Comparing merge-tests between 3.4 and 3.6. Day long simulation. Final comparison.
- Rivers turned on, key_ldfslp turned off, constant vertical mixing
In [1]:
%matplotlib inline
from matplotlib import pylab
import matplotlib.pyplot as plt
import matplotlib as mpl
import netCDF4 as NC
import numpy as np
import scipy.interpolate as sp
import math
import datetime
from salishsea_tools import stormtools, nc_tools, viz_tools
In [2]:
#load up the bathymetry.
grid = NC.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc','r')
bathy = grid.variables['Bathymetry'][:,:]
X = grid.variables['nav_lon'][:,:]
Y = grid.variables['nav_lat'][:,:]
In [3]:
#load in the datas. Trying something new with dict objects...
#first run (3.4)
r1 = 'three-four';
runname1 = '/data/nsoontie/MEOPAR/SalishSea/results/merge-tests/merg2015/final_NEMO34_NEMO36_compare/'
print runname1
#second run (3.6)
r2 = 'three-six'
runname2 = ('/ocean/sallen/allen/research/MEOPAR/nemo-3.6-code/NEMOGCM/CONFIG/SalishSea/'
'EXP00/final_NEMO34_NEMO36_comparison/')
print runname2
runs = {r1: runname1, r2: runname2}
/data/nsoontie/MEOPAR/SalishSea/results/merge-tests/merg2015/final_NEMO34_NEMO36_compare/ /ocean/sallen/allen/research/MEOPAR/nemo-3.6-code/NEMOGCM/CONFIG/SalishSea/EXP00/final_NEMO34_NEMO36_comparison/
In [4]:
fUs={}; fVs={}; fTs={}
for key, path in runs.iteritems():
if key == r1:
fUs[key] = NC.Dataset(path +'/SalishSea_1h_20121214_20121214_grid_U.nc','r');
fVs[key] = NC.Dataset(path +'/SalishSea_1h_20121214_20121214_grid_V.nc','r');
fTs[key] = NC.Dataset(path +'/SalishSea_1h_20121214_20121214_grid_T.nc','r');
else:
fUs[key] = NC.Dataset(path +'/SalishSea_1h_20121214_20121215_grid_U.nc','r');
fVs[key] = NC.Dataset(path +'/SalishSea_1h_20121214_20121215_grid_V.nc','r');
fTs[key] = NC.Dataset(path +'/SalishSea_1h_20121214_20121215_grid_T.nc','r');
Show info about the files
In [5]:
for key in runs:
print key
nc_tools.show_dataset_attrs(fUs[key])
three-four file format: NETCDF4 Conventions: CF-1.1 production: An IPSL model TimeStamp: 14/07/2015 13:09:02 -0700 file_name: SalishSea_1h_20121214_20121214_grid_U.nc history: Tue Jul 14 13:11:40 2015: ncks -4 -L4 -O SalishSea_1h_20121214_20121214_grid_U.nc SalishSea_1h_20121214_20121214_grid_U.nc NCO: 4.4.5 three-six file format: NETCDF4 name: SalishSea_1h_20121214_20121215 description: ocean U grid variables conventions: CF-1.1 production: An IPSL model timeStamp: 2015-Jul-13 11:09:49 PDT
In [6]:
for key in runs:
print key
nc_tools.show_variable_attrs(fUs[key])
three-four
<type 'netCDF4.Variable'>
float32 depthu(depthu)
axis: Z
standard_name: model_level_number
units: m
positive: down
valid_min: 0.5
valid_max: 441.466
title: depthu
long_name: Vertical U levels
unlimited dimensions:
current shape = (40,)
filling on, default _FillValue of 9.96920996839e+36 used
<type 'netCDF4.Variable'>
float32 nav_lat(y, x)
standard_name: latitude
units: degrees_north
valid_min: 46.8606
valid_max: 51.1056
long_name: Latitude
nav_model: Default grid
unlimited dimensions:
current shape = (898, 398)
filling on, default _FillValue of 9.96920996839e+36 used
<type 'netCDF4.Variable'>
float32 nav_lon(y, x)
standard_name: longitude
units: degrees_east
valid_min: -126.397
valid_max: -121.316
long_name: Longitude
nav_model: Default grid
unlimited dimensions:
