def band_fraction(self, value):
self.num_channels = value.size
# abstract axis for channels
ax = axis.Axis(num_points=self.num_channels)
self.channel_ax = {'channel': ax}
dom = domain._Domain(axes=self.channel_ax)
# fraction of the total solar flux in each band:
self._band_fraction = field.Field(value, domain=dom)
def _compute_absorptivity(self):
# assume that the water vapor etc is current
optical_path = self._compute_optical_path()
# account for finite layer depth
absorptivity = 1. - np.exp(-optical_path)
axes = copy(self.Tatm.domain.axes)
# add these to the dictionary of axes
axes.update(self.channel_ax)
dom = domain.Atmosphere(axes=axes)
self.absorptivity = field.Field(absorptivity, domain=dom)
from climlab import domain
from climlab.domain import field
from climlab.utils.legendre import P2
import numpy as np
import matplotlib.pyplot as plt
# create domain
sfc = domain.zonal_mean_surface(num_lat=36)
lat = sfc.lat.points
lat_rad = np.deg2rad(lat)
# define initial temperature distribution
T0 = 15.
T2 = -20.
Ts = field.Field(T0 + T2 * P2(np.sin(lat_rad)), domain=sfc)
# create budyko transport process
budyko_transp = BudykoTransport(b=4., state=Ts)
### Integrate & Plot ###
fig = plt.figure()
ax = fig.add_subplot(111)
for i in np.arange(0, 3, 1):
ax.plot(lat, budyko_transp.default, label='day %s' % (i * 40))
budyko_transp.integrate_days(40.)
ax.set_title('Standalone Budyko Transport')
ax.set_xlabel('latitude')
## NEED TO FIX THE PASSING OF INITIAL PARAMETERS
# especially timestep
# how easy is to implement the Stommel 1961 box model in climlab?
# currently... it still requires a fair bit of code:
import numpy as np
from climlab.process.time_dependent_process import TimeDependentProcess
from climlab.domain import domain, field
box = domain.box_model_domain()
print box.shape
# initial condition
x = field.Field([1., 0.], domain=box)
y = field.Field([1., 1.], domain=box)
state = {'x': x, 'y': y}
# define the process
class StommelBox(TimeDependentProcess):
def compute(self):
x = self.state['x']
y = self.state['y']
term = np.abs(-y + self.param['R'] * x) / self.param['lam']
self.tendencies['y'] = (1 - y - y * term)
self.tendencies['x'] = (self.param['delta'] * (1 - x) - x * term)
# make a parameter dictionary
param = {'R': 2., 'lam': 1., 'delta': 1., 'timestep': 0.01}
# instantiate the process