Planetary atmosphere observation analysis

Abstract:

Despite constituting a tiny fraction of the Mars atmosphere, trace gases can play an important role in controlling atmospheric chemical cycling. However, some discrepancies between observed distributions of trace gases and modelled values indicate that there are ongoing processes in Mars’ atmosphere that are not fully understood.Hydrogen chloride (HCl) was the first new gas detected by the ExoMars Trace Gas Orbiter (TGO)[1,2], with observations from the Atmospheric Chemistry Suite (ACS) and Nadir and Occultation for Mars Discovery (NOMAD) instruments showing a strong seasonal variation over the last several Mars years. With a few exceptions, detections almost exclusively occur during the second half of the year (at solar longitudes between 180° and 360°). This is during southern hemisphere spring and summer, when temperatures, atmospheric dust content, and water vapour concentrations are higher, and ozone concentrations are low. Previous modelling has shown that heterogeneous chemical reactions involving dust or ice aerosols play a key role in controlling this seasonal pattern[3,4,5,6].Here we use the Mars Planetary Climate Model[7,8], a 3-D global circulation model with photochemistry, to investigate some potential sources and sinks of HCl in the Martian atmosphere, which could account for the seasonal variation in measurements and the observed correlations and anticorrelations with other atmospheric factors. Firstly, we examine the indirect effect of heterogeneous chemistry of OH and HO2 interacting with ice. We compare the effect of two different heterogeneous chemical schemes[9,10] on HCl distributions via their effect on other oxidative species such as O and O3. Secondly, we investigate the isolated effects of two further heterogeneous pathways involving water ice—one HCl source and one sink. Specifically, we model the uptake of HCl onto water ice, which has been studied before, and its subsequent release back to the atmosphere during ice sublimation, which has not been included in previous models.Our preliminary results (e.g., Figure 1) show that the latter cycling could account for some of the seasonality of the HCl observations. HCl concentrations remain close to the ground during the first half of the year, and then increase at higher altitudes during the second half of the year, where they could be detected by TGO's instruments. Even without the addition of other HCl sources and sinks in the model, we expect this pattern to be repeated over multiple Mars years, reproducing at least part of the annual appearance and disappearance of HCl through the recycling of chlorine.Continuing to reconcile models and observations of the cycling of HCl and other trace gases is important for achieving a more complete understanding of atmospheric processes operating on Mars today, as well as those that have played a key role over Mars’ history.Figure 1: Preliminary model results showing seasonal distributions of HCl over more than one Mars year. Each panel shows zonally-averaged HCl volume mixing ratios with altitude and latitude, and there are 30° of solar longitude between each panel. The black contour indicates a mixing ratio of 0.5 ppbv, which is the detection limit for TGO ACS.[1] Korablev O. I. et al. (2021). Sci. Adv., 7, eabe4386. [2] Olsen K. S. et al. (2021). Astron. Astrophys., 647, A161. [3] Benne, B., et al. (2025). Astron. Astrophys., 699, A362. [4] Rajendran, K. et al. (2025). JGR: Planets 130(3), p.e2024JE008537. [5] Streeter, P. M. et al. (2025). GRL 52(6), p.e2024GL111059. [6] Taysum, B. M. et al. (2024). Astron. Astrophys., 687, A191. [7] Forget, F., et al. (1999). JGR: Planets 104, E10. [8] Lefèvre, F., et al. (2004). JGR: Planets 109, E7. [9] Brown M. A. J. et al. (2022). JGR: Planets, 127, p.e2022JE007346. [10] Lefèvre, F., et al. (2021) JGR: Planets, 126, p.e2021JE006838.

