Graph-Based Learning for Multi-Horizon Martian Atmospheric Forecasting
Martian weather forecasting is important for future exploration, but atmospheric behaviour on Mars combines spatial, temporal, vertical, and dust-driven processes in ways that challenge current modelling and forecasting approaches. This paper introduces MaGMA (Martian Graph-based Multi-horizon Atmospheric Forecasting), a graph-based data engineering framework that transforms OpenMARS reanalysis fields into structured learning objects for Martian atmospheric forecasting. Local atmospheric patches are represented as graph nodes and linked through spatial neighbourhoods, temporal continuity, longer temporal dependencies, and dynamically similar atmospheric states. The model integrates recent...
| Authors | Gary Myler, James Holmes, Manish Patel et al. |
|---|---|
| Primary category | cs.SE |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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