def calc_q_from_rh(dp, p):
"""
Input :
dp : dew point (deg Celsius)
p : pressure (hPa)
Output :
q : Specific humidity values
Function to estimate specific humidity from the relative humidity,
temperature and pressure in the given dataset. This function uses MetPy's
functions to get q:
(i) mpcalc.dewpoint_from_relative_humidity()
(ii) mpcalc.specific_humidity_from_dewpoint()
"""
q = mpcalc.specific_humidity_from_dewpoint(dp * units.degC,
p * units.hPa).magnitude
return q
def convert_variables(input_data):
output_data = input_data
for i in range(input_data.shape[0]):
## tdc to specific humidity
prs = input_data[i, :, 0]
tdc = input_data[i, :, 2]
q = mpcalc.specific_humidity_from_dewpoint(tdc * units.degC,
prs * units.hPa)
output_data[i, :, 2] = np.array(q)
## tc to potential temperature
tc = input_data[i, :, 1]
# theta = mpcalc.potential_temperature(prs * units.hPa,
# tc * units.degC)
thv = mpcalc.virtual_potential_temperature(prs * units.hPa,
tc * units.degC, q)
output_data[i, :, 1] = np.array(thv)
return output_data
def Crosssection_Wind_Theta_e_Qv(
initial_time=None,
fhour=24,
levels=[1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 200],
day_back=0,
model='ECMWF',
output_dir=None,
st_point=[20, 120.0],
ed_point=[50, 130.0],
map_extent=[70, 140, 15, 55],
h_pos=[0.125, 0.665, 0.25, 0.2]):
# micaps data directory
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='RH',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='TMP',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='HGT',
lvl='500')
]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if (initial_time != None):
filename = utl.model_filename(initial_time, fhour)
else:
filename = utl.filename_day_back_model(day_back=day_back, fhour=fhour)
# retrieve data from micaps server
rh = get_model_3D_grid(directory=data_dir[0][0:-1],
filename=filename,
levels=levels,
allExists=False)
if rh is None:
return
rh = rh.metpy.parse_cf().squeeze()
u = get_model_3D_grid(directory=data_dir[1][0:-1],
filename=filename,
levels=levels,
allExists=False)
if u is None:
return
u = u.metpy.parse_cf().squeeze()
v = get_model_3D_grid(directory=data_dir[2][0:-1],
filename=filename,
levels=levels,
allExists=False)
if v is None:
return
v = v.metpy.parse_cf().squeeze()
v2 = get_model_3D_grid(directory=data_dir[2][0:-1],
filename=filename,
levels=levels,
allExists=False)
if v2 is None:
return
v2 = v2.metpy.parse_cf().squeeze()
t = get_model_3D_grid(directory=data_dir[3][0:-1],
filename=filename,
levels=levels,
allExists=False)
if t is None:
return
t = t.metpy.parse_cf().squeeze()
gh = get_model_grid(data_dir[4], filename=filename)
if t is None:
return
resolution = u['lon'][1] - u['lon'][0]
x, y = np.meshgrid(u['lon'], u['lat'])
dx, dy = mpcalc.lat_lon_grid_deltas(u['lon'], u['lat'])
for ilvl in levels:
u2d = u.sel(level=ilvl)
#u2d['data'].attrs['units']=units.meter/units.second
v2d = v.sel(level=ilvl)
#v2d['data'].attrs['units']=units.meter/units.second
absv2d = mpcalc.absolute_vorticity(
u2d['data'].values * units.meter / units.second,
v2d['data'].values * units.meter / units.second, dx, dy,
y * units.degree)
if (ilvl == levels[0]):
absv3d = v2
absv3d['data'].loc[dict(level=ilvl)] = np.array(absv2d)
else:
absv3d['data'].loc[dict(level=ilvl)] = np.array(absv2d)
absv3d['data'].attrs['units'] = absv2d.units
