years = np.array(years)
slp_full = np.array(slp_full)
###########################
# Get the necessary indices
###########################
[ind1a] = np.where(years == analysis_years[0])
[ind1b] = np.where(years == analysis_years[1])
[ind1c] = np.where(years == plot_year[0])
ind1a = int(ind1a)
ind1b = int(ind1b)
ind1c = int(ind1c)
nyears, nmonths = np.shape(slp_full)
slp_full = um.filter_numeric_nans(slp_full, 0, float('NaN'), 'low')
slp_yearin = slp_full[ind1c, :]
###########################
# Calculate the monthly mean
###########################
slp_monthly_mean = np.nanmean(slp_full[ind1a:ind1b + 1, :], 0)
print(slp_monthly_mean)
print(nyears, nmonths)
######################
# mass transport calculations
######################
dslp_full = np.empty([nyears, nmonths])
dslp_yearin = np.empty(nmonths)
cmap_opt = plt.cm.RdBu_r
figname = "wrf_cross_section_ttendf"
if dlat_dlon[0] == 0:
figname = "wrf_cross_section_ew_tdynamics"
if dlat_dlon[1] == 0:
figname = "wrf_cross_section_ns_tdynamics"
elif plot_option == 14:
u = nc.variables['U'][timeindex, :, :, :].squeeze()
v = nc.variables['V'][timeindex, :, :, :].squeeze()
ghgt = nc.variables['HEIGHT'][timeindex, :, :, :].squeeze()
ghgt = um.nan2zero(ghgt)
ug, vg = wm.geostrophic_cartesian(u, v, ghgt, lats, dy, dx)
ug = um.filter_numeric_nans(ug, 200, float('NaN'), 'high')
ug = um.filter_numeric_nans(ug, -200, float('NaN'), 'low')
vg = um.filter_numeric_nans(vg, 200, float('NaN'), 'high')
vg = um.filter_numeric_nans(vg, -200, float('NaN'), 'low')
thickness = np.zeros_like(ghgt).astype('f')
thickness[1:-1, :, :] = (ghgt[2:, :, :] - ghgt[:-2, :, :]) / 2
thickness[0, :, :] = (ghgt[1, :, :] - ghgt[0, :, :])
thickness[-1, :, :] = (ghgt[-1, :, :] - ghgt[-2, :, :])
zeta_a = np.zeros_like(ghgt).astype('f')
[iz, iy, ix] = np.shape(zeta_a)
for ii in range(iz):
zeta_a[ii, :, :] = wm.vertical_vorticity_cartesian(
ug[ii, :, :].squeeze(), vg[ii, :, :].squeeze(), lats, dx, dy, 1)
#cbar_max_trth = 335
cbar_max_trth = 380
#cbar_max_trth = 400
cint_trth = 1
cflevs_trth = np.arange(cbar_min_trth, cbar_max_trth, cint_trth)
#cflevs_trth_cntrs = cflevs_trth[0:12]
cflevs_trth_cntrs = cflevs_trth
cflevs_trth_ticks = np.arange(cbar_min_trth,cbar_max_trth,4*cint_trth)
cmap_opt = plt.cm.jet
#cmap_opt = plt.cm.Blues_r
trth = ndimage.gaussian_filter(trth,0.75)
trth = um.filter_numeric_nans(trth,trth_thresh+cint_trth,trth_thresh+cint_trth,'high')
plotvar = trth
mstats(plotvar)
#plotvar, lons = um.addcyclic(trth[tt,:,:].squeeze(), lonin)
#plotvar = ndimage.gaussian_filter(plotvar,0.75)
titletext1 = 'Tropopause potential temperature gradient %s at %s UTC' % (dt.strftime('%d %b %Y'), dt.strftime('%H00'))
if plot_field == 'trpr' :
cbarlabel = 'Pressure (hPa)'
cint_trth = 20
cbar_max_trth = 800.
