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

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.

Knowledge Graph-Driven Weather Overview Generation for the Beijing 2022 Winter Olympic Games

Weather Overview is regarded as one of the crucial meteorological services supporting the Beijing 2022 Olympic and Paralympic Winter Games(hereafter as Beijing 2022).As generation of Weather Overview involves multipledata,large-scale weather conditions,and vulnerability to weather changes,there still exist quite some challenges in obtaining Weather Overview.At present,knowledge graph(KG)is believed to be an effective way to describe information and knowledge.Thus,this study focuses on development of a framework to automatically generate Weather Overview using KG.We first present a three-layer KG model to generate accurate content of Weather Overview:(1)knowledge acquisition of entities and relationships to construct the specific corpora;(2)knowledge representation of the relationships between weather conditions and the events based on ontology;and(3)knowledge application of corpora,variables,and weather conditions to query and reason knowledge with Neo4j.Moreover,an XML Schema is used to achieve the standardized Weather Overview,which is formed by sentence-paragraph-text generation.This model is validated for a typical case at the Yanqing National Alpine Skiing Centre in Beijing 2022.Compared to the manual method,the accuracy and standardization of Weather Overview can be maintained above 90%,and it can be automatically generated within seconds.The method proposed in this study provides a helpful meteorological service solution to other large-scale sports events.