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WANG Kai, LI Zhenhong, ZHANG Juqing. A Novel Regional Weighted Mean Temperature Model Through Multi-Source Data Fusion with Voronoi Diagrams: A Case Study in Northwest China[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240009
Citation: WANG Kai, LI Zhenhong, ZHANG Juqing. A Novel Regional Weighted Mean Temperature Model Through Multi-Source Data Fusion with Voronoi Diagrams: A Case Study in Northwest China[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240009
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A Novel Regional Weighted Mean Temperature Model Through Multi-Source Data Fusion with Voronoi Diagrams: A Case Study in Northwest China

  • 1. College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China;

  • 2. Key Laboratory of Loess, Xi’an 710054, China;

  • 3. Big Data Center for Geosciences and Satellites, Chang’an University, Xi’an 710054, China;

  • 4. Key Laboratory of Western China's Mineral Resources and Geological Engineering, Ministry of Education, Xi’an, 710054, China

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  • Accepted Date: April 14, 2024
  • Available Online: April 14, 2024
    Graphical Abstract
    • 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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    • References (27)
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