def get_cape(filename, flag, coords=None):
fil = hdf.File(filename, 'r')
height = getvar(fil, 'z_coords')
if height.size < 3:
height = getvar(
'/nobackup/rstorer/convperts/revu/aug11-control/aug11-control-revu-001.h5',
'z_coords')
dz = getdz(
'/nobackup/rstorer/convperts/revu/aug11-control/aug11-control-revu-001.h5'
)
else:
dz = getdz(fil)
tempk = getvar(fil, 'tempk')
q = getvar(fil, 'vapor')
press = getvar(fil, 'press')
fil.close()
# except:
# print 'error reading variables'
if type(coords) is tuple:
c0, c1 = coords
capevar, cinvar = capecalc(height, tempk[:, c0, c1], q[:, c0, c1],
press[:, c0, c1], flag)
else:
capevar = np.zeros((tempk.shape[1], tempk.shape[2]))
cinvar = np.zeros_like(capevar)
for c0 in range(capevar.shape[0]):
for c1 in range(capevar.shape[1]):
capevar[c0, c1], cinvar[c0,
c1] = capecalc(height, tempk[:, c0,
c1],
q[:, c0, c1],
press[:, c0, c1], flag)
return capevar, cinvar
def getrh(fil):
heights = getvar(fil, 'z_coords')[1:]
tempk = np.mean(np.mean(getvar(fil, 'tempk')[1:, :, :], 1), 1)
press = np.mean(np.mean(getvar(fil, 'press')[1:, :, :], 1), 1)
vapor = np.mean(np.mean(getvar(fil, 'vapor')[1:, :, :], 1), 1)
rho = (press * 100.) / (287. * tempk)
dz = getdz(fil)[:-1]
es = 611.2 * np.exp(17.67 * (tempk - 273.15) / (tempk - 29.65))
rvs = 622 * es / ((press * 100.) - es)
vsat = rvs * 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 = np.sum(
vact[lows]) #100. * (np.sum(vact[lows]) / np.sum(vsat[lows]))
midsat = np.sum(
vact[mids]) #100. * (np.sum(vact[mids]) / np.sum(vsat[mids]))
highsat = np.sum(
vact[highs]) #100. * (np.sum(vact[highs]) / np.sum(vsat[highs]))
allsat = np.sum(vact) #100. * (np.sum(vact) / np.sum(vsat))
return lowsat, midsat, highsat, allsat
def getrh(filename):
hfile = hdf.File(filename, 'r')
press = getvar(hfile, 'press')
tempk = getvar(hfile, 'tempk')
relhum = getvar(hfile, 'relhum')
rho = (press * 100.) / (287. * tempk)
vapor = getvar(hfile, 'vapor')
sat = satmixratio(tempk, press) * 1000.
dz = getdz(hfile)
hfile.close()
vsatall = sat * rho * dz[:, None, None]
vactall = vapor * rho * dz[:, None, None]
vsat = np.mean(np.mean(vsatall, 1), 1)
vact = np.mean(np.mean(vactall, 1), 1)
mpress = np.mean(np.mean(press, 1), 1)
lows = np.where(mpress > 850.)
mids = np.logical_and(mpress < 850, mpress > 500)
highs = np.where(mpress < 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]))
return lowsat, midsat, highsat
def get3levels(filename,varname):
hfile = hdf.File(filename,'r')
press = getvar(hfile,'press')
tempk = getvar(hfile,'tempk')
rho = (press*100.)/ (287.*tempk)
vartoavg = getvar(hfile,varname)
dz = getdz(hfile)
hfile.close()
vall = vartoavg * rho * dz[:,None,None]
mass = rho * dz[:,None,None]
vtop = np.mean(np.mean(vall,1),1)
vbot = np.mean(np.mean(mass,1),1)
mpress = np.mean(np.mean(press,1),1)
lows = np.where(mpress > 850.)
mids = np.logical_and(mpress < 850, mpress > 500)
highs = np.where(mpress < 500)
lowsat = np.sum(vtop[lows]) / np.sum(vbot[lows])
midsat = np.sum(vtop[mids]) / np.sum(vbot[mids])
highsat = np.sum(vtop[highs]) / np.sum(vbot[highs])
allsat = np.sum(vtop) / np.sum(vbot)
return lowsat, midsat, highsat, allsat
def makeoneplot(i,fil1,fil2,yval):
outdir = '/nobackup/rstorer/plots/budgetslices/'
cond1 = getvar(fil1, 'total_cond')/1000.
