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AI Emulation of Stochastic Sudden Stratospheric Warming with Interpretable Latent Structure
physics.ao-phcs.LG C. Daniel Boscu, Daniel Hernandez, Fabio Alvarez Ventura et al. · Oct 1, 2026

Rare weather regime transitions such as sudden stratospheric warmings are challenging to emulate because training data is overwhelmingly dominated by stable regimes. This paper develops a probabilistic ResNet-inspired Conditional Variational Autoencoder (CVAE) for the stochastic Holton-Mass model, a 75-dimensional idealized system with two metastable vortex states and rare noise-driven transitions. By training only on one-day-ahead predictions, the authors show that the emulator reproduces both short-term dynamics and demanding long-term statistics, and that unsupervised clustering of its 32-dimensional latent space yields four physically interpretable regime clusters. The results suggest a proof-of-concept pathway toward interpretable early warning systems for extreme events, although the study remains confined to a simplified testbed.

Rare weather regime transitions pose a challenge for data-driven modeling due to class imbalance. In this study, we develop a probabilistic deep learning emulator for a prototypical system with regime transitions, the stochastic Holton--Mass model of stratospheric variability, and analyze the structure of its learned latent space. The Holton--Mass model exhibits two metastable regimes, a strong and a weak polar vortex, maintained by nonlinear wave--mean flow interactions, with weak stochastic forcing intermittently triggering rare transitions between these regimes that qualitatively represent SSW events. We employ a ResNet-inspired Conditional Variational Autoencoder with six-layer encoder and decoder layers and explicit current-state conditioning to model the distribution of the system's state at the next time step (one day). The emulator accurately reproduces short-term dynamics, steady-state probability distributions, regime persistence statistics, rare transition rates, the transition committor function, and the transition expected lead time of the physical model. Beyond emulation fidelity, we interrogate the learned latent representation to understand how the model internalizes the underlying metastable structure of the dynamics. Principal Component Analysis of the 32-dimensional latent space reveals a clear and unsupervised separation into four physically interpretable clusters corresponding to strong versus weak vortex regimes and stable versus transition-prone configurations. Such emergent regime separation in latent space is hard to identify for deep generative models applied to high-dimensional stochastic systems. Our results show that carefully designed probabilistic emulators can uncover physically meaningful manifolds governing extreme-event dynamics, potentially aiding the development of improved operational advanced warning systems.
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arXivPDF
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Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography
physics.ao-phcs.LG Alberto Pennino, Francesco Zanetta, Michele Cattaneo et al. · Oct 1, 2026

Varda-single-1.0 is a deterministic, data-driven weather forecasting system built for the Alpine domain, delivering hourly forecasts at 1 km resolution via a stretched-grid Graph Transformer architecture. It combines a 6-hourly autoregressive forecaster with a temporal downscaler to balance forecast horizon and temporal fidelity. The work demonstrates that machine learning models can match or exceed operational numerical weather prediction baselines in complex topography, and it openly releases model weights and software to enable operational adoption and downstream research.

We present Varda-single-1.0, a medium-range data-driven weather prediction system built for the Alpine domain. It provides hourly deterministic regional forecasts on a mesh of 1 km resolution and global forecasts on a 31 km mesh. The system comprises two independently trained stretched-grid Graph Transformer models with encoder-processor-decoder architecture, developed in the Anemoi framework: a 6-hourly autoregressive forecaster and a temporal downscaler reconstructing hourly forecasts between the forecaster's steps. Its training curriculum includes pre-training on ERA5 reanalysis data, followed by training on a 20-year kilometre-scale regional reanalysis, and finally fine-tuning on operational kilometre-scale analyses. Verified over one year against operational analyses and surface station observations, Varda-single is competitive with or improves on MeteoSwiss' operational numerical weather prediction baselines for most headline scores and variables. It broadly matches the skill of the high-resolution 1 km ICON-CH1-EPS control at lead times up to +33 h and generally outperforms the 2 km ICON-CH2-EPS control at lead times up to +120 h. Despite competitive aggregate scores, Varda-single underestimates some local wind maxima and produces overly smooth convective precipitation fields, consistent with the smoothing associated with squared-error training. To gain insight into the model's behaviour, we investigate three case studies beyond the aggregated headline scores, and find particular weaknesses in Varda-single's representation of local winds over complex terrain. Varda-single represents an important step in the development of high-resolution ML forecasting over complex terrain, in complementing the operational regional numerical weather prediction models of MeteoSwiss with data-driven models and in providing a pretrained model for researchers and user-specific applications.
AI reviewed
arXivPDF
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Sonny: Breaking the Compute Wall in Medium-Range Weather Forecasting
cs.LGcs.AIcs.CV Minjong Cheon · Mar 22, 2026

Sonny tackles the compute barrier in medium-range weather forecasting by proposing a hierarchical transformer that trains on a single A40 GPU in 5.5 days. The core idea is a two-stage StepsNet pipeline: a narrow 'slow path' processes large-scale dynamics (U,V,Z,P) first, then a full-width 'fast path' integrates thermodynamics (T,Q). Combined with EMA during training, randomized dynamics forecasting, and pressure-weighted losses, Sonny aims to deliver competitive forecast skill without the TPU/GPU cluster requirements of models like Pangu-Weather or GraphCast.

Weather forecasting is a fundamental problem for protecting lives and infrastructure from high-impact atmospheric events. Recently, data-driven weather forecasting methods based on deep learning have demonstrated strong performance, often reaching accuracy levels competitive with operational numerical systems. However, many existing models rely on large-scale training regimes and compute-intensive architectures, which raises the practical barrier for academic groups with limited compute resources. Here we introduce Sonny, an efficient hierarchical transformer that achieves competitive medium-range forecasting performance while remaining feasible within reasonable compute budgets. At the core of Sonny is a two-stage StepsNet design: a narrow slow path first models large-scale atmospheric dynamics, and a subsequent full-width fast path integrates thermodynamic interactions. To stabilize medium-range rollout without an additional fine-tuning stage, we apply exponential moving average (EMA) during training. On WeatherBench2, Sonny yields robust medium-range forecast skill, remains competitive with operational baselines, and demonstrates clear advantages over FastNet, particularly at extended tropical lead times. In practice, Sonny can be trained to convergence on a single NVIDIA A40 GPU in approximately 5.5 days.
AI reviewed
arXivPDF