Notebook to investigate the negative salinity error that occurred in the 3 April 2015 Nowcast

In [15]:

from __future__ import division

import matplotlib.pyplot as plt
import netCDF4 as nc
import numpy as np

from salishsea_tools import viz_tools
%matplotlib inline

In [3]:

grid = nc.Dataset('../../NEMO-forcing/grid/bathy_meter_SalishSea2.nc')
bathy = grid.variables['Bathymetry'][:]

In [5]:

fig, ax = plt.subplots(1, 1, figsize=(10, 8))
viz_tools.set_aspect(ax)
mesh = ax.pcolormesh(bathy, cmap='winter_r')
fig.colorbar(mesh)
ax.plot(173,696,'ok')

Out[5]:

[<matplotlib.lines.Line2D at 0x7f9f6fe49c90>]

In [9]:

fig, ax = plt.subplots(1, 1, figsize=(10, 8))
viz_tools.set_aspect(ax)
cmap = plt.get_cmap('winter_r')
cmap.set_bad('burlywood')
mesh = ax.pcolormesh(bathy, cmap=cmap)
fig.colorbar(mesh)
plt.axis((150, 250, 650, 750))
ax.plot(173,696,'ok')

Out[9]:

[<matplotlib.lines.Line2D at 0x7f9f40935bd0>]

Right off the south edge of Savary Island. Probably not a River problem.

In [18]:

data = nc.Dataset('/data/dlatorne/MEOPAR/SalishSea/nowcast/03apr15/SalishSea_1h_20150403_20150403_grid_T.nc')
salinity = data.variables['vosaline'][:]
surfaceheight = data.variables['sossheig'][:]

In [19]:

print salinity.shape
# mask salinity
m = salinity == 0
sal = np.ma.array(salinity, mask=m)
m = surfaceheight == 0
eta = np.ma.array(surfaceheight, mask=m)

(18, 40, 898, 398)

In [17]:

fig, ax = plt.subplots(1, 1, figsize=(10, 8))
viz_tools.set_aspect(ax)
mesh = ax.pcolormesh(sal[17,0,650:750,150:250], cmap=cmap)
fig.colorbar(mesh)

Out[17]:

<matplotlib.colorbar.Colorbar instance at 0x7f9f34503170>

In [20]:

fig, ax = plt.subplots(1, 1, figsize=(10, 8))
viz_tools.set_aspect(ax)
mesh = ax.pcolormesh(eta[17,650:750,150:250], cmap=cmap)
fig.colorbar(mesh)

Out[20]:

<matplotlib.colorbar.Colorbar instance at 0x7f9f33f1e908>

In [35]:

atmos = nc.Dataset('/ocean/sallen/allen/research/MEOPAR/Operational/ops_y2015m04d03.nc')
precip = atmos.variables['precip'][:]
u_wind = atmos.variables['u_wind'][:]
v_wind = atmos.variables['v_wind'][:]
print precip.shape

(24, 266, 256)

In [28]:

fig, ax = plt.subplots(1, 1, figsize=(10, 8))
mesh = ax.pcolormesh(precip[19])
ax.set_xlim((0,256))
ax.set_ylim((0,266))
fig.colorbar(mesh)

Out[28]:

<matplotlib.colorbar.Colorbar instance at 0x7f9f26ddb9e0>

In [30]:

fig, ax = plt.subplots(1, 1, figsize=(10, 8))
mesh = ax.pcolormesh(u_wind[19])
ax.set_xlim((0,256))
ax.set_ylim((0,266))
fig.colorbar(mesh)

Out[30]:

<matplotlib.colorbar.Colorbar instance at 0x7f9f24f90440>

In [36]:

fig, ax = plt.subplots(1, 1, figsize=(10, 8))
mesh = ax.pcolormesh(v_wind[19])
ax.set_xlim((0,256))
ax.set_ylim((0,266))
fig.colorbar(mesh)

Out[36]:

<matplotlib.colorbar.Colorbar instance at 0x7f9f1f176488>

In [37]:

print u_wind[19,10,10], v_wind[19,10,10]
print u_wind[19].max(), u_wind[19].min()
print v_wind[19].max(), v_wind[19].min()

-- --
-- --
-- --

So u_wind values are all NaN's

In [38]:

fig, ax = plt.subplots(1, 1, figsize=(10, 8))
mesh = ax.pcolormesh(u_wind[18])
ax.set_xlim((0,256))
ax.set_ylim((0,266))
fig.colorbar(mesh)

Out[38]:

<matplotlib.colorbar.Colorbar instance at 0x7f9f34a241b8>

In [39]:

fig, ax = plt.subplots(1, 1, figsize=(10, 8))
mesh = ax.pcolormesh(u_wind[20])
ax.set_xlim((0,256))
ax.set_ylim((0,266))
fig.colorbar(mesh)

Out[39]:

<matplotlib.colorbar.Colorbar instance at 0x7f9f1b69fea8>

In [ ]: