A Novel Regional Weighted Mean Temperature Model Through Multi-Source Data Fusion with Voronoi Diagrams: A Case Study in Northwest China
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摘要: 加权平均温度(weighted mean temperature,Tm)是全球导航卫星系统(global navigationsatellite system,GNSS)反演可降水量(precipitable water vapour,PWV)的关键参数,且其与地表温度联合可建立线性模型,但随着区域不同线性模型通常有所不同。本文在传统Tm回归模型中引入了Voronoi图思想,建立了Tm区域增强模型(Tm Augmentation Model,TAM)。以地形起伏大、范围广的中国西北地区为研究区域,利用2017-2021年西北区域内24个探空站数据建立Voronoi区域回归模型,加入欧洲天气预报中心的再分析数据与全球大地观测系统(global geodetic observing system,GGOS)大气Tm格网数据进行增强融合,最终建立TAM模型。在融合过程中,顾及Tm在近地空间上的垂直递减变化,提出三次多项式垂直递减函数模型,用于Tm数据在垂向上的改正。TAM模型分别与2022年西北区域内探空站及GGOSTm格网数据进行了验证,结果表明:TAM模型较传统模型的精度有着显著的提升,模型估计的Tm表现出最优的空间分布结果,特别是在青藏高原等高海拔地区。TAM模型可望在山区的GNSS高精度水汽监测中具有重要的应用价值。Abstract: Objectives: The weighted mean temperature (Tm) is a key parameter for estimating precipitable water vapor (PWV) from Global Navigation Satellite System (GNSS) observations. The environment in northwest China is complicated, so it is necessary to establish a suitable atmospheric weighted mean temperature model. Methods: A novel Voronoi region regression model is presented by incorporating the Voronoi diagram concept into the traditional Tm regression model and a Voronoi region regression model is built using data from 24 radiosonde stations within the study region from 2017 to 2021. European Centre for Medium-Range Weather Forecasts reanalysis data and Global Geodetic Observing System Atmosphere Tm grid data are then integrated to generate a suitable for the Northwest regional Tm Augmentation Model (TAM). During the integration process, considering the fact that Tm decreases with elevation in the near-surface environment, a cubic polynomial model is proposed to correct the Tm values in the vertical direction. Results: The model is validated using data from radiosonde stations and GGOS Tm grid within the study region in 2022, suggesting that TAM model exhibits high accuracy and applicability in the Northwest region, with accuracy improvements of 19.7% and 50.7% compared to the Bevis model, 44.4% and 18.4% compared to GPT3-1 model, 53.8% and 41% compared to GPT3-5 respectively. Conclusions: The spatial distribution of Tm estimated by the TAM model exhibits optimal performance, especially in the Qinghai-Tibet Plateau and other high-altitude areas. It is believed that the TAM model holds great potential for high-precision water vapor monitoring with GNSS in mountainous areas.
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