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

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

doi:10.1007/s13351-024-3184-0

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Original Paper

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

Zhuo SUN^{¹^,²^,³}, Jiangbo LI^{¹^,²^,³}(), Ruiqiang GUO^⁴, Yiran ZHANG^⁴, Gang ZHU^{¹^,²^,³}, Xiaoliang YANG^{¹^,²^,³}

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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Abstract

Weather forecasting for the Zhangjiakou competition zone of the Beijing 2022 Winter Olympic Games is a challenging 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 dynamics, 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 integrating 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 automatic 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 performance 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 outperforms on extreme gust samples (42.7% compared with 38.7% obtained by ECMWF-HRES in terms of RMSE reduction). Sensitivity analysis of predictors in the four categories shows that ENSL fits their feature importance rankings and physical explanations effectively.

Keywords

machine learning ensemble learning post-processing cold air pools mountain Beijing 2022 Winter Olympic Games

References

Allard, D., A. Comunian, and P. Renard, 2012: Probability aggregation methods in geoscience. Math. Geosci., 44, 545–581. doi: 10.1007/s11004-012-9396-3.