# Backward Euler z = 1.0/((1-X)**(2.0) + (Y)**(2.0) ) # In[5]: # Set global figure properties golden = (pylab.sqrt(5)+1.)/2. figprops = dict(figsize=(8., 16./golden),dpi=128) adjustprops = dict(left=0.15, bottom=0.1, right=0.90, top = 0.93, wspace=0.2, hspace=0.2) cint = 0.1 clevs = np.arange(0.0,2.0+(cint/2),cint) cmap_opt = plt.cm.RdBu_r z = um.filter_numeric_nans(z,np.max(clevs),np.max(clevs),'high') # In[6]: fig = plt.figure(**figprops) # New figure ax1 = fig.add_axes([0.1,0.1,0.8,0.8]) CS1 = plt.contourf(X,Y,z,cmap=cmap_opt,levels=clevs) cbar = plt.colorbar(CS1, shrink=0.95, orientation='horizontal',extend='both',pad=0.10) CS2 = ax1.contour(X,Y,z,levels=clevs, colors='k',extend='both',zorder=1,linewidths=2) plt.clabel(CS2, inline=1, fontsize=10) CS3 = plt.contour(X,Y,z,(1.0,),colors='r',extend='both',zorder=1,linewidths=4)
def find_amplitude(datain,dataininds,thresh,min_or_max):
"""
Finds the amplitude of datain
Input:
datain - 2D data array
dataininds - masked array of indices denoting section of datain array to search for minimum or maximum
thresh - threshold min/max of datain
min_or_max - 'min' to find the amplitude from a minimum and 'max' to find the amplitude from a maximum
Steven Cavallo
February 2013
University of Oklahoma
"""
import utilities_modules as um
import numpy as np
datain = um.filter_numeric_nans(datain,thresh,thresh,'high')
datasave = datain
if min_or_max == 'min':
mininds=np.where(datain==np.min(datain[dataininds]))
else:
mininds=np.where(datain==np.max(datain[dataininds]))
mininds2 = np.ma.getdata(mininds)
minval = datasave[mininds2[0],mininds2[1]]
#datain = ndimage.gaussian_filter(datain,0.75)
datain = ndimage.gaussian_filter(datain,5.0)
[iy,ix] = np.shape(datain)
minsearch = np.min(mininds2)
amps = np.zeros(8)
for ii in range(0,8):
for jj in range(minsearch-1,0,-1):
# Search up
if ii==0:
try:
if datain[jj-1,mininds2[1]] < datain[jj,mininds2[1]]:
amps[ii] = datasave[jj,mininds2[1]] - minval
break
except:
continue
# Search NE
if ii==1:
try:
if datain[jj-1,jj+1] < datain[jj,jj]:
amps[ii] = datasave[jj,jj] - minval
break
except:
continue
# Search right
if ii==2:
try:
if datain[mininds2[0],jj+1] < datain[mininds2[0],jj]:
amps[ii] = datasave[mininds2[0],jj] - minval
break
except:
continue
# Search SE
if ii==3:
try:
if datain[jj+1,jj+1] < datain[jj,jj]:
amps[ii] = datasave[jj,jj] - minval
break
except:
continue
# Search down
if ii==4:
try:
if datain[jj+1,mininds2[1]] < datain[jj,mininds2[1]]:
amps[ii] = datasave[jj,mininds2[1]] - minval
break
except:
continue
# Search SW
if ii==5:
try:
if datain[jj+1,jj-1] < datain[jj,jj]:
amps[ii] = datasave[jj,jj] - minval
break
except:
continue
# Search left
if ii==6:
try:
if datain[mininds2[0],jj-1] < datain[mininds2[0],jj]:
amps[ii] = datasave[mininds2[0],jj] - minval
break
except:
continue
# Search NW
if ii==7:
try:
if datain[jj-1,jj-1] < datain[jj,jj]:
amps[ii] = datasave[jj,jj] - minval
break
except:
continue
try:
ampinds = np.where((amps>=1) & (amps<=50))
#ampout = np.min(amps[ampinds])
ampsort = np.sort(amps[ampinds])
ampout = np.median(ampsort[0:2])
except:
ampout = float('NaN')
#ampout = np.median(amps)
return ampout
latarr = np.zeros_like(trth).astype('f')
lonarr = np.zeros_like(trth).astype('f')
for ii in range(0, len(lons)):
for jj in range(0, len(lats)):
latarr[jj, ii] = lats[jj]
lonarr[jj, ii] = lons[ii]
cint_bg = 1
cbar_min_bg = 270
cbar_max_bg = 380
cmap_opt_bg = plt.cm.jet
cflevs_bg = np.arange(cbar_min_bg, cbar_max_bg + (cint_bg / 2),
cint_bg)
plotvar_bg = um.filter_numeric_nans(plotvar_bg,
cbar_max_bg - (cint_bg / 2),
cbar_max_bg, 'high')
plotvar_bg = um.filter_numeric_nans(plotvar_bg, cbar_min_bg, cbar_min_bg,
'low')
golden = (np.sqrt(5) + 1.) / 2.
