The Orbiting Carbon Observatory-2 (OCO-2) carries and points a three-channel imaging grating spectrometer designed to collect high-resolution, co-boresighted spectra of reflected sunlight within the ...molecular oxygen (O2) A-band at 0.765 microns and the carbon dioxide (CO2) bands at 1.61 and 2.06 microns. These measurements are calibrated and then combined into soundings that are analyzed to retrieve spatially resolved estimates of the column-averaged CO2 dry-air mole fraction, XCO2. Variations of XCO2 in space and time are then analyzed in the context of the atmospheric transport to quantify surface sources and sinks of CO2. This is a particularly challenging remote-sensing observation because all but the largest emission sources and natural absorbers produce only small (< 0.25 %) changes in the background XCO2 field. High measurement precision is therefore essential to resolve these small variations, and high accuracy is needed because small biases in the retrieved XCO2 distribution could be misinterpreted as evidence for CO2 fluxes. To meet its demanding measurement requirements, each OCO-2 spectrometer channel collects 24 spectra s−1 across a narrow (< 10 km) swath as the observatory flies over the sunlit hemisphere, yielding almost 1 million soundings each day. On monthly timescales, between 7 and 12 % of these soundings pass the cloud screens and other data quality filters to yield full-column estimates of XCO2. Each of these soundings has an unprecedented combination of spatial resolution (< 3 km2/sounding), spectral resolving power (λ∕Δλ > 17 000), dynamic range (∼ 104), and sensitivity (continuum signal-to-noise ratio > 400). The OCO-2 instrument performance was extensively characterized and calibrated prior to launch. In general, the instrument has performed as expected during its first 18 months in orbit. However, ongoing calibration and science analysis activities have revealed a number of subtle radiometric and spectroscopic challenges that affect the yield and quality of the OCO-2 data products. These issues include increased numbers of bad pixels, transient artifacts introduced by cosmic rays, radiance discontinuities for spatially non-uniform scenes, a misunderstanding of the instrument polarization orientation, and time-dependent changes in the throughput of the oxygen A-band channel. Here, we describe the OCO-2 instrument, its data products, and its on-orbit performance. We then summarize calibration challenges encountered during its first 18 months in orbit and the methods used to mitigate their impact on the calibrated radiance spectra distributed to the science community.
Jupiter's atmosphere is dominated by multiple jet streams which are strongly tied to its 3D atmospheric circulation. Lacking a rigid bottom boundary, several models exist for how the meridional ...circulation extends into the planetary interior. Here, we show, collecting evidence from multiple instruments of the Juno mission, the existence of midlatitudinal meridional circulation cells which are driven by turbulence, similar to the Ferrel cells on Earth. Different than Earth, which contains only one such cell in each hemisphere, the larger, faster rotating Jupiter can incorporate multiple cells. The cells form regions of upwelling and downwelling, which we show are clearly evident in Juno's microwave data between latitudes 60°S $60{}^{\circ}\mathrm{S}$ and 60°N $60{}^{\circ}\mathrm{N}$. The existence of these cells is confirmed by reproducing the ammonia observations using a simplistic model. This study solves a long‐standing puzzle regarding the nature of Jupiter's subcloud dynamics and provides evidence for eight cells in each Jovian hemisphere.
Plain Language Summary
The cloud layer of Jupiter is divided into dark and bright bands that are shaped by strong east‐west winds. Such winds in planetary atmospheres are thought to be tied with a meridional circulation. The Juno mission collected measurements of Jupiter's atmosphere at various wavelengths, which penetrate the cloud cover. Here, we provide evidence, using the Juno data, of eight deep Jovian circulation cells in each hemisphere encompassing the east‐west winds, gaining energy from atmospheric waves, and extending at least to a depth of hundreds of kilometers. Different than Earth, which has only one analogous cell in each hemisphere, known as a Ferrel cell, Jupiter can contain more cells due to its larger size and faster spin. To support the presented evidence, we modeled how ammonia gas would spread under the influence of such cells and compared it to the Juno measurements. The presented results shed light on the unseen flow structure beneath Jupiter's clouds.