current shape = (898, 398)
filling on, default _FillValue of 9.96920996839e+36 used
<type 'netCDF4.Variable'>
float64 time_counter(time_counter)
axis: T
standard_name: time
units: seconds since 2012-12-14 00:00:00
calendar: gregorian
title: Time
long_name: Time axis
time_origin: 2012-DEC-14 00:00:00
bounds: time_counter_bnds
unlimited dimensions: time_counter
current shape = (24,)
filling on, default _FillValue of 9.96920996839e+36 used
<type 'netCDF4.Variable'>
float64 time_counter_bnds(time_counter, tbnds)
unlimited dimensions: time_counter
current shape = (24, 2)
filling on, default _FillValue of 9.96920996839e+36 used
<type 'netCDF4.Variable'>
float32 u_wind_stress(time_counter, y, x)
units: N/m2
standard_name: Wind Stress along i-axis
_FillValue: 9.96921e+36
long_name: Wind Stress along i-axis
online_operation: ave(X)
interval_operation: 10.0
interval_write: 3600.0
coordinates: time_counter nav_lat nav_lon
unlimited dimensions: time_counter
current shape = (24, 898, 398)
filling on
<type 'netCDF4.Variable'>
float32 vozocrtx(time_counter, depthu, y, x)
units: m/s
standard_name: ocean current along i-axis
_FillValue: 9.96921e+36
long_name: ocean current along i-axis
online_operation: ave(X)
interval_operation: 10.0
interval_write: 3600.0
coordinates: time_counter depthu nav_lat nav_lon
unlimited dimensions: time_counter
current shape = (24, 40, 898, 398)
filling on
three-six
<type 'netCDF4.Variable'>
float32 nav_lat(y, x)
axis: Y
standard_name: latitude
long_name: Latitude
units: degrees_north
nav_model: grid_U
unlimited dimensions:
current shape = (898, 398)
filling off
<type 'netCDF4.Variable'>
float32 nav_lon(y, x)
axis: X
standard_name: longitude
long_name: Longitude
units: degrees_east
nav_model: grid_U
unlimited dimensions:
current shape = (898, 398)
filling off
<type 'netCDF4.Variable'>
float32 depthu(depthu)
axis: Z
long_name: Vertical U levels
units: m
positive: down
unlimited dimensions:
current shape = (40,)
filling off
<type 'netCDF4.Variable'>
float32 vozocrtx(time_counter, depthu, y, x)
long_name: ocean current along i-axis
units: m/s
online_operation: average
interval_operation: 10s
interval_write: 1h
_FillValue: 1e+20
missing_value: 1e+20
coordinates: time_centered depthu nav_lon nav_lat
unlimited dimensions: time_counter
current shape = (24, 40, 898, 398)
filling on
<type 'netCDF4.Variable'>
float64 time_centered(time_counter)
standard_name: time
long_name: Time axis
title: Time
calendar: gregorian
units: seconds since 1900-01-01 00:00:00
time_origin: 1900-01-01 00:00:00
bounds: time_centered_bounds
unlimited dimensions: time_counter
current shape = (24,)
filling off
<type 'netCDF4.Variable'>
float64 time_centered_bounds(time_counter, time_bounds)
unlimited dimensions: time_counter
current shape = (24, 2)
filling off
<type 'netCDF4.Variable'>
float64 time_counter(time_counter)
axis: T
standard_name: time
long_name: Time axis
title: Time
calendar: gregorian
units: seconds since 1900-01-01 00:00:00
time_origin: 1900-01-01 00:00:00
bounds: time_counter_bounds
unlimited dimensions: time_counter
current shape = (24,)
filling off
<type 'netCDF4.Variable'>
float64 time_counter_bounds(time_counter, time_bounds)
unlimited dimensions: time_counter
current shape = (24, 2)
filling off
Different sizes because initial is 48 hours.
