Prépublication
· 2026 arXiv
EN
Data assimilation (DA) is the process of combining forecasts from a model with observations in order to optimally estimate the state of a system. This is critical for chaotic systems, such as the atmosphere, since if observations are not continually assimilate...
Eviatar Bach
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
Deep learning has revolutionised weather forecasting in recent years, especially through atmospheric foundation models, which offer competitive skill for a fraction of the computational costs of classic physics-based models. However, most existing foundation m...
Eliot Walt, Miltiadis Kofinas et al.
#cs.LG
Prépublication
· 2026 arXiv
EN
Despite substantial progress in short-to-medium-range weather forecasting, predicting high-impact events such as flash droughts remains a key challenge for both early warning operations and physically-based subseasonal-to-seasonal (S2S) prediction systems. Her...
Noelia Otero, Atahan Özer et al.
#cs.LG
Prépublication
· 2026 arXiv
EN
We develop structure-preserving methods for the compressible Euler equations with gravity in vector-invariant form and potential temperature as a prognostic variable within the flux-differencing discontinuous Galerkin spectral element method (DGSEM) framework....
Marco Artiano, Kieran Ricardo et al.
#math.NA
Prépublication
· 2026 arXiv
EN
Analytical greenhouse models traditionally rely on the gray approximation, simplifying radiative transfer but treating optical depth as an unconstrained parameter that yields unrealistic surface temperatures. We derive an exact, closed-form solution directly f...
Carmine Serio
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
We present ClimateBench v2, a standardized protocol for evaluating climate models on diagnostics expected to be informative for their skill in projecting mid-century regional temperature and precipitation changes. The protocol is designed to evaluate any physi...
Duncan Watson-Parris, Willa Tobin et al.
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
The Gálvez Davison Index (GDI) is a thermodynamic diagnostic designed to summarize convective favorability in the tropical regions, yet its transferability across complex terrain and heterogeneous hydroclimates remains uncertain. Here we assess the climatologi...
Mauricio López-Reyes, Carlos Calvo-Sancho et al.
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
Climate oscillations are intrinsically out-of-equilibrium phenomena, yet the connection between their predictability and nonequilibrium thermodynamic properties remains unexplored. We develop a coarse-grained stochastic thermodynamic framework for Ornstein-Uhl...
Roberta Benincasa, Danni Du et al.
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
As we move through the world and carry out everyday tasks, we encounter objects that may become relevant only later. We are capable of recalling where we left something or what was inside a container, even without knowing we would need it later. Here, we study...
Shravan Chaudhari, William Paul et al.
#cs.CV
Prépublication
· 2026 arXiv
EN
Modern language models undergo reinforcement learning with verifiable rewards (RLVR) on top of already-trained checkpoints. A key promise of RLVR is the discovery of new reasoning strategies. In principle, a model can sample novel ideas absent from its prior t...
Saif Punjwani, Micah Goldblum
#cs.LG
Prépublication
· 2026 arXiv
EN
Conditional memory architectures such as DeepSeek Engram use input n-grams to look up learned embeddings, expanding the capacity of large language models (LLMs) with limited additional computation. Beyond model scaling, this architecture has demonstrated the p...
Hongru Cai, Ran Wei et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action. We present Long-WAM, a model-system framework for scaling the context of causal world-action models under real-time control c...
Wei Huang, Bohan Zhang et al.
#cs.RO