dt = 3600. # time step in seconds
fhout = 12. # output interval (hours)
gridtype = 'gaussian' # 'regular' or 'gaussian'
output_file = 'truth_twolevel_t%s_%sh_tst.nc' % (ntrunc, int(fhout))
# create spherical harmonic instance.
rsphere = 6.37122e6 # earth radius
sp = Spharmt(nlons, nlats, ntrunc, rsphere, gridtype=gridtype)
nstart = int((200. * 86400.) / dt) # end of spinup period
nmax = int((1300. * 86400.) / dt) # total duration of run
# create model instance
model = TwoLevel(sp, dt)
print 'pole/equator temp diff = ', model.thetaref.max() - model.thetaref.min()
# vort, div initial conditions
psipert = np.zeros((2, model.nlat, model.nlon), np.float)
psipert[1, :, :] = 5.e6 * np.sin(
(model.lons - np.pi))**12 * np.sin(2. * model.lats)**12
psipert = np.where(model.lons[np.newaxis, :, :] > 0., 0, psipert)
psipert[1, :, :] += np.random.normal(scale=1.e6, size=(sp.nlats, sp.nlons))
ug = np.zeros((2, model.nlat, model.nlon), np.float)
vg = np.zeros((2, model.nlat, model.nlon), np.float)
ug[1, :, :] = model.umax * np.sin(2. * model.lats)**model.jetexp
vrtspec, divspec = sp.getvrtdivspec(ug, vg)
vrtspec = vrtspec + model.lap * sp.grdtospec(psipert)
thetaspec = model.nlbalance(vrtspec)
divspec = np.zeros(thetaspec.shape, thetaspec.dtype)
nlats = (nlons / 2) + 1 # for regular grid. gridtype = 'regular' dt = 900 # time step in seconds tdiab = 12. * 86400 # thermal relaxation time scale tdrag = 4. * 86400. # lower layer drag efold = 4 * dt # hyperdiffusion time scale rsphere = 6.37122e6 # earth radius jetexp = 2 umax = 40 moistfact = 0.1 # create spherical harmonic instance. sp = Spharmt(nlons, nlats, rsphere=rsphere, gridtype=gridtype) # create model instance. model =\ TwoLevel(sp,dt,ntrunc,efold=efold,tdiab=tdiab,tdrag=tdrag,jetexp=jetexp,umax=umax,moistfact=moistfact) # initial state is equilbrium jet + random noise. vg = np.zeros((sp.nlat, sp.nlon, 2), np.float32) ug = model.uref vrtspec, divspec = sp.getvrtdivspec(ug, vg, model.ntrunc) psispec = np.zeros(vrtspec.shape, vrtspec.dtype) psispec.real += npran.normal(scale=1.e4, size=(psispec.shape)) psispec.imag += npran.normal(scale=1.e4, size=(psispec.shape)) vrtspec = vrtspec + model.lap[:, np.newaxis] * psispec thetaspec = model.nlbalance(vrtspec) divspec = np.zeros(thetaspec.shape, thetaspec.dtype) model.vrt = sp.spectogrd(vrtspec) model.theta = sp.spectogrd(thetaspec) model.heat = np.zeros(model.theta.shape, model.theta.dtype)
np.random.seed(42)
# model nature run to sample initial ensemble and draw additive noise.
modelclimo_file = 'truth_twolevel_t%s_12h.nc' % ntrunc
# 'truth' nature run to sample obs
# (these two files can be the same for perfect model expts)
# file to sample additive noise.
truth_file = 'truth_twolevel_t32_12h.nc'
# create spherical harmonic transform instance
sp = Spharmt(nlons,nlats,ntrunc,rsphere,gridtype=gridtype)
spout = sp
models = []
for nanal in range(nanals):
models.append(TwoLevel(sp,dt))
# weights for computing global means.
globalmeanwts = models[0].globalmeanwts
# read nature run, create obs.
nct = Dataset(truth_file)
lats = nct.variables['lat'][:]
lons = nct.variables['lon'][:]
spin = Spharmt(len(lons),len(lats),int(nct.ntrunc),rsphere,gridtype=gridtype)
samegrid = spin.nlons == spout.nlons and spin.nlats == spout.nlats
# find fibonacci points between latmin and latmax
if nobs == 1:
nobsall = 1
# single ob test.
oblonsall = np.array([180.], np.float)
dt = 3600. # time step in seconds fhout = 12. # output interval (hours) gridtype = 'gaussian' # 'regular' or 'gaussian' output_file = 'truth_twolevel_t%s_%sh_tst.nc' % (ntrunc,int(fhout)) # create spherical harmonic instance. rsphere = 6.37122e6 # earth radius sp = Spharmt(nlons,nlats,ntrunc,rsphere,gridtype=gridtype) nstart = int((200.*86400.)/dt) # end of spinup period nmax = int((1300.*86400.)/dt) # total duration of run # create model instance model = TwoLevel(sp,dt) print 'pole/equator temp diff = ',model.thetaref.max()-model.thetaref.min() # vort, div initial conditions psipert = np.zeros((2,model.nlat,model.nlon),np.float) psipert[1,:,:] = 5.e6*np.sin((model.lons-np.pi))**12*np.sin(2.*model.lats)**12 psipert = np.where(model.lons[np.newaxis,:,:] > 0., 0, psipert) psipert[1,:,:] += np.random.normal(scale=1.e6,size=(sp.nlats,sp.nlons)) ug = np.zeros((2,model.nlat,model.nlon),np.float) vg = np.zeros((2,model.nlat,model.nlon),np.float) ug[1,:,:] = model.umax*np.sin(2.*model.lats)**model.jetexp vrtspec, divspec = sp.getvrtdivspec(ug,vg) vrtspec = vrtspec + model.lap*sp.grdtospec(psipert) thetaspec = model.nlbalance(vrtspec) divspec = np.zeros(thetaspec.shape, thetaspec.dtype)
