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Enregistrement W3103651674 · doi:10.5194/epsc2020-911

Martian Water-Ice Cloud Optical Properties Following the Mars Year 34 Global Dust Storm

2020· article· en· W3103651674 sur OpenAlexaff
A. C. Innanen, Brittney A. Cooper, Jacob L. Kloos, Charissa Campbell, John E. Moores

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomainePhysics and Astronomy
ThématiquePlanetary Science and Exploration
Établissements canadiensYork University
Organismes subventionnairesnon disponible
Mots-clésMars Exploration ProgramMartianDust stormStormAtmospheric sciencesAtmosphere of MarsEnvironmental scienceImpact craterAstrobiologyMeteorologyPhysics

Résumé

récupéré en direct d'OpenAlex

ABSTRACTThe Global Dust Storm of Mars Year 34 was observed by the Mars Science Laboratory (MSL) in Gale crater, and many atmospheric effects were seen during its duration. Using atmospheric observations taken by MSL, water-ice cloud opacity and scattering phase function are calculated, allowing comparison of these quantities before and after the dust storm. The average phase function was calculated for the Mars Year 34 Aphelion Cloud Belt (ACB) season, before the global dust storm, and will be calculated for the ACB season following. Opacity calculations are computed throughout the Martian year. The results of these calculations will indicate any impacts the global dust storm may have on Martian water-ice clouds.1. INTRODUCTIONThe Martian atmosphere has long been the subject of study, both in situ and remotely, and as a result we are able to observe its dynamic nature. Water-ice clouds are one such dynamic feature, and their similarities to Earth-based clouds provide an opportunity to learn more not just about Martian weather but our own climate processes.The Mars Science Laboratory (MSL) in Gale Crater has been observing cloud activity in the region for the entirety of its mission to date. Cloud movies captured by MSL can be analysed in order to determine optical parameters such as opacity[1] and scattering phase function[2]. The most important time of year for cloud activity around Gale Crater is the Aphelion Cloud Belt season, which tends to peak between solar longitudes (Ls) 60-100° and is characterised by an increase in equatorial cloud formation[3].MSL has been in Gale Crater for nearly four Martian Years, and as a result has been able to observe the time-varying nature of the Martian climate on daily, seasonal, and annual scales. The presence of a global dust storm in 2018 (Mars Year 34) gave further opportunity to study not only the event itself, but its lasting effect on the Martian atmosphere.2. BACKGROUND2.1 The Mars Year 34 Global Dust StormThe global dust storm of Mars Year (MY) 34 was a planet encircling dust storm which lasted from about Ls~188° to Ls~250°[4]. Global dust storms typically occur every 3-4 years[5] and significantly impact the atmosphere, causing temperature and pressure fluctuations[4] and an increase in water vapour in the middle atmosphere[6]. While the decrease in visibility made it difficult to observe clouds during the global dust storm, a before-and-after comparison of cloud opacity and scattering phase function provide understanding about longstanding atmospheric effects of global dust storms.2.2 MSL Atmospheric MoviesThe navigation cameras onboard MSL take two types of movies year-round, Zenith Movies (ZM) and Suprahorizon Movies (SHM), and a third observation during the ACB season, the Phase Function Sky Survey (PFSS). ZM and SHM are both 8 frame movies taken over 6 minutes, with ZMs pointing to the Zenith and SHMs pointing just above the crater rim. The PFSS is a mosaic of 9 3-frame movies at a variety of pointings making a dome surrounding the rover. In order to better identify clouds, all three types of movies undergo mean frame subtraction, a process by which an average frame is subtracted from each frame of the movie, leaving only the time variable component. This can be seen in Figure 1.3. METHODSZM and SHM can be analysed to determine cloud optical thickness, which has been computed for the first two Martian Years of MSL’s mission (Ls=160° of MY 31 to Ls=160° of MY 33)[7]. After mean frame subtraction, a radiance map (Figure 2) is made of a single frame of the movie, which is examined manually for a region containing cloud and empty sky, giving a variation in spectral radiance (Iλ,VAR) which is then used to compute the optical thickness. During the ACB season, the high-cloud formula, derived by Kloos et al, is used to estimate opacity, which assumes clouds are high, optically thin, and composed of water-ice crystals[1].The PFSS was first instituted in MY 34, and an average scattering phase function determined for that year[2]. As with optical thickness, Iλ,VAR is determined from a radiance map for each movie and used to compute the phase function, using the formula derived by Cooper et al[2]. With the MY 35 ACB season ending in February of 2020, there is now another complete set of 26 PFSS observations, giving 234 total movies.4. RESULTS AND DISCUSSIONCurrently ZM and SHM have been processed and have radiance maps from sol 1706 to sol 2748, a total of 466 movies. All of these must be checked manually for quality, and to identify high and low radiance points. This process is ongoing. Following the calculation of optical thickness for all cloud movies, the catalogue of MSL cloud opacities will be extended from two Martian years to four.In addition, the 234 PFSS movies must likewise be checked, a process that is nearing completion at time of writing. By the time of the conference, it is anticipated that an average phase function for MY 35 will be calculated, allowing comparison with that of MY 34. Due to the larger size of the opacity dataset, opacity calculations are anticipated to take more time.These observations will allow us to better understand not only the lasting atmospheric effects of global dust storms, but also will extend our understanding of variability in water-ice cloud optical properties over larger timescales.REFERENCES[1] Kloos, J.L, et al, Adv. Space Res., 2016.[2] Cooper, B.A., et al, P&SS, 2019.[3] Wolff, M.J., et al, JGR, 1999.[4] Guzewich, S.D. et al, Geophys. Res. Lett., 2019.[5] Zurek, R.W., and Martin, L.J., JGR, 1993.[6] Aoki, S.A., et al, JGR-Planets, 2019.[7] Kloos, J.L. et al, JGR-Planets, 2018.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,040

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,024
Tête enseignante GPT0,208
Écart entre enseignants0,184 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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