MétaCan
Menu
Back to cohort
Record W4412121891 · doi:10.5194/epsc-dps2025-1551

Martian atmospheric aerosol latitudinal and seasonal analysis over 3 full MYs from Nomad/TGO solar occultation observations

2025· preprint· en· W4412121891 on OpenAlexaboutno aff
Miguel Ángel Gamonal García-Galán, M. Á. López‐Valverde, Adrián Brines, Aurélien Stolzenbach, Ashimananda Modak, Francisco González‐Galindo, B. Funke, J. J. López‐Moreno, Julio Gómez, Rosario Sanz-Mesa, Frank Daerden, Bojan Ristic, G. Belucci, Manish Patel, Ian Thomas

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
Fundersnot available
KeywordsOccultationMartianEnvironmental scienceAtmospheric sciencesAerosolAtmosphere of MarsAstrobiologyMeteorologyGeographyMars Exploration ProgramAstronomyGeologyPhysics

Abstract

fetched live from OpenAlex

On Mars, the most common types of atmospheric aerosols are composed of mineral dust and/or water ice. They have large effects on Martian climate, such as the absorption of solar radiation, altering the radiative balance of the planet, and they are key to atmospheric dynamics and circulation. In the case of dust, it can act as a base to form water ice or CO2 ice clouds, and it can affect observations from both orbiting satellites and rovers on ground, especially at the dusty season around perihelion. The instrument Nadir and Occultation for Mars Discovery (NOMAD) onboard the ExoMars/Trace Gas Orbiter (ExoMars/TGO) is a suite of three spectrometers which has been observing the Martian atmosphere routinely since April 2018. Using data from its solar occultation channel (SO), we combine several sets of diffraction orders, or wavelengths to retrieve the aerosol properties and distribution during that period with a very fine resolution in the vertical from the ground up to the thermosphere. Our retrieval approach consists in three main steps. First, we perform a "cleaning" of the NOMAD observations, provided as transmittance spectra at the tangent altitudes, using an in-house pre-processing algorithm developed at IAA/CSIC. This is intended to eliminate residual imperfections in the calibrated transmittances, such as spectral shifts and continuum curvatures. Second, the cleaned spectra are used to retrieve the aerosol extinction vertical profiles following a global fit approach. This is performed with a retrieval program (RCP) together with a radiative transfer model (KOPRA) which has been well tested in Earth's atmospheric remote sounding. Finally, we implement a fitting algorithm to compare the retrieved extinctions, coming as spectral ratios of the retrieved extinctions, with the extinction ratios simulated using a Lorenz-Mie code by Mishchenko et. al., 2002. The aerosol properties inferred are the size distribution, which is described by an effective radius and its effective variance, nature (mineral dust and water ice proportions), mass of the particles and number density, as well as their vertical distribution and time variability. In this talk we will discuss the results obtained by analyzing more than three full Martian Years. This is a significant extension of a previous first analysis by our team (Stolzenbach et. al, 2023 a,b), focused on the 1st year of NOMAD data. We have also improved a couple of aspects from the previous work. First, the wavelength coverage has been extended so that we are able to retrieve the aerosol information using any order combination, making us able to cover the SO spectral range as widely as possible, as well as exploiting the wavelengths where the aerosol nature can be better determined. Second, we have developed a new methodology to describe the uncertainties of our retrievals by computing the mean of the transmittances from two distinct regions inside the diffraction order.We will describe the major results obtained on the global distribution and properties of the aerosols, analyzing latitudinal and seasonal trends during the time range studied. The NOMAD experiment is led by the Royal Belgian Institute for Space Aeronomy (IASB-BIRA) with 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, 4000140753, 4000140863); by the Spanish Ministry of Science and Innovation (MCIU) and European funds (grants PGC2018-101836-B-I00 and ESP2017-87143-R; MINECO/FEDER), from the Severo Ochoa (CEX2021-001131-S) and from MCIN/AEI/10.13039/501100011033 (grants PID2022-137579NB-I00, RTI2018-100920-J-I00 and PID2022-141216NB-I00); by the UK Space Agency (grants ST/V002295/1, ST/V005332/1, ST/X006549/1, ST/Y000234/1 and ST/R003025/1); and by the Italian Space Agency (grant 2018-2-HH.0). This work was supported by the Belgian Fonds de la Recherche Scientifique – FNRS (grant 30442502; ET_HOME). US investigators were supported by the National Aeronautics and Space Administration. Canadian investigators were supported by the Canadian 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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.241
Teacher spread0.219 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicPlanetary Science and ExplorationFrench-language works237,207