#rh=rh.rename(dict(lat='latitude',lon='longitude'))
cross = cross_section(rh, st_point, ed_point)
cross_rh = cross.set_coords(('lat', 'lon'))
cross = cross_section(u, st_point, ed_point)
cross_u = cross.set_coords(('lat', 'lon'))
cross = cross_section(v, st_point, ed_point)
cross_v = cross.set_coords(('lat', 'lon'))
cross_u['data'].attrs['units'] = units.meter / units.second
cross_v['data'].attrs['units'] = units.meter / units.second
cross_u['t_wind'], cross_v['n_wind'] = mpcalc.cross_section_components(
cross_u['data'], cross_v['data'])
cross = cross_section(t, st_point, ed_point)
cross_t = cross.set_coords(('lat', 'lon'))
cross = cross_section(absv3d, st_point, ed_point)
cross_Td = mpcalc.dewpoint_rh(cross_t['data'].values * units.celsius,
cross_rh['data'].values * units.percent)
rh, pressure = xr.broadcast(cross_rh['data'], cross_t['level'])
Qv = mpcalc.specific_humidity_from_dewpoint(cross_Td, pressure)
cross_Qv = xr.DataArray(np.array(Qv) * 1000.,
coords=cross_rh['data'].coords,
dims=cross_rh['data'].dims,
attrs={'units': units('g/kg')})
Theta_e = mpcalc.equivalent_potential_temperature(
pressure, cross_t['data'].values * units.celsius, cross_Td)
cross_Theta_e = xr.DataArray(np.array(Theta_e),
coords=cross_rh['data'].coords,
dims=cross_rh['data'].dims,
attrs={'units': Theta_e.units})
crossection_graphics.draw_Crosssection_Wind_Theta_e_Qv(
cross_Qv=cross_Qv,
cross_Theta_e=cross_Theta_e,
cross_u=cross_u,
cross_v=cross_v,
gh=gh,
h_pos=h_pos,
st_point=st_point,
ed_point=ed_point,
levels=levels,
map_extent=map_extent,
output_dir=output_dir)
#########################################
##### A) SMN: Surface measurement
#########################################
SMN_data = xr.open_dataset(SMN_archive + '/SMN_concat1.nc').to_dataframe()
SMN_data = SMN_data[SMN_data.time_YMDHMS == int(
SMN_time.strftime('%Y%m%d%H%M%S'))]
SMN_data = SMN_data[SMN_data.pressure_hPa !=
1000] # delete first row (undefined values)
SMN_data = SMN_data.reset_index(drop=True)
pressure_SMN = SMN_data.pressure_hPa.values * units.hPa
temperature_SMN = SMN_data.temperature_degC.values * units.degC
RH_SMN = SMN_data.relative_humidity_percent.values * units.percent
temperature_d_SMN = cc.dewpoint_from_relative_humidity(temperature_SMN, RH_SMN)
specific_humidity_SMN = cc.specific_humidity_from_dewpoint(
temperature_d_SMN, pressure_SMN)
##########################################
##### B) RS: RADIOSONDE
##########################################
### read Radiosonde file and save as dataframe ###
RS_data = xr.open_dataset(RS_archive + '/RS_concat.nc').to_dataframe()
#RS_data = pd.read_csv('/data/COALITION2/PicturesSatellite/results_NAL/Radiosondes/RS_20200722000000')
RS_data = RS_data.rename(
columns={
'termin': 'time_YMDHMS',
'744': 'pressure_hPa',
'745': 'temperature_degC',
'746': 'relative_humidity_percent',
'742': 'geopotential_altitude_m',
'748': 'wind_speed_ms-1',
# Derive pressure levels from the barometric equation (i.e. assume neutral stratification with a
# lapse rate of g/cp)
pk = np.zeros(np.shape(Tk))
for t in range(ntimesteps):
pk[t, :] = barometric_formula(zk, 300, 101325)
# Derive the potential temperature
ptk = np.zeros(np.shape(Tk))
for t in range(ntimesteps):
ptk[t, :] = Tk[t, :] / exner_function(pk[t, :])
# Calculate the specific humidity from the dew point temperature and pressure
qk = np.copy(uk)