Abstract:

MoonTools is a software framework, written in the Julia programming language [9], allowing straightforward, flexible, and performant processing of multispectral and hyperspectral data products. Designed originally to operate on M3 observations [4, 5], our framework is readily extensible to a wide range of datasets.Drawing from functional programming [6], our framework emphasizes composition of disparate operations. Processing pipelines are constructed in native Julia, parametrised by partial function application. This approach allows for flexibility of use and ease of extensibility, and distinguishes our work from similar tools, e.g. [7]; further, Julia’s just-in-time compilation  and parallel-programming tools allow for fast, multithreaded operations on multi-terabyte datasets, including for user-supplied inputs.Implemented operations include thermal and photometric corrections of multispectral radiance cubes, reflectance retrievals, spectral parameter determination, and post-processing amongst others. Additional utilities allow users to search datasets for targets by nomenclature, terrain type, and local solar time. Various dataset export options are available, including HDF5 products and “at a glance” views of regions of interest.We provide an example Julia pipeline in Listing 1, reproducing the detection of spinel at Theophilus crater [1,2]. We begin by importing the MoonTools package; then, we define a RATIO parameter expression. The spectral parameters SPINEL and PYROXENE are implemented as in [2] up to a constant factor using the RATIO definition. Invoked macros produce multithreaded CPU and GPU-kernel implementations of these parameters transparently to the user. Finally, a pipeline is composed: we search M3 data for observations of Theophilus crater, apply parameters, and produce “quicklook” plots of all matching observations; one such plot is shown in Figure 1.Listing 1: Pipeline invocation, including parameter definitions, required to produce Figure 1.using MoonTools@paramdef RATIO(λs, R; λ1, λ2) = sum(R[λ1]) / sum(R[λ2])@param SPINEL   RATIO [1400]       [1750]@param PYROXENE RATIO [0700, 1200] [0950]observations(:m3) > by_name("Theophilus") > PYROXENE > SPINEL > quicklookFigure 1: One of several quicklook outputs, showing Theophilus crater. Quicklooks are intended to provide overviews of regions of interest (RoIs) indicated by pipeline construction. Plots on the left include a reference narrowband reflectance, and PYROXENE and SPINEL parameter maps across the RoI. The RoI is partitioned into a 3x3 grid of zones; spectra sampled from each zone are plotted on the right in corresponding positions.Striping artifacts exist throughout the M3 dataset, and are prominent in spectral parameter products; state-of-the-art tooling must destripe these images [7,8]. We provide a bespoke destriping algorithm using a wavelet packet decomposition [3]. The modified pipeline is given in Listing 2; a destriped spinel map is shown in Figure 2.Listing 2: Pipeline altered from Listing 1; outputs are shown in Figure 2.observations(:m3) > by_name("Theophilus") > SPINEL > destripe!Figure 2: Destriped spinel parameter map. The before and after of the destriping operation are shown in the left and center plots; the removed signal is shown on the right.Software development is progressing rapidly. We anticipate a release of MoonTools to the scientific community in the coming months; MoonTools will be distributed under the terms of an open-source software license. We will welcome bug reports, feature requests, and contributions.References[1] Dhingra, D., Pieters, C.M., Boardman, J.W., Head, J.W., Isaacson, P.J. and Taylor, L.A., 2011. Compositional diversity at Theophilus Crater: Understanding the Geological Context of Mg‐Spinel-Bearing Central Peaks. Geophysical Research Letters, 38(11).[2] Pieters, C.M., Hanna, K.D., Cheek, L., Dhingra, D., Prissel, T., Jackson, C., Moriarty, D., Parman, S. and Taylor, L.A., 2014. The distribution of Mg-spinel across the Moon and constraints on crustal origin. American Mineralogist, 99(10), pp.1893-1910.[3] Mallat, S., 1999. A Wavelet Tour of Signal Processing. Elsevier.[4] Chandrayaan-1 Moon Mineralogy Mapper Science Team (2011). M3 L1B Gridded Spectral Radiance, Version 3. PDS Cartography and Imaging Sciences Node. https://doi.org/10.17189/1520248.[5] Chandrayaan-1 Moon Mineralogy Mapper Science Team (2011). L2 Gridded Spectral Reflectance (version 1) products. https://doi.org/10.17189/1520414.[6] Backus, J., 1978. Can Programming be Liberated from the von Neumann Style? A Functional Style and its Algebra of Programs. Communications of the ACM, 21(8), pp.613-641.[7] Suárez‐Valencia, J.E., Rossi, A.P., Zambon, F., Carli, C. and Nodjoumi, G., 2024. MoonIndex, an open‐source tool to generate spectral indexes for the moon from M3 data. Earth and Space Science, 11(6), p.e2023EA003464.[8] Shkuratov, Y., Surkov, Y., Ivanov, M., Korokhin, V., Kaydash, V., Videen, G., Pieters, C. and Stankevich, D., 2019. Improved Chandrayaan-1 M3 data: A northwest portion of the Aristarchus Plateau and contiguous maria. Icarus, 321, pp.34-49.[9] Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A., 2012. Julia: A Fast, Dynamic Language for Technical Computing. arXiv preprint arXiv:1209.5145.

