Predicting Sudden Stratospheric Warmings Using Video Prediction Methods

Abstract

Sudden Stratospheric Warmings (SSWs) are weather phenomena occurring in polar regions, and have a profound impact on mid-latitude cold waves. In this paper, within a deep learning framework, we introduce video prediction techniques into SSW events forecasting for the first time. We develop a Global Attention Motion Decoupled Recurrent Neural Network (GMRNN) to better capture the detailed changes of the polar vortex. Through experiments on representative SSW events in 2018, 2019, and 2021, our model can stably predict SSW events 20 days in advance and accurately capture the morphological changes of the stratospheric polar vortex. Furthermore, we compared our model with baseline models, including PredRNN, MotionRNN, and the sub-seasonal to seasonal (S2S) integrated forecast models from ECMWF, CMA, and ECCC. The results indicate that our model outperforms these models across various evaluation metrics, compare with ensemble prediction results GMRNN's Structural Similarity increased by approximately 11.2%, and the Anomaly Correlation Coefficient increased by approximately 9.5%. The GMRNN model exhibits superior stability and possesses prediction potential over a longer period.