Planetary atmosphere observation analysis

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 (

Abstract:

Titan is renowned for its complex atmosphere, where ongoing photochemistry leads to a rich mixture of organic molecules. Beginning with the splitting of methane by sunlight and other energetic particles, multi-carbon molecules are built up by successive addition of CxHy radicals and ions to one another. This process leads to the formation of ever-larger  molecules and eventually particulates, that sediment out on the surface. Our experimental knowledge of the molecular inventory comes from two techniques: direct sampling mass spectrometry, and remote sensing.  While the former has shown the presence of species at a very wide range of masses from 1-100+ Da, their structure and even stoichiometry is poorly known. In this respect, remote sensing spectroscopy is more robust, providing definitive detections of individual molecular types via unique patterns of IR and sub-millimeter energy transitions, however for a more limited range of species. Currently, 25 species have been definitively identified by remote sensing, ranging in size from H2 to benzene (C6H6). These include 12 hydrocarbons, with the rest a mixture of diatomics, nitriles and small oxygen compounds (H2O, CO, CO2). With direct sampling currently impossible before the Dragonfly mission returns a spacecraft to Titan in 2034, astronomers have been pushing forward with chemical identifications using a range of ground and space-based observatories. We report here on recent attempts to identify new C3 and C4 hydrocarbons in Titan’s atmosphere using the high-resolution (R~100000) TEXES spectrometer at the Infrared Telescope Facility (IRTF) – see examples in Fig. 1. Associated laboratory spectroscopy work is ongoing at the Jet Propulsion Laboratory (JPL) using a Bruker FTS spectrometer to identify the positions and intensities of the strongest gas bands, to assist with targeting the telescope searches, and interpretation of the data.  Identifications of new, heavy molecular species are urgently needed to constrain photochemical and dynamical models, and make advances in our understanding of the workings of Titan’s atmosphere, and its potential for astrobiology. Such work is also important for planning data collection and analysis from the upcoming NASA Dragonfly mission, where a sensitive mass spectrometer will assess the composition of surface materials and their relation to the atmospheric constituents, as well as Titan atmospheric data from other telescopes such as ALMA and JWST.Figure 1: Examples of currently undetected molecules in Titan's atmosphere: isomers of C4H8 and C4H10. We report on ongoing searches for these species with IRTF/TEXES.

Abstract:

Over four decades have elapsed since the last in situ spectrophotometric observations of the Venusian atmosphere, specifically from the Venera 11 (1978) and Venera 13 and 14 (1982) missions. These missions recorded spectral data during their descent from approximately 62 km to the surface. Unfortunately, the original data were lost; however, a portion has been reconstructed by digitising the graphical outputs that were generated during the initial data processing phase of each of the three missions [1]. This reconstructed data is crucial as it remains the sole set of in situ spectrophotometric observations of Venus’s atmosphere and is likely to be so for the foreseeable future.While re-analysing the reconstructed Venera datasets, we identified several artefacts, errors and sources of noise, necessitating the implementation of some corrections and validation checks to isolate the most unaffected part of the reconstructed data. Then, using NEMESIS, a radiative transfer and retrieval tool [2], we conducted a series of retrievals to simultaneously fit the downward-going spectra at all altitudes. During this process, several parameters were retrieved. The first set of retrievals focused on the structure of the main cloud deck (MCD), which includes the cloud base altitude and abundance profiles of all four cloud modes. Previous corrections that were used to account for the effect of the unknown UV absorber did not result in good fits with the spectra shortward of 0.6 µm. Hence, we derived a new correction by retrieving the imaginary refractive index spectra of the Mode 1 particles.In the next phase, the MCD retrievals were used to update the model atmospheres for each of the missions. Then, the H2O volume mixing ratio profiles were retrieved and compared with the previous retrievals using the same data by [1] along with other remote sensing observations. The final retrieval phase concentrated on characterising particulate matter in the deep atmosphere. In [3], we outlined a methodology for retrieving a near-surface particulate layer using the reconstructed Venera 13 dataset. In this new work, we apply this methodology to encompass the Venera 11 and 14 datasets and compare the retrievals from the three datasets.This research thus provides a comprehensive overview of three distinct retrievals: 1) main cloud deck, 2) H2O, and 3) near-surface particulates using the reconstructed spectrophotometric data of Venera 11, 13, and 14.References: [1] Ignatiev, N. I., Moroz, V. I., Moshkin, B. E., Ekonomov, A. P., Gnedykh, V. I., Grigor’ev, A. V., and Khatuntsev, I. V. Cosmic Research 35(1), 1–14 (1997).[2] Irwin, P. G., Teanby, N. A., de Kok, R., Fletcher, L. N., Howett, C. J., Tsang, C. C., Wilson, C. F., Calcutt, S. B., Nixon, C. A., and Parrish, P. D. Journal of Quantitative Spectroscopy and Radiative Transfer 109(6), 1136–1150 (2008).[3] Kulkarni, S. V., Irwin, P. G. J., Wilson, C. F., & Ignatiev, N. I. Journal of Geophysical Research: Planets, 130, e2024JE008728, (2025).

Abstract:

We present a re-analysis of archival data-cubes of Neptune obtained with the GEMINI Near-Infrared Integral Field Spectrometer (NIFS), aiming to refine constraints on the abundance of hydrogen sulphide (H₂S) in Neptune's atmosphere. To enhance spatial and spectral fidelity, we employ a modified CLEAN algorithm that effectively deconvolves the data while conserving flux. To mitigate observational and instrumental artifacts, we utilize Singular Spectrum Analysis (SSA) on single-wavelength images and apply Principal Component Analysis (PCA) across the full data-cube to suppress both random and systematic noise. Spectral retrievals are conducted using ArchNemesis, an optimal estimation inverse modeling tool. We retrieve vertical profiles at individual locations, and use Minnaert-corrected reflectivity functions across latitude bands to investigate latitudinal variability. Using the deconvolution and data analysis techniques, we are able to extract more scientific utility from legacy datasets and describe a template that can be repeated for similar datasets.