Prépublication
· 2026 arXiv
EN
Accurate precipitation estimation at fine spatial scales is critical for hydrology, agriculture, and climate studies. Infrared brightness temperatures from geostationary satellites offer excellent temporal coverage over continental-scale domains. However, beca...
Matthieu Meignin, Cécile Mallet et al.
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
Deep learning has made rapid advances in weather forecasting: autoregressive models trained on atmospheric reanalyses now rival dynamical models across nowcasting, medium-range, and subseasonal-to-seasonal lead times, producing well-calibrated ensemble forecas...
Tom Beucler, J. David Neelin et al.
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
Atmospheric warming raises evaporative demand, but actual evapotranspiration (ETa) may track demand or decouple under water limitation. Here, we quantified demand-tracking ability from regression slopes relating deseasonalized ETa and reference evapotranspirat...
Yan Zhang, Veronica J. Berrocal et al.
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
A combination of remote sensing techniques, open space data and field testing was developed to derive astronomical and meteorological parameters and assess Dark Sky brightness of the hitherto unstudied Schist Villages region in the mountainous Interior Center...
Domingos Barbosa, Dalmiro Maia et al.
#astro-ph.IM
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
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
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