刘媛媛,张丽,李磊,刘业森,陈柏纬,张文海.基于多变量 LSTM 神经网络模型的风暴潮临近预报[J].海洋通报,2020,(6):
基于多变量 LSTM 神经网络模型的风暴潮临近预报
Storm surge nowcasting based on multivariable LSTM neural network model
投稿时间:2020-04-08  修订日期:2020-05-07
DOI:10.11840/j.issn.1001-6392.2020.06.006
中文关键词:  LSTM 神经网络模型  热带气旋  风暴潮  临近预报
英文关键词:long short-term memory  tropical cyclone  storm surge  nowcasting
基金项目:国家重点研发计划 (2016YFC0803107;2016YFC0803109)
作者单位E-mail
刘媛媛 中国水利水电科学研究院北京 100038 50237479@qq.com 
张丽 深圳市国家气候观象台 广东 深圳 519082 ziluolan_123@163.com 
李磊 中山大学大气科学学院广东 珠海 519082  
刘业森 中国水利水电科学研究院北京 100038  
陈柏纬 香港天文台香港 九龙 999077  
张文海 深圳市强风暴研究院广东 深圳 518057  
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中文摘要:
      台风的风暴潮是台风引发的一种重要次生灾害,对沿海城市带来的威胁是多方面的。及时准确地预报风暴潮,对沿海地区采取合理措施减少人员伤亡和经济损失具有重要意义。本文利用长短期记忆神经网络 (LSTM) 模型,综合考虑风速、 风向、气压等气象因素和前时序的潮位数据,建立了风暴潮的临近预报模型。结果表明,基于 LSTM 的临近预报模型具有相当的预报技巧,利用前时序的风速和风向数据以及潮位数据建立的模型可对风暴潮潮位进行准确地预测。研究还表明,仅考虑前时序潮位的预测模型误差最大,考虑气压后的模型预测能力有一定进步,而考虑风的要素以后,预测的效果提升更为明显。
英文摘要:
      A storm surge is an important secondary disaster caused by typhoons, and its threat to coastal cities is manifold. It is of great significance to forecast storm surges timely and accurately, and to adopt reasonable measures to reduce casualties and economic losses in coastal areas. This paper proposes a new algorithm which comprehensively considers wind speed, wind direction, atmospheric pressure, and other factors, as well as the tidal level observation data of preceding time series,and establishes a storm surge nowcasting model by using the long short-term memory (LSTM) neural network algorithm. The results reveal that the LSTM-based nowcasting model has considerable forecasting skills and that the forecasting error is related to the time level advance of the preceding data used. The study also reveals that the forecasting model which only considers the tidal level of the preceding time series has the largest error. Furthermore, the forecasting ability of the model improves to an extent after the atmospheric pressure is taken into account, while the forecasting effect improves more significantly after wind is considered.
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