首页|Machine Learning-Based Temperature and Wind Forecasts in the Zhangjiakou Competition Zone during the Beijing 2022 Winter Olympic Games

Machine Learning-Based Temperature and Wind Forecasts in the Zhangjiakou Competition Zone during the Beijing 2022 Winter Olympic Games

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
Weather forecasting for the Zhangjiakou competition zone of the Beijing 2022 Winter Olympic Games is a challen-ging task due to its complex terrain.Numerical weather prediction models generally perform poorly for cold air pools and winds over complex terrains,due to their low spatiotemporal resolution and limitations in the description of dy-namics,thermodynamics,and microphysics in mountainous areas.This study proposes an ensemble-learning model,named ENSL,for surface temperature and wind forecasts at the venues of the Zhangjiakou competition zone,by in-tegrating five individual models-linear regression,random forest,gradient boosting decision tree,support vector machine,and artificial neural network(ANN),with a ridge regression as meta model.The ENSL employs predictors from the high-resolution ECMWF model forecast(ECMWF-HRES)data and topography data,and targets from auto-matic weather station observations.Four categories of predictors(synoptic-pattern related fields,surface element fields,terrain,and temporal features)are fed into ENSL.The results demonstrate that ENSL achieves better perform-ance and generalization than individual models.The root-mean-square error(RMSE)for the temperature and wind speed predictions is reduced by 48.2％and 28.5％,respectively,relative to ECMWF-HRES.For the gust speed,the performance of ENSL is consistent with ANN(best individual model)in the whole dataset,whereas ENSL outper-forms on extreme gust samples(42.7％compared with 38.7％obtained by ECMWF-HRES in terms of RMSE reduc-tion).Sensitivity analysis of predictors in the four categories shows that ENSL fits their feature importance rankings and physical explanations effectively.

machine learningensemble learningpost-processingcold air poolsmountainBeijing 2022 Winter Olympic Games

Zhuo SUN、Jiangbo LI、Ruiqiang GUO、Yiran ZHANG、Gang ZHU、Xiaoliang YANG

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China Meteorological Administration Xiong'an Atmospheric Boundary Layer Key Laboratory,Xiong'an New Area 071800

Hebei Key Laboratory of Meteorology and Ecological Environment,Shijiazhuang 050021

Hebei Meteorological Observatory,Shijiazhuang 050021

College of Computer and Cyber Security,Hebei Normal University,Shijiazhuang 050024

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National Key Research and Development Program of ChinaKey Research and Development Program of Hebei Province of ChinaAfter-Action-Review Project of China Meteorological Administration

2018 YDD030010421375404DFPZJ2023-014

2024

10.1007/s13351-024-3184-0

气象学报(英文版)
中国气象学会

气象学报(英文版)

CSTPCD
影响因子：0.57
ISSN：0894-0525
年,卷(期)：2024.38(4)