant_tornado(sbcape, delta_height(self.p_i[0], lcl_p), srh_tot, wshr6km)[0]
# Plotting
self.ax.set_ylim(1050, 100)
self.ax.set_xlim(-40, 50)
self.plot(self.p_i, self.t_i, 'r', linewidth=1)
self.plot(self.p_i[:self.dp_idx], self.td_i[:self.dp_idx], 'g', linewidth=1)
self.plot_barbs(self.p_i[:index_p100], self.u_i[:index_p100] * 1.94, self.v_i[:index_p100] * 1.94)
self.plot(lcl_p, lcl_t, 'ko', markerfacecolor='black')
self.plot(self.p_i, prof, 'k', linewidth=2)
if cin.magnitude < 0:
chi = -1 * cin.magnitude
self.shade_cin(self.p_i, self.t_i, prof)
elif cin.magnitude > 0:
chi = cin.magnitude
self.shade_cin(self.p_i, self.t_i, prof)
else:
chi = 0.
self.shade_cape(self.p_i, self.t_i, prof)
self.plot_dry_adiabats(linewidth=0.5)
self.plot_moist_adiabats(linewidth=0.5)
self.plot_mixing_lines(linewidth=0.5)
plt.title('Skew-T Plot \nStation: {} Time: {}'.format(self.st, self.time.strftime('%Y.%m.%d %H:%M')), fontsize=14, loc='left')
# Add hodograph
ax = self._fig.add_axes([0.95, 0.71, 0.17, 0.17])
h = Hodograph(ax, component_range=50)
h.add_grid(increment=20)
h.plot_colormapped(self.u_i[:index_p100], self.v_i[:index_p100], self.alt[:index_p100], linewidth=1.2)
# Annotate parameters
# Annotate names
namelist = ['CAPE', 'CIN', 'MUCAPE', 'PWAT', 'K', 'A', 'SWEAT', 'LCL', 'LFC', 'EL', 'SI', 'LI', 'T850-500',
'θse850-500', 'SRH', 'STP']
xcor = -50
ycor = -90
spacing = -9
for nm in namelist:
ax.text(xcor, ycor, '{}: '.format(nm), fontsize=10)
ycor += spacing
# Annotate values
varlist = [cape, chi, mucape, pwat, k, a, sw, lcl_p, lfc_p, el_p, si, li, self.t_i[i8] - self.t_i[i5], thetadiff,
srh_tot, sigtor]
xcor = 10
ycor = -90
for v in varlist:
if hasattr(v, 'magnitude'):
v = v.magnitude
ax.text(xcor, ycor, str(np.round_(v, 2)), fontsize=10)
ycor += spacing
# Annotate units
unitlist = ['J/kg', 'J/kg', 'J/kg', 'mm', '°C', '°C', '', 'hPa', 'hPa', 'hPa', '°C', '°C', '°C', '°C']
xcor = 45
ycor = -90
for u in unitlist:
ax.text(xcor, ycor, ' {}'.format(u), fontsize=10)
ycor += spacing