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深度學習在天氣預報領域的應用分析及研究進展綜述

 2023-10-11 14:35:08  點擊:

董潤婷,吳利,王曉英,. 深度學習在天氣預報領域的應用分析及研究進展綜述[J]. 計算機應用,2023,43(6):1958-1968. DOI:10.11772/j.issn.1001-9081.2022050745.



摘要:

隨著傳感器網絡和全球定位系統等技術的進步,兼有時間與空間特性的氣象數據體量呈爆炸式增長,針對時空序列預測(STSF)的深度學習模型研究得到了迅猛發展。然而,長期以來用于天氣預報的傳統機器學習方法在提取數據的時間相關性與空間依賴性方面的效果往往并不理想。與此同時,深度學習方法通過人工神經網絡自動提取特征,可以有效提高天氣預報的準確度,并且在編碼長期空間信息的建模方面有相當優秀的效果。同時,由觀測數據驅動的深度學習模型與基于物理理論的數值天氣預報(NWP)模型結合的方式可以構建擁有更高預測精度與更長預報時間的混合模型;谶@些,將深度學習在天氣預報領域的應用分析及研究進展進行了綜述。首先,將天氣預報領域的深度學習問題與經典深度學習問題從數據格式、問題模型與評價指標這3個方面進行了對比研究;然后,回顧了深度學習在天氣預報領域的發展歷程與應用現狀,并總結分析了深度學習技術與NWP結合的最新進展;最后,展望了未來的發展方向和研究重點,為天氣預報領域的深度學習研究提供參考。


關鍵詞: 深度學習, 天氣預報, 時空序列預測, 數值天氣預報


Abstract:

With the advancement of technologies such as sensor networks and global positioning systems, the volume of meteorological data with both temporal and spatial characteristics has exploded, and the research on deep learning models for Spatiotemporal Sequence Forecasting (STSF) has developed rapidly. However, the traditional machine learning methods applied to weather forecasting for a long time have unsatisfactory effects in extracting the temporal correlations and spatial dependences of data, while the deep learning methods can extract features automatically through artificial neural networks to improve the accuracy of weather forecasting effectively, and have a very good effect in encoding long-term spatial information modeling. At the same time, the deep learning models driven by observational data and Numerical Weather Prediction (NWP) models based on physical theories are combined to build hybrid models with higher prediction accuracy and longer prediction time. Based on these, the application analysis and research progress of deep learning in the field of weather forecasting were reviewed. Firstly, the deep learning problems in the field of weather forecasting and the classical deep learning problems were compared and studied from three aspects: data format, problem model and evaluation metrics. Then, the development history and application status of deep learning in the field of weather forecasting were looked back, and the latest progress in combining deep learning technologies with NWP was summarized and analyzed. Finally, the future development directions and research focuses were prospected to provide a certain reference for future deep learning research in the field of weather forecasting.


Key words: deep learning, weather forecast, SpatioTemporal Sequence Forecasting (STSF), Numerical Weather Prediction (NWP)


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