arXiv:2609. 21872v1 Announce Type: cross Abstract: We introduce Chronosphere, a spatio-temporal neural field that learns representations of climate.
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The study evaluates whether Earth‑observation foundation models encode geographic location information by attempting to predict coordinates from their embeddings. Using 284 verified European solar farms, the authors tested Tessera v1, Tessera v1.1, and AlphaEarth, finding that all three models contain recoverable geographic data. AlphaEarth showed the strongest correlation between embedding distance and geographic distance, while both Tessera variants outperformed Sentinel‑2 controls, suggesting that geographic information should be considered when auditing such models.
arXiv:2608.27521v1 Announce Type: new Abstract: Many dynamical processes unfold on the sphere but the default scientific machine learning architectures are Euclidean. Applying these architectures on...
arXiv:2602. 13416v2 Announce Type: replace Abstract: The proliferation of data-driven models in weather and climate sciences has marked a significant paradigm shift, with advanced models demonstrating exceptional skill in medium-range forecasting.
arXiv:2608. 12271v1 Announce Type: new Abstract: Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties.
arXiv:2607. 29527v1 Announce Type: cross Abstract: A defining problem of the Anthropocene is to model the physical Earth and human societies as one coupled system, yet no learned representation spans their observational breadth.
arXiv:2607. 17037v1 Announce Type: new Abstract: High-resolution atmospheric data are required to resolve mesoscale and localized meteorological structures, however such datasets remain limited in many regions of the world.