In [7]:
#U,V,SSH,Salinity,Temperature
Us={}; Vs={}; Es={}; Ss={}; Ts={}; Ds={}
tims = {}
t_orig = {}
tim_count={}
for key in runs:
Us[key] = fUs[key].variables['vozocrtx']
Vs[key] = fVs[key].variables['vomecrty']
Es[key] = fTs[key].variables['sossheig']
Ss[key] = fTs[key].variables['vosaline']
Ts[key] = fTs[key].variables['votemper']
Ds[key] = fTs[key].variables['deptht']
tim_count[key] = fUs[key].variables['time_counter']
t_orig[r1] = datetime.datetime.strptime(
tim_count[r1].time_origin.title(), ' %Y-%b-%d %H:%M:%S')
t_orig[r2] = datetime.datetime.strptime(
tim_count[r2].time_origin.title(), '%Y-%m-%d %H:%M:%S')
Comparison plots¶
Quick look at surface fields near the start adn end of first day
In [8]:
def plot_variable(t,depthlevel,var, var_name,vmin=-3,vmax=3,diffmin=-0.1, diffmax=0.1,figsize=(15,5),ssh_flag=False):
#plot
fig, axs=plt.subplots(1,3,figsize=figsize)
for key,ax in zip(runs,axs[0:2]):
tims = tim_count[key][t]
out= t_orig[key] + datetime.timedelta(seconds=tims)
if ssh_flag:
mesh=ax.pcolormesh(var[key][t,:,:],vmin=vmin,vmax=vmax)
else:
mesh=ax.pcolormesh(var[key][t,depthlevel,:,:],vmin=vmin,vmax=vmax)
ax.set_title('{} at {}'.format(key,out.strftime('%d-%b-%Y %H:%M:%S')))
cbar = plt.colorbar(mesh,ax=ax)
cbar.set_label(var_name)
viz_tools.plot_land_mask(ax,grid,isobath=depthlevel)
ax=axs[2]
if ssh_flag:
diff = var[r1][t,:,:]-var[r2][t,:,:]
else:
diff = var[r1][t,depthlevel,:,:]-var[r2][t,depthlevel,:,:]
mesh = ax.pcolormesh(diff, vmin=diffmin, vmax=diffmax, cmap = 'bwr')
ax.set_title(r1+' - ' +r2)
cbar = plt.colorbar(mesh,ax=ax)
cbar.set_label('difference in ' +var_name)
viz_tools.plot_land_mask(ax,grid,isobath=depthlevel)
Initial time, surface
In [9]:
t=0
depthlevel=0
print 'Time = {}, depth level = {}'.format(t,depthlevel)
plot_variable(t,depthlevel,Us,'U [m/s]')
plot_variable(t,depthlevel,Vs,'V [m/s]')
plot_variable(t,depthlevel,Es,'SSH [m]', ssh_flag=True)
plot_variable(t,depthlevel,Ss,'Sal [psu]', vmin=0,vmax=34)
plot_variable(t,depthlevel,Ts,'Temp [deg C]', vmin=0,vmax=12)
Time = 0, depth level = 0
Minor differences in the U/V fields at the SJdF open boundary.
2015/06/06: No differences at the 0.1 level in any fields.
In [10]:
t=23
depthlevel=0
print 'Time = {}, depth level = {}'.format(t,depthlevel)
plot_variable(t,depthlevel,Us,'U [m/s]')
plot_variable(t,depthlevel,Vs,'V [m/s]')
plot_variable(t,depthlevel,Es,'SSH [m]', ssh_flag=True)
plot_variable(t,depthlevel,Ss,'Sal [psu]', vmin=0,vmax=34)
plot_variable(t,depthlevel,Ts,'Temp [deg C]', vmin=0,vmax=12)
Time = 23, depth level = 0
- Similar differences as in the no rivers case.