Abstract:

Introduction: Phobos’ formation remains uncertain, with two main hypotheses: accretion of debris following a high-energy impact between Mars and an asteroid [1] or capture of a primitive asteroid [2]. To solve this, JAXA’s Martian Moons eXploration (MMX) mission aims to return samples from Phobos by 2031 [3]. The characterisation of these samples will determine the origin of Phobos.Current observations of Phobos are limited to remote measurements that are interpreted without direct mineralogical ground-truth. In this study, we have characterised the infrared (IR) reflectance spectra and mineralogy of meteorites considered good analogues for materials likely to be present on the surface of Phobos. These measurements provide a link between remote sensing data and physical sample analysis by building a spectral-mineralogical reference catalogue using powdered meteorites. This catalogue will help interpret the initial remote observations (prior to landing on Phobos’ surface) of the upcoming MMX mission, inform sampling site choices, and then help evaluate the later returned sample spectra to ultimately constrain the origin of Phobos. In addition, the mineralogical-spectral correlations can be referred to for future spectral calibration across other small bodies in the Solar System.Methods: We have characterised the mineralogy and spectral properties of six CM (Mighei-like) carbonaceous chondrites, Tarda (C2-ung), the CO (Ornans-like) chondrite Kainsaz, CRs (Renazzo-like) NWA 801 and 1567, a range of shock darkened ordinary chondrites (mostly falls) including L4-6 and H5-6, four ureilites, Martian meteorites Nakhla and Tissint (shergottite), and a Tagish Lake (C2-ung) based simulant created by the University of Tokyo, known as UTPS-TB [5].We performed FTIR and XRD measurements on the same powder (~50 mg, grain size

Abstract:

