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The Microwave Radiometer (MWR), Juno’s remote-sensing experiment, captures the thermal and non-thermal radiation emitted by the atmosphere and the magnetosphere, which is present in the Jovian orbital environment. Other scientific instruments on the spacecraft record the signatures of space-charged particles and the planet’s magnetic field. To retrieve the atmospheric composition values from MWR’s low-frequency radiative observation, the contributions from three existing emission sources (the cosmic microwave background (CMB), the planet, and synchrotron radiation belts) must be untangled numerically. The multi-parameter, multi-zonal model of Levin et al. (2001) for synchrotron emission employs an empirical electron-energy distribution. Initially, this distribution has been adjusted exclusively from Very Large Array (VLA) observations made from Earth before the Juno mission. This is a report on the recent model update based on a subset of MWR in-situ data. The approaches considered, challenges confronted, and the latest results are discussed here.

We analyze the Juno microwave observations of Jupiter’s atmosphere and find a warmer interior temperature than previously assumed based on the Voyager’s radio occultation measurement (Lindal et al., 1981, JGR-Space Physics, 86.A10, 8721-8727) and the Galileo Probe (Seiff et al., 1998, JGR-Planets, 103.E10, 22857-22889). By analyzing globally averaged observations from 1.4 – 50 cm wavelength, we find that the deep isentrope of Jupiter is at 169 +/- 1 K referenced at 1 bar pressure level. The globally averaged kinetic temperature at 1-bar is closer to 175 K and Jupiter’s weather layer is stably stratified. On the other hand, the 1-bar temperature inverted from Juno microwave observations at the Equatorial Zone between 0 and 5 oN remains low at 166 K, consistent with the previous remote sensing measurements made at the equator from the infrared (Fletcher et al., 2016, Icarus 278, 128-161). This also implies a vertical temperature gradient at the equator which is super-adiabatic. Our results suggest that the potential temperature difference between 1-bar and the deep isentrope is approximately 2.8 +/- 1.4 K. To avoid dynamic instability, the super-adiabatic temperature gradient must be stabilized by a change of mean molecular weight within the 5 – 10 bars pressure levels which only water can provide. The result implies that an abundance of water at the equator is constrained to a value between 2.2 and 6.2 times solar.

Jupiter has ubiquitous clouds and enormous surface structures shrouding the planet. Juno MWR provides the unprecedented chance to answer remaining major questions about the composition and dynamical properties of the great bulk of the atmosphere that lies beneath. Since the launch of Juno, there has been a large effort to collect complementary ground- and space-based observations to help interpret the MWR data. The Jovian Infrared Auroral Mapper (JIRAM) onboard Juno complements the observations of MWR, by giving alternative and reference tropospheric measurements that provides the boundary condition for the interpretation of the MWR data [Adriani et al 2014]. Similarly, HST has a 6-month overlap with 13 Juno orbits and color images were constructed from images of Jupiter in red, green, and blue filters by JunoCam [Hansen et al., 2014]. We study the dynamics within the atmosphere by relating the exterior information provided by these surface maps to the deep interior detected by MWR.Since Aug 27, 2016, MWR has obtained over 40 perijoves, all scanning Jupiter’s atmosphere from North to South, covering various longitudes. We extend our calibration stability investigation to cover 6 years of observations, using our error analysis process. After removing the calibration drift, we combine observations from all perijoves to study the global-averaged atmosphere and the discrete features. We compare them with Jupiter’s surface atmosphere images taken by JunoCam, HST and JIRAM, and retrieve the corresponding NH3 volume mixing ratio from surface to over 100 bars.

The Juno Microwave Radiometer (MWR) has extended our knowledge of the structure and composition of Jupiter's atmosphere down to several hundred bars, revealing meridional variability at great depths (e.g. Li et al. 2017, Fletcher et al. 2021). It has revealed that some cyclonic and anticyclonic vortices may have roots at depths of hundreds of bars of pressure (Bolton et al. 2022), but 5-µm hot spots and associated plumes are restricted to shallow depths above the water cloud (Fletcher et al. 2021). We report ongoing work on evolution of axisymmetric bands, concentrating on two regions where large-scale changes have been observed in the visible and infrared. One of these is the Equatorial Zone (EZ), for which Figure 1 illustrates a dramatic color change. The color change in the central component (EZc, ~3°S – 1°N, planetocentric latitude) is more prominent than the northern component (EZn, ~2° - 6°N). This change began in 2018, and by 2019 was as prominent as shown in 2021. In near-infrared bands of strong gaseous absorption, the EZc reflectivity increased dramatically (Fig. 2), but only temporarily for the EZn. Another region is the northern component of the North Equatorial Belt (NEBn, ~12°N to 15°N), whose change from a visibly dark to a bright region is also illustrated in Figure 1, with the southern component (NEBs, ~7°N to 11°N) remaining its typical dark color. Figure 3 shows that this color is associated with a remarkable drop of its 5-µm brightness which dropped down to the faint emissions of the nearby cloudy and visually bright zones. This implies a major increase in the opacity of 0.7-5 bar clouds that are similar but more extreme than the quasi-periodic northward expansions of the NEB (Fletcher et al. 2017). This transformation took place in early 2021 when Jupiter was in solar conjunction. The very preliminary results of our initial examination of MWR observations (Fig. 4) plot antenna temperatures derived using averages over all longitudes sensed in which the center of the field of view lay within specified latitude ranges. Observations were selected only if 99% or more of the field of view included the planet and the emission angle was limited to 65° or less, after which they were converted to a nadir-equivalent emission using limb-darkening models that were fit to every latitude and each channel. All observations were made at close approaches of the spacecraft to Jupiter, known as ‘perijoves’ or PJs. Many perijoves between 2019 and 2022 did not contain any measurements of these regions meeting those selection criteria, due to unfavorable spacecraft pointing. Exceptions included special spacecraft orientations. The EZc appears invariable in time, but the EZn underwent a ~7K drop in Channel-3 antenna temperatures - sensitive to conditions near ~9 bars - starting in early 2017, reaching a minimum in late 2017, then returning to its original values by early 2019. Similar variability is evident in Channel 4, sensitive to the ~3-bar level, and a smaller one in Channel 5, which is sensitive to the ~1.5-bar level. No change is detectable in Channel 6, sensitive to the ~0.7-bar level. The 2017 temperature drop has no obvious counterpart in reflected sunlight, although its “recovery” occurs during the reflectivity changes in 2019 (Figs. 1-2). To link the two, one must devise a causal relationship between a short-lived variation of absorber, likely gaseous ammonia, at 1.5-9 bars at 2016-2019 between 2°N and 6°N, and conditions at higher altitudes over a wider latitude range. If the NEBn variability between 2020 and 2021 (Figs. 1, 3) implies an increase of ammonia absorption, we would expect a decrease in antenna temperatures between our last trustworthy observation in 2019 April and observations in late 2021. This is indeed the case at 0.7 bars, represented by the 6-7K drop in Channel-6 antenna temperatures for the NEBn. This is also present in Channel 5 as a ~5K drop, but it is not detectable above the noise in the deeper-sounding channels, so this is not substantially present at pressures higher than ~1.5 bars. A ~5K drop in antenna temperatures in late 2016 is followed by a slower rise to its previous range by the end of 2017 in both Channels 5 and 6. Other channels do not show this variability, so this is another “shallow” phenomenon with no obvious connection to changes in cloud reflectivity. We will continue to examine variability in cloud reflectivity associated with these changes, observe with increasingly favorable geometries for the next few perijoves, and examine other latitudes for variability. References: Bolton, et al. 2021. Science 374, 968-972. Fletcher, et al. 2017. Geophys. Res. Lett. 44, 7140-7148 Fletcher, et al. 2020. J. Geophys. Res. 125, E06399. Fletcher, et al. 2021. J. Geophys. Res. 126, E06858. Li, et al. 2017. G. Res. Lett. 44, 5317-5325. Simon, et al. 2015. Ap. J. 812, 55. Figure 1. Color-composite cylindrical maps of Jupiter from the Hubble Space Telescope OPAL program (Simon et al. 2015). Note the darkening of the EZ central and northern components, which began in 2018. Note also that in 2021 the northern part of the North Equatorial Belt has become lighter in color compared with 2017, a process that took place in early 2021. Figure 2. Reflectivity of the central and northern components of the Equatorial Zone vs time at 2.166 µm, a wavelength sensitive to reflected sunlight from ~80-mbar aerosols. This plot implies that the EZn contained a denser population of aerosols near this level, most likely from more vigorous lofting. Figure 3. Images of Jupiter at 5.1 µm: these and data in Fig. 2 were taken using NASA’s Infrared Telescope Facility (IRTF) SpeX guide camera. Note the drop in radiance of the northern section of the North Equatorial Belt from 2020 to 2021. Figure 4. Preliminary results on radiances measured by Juno’s Microwave Radiometer (MWR). Channels 3 and 6 sense radiances at 2.6 and 22 GHz, respectively. For clarity, other channels are not shown. Error bars represent the standard deviation of antenna temperatures included in the latitude bin.

