Studying the middle/upper atmosphere of Venus and Mars combining 3D modeling and observations
Bibliographic record
Abstract
Our understanding of Venus and Mars climate has been noticeably improved thanks to progress with General Circulation Models (GCM) (e.g., Forget et al. 1999, Lebonnois et al. 2010, Gilli et al. 2021) and increasing measurements, both from space missions and ground-based telescopes. While there are 13 operational missions currently dedicated to Mars, a new era in the exploration of “our sister” planet Venus is coming in the next decades with the selection of 3 missions: DAVINCI and VERITAS by NASA, EnVision by ESA, in addition to the Indian orbiter mission, Shukrayyan-1 (planned for 2025). Nevertheless, our view of the upper layers of those planets (i.e., above approximately 80 km and 60 km on Venus and Mars, respectively) remains incomplete. The observed high variability of those regions (e.g., Gerard et al. 2014, Gonzalez-Galindo et al. 2015) is very challenging to predict by 3D models. Planetary waves (e.g., Kelvin waves) are suggested to play an important role in the variability in the so-called transition region on Venus (between super-rotation and day-to-night circulation) (Navarro et al. 2021) and gravity waves are recognized to produce a significant impact on the thermal tides of Mars (Gilli et al. 2020). In this talk, I will give a brief overview of recent 3D GCM developments done in collaboration with the Institut Pierre-Simon Laplace (IPSL) laboratories in France and the Instituto de Astrofisica de Andalucia (IAA) in Spain, such as the inclusion of a stochastic non-orographic gravity wave parameterization and improvements on the parameterization of non-LTE CO2 heating rates (Martinez et al. 2022, submitted), to provide a more realistic picture of those upper regions of the Venus and Mars atmosphere. References: Forget et al. 1999, JGR, 104, 155-24 Lebonnois et al. 2010, JGR-Planets, 115, 6006 Gilli et al. 2021, Icarus, Vol. 366, 114432 Navarro et al. 2021, Icarus, Vol. 366, 114400 Gilli et al. 2020, JGR-Planets, 125-3 Gilli et al. 2017, Icarus, Vol.248, 478-498 Gerard et al. 2014, Icarus, 236, 92-103 Gonzalez-Galindo et al. 2015. JGR-Planets, 120, 2020-2035 Martinez et al. 2022, submitted to Icarus Acknowledgments: GG is funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, the Agencia Estatal de Investigación and EC-FEDER funds under project RTI2018-100920-J-I00, and acknowledges financial support from the State Agency for Research of the Spanish MCIU through the “Center of Excellence Severo Ochoa” award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709). This research was also supported by Fundação para a Ciência e a Tecnologia (FCT) through the research grants UIDB/04434/2020, UIDP/04434/2020, P-TUGA PTDC/FIS-AST/29942/2017.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".