Inverting Juno Gravity Field Measurements into the Atmospheric Dynamics of Jupiter - a Study Using a Dynamical Model and Its Adjoint Counterpart

Tuesday, 16 December 2014
Eli Galanti and Yohai Kaspi, Weizmann Institute of Science, Rehovot, Israel
In approximately two years Juno will perform close flybys of Jupiter, obtaining a high precision gravity spectrum for the planet. This data can potentially be used to estimate the depth of the observed flows on the Jupiter. Here, we propose a new methodology for the inversion of the gravity data into into the full three-dimensional flow on Jupiter. Using the adjoint method we construct an inverse model for a dynamical model in which the gravity field is calculated from the observed surface wind, thus allowing its backward integration, from the gravity field to the wind. Given a gravity field, the adjoint based model finds the atmospheric dynamics that can explain best the gravity field (minimum difference). The dynamical model is set up to allow either zonal flow only, or a full horizontal flow in both zonal and meridional directions based on the observed cloud-level wind. In addition, dynamical perturbations resulting from the the non-spherical shape of the planet are accounted for. The dynamical model, together with its adjoint counterpart, are used for examination of various scenarios, including cases in which the depth of the wind depend on latitudinal position.

We show that given the expected sensitivities of Juno, it is possible to use the gravity measurements to derive the depth of the wind on Jupiter. This holds for a large range of zonal wind possible penetration depths, from 100km to 10,000km, and for winds depth that vary with latitude. This method proves to be useful also when incorporating the full horizontal flow, and thus taking into account gravity perturbations that vary with longitude. We show that our adjoint based inversion method allows not only to estimate the depth of the circulation, but allows via iterations with the spacecraft trajectory estimation model to improve the inferred gravity field.