for t in range(ntimesteps):
qk[t, :] = mpcalc.specific_humidity_from_dewpoint(TDk[t, :] * units.kelvin,
pk[t, :] * units.pascal)
#%% Read SMEARIII DMPS data
if not precursor:
dmps = np.genfromtxt(fname_smear_dmps)
dmid_dmps = dmps[0, 2::]
day_dmps = dmps[:, 0]
ntot_dmps = dmps[:, 1] # 1/cm3
# Change the days from the beginning of the year (day_dmps) to datetime (time_dmps)
for i in range(len(day_dmps)):
# days from the beginning of the year to datetime
timecol = dtime.datetime(sim_year, 1,
def Crosssection_Wind_Theta_e_Qv(
initTime=None, fhour=24,
levels=[1000, 950, 925, 900, 850, 800, 700,600,500,400,300,200],
day_back=0,model='GRAPES_GFS',data_source='MICAPS',
lw_ratio=[16,9],
output_dir=None,
st_point = [20, 120.0],
ed_point = [50, 130.0],
map_extent=[70,140,15,55],
h_pos=[0.125, 0.665, 0.25, 0.2],**kwargs ):
if(data_source == 'MICAPS'):
try:
data_dir = [utl.Cassandra_dir(data_type='high',data_source=model,var_name='RH',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='UGRD',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='VGRD',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='TMP',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='HGT',lvl='500'),
utl.Cassandra_dir(data_type='surface',data_source=model,var_name='PSFC')]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if(initTime != None):
filename = utl.model_filename(initTime, fhour)
else:
filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour)
# retrieve data from micaps server
rh=get_model_3D_grid(directory=data_dir[0][0:-1],filename=filename,levels=levels, allExists=False)
u=get_model_3D_grid(directory=data_dir[1][0:-1],filename=filename,levels=levels, allExists=False)
v=get_model_3D_grid(directory=data_dir[2][0:-1],filename=filename,levels=levels, allExists=False)
v2=get_model_3D_grid(directory=data_dir[2][0:-1],filename=filename,levels=levels, allExists=False)
t=get_model_3D_grid(directory=data_dir[3][0:-1],filename=filename,levels=levels, allExists=False)
gh=get_model_grid(data_dir[4], filename=filename)
psfc=get_model_grid(data_dir[5], filename=filename)
if(data_source == 'CIMISS'):
# get filename
if(initTime != None):
filename = utl.model_filename(initTime, fhour,UTC=True)
else:
filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour,UTC=True)
try:
rh=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='RHU'),
fcst_levels=levels, fcst_ele="RHU", units='%')
u=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIU'),
fcst_levels=levels, fcst_ele="WIU", units='m/s')
v=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIV'),
fcst_levels=levels, fcst_ele="WIV", units='m/s')
v2=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIV'),
fcst_levels=levels, fcst_ele="WIV", units='m/s')
t=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='TEM'),
fcst_levels=levels, fcst_ele="TEM", units='K')
t['data'].values=t['data'].values-273.15
gh=CMISS_IO.cimiss_model_by_time('20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='GPH'),
fcst_level=500, fcst_ele="GPH", units='gpm')
gh['data'].values=gh['data'].values/10.
psfc=CMISS_IO.cimiss_model_by_time('20'+filename[0:8], valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='PRS'),
fcst_level=0, fcst_ele="PRS", units='Pa')
psfc['data']=psfc['data']/100.