cond2 = getvar(fil2, 'total_cond')/1000.
micro2 = (getvar(fil2, 'nuccldrt') + getvar(fil2, 'nucicert') + getvar(fil2, 'vapliqt') + getvar(fil2, 'vapicet'))/1000.
diff = cond2-cond1
height = getvar(fil1, 'z_coords')
xs = np.arange(400)*.25
dz = getdz(fil1)
plt.subplot(2,1,1)
plt.contourf(xs[160:320], height, diff[:,yval,160:320])
plt.ylim(0,18000)
plt.ylabel('height')
plt.title('Condensate Difference')
plt.colorbar()
plt.subplot(2,1,2)
plt.contourf(xs[160:320], height, micro2[:,yval,160:320])
plt.ylim(0,18000)
plt.ylabel('height')
plt.title('microphysics')
plt.colorbar()
plt.savefig(outdir+'time_'+i+'_y'+str(yval)+'.png')
plt.clf()
def getbudgetvars(fil1, dt):
cond = getvar(fil1, 'total_cond') / 1000.
rho = (getvar(fil1, 'press') * 100.) / (getvar(fil1, 'tempk') * 287.)
w = getvar(fil1, 'w')
pcp = getvar(fil1, 'precip3d') * -1
dz = getdz(fil1)
massflux = cond * rho * w
return massflux, w, pcp
def gettracer(dirname):
files = sorted(glob.glob('/nobackup/rstorer/convperts/revu/'+dirname+'/*h5'))
outdir = '/nobackup/rstorer/plots/'
nt = len(files)
t2 = getvar(files[0],'tracer002')[:,100,100]
dz = getdz(files[0])
x = t2*dz*100*100*100
return np.sum(x)
def getcapeparcel(filename, flag):
try:
fil = hdf.File(filename, 'r')
height = getvar(fil, 'z_coords')
if height.size < 3:
height = getvar(
'/nobackup/rstorer/convperts/revu/aug11-control/aug11-control-revu-001.h5',
'z_coords')
dz = getdz(
'/nobackup/rstorer/convperts/revu/aug11-control/aug11-control-revu-001.h5'
)
else:
dz = getdz(fil)
tempk = getvar(fil, 'tempk')[:, 10, 10]
q = getvar(fil, 'vapor')[:, 10, 10]
press = getvar(fil, 'press')[:, 10, 10]
fil.close()
except:
print 'error reading variables'
if flag == 'sfc':
zlcl, lcl, tlcl = find_lcl_sfc(height, tempk, q, press)
elif flag == 'mu':
zlcl, lcl, tlcl, zstart = find_lcl_mostunstable(
height, tempk, q, press)
else:
zlcl, lcl, tlcl = find_lcl_ml100(height, tempk, q, press)
parcel = moistadiabat(height, press, zlcl, tlcl, tempk, q)
parceltv = parcel * (1 + (.61 * satmixratio(parcel, press)))
parceltv[:zlcl + 1] = parcel[:zlcl + 1] * (1 + (.61 * q[0]))
Tv = tempk * (1 + (.61 * (q / 1000.)))
posarea = np.where(parceltv > Tv)
intvar = (parceltv - Tv) * (G / Tv)
lfc = np.min(np.asarray(posarea))
el = np.max(np.asarray(posarea))
if lfc < zlcl:
lfc = zlcl
intvarpos = intvar[lfc:el + 1]
zpos = height[lfc:el + 1]
cape = np.trapz(intvarpos, dx=np.diff(zpos))
return cape, parceltv - Tv
def gettavg(fil,bottom,top):
tempk = np.mean(np.mean(getvar(fil,'tempk')[1:,:,:],1),1)
dz = getdz(fil)[:-1]
press = np.mean(np.mean(getvar(fil,'press')[1:,:,:],1),1)
layer = np.logical_and(press < bottom, press > top)
vact = tempk*dz
tavg = np.sum(vact[layer])/ np.sum(dz[layer])
return tavg
def plots(maindir, stagevar, xdir, locvars, flag):
if flag == '500mb':
zval = 39
if flag == '8km':
zval = 46
i1, i2, j1, j2, location = locvars
stage, dt = stagevar
filesrams = sorted(glob.glob(maindir + stage + '/' + xdir + '/*h5'))
nt = len(filesrams)
totflux = np.zeros(nt)
condflux = np.zeros(nt)
totmass = np.zeros(nt)
massdiff = np.zeros(nt)
pcp3d = np.zeros(nt)
for tim in range(nt):
fil = hdf.File(filesrams[tim], 'r')
w = getvar(fil, 'w')
q = getvar(fil, 'total_cond') / 1000.
