现代信息科技2023，Vol.7Issue(24)：16-20,5.DOI:10.19850/j.cnki.2096-4706.2023.24.004
基于机器学习方法的内蒙古地区格点风场模式预报产品研究
Research on Grid Wind Field Model Prediction Products in Inner Mongolia Based on Machine Learning Method
刘辉1
作者信息
- 1. 内蒙古自治区气象数据中心,内蒙古 呼和浩特 010010
- 折叠
摘要
Abstract
In order to improve the accuracy of grid wind field prediction products,a set of wind speed and direction prediction methods based on deep learning and ensemble learning is proposed.The real-time data of station wind in the time series and the numerical model prediction products in the spatial range are used to establish the spatio-temporal information matching model,and the Long short-term memory method(LSTM)and extreme gradient lifting(XGBoost)are used to establish the joint prediction product correction model with spatio-temporal matching,forming the 72 hour SCMOC wind speed and direction prediction products with spatio-temporal resolution of 3 hours and 5 kilometers.The assessment results show that the mean absolute error(MAE)of wind speed realized by the joint model is 14.17%lower than that of SCMOC prediction,and the mean absolute error of wind direction is 23.61%lower than that of SCMOC prediction.The model has significantly improved the accuracy of wind speed and direction for SCMOC wind farm products,and the product has a good interpretation effect.关键词
LSTM/XGBoost/时空匹配/指导预报产品/产品释用Key words
LSTM/XGBoost/spatiotemporal matching/guiding forecast product/product interpretation分类
信息技术与安全科学引用本文复制引用
刘辉..基于机器学习方法的内蒙古地区格点风场模式预报产品研究[J].现代信息科技,2023,7(24):16-20,5.
