#compute the mean shear velocity in the BBL as theoretically it should be constant
shear_velocity = np.zeros(number_of_profiles)
for profile in range(number_of_profiles):
BBL_from = int(list_of_BBL_indices[profile])
BBL_to = int(list_of_bathymetry_indices[profile])
#print(list_of_BBL_indices[profile],list_of_bathymetry_indices[profile])
BBL_shear_velocity = np.nanmean(shear_velocity_grid[BBL_from:BBL_to])
law_of_the_wall_turbulent_diffusivity = thesis.von_Karman_constant * BBL_shear_velocity * distance_from_ground_grid[profile,BBL_from:BBL_to]
#replace the corresponding bins with the turbulent diffusivity from the law of the wall
turbulent_diffusivity_Osborn_grid[profile,BBL_from:BBL_to] = law_of_the_wall_turbulent_diffusivity
turbulent_diffusivity_Shih_grid[profile,BBL_from:BBL_to] = law_of_the_wall_turbulent_diffusivity
oxygen_gradient_grid = thesis.central_differences(eps_oxygen_grid)/thesis.central_differences(eps_depth) #in units of micromol/(m*l)
unit_conversion_grid = 86400 #to convert from m*micromol/(l*s) to mmol/(m^2*d) ; l to m^3 and micro to milli cancel each other out (factor of 1000)
oxygen_flux_Osborn_grid = - turbulent_diffusivity_Osborn_grid * oxygen_gradient_grid * unit_conversion_grid
oxygen_flux_Shih_grid = - turbulent_diffusivity_Shih_grid * oxygen_gradient_grid * unit_conversion_grid
#oxygen_flux_BB_grid = - turbulent_diffusivity_BB_grid * oxygen_gradient_grid * unit_conversion_grid
# check if the absolute Osborn fluxes are higher than the absolute Shih fluxes (as they should be, because Gamma_Osborn >= Gamma_Shih)
assert np.all(np.isnan(oxygen_flux_Osborn_grid) == np.isnan(oxygen_flux_Shih_grid))
test_Osborn = oxygen_flux_Osborn_grid[~np.isnan(oxygen_flux_Osborn_grid)]
test_Shih = oxygen_flux_Shih_grid[~np.isnan(oxygen_flux_Shih_grid)]
assert np.all( np.abs(test_Osborn) >= np.abs(test_Shih))
for profile in range(number_of_profiles):
100 * number_of_fluxes_over_the_threshold / total_number_of_fluxes,
"%")
print(
"Sum:", 100 * amount_of_missing_values / total_number_of_fluxes +
100 * number_of_zero_flux / total_number_of_fluxes +
100 * number_of_fluxes_over_the_threshold / total_number_of_fluxes,
"%")
distance_list = np.zeros(np.shape(longitude_list))
first_point = (np.mean(lat), longitude_list[0])
for i in range(len(longitude_list)):
current_point = (np.mean(lat), longitude_list[i])
distance_list[i] = geo.geodesic(first_point,
current_point).km #Distance in km
delta_X = thesis.central_differences(distance_list)
print("\n\n\n", cruisename, "flux sum:")
print("Osborn rolling mean", np.nansum(rolling_mean_Osborn_flux * delta_X),
"Osborn profiles", np.nansum(mean_Osborn_flux * delta_X))
print("Shih rolling mean", np.nansum(rolling_mean_Shih_flux * delta_X),
"Shih profiles", np.nansum(mean_Shih_flux * delta_X))
print("\n\n\n")
np.savetxt(
"./data/" + cruisename + '_coarse_flux_results.txt',
np.transpose([
longitude_list, distance_list, mean_Osborn_flux,
rolling_mean_Osborn_flux, mean_Shih_flux, rolling_mean_Shih_flux
]),
header=
#conversion from pressure coordinates to depth
eps_depth = gsw.z_from_p(eps_pressure, np.mean(
lat)) #mean lat should be sufficient, because the transect is east-west
eps_depth_grid = np.reshape(eps_depth, (1, -1)) * np.ones(np.shape(eps_grid))
#########################################
#Calculate fluxes########################
#########################################
Gamma_Osborn_eps_grid = thesis.Osborn(eps_Reynolds_bouyancy_grid)
turbulent_diffusivity_Osborn_grid = Gamma_Osborn_eps_grid * eps_grid / (
eps_N_squared_grid)
#remove negative diffusivity
turbulent_diffusivity_Osborn_grid[
turbulent_diffusivity_Osborn_grid < 0] = np.nan
oxygen_flux_osborn_grid = -turbulent_diffusivity_Osborn_grid[:, :] * thesis.central_differences(
eps_oxygen_grid) / thesis.central_differences(eps_depth)
#convert from m*micromol/(kg*s) to mmol/(m^2*d)
oxygen_flux_osborn_grid = oxygen_flux_osborn_grid * 86400 * (1000 /
eps_density_grid)
