def try_caltra_ridge_rev(k=0):
# Create a mapping from datetime objects to filenames
basetime = datetime.datetime(2016, 9, 30, 12)
basetime_str = basetime.strftime('%Y%m%d_%H')
datadir = '/export/cloud/NCASweather/ben/nawdex/mi-ar482/{}/'.format(
basetime_str)
timestep = datetime.timedelta(hours=6)
hoursafterinit = [42, 72, 96, 96]
hoursuntil = [156, 156, 156, 156]
times = [
basetime + timestep * i
for i in range(hoursafterinit[k] / 6, hoursuntil[k] / 6 + 1)
]
print times[0]
#creates mapping from selected outflow time until selected end time
mapping = {}
for t in times:
leadtimehr = np.int((t - basetime).total_seconds()) / 3600
fn = 'prodm_op_gl-mn_{0}_d{1:03d}_thgrid.pp'.\
format(basetime_str, 12 * (leadtimehr / 12))
mapping[t] = datadir + fn
if k != 3:
trainp_th = np.load(
'/glusterfs/msc/users_2018/bn826011/NAWDEX/IOP67ridge/' +
times[0].strftime("%m%d_%H") + '.npy')
#trajectory input on theta levels
else:
trainp_th = np.load(
'/glusterfs/msc/users_2018/bn826011/NAWDEX/IOP67ridge/' +
times[0].strftime("%m%d_%H") + '_ridge2.npy')
levels = ('air_potential_temperature',
[trainp_th[0, 2] + 5 * i for i in range(3)])
# three isentropic levels including that of the first data point
print levels
tracers = [
'altitude', 'x_wind', 'y_wind', 'upward_air_velocity', 'air_pressure'
]
#need these for circulation integrals
traout = caltra.caltra(trainp_th,
mapping,
fbflag=1,
nsubs=12,
tracers=tracers,
levels=levels)
# 12 steps between files = 30 mins apart
traout.save(
'/glusterfs/msc/users_2018/bn826011/NAWDEX/IOP67ridge/{}_TrajectoryEnsemble_forward'
.format(basetime_str) + str(k + 1))
return traout
def inflow_caltra(k=0,
levs=3,
hoursafterinit=[42, 36, 42, 24],
folder='IOP3/T42',
strapp=''):
# Create a mapping from datetime objects to filenames
basetime = [
datetime.datetime(2016, 9, 22, 12),
datetime.datetime(2016, 9, 26, 12),
datetime.datetime(2016, 9, 30, 12),
datetime.datetime(2016, 10, 03, 12)
]
basetime_str = basetime[k].strftime('%Y%m%d_%H')
#datadir = '/export/cloud/migrated-NCASweather/ben/nawdex/mi-ar482/{}/'.format(basetime_str)
datadir = '/storage/silver/NCAS-Weather/ben/nawdex/mi-ar482/{}/'.format(
basetime_str)
timestep = datetime.timedelta(hours=6)
times = [
basetime[k] + timestep * i for i in range(hoursafterinit[k] / 6 + 1)
]
print times[-1]
#creates mapping up to and including selected outflow time
mapping = {}
for t in times:
leadtimehr = np.int((t - basetime[k]).total_seconds()) / 3600
fn = [datadir + 'prodm_op_gl-mn_{0}_d{1:03d}_thgrid.pp'.\
format(basetime_str, 12 * (leadtimehr / 12)),
datadir + 'prodm_op_gl-mn_{0}_c{1:03d}.pp'.\
format(basetime_str, 6 * (leadtimehr / 6))] # new addition of c to mapping
mapping[t] = fn #datadir + fn
#trainp_th = np.load('/glusterfs/msc/users_2018/bn826011/NAWDEX/Final/' + folder + '/inflow/' + basetime_str + strapp + 'initial_grid.npy')
trainp_th = np.load(
'/storage/silver/scenario/bn826011/WCB_outflow/Final/' + folder +
'/inflow/' + basetime_str + strapp + 'initial_grid.npy')
#trajectory input on theta levels
tracers = [
'air_potential_temperature', 'air_pressure', 'derived_pv', 'dPV_tot',
'adv_only_PV', 'dPV_LW', 'dPV_mic', 'dPV_conv', 'dPV_adv', 'dPV_SW',
'dPV_ph1', 'dPV_bl', 'dPV_cld', 'dPV_mass'
] # 'specific_humidity', 'mass_fraction_of_cloud_ice_in_air', 'mass_fraction_of_cloud_liquid_water_in_air', 'derived_pv']
#need these for circulation integrals
traout = caltra.caltra(trainp_th,
mapping,
fbflag=-1,
nsubs=12,
tracers=tracers)
# 12 steps between files = 30 mins apart
traout.save('/storage/silver/scenario/bn826011/WCB_outflow/Final/' +
folder +
'/inflow/{}_3DTrajectoryEnsemble_dPV'.format(basetime_str) +
strapp)
return traout
def backward_isentropic_trajectories_from_outflow():
"""Calculate isentropic backward trajectories around boundary of the outflow
"""
traout = caltra.caltra(trainp, mapping, fbflag=-1, tracers=tracers,
levels=('air_potential_temperature', [320]))
