def makefile(infile,outfilename):
rfile = hdf.File(infile, 'r')
heights = rfile['z_coords'].value[1:]
tempk = np.zeros_like(heights)
press = np.zeros_like(heights)
vapor = np.zeros_like(heights)
for i in range(len(heights)):
tempk[i] = np.mean(rfile['tempk'].value[0,i+1,:,:])
press[i] = np.mean(rfile['press'].value[0,i+1,:,:])
vapor[i] = np.mean(rfile['vapor'].value[0,i+1,:,:])
rho = (press*100.)/ (287.*tempk)
relhum = rfile['relhum'].value[0,1:,0,0]
u = rfile['u'].value[0,1:,0,0]
v = rfile['v'].value[0,1:,0,0]
data = {'T':tempk, 'RH':relhum, 'p':press*100.}
dewpt = atmos.calculate('Td', **data)
sat = atmos.calculate('rvs', **data) * 1000.
dz = np.zeros_like(heights)
dz[0:-1] = np.diff(heights)
dz[-1] = dz[-2]
vsat = sat * rho * dz
vact = vapor * rho * dz
lows = np.where(press > 850.)
mids = np.logical_and(press < 850, press > 500)
highs = np.where(press < 500)
lowsat = 100. * (np.sum(vact[lows]) / np.sum(vsat[lows]))
midsat = 100. * (np.sum(vact[mids]) / np.sum(vsat[mids]))
highsat = 100. * (np.sum(vact[highs]) / np.sum(vsat[highs]))
print outfilename
print lowsat, midsat, highsat
print ' '
outfile = Dataset(outfilename, "w", format="NETCDF4")
height = outfile.createDimension("height", len(heights))
dim = outfile.createVariable('height',"f4",('height'))
outfile.variables['height'][:]=heights
a = outfile.createVariable('temp',"f4",('height'))
outfile.variables['temp'][:]=tempk-273.15
aa = outfile.createVariable('press',"f4",('height'))
outfile.variables['press'][:]=press
a = outfile.createVariable('dewpoint',"f4",('height'))
outfile.variables['dewpoint'][:]=dewpt-273.15
a = outfile.createVariable('v',"f4",('height'))
outfile.variables['v'][:]=v
a = outfile.createVariable('u',"f4",('height'))
outfile.variables['u'][:]=u
outfile.close()
def get_metcro2d_rh(self, lay=None):
import atmos
data = {
'T': self.dset['TEMP2'][:].compute().values,
'rv': self.dset['Q2'][:].compute().values,
'p': self.dset['PRSFC'][:].compute().values
}
if lay is None:
a = atmos.calculate('RH', **data)[:, :, :, :].squeeze()
else:
atmos.calculate('RH', **data)[:, lay, :, :].squeeze()
return a
def get_air_density(t):
"""
Returns air density given the temperature
:param t:
:return:
"""
return atmos.calculate('rho', Tv=t + 273.15, p=101325)
def dMarkFreezRain_(pr,
ps,
tas,
t925,
t850,
t700,
q925,
q850,
q700,
pr_min=1.8e-5,
t_cold=273.24,
t_melt=272.51,
rh_melt=89.,
h_cold=6900):
import atmos
aa = _rl(pr) > pr_min
bb = _rl(tas) <= t_cold
ta = np.vstack((_rl(t925), _rl(t850), _rl(t700)))
qa = np.vstack((_rl(q925), _rl(q850), _rl(q700)))
prl = np.asarray([92500, 85000, 70000]).reshape((3, 1))
rh = atmos.calculate('RH', qv=qa, T=ta, p=prl)
cc = ta > t_melt
dd = rh >= rh_melt
ee = (_rl(ps) - h_cold - prl) >= 0
ff = np.any(np.logical_and(cc, dd, ee), axis=0)
return np.logical_and(aa, bb, ff)
def mf2rh(P, T, mf):
mix2mass = (18 / (0.8 * 28 + 0.2 * 32))
W = np.copy(mf)
W[W < 0] = 0
W[:, P < 101325 * np.exp(-3)] = 0
RH = atmos.calculate('RH', T=T, p=P, rt=W * mix2mass)
RH[(RH < 0) | (W == 0)] = 0
return RH
def get_abs_humidity(sense):
"""Get the absolute humidity,
handles unit conversions(why does sense hat not use SI units?)"""
