Equatorial plots

%load_ext autoreload
%autoreload 2

%%capture 
# comment above line to see details about the run(s) displayed
from misc import *
import intake, yaml
%matplotlib inline

# load obs
catalog = intake.open_catalog('/glade/u/home/gmarques/libs/oce-catalogs/reference-datasets.yml')
obs = catalog['woa-2018-tx2_3v2-annual-all'].to_dask()
obs['xh'] = grd[0].xh
obs['yh'] = grd[0].yh
thetao_obs = obs.thetao
salt_obs = obs.so
obs_label = catalog['woa-2018-tx2_3v2-annual-all'].metadata['prefix']+' '+str(catalog['woa-2018-tx2_3v2-annual-all'].metadata['version'])

def plot_comparison(model, var, obs, vmin, vmax, diff, cname):
  fig, ax = plt.subplots(nrows=3, ncols=1,
                       sharex=True,sharey=True,
                       figsize=(12,14))

  ax1 = ax.flatten()

  model[var].plot(ax=ax1[0], ylim=(500,0),
                     y="z_l",vmin=vmin, vmax=vmax,
                     yincrease=False);
      
  obs.sel(yh=0., 
               method="nearest").plot(ax=ax1[1], ylim=(500, 0),
                                      y="z_l",vmin=vmin, vmax=vmax,
                                      yincrease=False);

  (model[var] - obs.sel(yh=0., 
               method="nearest")).plot(ax=ax1[2], ylim=(500, 0),
                                      y="z_l", cmap="bwr", vmin=-diff, vmax=diff, 
                                      yincrease=False);
  # thermocline  
  if var == 'temp_eq':
      model[var].plot.contour(ax=ax1[0], ylim=(500,0),
                     y="z_l",levels=[20.], colors='k',
                     yincrease=False);
      obs.sel(yh=0., method="nearest").plot.contour(ax=ax1[1], ylim=(500, 0),
                    y="z_l",levels=[20.], colors='r',
                    yincrease=False);
      model[var].plot.contour(ax=ax1[2], ylim=(500,0),
                     y="z_l",levels=[20.], colors='k',
                     yincrease=False);
      obs.sel(yh=0., method="nearest").plot.contour(ax=ax1[2], ylim=(500, 0),
                    y="z_l",levels=[20.], colors='r',
                    yincrease=False);

  # halocline
  if var == 'salt_eq':
      model[var].plot.contour(ax=ax1[0], ylim=(500,0),
                     y="z_l",levels=[35.], colors='k',
                     yincrease=False);
      obs.sel(yh=0., method="nearest").plot.contour(ax=ax1[1], ylim=(500, 0),
                    y="z_l",levels=[35.], colors='r',
                    yincrease=False);
      model[var].plot.contour(ax=ax1[2], ylim=(500,0),
                     y="z_l",levels=[35.], colors='k',
                     yincrease=False);
      obs.sel(yh=0., method="nearest").plot.contour(ax=ax1[2], ylim=(500, 0),
                    y="z_l",levels=[35.], colors='r',
                    yincrease=False);
      
  ax[0].set_title('Model, casename = {}'.format(cname)); ax[0].set_xlabel('')
  ax[1].set_title('Obs (WOA18)'); ax[1].set_xlabel('')
  ax[2].set_title('Model - Obs'); ax[2].set_xlabel('Longitude');
  return

Potential temperature

for path, case, i in zip(ocn_path, casename, range(len(casename))):
  ds_temp = xr.open_dataset(ocn_path[i]+casename[i]+'_temp_eq.nc').rename({'zl':'z_l'})
  plot_comparison(ds_temp,'temp_eq',thetao_obs,10,30,5,label[i])

Salinity

for path, case, i in zip(ocn_path, casename, range(len(casename))):
  ds_salt = xr.open_dataset(ocn_path[i]+casename[i]+'_salt_eq.nc').rename({'zl':'z_l'})
  plot_comparison(ds_salt,'salt_eq',salt_obs,33,37,1,label[i])

Pacific

Zonal velocity

import glob
from IPython.display import Image, display


fnames = ['Equatorial_Pacific_uo', 'uo_143.0', 'uo_156.0', 'uo_165.0',
           'uo_180.0', 'uo_190.0', 'uo_205.0', 'uo_220.0', 'uo_235.0',
           'uo_250.0', 'uo_265.0']
for f in fnames:
  for i in range(len(casename)):
    # List PNG files in the directory
    png_files = glob.glob(ocn_path[i]+'/../PNG/Equatorial/*_{}.png'.format(f))

    # Display PNG files in the Jupyter notebook
    for png_file in png_files:
      display(Image(filename=png_file))

Temperature

fnames = ['Equatorial_Global_temperature', 'temperature_143.0', 'temperature_156.0', 'temperature_165.0',
           'temperature_180.0', 'temperature_190.0', 'temperature_205.0', 'temperature_220.0', 'temperature_235.0',
           'temperature_250.0', 'temperature_265.0']
for f in fnames:
  for i in range(len(casename)):
    # List PNG files in the directory
    png_files = glob.glob(ocn_path[i]+'/../PNG/Equatorial/*_{}.png'.format(f))

    # Display PNG files in the Jupyter notebook
    for png_file in png_files:
      display(Image(filename=png_file))

Salinity

import glob
from IPython.display import Image, display

fnames = ['Equatorial_Global_salinity', 'salinity_143.0', 'salinity_156.0', 'salinity_165.0',
           'salinity_180.0', 'salinity_190.0', 'salinity_205.0', 'salinity_220.0', 'salinity_235.0',
           'salinity_250.0', 'salinity_265.0']
for f in fnames:
  for i in range(len(casename)):
    # List PNG files in the directory
    png_files = glob.glob(ocn_path[i]+'/../PNG/Equatorial/*_{}.png'.format(f))

    # Display PNG files in the Jupyter notebook
    for png_file in png_files:
      display(Image(filename=png_file))

Tropical Atmosphere Ocean (TAO) Moorings

for i in range(len(casename)):
  # List PNG files in the directory
  png_files = glob.glob(ocn_path[i]+'/../PNG/TAOMooring/*.png')
  # Display PNG files in the Jupyter notebook
  for png_file in png_files:
      display(Image(filename=png_file))