return
def try_caltra_rev(k = 0, levs = 3, hoursafterinit = [42, 36, 42, 24], hoursuntil = [90, 78, 156, 84], filtersize = 30, folder = 'IOP3/contours_2019', strapp = '', theta_max = 315):
# Create a mapping from datetime objects to filenames
basetime = [datetime.datetime(2016, 9, 22, 12), datetime.datetime(2016, 9, 26, 12), datetime.datetime(2016, 9, 30, 12), datetime.datetime(2016, 10, 03, 12)]
basetime_str = basetime[k].strftime('%Y%m%d_%H')
datadir = '/export/cloud/migrated-NCASweather/ben/nawdex/mi-ar482/{}/'.format(basetime_str)
timestep = datetime.timedelta(hours=6)
#hoursafterinit = [42, 42, 42, 72, 96]#42
#hoursuntil = [78, 78, 78, 108]
times = [basetime[k] + timestep * i for i in range(hoursafterinit[k]/6, hoursuntil[k]/6 + 1)]
print times[0]
#creates mapping from selected outflow time until selected end time
print 7
mapping = {}
for t in times:
leadtimehr = np.int((t - basetime[k]).total_seconds()) / 3600
fn = 'prodm_op_gl-mn_{0}_d{1:03d}_thgrid.pp'.\
format(basetime_str, 12 * (leadtimehr / 12))
mapping[t] = datadir + fn
#trainp_th = np.load('outflow/T42_mfs' + str(filtersize) + '/' + times[0].strftime("%m%d_%H") + '.npy')
#trainp_th = np.load('/glusterfs/msc/users_2018/bn826011/NAWDEX/Final/' + folder + '/' + times[0].strftime("%m%d_%H") + strapp + '.npy')
trainp_th = np.load('/storage/silver/scenario/bn826011/WCB_outflow/Final/' + folder + '/' + times[0].strftime("%m%d_%H") + strapp + '.npy')
#trainp_th = np.load('/storage/silver/scenario/bn826011/WCB_outflow/Final/' + folder + 2)
#trajectory input on theta levels
if len(trainp_th[0]) >= 3:
# the input file has already been given theta levels
levels = ('air_potential_temperature', [trainp_th[0, 2] + 5*i for i in range(levs)])
# three isentropic levels including that of the first data point
else:
levels = ('air_potential_temperature', [theta_max - 5*i for i in range(levs)])
trainp_th_new = np.concatenate([trainp_th, np.transpose([theta_max*np.ones_like(trainp_th[:, 0])])], axis = 1)
for i in range(1, levs):
trainp_th_new = np.concatenate([trainp_th_new, np.concatenate([trainp_th, np.transpose([(theta_max - 5*i)*np.ones_like(trainp_th[:, 0])])], axis = 1)], axis = 0)
trainp_th = trainp_th_new
print levels
tracers = ['altitude', 'x_wind', 'y_wind', 'upward_air_velocity', 'air_pressure']
#need these for circulation integrals
traout = caltra.caltra(trainp_th, mapping, fbflag=1, nsubs = 12, tracers = tracers, levels=levels)
# 12 steps between files = 30 mins apart
#np.save('outflow/{}_trajectories.npy'.format(basetime_str), traout)
#saving it as a numpy file removes functionality oof trajectory ensemble class, making it effectively useless, yay!
#traout.save('outflow/T42_mfs' + str(filtersize) + '/{}_TrajectoryEnsemble_forward'.format(basetime_str))
#traout.save('/glusterfs/msc/users_2018/bn826011/NAWDEX/Final/' + folder + '/{}_TrajectoryEnsemble_forward'.format(basetime_str) + strapp)
traout.save('/storage/silver/scenario/bn826011/WCB_outflow/Final/' + folder + '/{}_TrajectoryEnsemble_forward'.format(basetime_str) + strapp)
return traout
def main(forecast, filename):
# Get the input positions
cubes = forecast.set_lead_time(hours=36)
trainp = get_startpoints(cubes)
# Calculate trajectories
mapping = forecast._loader._files
traout = caltra.caltra(trainp, mapping, fbflag=-1, tracers=tracers)
# Save the trajectories
traout.to_pickle(filename)
def try_caltra(k = 0, levs = 3, hoursafterinit = [42, 36, 42, 24], filtersize = 30, folder = 'IOP3/T42', strapp = ''):
# Create a mapping from datetime objects to filenames
basetime = [datetime.datetime(2016, 9, 22, 12), datetime.datetime(2016, 9, 26, 12), datetime.datetime(2016, 9, 30, 12), datetime.datetime(2016, 10, 03, 12)]
basetime_str = basetime[k].strftime('%Y%m%d_%H')
#datadir = '/export/cloud/migrated-NCASweather/ben/nawdex/mi-ar482/{}/'.format(basetime_str)
datadir = '/storage/silver/NCAS-Weather/ben/nawdex/mi-ar482/{}/'.format(basetime_str)