v = getvar(fil, 'vapor') / 1000.
precip = getvar(fil, 'precip3d')
rho = (getvar(fil, 'press') * 100) / (getvar(fil, 'tempk') * rd)
dz = getdz(fil)
fil.close()
massfluxcond = w * q * rho
massfluxtot = w * (q + v) * rho
masstot = (q + v) * rho * dz[:, None, None]
totmass[tim] = np.sum(masstot[zval:, i1:i2, j1:j2]) * 250 * 250
totflux[tim] = np.sum(massfluxtot[zval, i1:i2, j1:j2]) * 250 * 250 * dt
condflux[tim] = np.sum(massfluxcond[zval, i1:i2,
j1:j2]) * 250 * 250 * dt
pcp3d[tim] = np.sum(precip[zval, i1:i2, j1:j2]) * 250 * 250 * dt * -1
massdiff[1:] = np.diff(totmass)
netcond = pcp3d + condflux
netvert = pcp3d + totflux
plt.plot(totflux, linewidth=3, label='total updraft flux')
plt.plot(condflux, linewidth=3, label='condensate updraft')
plt.plot(pcp3d, linewidth=3, label='precip')
plt.plot(netcond, linewidth=3, label='net condensate flux')
plt.plot(netvert, linewidth=3, label='net vertical flux')
plt.plot(massdiff, linewidth=3, label='Actual Mass Change')
plt.legend()
plt.title('Mass Budget above ' + flag + ' (kg)')
plt.savefig('/nobackup/rstorer/plots/massfluxplots/newbudget-' + xdir +
'-' + stage + '-' + location + '-' + flag + '.png')
plt.clf()
return [totflux, condflux, pcp3d, netcond, netvert, massdiff]
def getbudgetvars(fil1,fil2,mlist,dt):
cond1 = getvar(fil1, 'total_cond')/1000.
cond2 = getvar(fil2, 'total_cond')/1000.
rho1 = (getvar(fil1, 'press') * 100.) / (getvar(fil1, 'tempk') *287.)
rho2 = (getvar(fil2, 'press') * 100.) / (getvar(fil2, 'tempk') *287.)
w1 = getvar(fil1, 'w')
micro2 = np.zeros_like(w1)
for files in mlist:
micro2 = micro2+ getvar(files, 'nuccldrt') + getvar(files, 'nucicert') + getvar(files, 'vapliqt') + getvar(files, 'vapicet')
pcp1 = getvar(fil1, 'precip3d')
dz = getdz(fil1)
micro2 = (micro2*rho2*dz[:,None,None])/(dt*1000)
massflux1 = cond1*rho1*w1
diff = ((cond2*rho2*dz[:,None,None])-(cond1*rho1*dz[:,None,None]))/dt
vert1 = massflux1-pcp1
return diff, vert1, micro2
def budgetplot(maindir, xdir, dx, dy, dt):
files = sorted(glob.glob(maindir + xdir + '/*.h5'))
nt = len(files)
updraft = np.zeros(nt)
vapflux = np.zeros(nt)
tracflux = np.zeros(nt)
totalmass = np.zeros(nt)
for t, filename in enumerate(files):
print t
f = hdf.File(filename, 'r')
w = getvar(f, 'w')
q = getvar(f, 'total_cond') / 1000.
vap = getvar(f, 'vapor') / 1000.