except KeyError:
raise ValueError('Can not find all data needed')
rh = rh.metpy.parse_cf().squeeze()
u = u.metpy.parse_cf().squeeze()
v = v.metpy.parse_cf().squeeze()
v2 = v2.metpy.parse_cf().squeeze()
psfc=psfc.metpy.parse_cf().squeeze()
t = t.metpy.parse_cf().squeeze()
resolution=u['lon'][1]-u['lon'][0]
x,y=np.meshgrid(u['lon'], u['lat'])
# +form 3D psfc
mask1 = (
(psfc['lon']>=t['lon'].values.min())&
(psfc['lon']<=t['lon'].values.max())&
(psfc['lat']>=t['lat'].values.min())&
(psfc['lat']<=t['lat'].values.max())
)
t2,psfc_bdcst=xr.broadcast(t['data'],psfc['data'].where(mask1, drop=True))
mask2=(psfc_bdcst > -10000)
psfc_bdcst=psfc_bdcst.where(mask2, drop=True)
# -form 3D psfc
dx,dy=mpcalc.lat_lon_grid_deltas(u['lon'],u['lat'])
#rh=rh.rename(dict(lat='latitude',lon='longitude'))
cross = cross_section(rh, st_point, ed_point)
cross_rh=cross.set_coords(('lat', 'lon'))
cross = cross_section(u, st_point, ed_point)
cross_u=cross.set_coords(('lat', 'lon'))
cross = cross_section(v, st_point, ed_point)
cross_v=cross.set_coords(('lat', 'lon'))
cross_psfc = cross_section(psfc_bdcst, st_point, ed_point)
cross_u['data'].attrs['units']=units.meter/units.second
cross_v['data'].attrs['units']=units.meter/units.second
cross_u['t_wind'], cross_v['n_wind'] = mpcalc.cross_section_components(cross_u['data'],cross_v['data'])
cross = cross_section(t, st_point, ed_point)
cross_t=cross.set_coords(('lat', 'lon'))
cross_Td = mpcalc.dewpoint_rh(cross_t['data'].values*units.celsius,
cross_rh['data'].values* units.percent)
rh,pressure = xr.broadcast(cross_rh['data'],cross_t['level'])
pressure.attrs['units']='hPa'
Qv = mpcalc.specific_humidity_from_dewpoint(cross_Td,
pressure)
cross_Qv = xr.DataArray(np.array(Qv)*1000.,
coords=cross_rh['data'].coords,
dims=cross_rh['data'].dims,
attrs={'units': units('g/kg')})
Theta_e=mpcalc.equivalent_potential_temperature(pressure,
cross_t['data'].values*units.celsius,
cross_Td)
cross_terrain=pressure-cross_psfc
cross_Theta_e = xr.DataArray(np.array(Theta_e),
coords=cross_rh['data'].coords,
dims=cross_rh['data'].dims,
attrs={'units': Theta_e.units})
crossection_graphics.draw_Crosssection_Wind_Theta_e_Qv(
cross_Qv=cross_Qv, cross_Theta_e=cross_Theta_e, cross_u=cross_u,
cross_v=cross_v,cross_terrain=cross_terrain,gh=gh,
h_pos=h_pos,st_point=st_point,ed_point=ed_point,
levels=levels,map_extent=map_extent,lw_ratio=lw_ratio,
output_dir=output_dir)
wd = wd * units.degrees
ws = df['speed'].to_numpy()
ws = ws * units.knot
u = df['u_wind'].to_numpy()
u = u * units.knot
v = df['v_wind'].to_numpy()
v = v * units.knot
rh = mpcalc.relative_humidity_from_dewpoint(t,td)
rh = rh * units.percent
rh = rh * 100.
#compute mixing ratio, pot. temp, spec. humidity, virtual pot. temp.
pt = mpcalc.potential_temperature(p,t)
# this is written for metpy 1.0, older versions of metpy want the arguments reversed in the below q to dp function
# check your version with metpy.__version__
q = mpcalc.specific_humidity_from_dewpoint(p,td)
mr = mpcalc.mixing_ratio_from_specific_humidity(q)
vpt = mpcalc.virtual_potential_temperature(p, t, mr)
mr = mr * 1000 * (units.gram/units.kilogram) #adjust units for viz
# if using NCAR research sondes
if grade == 1:
file_path = '/Users/elizabeth.smith/Documents/CHEESEHEAD/sondes/'
snd_name = 'CHEESEHEAD_ISS1_RS41_v1_'+f"{date:%Y%m%d_%H}"
snd_file = glob.glob(file_path+snd_name+'*.nc')
if len(snd_file)<1: #glob found none
#print("Missing obs on "+f"{date:%Y%m%d_%H}")
continue
else:
snd_file = np.sort(glob.glob(file_path+snd_name+'*.nc'))[0]
print("Found obs on "+f"{date:%Y%m%d_%H}")