This notebook looks at the vertical eddy viscosity/diffusivity during a deep water renewal event in late August 2003.
Compares a kw closure with k-eps closure
Both have no wind, rm_avm0 =1e-4, rn_avt0 = 1e-5, no bbl, isoneutral mixing
dwr_kw compared to dwr_isoneutral
In [1]:
%matplotlib inline
from matplotlib import pylab
import matplotlib.pyplot as plt
import netCDF4 as NC
import numpy as np
import os
from salishsea_tools import (nc_tools,viz_tools)
from salishsea_tools.nowcast import analyze
In [2]:
# Load the data. Path name can be changed to look at different data.
runs=['dwr_kw','dwr_isoneutral']
base='/data/nsoontie/MEOPAR/SalishSea/results/stratification/'
sals={}; depths={}; avms={}; avds={}; Ws={};depthws={}; Us={}; Vs={}
for run in runs:
path = os.path.join(base,'{}/SalishSea_1d_20030819_20030927_grid_T.nc'.format(run))
f = NC.Dataset(path,'r');
sals[run]=f.variables['vosaline']
depths[run] = f.variables['deptht']
T_lat = f.variables['nav_lat']
T_lon = f.variables['nav_lon']
#Loading eddy viscosity/diffusivity data on the vertical grid
path = os.path.join(base,'{}/SalishSea_1d_20030819_20030927_grid_W.nc'.format(run))
f = NC.Dataset(path,'r');
avms[run]=f.variables['ve_eddy_visc']
avds[run]= f.variables['ve_eddy_diff']
Ws[run]=f.variables['vovecrtz'][:]
depthws[run] = f.variables['depthw']
#Loading data on the ugrid
path = os.path.join(base,'{}/SalishSea_1d_20030819_20030927_grid_U.nc'.format(run))
f = NC.Dataset(path,'r');
Us[run]=f.variables['vozocrtx']
#Loading data on the ugrid
path = os.path.join(base,'{}/SalishSea_1d_20030819_20030927_grid_V.nc'.format(run))
f = NC.Dataset(path,'r');
Vs[run]=f.variables['vomecrty']
grid = NC.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')
bathy=grid.variables['Bathymetry']
Basic Comparison¶
Which case has higher eddy viscosity? Higher average? How does it change over time? Where are the max values?
In [3]:
maxes_diff={}; maxes_visc={}; means_diff={}; means_visc={}; inds_diff={}; inds_visc={}
for run in runs:
maxes_diff[run]=[]; maxes_visc[run]=[]; means_diff[run]=[]; means_visc[run]=[]; inds_diff[run]=[]
inds_visc[run]=[]
for t in np.arange(0,sals[run].shape[0]):
#mask
mu = avds[run][t,0:,:,:] == 0
avd_mask = np.ma.array(avds[run][t,::,:,:],mask=mu)
mu = avms[run][t,0:,:,:] == 0
avm_mask = np.ma.array(avms[run][t,::,:,:],mask=mu)
maxes_diff[run].append(np.nanmax(avd_mask))
ind =np.nanargmax(avd_mask)
inds_diff[run].append(np.unravel_index(ind, avd_mask.shape))
maxes_visc[run].append(np.nanmax(avm_mask))
ind =np.nanargmax(avm_mask)
inds_visc[run].append(np.unravel_index(ind, avm_mask.shape))
means_diff[run].append(np.nanmean(avd_mask))
means_visc[run].append(np.nanmean(avm_mask))
In [4]:
for run in runs:
print run
print inds_diff[run]
print inds_visc[run]
