Hyan: State-Adaptive Hidden Perturbations for Efficient Ensemble Weather Forecasting

Reliable weather forecasting requires both accurate predictions and calibrated uncertainty estimates, yet diffusion-based ensemble methods often incur high inference costs due to iterative sampling. We propose Hyan, an efficient ensemble weather forecasting model with state-adaptive noise injection into intermediate representations, and Hyan DE, its deep-ensemble variant. At each injection layer, a lightweight bottleneck network uses local latent features to predict mixing coefficients that control the relative contributions of latent features and stochastic noise. This mechanism produces spatially varying, state-dependent perturbations and requires only one forward pass per ensemble member at each forecast step. On WeatherBench2 at $1.5^{\circ }$ resolution, Hyan DE reduces CRPS by an average of 7.92% relative to IFS ENS over the first five forecast days and outperforms ArchesWeatherGen at only 10.76% of its inference cost. Ablation experiments show that explicit state conditioning improves probabilistic forecast skill and the agreement between ensemble spread and forecast error. Visualizations further reveal distinct spatial modulation patterns across perturbation layers, vertical groups, and forecast lead times.