Radio observations probe the radiolucent layers of planetary atmospheres, and often probe tens to hundreds of bars below the cloud deck, enabling exploration of the troposphere and its dynamics. The presence of radio-opaque trace gases, modulates the atmospheric thermal emission and is the basis for interpreting radio measurements.
We present a retrieval approach that matches radio observations, such as those obtained from NRAO’s Very Large Array or NASA’s Juno mission, by parameterizing the atmosphere based on a thermo-equilibrium model in conjunction with radiative transfer modeling. The key to interpreting a dynamic atmosphere with an equilibrium model is to allow for and incorporate perturbations to equilibrium. We disturb our models by introducing factors that affect the cloud formation efficiency and abundance of major trace gases. We use a combination of an optimizer and a Markov Chain Monte Carlo sampler to obtain the best fit to the observations and the corresponding uncertainties of the retrieved parameters.
We demonstrate the model by matching radio observations from Juno and the Very Large Array and discuss the implications of the retrieved atmosphere for various atmospheric models.