京津冀大气加权平均温度的时空异质性建模及气象因子耦合分析

doi:10.13474/j.cnki.11-2246.2025.1222

测绘通报 ›› 2025, Vol. 0 ›› Issue (12): 126-133.doi: 10.13474/j.cnki.11-2246.2025.1222

• 技术交流 • 上一篇

京津冀大气加权平均温度的时空异质性建模及气象因子耦合分析

于亚杰¹, 李卫国², 王兴坤¹, 唐江森¹, 丁文玉¹

1. 河北省第二测绘院, 河北石家庄 050031;
2. 河北地质大学土地科学与空间规划学院, 河北石家庄 050031

收稿日期:2025-04-03 发布日期:2025-12-31
通讯作者: 李卫国。E-mail:002061@hgu.edu.cn
作者简介:于亚杰(1982—),男,硕士,研究方向为测绘地理信息数据处理及应用系统研发。E-mail:565399594@qq.com
基金资助:
2024年度河北省“三三三人才工程”资助(B2024004);2024年度河北省燕赵黄金台聚才计划骨干人才项目(留学回国平台)(B2024014)

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

YU Yajie¹, LI Weiguo², WANG Xingkun¹, TANG Jiangsen¹, DING Wenyu¹

1. Hebei Province No. 2 Institute of Surveying and Mapping, Shijiazhuang 050031, China;
2. School of Land Science and Spatial Planning, Hebei GEO University, Shijiazhuang 050031, China

Received:2025-04-03 Published:2025-12-31

摘要/Abstract

摘要： 京津冀极端天气频发,对大气监测精度要求提高。大气加权平均温度(T_m)是 GNSS 反演可降水量的关键参数,但现有经验模型在该区域存在偏差。本文基于京津冀 5 个探空站数据,构建多因子回归模型,分析不同因变量组合的影响。研究表明,气象因子(P、T)与时间因子(DOY)影响最显著(相关性分别达 0.83、0.95),地理因子影响较小。经对16 种模型进行对比,以均方根误差(RMSE)等评估,选定模型 8(P、T、DOY)为最优,较 Bevis 模型精度提升11.3%,偏差最低,适应性更优且残差无系统性偏差。本文优化了京津冀地区T_m预测模型,提升了区域适应性与精度,未来将扩展数据并引入非线性建模,以增强对极端天气的适应能力。

关键词: 加权平均温度, GNSS气象学, 多因子回归模型, 大气监测, 模型优化

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

中图分类号:

P237

于亚杰, 李卫国, 王兴坤, 唐江森, 丁文玉. 京津冀大气加权平均温度的时空异质性建模及气象因子耦合分析[J]. 测绘通报, 2025, 0(12): 126-133.

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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京津冀大气加权平均温度的时空异质性建模及气象因子耦合分析

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

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