Key Points
Measurements from multiple instruments of the Juno mission are interpreted to reveal the meridional circulation beneath Jupiter's clouds
16 Jet‐paired deep cells, extending from the cloud deck down to at least 240 bar, are revealed between latitudes 60°S $60{}^{\circ}\mathrm{S}$ and 60°N $60{}^{\circ}\mathrm{N}$, driven by turbulence similar to Earth's Ferrel cells
The findings are supported by modeling the advection of tracers due to the cells, showing agreement with NH3 ${\mathrm{N}\mathrm{H}}_{3}$ data
Abstract
Water and ammonia vapors are known to be the major sources of spectral absorption at pressure levels observed by the microwave radiometer (MWR) on Juno. However, the brightness temperatures ...and limb darkening observed by the MWR at its longest-wavelength channel of 50 cm (600 MHz) in the first nine perijove passes indicate the existence of an additional source of opacity in the deep atmosphere of Jupiter (pressures beyond 100 bar). The absorption properties of ammonia and water vapor, and their relative abundances in Jupiter’s atmosphere, do not provide sufficient opacity in the deep atmosphere to explain the 600 MHz channel observation. Here we show that free electrons due to the ionization of alkali metals, i.e., sodium and potassium, with subsolar metallicity, M/H (log-based 10 relative concentration to solar) in the range of M/H = −2 to M/H = −5, can provide the missing source of opacity in the deep atmosphere. If the alkali metals are not the source of additional opacity in the MWR data, then their metallicity at 1000 bars can only be even lower. This upper bound of −2 on the metallicity of the alkali metals contrasts with the other heavy elements—C, N, S, Ar, Kr, and Xe—that are all enriched relative to their solar abundances, having a metallicity of approximately +0.5.
The latitude‐altitude map of ammonia mixing ratio shows an ammonia‐rich zone at 0–5°N, with mixing ratios of 320–340 ppm, extending from 40–60 bars up to the ammonia cloud base at 0.7 bars. ...Ammonia‐poor air occupies a belt from 5–20°N. We argue that downdrafts as well as updrafts are needed in the 0–5°N zone to balance the upward ammonia flux. Outside the 0–20°N region, the belt‐zone signature is weaker. At latitudes out to ±40°, there is an ammonia‐rich layer from cloud base down to 2 bars that we argue is caused by falling precipitation. Below, there is an ammonia‐poor layer with a minimum at 6 bars. Unanswered questions include how the ammonia‐poor layer is maintained, why the belt‐zone structure is barely evident in the ammonia distribution outside 0–20°N, and how the internal heat is transported through the ammonia‐poor layer to the ammonia cloud base.
Key Points
The altitude‐latitude map of Jupiter's ammonia reveals unexpected evidence of large‐scale circulation down at least to the 50‐bar level
A narrow equatorial band is the only region where ammonia‐rich air from below the 50‐bar level can reach the ammonia cloud at 0.7 bars
At higher latitudes the ammonia‐rich air appears to be blocked by a layer of ammonia‐poor air between 3 and 15 bars
Plain Language Summary
Jupiter is a fluid planet. It has no solid continents to stabilize the weather. Scientists have wondered what the weather is like below the clouds because it might explain why storms last for decades or hundreds of years on Jupiter. The Juno spacecraft is the first chance we have had to take a look beneath the clouds, and this is the first analysis of the Juno data. The surprise is that, deep down, Jupiter's weather looks a lot like Earth's, with ammonia gas taking the place of water vapor. There is a band of high humidity at the equator and bands of low humidity on either side of the equator, like Earth's tropical and subtropical bands. What is different is that the bands go much deeper than anyone expected and this is all taking place on a planet without an ocean or a solid surface.
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° from 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 top‐most 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.
