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Record W4400289642 · doi:10.5194/epsc2024-994

Martian dust properties through NOMAD UVIS-LNO nadir datasets’ investigation: analysis update

2024· preprint· en· W4400289642 on OpenAlexaboutno aff
Fabrizio Oliva, E. D’Aversa, G. Bellucci, F. G. Carrozzo, Luca Ruiz Lozano, Özgür Karatekin, Francesca Altieri, Frank Daerden, Ian Thomas, Bojan Ristic, Manish Patel, Jon Mason, Yannick Willame, C. Depiesse, Miguel Ángel López Valverde, Ann Carine Vandaele, Giuseppe Sindoni

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
Fundersnot available
KeywordsMartianNadirAstrobiologyMars Exploration ProgramPhysicsChemistryAstronomySatellite

Abstract

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AbstractIn this work we present an update on the analysis described in [15,21], focused on the characterization of Martian dust microphysical properties through the investigation of the TGO/NOMAD [1] UVIS and LNO channels’ combined nadir data. These observations cover ultraviolet-visible and near-infrared wavelengths respectively, an extended range that allows constraining the dust densities and sizes. Spatially and temporally coincident data are analysed through the MITRA radiative transfer (RT) tool [2,3,4,16].Being the spectral surface albedo a key element in the RT simulations, we define a method to derive it by exploiting MEx/OMEGA data. As a by-product of this analysis, we plan to obtain a global Mars surface albedo map covering visual (VIS) and near-infrared (NIR) wavelengths.Introduction Airborne dust drives the Red Planet’s thermal structure and climate [6,7,8,9,10], the distribution and circulation of atmospheric gases and has a role in triggering water ice clouds formation [5,17]. These mechanisms are affected by dust composition, abundance and microphysics. The investigation of NOMAD UVIS and LNO nadir data, can provide significant information on the properties of the integrated dust column down to the surface, hence contributing in our understanding of the evolution of Mars’ atmosphere.Instrument and observations Among NOMAD’s three spectrometers [1], UVIS and LNO channels can observe in nadir geometry in the ultraviolet-visible (UV-VIS, 0.2 – 0.65 µm) and NIR (2.2 – 3.8 µm) ranges respectively. Therefore, if combined, they allow retrieving the dust microphysical properties in the whole atmospheric integrated column. We consider observations encompassing from the second half of Martian Year (MY) 34 to the first half of MY37, an extended interval within which dust global and seasonal trends can be analyzed.Method UVIS data are exploited down to 0.36 μm, matching the lower wavelength of the surface albedo spectra ingested in the RT model. These are obtained by processing MEx/OMEGA data with a modified version of the SAS technique [14], nominally correcting the spectral shape from the gases and aerosols contribution. We modify the method in order to determine if the observations can be considered as aerosols-free, hence avoiding biases deriving from the assumed aerosols properties in the original correction. As far as LNO is concerned, only spectral orders from 168 to 202 are adopted [15,21], since they cover a wavelength range (2.20 - 2.55 μm) that is approximately devoid of strong absorption lines, hence allowing a reliable estimation of the spectral continuum. This way, no gases correction is required in our modified SAS.The retrievals are performed through MITRA tool, deriving the temperature-pressures profiles from [11] and considering dust optical constants from [12,13]. A benchmarking with the ones recently published in [19] is also foreseen.SummaryThis study presents an update of the method described in [15,21], focused on retrieving Martian dust microphysical properties from NOMAD UVIS and LNO nadir observations. We updated the method for deriving the spectral surface albedo in order to reduce eventual biases introduced in the original correction.We plan to analyze all spatially and temporally coincident UVIS and LNO observations, in order to track the evolution of dust properties in different MYs and verify how they compare to those retrieved at high altitude with NOMAD SO channel’s data [20].AcknowledgementsExoMars is a space mission of the European Space Agency (ESA) and Roscosmos. The NOMAD experiment is led by the Royal Belgian Institute for Space Aeronomy (IASB- BIRA), assisted by Co-PI teams from Spain (IAA-CSIC), Italy (INAF-IAPS), and the United Kingdom (Open University). This project acknowledges funding by the Belgian Science Policy Office (BELSPO), with the financial and contractual coordination by the ESA Prodex Office (PEA 4000103401, 4000121493), by the Spanish MICINN through its Plan Nacional and by European funds under grants PGC2018-101836-B-I00 and ESP2017-87143-R (MINECO/FEDER), as well as by UK Space Agency through grants ST/V002295/1, ST/V005332/1, ST/Y000234/1 and ST/X006549/1 and Italian Space Agency through grant 2018-2-HH.0. The IAA/CSIC team acknowledges financial support from the State Agency for Research of the Spanish MCIU through the ‘Center of Excellence Severo Ochoa’ award for the Instituto de Astrofísica de Andalucía (SEV-2017-0709). This work was supported by the Belgian Fonds de la Recherche Scientifique – FNRS under grant numbers 30442502 (ET_HOME) and T.0171.16 (CRAMIC) and BELSPO BrainBe SCOOP Project. US investigators were supported by the National Aeronautics and Space Administration. Canadian investigators were supported by the Canada Space Agency.References[1] Neefs, E., et al, 2015. Appl. Opt. 54, 28, 8494-8520.[2] Oliva, F., et al, 2016. Icarus 278, 215-237.[3] Sindoni, G., et al, 2013. EPSC2013.[4] Oliva, F., et al, 2018. Icarus 300, 1-11.[5] Vandaele, A.C., et al, 2019. Nature 568, 521-525.[6] Kahre, M.A., et al, 2008. Icarus 195, 576-597.[7] Korablev, O. ,et al, 2005. Adv. Space Res. 35, 21–30.[8] Gierasch, P.G., Goody, R.M., 1972. J. Atmos. Sci. 29, 400–402.[9] Pollack, J.,et al, 1979. J. Geophys. Res. 84, 2929–2945.[10] Määttänen, A., et al, 2009. Icarus 201, 504-516.[11] Millour, E., et al., 2019. EPSC-DPS 2019[12] Wolff, M.J., et al, 2009. J. Geophys. Res., 114, E9.[13] Wolff, M.J., et al, 2010. Icarus, 208.[14] Geminale, A., et al, 2015. Icarus 253, 51-65.[16]Aoki, S., et al. 2019. J. Geophys. Res.: Planets,124, 3482-3497.[15] Oliva, F., et al., 2021. 15th EPSC, EPSC2021-501.[16] D'Aversa, E., Oliva, et al., 2022. Icarus, 371, 114702.[17] Aoki, S., et al. 2019. JGR: Planets,124, 3482-3497.[18] Wolff, M. et al., 2019. Icarus, 332, 24-29.[19] Martinkainen, J., et al., 2023. APJ Suppl.Ser. 268:47[20] Stolzenbach, A., et al., 2023. JGR Planets, 128[21] Oliva, F., et al., 2024. XIX Congr. Naz. Sc. Pl., Bormio 2024.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.054
GPT teacher head0.259
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes1
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