Modeling spatio-temporal heterogeneity and meteorological factor coupling in atmospheric weighted mean temperature over the Beijing-Tianjin-Hebei region

doi:10.13474/j.cnki.11-2246.2025.1222

Abstract

Abstract: Extreme weather events occur frequently in the Beijing-Tianjin-Hebei region,necessitating higher accuracy in atmospheric monitoring.The atmospheric weighted mean temperature (T_m) is a key parameter for GNSS-derived precipitable water vapor retrieval,but existing empirical models exhibit biases in this region.Based on data from five sounding stations in the Beijing-Tianjin-Hebei region,this study constructs a multi-factor regression model to analyze the impact of different combinations of dependent variables.The research indicates that meteorological factors (P,T)and temporal factors (DOY)have the most significant impact (with correlations of 0.83 and 0.95,respectively),while geographical factors have a smaller impact.After comparing 16models and evaluating using metrics such as root mean square error (RMSE),model 8 (P,T,DOY)is selected as the optimal model,achieving an 11.3% improvement in accuracy compared to the Bevis model,with the lowest bias,superior adaptability,and no systematic bias in residuals.This study optimizes the T_m prediction model in the Beijing-Tianjin-Hebei region,enhancing regional adaptability and accuracy.In the future,the data will be expanded and nonlinear modeling will be introduced to enhance adaptability to extreme weather events.

Key words: weighted mean temperature, GNSS meteorology, multi-factor regression model, atmospheric monitoring, model optimization

CLC Number:

P237

YU Yajie, LI Weiguo, WANG Xingkun, TANG Jiangsen, DING Wenyu. Modeling spatio-temporal heterogeneity and meteorological factor coupling in atmospheric weighted mean temperature over the Beijing-Tianjin-Hebei region[J]. Bulletin of Surveying and Mapping, 2025, 0(12): 126-133.

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