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Abstract:

We present a new method, based on a joint application of a principal component analysis (PCA) and Gaussian mixture models (GMM), to automatically find similar groups of spectra in a collection. We applied the method (condensed in the public code chopper.py ) to archival Jupiter spectral data in the 2–5 µm range collected by NASA Juno/JIRAM in its first perijove passage (August 2016) and to mosaics of the great red spot (GRS) acquired by JWST/NIRSpec (July 2022). Using JIRAM data analyzed in previous work, we show that using a PCA+GMM clustering can increase the efficiency of the retrieval stage without any loss of accuracy in terms of the retrieved parameters. We show that a PCA+GMM approach is able to automatically identify spectra of known regions of interest (e.g., belts, zones, GRS) belonging to different clusters. The application of the method to the NIRSpec data leads to detection of substructures inside the GRS, which appears to be composed of an outer halo characterized by low reflectivity and an inner brighter main oval. By applying these techniques to JIRAM data, we were able to identify the same substructure. We remark that these new structures have not been seen before at visible wavelengths. In both cases, the spectra belonging to the inner oval have solar and thermal signals comparable to those belonging to the halo, but they present broadened 2.73 µm solar-reflected peaks. Performing forward simulations with the NEMESIS radiative transfer suite, we propose that the broadening may be caused by differences in the vertical extension of the main cloud layer. This finding is consistent with recent 3D fluid dynamics simulations.

Abstract:

Venus is home to thousands of volcanoes, with a wide range of volumes and sizes. Its surface is relatively young, with a temperature of approximately 735 K and an atmosphere of 92 bar. Past and possible ongoing volcanic outgassing is expected to provide a source to the sustenance of this massive atmosphere, dominated by CO2 and SO2. The lower atmosphere can be investigated in the near-infrared transparency windows on the nightside, such as the 2.3μm thermal emission window, which provides a chance of detection of species with volcanic origin, such as water vapor. The Planetary Spectrum Generator was used to simulate the nightside 2.3μm thermal emission window of Venus. We simulated the effect of a volcanic gas plume rising to a ceiling altitude, for species such as H2O, CO, OCS, HF and SO2. The sensitivity of the radiance spectrum at different wavelengths was explored as an attempt to qualitatively access detection for future measurements of both ground-based and space-instrumentation. We conclude from our qualitative analysis that for the H2O, CO and OCS plumes simulated there is potential to achieve a detection in the future, given a minimum required signal-to-noise ratio of 50. For SO2 and HF plumes, a higher signal-to-noise ratio would be needed.

Abstract:

Context. Hydrogen chloride (HCl) was independently detected in the Martian atmosphere by the Nadir and Occultation for MArs Discovery (NOMAD) and Atmospheric Chemistry Suite (ACS) spectrometers aboard the ExoMars Trace Gas Orbiter (TGO). Photochemical models show that using gas-phase chemistry alone is insufficient to reproduce these data. Recent work has developed a heterogeneous chemical network within a 1D photochemistry model, guided by the seasonal variability in HCl. This variability includes detection almost exclusively during the dust season, a positive correlation with water vapour, and an anticorrelation with water ice. Aims. The aim of this work is to show that incorporating heterogeneous chlorine chemistry into a global 3D model of Martian photochemistry with conventional gas-phase chemistry can reproduce spatial and temporal changes in hydrogen chloride on Mars, as observed by instruments aboard the TGO. Methods. We incorporated this heterogeneous chlorine scheme into the Mars Planetary Climate Model (MPCM). After some refinements to the scheme, mainly associated with it being employed in a 3D model, we used it to model chlorine photochemistry during Mars Years (MYs) 34 and 35. These two years provide contrasting dust scenarios, with MY 34 featuring a global dust storm. We also examined correlations in the model results between HCl and other key atmospheric quantities, as well as production and loss processes, to understand the impact of different factors driving changes in HCl. Results. We find that the 3D model of Martian photochemistry using the proposed heterogeneous chemistry is consistent with the changes in HCl observed by ACS in MY 34 and MY 35, including detections and 70% of non-detections. For the remaining 30% of non-detections, model HCl is higher than the ACS detection limit due to biases associated with water vapour, dust, or water ice content at these locations. As with previous 1D model calculations, we find that heterogeneous chemistry is required to describe the loss of HCl, resulting in a lifetime of a few sols that is consistent with the observed seasonal variation in HCl. As a result of this proposed chemistry, modelled HCl is correlated with water vapour, airborne dust, and temperature, and anticorrelated with water ice. Our work shows that this chemical scheme enables the reproduction of aphelion detections in MY 35.

Abstract:

Research into compositions of small bodies and planetary surfaces, such as asteroids, is key to understanding the origin of water and organics on Earth [1], as well as placing constraints on planetary dynamics and migration models [2] that can help understand how planetary systems around other stars may form and evolve. Compositional estimates can be found with thermal infrared (TIR; 5-25μm) spectroscopy, as the TIR region is rich in diagnostic information and can be used in remote sensing observations and laboratory measurements. However, TIR spectra of the same material may appear differently depending on several factors, such as particle size, surface roughness, porosity etc. This work quantifies the changes in spectral morphology (i.e., shapes and depths of spectral features) as particle size transitions from fine (90%), at several size fractions, aimed to be

Abstract:

Jupiter, the largest planet in our solar system, is a vital reference point for understanding gaseous exoplanets and their atmospheres. While we know its upper tropospheric chemical composition well, the nature and structure of its clouds remain puzzling. We, therefore, rely on theoretical models and remote sensing data to address this.While traditional equilibrium chemistry condensation models (ECCM) are sensitive to input parameters, advanced models [1] offer more realistic cloud property predictions. Remote sensing data can help determine cloud properties and test theoretical predictions thanks to the application of multiple scattering atmospheric retrieval. Still, the process is highly degenerate and, therefore, computationally demanding. The predicted tropospheric layers are upper ammonia ice (∼0.7 bar) and ammonium hydrosulfide (∼2 bar) clouds [2], but their spectral detection has been limited to small, dynamically active regions (