trac = getvar(f, 'tracer002') * (100.**3)
height = getvar(f, 'z_coords')
rho = getrho(f)
dz = getdz(f)
f.close()
nz, nx, ny = q.shape
azz = np.argmin(np.absolute(height - 8000))
bzz = nz - 1
advw = rho * w * q
advv = rho * w * vap
advt = w * trac
updraft[t] = dx * dy * dt * (np.sum(advw[azz, :, :]) -
np.sum(advw[bzz, :, :]))
vapflux[t] = dx * dy * dt * (np.sum(advv[azz, :, :]) -
np.sum(advv[bzz, :, :]))
tracflux[t] = dx * dy * dt * (np.sum(advt[azz, :, :]) -
np.sum(advt[bzz, :, :]))
totalmass[t] = np.sum(q[azz:bzz, :, :] * rho[azz:bzz, :, :] *
dz[azz:bzz, None, None]) * dx * dy
for t, filename in enumerate(files):
print t
# advw = rho * w * q
# advv = rho * w * vap
# advt = w * trac
# updraft[t] = dx * dy * dt * (np.sum(advw[azz,:,:]) - np.sum(advw[bzz,:,:]))
# vapflux[t] = dx * dy * dt * (np.sum(advv[azz,:,:]) - np.sum(advv[bzz,:,:]))
# tracflux[t] = dx * dy * dt * (np.sum(advt[azz,:,:]) - np.sum(advt[bzz,:,:]))
# totalmass[t] = np.sum(q[azz:bzz,:,:]*rho[azz:bzz,:,:] * dz[azz:bzz,None,None]) * dx * dy
return updraft, vapflux, tracflux, totalmass
def getrh(fil, bottom, top):
heights = getvar(fil, 'z_coords')[1:]
tempk = np.mean(np.mean(getvar(fil, 'tempk')[1:, :, :], 1), 1)
press = np.mean(np.mean(getvar(fil, 'press')[1:, :, :], 1), 1)
vapor = np.mean(np.mean(getvar(fil, 'vapor')[1:, :, :], 1), 1)
rho = (press * 100.) / (287. * tempk)
dz = getdz(fil)[:-1]
es = 611.2 * np.exp(17.67 * (tempk - 273.15) / (tempk - 29.65))
rvs = 622 * es / ((press * 100.) - es)
vsat = rvs * rho * dz
vact = vapor * rho * dz
layer = np.logical_and(press < bottom, press > top)
columnvapor = np.sum(vact[layer])
columnrh = 100. * (np.sum(vact[layer]) / np.sum(vsat[layer]))
return columnvapor, columnrh
def getbudgetvars(fil1, fil2, dt):
cond1 = getvar(fil1, 'total_cond') / 1000.
cond2 = getvar(fil2, 'total_cond') / 1000.
rho1 = (getvar(fil1, 'press') * 100.) / (getvar(fil1, 'tempk') * 287.)
rho2 = (getvar(fil2, 'press') * 100.) / (getvar(fil2, 'tempk') * 287.)
w = getvar(fil1, 'w')
u = getvar(fil1, 'u')
v = getvar(fil1, 'v')
micro2 = (getvar(fil2, 'nuccldrt') + getvar(fil2, 'nucicert') +
getvar(fil2, 'vapliqt') + getvar(fil2, 'vapicet')) / 1000.
pcp3d = getvar(fil1, 'precip3d')
z = getvar(fil1, 'z_coords')
dz = getdz(fil1)
diff = ((cond2 * rho2 * dz[:, None, None]) -
(cond1 * rho1 * dz[:, None, None])) / dt
mic = (micro2 * rho2 * dz[:, None, None]) / dt
dzdz = np.zeros_like(w)
dzdz[1:-1, :, :] = (cond1[2:, :, :] * rho1[2:, :, :] * w[2:, :, :]) - (
cond1[:-2, :, :] * rho1[:-2, :, :] * w[:-2, :, :])
dzdx = np.zeros_like(u)
dzdx[:, :, 1:-1] = (cond1[:, :, 2:] * rho1[:, :, 2:] * u[:, :, 2:]) - (
cond1[:, :, :-2] * rho1[:, :, :-2] * u[:, :, :-2])
dzdy = np.zeros_like(v)
dzdy[:, 1:-1, :] = (cond1[:, 2:, :] * rho1[:, 2:, :] * v[:, 2:, :]) - (
cond1[:, :-2, :] * rho1[:, :-2, :] * v[:, :-2, :])
term = np.zeros_like(w)
term[1:-1, :, :] = (pcp3d[2:, :, :] - pcp3d[:-2, :, :])
vert = dzdz * -1
horiz = (dzdx + dzdy) * -1
rhs = mic + horiz + vert + term
res = diff - rhs
return w, diff, horiz, vert, term, mic, res
def plots(maindir, stagevar, xdir, locvars, flag):
if flag == '500mb':
zval = 39
if flag == '8km':
zval = 46
i1, i2, j1, j2, location = locvars
stage, dt = stagevar
filesrams = sorted(glob.glob(maindir+stage+'/'+xdir+'/*h5'))
nt = len(filesrams)
totflux= np.zeros(nt)
condflux= np.zeros(nt)
totmass = np.zeros(nt)
massdiff = np.zeros(nt)
pcp3d = np.zeros(nt)
for tim in range(nt):
fil = hdf.File(filesrams[tim],'r')
w = getvar(fil, 'w')
q = getvar(fil,'total_cond')/1000.