Gamma_BB_eps_grid = thesis.BB(eps_Reynolds_bouyancy_grid)
turbulent_diffusivity_BB_grid = Gamma_BB_eps_grid * eps_grid / (
eps_N_squared_grid)
#remove negative diffusivity
turbulent_diffusivity_BB_grid[turbulent_diffusivity_BB_grid < 0] = np.nan
oxygen_flux_BB_grid = -turbulent_diffusivity_BB_grid[:, :] * thesis.central_differences(
eps_oxygen_grid) / thesis.central_differences(eps_depth)
#convert from m*micromol/(kg*s) to mmol/(m^2*d)
oxygen_flux_BB_grid = oxygen_flux_BB_grid * 86400 * (1000 / eps_density_grid)
Gamma_Skif_eps_grid = thesis.Skif(eps_Reynolds_bouyancy_grid)
axis3.plot(longitude,
rolling_mean_Shih,
"-",
color=color,
label=cruise_name +
r" $\langle$Shih flux$\rangle_{\mathrm{z,lon}}$ ")
axis3.set_title(cruise_name)
axis3.legend(loc="lower center")
axis3.set_xlabel("Shih Oxygen flux")
##########################################################################################
distance_from_ground_bin_edges = np.arange(0, 80, 2.5)
#sorted(list(set(transect_bathymetry)))
diff_distance = thesis.central_differences(distance)
#diff_distance = np.diff(distance)
#assert np.all(diff_distance > 0)
#print(np.round(diff_distance,3))
#print(diff_distance >= 0)
#integral_axis[0,cruise_index].bar(distance,raw_Osborn,alpha = 0.5, width = diff_distance, align = "edge")
#integral_axis[0,cruise_index].plot(distance,raw_Osborn,"k-")
#integral_axis[1,cruise_index].bar(distance,rolling_mean_Shih,alpha = 0.5, width = diff_distance, align = "edge")
#integral_axis[1,cruise_index].plot(distance,rolling_mean_Shih,"k-")
print(
"-------------------------------------------------------------------------------"
)
print(cruise_name)
print("For a basin with a halocline at depth", halocline_depth,
#test if the saturation at that depth is under a certain level
if eps_oxygen_sat_grid[profile, test_index] < 50:
print("Oxycline drops too fast!",
eps_oxygen_sat_grid[profile, test_index])
outlier_count += 1
list_of_bad_profiles.append([
"_".join((cruisename, transect_name, str(profile))),
"\t\t\toxygen_drops_too_fast"
])
continue
try:
#test if the saturation at that depth is under a certain level
if eps_pressure[np.nanargmin(
thesis.central_differences(
eps_oxygen_sat_grid[profile, :]))] < 50:
print(
"Oxycline is too high!", eps_pressure[np.nanargmin(
thesis.central_differences(
eps_oxygen_sat_grid[profile, :]))])
outlier_count += 1
#list_of_bad_profiles.append(["_".join((cruisename,transect_name,str(profile))),"oxycline_is_too_shallow"])
#continue
except ValueError:
#if np.all(np.isnan(eps_oxygen_sat_grid[profile,:])):
print("All NaN profile")
outlier_count += 1
list_of_bad_profiles.append([
"_".join((cruisename, transect_name, str(profile))),
"\t\t\tNaN_profile"
c=color,
cmap="viridis",
vmin=vmin,
vmax=vmax,
marker=",",
s=1.4)
image = density_axarr[1].scatter(oxygen_flux_Skif_grid[profile, :],
eps_pot_density_grid[profile, :],
c=color,
cmap="viridis",
vmin=vmin,
vmax=vmax,
marker=",",
s=1.4)
oxygen_axarr[0].scatter(thesis.central_differences(
eps_oxygen_sat_grid[profile, :] /
thesis.central_differences(eps_depth)),
eps_pot_density_grid[profile, :],
c=color,
cmap="viridis",
vmin=vmin,
vmax=vmax,
marker=",",
s=1.4)
image_oxy = oxygen_axarr[1].scatter(
eps_pressure,
eps_pot_density_grid[profile, :],
c=color,
cmap="viridis",
vmin=vmin,
vmax=vmax,
#Test of different BBL conditions
exp_results = experimental_BBL(number_of_profiles,interp_pressure,density_grid,oxygen_grid,height_above_ground = 10,minimal_density_difference = 0.02)
exp_bathymetrie,exp_list_of_bathymetrie_indices = results[0]
exp_BBL,exp_list_of_BBL_indices = results[1]
exp_BBL_range,exp_list_of_BBL_range_indices = results[2]
"""
#TODO
turbulent_diffusivity_Osborn_grid = 0.2 * eps_grid / (eps_N_squared_grid)
turbulent_diffusivity_Osborn_grid[turbulent_diffusivity_Osborn_grid<0] = 0
eps_depth = gsw.z_from_p(eps_pressure,np.mean(lat)) #mean lat should be sufficient, because the transect is east-west
print(min(eps_depth),max(eps_depth))
#TODO: Which differention scheme should I use?
