摘要
高温是一种比较常见的气象灾害,对人们日常生活和健康以及国民经济都有一定的影响。准确地预报未来10-30天(次季节)的每日高温天气状况,有助于人们及早地做出对应的决策和降低财产损失。本文设计了一种深度学习模型,通过对两个Encoder提取的大气环流和地表高温的特征进行延迟融合,并结合Decoder逐日输出高温预报结果。实验结果表明,本文的方法有较好的预测效果,相较于单个Encoder的深度学习方法,均方根误差降低了2.12%,异常相关系数提高了2.72%,决定系数提高了0.04。
High temperature is a common meteorological disaster that has a certain impact on people’s daily life and economy.Accurate forecasting of daily high temperature weather for the next 10-30 days(sub-seasonal)can help people make early decisions and reduce damage.In the paper,a deep learning model was design by late fusion the futures of atmospheric circulation data and surface high temperature extracted by two Encoders,and combining with Decoder to output day-by-day high temperature forecasting results.The experimental results show that the proposed method has better prediction effect,with a 2.12%reduction in root mean square error,a 2.72%improvement in anomaly correlation coefficient and a 0.04%improvement in coefficient of determination compared to the deep learning method with a single Encoder.
作者
邓敏
游立军
翁彬
郑子华
叶锋
DENG Min;YOU Lijun;WENG Bin;ZHENG Zihua;YE Feng(College of Computer and Cyber Security,Fujian Normal University,Fuzhou,China,350117;Digital Fujian Institute of Big Data Security Technology,Fuzhou,China,350117;Engineering Technology Research Center for Public Service Big Data Mining and Application of Fujian Province,Fuzhou,China,350117;Fujian Institute of Meteorological Sciences,Fuzhou,China,350007)
出处
《福建电脑》
2023年第2期1-5,共5页
Journal of Fujian Computer
基金
国家重点研发计划“重大自然灾害监测预警与防范”专项(No.2018YFC1505805)
福建省引导性项目“福建省前汛期持续性强降水过程延伸期预报的人工智能技术研究”(No.2021Y0057)
福建省引导性项目“基于深度时空多尺度交叉注意力的华南前汛期极端降水延伸期预报研究”(No.2022Y0008)资助。
关键词
高温次季节预报
深度学习
气象预报数据
Sub-seasonal Horecast of High Temperature
Deep Learning
Meteorological Forecast Data