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Commit 2d2041c4 authored by Stefano Serafin's avatar Stefano Serafin
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minor tgraphical weaks to ffigure 1

parent 72523879
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......@@ -54,15 +54,16 @@ if __name__ == '__main__':
'do_parameter_estimation' : False,
'parameter_inflation_rtps_alpha' : np.array([1.,1.,1.0]),
}
integration_dt = 0.25*cbl_settings["dz"]**2/cbl_settings["Kmax"]
CASE = 'fig01'
CASE = 'fig02'
#CASE = 0 # eddy diffusivity plots
#CASE = 1 # deterministic run
#CASE = 2 # ensemble run, no data assimilation
#CASE = 3 # single assimilation
#CASE = 4 # assimilation cycle
#CASE = 5 # parameter identifiability
#CASE = 6 # parameter estimation
#CASE = 0 # eddy diffusivity plots
if CASE == 'fig01':
......@@ -72,19 +73,19 @@ if __name__ == '__main__':
cbl_det.run(output_full_history=True)
# Panel b) ensemble run
cbl_settings['perturbations_theta_amplitude'] = 1.0
cbl_settings['perturbations_type'] = "smooth"
cbl_settings['perturb_ensemble_state'] = False
cbl_settings['perturbations_theta_amplitude'] = 0.1
cbl_settings['perturbations_type'] = "uniform"
cbl_settings['perturb_ensemble_state'] = True
cbl_settings['perturb_ensemble_parameters'] = True
cbl_settings['perturbations_symmetric'] = True
cbl_settings['parameter_number'] = 1
cbl_settings['parameter_transform'] = [ parameter_transform_log ]
cbl_settings['parameter_ensemble_min'] = np.array([1])
cbl_settings['parameter_ensemble_max'] = np.array([4])
cbl_settings['parameter_ensemble_min'] = np.array([0.5])
cbl_settings['parameter_ensemble_max'] = np.array([4.5])
cbl_settings['parameter_true'] = np.array([1.5])
cbl_settings['do_parameter_estimation'] = False
cbl_ens = CBL(cbl_settings)
cbl_ens.initialize(20)
cbl_ens.initialize(200)
cbl_ens.run(output_full_history=True)
# Panel c) sensitivity to p
......@@ -135,7 +136,7 @@ if __name__ == '__main__':
fig.savefig('fig01.png',format='png',dpi=300)
p.close(fig)
if CASE == 0: # plot K profile examples
elif CASE == 0: # plot K profile examples
p_factors = [0.2,0.5,1,2,4,6]
theta_profiles = []
......
......@@ -232,7 +232,7 @@ def plot_CBL_identifiability(cbl,figtitle,ax=None):
c = ax.pcolormesh(times/3600.,zt,p_x_correlation,vmin=-1,vmax=1,cmap='RdBu_r')
ax.contour(times/3600.,zt,theta.mean(axis=0),
np.linspace(cbl.theta_0,cbl.theta_0+cbl.gamma*zmax,ncont),
colors='lightgrey',
colors='black',
linestyles='--',
linewidths=0.75)
ax.set_ylim([0,zmax])
......
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