Profiles¶
In [11]:
def compare_profile(var,var_name, i,j,t,zmin=0,zmax=400,vmin=0,vmax=34):
"""Function to compare a variable over depth at point i,j and time t.
Plots each sim on the same axis and then their difference over depth"""
fig,axs=plt.subplots(1,2,figsize=(10,5))
ax=axs[0]
for key in runs:
tims = tim_count[key][t]
out= t_orig[key] + datetime.timedelta(seconds=tims)
ax.plot(var[key][t,:,j,i],Ds[key], '-o',label=key)
ax.set_title(out.strftime('%d-%b-%Y %H:%M:%S'))
ax.set_xlabel(var_name)
ax.set_ylabel('Depth [m]')
ax.set_ylim([zmax,zmin])
ax.set_xlim([vmin,vmax])
ax.legend(loc=0)
ax=axs[1]
#difference
diff = var[r1][t,:,j,i]-var[r2][t,:,j,i]
ax.plot(diff,Ds[key], '-ro')
ax.set_title('Difference: ' +r1+' - ' +r2)
ax.set_xlabel('Difference in ' + var_name)
ax.set_ylabel('Depth [m]')
ax.set_ylim([zmax,zmin])
In [12]:
tp = 23
ip = 300
jp = 450
zmax=30
compare_profile(Ss,'Sal [psu]', ip,jp,tp,zmin=0,zmax=zmax,vmin=15,vmax=32)
compare_profile(Ts,'Temp [deg C]', ip,jp,tp,zmin=0,zmax=zmax,vmin=4,vmax=15)
- Central SoG
In [13]:
tp = 23
ip = 300
jp = 400
zmax=30
compare_profile(Ss,'Sal [psu]', ip,jp,tp,zmin=0,zmax=zmax,vmin=15,vmax=32)
compare_profile(Ts,'Temp [deg C]', ip,jp,tp,zmin=0,zmax=zmax,vmin=5,vmax=9)
- Closer to Boundary Pass
In [14]:
tp = 23
ip = 310
jp = 430
zmax=10
compare_profile(Ss,'Sal [psu]', ip,jp,tp,zmin=0,zmax=zmax,vmin=0,vmax=20)
compare_profile(Ts,'Temp [deg C]', ip,jp,tp,zmin=0,zmax=zmax,vmin=0,vmax=6)
- Right in plume
- 3.4 is a little bit saliter right plume area. There might be a small difference in how the rivers are treated but it doens't seem to affect the large scale
Differences¶
Calculate simple staistics on the differences between fields. min/max/mean...
In [15]:
def summarize_differences(var,var_name,imin=0,imax=396,jmin=0,jmax=896):
diff = var[r1][0:24,...,jmin:jmax+1,imin:imax+1]-var[r2][...,jmin:jmax+1,imin:imax+1]
inds_min = np.argmin(diff); inds_min=np.unravel_index(inds_min,diff.shape)
inds_max = np.argmax(diff); inds_max=np.unravel_index(inds_max,diff.shape)
print var_name
print 'min diff: {0:.3}, index {1}'.format(diff.min(),inds_min)
print 'max diff: {0:.3}, index {1}'.format(diff.max(),inds_max)
print 'mean diff {0:.3}'.format(diff.mean())
In [16]:
variables = {'U [m/s]': Us, 'V [m/s]': Vs, 'SSH [m]': Es, 'Sal [psu]': Ss, 'Temp [deg C]': Ts}
for key in variables:
summarize_differences(variables[key],key)
V [m/s] min diff: -1.68, index (9, 15, 771, 118) max diff: 3.55, index (16, 0, 772, 119) mean diff -1.28e-06 Temp [deg C] min diff: -0.438, index (14, 0, 346, 292) max diff: 0.406, index (12, 20, 348, 290) mean diff 7.16e-05 SSH [m] min diff: -2.22, index (21, 896, 41) max diff: 2.7, index (3, 896, 41) mean diff -0.00114 U [m/s] min diff: -2.59, index (9, 0, 771, 117) max diff: 3.3, index (9, 0, 773, 117) mean diff -2.32e-06 Sal [psu] min diff: -1.51, index (6, 3, 147, 311) max diff: 0.771, index (18, 11, 749, 123) mean diff -2.81e-05
- Mean diff is perhaps unfair because the fields aren't masked. So there are a whole lot of zeros
- U/V have largest differences near the north end of the domain.