# Get relative humidity
relh = sense.get_humidity()
# Get pressure, and convert from mB to Pa
pressure = sense.get_pressure() * 100
# Get temperature, and convert from degrees Celsius to kelvin
temp = sense.get_temperature() + 273.15
# Get absolute humidity
abshumidity = atmos.calculate("AH", RH=relh, T=temp, p=pressure)
# Why is the SI unit the KILOgram?
return abshumidity * 1000
T = nc.variables['T'][:].squeeze()
nc.close()
# buscando indices de niveles superior e inferior
us = np.nanmean( (u[:,zs:zi+1,:,:].copy())**2, axis=0)
del u
vs = np.nanmean( (v[:,zs:zi+1,:,:].copy())**2, axis=0)
del v
ms = np.sqrt(us + vs)
qs = np.nanmean( q[:,zs:zi+1,:,:].copy(), axis=0)
del q
ts = np.nanmean( T[:,zs:zi+1,:,:].copy(), axis=0)
del T
# calculando rho
rho = atmos.calculate('rho',p = zl*100.0, Tv = ts)
# calculando el wind speed
ws.append ( (np.nansum( rho * ms, axis=0)) / (np.nansum(rho, axis=0) ) )
# calculando Presion
Ps.append ( (np.nansum( rho * ms * zl, axis=0 )) / (np.nansum(rho * ms, axis=0) ) )
# calculando latitud
ls_top = np.nansum ( rho * ms, axis=0) * y
ls_top = np.nansum ( ls_top[ys:yi,:], axis = 0 )
ls_inf = np.nansum ( rho * ms, axis=0)
ls_inf = np.nansum ( ls_inf[ys:yi,:], axis = 0 )
ls.append( ls_top/ls_inf )
t.append(f[i][7:-3])
def calculateAbsoluteHumidity(rel_humidity, temperature):
temp_kelvin = temperature + 273.15
absolute_humidity = calculate('AH', RH=rel_humidity, p=1e5, T=temp_kelvin, debug=True)[0]
# convert from kg/m^3 to g/m^3
absolute_humidity = absolute_humidity * 1000
return absolute_humidity
from matplotlib import gridspec
import matplotlib.image as mpimg
filename = 'KWAJEX_Soundings_11Aug1999/sounding_11aug1999_10mb_control.txt'
var = np.loadtxt(filename, dtype=float)
p = var[:,0]
T = var[:,1]
rv = var[:,2]*1000.
u = var[:,3]
v = var[:,4]
data = {'T':T+273.15, 'rv':rv/1000., 'p':p*100.}
Td = atmos.calculate('Td', **data) -273.15
Tv = atmos.calculate('Tv', **data)
data = {'Tv':Tv, 'rv':rv/1000., 'p':p*100.}
rho = atmos.calculate('rho', **data)
dpdz = rho * 9.8
heights = np.zeros_like(p)
heights[0] = 10.0
for i in range(1,len(heights)):
heights[i] = ((((p[i-1] - p[i])*100.)) / dpdz[i-1]) + heights[i-1]
p=p*units.hPa45
T=T*units.degC
Td=Td*units.degC
fig = plt.figure(figsize=(9, 9))45
'ccmap': "viridis_r",
'llevels': np.arange(970, 1040, 4),
'llevels2': [1000],
'time_index': tindex,
'times': times,
'skip': skip,
'mapscale': mapscale,
'mapparms': mapparms,
'fontsize': fontsize
}
if model == 'SMN_WRF':
cffield = data['TMP_GDS3_HTGL']
cffield.values = 273.15 + atmos.calculate(
'thetae',
T=data['TMP_GDS3_HTGL'].values,
p=data['PRES_GDS3_SFC'].values,
qv=data['SPF_H_GDS3_HTGL'].values)
cffield.attrs['long_name'] = '2 m equivalent potential temperature'
cffield.attrs['units'] = 'K'
lfield = data['PRMSL_GDS3_MSL'] / 100.
lfield2 = topo
ufld = data['U_GRD_GDS3_HTGL'] * 1.94
vfld = data['V_GRD_GDS3_HTGL'] * 1.94
else:
cffield = getvar(files,
params['cfield'],
timeidx=params['time_index'],
units='K')
cffield = cffield.isel(bottom_top=0)
lfield = getvar(files, 'slp', timeidx=params['time_index'], units='hPa')
def rho_fr_t_q_p_(t, q, p):
import atmos
return atmos.calculate('rho', T=_rl(t), qv=_rl(q), p=_rl(p))