Planetary atmosphere observation analysis: publications

High-Resolution Mapping of Titan’s N-S Atmospheric Boundary from Cassini/CIRS

(2026)

Authors:

Lucy Wright, Nicholas A Teanby, Patrick GJ Irwin, Conor A Nixon, Joshua S Ford

Abstract:

Introduction: Titan’s atmosphere has a north-south haze dichotomy (Fig.1), with an unexpectedly sharp boundary near the equator (e.g., Lorenz et al. 1997; Roos-Serote 2005). The boundary does not sit exactly at the equator, nor does it remain stationary throughout Titan’s year (R. Lorenz 1999; Roman et al. 2009; Kutsop et al. 2022; Vashist et al. 2023; Snell and Banfield 2024). Instead, the boundary migrates in latitude seasonally and is seen to disappear post-equinox then reappear with the dichotomy reversed shortly after. The sharpness of the boundary suggests that there is limited horizontal mixing over the equator. This, in addition to the boundary’s seasonal migration, makes Titan’s equator a dynamically intriguing region. Previously, dynamics in Titan’s stratosphere have been constrained observationally using the thermal wind relation (Sharkey et al. 2021; Achterberg 2023; Wright et al. 2025), but this equation breaks down at low latitudes. We instead map infrared-active trace species in Titan’s stratosphere to inspect the dynamics in Titan’s equatorial region.Data & Method: We use infrared spectra acquired by Cassini’s Composite Infrared Spectrometer (CIRS) instrument from 70 fly-bys of Titan spanning the entire 13-year mission. CIRS had an adjustable spectral resolution, typically observing at FWHM~0.5, 2.5, or 14.5 cm-1. We use CIRS FP3/4 observations acquired at a low spectral resolution (FWHM~14.5 cm-1), which achieved the best combination of seasonal and spatial coverage with high spatial resolution, allowing us to discern compositional variations over finer length-scales than in previous studies (Teanby et al. 2006; Teanby et al. 2010). Wright et al. 2024 showed that these data can be reliably forward-modelled despite having subtle and often blended spectral peaks. We use archNEMESIS (Alday et al. 2025) – an open-source Python package based on the NEMESIS (Irwin et al. 2008) radiative transfer and retrieval code – to fit CIRS FP3/4 spectra. We fit CIRS FP4 spectra by retrieving continuous temperature profiles and fit mid-IR spectra from 600-1100 cm-1 by scaling vertical profiles of gas volume mixing ratio.Results: We present maps of the variation in abundances of HCN, C2H2, C2H6, C3H4, C4H2, CO2 in Titan’s stratosphere (~5 mbar pressure) with the highest resolution mapping achieved to date, covering 40oS to 40oN throughout 2004—2017. Many species are seen to have a rapid change in abundance over the equator, with HCN exhibiting the steepest latitudinal gradient (e.g., Fig.2). The improved spatial resolution achieved here allows us to track the migration of the compositional gradient over time. We find that it follows a similar migration to Titan’s north-south haze boundary during the Cassini mission. Haze and HCN distributions appear to behave similarly at the equator, suggesting that the boundary is induced by dynamics, rather than by chemistry or microphysical processes.In addition, we use the meridional composition gradient to predict the tilt offset of Titan’s stratosphere, following the method of (Teanby et al. 2010). We do this over the full 13-year Cassini mission to inspect the seasonal evolution of Titan’s tilted stratosphere. This is compared to the tilt evolution inferred from temperature (Wright et al. 2025) and from images (Snell and Banfield 2024).Fig 1. Titan’s north-south albedo asymmetry. Infrared image taken in 2007 by Cassini’s Imaging Science Subsystem (ISS) Narrow-Angled Camera (NAC) using a 890 nm filter.Fig 2. Retrieved HCN volume mixing ratio (VMR) in Titan’s equatorial region, at 5 mbar. Example from observations taken during 2008. Different colours identify different observation sequences. The steepest gradient is seen to be ~5oS at this time (dashed line, shaded region is the uncertainty).ReferencesAchterberg, R. K. 2023. The Planetary Science Journal 4 (8): 140. https://doi.org/10.3847/PSJ/acebea.Alday, J., J. Penn, P. Irwin, J. Mason, J. Yang, and J. Dobinson. 2025. Journal of Open Research Software 13: 10. https://doi.org/10.5334/jors.554.Irwin, P. G. J., N. A. Teanby, R. de Kok, et al. 2008. Journal of Quantitative Spectroscopy and Radiative Transfer 109 (6): 1136–50. https://doi.org/10.1016/j.jqsrt.2007.11.006.Kutsop, N. W., A. G. Hayes, P. M. Corlies, et al. 2022. The Planetary Science Journal 3 (5): 114. https://doi.org/10.3847/PSJ/ac582d.Lorenz, R. 1999. Icarus 142 (2): 391–401. https://doi.org/10.1006/icar.1999.6225.Lorenz, R. D., P. H. Smith, M. T. Lemmon, E. Karkoschka, G. W. Lockwood, and J. Caldwell. 1997. Icarus 127 (1): 173–89. https://doi.org/10.1006/icar.1997.5687.Roman, M. T., R. A. West, D. J. Banfield, et al. 2009. Icarus 203 (1): 242–49. https://doi.org/10.1016/j.icarus.2009.04.021.Roos-Serote, M. 2005. Space Science Reviews 116 (1–2): 201–10. https://doi.org/10.1007/s11214-005-1956-0.Sharkey, J., N. A. Teanby, Melody Sylvestre, et al. 2021. Icarus 354 (January): 114030. https://doi.org/10.1016/j.icarus.2020.114030.Snell, C., and D. Banfield. 2024. The Planetary Science Journal 5 (1): 12. https://doi.org/10.3847/PSJ/ad0bec.Teanby, N. A., P. G. J. Irwin, and R. de Kok. 2010. Planetary and Space Science 58 (5): 792–800. https://doi.org/10.1016/j.pss.2009.12.005.Teanby, N., P. Irwin, R. Dekok, et al. 2006. Icarus 181 (1): 243–55. https://doi.org/10.1016/j.icarus.2005.11.008.Vashist, A. S., M. F. Heslar, J. W. Barnes, C. Hennen, and R. D. Lorenz. 2023. The Planetary Science Journal 4 (6): 118. https://doi.org/10.3847/PSJ/acdd05.Wright, L., N. A. Teanby, P. G. J. Irwin, and C. A. Nixon. 2024. Experimental Astronomy 57 (2): 15. https://doi.org/10.1007/s10686-024-09934-y.Wright, L, N. A. Teanby, P. G. J. Irwin, et al. 2025. The Planetary Science Journal 6 (5): 114. https://doi.org/10.3847/PSJ/adcab3.