#Here I remove the uppermost row of the diffusivity to get the same shape (diff removes one row)
oxygen_flux_osborn_grid = turbulent_diffusivity_Osborn_grid[:,:] * thesis.central_differences(eps_oxygen_grid)/thesis.central_differences(eps_depth)
#convert from m*micromol/(l*s) to mmol/(m^2*d)
oxygen_flux_osborn_grid = oxygen_flux_osborn_grid*86400/(1000**2)
transect_index = -5
print("\n\n\n\n")
print("pressure\tdepth\teps\t","N^2\t","k\t","dO_2/dz\t","flux\t")
for i in range(eps_pressure.size-1):
print(eps_pressure[i],"\t",np.round(eps_depth[i],1),"\t",'{:0.3e}'.format(eps_grid[transect_index,i]),"\t",'{:0.3e}'.format(eps_N_squared_grid[transect_index,i]),"\t",'{:0.3e}'.format(turbulent_diffusivity_Osborn_grid[transect_index,i]),"\t",'{:0.3e}'.format((np.diff(eps_oxygen_grid)/np.diff(eps_depth))[transect_index,i]),"\t",'{:0.3e}'.format(oxygen_flux_osborn_grid[transect_index,i]))
print("\n\n\n\n")
#append the last distance plus the last difference (for plotting all the n profiles we need a distance array of size n+1
plotmesh_distance = np.append(distance,2*distance[-1]-distance[-2])
plotmesh_longitude = np.append(lon,2*lon[-1]-lon[-2])
BBL_from = int(list_of_BBL_indices[profile])
BBL_to = int(list_of_bathymetry_indices[profile])
#print(list_of_BBL_indices[profile],list_of_bathymetry_indices[profile])
BBL_shear_velocity = np.nanmean(shear_velocity_grid[BBL_from:BBL_to])
law_of_the_wall_turbulent_diffusivity = thesis.von_Karman_constant * BBL_shear_velocity * distance_from_ground_grid[
profile, BBL_from:BBL_to]
#replace the corresponding bins with the turbulent diffusivity from the law of the wall
turbulent_diffusivity_Osborn_grid[
profile, BBL_from:BBL_to] = law_of_the_wall_turbulent_diffusivity
turbulent_diffusivity_Shih_grid[
profile, BBL_from:BBL_to] = law_of_the_wall_turbulent_diffusivity
oxygen_gradient_grid = thesis.central_differences(
eps_oxygen_grid) / thesis.central_differences(eps_depth)
unit_conversion_grid = 86400 * (
1000 / eps_density_grid
) #to convert from m*micromol/(kg*s) to mmol/(m^2*d)
oxygen_flux_Osborn_grid = -turbulent_diffusivity_Osborn_grid * oxygen_gradient_grid * unit_conversion_grid
oxygen_flux_BB_grid = -turbulent_diffusivity_BB_grid * oxygen_gradient_grid * unit_conversion_grid
oxygen_flux_Shih_grid = -turbulent_diffusivity_Shih_grid * oxygen_gradient_grid * unit_conversion_grid
interior_Shih_flux_for_different_intervals = []
interior_Osborn_flux_for_different_intervals = []
edge_Shih_flux_for_different_intervals = []
edge_Osborn_flux_for_different_intervals = []
example_profile_index = np.argmin(abs(lon - 20.56))
BBL_from = int(list_of_BBL_indices[profile])
BBL_to = int(list_of_bathymetry_indices[profile])
#print(list_of_BBL_indices[profile],list_of_bathymetry_indices[profile])
BBL_shear_velocity = np.nanmean(shear_velocity_grid[BBL_from:BBL_to])
law_of_the_wall_turbulent_diffusivity = thesis.von_Karman_constant * BBL_shear_velocity * distance_from_ground_grid[
profile, BBL_from:BBL_to]
#replace the corresponding bins with the turbulent diffusivity from the law of the wall
turbulent_diffusivity_Osborn_grid[
profile, BBL_from:BBL_to] = law_of_the_wall_turbulent_diffusivity
turbulent_diffusivity_Shih_grid[
profile, BBL_from:BBL_to] = law_of_the_wall_turbulent_diffusivity
oxygen_gradient_grid = thesis.central_differences(
eps_oxygen_grid) / thesis.central_differences(
eps_depth) #in units of micromol/(m*l)
unit_conversion_grid = 86400 #to convert from m*micromol/(l*s) to mmol/(m^2*d) ; l to m^3 and micro to milli cancel each other out (factor of 1000)
oxygen_flux_Osborn_grid = -turbulent_diffusivity_Osborn_grid * oxygen_gradient_grid * unit_conversion_grid
oxygen_flux_Shih_grid = -turbulent_diffusivity_Shih_grid * oxygen_gradient_grid * unit_conversion_grid
#remove negative diffusivity for the plot
turbulent_diffusivity_Osborn_grid[turbulent_diffusivity_Osborn_grid ==
0] = 1e-7
profile_index = np.argmin(np.abs(lon - 20.57))
print(cruise_name, transect_name, lon[profile_index])
"""
def make_patch_spines_invisible(ax):
ax.set_frame_on(True)