dwr_kw [(1, 773, 119), (1, 773, 119), (7, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (4, 773, 119), (2, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (2, 773, 119), (2, 773, 119), (3, 773, 119), (3, 773, 119), (3, 773, 119), (4, 773, 119), (3, 773, 119), (2, 773, 119), (2, 773, 119), (2, 773, 119), (2, 773, 119), (2, 773, 119), (3, 773, 119), (3, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119)] [(1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (2, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (2, 773, 119), (2, 773, 119), (2, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (2, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119)] dwr_isoneutral [(1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119)] [(1, 801, 130), (1, 801, 130), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 778, 122), (1, 778, 122), (1, 778, 122), (1, 778, 122), (1, 778, 122), (1, 778, 122), (1, 778, 121), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 778, 122), (1, 778, 122), (1, 778, 122), (1, 778, 122), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 773, 119), (1, 778, 122), (1, 778, 122), (1, 778, 122)]
Where are the highest vertical eddy coeffcients? Notsmooth changes over time but it looks like it is always in the northern part of the domain
In [5]:
fig,ax=plt.subplots(1,1,figsize=(5,8))
viz_tools.plot_coastline(ax,grid)
for run in runs:
ax.plot(inds_diff[run][0][2],inds_diff[run][0][1],'o',label='diff ' + run)
ax.plot(inds_visc[run][0][2],inds_visc[run][0][1],'o',label='visc ' + run)
plt.legend(loc=0)
Out[5]:
<matplotlib.legend.Legend at 0x7efbd053b4d0>
In [6]:
fig,axs=plt.subplots(2,1,figsize=(10,8))
ts=np.arange(0,sals[runs[0]].shape[0])
#maxes
ax=axs[0]
run1=runs[0]
ax.plot(ts,maxes_diff[run1],'*-r',label=run1 +' diffusivity')
ax.plot(ts,maxes_visc[run1],'s-r',label=run1 +' viscosity')
run2=runs[1]
ax.plot(ts,maxes_diff[run2],'*-b',label=run2 +' diffusivity')
ax.plot(ts,maxes_visc[run2],'s-b',label=run2 +' viscosity')
ax.set_xlabel('time stamp')
ax.set_ylabel('Eddy diffusivity/Viscosity (m^2/s)')
ax.set_title('Comparison of Maximum Vertical Eddy Coefficent')
plt.legend(loc=0)
#means
ax=axs[1]
ax.plot(ts,means_diff[run1],'*-r',label=run1 +' diffusivity')
ax.plot(ts,means_visc[run1],'s-r',label=run1+' viscosity')
ax.plot(ts,means_diff[run2],'*-b',label=run2 +' diffusivity')
ax.plot(ts,means_visc[run2],'s-b',label=run2 +' viscosity')
ax.set_xlabel('time stamp')
ax.set_ylabel('Eddy diffusivity/Viscosity (m^2/s)')
ax.set_title('Comparison of Mean Vertical Eddy Coefficent')
plt.legend(loc=0)
/home/nsoontie/anaconda/lib/python2.7/site-packages/matplotlib/axes/_axes.py:475: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.
warnings.warn("No labelled objects found. "
Out[6]:
<matplotlib.legend.Legend at 0x7efbd02d35d0>
- Much difference values for maxmimm/mean eddy coefficients. Lower with kw means less mixing.
Thalweg¶
Plotting salinity and eddy viscosity/diffusivity along thalweg over time. Daily average outputs over 10 days.
In [7]:
lines = np.loadtxt('/data/nsoontie/MEOPAR/tools/bathymetry/thalweg_working.txt', delimiter=" ", unpack=False)
lines = lines.astype(int)
T_lon = T_lon[:]
T_lat=T_lat[:]
thalweg_lon = T_lon[lines[:,0],lines[:,1]]
thalweg_lat = T_lat[lines[:,0],lines[:,1]]
ds=np.arange(0,lines.shape[0],1);
vs=np.arange(34,27.5,-0.5);
XX_T={}; ZZ_T={}; XX_W={}; ZZ_W={}
for run in runs:
XX_T[run],ZZ_T[run] = np.meshgrid(ds,-depths[run][:])
XX_W[run],ZZ_W[run] = np.meshgrid(ds,-depthws[run][:])
Salinity difference along thalweg over time.
