摘要
随着传感器网络和全球定位系统等技术的进步,兼有时间与空间特性的气象数据体量呈爆炸式增长,针对时空序列预测(STSF)的深度学习模型研究得到了迅猛发展。然而,长期以来用于天气预报的传统机器学习方法在提取数据的时间相关性与空间依赖性方面的效果往往并不理想。与此同时,深度学习方法通过人工神经网络自动提取特征,可以有效提高天气预报的准确度,并且在编码长期空间信息的建模方面有相当优秀的效果。同时,由观测数据驱动的深度学习模型与基于物理理论的数值天气预报(NWP)模型结合的方式可以构建拥有更高预测精度与更长预报时间的混合模型。基于这些,将深度学习在天气预报领域的应用分析及研究进展进行了综述。首先,将天气预报领域的深度学习问题与经典深度学习问题从数据格式、问题模型与评价指标这3个方面进行了对比研究;然后,回顾了深度学习在天气预报领域的发展历程与应用现状,并总结分析了深度学习技术与NWP结合的最新进展;最后,展望了未来的发展方向和研究重点,为天气预报领域的深度学习研究提供参考。
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.
作者
董润婷
吴利
王晓英
曹腾飞
黄建强
管琴
吴洁瑕
DONG Runting;WU Li;WANG Xiaoying;CAO Tengfei;HUANG Jianqiang;GUAN Qin;WU Jiexia(Department of Computer Technologies and Applications,Qinghai University,Xining Qinghai 810016,China;Qinghai Meteorological Observatory,Xining Qinghai 811300,China;Beijing PRESKY Technology Company Limited,Beijing 100195,China)
出处
《计算机应用》
CSCD
北大核心
2023年第6期1958-1968,共11页
journal of Computer Applications
基金
国家自然科学基金资助项目(62162053)
清华大学-宁夏银川水联网数字治水联合研究院横向课题(SKL-IOW-2020TC2004-01)
青海省自然科学基金资助项目(2020-ZJ-943Q)
青海省科技厅应用基础研究项目(2022-ZJ-701)。
关键词
深度学习
天气预报
时空序列预测
数值天气预报
deep learning
weather forecast
SpatioTemporal Sequence Forecasting(STSF)
Numerical Weather Prediction(NWP)