On November 29th, 2021, the Juno Spacecraft completed its 38th perijove as part of its Extended Mission. Three of the spacecraft’s instruments, JunoCam, JIRAM, and MWR, imaged a thunderstorm in the North Equatorial Belt (NEB) at approximately 9oN planetocentric latitude. JunoCam and the MWR captured data from an altitude of a few thousand kilometers, following JIRAM’s images of the storm four hours before. Ground-based observers tracked this storm over a period of a few days, providing a planetary-scale perspective to Juno’s observations. This region of the planet has been quite active throughout 2021 and 2022. The morphology of the storm as shown in JunoCam’s RGB filters (observations with the methane filter were not conducted), and from ground-based observers, is highly suggestive of a moist-convective thunderstorm complex with clouds reaching the upper troposphere. Furthermore, JunoCam images suggest that the storm is shaped by vertical shear as the presumed anvil is offset from a thicker region of white clouds. On Earth, vertical shear is necessary for non-tropical cyclone thunderstorm systems to persist for prolonged periods. JunoCam imaging also suggests a previous anvil top located to the west of the optically thick clouds, which may indicate a temporarily-varying nature to the convection, which is consistent with ground-based observations showing upwelling at this location for several days before the Juno images. JIRAM’s observations show a cold spot at 4.78 µm near the region of the thickest white clouds, which would be expected from optically thick clouds blocking heat transport to space. Spectroscopic retrievals show a slight enhancement of H20 and PH3 compared to the surrounding region, which is expected from upwelling from the interior. The microwave radiometer (MWR) instrument detected numerous lightning flashes at 0.6 GHz (Channel 1) and several flashes at 1.2 and 2.4 GHz (Channels 2 and 3, respectively), which are correlated with JunoCam and JIRAM’s observations of optically thick clouds. However, the brightness temperature signals of the storm in the MWR observations appears to be confined to the upper most 9 bars of the atmosphere ("weather layer") indicating that a deep-seated convective plume originating from beneath the water-cloud base is probably not responsible for this storm system. Instead, a humidity or temperature front contained within the weather layer may be a likely source. These observations may ultimately shed light on the mechanisms that form, sustain, and characterize moist convective storms in hydrogen-dominated atmospheres. Here we summarize our observations to date and compare the PJ38 storm to other storms in Jupiter's NEB and those on Saturn.

The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm has been applied to multi-year records of observations from NASA's Orbiting Carbon Observatory -2 and -3 sensors (OCO-2 and OCO-3, respectively) to provide estimates of the carbon dioxide (CO2) column-averaged dry-air mole fraction (XCO2). In this study, a number of improvements to the ACOS v10 L2FP algorithm are described. The post-processing quality filtering and bias correction of the XCO2 estimates against multiple truth proxies are also discussed. The OCO v10 data volumes and XCO2 estimates from the two sensors for the time period August 2019 through February 2022 are compared, highlighting differences in spatiotemporal sampling, but demonstrating broad agreement between the two sensors where they overlap in time and space. A number of evaluation sources applied to both sensors suggest they are broadly similar in data and error characteristics. Mean OCO-3 differences relative to collocated OCO-2 data are approximately 0.2 ppm and −0.3 ppm for land and ocean observations, respectively. Comparison of XCO2 estimates to collocated Total Carbon Column Observing Network (TCCON) measurements show root mean squared errors (RMSE) of approximately 0.8 ppm and 0.9 ppm for OCO-2 and OCO-3, respectively. An evaluation against XCO2 fields derived from atmospheric inversion systems that assimilated only near-surface CO2 observations, i.e., did not assimilate satellite CO2 measurements, yielded RMSEs of 1.0 ppm and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of errors in small areas, as well as biases across land-ocean crossings, also show encouraging results, for each sensor and in their agreement. Taken together, our results demonstrate a broad consistency of OCO-2 and OCO-3 XCO2 measurements, suggesting they may be used together for scientific analyses.

