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Record W4412122346 · doi:10.5194/epsc-dps2025-1416

Biases on the retrieval of aerosols' properties from VIS-NIR data: the NOMAD case study

2025· preprint· en· W4412122346 on OpenAlexaboutno aff
Fabrizio Oliva, E. D’Aversa, G. Bellucci, F. G. Carrozzo, Ian Thomas, Luca Ruiz Lozano, Özgür Karatekin, Francesca Altieri, Frank Daerden, Bojan Ristic, Manish Patel, Jon Mason, Yannick Willame, M. Á. López‐Valverde, Ann Carine Vandaele, Giovanni Valentini

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsnot available
Fundersnot available
KeywordsInformation retrievalEnvironmental scienceComputer scienceRemote sensingGeology

Abstract

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Data acquired by spaceborne spectrometers operating in the visual (VIS) and near-infrared (NIR) spectral ranges, are commonly exploited to derive the microphysical properties of planetary atmospheric aerosols (e.g. Adriani et al., 2015; Sindoni et al., 2017; Fedorova et al., 2024; Oliva et al., 2016; Oliva et al., 2018; D’Aversa et al., 2022). The precision with which these properties can be constrained, depends on several parameters (e.g. optical constants, surface albedo spectrum, particles’ shape and size distribution, ecc.) and on the spectral information content in the data. VIS wavelengths alone (~ 0,4 – 0,7 μm) provide information on aerosols’ composition, particles’ density and sizes, allowing the estimation of the optical depth integrated along the line of sight (Oliva et al., 2016). However, in order to constrain these parameters with enhanced precision, the NIR range (~ 0,7 – 3,0 μm) is also required, since it allows to assess how the spectral shape bends towards longer wavelengths (Oliva et al., 2018).Our goal is to exploit the combined nadir datasets of the UVIS (0.2 – 0.65 µm) and LNO (2.2 – 3.8 µm) channels of the NOMAD spectrometer (Neefs et al., 2015) to contrain the microphysical properties of Martian dust (Oliva et al., 2025). The two spectral ranges present a large gap among each other, and this introduces biases in the retrieved parameters due to the unconstrained spectral information in the missing wavelengths. In order to benchmark these biases, we exploit MEx/OMEGA (Bibring et al., 2004) VNIR (0,35 – 1,05 µm) and SWIR-C (0.93 - 2.73 µm) channels’ data. Such an extended interval allows the retrieval of Martian dust clouds’ height and microphysical properties (e.g. Oliva et al., 2018; D’Aversa et al., 2022), that can be used as a proxy for the NOMAD retrievals. By studying dust’s densities and sizes 1) from the full OMEGA spectrum and 2) from the spectrum only covering UVIS and LNO wavelengths, we can derive the bias in the retrieved parameters. Moreover, this analysis allows to calibrate how many UVIS and LNO spectral points need to be considered in the retrieval, in order to balance the information content of the two channels.Preliminary results suggest that dust densities are systematically overestimated (as well as grains’ sizes to a lesser degree) if NIR wavelengths are completely neglected, while such a bias is reduced if LNO range is taken into account.ReferencesAdriani et al., 2015. Faint luminescent Ring over Saturn’s polar hexagon. Astrophys. J. Lett. 808 (1), 5. L16.Bibring et al., 2004. Omega: Observatoire pour La minéralogie, l’eau, Les Glaces Et l’activité. ESA SP-1240: Mars Express: The Scientific Payload. ESA Publications Division, Estec, Noordwijk, The Netherlands, pp. 37–49.D’Aversa et al., 2022. Vertical distribution of dust in the Martian atmosphere: OMEGA/Mex limb observations. Icarus 371, 114702.Fedorova et al., 2024. Distribution of atmospheric aerosols during the 2007 Mars dust storm (MY 28): Solar infrared occultation observations by SPICAM. Icarus 415, 116030Oliva et al., 2016. Clouds and hazes vertical structure of a Saturn’s giant vortex from Cassini/VIMS-V data analysis. Icarus 278, 215–237.Oliva et al., 2018. Properties of a Martian local dust storm in Atlantis Chaos from OMEGA/MEX data. Icarus.Oliva et al., 2025. Martian dust characterization: reanalysis of TGO/NOMAD UVIS and LNO channels’ nadir data. XX Congresso Nazionale di Scienze Planetarie, Pescara, 3-7 Febbraio 2025.Neefs et al, 2015. NOMAD spectrometer on the ExoMars trace gas orbiter mission: part 1—design, manufacturing and testing of the infrared channelsApplied Optics 54, 28, 8494-8520.Sindoni et al., 2017. Characterization of the white ovals on Jupiter’s southern hemisphere using the first data by the Juno/JIRAM instrument. Geophys. Res. Lett. https://doi.org/10.1002/2017gl072940.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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.270
GPT teacher head0.341
Teacher spread0.071 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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