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
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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.
Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics numerical weather prediction models can represent the processes governing RI, resolving storm-environment interactions remains computationally expensive, while purely data-d...
Shi-Jie Xiao, Jonathan Lin, Thomas Ehrmann et al.· 0 citations
A stretched-grid artificial intelligence weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resolution elsewhere globally provides its greatest value for localized precipitation extremes and intense AR-related moisture transport.
Nazak Rouzegari, V. A. Gorooh, A. Sengupta et al.· 0 citations
Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from high-fidelity data. However, this so far mostly involves local-in-time, diagnostic parameterizations, in which the subgrid state depends only on the current coarse state w...
Jurij Sch\"onfeld, Tom Beucler, Julien Savre et al.· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