The Juno spacecraft’s polar orbit with periapses (perijove; PJ) within a few thousand kilometers of the 1-bar level has allowed for detailed observations of Jupiter’s thunderstorms from multiple instruments at unprecedented resolution. Here, we detail the observations of a 2,500-km wide thunderstorm feature located in the North Equatorial Belt (at planetocentric latitude 9°N; Fig. 1) from the six channels of the Microwave Radiometer Instrument (MWR), from JunoCam’s RGB filters, from the Jovian Infrared Auroral Mapper’s (JIRAM) 5-micron band, and from supporting Earth-Based images taken during near the time of the 38th perijove. Juno flew over one such thunderstorm complex at close range (~5,000 km) on 29 Nov. 2021 for the first time with a favorable alignment to observe such a feature at low emission angles. JunoCam and MWR observations were taken nearly simultaneously while JIRAM’s data was collected approximately 4.5 hours prior. Moist convection is widely thought to play a large role in transporting heat from Jupiter’s interior through the weather layer (here defined as 10 to 0.7 bar) and then to space. In the process, heat transport allows for moist convection, which powers thunderstorms and generates small-scale turbulence that, through the inverse-cascade mechanism, generates large-scale vortices and zonal jets. Convective instability allows for strong updrafts that carry volatiles such as NH3 and H2O from the base of the water-cloud (and deeper) upwards to form cumulonimbus (CB) clouds. The tops of these CB towers diverge outwards and are shaped by local winds to form an icy anvil cloud much as they do on Earth. Lightning is a defining characteristic of CB clouds and the MWR instrument detected numerous flashes in and around the bright white feature with a storm-like morphology clearly observed by JunoCam. JIRAM’s M-band filter images clearly show the structure of the cloud tops, matching observations from JunoCam once the zonally-averaged motion over the 4.5-hour time separation is accounted for. Figure 2 is a JIRAM image with a noticeable dark ‘notch,’ which is where the optically thick storm clouds are located. Preliminary spectral analysis from JIRAM shows a slightly enhanced signal of H2O and PH3 near the anvil top but NH3 ice is undetectable in these night-time JIRAM observations. Gaseous NH3 and H2O are partially opaque to wavelengths sensed by the MWR instrument. Each of the six channels of MWR have a sensitivity that peaks at different altitudes in the atmosphere, which allows us to sound the brightness temperature and the vertical structure from the cloud tops down to many tens of bars. The brightness temperature is a combination of air temperature and humidity, and, at present, we are unable to deconvolve these two measurements. Nevertheless, sounding the brightness temperature provides information on the depth and structure of a tall atmospheric feature and we present brightness temperature maps for all six channels. We observe that this particular thunderstorm complex is visible from the cloud tops (~0.7 bar) down to approximately the level of the water cloud. Below this level, the thunderstorm signal is no longer apparent, which indicates that any dry convective updraft carrying MWR opacity-inducing vapor is either not present, or its air temperature and humidity are combined in such a way to mask its presence perfectly. If this thunderstorm is a result of dry convection from below the base of the water cloud lifting moist air to the level of free convection (LFC) then the effect of the dry convection is to lift vapor already located around the base of the water-cloud level rather than bringing up significantly moist air from deep below the base of the water cloud, or otherwise a signal would be detected in the short wavelength channels of MWR (See Fig. 3 for a cloud-top MWR map). The next several perijoves will feature an orientation for the MWR instrument that is conducive for low emission angle observations. Additionally, the latitude of perijove is slowly migrating northward and, if Juno does fly over new thunderstorms, we may have the opportunity to compare the vertical structure of multiple thunderstorms taken from different regions of the planet at high resolution from multiple instruments. Figure 1: PJ38 Thunderstorm from JunoCam Image JNCE_2021333_38C00030_V01 (left) and close-up (right: Image Credit: NASA/JPL-Caltech/SwRI/MSSS/Kevin M. Gill) Figure 2: Map-Projected Processed Close-Up Image of PJ38 Thunderstorm from JIRAM M-Band Image (Image Credit: NASA/JPL-Caltech/INAF-IAPS). Circle denotes location of optically thick storm clouds. Figure 3: Map-Projected MWR Ch 6 Brightness Temperature

Jupiter’s cyclonic features are known to undergo transitions between quiescent states with smooth edges (often appearing as dark brown ‘barges’) to states with convective outbursts of billowing white clouds, chaotically churned into filamentary structures. Cyclones in the latter state are known as ‘Folded Filamentary Regions’ (FFRs), and Voyager images (Ingersoll+1979, doi: 10.1038/280773a0) revealed them to be rapidly-varying turbulent regions, occurring in cyclonic domains on the poleward side of Jupiter’s prograde jets. JunoCam visible-light observations (Orton+2017, doi:10.1002/2016GL072443, Rogers+2021, doi:10.1016/j.icarus.2021.114742), reveal the increasing prevalence of FFRs at mid-to-high latitudes. They dominate the polar domain alongside smaller anticyclonic white ovals, drifting westward in latitude bands between the narrow prograde jets, and rapidly evolving over timescales of days. The present study makes use of Juno’s ever-improving microwave observations of the north polar domain, as the latitude of closest-approach (“perijove”) moves northward. We therefore focus on FFRs in the northern hemisphere, where we find them to occur in zonally-organised latitude bands even at high latitudes. Statistics of the FFRs suggest that they occur on the poleward sides of the N4 (43.3oN, centric), N5 (52.3oN), and N7 (66.1oN) prograde jets (N6 at 61.2oN coincides with a ‘bland zone’ lacking notable FFRs), and scattered in the polar domain up to the octagon of circumpolar cyclones at 85oN. JIRAM 5-µm imaging of both poles reveal FFRs as generally dark structures with elevated aerosol opacity blocking thermal infrared emission from the 4-6 bar level, coinciding with the white stratiform clouds observed by JunoCam. Clusters of small cumulus-like clouds, as well as curvilinear cloud streaks, provide texture to the flat stratiform clouds to give the appearance of a network of filaments. Visibly-dark lanes border the brighter filaments, creating an intricate network of narrow, aerosol-free, and 5-µm-bright striations within each FFR. This is in contrast to cyclonic features such as barges at lower latitudes, where an absence of overlying aerosols generally renders them 5-µm bright. A survey of 1.4-50 cm observations acquired by Juno’s Microwave Radiometer (MWR) between PJ20 (May 2019) to PJ37 (October 2021) reveals that FFRs share a key characteristic with their low-latitude counterparts: they are microwave-bright in channels sounding the 0.6-2.0 bar range (1.4-3.0 cm), become hard to distinguish from their surroundings near 5 bars (5.75 cm), but are then microwave-dark in the channel sounding 10-15 bar (11.5 cm). This suggests FFRs are depleted in ammonia gas and/or locally warmer at levels above the putative location of Jupiter’s water cloud, the latter implying a decay of cyclonic winds with altitude. This shallow ammonia depletion is surprising, given the apparent convective nature of the FFRs - maybe NH3-rich plumes occupy a sufficiently small area of the cyclonic structure, so that they have negligible impact on the warm emission observed by MWR. This shallow depletion is balanced by a local NH3 enrichment (or local cooling) at depth, below the water cloud, like a cyclonic lens. However, the extension of FFR signatures to deeper levels (p>20 bars) is currently unclear due to insufficient spatial coverage and resolution at the longest-wave channels, and confusion arising from auroral contributions to the MWR dataset at 50 cm. An inversion in microwave brightness with depth was previously identified for Jupiter’s larger-scale belts and zones (Fletcher+2021, doi:10.1029/2021JE006858) and mid-latitude discrete features (Bolton+2021, doi:10.1126/science.abf1015), and now appears to be common at high latitudes as well. Geostrophy implies that cyclonic circulations (low-pressure centers) cause a rise in potential-temperature surfaces in deeper layers, potentially triggering moist convection in the water cloud (e.g., Dowling & Gierasch, 1989 Bull. Amer. Astron. Soc 21, 946; Fletcher+2017, doi:10.1016/j.icarus.2017.01.001), producing the distinct convective cloud structure observed by visible and infrared imaging: juxtaposed tall convective towers, deep water clouds, and narrow clear lanes (Imai+2020, doi:10.1029/2020GL088397). Lightning sferics measured by MWR at 50 cm/600 MHz are more frequent in the N4, N5, and N7 domains where FFRs are common (Brown+2018, doi:10.1038/s41586-018-0156-5), and are further clustered in FFRs themselves (Wong+2020, doi:10.3847/1538-4365/ab775f). Thus the evolution of the cyclonic FFRs, and their penetration to the moist depths below the water condensation level, may hold the key to understanding the distribution of lightning activity on Jupiter.

Jupiter’s atmosphere is governed by multiple jet streams, which are strongly tied to its three-dimensional atmospheric circulation. Lacking a solid surface, several theories exist for how the meridional circulation extends into the interior. Here we show, collecting evidence from multiple instruments of the Juno mission, the existence of mid-latitudinal, turbulent driven, meridional circulation cells, similar to the Ferrel cells on Earth. Different than Earth, which contains only one such cell in each hemisphere, Jupiter can incorporate multiple cells due to its large size and fast spin. The cells form regions of upwelling and downwelling, which we show are clearly evident in Juno’s MWR data between latitudes 60S and 60N. The existence of these cells is confirmed by reproducing the ammonia observations using an advection-relaxation model. This study solves a long-standing puzzle regarding the nature of Jupiter’s sub-cloud dynamics and provides evidence for 8 cells in each Jovian hemisphere.