- Large differences in ssh are probaly because of how the open boundary is treated. 3.4 has an extra layer of zeros at each boundary on the T grid. Perhpas this can explain the U/V differences too?
- Largest temp and salinity difference looks to be just after Boundary Pass at the surface.
Exclude layer surrounding the open boundaries
In [17]:
imin=2; imax=396
jmin=2; jmax=895
for key in variables:
summarize_differences(variables[key],key,imin,imax,jmin,jmax)
V [m/s] min diff: -1.68, index (9, 15, 769, 116) max diff: 3.55, index (16, 0, 770, 117) mean diff -4.36e-07 Temp [deg C] min diff: -0.438, index (14, 0, 344, 290) max diff: 0.406, index (12, 20, 346, 288) mean diff 7.22e-05 SSH [m] min diff: -0.306, index (10, 771, 115) max diff: 0.268, index (8, 770, 118) mean diff -0.00116 U [m/s] min diff: -2.59, index (9, 0, 769, 115) max diff: 3.3, index (9, 0, 771, 115) mean diff -2.59e-06 Sal [psu] min diff: -1.51, index (6, 3, 145, 309) max diff: 0.771, index (18, 11, 747, 121) mean diff -2.83e-05
Bounday conditions on SSH¶
How do the ssh/u/v near the boundaries compare?
In [18]:
sshs={}
us={}
vs={}
i=1
j=450
dtimes={}
for key in runs:
sshs[key] = fTs[key].variables['sossheig'][:,j,i]
us[key] = fUs[key].variables['vozocrtx'][:,0,j,i]
vs[key] = fVs[key].variables['vomecrty'][:,0,j,i]
time_count = fTs[key].variables['time_counter'][:]
dtimes[key]= [t_orig[key]+datetime.timedelta(seconds=t) for t in time_count]
In [19]:
fig,ax=plt.subplots(1,1,figsize=(10,5))
for key in runs:
ax.plot(dtimes[key],sshs[key],label=key)
ax.set_ylabel('ssh [m]')
ax.set_xlabel('output index')
ax.legend(loc=0)
ax.grid()
fig,ax=plt.subplots(1,1,figsize=(10,5))
for key in runs:
ax.plot(dtimes[key],us[key],label=key)
ax.set_ylabel('u [m/s]')
ax.set_xlabel('output index')
ax.legend(loc=0)
ax.grid()
fig,ax=plt.subplots(1,1,figsize=(10,5))
for key in runs:
ax.plot(dtimes[key],vs[key],label=key)
ax.set_ylabel('v [m/s]')
ax.set_xlabel('output index')
ax.legend(loc=0)
ax.grid()
Why is second index zero in initial but not three-six.