The discovery of widespread hydration across the lunar surface [1,2,3] is one of the most surprising results of the last twenty years in the field of planetary science. Further considerations have extended to exospheric migration of water on other airless bodies, such as Mercury and Ceres [4]; other works have considered the coupling of multiple volatile species within the lunar volatile inventory [5,6], or have considered the icy moons of the outer Solar System [7], with the seasonal migration of CO2 on the Uranian moon Ariel [8,13] being one such example.A difficulty in modelling volatile transport is the interplay between various physical and chemical processes, including adsorption kinetics, photochemistry, and transport kinematics. Hence, Monte Carlo modelling has remained the dominant mode of investigation, producing qualitative outputs surveying emergent phenomena. The development of a comprehensive and quantitative forward-modelling approach has remained an outstanding problem.To produce such a model, we turn to Markov processes [9]. The governing equations of these processes generate, via Kramers-Moyal expansion [10], such familiar results as the Fokker-Planck, continuity, and diffusion equations.We derive a Markov master equation for a global volatile ensemble from mass-balance. With straightforward probability theory, we model adsorption kinetics at a molecular level, and ballistic transport on a global scale, providing an integrated analytic approach to global volatile dynamics.Going further, we use tensor products between Markov processes [11,12] to model the interplay between volatile species, allowing for capture of kinetic schemes driven by surficial and photochemical reactions.We implement a simplified, straightforward model. The global ensemble was taken to be 10^30 water molecules. 4200 timesteps per lunation are calculated for 100,000 timesteps - approximately 607 seconds per timestep. We neglect implantation and loss mechanisms as well as topography. We use an analytic model of lunar surface temperatures [14]. Desorption probabilities are calculated from the Eyring-Polanyi equation [15,16], with an activation energy of 0.7 eV. The Armand distribution [17,18] drives ballistic-hop transport.Figure 1: Lunar surficial water abundance; simplified model run, timestep 8200.Timestep 8200 is shown in Figure 1. Volatile concentration is heightened in the southern winter. The initial volatile distribution was random; onset of equilibrium conditions is rapid. A dusk-dawn asymmetry is present, reproducing the results of Schörghofer [19]. Latitudinal stratification is visible, with the band of minimal concentration dependent on solar declination. We conclude by presenting paths forward in volatile modelling efforts enabled by this approach. [1] Pieters, C.M., Goswami, J.N., Clark, R.N., Annadurai, M., Boardman, J., Buratti, B., Combe, J.P., Dyar, M.D., Green, R., Head, J.W. and Hibbitts, C., 2009. Character and spatial distribution of OH/H2O on the surface of the Moon seen by M3 on Chandrayaan-1. science, 326(5952), pp.568-572.[2] Sunshine, J.M., Farnham, T.L., Feaga, L.M., Groussin, O., Merlin, F., Milliken, R.E. and A’Hearn, M.F., 2009. Temporal and spatial variability of lunar hydration as observed by the Deep Impact spacecraft. Science, 326(5952), pp.565-568.[3] Clark, R.N., 2009. Detection of adsorbed water and hydroxyl on the Moon. Science, 326(5952), pp.562-564.[4] Schörghofer, N., Benna, M., Berezhnoy, A.A., Greenhagen, B., Jones, B.M., Li, S., Orlando, T.M., Prem, P., Tucker, O.J. and Wöhler, C., 2021. Water group exospheres and surface interactions on the Moon, Mercury, and Ceres. Space Science Reviews, 217(6), p.74.[5] Huebner, W.F. and Mukherjee, J., 2015. Photoionization and photodissociation rates in solar and blackbody radiation fields. Planetary and Space Science, 106, pp.11-45.[6] Smolka, A., Nikolić, D., Gscheidle, C. and Reiss, P., 2023. Coupled H, H2, OH, and H2O lunar exosphere simulation framework and impacts of conversion reactions. Icarus, 397.[7] Steckloff, J.K., Goldstein, D., Trafton, L., Varghese, P. and Prem, P., 2022. Exosphere-mediated migration of volatile species on airless bodies across the solar system. Icarus, 384, p.115092.[8] Cartwright, R.J., Nordheim, T.A., DeColibus, R.A., Grundy, W.M., Holler, B.J., Beddingfield, C.B., Sori, M.M., Lucas, M.P., Elder, C.M., Regoli, L.H. and Cruikshank, D.P., 2022. A CO2 Cycle on Ariel? Radiolytic production and migration to low-latitude cold traps. The Planetary Science Journal, 3(1), p.8.[9] Livi, R. and Politi, P., 2025. Nonequilibrium Statistical Physics: a Modern Perspective. 2nd edition. Cambridge University Press.[10] Kramers, H.A., 1940. Brownian motion in a field of force and the diffusion model of chemical reactions. physica, 7(4), pp.284-304.[11] Dayar, T., 2012. Analyzing Markov chains using Kronecker products: theory and applications. Springer Science & Business Media.[12] Giry, M., 2006, October. A categorical approach to probability theory. In Categorical Aspects of Topology and Analysis: Proceedings of an International Conference Held at Carleton University, Ottawa, August 11–15, 1981 (pp. 68-85). Berlin, Heidelberg: Springer Berlin Heidelberg.[13] Grundy, W.M., Young, L.A., Spencer, J.R., Johnson, R.E., Young, E.F. and Buie, M.W., 2006. Distributions of H2O and CO2 ices on Ariel, Umbriel, Titania, and Oberon from IRTF/SpeX observations. Icarus, 184(2), pp.543-555.[14] Crider, D.H. and Vondrak, R.R., 2000. The solar wind as a possible source of lunar polar hydrogen deposits. Journal of Geophysical Research: Planets, 105(E11), pp.26773-26782.[15] Eyring, H., 1935. The activated complex in chemical reactions. The Journal of chemical physics, 3(2), pp.107-115.[16] Evans, M.G. and Polanyi, M., 1935. Some applications of the transition state method to the calculation of reaction velocities, especially in solution. Transactions of the Faraday Society, 31, pp.875-894.[17] Armand, G., 1977. Classical theory of desorption rate velocity distribution of desorbed atoms; possibility of a compensation effect. Surface Science, 66(1), pp.321-345.[18] Schörghofer, N., 2022. Statistical thermodynamics of surface-bounded exospheres. Earth, Moon, and Planets, 126(2), p.5.[19] Schorghofer, N., 2014. Migration calculations for water in the exosphere of the Moon: Dusk‐dawn asymmetry, heterogeneous trapping, and D/H fractionation. Geophysical Research Letters, 41(14), pp.4888-4893.