v = getvar(fil,'vapor')/1000.
precip = getvar(fil, 'precip3d')
rho = (getvar(fil,'press')*100)/(getvar(fil,'tempk')*rd)
dz = getdz(fil)
fil.close()
massfluxcond = w*q*rho
massfluxtot = w*(q+v)*rho
masstot = (q+v)*rho*dz[:,None,None]
totmass[tim] = np.sum(masstot[zval:,i1:i2,j1:j2])*250*250
totflux[tim]=np.sum(massfluxtot[zval,i1:i2,j1:j2])*250*250*dt
condflux[tim] = np.sum(massfluxcond[zval,i1:i2,j1:j2])*250*250*dt
pcp3d[tim] = np.sum(precip[zval,i1:i2,j1:j2])*250*250*dt*-1
massdiff[1:]=np.diff(totmass)
netcond = pcp3d + condflux
netvert = pcp3d + totflux
return [totflux,condflux,pcp3d,netcond,netvert,massdiff]
def budgetplot(maindir, xdir, dx, dy, dt):
files = sorted(glob.glob(maindir + xdir + '/*.h5'))
nt = len(files)
updraft = np.zeros(nt)
onlyup = np.zeros(nt)
for t, filename in enumerate(files):
print t
f = hdf.File(filename, 'r')
w = getvar(f, 'w')
height = getvar(f, 'z_coords')
rho = getrho(f)
dz = getdz(f)
f.close()
nz, nx, ny = w.shape
azz = np.argmin(np.absolute(height - 8000))
bzz = nz - 1
advw = rho * w
upw = copy.deepcopy(advw)
upw[w < 1] = 0.
updraft[t] = dx * dy * dt * (np.sum(advw[azz, :, :]) -
np.sum(advw[bzz, :, :]))
onlyup[t] = dx * dy * dt * (np.sum(upw[azz, :, :]) -
np.sum(upw[bzz, :, :]))
return updraft, onlyup
def plots(maindir, stage, xdir):
filesrams = sorted(glob.glob(maindir + stage + '/' + xdir + '/*h5'))
nt = len(filesrams)
timemax = np.zeros(nt)
massadd = np.zeros(nt)
masscondflux = np.zeros(nt)
flux500 = []
w5 = []
w8 = []
totmass = np.zeros(nt)
massdiff = np.zeros(nt)
microadd = np.zeros(nt)
pcp3d = np.zeros(nt)
for tim in range(nt):
fil = hdf.File(filesrams[tim], 'r')
w = getvar(fil, 'w')
q = getvar(fil, 'total_cond') / 1000.
v = getvar(fil, 'vapor') / 1000.
precip = getvar(fil, 'precip3d')
rho = (getvar(fil, 'press') * 100) / (getvar(fil, 'tempk') * rd)
micro = (getvar(fil, 'nuccldrt') + getvar(fil, 'nucicert') +
getvar(fil, 'vapicet') + getvar(fil, 'vapliqt')) / 1000.