Investigating the Detectability of Subsurface Lunar Water-Ice Beneath Regolith Dust Using Infrared Reflectance Spectroscopy

(2026)

Authors:

Fiona Henderson, Neil Bowles, Katherine Shirley, Jon Temple, Henry Eshbaugh

Abstract:

Hydration on the lunar surface has been widely identified in orbital datasets (e.g., M³, LCROSS, LAMP), yet the physical form, abundance, and spatial distribution of lunar volatiles remain poorly constrained. Interpretation is complicated by fine-grained regolith, which modifies local thermophysical conditions, obscures underlying volatiles, and alters diagnostic spectral features through scattering and photometric effects. These uncertainties are particularly significant for permanently shadowed regions (PSRs) and high latitudes , where temperatures below ~120 K may preserve water-ice over geological timescales and where several upcoming missions (e.g., Chang’e-7, PROSPECT, CLPS payloads, LEAP) aim to investigate insitu volatiles.We present the development of the Polar Analogue of Dust Overlying Regolith–Ice (PANDOR-I), a demountable laboratory vacuum chamber designed to simulate lunar polar conditions for infrared studies of water-ice and regolith mixtures. The system is engineered to operate under high vacuum and cryogenic conditions (~10⁻⁶ mbar; ≤120 K) and supports variable illumination geometries relevant to polar environments. PANDOR-I operates in two configurations: (1) coupled to a Bruker Vertex 70v FTIR spectrometer for laboratory reflectance measurements across 1.8–20 µm, and (2) integrated with existing flight-instrument thermal-vacuum facilities to enable direct observations by flight-ready infrared instruments.As an initial experimental phase prior to full cryogenic integration, the FTIR sample compartment has been isolated using KBr windows to enable controlled low-pressure (~0.2 mbar) reflectance measurements of hydrated and anhydrous regolith analogue configurations. These preliminary experiments investigate how dust layering, grain size, regolith maturity, composition, ice abundance, and mixing state influence the spectral expression of hydration features, with emphasis on the ~3 µm O–H stretching region and the diagnostic ~6 µm H–O–H bending mode of molecular water. Laboratory spectra will additionally be compared with Mie–Hapke forward models to examine band depth suppression, spectral mixing behaviour, and detectability thresholds under dusty polar conditions.This work reviews the laboratory framework for constraining infrared water-ice detection limits under mission-relevant lunar conditions and provides initial calibration datasets relevant to upcoming orbital and surface investigations of lunar polar volatiles.1. Honniball, C.I., Lucey, P.G., Hayne, P.O., Little, R.C., Greenhagen, B.T., Malespin, C. and Orlando, T.M., 2021. Molecular water detected on the sunlit Moon by SOFIA. Nature Astronomy, 5(2), pp.121–127. https://doi.org/10.1038/s41550-020-01222-x2. Saal, A.E., Hauri, E.H., Cascio, M.L., Van Orman, J.A., Rutherford, M.C. and Cooper, R.F., 2008. Volatile content of lunar volcanic glasses and the presence of water in the Moon’s interior. Nature, 454(7201), pp.192–195. https://doi.org/10.1038/nature070473. Buffo, J.J., Shepherd, J.D., Xu, J., Whisner, C., Devore, E., Shay, P. and Crites, S.T., 2025. Quantifying Regolith Cover Effects on 3 and 6 µm Water Ice Bands. 