'H1': 500.0,
'H2': 2000.0,
'U1': 5e-2,
'U2': 1e-2,
'bottomDrag': 1e-7,
'nx': 128,
'dt': 1e3,
'visc': 1e9,
'viscOrder': 4.0,
'timeStepper': 'AB3',
'nThreads': 4,
'useFilter': False,
}
# Create the two-layer model
qg = twoLayerQG.model(**qgParams)
qg.describe_model()
# Initial condition to seed baroclinic instability
Ro = 1.0e-3
f0 = 1.0e-4
q1 = Ro * f0 * np.random.standard_normal(qg.physVarShape)
q2 = Ro * f0 * np.random.standard_normal(qg.physVarShape)
qg.set_q1_and_q2(q1, q2)
# Gaussian hill topography
(x0, y0, r) = (qg.Lx / 2.0, qg.Ly / 2.0, qg.Lx / 20.0)
h = 0.1 * qg.H2 * np.exp(-((qg.x - x0)**2.0 + (qg.y - y0)**2.0) /
(2.0 * r**2.0))
'H1': 500.0,
'H2': 2000.0,
'U1': 2.5e-2,
'U2': 0.0,
'bottomDrag': 1.0e-7,
'nx': 128,
'dt': 1.0e3,
'visc': 2.0e8,
'viscOrder': 4.0,
'timeStepper': 'AB3',
'nThreads': 4,
'useFilter': False,
}
# Create the two-layer model
qg = twoLayerQG.model(**params)
qg.describe_model()
# Initial condition:
Ro = 1.0e-3
f0 = 1.0e-4
q1 = Ro * f0 * np.random.standard_normal(qg.physVarShape)
q2 = Ro * f0 * np.random.standard_normal(qg.physVarShape)
qg.set_q1_and_q2(q1, q2)
# Run a loop
nt = 1e3
for ii in np.arange(0, 1e3):
'H1': pyqgParams['H1'],
'H2': pyqgParams['H1'] / pyqgParams['delta'],
'U1': pyqgParams['U1'],
'U2': pyqgParams['U2'],
'bottomDrag': pyqgParams['rek'],
'nx': pyqgParams['nx'],
'dt': pyqgParams['dt'],
'visc': 0e6,
'viscOrder': 4.0,
'timeStepper': 'AB3',
'nThreads': pyqgParams['ntd'],
'useFilter': True,
}
m = pyqg.QGModel(**pyqgParams)
qg = twoLayerQG.model(**myParams)
qg.describe_model()
# Initial condition
(f0, Ro) = (1.0e-4, 1.0e-3)
q1 = Ro * f0 * np.random.standard_normal((m.ny, m.nx))
q2 = Ro * f0 * np.random.standard_normal((m.ny, m.nx))
m.set_q1q2(q1, q2)
qg.set_q1_and_q2(q1, q2)
# Run models, interleaved within one another.
nt = 1e3
for snapshot in m.run_with_snapshots(tsnapstart=0, tsnapint=nt * m.dt):