基于LightGBM机器学习算法的江西气温短期预报模型研究  

Study on Short Term Temperature Forecast Model in Jiangxi Province based on LightGBM Machine Learning Algorithm

作　　者：孙康慧 肖安[1] 夏侯杰 SUN Kanghui;XIAO An;XIA Houjie(Jiangxi Meteorological Observatory,Weather Forecast Open Laboratory of Jiangxi Meteorological Bureau,Nanchang 330096,Jiangxi,China)

机构地区：[1]江西省气象台江西省气象局天气预报开放实验室,江西南昌330096

出　　处：《高原气象》2024年第6期1520-1535,共16页Plateau Meteorology

基　　金：华东区域气象科技协同创新基金项目(QYHZ202315);江西省气象局重点项目(JX2023Z05)。

摘　　要：为进一步提高站点气温的预报精度,增强对极端气温的预报能力,本研究利用2017-2019年江西省91个国家站地面观测数据和ECMWF模式高空和地面预报数据,基于LightGBM机器学习算法和MOS预报框架,建立了江西省24 h国家站日最高(低)气温预报模型。2020年评估结果表明:LightGBM模型日最高(低)气温预报和观测变化趋势一致,年平均预报效果优于ECMWF、CMA-SH9、CMA-GFS三家数值模式、RF和SVM两种机器学习产品以及主观订正产品。从预报误差的时空分布来看,模型冬、春季日最高(低)气温预报误差略大于夏、秋季;日最高气温预报误差呈现“南大北小、周边大于中心”的空间分布特征,日最低气温则与之大致相反。从重要天气过程来看,在高温过程中,LightGBM模型在七种产品中预报效果最优;在强冷空气过程中,LightGBM模型预报效果仍优于三家数值模式产品和另外两种机器学习模型,但日最低气温预报效果不如主观订正产品。针对强冷空气过程中低温预报误差进行简单经验订正后,模型低温预报效果与主观订正产品接近。模型重要性分析显示临近地面观测特征对模型建立也有较大贡献,该结果可以为模式改进和气温预报产品研发提供参考。目前,LightGBM模型气温预报产品已应用于江西省气象业务。In order to achieve further improvement in the forecast accuracy of station temperatures and enhance the forecast capability for extreme temperatures,this study establishes a 24-hour national station daily maximum(minimum)temperature forecast model for Jiangxi Province based on the LightGBM machine-learning algorithm and the MOS forecast framework by using the surface observation data of 91 national stations in Jiangxi Province and the upper-air and surface forecast data of the ECMWF model from 2017 to 2019.The results of the 2020 evaluation show that the LightGBM model daily maximum(minimum)temperature forecast is consistent with the observed trend,and the annual average forecast is better than that of three numerical models,ECMWF,CMA-SH9 and CMA-GFS,two machine learning products,RF and SVM,and subjective revision products.In terms of the spatial and temporal distribution of forecast errors,the model's daily maximum(minimum)temperature forecast errors in winter and spring are slightly larger than those in summer and autumn;the daily maximum temperature forecast errors show the spatial distribution characteristics of"larger in the south and smaller in the north,and larger in the periphery than in the centre",while the opposite is true for the daily minimum temperatures.In terms of important weather processes,the LightGBM model has the best prediction effect among the seven products in the high temperature process;in the strong cold air process,the LightGBM model is still better than the three numerical model products and the other two machine-learning models,but the prediction effect of the daily minimum temperature is not as good as that of the subjective revision products.After a simple empirical correction for the low-temperature forecast error in the strong cold air process,the model low-temperature forecast effect is close to that of the subjective revision product.The model significance analysis shows that the recent surface observation features also contribute to the model construction,and the results can

关 键 词：气温预报 ECMWF 机器学习 LightGBM 预报效果评估 

分 类 号：P456.9[天文地球—大气科学及气象学]