气象科学2025，Vol.45Issue(4)：535-548,14.DOI:10.12306/2023jms.0059
基于因果分析和机器学习算法的站点气温短临预报试验
Experiments on short-term station temperature forecast based on causality analysis and machine learning algorithms
摘要
Abstract
This paper constructed a model for short term forecasting of station temperature based on causal analysis of information flow and machine learning algorithms,taking Songshan Station in Taiwan as the test samples,using ECMWF reanalysis data,measured meteorological data of Songshan Station and CLDAS-v2.0 near-real-time product data,combined with causal analysis and correlation analysis,using four Machine Learning(ML)algorithms:BP neural network,Random Forest(RF),Least Squares Support Vector Machine(LSSVM)and Bayesian Network(BN)to carry out temperature short-term forecasting experiments.The conclusions are as follows:(1)driven by any of the data selected in this paper,for BP neural network,RF,and BN,the prediction results based on causal analysis are better than those of correlation analysis,and the average reduction of RMSE is between 1％-2％.For LSSVM,the difference between causal analysis and correlation analysis is small,which verifies that causal analysis has better correlation mining capabilities;(2)adding adjacent spatial prediction factors in the forecast model can improve the temperature prediction effect,and the average reduction of the RMSE of the improved temperature prediction model is between 2％-8％;(3)in the case of small samples,the forecasting effect based on CLDAS-V2.0 data is better than that of ECMWF reanalysis data,and the average reduction of RMSE is between 4％-8％,which verifies that the quality of CLDAS-V2.0 data in China is indeed better than that of similar international products.关键词
气温短临预报/机器学习/因果分析Key words
short-term temperature forecast/machine learning/causality analysis分类
海洋科学引用本文复制引用
李洪臣,李明,王鹏皓..基于因果分析和机器学习算法的站点气温短临预报试验[J].气象科学,2025,45(4):535-548,14.基金项目
国家自然科学基金资助项目(62073332) （62073332）
