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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-04-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2024GL113993 |
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| _version_ | 1849397299201966080 |
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| author | Yuhao Du Jiankai Zhang Xinyuan Cheng Yixiong Lu Douwang Li Wenshou Tian |
| author_facet | Yuhao Du Jiankai Zhang Xinyuan Cheng Yixiong Lu Douwang Li Wenshou Tian |
| author_sort | Yuhao Du |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b84ca5da134746c49ef5d1202cbb4499 |
| institution | Kabale University |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-b84ca5da134746c49ef5d1202cbb44992025-08-20T03:39:04ZengWileyGeophysical Research Letters0094-82761944-80072025-04-01528n/an/a10.1029/2024GL113993Predicting Sudden Stratospheric Warmings Using Video Prediction MethodsYuhao Du0Jiankai Zhang1Xinyuan Cheng2Yixiong Lu3Douwang Li4Wenshou Tian5College of Atmospheric Sciences Lanzhou University Lanzhou ChinaCollege of Atmospheric Sciences Lanzhou University Lanzhou ChinaCenter for Information and Language Processing University of Munich (LMU) Munich GermanyEarth System Modeling and Prediction Centre China Meteorological Administration Beijing ChinaCollege of Atmospheric Sciences Lanzhou University Lanzhou ChinaCollege of Atmospheric Sciences Lanzhou University Lanzhou ChinaAbstract 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.https://doi.org/10.1029/2024GL113993sudden stratospheric warmingsdeep learningvideo prediction |
| spellingShingle | Yuhao Du Jiankai Zhang Xinyuan Cheng Yixiong Lu Douwang Li Wenshou Tian Predicting Sudden Stratospheric Warmings Using Video Prediction Methods Geophysical Research Letters sudden stratospheric warmings deep learning video prediction |
| title | Predicting Sudden Stratospheric Warmings Using Video Prediction Methods |
| title_full | Predicting Sudden Stratospheric Warmings Using Video Prediction Methods |
| title_fullStr | Predicting Sudden Stratospheric Warmings Using Video Prediction Methods |
| title_full_unstemmed | Predicting Sudden Stratospheric Warmings Using Video Prediction Methods |
| title_short | Predicting Sudden Stratospheric Warmings Using Video Prediction Methods |
| title_sort | predicting sudden stratospheric warmings using video prediction methods |
| topic | sudden stratospheric warmings deep learning video prediction |
| url | https://doi.org/10.1029/2024GL113993 |
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