In Jupiter’s vicinity, Juno’s remote-sensing experiment, the Microwave Radiometer (MWR), captures thermal and non-thermal emissions from the atmosphere and magnetosphere. Furthermore, other scientific instruments on the spacecraft register the signatures of space charged particles and the planet’s magnetic field. The separation of contributions from several existing emission sources (cosmic microwave background, galactic emission, planetary thermal emission and synchrotron radiation belts) is a necessary step in the retrieval of atmospheric composition values from MWR’s low-frequency radiative observations. The ad hoc multi-parameter, multi-zonal model of Levin et al. (2001) for synchrotron emission has been updated based on a subset of the MWR in-situ data. This model employs an empirical electron-energy distribution, which originally has been adjusted exclusively from Very Large Array (VLA) observations made prior to the Juno mission. The approaches considered and challenges confronted are discussed here. The model will be updated frequently as additional observations from the MWR and magnetometer instruments are taken into account.

This study continues the work from Hodges et al. (2020) to further analyze microwave emissions associated with Jupiter’s aurorae as seen by the 600 MHz channel of the MicroWave Radiometer (MWR) onboard the Juno spacecraft. The MWR can obtain spatial maps of the northern aurora. These maps allow a two-dimensional comparison of auroral observations at microwave, ultraviolet, and infrared frequencies. Each spectral region provides information on different particles of the auroral plasma. For example, microwave observations provide information on the electron density content and structure. Ultraviolet observations provide insight on the content and morphology of the Lyman series of H and the Lyman, Werner, and Rydberg bands of H­2. Lastly, infrared observations provide information on the content and structure of H3+ ions. The UltraViolet Spectrograph (UVS) and the Jovian Infrared Auroral Mapper (JIRAM) have higher resolution observations than the MWR (Gladstone et al. 2014; Adriani et al. 2014; Janssen et al. 2017). To compare observations from these three instruments, the UVS and JIRAM observations are convolved with the antenna beam-pattern of the 5x5 patch antenna array for the 600 MHz channel with a half-power beamwidth 20° (Janssen et al. 2017). The convolution allows UVS and JIRAM data to smear and provide a resolution similar to MWR observations. This process facilitates the comparative analysis of microwave, ultraviolet, and infrared observations of Jupiter’s northern aurora. This work reports on the results of the convolved UVS and JIRAM maps compared to MWR observations from previous perijoves.

Jupiter has ubiquitous clouds and enormous surface structures shrouding the planet. Juno MWR provides the unprecedented chance to answer remaining major questions about the composition and dynamical properties of the great bulk of the atmosphere that lies beneath. Since the launch of Juno, there has been a large effort to collect complementary ground- and space-based observations to help interpret the MWR data. The Jovian Infrared Auroral Mapper (JIRAM) onboard Juno complements the observations of MWR, by giving alternative and reference tropospheric measurements that provides the boundary condition for the interpretation of the MWR data [Adriani et al 2014]. Similarly, HST has a 6-month overlap with 13 Juno orbits and color images were constructed from images of Jupiter in red, green, and blue filters by JunoCam [Hansen et al., 2014]. We study the dynamics within the atmosphere by relating the exterior information provided by these surface maps to the deep interior detected by MWR.During Aug 27, 2016 to October 24, 2017, MWR obtained 8 perijoves (PJ1, 3, 4, 5, 6, 7, 8, 9), all scanning Jupiter’s atmosphere from North to South, covering various longitudes. By combing observations from these perijoves, we are able to study the global-averaged atmosphere and the anomalies to be compared with top atmosphere maps. The success of such a study depends on the stability of calibration between different perijoves. In order to combine those data, we investigate and remove the calibration drift with respect to time using our error analysis process. We report two outcomes from the error analysis: 1) The atmosphere stability with respect to longitude and time, as compared to the latitudinal belt and zone structures. 2) The spotted points-of-interest which lie 2 standard deviations away from the global-averaged atmosphere. We compare them with Jupiter’s surface atmosphere images taken by JunoCam, HST and JIRAM, and retrieve the corresponding NH3 volume mixing ratio from surface to over 100 bars.

Measuring the depth of Jupiter’s storms The atmosphere of Jupiter consists of bands of winds rotating at different rates, punctuated by giant storms. The largest storm is the Great Red Spot (GRS), which has persisted for more than a century. It has been unclear whether the storms are confined to a thin layer near the top of the atmosphere or if they extend deep into the planet. Bolton et al . used microwave observations from the Juno spacecraft to observe several storms and vortices. They found that the storms extended below the depths at which water and ammonia are expected to condense, implying a connection with the deep atmosphere. Parisi et al . analyzed gravity measurements taken while Juno flew over the GRS. They detected a perturbation in the planet’s gravitational field caused by the storm, finding that it was no more than 500 kilometers deep. In combination, these results constrain how Jupiter’s meteorology links to its deep interior. —KTS

We present our latest model of electron radiation belts developed for a large region of Jupiter's magnetosphere (1-50 Rj). For the region inward of Io, electron distributions are computed from a computational code that solves the governing three-dimensional Fokker-Planck equation. This physics-based model accounts for different mechanisms to discuss the energy and spatial distributions of electrons for L values between 1 and 5. The model for the innermost magnetospheric region is expanded to the middle magnetosphere using an empirical approach. In this paper, we first show how our large-scale model of Jupiter's electron radiation belts agrees with data sets from past missions (Pioneer 10 and 11 GTT, Galileo EPD and EPI measurements). We then focus on our effort to combine Juno (JEDI, JADE Electron Ambient Background Counts) and Galileo EPD (> 1.5, 11.5 MeV) datasets to improve our model for both the region beyond Io and the inner edge of the Jovian electron radiation belts. Finally, simulations of Jupiter's synchrotron emission are presented to gauge the contribution of ultra-energetic electrons trapped beyond L ~ 3 at different latitudes to radio emission observed by Juno MWR.

