Accurate global Subseasonal-to-Seasonal (S2S) climate forecasting is critical for disaster preparedness and resource management, yet it remains challenging due to chaotic atmospheric dynamics. Despite advances in geometry-aware representations, existing methods do not specify how the zonal and meridional interactions t...
Zi-Yu Zhou, Yu-Chen Fang, Wei-Lin Ruan et al.· 0 citations
Microsoft Weather Nowcast is presented, a six-hour ensemble radar nowcasting model that jointly learns a deterministic predictor to capture organised precipitation structure shared across ensemble members, and a generator to produce diverse local residuals around this shared prediction.
Ning Wang, Zu-Liang Fang, Wei-Xin Jin et al.· 0 citations
A calibrated comparison of GraphCast, GenCast and the Functional Generative Network against the physical NWP model IFS for rainfall prediction across Africa highlights the potential of calibrated AI weather prediction to provide accessible and computationally efficient rainfall forecasts, while demonstrating the contin...
S. Nath, Docko Sow, Koomi Toussaint Amoussouvi et al.· 0 citations
By establishing a rigorous reference baseline for what univariate Hs models can and cannot achieve, this study provides a benchmark against which future multivariate and physics-informed approaches can be calibrated, and offers practical guidance for lightweight buoy-level forecasting in mid-latitude storm-dominated an...
A mechanism-aware conditioning plug-in framework that turns a nudged coarse ensemble into a non-intrusive sensor of local instability geometry, showing that local instability geometry is not merely interpretable post hoc, but an actionable conditioning signal for data-efficient rare-event emulation.
Isabella Thiel, J. Bello-Rivas, Y. Kevrekidis et al.· 0 citations
Reconstructing high-resolution regional significant wave height (SWH) fields from sparse buoy observations is a critical challenge for ocean monitoring. We introduce AUWave, a hybrid deep learning framework that fuses a station-wise encoder with a multi-scale U-Net enhanced by self-attention to recover regional SWH fie...
An explainable contrastive learning model that maps 5 monthly cloud and radiation fields into a shared representation space and demonstrates that explainable representations of climate fields can attribute model differences to specific variables, regions, seasons, and physical parameters.
Da Fan, D. J. Gagne, G. Elsaesser et al.· 0 citations
DeepX‐GAN (Dependence‐Enhanced Embedding for Physical eXtremes—Generative Adversarial Network) is introduced, a deep generative model that explicitly captures the spatial structure of rare extremes and enables the simulation of statistically plausible extremes beyond the observed record, evaluated against long climate...
Xin-Yue Liu, Xiao Peng, Shu-Yue Yan et al.· Earth's Future· 0 citations
HlimRep-Ocean is presented, an ocean emulator that operates directly on the native unstructured mesh of FESOM2, and achieves the lowest RMSE against GLORYS reanalysis among all assessed systems, confirming the competitiveness of the native-mesh approach.
K. Nowak, A. Koldunov, Nikolay Koldunov et al.· 0 citations
This work forms this reconstruction problem as generative atmospheric super-resolution and introduces composable observation interfaces for conditioning a single pretrained 13-variable atmospheric diffusion model without retraining the underlying model.
Yang Xu, Dibyajyoti Chakraborty, Hai-Wen Guan et al.· 0 citations
Diffusion models have shown strong potential for kilometer-scale precipitation downscaling, but their performance in geographically unseen regions and event regimes remains insufficiently understood. Building on the wavelet diffusion model (WDM) framework, this study evaluates cross-region and cross-event generalizatio...
Wei-Kang Qian, Yi-Xin Wen, Chu-Gang Yi et al.· 0 citations
PR-Smoother is introduced, a simulator-preserving amortized smoother designed for this prescribed-simulator DA regime that yields an explicit non-Gaussian smoothing distribution over physical trajectories and supports joint state, parameter, and sensor-bias learning from observations alone.
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