In [8]:
fig,axs=plt.subplots(8,5,figsize=(20,20),sharex=True,sharey=True)
smin=-.5;smax=.5
diff = sals[run1][:]-sals[run2][:]
for t,ax in zip(np.arange(40),axs.flat):
mesh=ax.pcolormesh(XX_T[run],ZZ_T[run],(diff[t,:,lines[:,0],lines[:,1]]).T,vmin=smin,vmax=smax,cmap='bwr')
CS=ax.contour(XX_T[run],ZZ_T[run],(diff[t,:,lines[:,0],lines[:,1]]).T,[-.4,-.2,0.2,.4])
plt.colorbar(mesh, ax=ax)
ax.clabel(CS,fontsize=9, inline=1)
ax.set_ylim(ax.get_ylim()[::-1])
ax.set_ylim([-400,0])
ax.set_title('t = ' +str(t))
print run1 + ' - ' + run2
dwr_kw - dwr_isoneutral
- kw has fresher SJdF deep and Haro Strait deep, SoG intermediate
- kw has saltier SJdF surface
- Really? This doesnt seem to match with smaller eddy coefficients...
- DWR in kw looks fresher.
Surface¶
In [9]:
fig,axs=plt.subplots(8,5,figsize=(15,20),sharex=True,sharey=True)
smin=-1;smax=1; dep=0
for t,ax in zip(np.arange(40),axs.flat):
salP=sals[run1][t,dep,:,:];
salP1=salP
salP=sals[run2][t,dep,:,:];
mesh=ax.pcolormesh(salP1-salP,vmin=smin,vmax=smax,cmap='bwr')
plt.colorbar(mesh, ax=ax)
viz_tools.plot_coastline(ax,grid)
ax.set_title('t = ' +str(t))
print run1 + ' - ' + run2
dwr_kw - dwr_isoneutral
- differences in surface SoG are variable.
- SJdF surface is consistently saltier in kw.
In [10]:
fig,axs=plt.subplots(1,2,figsize=(8,5))
smin=0;smax=34; dep=0; t=39
mesh=axs[0].pcolormesh(sals[run1][t,dep,:,:],vmin=smin,vmax=smax,cmap='jet')
cbar=plt.colorbar(mesh,ax=axs[0])
viz_tools.plot_land_mask(axs[0],grid,color='burlywood')
axs[0].set_title(run1)
mesh=axs[1].pcolormesh(sals[run2][t,dep,:,:],vmin=smin,vmax=smax,cmap='jet')
cbar=plt.colorbar(mesh,ax=axs[1])
viz_tools.plot_land_mask(axs[1],grid,color='burlywood')
axs[1].set_title(run2)
Out[10]:
<matplotlib.text.Text at 0x7efbbc849fd0>
No winds
Preparing for quivers
In [11]:
def quiver_salinity(t,dep,imin=1,imax=396,jmin=1,jmax=896,st=5):
"compare rivers and salinity at t, dep in box. st is quiver arrow interval"
fig,axs = plt.subplots(1,2,figsize=(12,5))
x=np.arange(imin,imax)
y=np.arange(jmin,jmax)
for key, ax in zip(runs,axs):
#trununcate U/V and unstagger
U= Us[key][t,dep,jmin-1:jmax,imin-1:imax]
V =Vs[key][t,dep,jmin-1:jmax,imin-1:imax]
lon=T_lon[jmin:jmax,imin:imax]
lat=T_lat[jmin:jmax,imin:imax]
S=sals[key][t,dep,jmin:jmax,imin:imax]
#masking
U = np.ma.masked_values(U,0)
V = np.ma.masked_values(V,0)
#unstagger
u,v = viz_tools.unstagger(U,V)
#rotate
theta = np.pi*29/180
uE = u*np.cos(theta) - v*np.sin(theta)
vN = u*np.sin(theta) +v*np.cos(theta)
#mesh
mesh=ax.pcolormesh(lon,lat,S,cmap='spectral')
viz_tools.plot_land_mask(ax,grid,coords='map',color='burlywood')
#quivers
quiver = ax.quiver(lon[::st,::st],lat[::st,::st],uE[::st,::st], vN[::st,::st],
pivot='mid', scale=4.5, color='white',width=0.005
)
ax.quiverkey(quiver,-123.7,48.5, 1,'1 m/s',
