def get_diffusivity_profile(self, model):
depths = self.environment_profiles['z']
wind, depth = np.meshgrid(self.wind_speed(), depths)
if model == 'windspeed_Large1994':
return verticaldiffusivity_Large1994(wind, depth)
elif model == 'windspeed_Sundby1983':
return verticaldiffusivity_Sundby1983(wind)
elif model == 'gls_tke':
if not hasattr(self, 'gls_parameters'):
self.logger.info('Searching readers for GLS parameters...')
for reader_name, reader in self.readers.items():
if hasattr(reader, 'gls_parameters'):
self.gls_parameters = reader.gls_parameters
self.logger.info('Found gls-parameters in ' + reader_name)
break # Success
if not hasattr(self, 'gls_parameters'):
self.logger.info('Did not find gls-parameters in any readers.')
self.gls_parameters = None
windstress = np.sqrt(self.environment.surface_downward_x_stress**2 +
self.environment.surface_downward_y_stress**2)
return gls_tke(windstress, depth, self.sea_water_density(),
self.environment.turbulent_kinetic_energy,
self.environment.turbulent_generic_length_scale,
gls_parameters)
else:
raise ValueError('Unknown diffusivity model: ' + model)
def test_vertical_diffusivity(self):
windspeeds = np.arange(0, 20, 5)
depths = np.arange(0, 80, 5)
wind, depth = np.meshgrid(windspeeds, depths)
KLarge = verticaldiffusivity_Large1994(wind, depth)
KSundby = verticaldiffusivity_Sundby1983(wind, depth)
self.assertAlmostEqual(KLarge.min(), 0, 3)
self.assertAlmostEqual(KLarge.max(), 0.2017, 3)
self.assertAlmostEqual(KSundby.min(), 0, 3)
self.assertAlmostEqual(KSundby.max(), 0.0585, 3)
====================
"""
#%%
# Plot the depth dependence of vertical diffusivity from the various analytical methods
import numpy as np
import matplotlib.pyplot as plt
from opendrift.models.physics_methods import verticaldiffusivity_Sundby1983, verticaldiffusivity_Large1994, verticaldiffusivity_stepfunction
depth = np.linspace(0, 60, 100)
windspeed = np.arange(0, 20, 5)
colors = ['r', 'g', 'b', 'k']
for w, c in zip(windspeed, colors):
plt.plot(np.ones(depth.shape)*verticaldiffusivity_Sundby1983(w, depth, 50),
depth, c + '-', label='Sundby, wind = %sm MLD=50' % w)
plt.plot(verticaldiffusivity_Large1994(w, depth, 50), depth,
c + '--', label='Large, wind = %s, MLD=50' % w)
plt.plot(verticaldiffusivity_Large1994(w, depth, 20), depth,
c + '-.', label='Large, wind = %s, MLD=20' % w)
plt.plot(verticaldiffusivity_stepfunction(depth), depth,
'-m', label='Stepfunction')
plt.xlabel('Vertical diffusivity [m/s2]')
plt.ylabel('Depth [m]')
plt.gca().set_ylim([0, depth.max()])
plt.gca().set_xlim([0, None])
plt.gca().invert_yaxis()
plt.legend()
"""
#%%
# Plot the depth dependence of vertical diffusivity from the various analytical methods
import numpy as np
import matplotlib.pyplot as plt
from opendrift.models.physics_methods import verticaldiffusivity_Sundby1983, verticaldiffusivity_Large1994, verticaldiffusivity_stepfunction
depth = np.linspace(0, 60, 100)
windspeed = np.arange(0, 20, 5)
colors = ['r', 'g', 'b', 'k']
for w, c in zip(windspeed, colors):
plt.plot(np.ones(depth.shape) *
verticaldiffusivity_Sundby1983(w, depth, 50),
depth,
c + '-',
label='Sundby, wind = %sm MLD=50' % w)
plt.plot(verticaldiffusivity_Large1994(w, depth, 50),
depth,
c + '--',
label='Large, wind = %s, MLD=50' % w)
plt.plot(verticaldiffusivity_Large1994(w, depth, 20),
depth,
c + '-.',
label='Large, wind = %s, MLD=20' % w)
plt.plot(verticaldiffusivity_stepfunction(depth),
depth,
'-m',