Key Points
A method to deconvolve Jupiter's thermal emission measured by the Juno microwave radiometer is presented and validated
Deconvolved nadir brightness temperatures and limb darkening results are presented for Juno observations between July 2016 and April 2018
The Juno spacecraft provides unique close-up views of Jupiter underneath the synchrotron radiation belts while circling Jupiter in its 53-day orbits. The microwave radiometer (MWR) onboard measures ...Jupiter thermal radiation at wavelengths between 1.37 and 50 cm, penetrating the atmosphere to a pressure of a few hundred bars and greater. The mission provides the first measurements of Jupiter's deep atmosphere, down to ~250 bars in pressure, constraining the vertical distributions of its kinetic temperature and constituents. As a result, vertical structure models of Jupiter's atmosphere may now be tested by comparison with MWR data. Taking into account the MWR beam patterns and observation geometries, we test several published Jupiter atmospheric models against MWR data. Our residual analysis confirms Li et al.'s (2017, https://doi.org/10.1002/2017GL073159) result that ammonia depletion persists down to 50–60 bars where ground-based Very Large Array was not able to observe. We also present an extension of the study that iteratively improves the input model and generates Jupiter brightness temperature maps which best match the MWR data. A feature of Juno's north-to-south scanning approach is that latitudinal structure is more easily obtained than longitudinal, and the creation of optimum two-dimensional maps is addressed in this approach.
The Juno microwave radiometer measured the thermal emission from Jupiter's atmosphere from the cloud tops at about 1 bar to as deep as a hundred bars of pressure during its first flyby over Jupiter ...(PJ1). The nadir brightness temperatures show that the Equatorial Zone is likely to be an ideal adiabat, which allows a determination of the deep ammonia abundance in the range
362−33+33 ppm. The combination of Markov chain Monte Carlo method and Tikhonov regularization is studied to invert Jupiter's global ammonia distribution assuming a prescribed temperature profile. The result shows (1) that ammonia is depleted globally down to 50–60 bars except within a few degrees of the equator, (2) the North Equatorial Belt is more depleted in ammonia than elsewhere, and (3) the ammonia concentration shows a slight inversion starting from about 7 bars to 2 bars. These results are robust regardless of the choice of water abundance.
Plain Language Summary
The distribution of ammonia gas on Jupiter's atmosphere was derived by fitting the microwave spectra measured by the Juno spacecraft. The result showed that the concentration of ammonia gas in the extratropics was much less than expected and had a local minimum near 7 bars of pressure.
Key Points
Jupiter's deep ammonia abundance is estimated using nadir brightness temperatures
Ammonia gas concentration is depleted with respect to the deep value down to at least 50 bars outside of the Equatorial Zone
An ammonia‐rich zone occupies 0‐5°N and extends from the deep atmosphere to the base of the ammonia cloud.
Mapping time-varying gravity via satellite-to-satellite tracking systems holds great potential as a new way to monitor the Earth's global climate system. Measurement noises and systematic ...deficiencies in sampling, both in time and space, cause global geoid or surface mass solutions to have a structured spherical harmonic error spectrum, with strong degree and order dependences and cross-correlations. To extract average values of geoid or surface mass variations around global gridpoints on Earth's surface and over various geographic regions, both the shape of the averaging kernel and the resulting average uncertainties must be considered quantitatively and statistically. We investigate two methods of the Backus and Gilbert continuous geophysical inverse formalism for optimal averages around points on Earth's surface. The first averaging kernel optimally approximates the Dirac-δ function. With an equivalent measure of deviation from the Dirac-δ function, the optimal average has greater (up to 2.6 times) accuracy than does the most widely used isotropic Gaussian filter for GRACE analysis. The second method was crafted to decrease the kernel weight as the distance from the point of interest increases. A new method is presented to use a modified Gaussian averaging kernel that reduces average uncertainties with minimum loss of resolution. The modified method has some advantages over using the kernel that optimally approximates the Dirac-δ function. Both methods are computationally efficient and are applied to simulated and real GRACE data to compute improved averages around fine-resolution global gridpoints and used with non-diagonal covariance matrices to intelligently reduce effects of correlated errors. The optimal probabilistic method of least squares with a priori information is discussed in the spherical harmonic domain. The property of optimality will be preserved when the estimates are mapped to the geographic domain for spatial averages. A regionally-bounded Gaussian a priori function is derived in the spherical harmonic domain to better represent different change regimes separated by major geographic boundaries. We also introduce an algorithm to derive the optimal regional average incorporating a constraint such that the average weight over the region is unity. Applications of such more realistic a priori information (and/or constraint) can produce improved average estimates using satellite gravity data.