#m = Basemap(projection='npstere',boundinglat=30,lon_0=270.,resolution='l')
# Get the cross section points. This is a function in utilities_modules.
if cross_method == 1:
m = Basemap(projection='npstere',
boundinglat=30,
lon_0=270.,
resolution='l')
def eliassen_palm_flux_sphere(geop,theta,lats,lons,levs):
"""
Computes the 3-D Eliassen-Palm flux vectors and divergence on a
latitude/longitude grid with any vertical coordinate
Computation is Equation 5.7 from:
R. A. Plumb, On the Three-dimensional Propagation of Stationary Waves, J. Atmos. Sci., No. 3, 42 (1985).
Input:
geop: 3D geopotential (m^2 s-2)
theta: 3D potential temperature (K)
lats,lons: 1D latitude and longitude vectors
levs: 1D pressure vector (Pa)
Output:
Fx, Fy, Fz: Eliassen-Palm flux x, y, z vector components
divF: Eliassen-Palm flux divergence
Steven Cavallo
March 2014
University of Oklahoma
"""
import utilities_modules as um
iz, iy, ix = geop.shape
# First need to filter out numeric nans
geop = um.filter_numeric_nans(geop,0,0,'low')
theta = um.filter_numeric_nans(theta,200,0,'low')
theta = um.filter_numeric_nans(theta,10000,0,'high')
theta_anom, theta_anom_std = spatial_anomaly(theta,1)
geop_anom , geop_anom_std = spatial_anomaly(geop,1)
latarr = np.zeros_like(geop).astype('f')
farr = np.zeros_like(geop).astype('f')
for kk in range(0,iz):
for jj in range(0,iy):
latarr[kk,jj,:] = lats[jj]
farr[kk,jj,:] = 2*omeg_e*np.sin(lats[jj]*(np.pi/180))
psi = geop / farr
psi_anom, psi_anom_std = spatial_anomaly(psi,1)
pres = np.zeros_like(theta_anom).astype('f')
for kk in range(0,iz):
pres[kk,:,:] = levs[kk]
coef = pres*np.cos(latarr*(np.pi/180))
arg1 = coef/( 2*np.pi*(R_earth**2)*np.cos(latarr*(np.pi/180))*np.cos(latarr*(np.pi/180)) )
arg2 = coef/( 2*np.pi*(R_earth**2)*np.cos(latarr*(np.pi/180)) )
arg3 = coef*(2*(omeg_e**2)*np.sin(latarr*(np.pi/180))*np.sin(latarr*(np.pi/180)))
dthdz, yy, xx = gradient_cendiff_latlon(theta, geop/g, lats, lons)
xx, dthdy, dthdx = gradient_cendiff_latlon(theta_anom, geop/g, lats, lons)
dpsidz, dpsidy, dpsidx = gradient_cendiff_latlon(psi_anom, geop/g, lats, lons)
d2psidxdz, d2psidxdy, d2psidx2 = gradient_cendiff_latlon(dpsidx, geop/g, lats, lons)
aaa, d2psidxdz, ccc = gradient_cendiff_latlon(dpsidz, geop/g, lats, lons)
N2 = (g/theta)*(dthdz)
arg4 = arg3/(N2*R_earth*np.cos(latarr*(np.pi/180)))
Fx = arg1*( dpsidx**2 - (psi_anom*d2psidx2))
Fy = arg2*((dpsidy*dpsidx) - (psi_anom*d2psidxdy))
Fz = arg4*((dpsidx*dpsidz) - (psi_anom*d2psidxdz))
Fx_z, Fx_y, Fx_x = gradient_sphere(Fx, geop/g, lats, lons)
Fy_z, Fy_y, Fy_x = gradient_sphere(Fy, geop/g, lats, lons)
Fz_z, Fz_y, Fz_x = gradient_sphere(Fz, geop/g, lats, lons)
divF = Fx_x + Fy_y + Fz_z
return Fx, Fy, Fz, divF
cbar_min = -300
cbar_max = 300+(cint/2)
cbar_labels = 'm K s-1'
titlestring = "Heat flux " + date_string
cmap_opt = plt.cm.RdBu
epvin = nc.variables['PV'][timeindex,:,:,:].squeeze()
epv_cross = epvin[:,xx,yy]
thetain = nc.variables['THETA'][timeindex,:,:,:].squeeze()
theta_cross = thetain[:,xx,yy]
nc.close
plot_cross = um.filter_numeric_nans(plot_cross,cbar_max-(cint/2),cbar_max,'high')
plot_cross = um.filter_numeric_nans(plot_cross,cbar_min,cbar_min,'low')
x, y = m(lons, lats)
clevs1 = np.arange(240, 360, 2)
#clevs2 = np.arange(250, 340, 2)
clevs2 = np.arange(cbar_min,cbar_max+(cint/2),cint)
clevs2_ticks = clevs2[::5]
slplevs = np.arange(948,1000,4)
slp_ticks = slplevs[::2]
golden = (np.sqrt(5)+1.)/2.
