kwargs['dt'] = 60.*5.
#kwargs['RestartFile'] = 'held_hou.nc'
kwargs['OutputFile'] = 'held_hou.nc'
kwargs['OutputFreq'] = 86400. * 5.
kwargs['MonitorFields'] = ['psi','U','T','q']
kwargs['MonitorFreq'] = 60 *60.*4.
kwargs['do_srf_lat_flx'] = 0
kwargs['do_srf_sen_flx'] = 0
# Grid
kwargs['lat'] = (arange(nlat)+0.5)*(MaxLat-MinLat)/nlat + MinLat
kwargs['lev'] = (arange(nlev)+0.5)*(MaxLev-MinLev)/nlev + MinLev
kwargs['q'] = zeros((nlev,nlat,1)) + 1.e-9
kwargs['q'][nlev*4/5,:] =1.
# Set up federation
dyn = climt.dynamics(scheme='axisymmetric')
tur = climt.turbulence()
fed = climt.federation(dyn,tur, **kwargs)
# Run
fed.step(Ndays*86400.)
try:
from matplotlib.pylab import *
show()
except:
pass
Ndays = 5.0 # Total length of run (days)
kwargs = {}
kwargs["lat"] = ["1.", "2."]
kwargs["solin"] = ["300.", "310."]
kwargs["dt"] = 60.0 * 10.0
# kwargs['RestartFile'] = 'two_column.nc'
kwargs["OutputFile"] = "two_column.nc"
kwargs["OutputFreq"] = 60 * 10.0
# kwargs['MonitorFields'] = ['U','T','q']
# kwargs['MonitorFreq'] = 60 *60.*4.
# Set up federation
dyn = climt.dynamics(scheme="two_column")
tur = climt.turbulence()
rad = climt.radiation(scheme="cam3")
con = climt.convection(scheme="hard")
oce = climt.ocean()
fed = climt.federation(dyn, tur, rad, con, oce, **kwargs)
# Run
fed.step(Ndays * 86400.0)
try:
from matplotlib.pylab import *
show()
except:
pass
Ndays = 5. # Total length of run (days)
kwargs={}
kwargs['lat'] = ['1.','2.']
kwargs['solin'] = ['300.','310.']
kwargs['dt'] = 60.*10.
#kwargs['RestartFile'] = 'two_column.nc'
kwargs['OutputFile'] = 'two_column.nc'
kwargs['OutputFreq'] = 60*10.
#kwargs['MonitorFields'] = ['U','T','q']
#kwargs['MonitorFreq'] = 60 *60.*4.
# Set up federation
dyn = climt.dynamics(scheme='two_column')
tur = climt.turbulence()
rad = climt.radiation(scheme='cam3')
con = climt.convection(scheme='hard')
oce = climt.ocean()
fed = climt.federation(dyn,tur,rad,con,oce, **kwargs)
# Run
fed.step(Ndays*86400.)
try:
from matplotlib.pylab import *
show()
except:
pass
# If RestartFile and OutputFile are the same, then output will be
# appended to the restart file (ie. a continuation run).
#kwargs['RestartFile'] = 'restart.nc'
# 2) If RestartFile is not given, then initial values for prognostic
# fields must be explicitly given, e.g.
nlev = climt.get_nlev()
stebol = climt.Parameters()['stebol']
kwargs['q'] = zeros(nlev) + 1.e-9
kwargs['T'] = zeros(nlev) + (kwargs['solin'] / 2. / stebol)**0.25
# -- Instantiate components and federation
rad = climt.radiation(UpdateFreq=kwargs['dt'] * 50, scheme='cam3')
con = climt.convection(scheme='emanuel')
dif = climt.turbulence()
oce = climt.ocean()
fed = climt.federation(dif, rad, oce, con, **kwargs)
# Main timestepping loop
RunLength = 2000. # Total length of run (days)
NSteps = int(RunLength * 86400. / fed['dt'])
for i in range(NSteps):
# With a line like the following, it is possible to manipulate
# field values each time step (the example here resets humidity
# to an almost dry value everywhere at each timestep)
#fed.State['q']=zeros(rad.nlev)+1.e-9
# The following code adds a uniform 1 K/day cooling rate to
# the internally-computed tendencies
dT = array([[-1. / 86400. * kwargs['dt'] * 2. * ones(rad.nlev)]
]).transpose()
fed.step(Inc={'T': dT})
printout(fed)
# -- Instantiate components and federation
rad = climt.radiation(UpdateFreq=kwargs['dt']*50)
con = climt.convection(scheme='emanuel')
dif = climt.turbulence()
ice = climt.seaice()
ins = climt.insolation(lat=85.,avg='daily')
kwargs['Hslab']=50.
kwargs['Pr']=100.