timestep = datetime.timedelta(hours=6)
#hoursafterinit = [42, 42, 42, 72, 96]#42
times = [basetime[k] + timestep * i for i in range(hoursafterinit[k]/6 + 1)]
print times[-1]
#creates mapping up to and including selected outflow time
mapping = {}
for t in times:
leadtimehr = np.int((t - basetime[k]).total_seconds()) / 3600
fn = [datadir + 'prodm_op_gl-mn_{0}_d{1:03d}_thgrid.pp'.\
format(basetime_str, 12 * (leadtimehr / 12)),
datadir + 'prodm_op_gl-mn_{0}_c{1:03d}.pp'.\
format(basetime_str, 6 * (leadtimehr / 6))] # new addition of c to mapping
mapping[t] = fn#datadir + fn
#trainp_th = np.load('outflow/T42_mfs' + str(filtersize) + '/' + times[-1].strftime("%m%d_%H") + '.npy')
#trainp_th = np.load('/glusterfs/msc/users_2018/bn826011/NAWDEX/Final/' + folder + '/' + times[-1].strftime("%m%d_%H") + strapp + '.npy')
trainp_th = np.load('/storage/silver/scenario/bn826011/WCB_outflow/Final/' + folder + '/inflow/' + basetime_str + strapp + 'initial_grid_thlevs.npy')
#trajectory input on theta levels
levels = ('air_potential_temperature', [trainp_th[0, 2] + 5*i for i in range(levs)])
# three isentropic levels including that of the first data point
#levels = ('air_potential_temperature', [325])
print levels
tracers = ['altitude', 'x_wind', 'y_wind', 'upward_air_velocity', 'air_pressure', 'derived_pv', 'dPV_tot', 'adv_only_PV', 'dPV_LW', 'dPV_mic', 'dPV_conv', 'dPV_adv', 'dPV_SW', 'dPV_ph1', 'dPV_bl', 'dPV_cld', 'dPV_mass']
#need these for circulation integrals
traout = caltra.caltra(trainp_th, mapping, fbflag=-1, nsubs = 12, tracers = tracers, levels=levels)
# 12 steps between files = 30 mins apart
#np.save('outflow/{}_trajectories.npy'.format(basetime_str), traout)
#saving it as a numpy file removes functionality oof trajectory ensemble class, making it effectively useless, yay!
#traout.save('outflow/T42_mfs' + str(filtersize) + '/{}_TrajectoryEnsemble_backward'.format(basetime_str))
#traout.save('/glusterfs/msc/users_2018/bn826011/NAWDEX/Final/' + folder + '/{}_TrajectoryEnsemble_backward'.format(basetime_str) + strapp)
#traout.save('/storage/silver/scenario/bn826011/WCB_outflow/Final/' + folder + '/{}_TrajectoryEnsemble_backward'.format(basetime_str) + strapp)
traout.save('/storage/silver/scenario/bn826011/WCB_outflow/Final/' + folder + '/inflow/{}_2DTrajectoryEnsemble_dPV'.format(basetime_str) + strapp)
return traout
def forward_trajectories(forecast):
"""Calculate 48 hour forward trajectories from low levels
Start trajectories from all points below 2km
"""
cubes = forecast.set_lead_time(hours=48)
z = convert.calc('altitude', cubes)
theta = convert.calc('air_potential_temperature', cubes)
theta_adv = convert.calc('advection_only_theta', cubes)
pv = convert.calc('ertel_potential_vorticity', cubes)
lon, lat = grid.true_coords(pv)
glon, glat = grid.get_xy_grids(pv)
time = grid.get_datetime(pv)
nz, ny, nx = pv.shape
eqlats = rossby_waves.eqlats
cs = iris.Constraint(time=time)
with iris.FUTURE.context(cell_datetime_objects=True):
eqlat = eqlats.extract(cs)[0]
# Interpolate to the theta and PV surfaces
eqlat = interpolate.main(eqlat, ertel_potential_vorticity=2)
eqlat = interpolate.main(eqlat,
potential_temperature=theta.data.flatten())
# Define the start points
trainp = []
for k in range(nz):
print(k)
for j in range(ny):
for i in range(nx):
if (theta_adv.data[k, j, i] < 300 < theta.data[k, j, i] and
pv.data[k, j, i] < 2 and
lat[j, i] > eqlat.data[k * ny * nx + j * nx + i]):
trainp.append([glon[j, i], glat[j, i], z.data[k, j, i]])
trainp = np.array(trainp)
plot.pcolormesh(pv[33], vmin=0, vmax=10, cmap='cubehelix_r', pv=pv[33])
plt.scatter(trainp[:, 0], trainp[:, 1])
plt.show()
# Calculate the trajectories
tracers = ['air_potential_temperature', 'air_pressure']
traout = caltra.caltra(trainp, mapping, fbflag=-1, tracers=tracers)
# Save the trajectories
traout.save(datadir + 'backward_trajectories.pkl')