摘要
基于海洋气象历史观测资料和再分析数据等,利用LSTM深度神经网络方法,开展在有监督学习情况下的海面风场短时预报应用研究。以中国近海5个代表站为研究区域,通过气象台站观测数据和ERA-Interim 6 h再分析数据构建数据集。选取21个变量作为预报因子,分别构建两个LSTM深度神经网络框架(OBS_LSTM和ALL_LSTM)。经与2017年WRF模式6 h预报结果对比分析,得出如下结论:构建的两个LSTM风速预报模型可以大幅降低风速预报误差,RMSE分别降低了41.3%和38.8%,MAE平均降低了43.0%和40.0%;风速误差统计和极端大风分析发现,LSTM模型能够抓住地形、短时大风和台风等敏感信息,对于大风过程预报结果明显优于WRF模式;两种LSTM模型对比发现,ALL_LSTM模型风速预报误差最小,具有很好的稳定性和鲁棒性,OBS_LSTM模型应用范围更广泛。
Based on historical meteorological observation data and reanalysis data, the application of LSTM(longs short-term memory) deep neural network in short-term forecasting of sea surface wind speed under supervised learning was studied. Five representative meteorological stations in the offshore were taken as research areas.Twenty-one variables, which has been quality control and preprocessing, were selected as the prediction factors,and two LSTM deep neural network frameworks(OBS_LSTM and ALL_LSTM) were constructed. The 6 h wind speed forecast of WRF model over two-nesting domains in 2017 was included to validate the real performance of the proposed model. The result indicate that, the LSTM wind speed forecast models could significantly reduce the forecast error, RMSE was reduced by 41.3% and 38.8%, and MAE was reduced by 43.0% and 40.0%, respectively;wind speed error statistics and strong wind events comparison shows that, LSTM model can grasp sensitive information such as topography and typhoon, and superior to WRF model. The ALL_LSTM model has the smallest prediction error, good stability and robustness, and OBS_LSTM model has a wider range of application.
作者
王国松
王喜冬
侯敏
齐义泉
宋军
刘克修
吴新荣
白志鹏
Wang Guosong;Wang Xidong;Hou Min;Qi Yiquan;Song Jun;Liu Kexiu;Wu Xinrong;Bai Zhipeng(College of Oceanography,Hohai University,Nanjing 210098,China;National Marine Data and Information Service,Tianjin 300171,China;Tianjin Binhai New Area Meteorology Administration,Tianjin 300457,China;Operational Oceanographic Institution,School of Marine Science and Environment Engineering,Dalian Ocean University,Dalian 116023,China;61741 Troops,Beijing 100094,China)
出处
《海洋学报》
CAS
CSCD
北大核心
2020年第1期67-77,共11页
基金
国家重点研发计划（2016YFC1401903）
国家自然科学基金（41776004）
河海大学中央高校基本科研业务费（2016B12514）
关键词
深度学习
LSTM
海面风速
短时预报
WRF模式
deep neural network
LSTM
sea surface wind speed
short-term forecasting
WRF model
作者简介
王国松(1987—),男,天津市人,助研,主要从事数值模拟与资料处理研究。E-mail:xifengbishu2110@163.com;通信作者:侯敏,工程师,主要从事海洋气象预报业务。E-mail:ande0604@163.com。
