def compute_rp(filename):
"""
Compute Rp - the ratio of the change in salinity
relative to temperature. Allows for identification
of profiles which are susceptible to double diffusion or salt
fingering
"""
plt.style.use('seaborn')
name = filename[:-3]
datadir = "Data/ctd_files/gridded_calibrated_updated"
data = xr.open_dataset(os.path.join(datadir, filename))
# fig,ax = plt.subplots(1,2,sharey=True)
temp = data['ptemp']
psal = data['ab_sal']
depth = data['DEPTH']
sigma_t = data['Gsw_sigma0A0']
[rho, alpha, beta] = gsw.rho_alpha_beta(psal, temp, depth)
rp = (beta[:-1] * np.diff(psal)) / (alpha[:-1] * np.diff(temp))
fig, ax = plt.subplots(2, 2)
fig.suptitle(name.replace('_', ' '))
ax[1, 1].plot(rp, depth[:-1] * -1)
ax[1, 0].plot(rho, depth * -1)
ax[0, 1].plot(psal, depth * -1)
ax[0, 0].plot(temp, depth * -1)
plt.show()
def raw_to_zref(d, zref):
temp = d['TEMP']
psal = d['PSAL']
pres = d['PRES']
temp_qc = d['TEMP_QC']
psal_qc = d['PSAL_QC']
pres_qc = d['PRES_QC']
lon = d['LONGITUDE']
lat = d['LATITUDE']
klist, ierr = remove_bad_qc(d)
if ierr == 0:
Tis = temp[klist]
SP = psal[klist]
p = pres[klist]
CT, SA, z = insitu_to_absolute(Tis, SP, p, lon, lat, zref)
Ti, Si, dTidz, dSidz = interp_at_zref(CT, SA, z, zref, klist)
pi = gsw.p_from_z(-zref, lat)
#Ri = gsw.rho(Si, Ti, pi)
Ri, alpha, beta = gsw.rho_alpha_beta(Si, Ti, pi)
g = gsw.grav(lat, pi)
BVF2i = g * (beta * dSidz - alpha * dTidz)
flag = True
else:
zero = zref * 0.
Ti, Si, Ri, BVF2i = zero, zero, zero, zero
flag = False
return {'CT': Ti, 'SA': Si, 'RHO': Ri, 'BVF2': BVF2i}, flag
def compute_alpha_beta(data, temperature_var='SST', salinity_var='SSS'):
p = 0 * data['lon']
SA = gsw.SA_from_SP(data[salinity_var], p, data['lon'], data['lat'])
CT = gsw.CT_from_t(SA, data[temperature_var], p)
rho, alpha, beta = gsw.rho_alpha_beta(SA, CT, p)
return data.assign(alpha=xr.DataArray(alpha, dims='time'),
beta=xr.DataArray(beta, dims='time'))
def compute_density(data):
p = 0 * data['lon']
SA = gsw.SA_from_SP(data['SSS'], p, data['lon'], data['lat'])
CT = gsw.CT_from_t(SA, data['SST'], p)
rho, alpha, beta = gsw.rho_alpha_beta(SA, CT, p)
#b = 9.81 * (1 - rho / 1025.)
data['SSS'].data = SA
data['SST'].data = CT
return data.assign(SSrho=xr.DataArray(rho, dims='time'),
SSalpha=xr.DataArray(alpha, dims='time'),
SSbeta=xr.DataArray(beta, dims='time'))
def brunt_vaisala(datadir, filenames, desc=''):
#Load required variables
lat = []
lon = []
temp = []
sal = []
depth = []
for i in filenames:
d = xr.open_dataset(os.path.join(datadir, i))
temp.append(d.TEMP.values)
sal.append(d.PSAL.values)
lon.append(np.nanmean(d.lon.values))
lat.append(np.nanmean(d.lat.values))
depth = -np.abs(d.DEPTH.values)
coords = lon
#compute alpha and beta
[rho, alpha, beta] = gsw.rho_alpha_beta(sal, temp, depth)
g = 9.8
N = np.sqrt(g * ((alpha[:, :-1] * (np.diff(temp) / np.diff(depth))) -
(beta[:, :-1] * (np.diff(sal) / np.diff(depth))))
) ##computed this way so that the influence of temperature
#vs salinity can be separated
#plot N
xx, yy = np.meshgrid(lon, depth[:-1])
plt.pcolormesh(xx, yy, N.T)
cb = plt.colorbar(extend='both')
cb.set_label('N')
#plt.show()
plt.savefig('Figures/Transect/transect_N.pdf')
plt.savefig('Figures/Transect/transect_N.png')
plt.close()
return N, np.asarray(rho), depth, lon, lat
def alphabeta(SA, CT, depth):
import gsw as gsw
_, y = np.meshgrid(SA[1, :], depth)
_, alpha, beta = gsw.rho_alpha_beta(SA, CT, y)
return alpha, beta