Juno Microwave Radiometer (MWR) observations of Jupiter's mid-latitudes reveal a strong correlation between brightness temperature contrasts and zonal winds, confirming that the banded structure extends throughout the troposphere. However, the microwave brightness gradient is observed to change sign with depth: the belts are microwave-bright in the $p10$ bar range. The transition level (which we call the jovicline) is evident in the MWR 11.5 cm channel, which samples the 5-14 bar range when using the limb-darkening at all emission angles. The transition is located between 4 and 10 bars, and implies that belts change with depth from being NH$_3$-depleted to NH$_3$-enriched, or from physically-warm to physically-cool, or more likely a combination of both. The change in character occurs near the statically stable layer associated with water condensation. The implications of the transition are discussed in terms of ammonia redistribution via meridional circulation cells with opposing flows above and below the water condensation layer, and in terms of the `mushball' precipitation model, which predicts steeper vertical ammonia gradients in the belts versus the zones. We show via the moist thermal wind equation that both the temperature and ammonia interpretations can lead to vertical shear on the zonal winds, but the shear is $\sim50\times$ weaker if only NH$_3$ gradients are considered. Conversely, if MWR observations are associated with kinetic temperature gradients then it would produce zonal winds that increase in strength down to the jovicline, consistent with Galileo probe measurements; then decay slowly at higher pressures., Published in JGR: Planets

The accuracy of atmospheric trace gas retrievals depends directly on the accuracy of the molecular absorption model used within the retrieval algorithm. For remote sensing of well-mixed gases, such as carbon dioxide (CO2), where the atmospheric variability is small compared to the background, the quality of the molecular absorption model is key. Recent updates to oxygen (O2) absorption coefficients (ABSCO) for the 0.76 µm A-band and the water vapor (H2O) continuum model within the 1.6 µm and 2.06 µm CO2 bands used within the Orbiting Carbon Observatory (OCO-2 and OCO-3) algorithm are described here. Updates in the O2 A-band involve the inclusion of new laboratory measurements within multispectrum fits to improve relative consistency between O2 line shapes and collision-induced absorption (CIA). The H2O continuum model has been updated to MT _ CKD v3.2, which has benefited from information from a range of laboratory studies relative to the model utilized in the previous ABSCO version. Impacts of these spectroscopy updates have been evaluated against ground-based atmospheric spectra from the Total Carbon Column Observing Network (TCCON) and within the framework of the OCO-2 algorithm, using OCO-2 soundings covering a range of atmospheric and surface conditions. The updated absorption coefficients (ABSCO version 5.1) are found to offer improved fitting residuals and reduced biases in retrieved surface pressure relative to the previous version (ABSCO v5.0) used within B8 and B9 of the OCO-2 retrieval algorithm and have been adopted for the OCO B10 Level 2 algorithm.

Introduction: The locations of Jupiter’s cloud-top east-west jets are relatively constant over time and define the cyclonic belts and their neighbouring anticyclonic zones. Thermal-infrared observations of the upper troposphere reveal cool temperatures, elevated abundances of condensate and disequilibrium gases, and enhanced cloud opacity over the zones, and the opposite over the belts (Gierasch et al., 1986, doi:10.1016/0019-1035(86)90125-9). This distribution implies upwelling motions in zones and subsidence in belts. However, this picture has been called into question by the observed eddy-momentum flux convergence into the eastward jets (and divergence from the westward jets), which suggests a compensating flow in the opposite direction, from belts into zones, which is partially supported by the distribution of lightning at low latitudes (Little et al., 1999, doi:10.1006/icar.1999.6195). It is possible that two different atmospheric regimes exist: a deep regime where eddies are able to drive the zonal flows, and a higher-altitude regime where those zonal flows decay with height. Reconciling this apparent inconsistency remains a key challenge for the understanding of Jupiter’s atmosphere, and Juno observations of the deeper atmosphere shed important light on this issue. Methodology: Juno’s Microwave Radiometer (MWR) examines the vertical structure of Jupiter’s belts and zones below the clouds by sounding in six channels from 1.4 to 50 cm, sensing from the cloud-tops at ~0.7 bar to pressures greater than 300 bar. Initial results (Li et al. 2017, doi:10.1002/2017GL073159) revealed contrasts at depth that bore a potential resemblance to the belt/zone structure in the upper troposphere. We report on progress in our analysis of averaged nadir microwave brightness and its emission-angle dependence from the first two years of Juno’s mission. We investigate the correlation between the meridional gradient of the brightness temperature at all emission angles and the cloud-top zonal winds. These brightness temperature gradients reflect changes in gaseous opacity (e.g., ammonia and water), kinetic temperature, or both. We explore the implications of the contrasts observed between belts and zones as a function of depth sounded by MWR. Preliminary Results: Meridional brightness temperature gradients above the clouds (poN, this is broadly consistent with results from both the Galileo Probe (Atkinson et al., 1998, doi:10.1029/98JE00060) and Cassini cloud-tracking (Li et al., 2006, doi:10.1029/2005JE002556), which suggested that the jet decayed with height from the ~5-bar level to the 0.5-bar level by more than 90 m/s. We will report on our initial exploration of how these correlations change at deeper pressure levels by looking at MWR wavelengths beyond 10 cm, probing well below the expected condensation level of Jupiter’s water clouds. Acknowledgements: Fletcher was supported by a Royal Society Research Fellowship and European Research Council Consolidator Grant (under the European Union's Horizon 2020 research and innovation programme, grant agreement No 723890) at the University of Leicester. Levin, Orton, and Oyafuso were supported by the National Aeronautics and Space Administration through funds distributed to the Jet Propulsion Laboratory, California Institute of Technology.

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.

Oxygen is the most common element after hydrogen and helium in Jupiter's atmosphere, and may have been the primary condensable (as water ice) in the protoplanetary disk. Prior to the Juno mission, in situ measurements of Jupiter's water abundance were obtained from the Galileo Probe, which dropped into a meteorologically anomalous site. The findings of the Galileo Probe were inconclusive because the concentration of water was still increasing when the probe died. Here, we initially report on the water abundance in the equatorial region, from 0 to 4 degrees north latitude, based on 1.25 to 22 GHz data from Juno Microwave radiometer probing approximately 0.7 to 30 bars pressure. Because Juno discovered the deep atmosphere to be surprisingly variable as a function of latitude, it remains to confirm whether the equatorial abundance represents Jupiter's global water abundance. The water abundance at the equatorial region is inferred to be $2.5_{-1.6}^{+2.2}\times10^3$ ppm, or $2.7_{-1.7}^{+2.4}$ times the protosolar oxygen elemental ratio to H (1$\sigma$ uncertainties). If reflective of the global water abundance, the result suggests that the planetesimals formed Jupiter are unlikely to be water-rich clathrate hydrates., Comment: 27 pages, 7 figures

We present multi-wavelength measurements of the thermal, chemical, and cloud contrasts associated with the visibly dark formations (also known as 5-$��$m hot spots) and intervening bright plumes on the boundary between Jupiter's Equatorial Zone (EZ) and North Equatorial Belt (NEB). Observations made by the TEXES 5-20 $��$m spectrometer at the Gemini North Telescope in March 2017 reveal the upper-tropospheric properties of 12 hot spots, which are directly compared to measurements by Juno using the Microwave Radiometer (MWR), JIRAM at 5 $��$m, and JunoCam visible images. MWR and thermal-infrared spectroscopic results are consistent near 0.7 bar. Mid-infrared-derived aerosol opacity is consistent with that inferred from visible-albedo and 5-$��$m opacity maps. Aerosol contrasts, the defining characteristics of the cloudy plumes and aerosol-depleted hot spots, are not a good proxy for microwave brightness. The hot spots are neither uniformly warmer nor ammonia-depleted compared to their surroundings at $p10$ bars, suggesting that the hot-spot/plume wave is a relatively shallow feature., 50 pages, 16 figures, accepted for publication in JGR-Planets

The accuracy of atmospheric trace gas retrievals depends directly on the accuracy of the molecular absorption model used within the retrieval algorithm. For remote sensing of well-mixed gases, such as carbon dioxide (CO 2 ), where the atmospheric variability is small compared to the background, the quality of the molecular absorption model is key. Recent updates to the 1.6 µm and 2.06 µm CO 2 absorption model used within the Orbiting Carbon Observatory (OCO-2) algorithm are described and validated. A set of 164 atmospheric spectra from the Total Carbon Column Observing Network (TCCON) is used to compare three models, both previous and current versions of absorption coefficient tables (largely derived from recent multispectrum fitting analyses targeted specifically at these bands) as well as a recent model constructed to use the HITRAN 2012 compilation. Both spectral residuals and retrieved column-averaged CO 2 mixing ratios (XCO 2 ) are included in the comparison. Absorption coefficients based on the updated multispectrum fitting analyses provide residuals comparable to or smaller than either the previous version of the multispectrum fits or the HITRAN 2012-based model. For the 2.06 µm band the updated model finds noticeably lower residuals for low water content cases. It is found that apart from a scaling factor the prior and updated absorption models result in similar retrieved values of XCO 2 for the 2.06 µm band and a slightly different airmass dependence for the 1.6 µm band.