dz = getdz(fil)
fil.close()
massfluxcond = w * q * rho
massflux = w * (q + v) * rho
timemax[tim] = np.max(massfluxcond[39, 125:175, 200:250])
flux500.append(massflux[39, 125:175, 200:250])
w[w < 1] = 0
w5.append(w[39, :, :])
w8.append(w[46, :, :])
mass = (q + v) * rho * dz[:, None, None]
totmic = micro * rho * dz[:, None, None]
totmass[tim] = np.sum(mass[39:, 125:175, 200:250]) * 250 * 250
massadd[tim] = np.sum(massflux[39, 125:175, 200:250]) * 250 * 250 * 30
masscondflux[tim] = np.sum(massfluxcond[39, 125:175,
200:250]) * 250 * 250 * 30
microadd[tim] = np.sum(totmic[39:, 125:175, 200:250]) * 250 * 250
pcp3d[tim] = np.sum(precip[39, 125:175, 200:250]) * 250 * 250 * 30 * -1
massdiff[1:] = np.diff(totmass)
plt.plot(massdiff, linewidth=3)
plt.title('mass difference (kg)')
plt.savefig('/nobackup/rstorer/plots/massfluxplots/massdiff.png')
plt.clf()
movie(flux500, '/nobackup/rstorer/plots/massfluxplots/' + xdir + stage,
'massflux500mb')
movie(w5, '/nobackup/rstorer/plots/massfluxplots/' + xdir + stage,
'w500mb')
movie(w8, '/nobackup/rstorer/plots/massfluxplots/' + xdir + stage, 'w8km')
plt.plot(massadd, linewidth=3)
plt.title('Mass added through vertical flux 500mb (kg)')
plt.savefig('/nobackup/rstorer/plots/massfluxplots/massfluxadded.png')
plt.clf()
plt.plot(timemax, linewidth=3)
plt.title('Max Mass Flux at 500mb (kg/m2s)')
plt.savefig('/nobackup/rstorer/plots/massfluxplots/timemax.png')
plt.clf()
plt.plot(totmass, linewidth=3)
plt.title('Condensate above 500mb (kg)')
plt.savefig('/nobackup/rstorer/plots/massfluxplots/totmass.png')
plt.clf()
plt.plot(massadd, linewidth=3, label='vertical flux')
plt.plot(masscondflux, linewidth=3, label='cond flux')
# plt.plot(microadd, linewidth = 3, label = 'micro')
plt.plot(pcp3d, linewidth=3, label='precip')
plt.plot((pcp3d + massadd), linewidth=3, label='sum')
plt.plot(massdiff, linewidth=3, label='diff')
plt.legend()
plt.title('Mass Budget above 500mb (kg)')
plt.savefig(
'/nobackup/rstorer/plots/massfluxplots/newbudget-growing-125-200.png')
plt.clf()
outdzdx = {}
outdzdy = {}
outdzdz = {}
outw = {}
outu = {}
outv = {}
for xdir in dirs:
print xdir
modelfiles = sorted(
glob.glob('/nobackup/rstorer/convperts/' + time + '/' + xdir +
'/*h5'))
radarfiles = sorted(
glob.glob('/nobackup/rstorer/convperts/' + time + '/quickbeam/' +
xdir + '*h5'))
height = getvar(modelfiles[0], 'z_coords')[37:]
dz = getdz(modelfiles[0])[37:]
nt = len(modelfiles)
outdzdt[xdir] = []
outdzdx[xdir] = []
outdzdy[xdir] = []
outdzdz[xdir] = []
outw[xdir] = []
outu[xdir] = []
outv[xdir] = []
for t in range(nt - 3):
print t
w = smoothedvar(getvar(modelfiles[t], 'w'))
u = smoothedvar(getvar(modelfiles[t], 'u'))
v = smoothedvar(getvar(modelfiles[t], 'v'))
ref = smoothedvar(getvar(radarfiles[t], 'reflectivity'))
ref2 = smoothedvar(getvar(radarfiles[t + 3], 'reflectivity'))
def makeplots(fil1, fil2, mlist, dt, num):
dz = getdz(fil1)
outnum = str(num)
if num < 10:
outnum = '0' + outnum
diff, vertflux, micro, w, cmf, pcp = getbudgetvars(fil1, fil2, mlist, dt)
udet, vdet, uadv, vadv = calcadvflux(fil1)
advprofs = advprofiles(fil1, uadv, vadv)
detprofs = advprofiles(fil1, udet, vdet)
vert = np.diff(vertflux, axis=0) * -1
diff1 = diff[:, 100:300, 100:300]
vert1 = vert[:, 100:300, 100:300]
flux = vertflux[:, 100:300, 100:300] * 250 * 250
updraftonly = cmf[:, 100:300, 100:300] * 250 * 250
pcponly = pcp[:, 100:300, 100:300] * 250 * 250
micro1 = micro[:, 100:300, 100:300]