# Plot plan view map with solid white line indicating the location of the cross section
fig = plt.figure(figsize=(8., 16./golden), dpi=128) # New figure
if ((plot_tpvs_at_specific_time == 'True')
and (plot_tpvs_within_timerange == 'False')):
plotTrack1 = False
#plotTrack = True
if (int(datestrinit <= date_firstrecord)
and (int(datestrfin >= date_firstrecord))):
plotTrack1 = True
print datestrinit, datestrfin
else:
#plotTrack1 = True
aa = 1
latnow = lat[sind:eind]
lonnow = lon[sind:eind]
lonnow = um.filter_numeric_nans(lonnow, 361., float('NaN'), 'high')
lonnow = um.filter_numeric_nans(lonnow, -1., float('NaN'), 'low')
latnow = um.filter_numeric_nans(latnow, 91., float('NaN'), 'high')
latnow = um.filter_numeric_nans(latnow, -91., float('NaN'), 'low')
x, y = m(lonnow, latnow)
if plot_cyclones_inbox == 'True':
plotTrack2 = False
for ii in xrange(0, len(latnow)):
if lonnow[ii] < 0:
lonnow[ii] = lonnow[ii] + 360.
distance = abs(tpvmod.rEarth * tpvmod.distance_on_unit_sphere(
box_center_latlon[0], box_center_latlon[1], latnow[ii],
lonnow[ii]))
distance = distance / 1000.
latarr = np.zeros_like(trth).astype('f')
lonarr = np.zeros_like(trth).astype('f')
for ii in range(0,len(lons)):
for jj in range(0,len(lats)):
latarr[jj,ii] = lats[jj]
lonarr[jj,ii] = lons[ii]
cint_bg = 1
cbar_min_bg = 270
cbar_max_bg = 380
cmap_opt_bg = plt.cm.jet
cflevs_bg = np.arange(cbar_min_bg,cbar_max_bg+(cint_bg/2),cint_bg)
plotvar_bg = um.filter_numeric_nans(plotvar_bg,cbar_max_bg-(cint_bg/2),cbar_max_bg,'high')
plotvar_bg = um.filter_numeric_nans(plotvar_bg,cbar_min_bg,cbar_min_bg,'low')
golden = (np.sqrt(5)+1.)/2.
#m = Basemap(projection='npstere',boundinglat=30,lon_0=270.,resolution='l')
# Get the cross section points. This is a function in utilities_modules.
if cross_method == 1:
m = Basemap(projection='npstere',boundinglat=30,lon_0=270.,resolution='l')
xx, yy = um.xsection_inds(lon_range[0], lat_range[0], lon_range[1], lat_range[1], lonarr, latarr, m)
#x, y = m(lonarr, latarr)
# Plot plan view map with solid white line indicating the location of the cross section
#cbar_max_trth = 335
cbar_max_trth = 380
#cbar_max_trth = 400
cint_trth = 1
cflevs_trth = np.arange(cbar_min_trth, cbar_max_trth, cint_trth)
#cflevs_trth_cntrs = cflevs_trth[0:12]
cflevs_trth_cntrs = cflevs_trth
cflevs_trth_ticks = np.arange(cbar_min_trth, cbar_max_trth,
4 * cint_trth)
cmap_opt = plt.cm.jet
#cmap_opt = plt.cm.Blues_r
trth = ndimage.gaussian_filter(trth, 0.75)
trth = um.filter_numeric_nans(trth, trth_thresh + cint_trth,
trth_thresh + cint_trth, 'high')
plotvar = trth
mstats(plotvar)
#plotvar, lons = um.addcyclic(trth[tt,:,:].squeeze(), lonin)
#plotvar = ndimage.gaussian_filter(plotvar,0.75)
titletext1 = 'Tropopause potential temperature gradient %s at %s UTC' % (
dt.strftime('%d %b %Y'), dt.strftime('%H00'))
if plot_field == 'trpr':
cbarlabel = 'Pressure (hPa)'
cint_trth = 20
cbar_max_trth = 800.