Since August 2016, measurements of Jupiter's microwave emissions at six wavelengths ranging from 1.3 cm to 50 cm have been made with the Juno Microwave Radiometer (MWR). In this paper, we introduce the first systematic set of in-situ observations of synchrotron radiation in a polar plane while describing the modeling approach we use to analyze this data (collected August 27th, 2016). Time series of brightness profiles at all six frequencies present similarities that are explained by the presence of known regions of intense synchrotron radiation. Our model predictions, though limited for now to the total intensity of the radiation, reproduce (qualitatively) the observation of temporal variations and allow to disentangle the synchrotron emission from the atmospheric emission. The discrepancies seen between the data and simulations confirm that physical conditions close to Jupiter affecting synchrotron emission (electron energy spectra, pitch-angle distributions, and the magnetic environment) are different than we anticipated.

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 which 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.

The Juno Microwave Radiometer (MWR) is a six-frequency scientific instrument designed and built to investigate the deep atmosphere of Jupiter. It is one of a suite of instruments on NASA’s New Frontiers Mission Juno launched to Jupiter on August 5, 2011. The focus of this paper is the description of the scientific objectives of the MWR investigation along with the experimental design, observational approach, and calibration that will achieve these objectives, based on the Juno mission plan up to Jupiter orbit insertion on July 4, 2016. With frequencies distributed approximately by octave from 600 MHz to 22 GHz, the MWR will sample the atmospheric thermal radiation from depths extending from the ammonia cloud region at around 1 bar to pressure levels as deep as 1000 bars. The primary scientific objectives of the MWR investigation are to determine the presently unknown dynamical properties of Jupiter’s subcloud atmosphere and to determine the global abundance of oxygen and nitrogen, present in the atmosphere as water and ammonia deep below their respective cloud decks. The MWR experiment is designed to measure both the thermal radiation from Jupiter and its emission-angle dependence at each frequency relative to the atmospheric local normal with high accuracy. The antennas at the four highest frequencies (21.9, 10.0, 5.2, and 2.6 GHz) have ∼12° beamwidths and will achieve a spatial resolution approaching 600 km near perijove. The antennas at the lowest frequencies (0.6 and 1.25 GHz) are constrained by physical size limitations and have 20° beamwidths, enabling a spatial resolution of as high as 1000 km to be obtained. The MWR will obtain Jupiter’s brightness temperature and its emission-angle dependence at each point along the subspacecraft track, over angles up to 60° from the normal over most latitudes, during at least six perijove passes after orbit insertion. The emission-angle dependence will be obtained for all frequencies to an accuracy of better than one part in 10^3, sufficient to detect small variations in atmospheric temperature and absorber concentration profiles that distinguish dynamical and compositional properties of the deep Jovian atmosphere.

We present an analysis of uncertainties in global measurements of the column averaged dry-air mole fraction of CO2 (XCO2) by the NASA Orbiting Carbon Observatory-2 (OCO-2). The analysis is based on our best estimates for uncertainties in the OCO-2 operational algorithm and its inputs, and uses simulated spectra calculated for the actual flight and sounding geometry, with measured atmospheric analyses. The simulations are calculated for land nadir and ocean glint observations. We include errors in measurement, smoothing, interference, and forward model parameters. All types of error are combined to estimate the uncertainty in XCO2 from single soundings, before any attempt at bias correction has been made. From these results we also estimate the "variable error" which differs between soundings, to infer the error in the difference of XCO2 between any two soundings. The most important error sources are aerosol interference, spectroscopy, and instrument calibration. Aerosol is the largest source of variable error. Spectroscopy and calibration, although they are themselves fixed error sources, also produce important variable errors in XCO2. Net variable errors are usually

We derived a new formula for calculating the moist adiabatic temperature profile of an atmosphere consisting of ideal gases with multiple condensing species. This expression unifies various formulas published in the literature and can be generalized to account for chemical reactions. Unlike previous methods, it converges to machine precision independent of mesh size. It accounts for any ratio of condensable vapors to dry gas, from zero to infinity, and for variable heat capacities as a function of temperature. Because the derivation is generic, the new formula is not only applicable to planetary atmosphere in the solar system, but also to hot Jupiters and brown dwarfs in which a variety of alkali metals, silicates and exotic materials condense. We demonstrate that even though the vapors are ideal gases, they interact in their effects on the moist adiabatic lapse rate. Finally, we apply the new thermodynamic model to study the effects of downdrafts on the distribution of minor constituents and thermal profile in the Galileo probe hotspot. We find that the Galileo Probe measurements can be interpreted as a strong downdraft that displaces an air parcel from 1 bar to the 4 bar level., 30 pages, 3 figures, accepted for publication in Journal of the Atmospheric Science

A need for precise air-mass retrievals utilizing the near-infrared O2 A-band has motivated measurements of the water-broadening in oxygen. Experimental challenges have resulted in very little water broadened oxygen data. Existing water broadening data for the O2 A-band is of insufficient precision for application to the atmospheric data. Line shape theory suggests that approximate O2 pressure broadening parameters for one spectral region, such as the A-band, may be obtained from comparable spectral regions such as the O2 60 GHz Q-branch, which is also used prominently in remote sensing. We have measured precise O2–H2O broadening for the 60 GHz Q-branch and the pure-rotational transitions at room temperature with a Zeeman-modulated absorption cell using a frequency-multiplier spectrometer. Intercomparisons of these data and other O2 pressure broadening data sets confirm the expectation of only minor band-to-band scaling of pressure broadening. The measurement provides a basis for fundamental parameterization of retrieval codes for the long-wavelength atmospheric measured values. Finally, we demonstrate the use of these measurements for retrievals of air-mass via remote sensing of the oxygen A-band.

We present well-calibrated, high-resolution maps of Saturn’s thermal emission at 2.2-cm wavelength obtained by the Cassini RADAR radiometer through the Prime and Equinox Cassini missions, a period covering approximately 6 years. The absolute brightness temperature calibration of 2% achieved is more than twice better than for all previous microwave observations reported for Saturn, and the spatial resolution and sensitivity achieved each represent nearly an order of magnitude improvement. The brightness temperature of Saturn in the microwave region depends on the distribution of ammonia, which our radiative transfer modeling shows is the only significant source of absorption in Saturn’s atmosphere at 2.2-cm wavelength. At this wavelength the thermal emission comes from just below and within the ammonia cloud-forming region, and yields information about atmospheric circulations and ammonia cloud-forming processes. The maps are presented as residuals compared to a fully saturated model atmosphere in hydrostatic equilibrium. Bright regions in these maps are readily interpreted as due to depletion of ammonia vapor in, and, for very bright regions, below the ammonia saturation region. Features seen include the following: a narrow equatorial band near full saturation surrounded by bands out to about 10° planetographic latitude that demonstrate highly variable ammonia depletion in longitude; narrow bands of depletion at −35° latitude; occasional large oval features with depleted ammonia around −45° latitude; and the 2010–2011 storm, with extensive saturated and depleted areas as it stretched halfway around the planet in the northern hemisphere. Comparison of the maps over time indicates a high degree of stability outside a few latitudes that contain active regions.