height = getvar(fil1, 'z_coords')
diffprof = np.sum(np.sum(diff1, 1), 1) * 250 * 250
vertprof = np.sum(np.sum(vert1, 1), 1) * 250 * 250
micprof = np.sum(np.sum(micro1, 1), 1) * 250 * 250
advprof = np.sum(advprofs, 1) * dz * 250
detprof = np.sum(detprofs, 1) * dz * 250
intdiff = np.zeros_like(diffprof)
intmicro = np.zeros_like(diffprof)
intadv = np.zeros_like(diffprof)
intdet = np.zeros_like(diffprof)
intflux = np.sum(np.sum(flux, 1), 1)
intpcp = np.sum(np.sum(pcponly, 1), 1) * -1
intcmf = np.sum(np.sum(updraftonly, 1), 1)
testvert = np.zeros_like(diffprof)
for z in range(82):
intdiff[z] = np.sum(diffprof[z:])
intmicro[z] = np.sum(micprof[z:])
intadv[z] = np.sum(advprof[z:])
intdet[z] = np.sum(detprof[z:])
testvert[z] = np.sum(vertprof[z:])
print intmicro.shape, intflux.shape, intadv.shape, intdet.shape
sumvar = intmicro + intflux + intadv + intdet
plt.plot(intdiff[37:],
height[37:],
color='black',
linewidth=3,
label='mass diff')
plt.plot(intmicro[37:],
height[37:],
color='blue',
linewidth=3,
label='micro')
plt.plot(intflux[37:], height[37:], color='red', linewidth=3, label='flux')
plt.plot(testvert[37:],
height[37:],
color='black',
linewidth=2,
linestyle='dashed',
label='fluxtestint')
plt.plot(intpcp[37:], height[37:], color='gray', linewidth=3, label='pcp')
plt.plot(intcmf[37:],
height[37:],
color='orange',
linewidth=3,
label='updraft')
plt.plot(intadv[37:],
height[37:],
color='green',
linewidth=3,
label='advection')
plt.plot(intdet[37:],
height[37:],
color='yellow',
linewidth=3,
label='detrainment')
plt.plot(sumvar[37:],
height[37:],
color='purple',
linewidth=3,
label='sum')
# plt.xlim(-.006,.010)
# plt.xlim(-400,500)
plt.legend()
plt.title('integrated')
plt.savefig(
'/nobackup/rstorer/plots/budgetslices/bigdomainsplitadvectionprofilesintegrated5-'
+ outnum + '.png')
plt.clf()
return intdiff[46], intmicro[46], intadv[46], intflux[46]
def getbudgetvars(fil1, dt):
cond = getvar(fil1, 'tracer002') * 100. * 100. * 100.
w = getvar(fil1, 'w')
dz = getdz(fil1)
massflux = cond * w
return massflux, w
for i in range(25):
cols.append('y')
initval = {}
profs = {}
allupdraft = {}
for i, dirname in enumerate(dirs):
print dirname
files = sorted(
glob.glob('/nobackup/rstorer/convperts/revu/' + dirname + '/' +
dirname + '*h5'))
#outdir = '/nobackup/rstorer/code/'
outdir = '.'
nt = len(files)
t2 = getvar(files[0], 'tracer002')[:, 100, 100]
dz = getdz(files[0])
x = t2 * dz * 100 * 100 * 100
initval[dirname] = np.sum(x)
profs[dirname] = t2
intup = np.zeros((nt, 82))
intnum = 0.
z = getvar(files[0], 'z_coords')
zarg = np.argmin(np.abs(z - 8000))
for num in range(1, nt - 1):
up = makecode(files[num], 300., num)
intup[num, :] = up
allupdraft[dirname] = intup
outdir = '/nobackup/rstorer/plots/budgetslices/'
cond1 = getvar(fil1, 'total_cond')/1000.
cond2 = getvar(fil2, 'total_cond')/1000.
rho1 = (getvar(fil1, 'press') * 100.) / (getvar(fil1, 'tempk') *287.)
rho2 = (getvar(fil2, 'press') * 100.) / (getvar(fil2, 'tempk') *287.)
w1 = getvar(fil1, 'w')
w2 = getvar(fil2, 'w')
micro1 = (getvar(fil1, 'nuccldrt') + getvar(fil1, 'nucicert') + getvar(fil1, 'vapliqt') + getvar(fil1, 'vapicet'))/1000.
micro2 = (getvar(fil2, 'nuccldrt') + getvar(fil2, 'nucicert') + getvar(fil2, 'vapliqt') + getvar(fil2, 'vapicet'))/1000.
pcp1 = getvar(fil1, 'precip3d')
pcp2 = getvar(fil2, 'precip3d')
height = getvar(fil1, 'z_coords')
xs = np.arange(400)*.25
dz = getdz(fil1)
#micro1=micro1*rho1*dz[:,None,None]/30.