Abstract:

The spectral signature of hydrogen sulphide (H2S) above the cloud tops in Uranus’ atmosphere was detected in 2018 [1]. The H2S humidity can be used as a tracer of Uranus’ overturning circulation [2] - peaks and troughs in the latitudinal humidity distribution may correspond to regions of local upwelling and downwelling near the H2S condensation level. We analysed observations from Gemini-NIFS and VLT-SINFONI, taken between 2009 and 2014, to study the H2S humidity distribution of Uranus. In our previous analysis of H2S on Neptune [3], we found a significant degeneracy between the methane (CH4) and H2S distributions, so we prescribe a latitudinally varying deep methane abundance previously derived from HST-STIS spectra [4]. We deconvolve the observations and extract spectra using the Minnaert limb-darkening approximation, which has been applied in several analyses of Ice Giant observations [3,4,5]. We fit a parameterised aerosol model and the H2S humidity to our extracted spectra with nested sampling using our open-source radiative transfer code, archNEMESIS [6]. Our atmospheric model has a large number of parameters, and to make nested sampling computationally feasible we utilise a trained neural network for early exploration of the parameter space during our retrievals. Since our observations span several years, we search for temporal changes. We find changes in the aerosol structure and aerosol spectral properties corresponding to the development of Uranus' north polar hood, in agreement with previous work [4,8]. If we assume that the CH4 distribution is stable over time, then our results show no significant changes in the H2S distribution.  Our results show a general equator-to-pole decrease in the H2S humidity, similar to what has been found in microwave analyses that are sensitive to the deep H2S distribution [7]. Superimposed on this are local increases, which are fairly evenly spaced in latitude. We found a somewhat similar pattern in our analysis of H2S on Neptune [3], and an analysis of Neptune with VLT/MUSE also found peaks in reflectivity with a similar spacing [5]. These results are suggestive of a complex circulation pattern near the deep H2S aerosol layer. [1] Irwin, P. G. J., et al. (2018). Detection of hydrogen sulfide above the clouds in Uranus's atmosphere. Nature Astronomy, 2(5), 420-427. [2] Fletcher, L.N., et al. (2020). Ice Giant Circulation Patterns: Implications for Atmospheric Probes. Space Sci Rev 216, 21[3] Penn, J., et al. (2026). Reconciling Near-Infrared and Microwave Analyses of Neptune’s Hydrogen Sulphide Distribution. Monthly Notices of the Royal Astronomical Society, 548, 2, [4] James, A., et al. (2023). The Temporal Brightening of Uranus' Northern Polar Hood From HST/WFC3 and HST/STIS Observations. Journal of Geophysical Research: Planets, 128(10), e2023JE007904.[5] Irwin, P. G. J., et al. (2023). Latitudinal Variations in Methane Abundance, Aerosol Opacity and Aerosol Scattering Efficiency in Neptune's atmosphere determined from VLT/MUSE. Journal of Geophysical Research: Planets, 128(11), e2023JE007980.[6] Alday, J., et al. (2025). archNEMESIS: An Open-Source Python Package for Analysis of Planetary Atmospheric Spectra. Journal of Open Research Software, doi:10.5334/jors.554.[7] Molter, E. M., et al. (2021). Tropospheric Composition and Circulation of Uranus with ALMA and the VLA. The Planetary Science Journal, 2(1), 3.[8] Sromovsky, L. A., et al. (2024). The puzzling north polar region of Uranus: Continued zero-shear winds and increasing brightness from 2015 through 2022 according to 7 years of Keck AO imaging. Icarus, 420, 116186.