arXiv:2509. 04991v2 Announce Type: replace-cross Abstract: Land surface temperature (LST) is a fundamental physical variable in land-atmosphere interactions, surface energy budgets, and climate processes.
By Tian Xie, Menghui Jiang, Chao Zeng, Huifang Li, Guanhao Zhang, Chan Li, Huanfeng Shen
arXiv:2510. 02415v3 Announce Type: replace-cross Abstract: Machine learning models for the global atmosphere that are capable of producing stable, multi-year simulations of Earth's climate have recently been developed.
By Bosong Zhang, Timothy M. Merlis
arXiv:2603.25779v2 Announce Type: replace-cross
Abstract: Groundwater represents a key element of the water cycle, yet it exhibits complex and context-dependent relationships that make its modeling c...
By Matteo Salis, Gabriele Sartor, Rosa Meo, Stefano Ferraris, Abdourrahmane M. Atto
The paper introduces a Physics Informed Recurrent Neural Network (PIRNN) that simultaneously predicts target time series and unobservable intermediate physical variables, enhancing robustness and interpretability. It adapts to any physical model with multiple equations and variables, demonstrated on groundwater level predictions using the Gardenia model. Experiments on twelve real‑world datasets show PIRNN outperforming several neural network baselines and the Gardenia model, with an ablation study confirming the value of physical knowledge.
By Etienne Lehembre (CA, LIFO), Pascal Audigane (BRGM), Vincent Nguyen (LIFO), Christel Vrain (LIFO, CA), Thi-Bich-Hanh Dao (LIFO, CA)
arXiv:2601.21151v3 Announce Type: replace
Abstract: Machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms, such as advection,...
By Carlos A. Pereira, St\'ephane Gaudreault, Valentin Dallerit, Christopher Subich, Shoyon Panday, Siqi Wei, Sasa Zhang, Siddharth Rout, Eldad Haber, Raymond J. Spiteri, David Millard
arXiv:2606. 11534v1 Announce Type: cross Abstract: Urban heat is amplified by impermeable surfaces and heterogeneous built environments, yet street-level variability remains difficult to quantify because multi-sensor observations are rarely available in consistent, analysis-ready form at the necessary spatiotemporal scales.
By Jonathan Starfeldt, Maria J. Molina, Alexander Kerr, Adam Yang, Thomas R. H. Holmes, Christopher R. Hain