#micro2 = micro2*rho2*dz[:,None,None]/30.
#plt.contourf(xs[160:320], height, cond1[:,170,160:320])
#plt.ylim(0,18000)
#plt.ylabel('height')
#plt.xlabel('km')
#plt.title('Condensate Time 1')
#plt.colorbar()
#plt.savefig(outdir+'cond1.png')
#plt.clf()
#
#plt.contourf(xs[160:320], height, cond2[:,170,160:320])
#meanprof2 = np.load('newtracerprofiles-meanprof-wgt2.npz')
#meanprof5 = np.load('newtracerprofiles-meanprof-wgt5.npz')
y2 = {}
y5 = {}
#make time series of 8km flux and get cumulative sum for powerlaw plot
for xdir in dirs:
y2[xdir] = makesum(var2d2[xdir], zarg)
y5[xdir] = makesum(var2d5[xdir], zarg)
#get initial values of tracer to weight flux by
initval = {}
for xdir in dirs:
fil = '/nobackup/rstorer/convperts/revu/' + xdir + '/' + xdir + '-revu-001.h5'
t0 = getvar(fil, 'tracer002')[:, 100, 100]
dz = getdz(fil)
x = t0 * dz * 100 * 100 * 100
initval[xdir] = np.sum(x)
rh0 = np.load('rhlow.npz')
rh = makearray(rh0, dirs)
plotprofiles(meanprof2, z, dirs, 'newtracerprofileswgt2.png', rh)
plotprofiles(meanprof5, z, dirs, 'newtracerprofileswgt5.png', rh)
#plotscatter(y2, rh, dirs, 'newtracerprofiles-scatter-wgt2.png', initval)
#plotscatter(y5, rh, dirs, 'newtracerprofiles-scatter-wgt5.png', initval)
coloredplot(meanprof2, z, dirs, 'newtracerprofileswgt2-zoom.png', rh)
coloredplot(meanprof5, z, dirs, 'newtracerprofileswgt5-zoom.png', rh)
import matplotlib
import numpy as np
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import h5py as hdf
from rachelutils.hdfload import getvar, getdz
import glob
from rachelutils.dumbnaming import pert75
dirs = pert75()
#dirs = ['aug11-control','feb23-control','aug17-control']
height = getvar('/nobackup/rstorer/convperts/revu/feb23-control/feb23-control-revu-001.h5','z_coords')
dz = getdz('/nobackup/rstorer/convperts/revu/feb23-control/feb23-control-revu-001.h5')
zarg = []
for outheight in range(2,13,2):
zarg.append(np.argmin(np.abs(height-(outheight*1000))))
for zval in zarg:
times = ['mature','growing']
for time in times:
outdzdt = {}
outdzdx = {}
outdzdy = {}
outdzdz = {}
outw = {}
outu = {}
outv = {}
for xdir in dirs:
print xdir
modelfiles = sorted(glob.glob('/nobackup/rstorer/code/budgetfiles/'+time+'*'+xdir+'*h5'))
import numpy as np
import copy
import matplotlib
matplotlib.use("Agg")
import glob
import matplotlib.pyplot as plt
from rachelutils.hdfload import getvar,getrho,getdz
from rachelutils.dumbnaming import pert75
height = getvar('/nobackup/rstorer/convperts/mature/feb23-control/feb23-control-mature-001.h5','z_coords')
h8km = np.argmin(np.abs(height-8000))
height=height[h8km:]
dz = getdz('/nobackup/rstorer/convperts/mature/feb23-control/feb23-control-mature-001.h5')[h8km:]
modeldirs = pert75()
modeldirs = ['feb23-control','aug11-control','aug17-control']
updraftfrac = {}
plt.set_cmap('summer')
for xdir in modeldirs:
print xdir
files = sorted(glob.glob('/nobackup/rstorer/convperts/revu/'+xdir+'/*h5'))
nt = len(files)
uptimes = np.zeros(nt)
downtimes = np.zeros(nt)
for t in range(nt):
w = (getvar(files[t],'w')[h8km:,:,:])
