arXiv Computer Vision
Oct 16

DIAL: Dynamic and Informed Adjustment of the Native Stochastic Law for Reliable Weather Ensembles

The paper introduces DIAL, a method that dynamically adjusts the native stochasticity of a frozen weather forecasting model to improve ensemble reliability across variables, scales, and lead times. DIAL learns from past forecasts to predict necessary spread adjustments and applies small noise updates during each forecast step, avoiding retraining or full backpropagation. Experiments on FourCastNet 3 show that DIAL outperforms post‑processing and reward‑based baselines, achieving an 8.27% improvement in multiscale CRPS over the unmodified model.

By Yucheng Wang, Haonan Guo, Tianhao Zhao, Di Wang, Yu Wu, Zengmao Wang, Bo Du
arXiv AI
Oct 19

Rescene: stable century-long climate emulation from a frozen weather model

Rescene is a 0.4 M‑parameter wrapper that transforms a frozen 1.5° weather model into a stochastic climate emulator. By anchoring the model to a calendar climatology with a deterministic slow clock and adding a band‑limited AR(1) perturbation tuned to the observed anomaly spectrum, Rescene produces century‑long runs with no detectable drift and realistic variability. A 100‑year, 8‑member experiment shows stable temperature trends, calibrated ensemble spread/skill, and accurate reproduction of day‑to‑day variability and blocking frequency.

By Minjong Cheon
arXiv Machine Learning
Aug 27

Forecasting Multiple Observables with SCROLL: Score-Trained Uncertainty for Stochastic Dynamics

The paper introduces SCROLL, a method for forecasting multiple observables in stochastic dynamical systems by composing each observable’s likelihood into per‑task free‑routed last‑layer beliefs on a shared backbone. This approach learns unit‑dependent loss scaling directly from data, enabling accurate predictive variance estimation without separate tuning. Experiments on the Ornstein–Uhlenbeck process, stochastic Lorenz‑63, and real air‑quality data show that SCROLL recovers analytic kernels, achieves superior negative log‑likelihood on state and regime tasks, and maintains calibration while reducing hyper‑parameter search costs.

By Pavel Prochazka