56th Lunar and Planetary Science Conference, Abstract 2152.4. Pieters, C.M., Goswami, J.N., Clark, R.N., Annadurai, M., Boardman, J., Buratti, B., Cheek, L., Dhingra, D.K., Green, R.O., Head, J.W., Hiesinger, H., Hypki, A., Isaacson, P., Jolliff, B.L., Klima, R.L., Kramer, G., Kumar, S., Lawrence, S.J., LeCorre, L., Li, S., Malaret, E., Mustard, J.F., Petro, N.E., Robinson, M.S., Samuelson, J., Sundaram, C.N. and Taylor, L.A., 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. https://doi.org/10.1126/science.11786585. McCord, T.B., Taylor, L.A., Combe, J.P., Klima, R.L., Tighe, R., Murray, K., Hayne, P.O., Clark, R.N., Pieters, C.M., Sunshine, J.M., Mellon, M.T., Hargraves, R.B., Dyar, M.D., Bussey, D.B.J., Paige, D.A. and Orlando, T.M., 2011. Sources and processes responsible for OH/H₂O in lunar soil. Journal of Geophysical Research: Planets, 116(E10). https://doi.org/10.1029/2010JE0037116. Ehlmann, B.L., Calvin, W.M., Bowles, N.E., Donaldson Hanna, K.L., Green, R.O., Greenhagen, B.T. and Shirley, K.A., 2022. Lunar Trailblazer: A pathfinding mission for lunar water and the lunar surface composition. IEEE Aerospace and Electronic Systems Magazine, 37(11), pp.6–22. https://doi.org/10.1109/AERO53065.2022.98436637. Bowles, N.E., Thomas, I.R., Calcutt, S.B., Donaldson Hanna, K.L., Ehlmann, B.L., Greenhagen, B.T. and Shirley, K.A., 2020. Lunar Thermal Mapper: Characterising the lunar surface in the mid-infrared. 51st Lunar and Planetary Science Conference, Abstract 1380.8. Colaprete, A., Schultz, P., Heldmann, J., Wooden, D., Ennico, K., Hermalyn, B., Marshall, W., Ricco, A., Shirley, M., Vergoz, J. and Yeomans, D., 2010. Detection of water in the LCROSS ejecta plume. Science, 330(6003), pp.463–468. https://doi.org/10.1126/science.11869869. Ogishima, A., Saiki, K., Okubo, A. and Sasaki, S., 2021. Development of a laboratory apparatus to reproduce lunar polar surface environment and measurements of reflectance spectra of frost on the regolith. Icarus, 358, 114192. https://doi.org/10.1016/j.icarus.2020.114192

Modelling interactions with ice to understand the seasonal variation of HCl

(2026)

Authors:

Bethan Gregory, Kevin Olsen, Ehouarn Millour, Megan Brown, Kylash Rajendran, Paul Streeter, Manish Patel, Franck Lefèvre

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.

MoonTools: A Framework for Hyperspectral Data Processing and Parameter Retrieval

(2026)

Authors:

Henry Eshbaugh, Katherine Shirley, Fiona Henderson, Namrah Habib, Emma Belhadfa, Robert Spry, Kevin Olsen, Neil Bowles

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.

Spectral–Mineralogical Correlations in Meteorite and Simulant Analogues: Implications for the Composition and Origin of Phobos

(2026)

Authors:

Emelia Branagan-Harris, Helena Bates, Katherine Shirley, Ashley King, Neil Bowles, Sara Russell

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

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