Mars

Towards a Foundation Model for the Martian Atmosphere

By Keith Cowing
Status Report
astro-ph.EP
June 1, 2026
Filed under , , , , , , ,
Towards a Foundation Model for the Martian Atmosphere
(left) An illustration of Ames MGCM in c48 (1.875°) cubed-sphere grid over martian topography. Tile boundaries are drawn in red. (from Ames MGCM User Guide); (right) Three stretched grids that illustrate the effect of stretch factor (S) on stretching a C16 cubed-sphere. (From Bindle et al., 2021) — astro-ph.EP

The martian atmosphere hosts dynamical phenomena ranging from planet-encircling dust storms to mesoscale orographic clouds and nocturnal low-level jets.

General circulation model show capability to simulate these phenomena, but is computationally expensive at resolution needed to resolve mesoscale features. While assimilation of satellite remote sensing observation enable forecasting capabilities using such models, observation record is often sparse, short and fragmented across instrument generators. These constraints motivate the development of a data-driven foundation model for the Martian atmosphere.

Foundation models live in a complex design landscape. There is an interplay between the available data, the physics of the underlying processes and corresponding developments in AI. Even though the idea of a foundation model is to address multiple use cases in a data- and compute-efficient manner, it is important to have a clear picture what applications can sensibly addressed by a single model.

The purpose of this paper is to elucidate this design landscape. We discuss available data ranging from atmospheric retrievals to reanalysis datasets as well as existing physical models. Moreover, we identify a wide range of candidate downstream applications. Finally, we consider relevant recent developments in artificial intelligence (AI) that can be leveraged in this context.

Here, we put a particular emphasis on AI models for atmospheric physics, data-driven approaches to data assimilation as well as methods to work in a limited data setting.

Sujit Roy, Udayshankar Nair, Yuling Wu, Georgios Priftis, Liping Wang, Anastasia Georgiou, Anne Jones, Björn Lütjens, Johannes Schmude, Campbell Watson, Rachel A. Slank, Ankur Kumar, Anirbit Mukherjee, Procheta Sen, Ramin Lolachi, Haonan Chen, Manil Maskey, Juan Bernabé-Moreno, Rahul Ramachandran

Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
Cite as: arXiv:2605.28851 [astro-ph.EP] (or arXiv:2605.28851v1 [astro-ph.EP] for this version) https://doi.org/10.48550/arXiv.2605.28851 Focus to learn more
Submission history
From: Sujit Roy [v1] Sat, 16 May 2026 20:37:05 UTC (16,909 KB) https://arxiv.org/abs/2605.28851

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