热带气象学报2024，Vol.40Issue(6)：896-905,10.DOI:10.16032/j.issn.1004-4965.2024.065
基于多种机器学习的模式PM2.5订正预报方法
PM2.5 Forecast Correction Based on Multi-Machine Learning
摘要
Abstract
Fine particulate matter(PM2.5)pollution in the atmosphere profoundly affects human health,atmospheric visibility,and climate change;therefore,accurate forecasting of PM2.5 concentration is essential.This study developed PM2.5 forecast correction models for the Beijing-Tianjin-Hebei region using the China Meteorological Administration Unified Atmospheric Chemistry Environment for Haze V3.0(CUACE-Haze 3.0)model and various machine learning methods,including random forest(RF),light gradient boosting machine(LightGBM),and extreme gradient boosting machine(XGBoost).The forecast performance and differences among these machine learning models were then compared and analyzed.The results show that the mean error(ME)and mean absolute error(MAE)for the three machine learning algorithms were significantly lower than those of the CUACE model.The variation ranges of ME and MAE under different forecast time intervals were smaller,indicating better stability of the PM2.5 forecasts obtained based on machine learning algorithms.Furthermore,among the three machine learning algorithms,RF exhibited the best forecast performance,with ME and MAE of-3.0 μg m-3 and 23.6 μg m-3,respectively.The improvement in MAE for RF was the most prominent,reaching 11.5%,and the proportion of stations with positive correction was 97.7% in this region,significantly better than those of LightGBM and XGBoost.Additionally,during the verification and evaluation of forecast for the haze from March 9 to 12,2024,RF achieved the highest threat score(TS),with TS scores of 0.43,0.19,and 0.03 for light pollution,moderate pollution,and severe pollution or above,respectively.This demonstrates that the forecast performance of the RF algorithm is superior,and the research results provide valuable references for operational forecasting.关键词
机器学习/PM2.5/中国气象局雾-霾数值预报系统/订正Key words
machine learning/PM2.5/CUACE-Haze 3.0/correction分类
天文与地球科学引用本文复制引用
刘超,宫宇,张碧辉,柯华兵..基于多种机器学习的模式PM2.5订正预报方法[J].热带气象学报,2024,40(6):896-905,10.基金项目
国家重点研发计划(2022YFC3701205) （2022YFC3701205）
中国气象局高影响天气(专项)重点开放实验室以及中国气象局第二批揭榜挂帅项目(CMAJBGS202308)共同资助 （专项）