coordinates='data', color='white', labelcolor='white')
ax.set_xlim([-124,-122.8])
ax.set_ylim([48.6,49.5])
return fig
Day 40
In [12]:
day=39
dep=0
fig=quiver_salinity(day,dep,st=6)
- Surface currents and salinity look really similar. Tgere are some small differences
In [13]:
def average_thalweg(depth, index1,index2, var):
#Averages the given variable along the thalweg at a depth and for indices between index1 and index2
var_thal = var[depth,lines[:,0],lines[:,1]]
#mask
mu = var_thal==0
var_thal=np.ma.array(var_thal,mask=mu)
var_average=np.nanmean(var_thal[index1:index2])
return var_average
In [14]:
#plot now
t1=0;t2=40;
fig,axs = plt.subplots(1,2,figsize=(15,10))
diffs = sals[run1][t1:t2,:,:,:]-sals[run2][t1:t2,:,:,:]
tm=np.arange(t1,t2)
inds1 = [0,100,0,200,450]; inds2= [450,450,200,450,600]
dep=0;
for ind1,ind2 in zip(inds1,inds2):
averages = []
for n in range(diffs.shape[0]):
averages.append(average_thalweg(dep,ind1,ind2,diffs[n,:,:,:]))
ax=axs[0]
ax.plot(tm,averages)
ax.set_xlabel('time output')
ax.set_ylabel('Surface salinity difference avergaed along thalweg in SJdF')
ax=axs[1]
viz_tools.plot_coastline(ax,grid)
ax.plot([lines[ind1,1],lines[ind2,1]],[lines[ind1,0],lines[ind2,0]],'o-')
axs[0].grid()
print run1 + ' - ' + run2
dwr_kw - dwr_isoneutral
In [15]:
smin=28; smax=31
emin=-4; emax=2
(fig,axs)=plt.subplots(10,2,figsize=(18,25),sharey=True)
ts=np.arange(30,40,1)
vs=np.arange(31.2,30,-0.1);
r1=400; r2=1100;
for t,ax1,ax2 in zip(ts,axs[:,0],axs[:,1]):
#salinity
run=runs[0]
salP=sals[run][:]
salP=salP[t,:,lines[r1:r2,0],lines[r1:r2,1]];
mu =salP == 0; salP= np.ma.array(salP,mask=mu)
mesh=ax1.pcolormesh(XX_T[run][:,r1:r2],ZZ_T[run][:,r1:r2],salP.T,vmin=smin,vmax=smax,cmap='rainbow')
CS=ax1.contour(XX_T[run][:,r1:r2],ZZ_T[run][:,r1:r2],salP.T,vs, colors='black')
ax1.clabel(CS,fontsize=9, inline=1)
ax1.set_title('t = ' +str(t))
run=runs[1]
salP=sals[run][:]
salP=salP[t,:,lines[r1:r2,0],lines[r1:r2,1]];
mu =salP == 0; salP= np.ma.array(salP,mask=mu)
mesh=ax2.pcolormesh(XX_T[run][:,r1:r2],ZZ_T[run][:,r1:r2],salP.T,vmin=smin,vmax=smax,cmap='rainbow')
CS=ax2.contour(XX_T[run][:,r1:r2],ZZ_T[run][:,r1:r2],salP.T,vs, colors='black')
ax2.clabel(CS,fontsize=9, inline=1)
ax2.set_title('t = ' +str(t))
print 'Left:', runs[0]
print 'Right:', runs[1]
Left: dwr_kw Right: dwr_isoneutral
/home/nsoontie/anaconda/lib/python2.7/site-packages/matplotlib/text.py:52: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
if rotation in ('horizontal', None):
/home/nsoontie/anaconda/lib/python2.7/site-packages/matplotlib/text.py:54: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
elif rotation == 'vertical':
- DWR less salty with kw
Average salinity over a volume¶
In this case, averaging will take into account that the grid spacing changes vertically. But I am assuming that horizontal grid boxes are equal area.