Next to boundary
In [20]:
sshs={}
us={}
vs={}
i=2
j=450
dtimes={}
for key in runs:
sshs[key] = fTs[key].variables['sossheig'][:,j,i]
us[key] = fUs[key].variables['vozocrtx'][:,0,j,i]
vs[key] = fVs[key].variables['vomecrty'][:,0,j,i]
time_count = fTs[key].variables['time_counter'][:]
dtimes[key]= [t_orig[key]+datetime.timedelta(seconds=t) for t in time_count]
fig,ax=plt.subplots(1,1,figsize=(10,5))
for key in runs:
ax.plot(dtimes[key],sshs[key],label=key)
ax.set_ylabel('ssh [m]')
ax.set_xlabel('output index')
ax.legend(loc=0)
ax.grid()
fig,ax=plt.subplots(1,1,figsize=(10,5))
for key in runs:
ax.plot(dtimes[key],us[key],label=key)
ax.set_ylabel('u [m/s]')
ax.set_xlabel('output index')
ax.legend(loc=0)
ax.grid()
fig,ax=plt.subplots(1,1,figsize=(10,5))
for key in runs:
ax.plot(dtimes[key],vs[key],label=key)
ax.set_ylabel('v [m/s]')
ax.set_xlabel('output index')
ax.legend(loc=0)
ax.grid()
North
In [21]:
sshs={}
us={}
vs={}
i=50
j=896
dtimes={}
for key in runs:
sshs[key] = fTs[key].variables['sossheig'][:,j,i]
us[key] = fUs[key].variables['vozocrtx'][:,0,j,i]
vs[key] = fVs[key].variables['vomecrty'][:,0,j,i]
time_count = fTs[key].variables['time_counter'][:]
dtimes[key]= [t_orig[key]+datetime.timedelta(seconds=t) for t in time_count]
fig,ax=plt.subplots(1,1,figsize=(10,5))
for key in runs:
ax.plot(dtimes[key],sshs[key],label=key)
ax.set_ylabel('ssh [m]')
ax.set_xlabel('output index')
ax.legend(loc=0)
ax.grid()
fig,ax=plt.subplots(1,1,figsize=(10,5))
for key in runs:
ax.plot(dtimes[key],us[key],label=key)
ax.set_ylabel('u [m/s]')
ax.set_xlabel('output index')
ax.legend(loc=0)
ax.grid()
fig,ax=plt.subplots(1,1,figsize=(10,5))
for key in runs:
ax.plot(dtimes[key],vs[key],label=key)
ax.set_ylabel('v [m/s]')
ax.set_xlabel('output index')
ax.legend(loc=0)
ax.grid()
Same here. At the north, initial has two points on the T grid that are zero...
In [22]:
sshs={}
us={}
vs={}
i=50
j=895
dtimes={}
for key in runs:
sshs[key] = fTs[key].variables['sossheig'][:,j,i]
us[key] = fUs[key].variables['vozocrtx'][:,0,j,i]
vs[key] = fVs[key].variables['vomecrty'][:,0,j,i]
time_count = fTs[key].variables['time_counter'][:]
dtimes[key]= [t_orig[key]+datetime.timedelta(seconds=t) for t in time_count]
fig,ax=plt.subplots(1,1,figsize=(10,5))
for key in runs:
ax.plot(dtimes[key],sshs[key],label=key)
ax.set_ylabel('ssh [m]')
ax.set_xlabel('output index')
ax.legend(loc=0)
ax.grid()
fig,ax=plt.subplots(1,1,figsize=(10,5))
for key in runs:
ax.plot(dtimes[key],us[key],label=key)
ax.set_ylabel('u [m/s]')
ax.set_xlabel('output index')
ax.legend(loc=0)
ax.grid()
fig,ax=plt.subplots(1,1,figsize=(10,5))
for key in runs:
ax.plot(dtimes[key],vs[key],label=key)
ax.set_ylabel('v [m/s]')
ax.set_xlabel('output index')
ax.legend(loc=0)
ax.grid()
Phases align!
