Getting started

import matplotlib.pyplot as plt
from typhon import plots

import konrad


plots.styles.use('typhon')

At the heart of each konrad simulation is the atmosphere component, which represents the thermodynamical state of the model column. In a first step, we create a vertical (pressure) grid and initialize the atmosphere. Internally, konrad distinguishes full-levels (plev) and half-levels (phlev). The thermodynamic state and the atmospheric composition are defined for each full-level. Half-levels are used to consistently represent (radiative) fluxes between full-levels. Therefore, the surface is also placed at the lowest half-level.

plev, phlev = konrad.utils.get_pressure_grids(1000e2, 1, 128)
atmosphere = konrad.atmosphere.Atmosphere(phlev)

We can access the state variables in a dict-like way and, for example, plot the initial temperature profile.

fig, ax = plt.subplots()
plots.profile_p_log(atmosphere['plev'], atmosphere['T'][-1, :])
ax.set_xlabel(r"$T$ / K")
ax.set_ylabel("$p$ / hPa")

Text(0, 0.5, '$p$ / hPa')

Next, we can compile the RCE object, which combines the different model component and allows the user to control the run.

# Initialize the setup for the radiative-convective equilibrium simulation.
rce = konrad.RCE(
    atmosphere,
    surface=konrad.surface.FixedTemperature(temperature=288.),  # Run with a fixed surface temperature.
    timestep='12h',  # Set timestep in model time.
    max_duration='100d',  # Set maximum runtime.
)

After defining our RCE model, we can perform the actual simulation

rce.run()  # Start the simulation.

Finally, we can plot the RCE state and compare it to the inital (standard) atmopshere

fig, ax = plt.subplots()
plots.profile_p_log(atmosphere['plev'], atmosphere['T'][-1, :], label="Init. state")
plots.profile_p_log(rce.atmosphere['plev'], rce.atmosphere['T'][-1, :], label="RCE")
ax.legend()
ax.set_xlabel(r"$T$ / K")
ax.set_ylabel("$p$ / hPa")

Text(0, 0.5, '$p$ / hPa')