Accessing all profiles at once

Conveniently, all the core mision profiles are compated in a single file, named: <FloatWmoID>_prof.nc

6901254/6901254_prof.nc

import numpy as np
import xarray as xr
import netCDF4
from matplotlib import pyplot as plt
%matplotlib inline

prof  = xr.open_dataset('./Data/6901254/6901254_prof.nc')

In this case, N_PROF is 66, since there are 66 two for the first cycle, the descending and the ascending. These profiles are just the ‘Primary sampling’, if you need the high-resolution upper 5dbar you have to use the individual cycle files.

fig , ax = plt.subplots(figsize=(10,10))
ax.contourf(prof.PSAL,40);

However, if we want to add the proper pressure levels since each profile has slightly different levels:

prof.PRES[3,:].values

array([   6.,    7.,    8.,    9.,   10.,   16.,   25.,   36.,   46.,
         55.,   66.,   76.,   86.,   96.,  106.,  115.,  125.,  135.,
        146.,  156.,  165.,  176.,  186.,  196.,  213.,  237.,  262.,
        288.,  313.,  338.,  363.,  388.,  413.,  438.,  463.,  488.,
        513.,  538.,  563.,  588.,  613.,  638.,  663.,  687.,  713.,
        738.,  763.,  788.,  813.,  838.,  863.,  888.,  914.,  938.,
        963.,  988., 1013., 1038., 1063., 1088., 1113., 1139., 1163.,
       1188., 1213., 1238., 1263., 1288., 1313., 1338., 1363., 1388.,
       1413., 1439., 1463., 1488., 1513., 1538., 1563., 1588., 1613.,
       1638., 1663., 1688., 1713., 1738., 1763., 1788., 1813., 1838.,
       1863., 1888., 1913., 1938., 1963., 1988., 2013., 2036.],
      dtype=float32)

prof.PRES[4,:].values

array([   6.,    7.,    8.,    9.,   10.,   16.,   25.,   35.,   45.,
         55.,   66.,   75.,   85.,   95.,  106.,  116.,  125.,  135.,
        145.,  156.,  165.,  175.,  186.,  196.,  213.,  238.,  263.,
        287.,  313.,  338.,  363.,  388.,  413.,  438.,  463.,  488.,
        513.,  538.,  563.,  588.,  613.,  638.,  663.,  689.,  713.,
        738.,  763.,  788.,  813.,  838.,  863.,  889.,  913.,  938.,
        963.,  988., 1013., 1038., 1063., 1089., 1113., 1138., 1163.,
       1188., 1213., 1238., 1263., 1289., 1313., 1338., 1363., 1388.,
       1413., 1438., 1462., 1488., 1513., 1538., 1563., 1588., 1613.,
       1639., 1663., 1688., 1713., 1737., 1763., 1788., 1813., 1837.,
       1863., 1888., 1913., 1938., 1963., 1978.,   nan,   nan],
      dtype=float32)

We will need to do a little of interpolation to use contour:

juld=prof.JULD.values
psal=prof.PSAL.values
pres=prof.PRES.values
prei=np.arange(5,2005,5)

psali= np.zeros((juld.shape[0],prei.shape[0]))
psali.fill(np.nan)

for ip in range(0,pres.shape[0]-1):
    psali[ip,:]=np.interp(prei,pres[ip,:],psal[ip,:])

fig, ax = plt.subplots(figsize=(15,10))

cs=ax.contourf(juld,prei,psali.transpose(),40,cmap="RdBu_r")
cs2=ax.contour(juld,prei,psali.transpose(),colors=('k'), levels=cs.levels[::4])

ax.invert_yaxis()
ax.clabel(cs2, fmt='%2.1f', colors='w', fontsize=10)

ax.set_title(f"Vertical Salinity section for float {prof.PLATFORM_NUMBER[0].astype(str).values}")
ax.set_xlabel(f"{prof.JULD.standard_name}")
ax.set_ylabel(f"{prof.PRES.long_name}")

cbar=fig.colorbar(cs,ax=ax)

or a TS diagram using the hydrographic data in TEMP and PSAl

import seawater as sw

temp=prof.TEMP.values.flatten()
psal=prof.PSAL.values.flatten()
pres=prof.PRES.values.flatten()

ptmp=sw.ptmp(psal, temp, pres, pr=0)

t_bins = np.linspace(2, 25, 200)
s_bins = np.linspace(35, 37.25, 200)

hist, xedges, yedges = np.histogram2d(psal, ptmp, (s_bins, t_bins))
xidx = np.clip(np.digitize(psal, xedges), 0, hist.shape[0]-1)
yidx = np.clip(np.digitize(ptmp, yedges), 0, hist.shape[1]-1)
c = hist[xidx, yidx]

fig, ax = plt.subplots(figsize=(10,10))
sc=ax.scatter(psal, ptmp, c=c,alpha=0.5, cmap="RdBu_r",vmin=0, vmax=10)
ax.set_title(f"T/S diagram for float {prof.PLATFORM_NUMBER[0].astype(str).values}")
ax.set_ylabel("potential temperature")
ax.set_xlabel(f"{prof.PSAL.long_name}")
fig.colorbar(sc,extend='both');

Metadata

All the metadata information for each profile is included:

for i1 in range(1,prof.dims['N_PROF'],10):
    print(f"Cycle {prof.data_vars['CYCLE_NUMBER'].values.astype(int)[i1]}"
          f" Direction {prof.data_vars['DIRECTION'].values.astype(str)[i1]}"
          f" WMO {prof.data_vars['PLATFORM_NUMBER'].values.astype(str)[i1]}"
          f" Data Center {prof.data_vars['DATA_CENTRE'].values.astype(str)[i1]}"   
          f" Project {prof.data_vars['PROJECT_NAME'].values.astype(str)[i1]}" )

Cycle 1 Direction A WMO 6901254  Data Center IF Project ARGO SPAIN                                                      
Cycle 11 Direction A WMO 6901254  Data Center IF Project ARGO SPAIN                                                      
Cycle 21 Direction A WMO 6901254  Data Center IF Project ARGO SPAIN                                                      
Cycle 31 Direction A WMO 6901254  Data Center IF Project ARGO SPAIN                                                      
Cycle 41 Direction A WMO 6901254  Data Center IF Project ARGO SPAIN                                                      
Cycle 51 Direction A WMO 6901254  Data Center IF Project ARGO SPAIN                                                      
Cycle 61 Direction A WMO 6901254  Data Center IF Project ARGO SPAIN