This work focuses on determining the latitudinal structure of ammonia vapor in Saturn's cloud layer near 1.5 bars using the brightness temperature maps derived from the Cassini RADAR (Elachi et al., 2004) instrument, which works in a passive mode to measure thermal emission from Saturn at 2.2-cm wavelength. We perform an analysis of five brightness temperature maps that span epochs from 2005 to 2011, which are presented in a companion paper by Janssen et al. (2013a, this issue). The brightness temperature maps are representative of the spatial distribution of ammonia vapor, since ammonia gas is the only effective opacity source in Saturn's atmosphere at 2.2-cm wavelength. Relatively high brightness temperatures indicate relatively low ammonia relative humidity (RH), and vice versa. We compare the observed brightness temperatures to brightness temperatures computed using the Juno atmospheric microwave radiative transfer (JAMRT) program which includes both the means to calculate a tropospheric atmosphere model for Saturn and the means to carry out radiative transfer calculations at microwave frequencies. The reference atmosphere to which we compare has a 3x solar deep mixing ratio of ammonia (we use 1.352x10(exp -4) for the solar mixing ratio of ammonia vapor relative to H2; see Atreya, 2010) and is fully saturated above its cloud base. The maps are comprised of residual brightness temperatures-observed brightness temperature minus the model brightness temperature of the saturated atmosphere.

The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO2) with the accuracy, resolution, and coverage needed to quantify CO2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014 and has gathered more than 2 years of observations. The v7/v7r operational data products from September 2014 to January 2016 are discussed here. On monthly timescales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO2 dry air mole fraction, XCO2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of XCO2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes XCO2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north–south XCO2 gradient is small. Enhanced XCO2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north–south XCO2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in XCO2 across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart XCO2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset.

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 (2 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 (2. Each of these soundings has an unprecedented combination of spatial resolution (2/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.

The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO2) with the accuracy, resolution, and coverage needed to quantify CO2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014, and joined the 705 km Afternoon Constellation on 3 August 2014. On monthly time scales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO2 dry air mole fraction, XCO2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of XCO2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes XCO2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north-south XCO2 gradient is small. Enhanced XCO2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north-south XCO2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in XCO2 across the northern hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart XCO2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high resolution, global data set.

Retrievals of atmospheric composition from near-infrared measurements require measurements of airmass to better than the desired precision of the composition. The oxygen bands are obvious choices to quantify airmass since the mixing ratio of oxygen is fixed over the full range of atmospheric conditions. The OCO-2 mission is currently retrieving carbon dioxide concentration using the oxygen A-band for airmass normalization. The 0.25% accuracy desired for the carbon dioxide concentration has pushed the required state-of-the-art for oxygen spectroscopy. To measure O2 A-band cross-sections with such accuracy through the full range of atmospheric pressure requires a sophisticated line-shape model (Rautian or Speed-Dependent Voigt) with line mixing (LM) and collision induced absorption (CIA). Models of each of these phenomena exist, however, this work presents an integrated self-consistent model developed to ensure the best accuracy. It is also important to consider multiple sources of spectroscopic data for such a study in order to improve the dynamic range of the model and to minimize effects of instrumentation and associated systematic errors. The techniques of Fourier Transform Spectroscopy (FTS) and Cavity Ring-Down Spectroscopy (CRDS) allow complimentary information for such an analysis. We utilize multispectrum fitting software to generate a comprehensive new database with improved accuracy based on these datasets. The extensive information will be made available as a multi-dimensional cross-section (ABSCO) table and the parameterization will be offered for inclusion in the HITRANonline database.

Here, we report preliminary estimates of the column averaged carbon dioxide (CO2) dry air mole fraction, XCO2, retrieved from spectra recorded over land by the Greenhouse gases Observing Satellite, GOSAT (nicknamed "Ibuki"), using retrieval methods originally developed for the NASA Orbiting Carbon Observatory (OCO) mission. After screening for clouds and other known error sources, these retrievals reproduce much of the expected structure in the global XCO2 field, including its variation with latitude and season. However, low yields of retrieved XCO2 over persistently cloudy areas and ice covered surfaces at high latitudes limit the coverage of some geographic regions, even on seasonal time scales. Comparisons of early GOSAT XCO2 retrievals with XCO2 estimates from the Total Carbon Column Observing Network (TCCON) revealed a global, −2% (7–8 parts per million, ppm, with respect to dry air) XCO2 bias and 2 to 3 times more variance in the GOSAT retrievals. About half of the global XCO2 bias is associated with a systematic, 1% overestimate in the retrieved air mass, first identified as a global +10 hPa bias in the retrieved surface pressure. This error has been attributed to errors in the O2 A-band absorption cross sections. Much of the remaining bias and spurious variance in the GOSAT XCO2 retrievals has been traced to uncertainties in the instrument's calibration, oversimplified methods for generating O2 and CO2 absorption cross sections, and other subtle errors in the implementation of the retrieval algorithm. Many of these deficiencies have been addressed in the most recent version (Build 2.9) of the retrieval algorithm, which produces negligible bias in XCO2 on global scales as well as a ~30% reduction in variance. Comparisons with TCCON measurements indicate that regional scale biases remain, but these could be reduced by applying empirical corrections like those described by Wunch et al. (2011b). We recommend that such corrections be applied before these data are used in source sink inversion studies to minimize spurious fluxes associated with known biases. These and other lessons learned from the analysis of GOSAT data are expected to accelerate the delivery of high quality data products from the Orbiting Carbon Observatory-2 (OCO-2), once that satellite is successfully launched and inserted into orbit.

This work describes the NASA Atmospheric CO2 Observations from Space (ACOS) XCO2 retrieval algorithm, and its performance on highly realistic, simulated observations. These tests, restricted to observations over land, are used to evaluate retrieval errors in the face of realistic clouds and aerosols, polarized non-Lambertian surfaces, imperfect meteorology, and uncorrelated instrument noise. We find that post-retrieval filters are essential to eliminate the poorest retrievals, which arise primarily due to imperfect cloud screening. The remaining retrievals have RMS errors of approximately 1 ppm. Modeled instrument noise, based on the Greenhouse Gases Observing SATellite (GOSAT) in-flight performance, accounts for less than half the total error in these retrievals. A small fraction of unfiltered clouds, particularly thin cirrus, lead to a small positive bias of ~0.3 ppm. Overall, systematic errors due to imperfect characterization of clouds and aerosols dominate the error budget, while errors due to other simplifying assumptions, in particular those related to the prior meteorological fields, appear small.