In [16]:
def average_over_box(varis,depths,t,imin,imax,jmin,jmax,dmin,dmax):
"""Average field stored in var over a box at a time t. """
var_av={}
#iteraring over variables in varis
for key, var in varis.iteritems():
#subdomain
sub = var[t,dmin:dmax+1,jmin:jmax+1,imin:imax+1]
sub_dep = depths[key][dmin:dmax+1]
#mask
sub=np.ma.masked_values(sub,0)
#averaing horizontally. Assuming horizontal grid boxes are equal area
sub = np.nanmean(sub,axis=2)
sub = np.nanmean(sub,axis=1)
var_av[key]=analyze.depth_average(sub,sub_dep,depth_axis=0)
return var_av
In [17]:
def compare_volume_average(imin,imax,jmin,jmax,dmin,dmax):
""""time series of volume averages comparison"""
#time series of average
keys=sals.keys()
sals_av = {keys[0]:[],keys[1]:[]}
for t in np.arange(sals[keys[1]].shape[0]):
avg=average_over_box(sals,depths,t,imin,imax,jmin,jmax,dmin,dmax)
for run, out in avg.iteritems():
sals_av[run].append(out)
#plotting
fig,axs=plt.subplots(1,2,figsize=(10,5))
#map
ax=axs[0]
viz_tools.plot_coastline(ax,grid)
ax.plot([imin,imax],[jmin,jmin],'r-')
ax.plot([imin,imax],[jmax,jmax],'r-')
ax.plot([imin,imin],[jmin,jmax],'r-')
ax.plot([imax,imax],[jmin,jmax],'r-')
#averages
ax=axs[1]
for key, sal_plot in sals_av.iteritems():
ax.plot(sal_plot,label=key)
ax.legend(loc=0)
ax.set_xlabel('output time')
ax.set_ylabel('Average Salinity [psu]')
ax.set_title('Depth range {0:.3} -{1:.3} m'.format(depths[key][dmin], depths[key][dmax]))
ax.grid()
ax.get_yaxis().get_major_formatter().set_useOffset(False)
diff = np.array(sals_av[keys[1]])-np.array(sals_av[keys[0]])
print 'Difference between {} and {} in [psu]'.format(keys[1], keys[0])
print 'Max diff: {0:.2}'.format(np.max(diff))
print 'Min diff: {0:.2}'.format(np.min(diff))
print 'Mean diff: {0:.2}'.format(np.mean(diff))
return fig
Juan de Fuca - surface, depth, whole water column¶
In [18]:
fig = compare_volume_average(0,200,200,500,0,23)
Difference between dwr_kw and dwr_isoneutral in [psu] Max diff: 0.046 Min diff: 0.00074 Mean diff: 0.027
In [19]:
fig = compare_volume_average(0,200,200,500,24,39)
Difference between dwr_kw and dwr_isoneutral in [psu] Max diff: -8.2e-05 Min diff: -0.0091 Mean diff: -0.0055
In [20]:
fig = compare_volume_average(0,200,200,500,0,39)
Difference between dwr_kw and dwr_isoneutral in [psu] Max diff: 0.002 Min diff: -0.0042 Mean diff: -0.00091
- Saliter SoG surface
- Fresher SoF deep
Gulf Islands - surface, depth, whole water column¶
In [21]:
fig = compare_volume_average(200,310,200,360,0,23)
Difference between dwr_kw and dwr_isoneutral in [psu] Max diff: 0.022 Min diff: -0.017 Mean diff: 0.0023
In [22]:
fig = compare_volume_average(200,310,200,360,24,39)
Difference between dwr_kw and dwr_isoneutral in [psu] Max diff: -0.0029 Min diff: -0.068 Mean diff: -0.047
In [23]:
fig = compare_volume_average(200,310,200,360,0,39)
Difference between dwr_kw and dwr_isoneutral in [psu] Max diff: -0.0023 Min diff: -0.059 Mean diff: -0.04
- Fresher at depth in the Gulf Islands
Strait of Georgia - surface(0-5), intermediate (25-100), depth (100-400), whole water column¶
In [24]:
fig = compare_volume_average(250,300,360,500,0,5)
