Angular Dependence and Spatial Distribution of Jupiter's Centimeter‐Wave Thermal Emission From Juno's Microwave Radiometer

Abstract

NASA's Juno spacecraft has been monitoring Jupiter in 53-day orbits since 2016. Its six-frequency microwave radiometer (MWR) is designed to measure black body emission from Jupiter over a range of pressures from a few tenths of a bar to several kilobars in order to retrieve details of the planet's atmospheric composition, in particular, its ammonia and water abundances. A key step toward achieving this goal is the determination of the latitudinal dependence of the nadir brightness temperature and limb darkening of Jupiter's thermal emission through a deconvolution of the measured antenna temperatures. We present a formulation of the deconvolution as an optimal estimation problem. It is demonstrated that a quadratic expression is sufficient to model the angular dependence of the thermal emission for the data set used to perform the deconvolution. Validation of the model and results from a subset of orbits favorable for MWR measurements is presented over a range of latitudes that cover up to 60°f rom the equator. A heuristic algorithm to mitigate the effects of nonthermal emission is also described. Plain Language Summary One of the instruments on the Juno spacecraft that is currently orbiting Jupiter every 53 days is the microwave radiometer (MWR). It has been sensing the atmosphere for the first time over a wide range of depths below the topmost clouds, covering pressures from less than the Earth's surface pressure to several thousand times that value. This enables a deeper exploration than ever before of how winds distribute gases that can condense, such as water (as in the Earth's atmosphere) and ammonia (which forms Jupiter's highest level clouds). One challenge in understanding the MWR data is to convert each of its raw measurements into an estimate of the true brightness temperature of Jupiter as though it were observed in a perfect, narrow beam, a process known as a deconvolution. We determined that this correction for the angular dependence can be done reliably with a three-term (quadratic) expression. The results of this approach have formed the basis of all of the analysis of MWR data to date, and we show some of the intriguing results from orbits that allowed for the best MWR observing geometry over latitudes that cover up to 60°from the equator.

Observations and Electron Density Retrievals of Jupiter's Discrete Auroral Arcs Using the Juno Microwave Radiometer

“…The GRAV orbits do not guarantee nadir look angles for the MWR instrument. The deconvolution errors for all orbit types were less than 0.5% (Oyafuso et al, 2017).…”

Residual Study: Testing Jupiter Atmosphere Models Against Juno MWR Observations

“…Other calibration errors such as uncertainties in the measured beam patterns are expected to result in residuals of this order or less (Janssen et al, 2017). Intrinsic receiver noise introduces variations of order 0.1% which leads to scatter in the residuals (Oyafuso et al, 2020). In practice, an atmospheric model would be considered inconsistent with the MWR measurements if the bulk residuals are larger than 2%.…”

“…According to theoretical and empirical deduction, the Jupiter limb darkening can be approximated with the three‐coefficient equation (Oyafuso et al, 2020): where θ is the emission angle and μ = cos ( θ ). A 0 is the nadir brightness temperature, and A 1 and A 2 are the limb‐darkening coefficients.…”

“… A 0 is the nadir brightness temperature, and A 1 and A 2 are the limb‐darkening coefficients. f ( θ ) is an empirical angular profile suggested by Oyafuso et al (2020) in order to account for the rapid brightness drop‐off at larger emission angles (Figure 2 in Oyafuso et al, 2020). At the end of each iteration and within each latitude bin, this method fits with respect to μ for all the observations in that latitude bin to the equation above.…”

“…de Pater et al (2019) also derived a vertical ammonia distribution from ground‐based Very Large Array (VLA) observations at 3–37 GHz, showing compatibility with a subset of the MWR data described by Li et al (2017). However, interpreting MWR data in this way requires complex data processing and a variety of assumptions about data smoothness, symmetry of the planet, and so forth (see, e.g., Oyafuso et al, 2020). Instead, in this paper we apply a model in the forward direction to produce synthetic MWR observations and then compare those predictions with the actual MWR data set.…”