We describe a method of evaluating systematic errors in measurements of total column dry-air mole fractions of CO2 (XCO2) from space, and we illustrate the method by applying it to the v2.8 Atmospheric CO2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over land. The approach exploits the lack of large gradients in XCO2 south of 25° S to identify large-scale offsets and other biases in the ACOS-GOSAT data with several retrieval parameters and errors in instrument calibration. We demonstrate the effectiveness of the method by comparing the ACOS-GOSAT data in the Northern Hemisphere with ground truth provided by the Total Carbon Column Observing Network (TCCON). We use the observed correlation between free-tropospheric potential temperature and XCO2 in the Northern Hemisphere to define a dynamically informed coincidence criterion between the ground-based TCCON measurements and the ACOS-GOSAT measurements. We illustrate that this approach provides larger sample sizes, hence giving a more robust comparison than one that simply uses time, latitude and longitude criteria. Our results show that the agreement with the TCCON data improves after accounting for the systematic errors, but that extrapolation to conditions found outside the region south of 25° S may be problematic (e.g., high airmasses, large surface pressure biases, M-gain, measurements made over ocean). A preliminary evaluation of the improved v2.9 ACOS-GOSAT data is also discussed.

We present the formulation of a global climate model (GCM) of Titan's atmosphere and surface and report initial results. The model is a fully three-dimensional, modified version of NCAR's terrestrial global climate model, CAM3. It includes forcing by Saturn's gravitational tides; a treatment of the planetary boundary layer and surface interactions; scattering and absorption of short-wave radiation; absorption and emission of long-wave radiation; thermal conduction in the soil, and a package for computing atmospheric chemistry. The physical properties and distribution of minor species and aerosols are constrained by Cassini observations. The simulations do not include the effects of a methane hydrological cycle, feedback between radiative forcing and transport of radiatively active minor species and aerosol, or aerosol microphysics. We report a set of baseline results obtained after more than ∼30 Titan-years of model integration. The model calculates a tropospheric circulation that is in-family with the results from other published GCMs, with a regime of weak retrograde winds dominating the summer hemisphere and a band of weak prograde winds appearing in the winter hemisphere. In the stratosphere, the model produces a polar jet in the winter hemisphere, but its peak velocity of 12 m s−1 is more than an order of magnitude weaker than the winter polar jet inferred from observations. We find that the globally integrated atmospheric angular momentum (AAM) undergoes a semiannual oscillation similar to that calculated by Tokano and Neubauer [Tokano, T., Neubauer, F.M., 2005. Wind-induced seasonal angular momentum exchange at Titan's surface and its influence on Titan's length-of-day. Geophys. Res. Lett. 32, L24203–L24206], but delayed in seasonal phase by 30° of Ls. The associated transfer of angular momentum to the surface implies that Titan's rate of rotation should be accelerating during the epoch of Cassini Mission observations, consistent with results obtained by the Cassini radar investigation [Stiles, B.W., Kirk, R.L., Lorenz, R.D., Hensley, S., Lee, E., Ostro, S.J., Allison, M.D., Callahan, P.S., Gim, Y., Iess, L., Persi del Marmo, P., Hamilton, G., Johnson, W.T.K., West, R.D., 2008. The Cassini RADAR Team: Determining Titan's spin state from Cassini RADAR images. Astron. J. 135, 1669–1680]. The amount of AAM transfer to the surface predicted by the model, when combined with knowledge of the state of rotation, points to a low value for Titan's effective moment of inertia. As has been suggested [Lorenz, R.D., Stiles, B.W., Kirk, R.L., Allison, M.D., Persi del Marmo, P., Iess, L., Lunine, J.I., Ostro, S.J., Hensley, S., 2008. Titan's rotation reveals an internal ocean and changing zonal winds. Science 319, 1649–1651], this may indicate significant decoupling between Titan's outer crust and its mantle, possibly due to the presence of an intervening subsurface ocean. We note, however, that existing Titan GCMs, as presently formulated, do not yield uniform results concerning AAM transfer to the surface and ignore some physical processes that could potentially alter the predicted magnitude and seasonal phasing of the transfer.

Atomistic computations of electronic properties for nanostructures with strain (such as self-assembled quantum dots) typically consist of two components - a calculation of the individual atomic positions and the eigenstates of interest in the resulting Hamiltonian. Such simulations ultimately require artificial boundary conditions either through a truncation of the simulation domain or by the imposition of periodic boundary conditions, which necessarily introduce inaccuracies in both components of the computation. In simulations that include up to about 20 million atoms, it is demonstrated that the simulation domain truncation has little impact on the direct computation of the electronic energies but causes considerable inaccuracies in the calculation of the atomic positions unless the simulation domain is made much larger than the central quantum dot structure. The long-range nature of the lattice distortions induced by lattice mismatch is consequently expected to significantly alter the electronic structure of nearby quantum dots.

An accurate modeling of phonons, strain distributions, and Gruneisen coefficients is essential for the qualitative and quantitative design of modern nanoelectronic and nanooptoelectronic devices. The challenge is the development of a model that fits within an atomistic representation of the overall crystal yet remains computationally tractable. A simple model for introducing the anharmonicity of the interatomic potential into the Keating two-parameter valence-force-field model is developed. The new method is used for the calculation of acoustic phonon and strain effects in zinc-blende semiconductors. The model is fitted to the Gruneisen coefficients for long-wavelength acoustic phonons and reproduces the response to strain throughout the Brillouin zone in reasonable agreement with experiment.

Electronic device scaling is ultimately limited by atomic dimensions. The simulation of electronic structure and electron transport on these length scales must be fundamentally quantum mechanical. This leads to computational models that account for fundamental physical interactions using an atomistic basis and tax even the largest available supercomputer when simulating measurable devices. The prototype development of a software tool that enables this class of simulation is presented. Realistically sized structures contain one million to tens of millions of atoms that need to be represented with an appropriate basis. The resulting sparse complex Hamiltonian matrix is of the order to tens of millions. A custom matrix–vector multiplication algorithm that is coupled to a Lanczos and/or Rayleigh–Ritz eigenvalue solver has been developed and ported to a Beowulf cluster as well as an Origin 2000. First benchmarking results of these algorithms as well as the first results of quantum dot simulations are reported.

The broadening of the conduction and valence band edges due to compositional disorder in alloyed materials of finite extent is studied using an sp3s* tight binding model. Two sources of broadening due to configuration and concentration disorder are identified. The concentrational disorder dominates for systems up to at least one million atoms and depends on problem size through an inverse square root law. Significant differences (over 12 meV) in band edge energies are seen depending on choice of granularity of alloy clusters.

We describe atmospheric validation of View the MathML source and View the MathML source CO_2 absorption coefficient databases for use by the Orbiting Carbon Observatory (OCO-2). The OCO-2 mission will collect the measurements needed to estimate column-averaged CO_2 dry air mole fraction within 1 ppm accuracy without the region- or airmass-dependent biases that would significantly degrade efforts to understand carbon sources and sinks on a global scale. To accomplish this, the forward radiative transfer model used to generate synthetic atmospheric spectra for retrievals must achieve unprecedented spectroscopic fidelity within the short wave infrared CO_2 bands sampled by the sensors. The failure of Voigt line shapes and conventional line mixing formulations for such objectives has motivated significant revisions to line shape models used to generate the gas absorption cross sections for the OCO-2 forward model. In this paper, we test line mixing and speed dependent line shapes combined with improved experimental line parameters. We evaluate pre-computed absorption coefficients in the two spectral regions of CO_2 absorbtion using high resolution FT-IR laboratory spectra, atmospheric spectra from the Total Carbon Column Observing Network (TCCON), and medium resolution soundings from the space-based Greenhouse Gases Observing Satellite (GOSAT).