Difference between dwr_kw and dwr_isoneutral in [psu] Max diff: 0.2 Min diff: -0.026 Mean diff: 0.067
In [25]:
fig = compare_volume_average(250,300,360,500,20,26)
Difference between dwr_kw and dwr_isoneutral in [psu] Max diff: -0.00015 Min diff: -0.037 Mean diff: -0.019
In [26]:
fig = compare_volume_average(250,300,360,500,26,39)
Difference between dwr_kw and dwr_isoneutral in [psu] Max diff: -7.4e-05 Min diff: -0.039 Mean diff: -0.014
In [27]:
fig = compare_volume_average(250,300,360,500,0,39)
Difference between dwr_kw and dwr_isoneutral in [psu] Max diff: -9.8e-05 Min diff: -0.034 Mean diff: -0.013
- Saltier surface SoG
- Fresher intermediate and deep SpG
Eddy Coefficients¶
In [28]:
smin=28; smax=34
emin=0; emax=2
(fig,axs)=plt.subplots(10,2,figsize=(15,15),sharey=True)
ts=np.arange(0,40,4)
for i, run in enumerate(runs):
for t, axV, in zip(ts,axs[:,i]):
#viscosity
avmP=avms[run][:];
avmP=np.ma.masked_values(avmP[t,:,lines[:,0],lines[:,1]],0);
mesh=axV.pcolormesh(XX_W[run],ZZ_W[run],avmP.T,vmin=emin, vmax=emax,cmap='hot')
axV.set_title('t = ' +str(t))
fig.subplots_adjust(wspace=0.05)
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.82, 0.25, 0.02, 0.5])
cbar=fig.colorbar(mesh, cax=cbar_ax)
cbar.set_label('Vertical eddy coefficient (m^2/s)')
print 'viscosity'
print 'Left: ', runs[0]
print 'Right: ', runs[1]
viscosity Left: dwr_kw Right: dwr_isoneutral
This is hard to make out any major differences in the viscosity.
In [29]:
smin=28; smax=34
emin=0; emax=2
(fig,axs)=plt.subplots(10,2,figsize=(15,15),sharey=True)
ts=np.arange(0,40,4)
for i, run in enumerate(runs):
for t, axD, in zip(ts,axs[:,i]):
#diffusivity
avdP=avds[run][:];
avdP=np.ma.masked_values(avdP[t,:,lines[:,0],lines[:,1]],0);
mesh=axD.pcolormesh(XX_W[run],ZZ_W[run],avdP.T,vmin=emin, vmax=emax,cmap='hot')
axD.set_title('t = ' +str(t))
fig.subplots_adjust(wspace=0.05)
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.82, 0.25, 0.02, 0.5])
cbar=fig.colorbar(mesh, cax=cbar_ax)
cbar.set_label('Vertical eddy coefficient (m^2/s)')
print 'diffusivity'
print 'Left: ', runs[0]
print 'Right: ', runs[1]
diffusivity Left: dwr_kw Right: dwr_isoneutral
Velocity¶
In [30]:
smin=28; smax=34
emin=0; emax=1
(fig,axs)=plt.subplots(10,2,figsize=(15,15),sharey=True)
ts=np.arange(0,40,4)
for i, run in enumerate(runs):
#viscosity
vel=np.sqrt(Us[run][:]**2 + Vs[run][:]**2);
for t, ax, in zip(ts,axs[:,i]):
velP=np.ma.masked_values(vel[t,:,lines[:,0],lines[:,1]],0);
mesh=ax.pcolormesh(XX_W[run],ZZ_W[run],velP.T,vmin=emin, vmax=emax,cmap='jet')
ax.set_title('t = ' +str(t))
fig.subplots_adjust(wspace=0.05)
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.82, 0.25, 0.02, 0.5])
cbar=fig.colorbar(mesh, cax=cbar_ax)
cbar.set_label('Velocity mangitude (m/s)')
print 'Left: ', runs[0]
print 'Right: ', runs[1]
Left: dwr_kw Right: dwr_isoneutral
Summary¶
This is bizarre. The eddy coefficients in the kw case are on average smaller, yet there is more mixing. It reminds me of the modifed resoultion in 2D.
Next¶
Read more about the difference between k-eps and k-omega. My understanding is that the definition of the turbulent length scale is that main difference.
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