Research on Subseasonal Ensemble Forecasting of Cold Surges over China Based on Physically-Constrained Perturbations in AI-based Weather Prediction Models

Cold waves are one of the most frequent extreme weather events in the winter mid- and high-latitude regions of the Northern Hemisphere. Due to their sudden onset, persistence, and wide-ranging effects, they often cause significant economic and social losses. Although progress has been made in short-term cold wave forecasting, sub-seasonal (3-4 weeks) forecasting remains a major challenge due to the loss of initial condition information, and the complex and nonlinear external forcings. Current numerical models, which dominate operational cold wave forecasting, are computationally expensive and difficult to run large-scale simulations. In contrast, machine learning models, particularly FuXi-S2S developed by Fudan University, offer significant potential due to their efficiency and accuracy in sub-seasonal forecasting.

In the preliminary work, the researcher identified the spatiotemporal characteristics and circulation evolution of cold events in Eurasia, proposing the "Cold Arctic-Warm Continent" mode and its interaction with tropical Pacific signals. Despite improvements in understanding the mechanisms of cold waves, predicting their occurrence at the sub-seasonal scale remains difficult due to uncertainties and complex nonlinear processes. Therefore, exploring new machine learning-based forecasting methods is essential to improve prediction accuracy.

The goal of this study is to: 1) identify key pre-cold wave factors at the sub-seasonal scale in China; 2) develop a probabilistic forecasting scheme based on FuXi-S2S with physically constrained perturbations. The research methodology includes composite analysis and the design of initial perturbations for the FuXi-S2S model with physical constraints, aimed at improving forecast accuracy. By comparing ensemble and deterministic forecasts, this study will evaluate the effectiveness of the proposed scheme and contribute to early warning strategies for cold wave events.