In [23]:
sshs={}
us={}
vs={}
i=50
j=894
dtimes={}
for key in runs:
sshs[key] = fTs[key].variables['sossheig'][:,j,i]
us[key] = fUs[key].variables['vozocrtx'][:,0,j,i]
vs[key] = fVs[key].variables['vomecrty'][:,0,j,i]
time_count = fTs[key].variables['time_counter'][:]
dtimes[key]= [t_orig[key]+datetime.timedelta(seconds=t) for t in time_count]
fig,ax=plt.subplots(1,1,figsize=(10,5))
for key in runs:
ax.plot(dtimes[key],sshs[key],label=key)
ax.set_ylabel('ssh [m]')
ax.set_xlabel('output index')
ax.legend(loc=0)
ax.grid()
fig,ax=plt.subplots(1,1,figsize=(10,5))
for key in runs:
ax.plot(dtimes[key],us[key],label=key)
ax.set_ylabel('u [m/s]')
ax.set_xlabel('output index')
ax.legend(loc=0)
ax.grid()
fig,ax=plt.subplots(1,1,figsize=(10,5))
for key in runs:
ax.plot(dtimes[key],vs[key],label=key)
ax.set_ylabel('v [m/s]')
ax.set_xlabel('output index')
ax.legend(loc=0)
ax.grid()
Closer look at currents at SJdF boundary¶
Compare fields over depth and along the JdF boundary
In [24]:
def compare_boundary(var, var_name, irange, jrange, t,vmin=-1,vmax=1,diffmin=-0.5,diffmax=0.5):
fig,axs=plt.subplots(1,3,figsize=(15,3))
for ax, key in zip(axs[0:2],[r1,r2]):
tims = tim_count[key][t]
out= t_orig[key] + datetime.timedelta(seconds=tims)
masked_var = np.ma.masked_values(var[key][t,:,jrange,irange],0)
mesh=ax.pcolormesh(masked_var,vmin=vmin,vmax=vmax)
ax.set_title('{} at {}'.format(key,out.strftime('%d-%b-%Y %H:%M:%S')))
cbar = plt.colorbar(mesh,ax=ax)
cbar.set_label(var_name)
ax.invert_yaxis()
ax=axs[2]
diff = var[r1][t,:,jrange,irange]-var[r2][t,:,jrange,irange]
diff=np.ma.masked_values(diff,0)
mesh = ax.pcolormesh(diff,vmin=diffmin,vmax=diffmax)
ax.set_title(r1+' - ' +r2)
cbar = plt.colorbar(mesh,ax=ax)
cbar.set_label('difference in ' +var_name)
ax.invert_yaxis()
Early time
In [25]:
t=0
compare_boundary(Us,'U [m/s]', 2, np.arange(300,500), t)
compare_boundary(Vs,'V [m/s]', 2, np.arange(300,500), t)
compare_boundary(Ss,'Sal [psu]', 2, np.arange(300,500), t,vmin=20,vmax=34)
compare_boundary(Ts,'Temp [dep C]', 2, np.arange(300,500), t,vmin=5,vmax=15)
print 'Mouth of JdF at t = {}'.format(t)
Mouth of JdF at t = 0
Max difference time
In [26]:
t=5
compare_boundary(Us,'U [m/s]', 2, np.arange(300,500), t)
compare_boundary(Vs,'V [m/s]', 2, np.arange(300,500), t)
compare_boundary(Ss,'Sal [psu]', 2, np.arange(300,500), t,vmin=20,vmax=34)
compare_boundary(Ts,'Temp [dep C]', 2, np.arange(300,500), t,vmin=5,vmax=15)
print 'Mouth of JdF at t = {}'.format(t)
Mouth of JdF at t = 5
Last time
In [27]:
t=23
compare_boundary(Us,'U [m/s]', 2, np.arange(300,500), t)
compare_boundary(Vs,'V [m/s]', 2, np.arange(300,500), t)
compare_boundary(Ss,'Sal [psu]', 2, np.arange(300,500), t,vmin=20,vmax=34)
compare_boundary(Ts,'Temp [dep C]', 2, np.arange(300,500), t,vmin=5,vmax=15)
print 'Mouth of JdF at t = {}'.format(t)
Mouth of JdF at t = 23
- Note, we both turned left the rivers turned off in this case.
Overall, not much different from the no rivers case.
Areas where the 3.4 and 3.6 differ:
- Boundary Pass
- Plume
- Johnstone Strait
- Some close to open boundary
Note the SSH is a few cm lower in the north SoG for 3.6. This also occured in the no rivers.
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