#kwargs['do_srf_lat_flx']=0
#kwargs['do_srf_sen_flx']=0
T0,q0 = climt.thermodyn.moistadiabat(rad['p'],273.,273.-90,.1)
kwargs['T'],kwargs['q'] = T0,q0
fed = climt.federation(dif, rad, ice, con, ins, **kwargs)
# Main timestepping loop
RunLength = 2000. # Total length of run (days)
NSteps = int( RunLength*86400./fed['dt'] )
TdotDyn = 2./86400.
for i in range(NSteps):
# With a line like the following, it is possible to manipulate
# field values each time step (the example here resets humidity
# to an almost dry value everywhere at each timestep)
#fed.State['q']= q0[:,None,None]
# Inject heating here
fed.State['T'][14:23] += TdotDyn*fed['dt']
# Also put in a cloud; we could reset the moisture here, too
#fed.State['cldf'][14:25] = 1.
#fed.State['clwp'][14:25] = 30.
# If RestartFile and OutputFile are the same, then output will be
# appended to the restart file (ie. a continuation run).
#kwargs['RestartFile'] = 'restart.nc'
# 2) If RestartFile is not given, then initial values for prognostic
# fields must be explicitly given, e.g.
nlev = climt.get_nlev()
stebol = climt.Parameters()['stebol']
kwargs['q'] = zeros(nlev) + 1.e-9
kwargs['T'] = zeros(nlev) + (kwargs['solin']/2./stebol)**0.25
# -- Instantiate components and federation
rad = climt.radiation(UpdateFreq=kwargs['dt']*50, scheme='cam3')
con = climt.convection(scheme='emanuel')
dif = climt.turbulence()
oce = climt.ocean()
fed = climt.federation(dif, rad, oce, con, **kwargs)
# Main timestepping loop
RunLength = 2000. # Total length of run (days)
NSteps = int( RunLength*86400./fed['dt'] )
for i in range(NSteps):
# With a line like the following, it is possible to manipulate
# field values each time step (the example here resets humidity
# to an almost dry value everywhere at each timestep)
#fed.State['q']=zeros(rad.nlev)+1.e-9
# The following code adds a uniform 1 K/day cooling rate to
# the internally-computed tendencies
dT= array([[-1./86400.*kwargs['dt']*2.*ones(rad.nlev)]]).transpose()
fed.step(Inc={'T':dT})
printout(fed)
kwargs['T'] = zeros((nlev,nlat)) + 100.
kwargs['q'] = zeros((nlev,nlat)) + 1.e-9
# Run length (days)
Ndays = 3000
########################## End user adjustble
kwargs['lat'] = (arange(nlat)+0.5)*(MaxLat-MinLat)/nlat + MinLat
kwargs['lev'] = (arange(nlev)+0.5)*(MaxLev-MinLev)/nlev + MinLev
# Set up federation
tur = climt.turbulence()
dyn = climt.dynamics()
ocn = climt.ocean()
con = climt.convection()
fed = climt.federation(dyn, tur, ocn, con, **kwargs)
# Run
Nsteps = int(Ndays*86400./kwargs['dt'])
ins = climt.insolation(lat=kwargs['lat'], avg='annual')
CoolingRate = 0./86400.
for l in range(Nsteps):
fed.State['SrfRadFlx'] = ins.State['solin']*0.5 - fed['stebol']*fed.State['Ts']**4
fed.step()
fed.State['T'][4:nlev,:,:] -= CoolingRate*kwargs['dt']*ones((nlev-4,nlat,1))
try:
from matplotlib.pylab import *
show()
except:
