The planetary boundary layer (PBL),located in the lower troposphere near the earth's surface,is profoundly influenced by surface friction,thermal processes,and evaporation.As a crucial component of the atmospheric system,the PBL acts as a bridge between the free atmosphere and the Earth's surface and serves as the primary space for human activity.Planetary boundary layer height (PBLH),a key structural characteristic,reflects physical processes such as turbulent mixing and convective development within the boundary layer.Accurately tracking its continuous changes and evolution is essential for advancing research in atmospheric science,environmental monitoring and pollution control.
Traditional methods for PBLH determination,such as sounding observations,offer high accuracy but are limited in spatial and temporal coverage,restricting their utility for multi-scale continuous observations.Remote sensing can provide continuous monitoring but is significantly affected by weather conditions and cannot fully capture PBLH dynamics.Numerical models,while useful,are subject to intrinsic model errors.A need remains to further investigate the relationship between near-surface atmospheric characteristics and PBLH.In this study,we apply a machine learning approach,XGBoost,to predict PBLH using long-term surface meteorological,wind radar,and sounding data from Beijing (January 2016 to May 2019) to train a model,which we subsequently employ to predict PBLH from June 2019 to May 2020.
Results indicate that the model performs optimally under clear-sky daytime conditions,achieving a high correlation with radiosonde-derived PBLH (correlation coefficient=0.86).Prediction accuracy is reduced at night.Surface temperature,relative humidity,and wind speed emerge as the most influential input features.The predicted PBLH displays a pronounced diurnal cycle,increasing rapidly after sunrise,gradually decreasing in the afternoon,and stabilizing at night.Seasonal analysis shows that daily PBLH variations are more pronounced in spring and summer,reaching up to 1 km,and are smaller in autumn and winter,around 700 m.
Overall,the XGBoost algorithm outperforms multiple linear regression and support vector regression in PBLH predictions,offering an efficient,intuitive method to continuously estimate PBLH's diurnal variation.This approach provides new insights into the diurnal and seasonal patterns of the PBL,supporting multi-period analysis.However,model performance for nighttime PBLH is limited,as it does not fully capture the stabilized boundary layer's vertical stricture due to strong radiative cooling and the weakened interaction between the PBL and the surface.Future work will incorporate vertical observation data to refine the model structure and compare results with other detection methods to validate the applicability of the XGBoost algorithm.